06_PolygenicRiskScore.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/luuloi/GWAS_Introduction_2023/blob/main/06_PolygenicRiskScore.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 0. Environment Setup\n",
        "\n"
      ],
      "metadata": {
        "id": "mK0jGtKj_0O8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download and Install Packages"
      ],
      "metadata": {
        "id": "imiGhruJmepZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Install using pip\n",
        "\n",
        "!pip install rpy2==3.5.1\n",
        "!pip install -q condacolab\n",
        "!pip install gdown"
      ],
      "metadata": {
        "id": "RnUE94KmGSYG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "646f465e-8ec9-41a8-9441-922a98f96cba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting rpy2==3.5.1\n",
            "  Downloading rpy2-3.5.1.tar.gz (201 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/201.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━\u001b[0m \u001b[32m194.6/201.7 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m201.7/201.7 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: cffi>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from rpy2==3.5.1) (1.15.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from rpy2==3.5.1) (3.1.2)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.10/dist-packages (from rpy2==3.5.1) (2023.3)\n",
            "Requirement already satisfied: tzlocal in /usr/local/lib/python3.10/dist-packages (from rpy2==3.5.1) (5.0.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.10.0->rpy2==3.5.1) (2.21)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->rpy2==3.5.1) (2.1.3)\n",
            "Building wheels for collected packages: rpy2\n",
            "  Building wheel for rpy2 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for rpy2: filename=rpy2-3.5.1-cp310-cp310-linux_x86_64.whl size=314931 sha256=62808c6ee01cd0edcd4ec3ec6697e081778d1fb35902136954f9c98b7f93f26b\n",
            "  Stored in directory: /root/.cache/pip/wheels/73/a6/ff/4e75dd1ce1cfa2b9a670cbccf6a1e41c553199e9b25f05d953\n",
            "Successfully built rpy2\n",
            "Installing collected packages: rpy2\n",
            "  Attempting uninstall: rpy2\n",
            "    Found existing installation: rpy2 3.4.2\n",
            "    Uninstalling rpy2-3.4.2:\n",
            "      Successfully uninstalled rpy2-3.4.2\n",
            "Successfully installed rpy2-3.5.1\n",
            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (4.6.6)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.12.2)\n",
            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from gdown) (1.16.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.1)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.11.2)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.4.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2023.7.22)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Handle Warnings"
      ],
      "metadata": {
        "id": "W8IlunCuUKHl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ignore rpy2's warnings\n",
        "\n",
        "import rpy2\n",
        "import warnings\n",
        "from rpy2.rinterface import RRuntimeWarning\n",
        "\n",
        "warnings.filterwarnings(\"ignore\", category=RRuntimeWarning)"
      ],
      "metadata": {
        "id": "rqsVRlvZbepp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Initialize Conda and Install PLINK"
      ],
      "metadata": {
        "id": "6ougjPAwURsj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize conda\n",
        "\n",
        "import condacolab\n",
        "condacolab.install()"
      ],
      "metadata": {
        "id": "nplSoFdMbgXY",
        "outputId": "19a4ab9d-1aff-421b-c85a-ee0e6daeba53",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✨🍰✨ Everything looks OK!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Install plink\n",
        "\n",
        "!conda install -c bioconda plink"
      ],
      "metadata": {
        "id": "xW-im8EObz-R",
        "outputId": "b94e4c4d-3f81-4275-fede-cd6de9084177",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "\n",
            "\n",
            "==> WARNING: A newer version of conda exists. <==\n",
            "  current version: 23.1.0\n",
            "  latest version: 23.7.2\n",
            "\n",
            "Please update conda by running\n",
            "\n",
            "    $ conda update -n base -c conda-forge conda\n",
            "\n",
            "Or to minimize the number of packages updated during conda update use\n",
            "\n",
            "     conda install conda=23.7.2\n",
            "\n",
            "\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - plink\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    ca-certificates-2023.7.22  |       hbcca054_0         146 KB  conda-forge\n",
            "    certifi-2023.7.22          |     pyhd8ed1ab_0         150 KB  conda-forge\n",
            "    openssl-3.1.2              |       hd590300_0         2.5 MB  conda-forge\n",
            "    plink-1.90b6.21            |       h031d066_5         6.8 MB  bioconda\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:         9.7 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  plink              bioconda/linux-64::plink-1.90b6.21-h031d066_5 \n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  ca-certificates                      2022.12.7-ha878542_0 --> 2023.7.22-hbcca054_0 \n",
            "  certifi                            2022.12.7-pyhd8ed1ab_0 --> 2023.7.22-pyhd8ed1ab_0 \n",
            "  openssl                                  3.1.0-h0b41bf4_0 --> 3.1.2-hd590300_0 \n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "openssl-3.1.2        | 2.5 MB    | :   0% 0/1 [00:00<?, ?it/s]\n",
            "plink-1.90b6.21      | 6.8 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "\n",
            "certifi-2023.7.22    | 150 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "openssl-3.1.2        | 2.5 MB    | :  11% 0.10524207778742557/1 [00:00<00:00,  1.02it/s]\n",
            "\n",
            "certifi-2023.7.22    | 150 KB    | :  53% 0.5326709625400706/1 [00:00<00:00,  5.17it/s]\u001b[A\u001b[A\n",
            "plink-1.90b6.21      | 6.8 MB    | :   0% 0.002284472932188773/1 [00:00<00:46, 47.07s/it]\u001b[A\n",
            "\n",
            "\n",
            "ca-certificates-2023 | 146 KB    | :  88% 0.876647827977126/1 [00:00<00:00,  8.37it/s]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "ca-certificates-2023 | 146 KB    | : 100% 1.0/1 [00:00<00:00,  8.37it/s]              \u001b[A\u001b[A\u001b[A\n",
            "\n",
            "certifi-2023.7.22    | 150 KB    | : 100% 1.0/1 [00:00<00:00,  5.17it/s]               \u001b[A\u001b[A\n",
            "openssl-3.1.2        | 2.5 MB    | : 100% 1.0/1 [00:00<00:00,  3.22it/s]\n",
            "plink-1.90b6.21      | 6.8 MB    | : 100% 1.0/1 [00:02<00:00,  3.11s/it]               \u001b[A\n",
            "                                                                        \n",
            "                                                                        \u001b[A\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\n",
            "Preparing transaction: / \b\bdone\n",
            "Verifying transaction: \\ \b\bdone\n",
            "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Set up R environment by installing required packages"
      ],
      "metadata": {
        "id": "Ai4uzAHTFjIl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# activate R magic\n",
        "%load_ext rpy2.ipython"
      ],
      "metadata": {
        "id": "ZwFsLbTocdMR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Install packages\n",
        "\n",
        "install.packages(\"ggplot2\")\n",
        "library(ggplot2)"
      ],
      "metadata": {
        "id": "o8DQ5Cb1lGXz",
        "outputId": "842014bd-dab2-4d72-ad7f-ad16a11606c2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "R[write to console]: Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/ggplot2_3.4.3.tar.gz'\n",
            "\n",
            "R[write to console]: Content type 'application/x-gzip'\n",
            "R[write to console]:  length 2190947 bytes (2.1 MB)\n",
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            "R[write to console]: \n",
            "\n",
            "R[write to console]: downloaded 2.1 MB\n",
            "\n",
            "\n",
            "R[write to console]: \n",
            "\n",
            "R[write to console]: \n",
            "R[write to console]: The downloaded source packages are in\n",
            "\t‘/tmp/RtmpjsXCaO/downloaded_packages’\n",
            "R[write to console]: \n",
            "R[write to console]: \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Loading"
      ],
      "metadata": {
        "id": "G1NqaIXpBq1D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "# Create folder to store neccesary data\n",
        "mkdir -p data/base\n",
        "mkdir -p data/target"
      ],
      "metadata": {
        "id": "pMecyXnWcKlE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "# Download the base and target data\n",
        "wget -O data/base/Height.gwas.txt.gz https://raw.githubusercontent.com/luuloi/GWAS_Introduction_2023/main/data/Height.gwas.txt.gz\n",
        "gdown -O data/target/EUR.zip https://drive.google.com/uc?id=1uhJR_3sn7RA8U5iYQbcmTp6vFdQiF4F2&export=download"
      ],
      "metadata": {
        "id": "CZ7Du0HNcImi",
        "outputId": "668ed16c-6037-4791-c522-1ca4c89dae91",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "--2023-08-18 00:49:47--  https://raw.githubusercontent.com/luuloi/GWAS_Introduction_2023/main/data/Height.gwas.txt.gz\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 22609185 (22M) [application/octet-stream]\n",
            "Saving to: ‘data/base/Height.gwas.txt.gz’\n",
            "\n",
            "     0K .......... .......... .......... .......... ..........  0% 25.4M 1s\n",
            "    50K .......... .......... .......... .......... ..........  0% 26.7M 1s\n",
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            "   200K .......... .......... .......... .......... ..........  1% 60.8M 1s\n",
            "   250K .......... .......... .......... .......... ..........  1% 94.8M 1s\n",
            "   300K .......... .......... .......... .......... ..........  1%  224M 0s\n",
            "   350K .......... .......... .......... .......... ..........  1%  155M 0s\n",
            "   400K .......... .......... .......... .......... ..........  2%  233M 0s\n",
            "   450K .......... .......... .......... .......... ..........  2%  211M 0s\n",
            "   500K .......... .......... .......... .......... ..........  2%  159M 0s\n",
            "   550K .......... .......... .......... .......... ..........  2%  104M 0s\n",
            "   600K .......... .......... .......... .......... ..........  2%  129M 0s\n",
            "   650K .......... .......... .......... .......... ..........  3%  238M 0s\n",
            "   700K .......... .......... .......... .......... ..........  3%  171M 0s\n",
            "   750K .......... .......... .......... .......... ..........  3%  110M 0s\n",
            "   800K .......... .......... .......... .......... ..........  3%  212M 0s\n",
            "   850K .......... .......... .......... .......... ..........  4%  101M 0s\n",
            "   900K .......... .......... .......... .......... ..........  4% 57.7M 0s\n",
            "   950K .......... .......... .......... .......... ..........  4%  124M 0s\n",
            "  1000K .......... .......... .......... .......... ..........  4% 91.6M 0s\n",
            "  1050K .......... .......... .......... .......... ..........  4%  137M 0s\n",
            "  1100K .......... .......... .......... .......... ..........  5%  116M 0s\n",
            "  1150K .......... .......... .......... .......... ..........  5%  161M 0s\n",
            "  1200K .......... .......... .......... .......... ..........  5% 29.2M 0s\n",
            "  1250K .......... .......... .......... .......... ..........  5% 52.9M 0s\n",
            "  1300K .......... .......... .......... .......... ..........  6% 32.4M 0s\n",
            "  1350K .......... .......... .......... .......... ..........  6% 21.6M 0s\n",
            "  1400K .......... .......... .......... .......... ..........  6% 34.2M 0s\n",
            "  1450K .......... .......... .......... .......... ..........  6% 31.4M 0s\n",
            "  1500K .......... .......... .......... .......... ..........  7%  121M 0s\n",
            "  1550K .......... .......... .......... .......... ..........  7%  213M 0s\n",
            "  1600K .......... .......... .......... .......... ..........  7%  242M 0s\n",
            "  1650K .......... .......... .......... .......... ..........  7% 79.2M 0s\n",
            "  1700K .......... .......... .......... .......... ..........  7% 91.6M 0s\n",
            "  1750K .......... .......... .......... .......... ..........  8%  217M 0s\n",
            "  1800K .......... .......... .......... .......... ..........  8%  185M 0s\n",
            "  1850K .......... .......... .......... .......... ..........  8%  197M 0s\n",
            "  1900K .......... .......... .......... .......... ..........  8% 50.8M 0s\n",
            "  1950K .......... .......... .......... .......... ..........  9%  101M 0s\n",
            "  2000K .......... .......... .......... .......... ..........  9%  487M 0s\n",
            "  2050K .......... .......... .......... .......... ..........  9%  515M 0s\n",
            "  2100K .......... .......... .......... .......... ..........  9%  524M 0s\n",
            "  2150K .......... .......... .......... .......... ..........  9%  459M 0s\n",
            "  2200K .......... .......... .......... .......... .......... 10%  464M 0s\n",
            "  2250K .......... .......... .......... .......... .......... 10%  550M 0s\n",
            "  2300K .......... .......... .......... .......... .......... 10%  547M 0s\n",
            "  2350K .......... .......... .......... .......... .......... 10%  273M 0s\n",
            "  2400K .......... .......... .......... .......... .......... 11%  315M 0s\n",
            "  2450K .......... .......... .......... .......... .......... 11%  557M 0s\n",
            "  2500K .......... .......... .......... .......... .......... 11%  369M 0s\n",
            "  2550K .......... .......... .......... .......... .......... 11%  565M 0s\n",
            "  2600K .......... .......... .......... .......... .......... 12%  542M 0s\n",
            "  2650K .......... .......... .......... .......... .......... 12%  536M 0s\n",
            "  2700K .......... .......... .......... .......... .......... 12%  462M 0s\n",
            "  2750K .......... .......... .......... .......... .......... 12%  551M 0s\n",
            "  2800K .......... .......... .......... .......... .......... 12%  412M 0s\n",
            "  2850K .......... .......... .......... .......... .......... 13%  520M 0s\n",
            "  2900K .......... .......... .......... .......... .......... 13%  347M 0s\n",
            "  2950K .......... .......... .......... .......... .......... 13%  256M 0s\n",
            "  3000K .......... .......... .......... .......... .......... 13%  519M 0s\n",
            "  3050K .......... .......... .......... .......... .......... 14%  559M 0s\n",
            "  3100K .......... .......... .......... .......... .......... 14%  333M 0s\n",
            "  3150K .......... .......... .......... .......... .......... 14%  630M 0s\n",
            "  3200K .......... .......... .......... .......... .......... 14%  250M 0s\n",
            "  3250K .......... .......... .......... .......... .......... 14%  395M 0s\n",
            "  3300K .......... .......... .......... .......... .......... 15%  553M 0s\n",
            "  3350K .......... .......... .......... .......... .......... 15%  410M 0s\n",
            "  3400K .......... .......... .......... .......... .......... 15%  362M 0s\n",
            "  3450K .......... .......... .......... .......... .......... 15%  261M 0s\n",
            "  3500K .......... .......... .......... .......... .......... 16%  405M 0s\n",
            "  3550K .......... .......... .......... .......... .......... 16%  383M 0s\n",
            "  3600K .......... .......... .......... .......... .......... 16% 74.3M 0s\n",
            "  3650K .......... .......... .......... .......... .......... 16% 46.1M 0s\n",
            "  3700K .......... .......... .......... .......... .......... 16% 61.4M 0s\n",
            "  3750K .......... .......... .......... .......... .......... 17% 84.5M 0s\n",
            "  3800K .......... .......... .......... .......... .......... 17% 62.2M 0s\n",
            "  3850K .......... .......... .......... .......... .......... 17% 53.3M 0s\n",
            "  3900K .......... .......... .......... .......... .......... 17% 53.0M 0s\n",
            "  3950K .......... .......... .......... .......... .......... 18% 72.8M 0s\n",
            "  4000K .......... .......... .......... .......... .......... 18% 37.1M 0s\n",
            "  4050K .......... .......... .......... .......... .......... 18% 37.0M 0s\n",
            "  4100K .......... .......... .......... .......... .......... 18% 21.8M 0s\n",
            "  4150K .......... .......... .......... .......... .......... 19% 74.3M 0s\n",
            "  4200K .......... .......... .......... .......... .......... 19% 29.0M 0s\n",
            "  4250K .......... .......... .......... .......... .......... 19% 34.4M 0s\n",
            "  4300K .......... .......... .......... .......... .......... 19% 88.0M 0s\n",
            "  4350K .......... .......... .......... .......... .......... 19% 53.1M 0s\n",
            "  4400K .......... .......... .......... .......... .......... 20% 99.9M 0s\n",
            "  4450K .......... .......... .......... .......... .......... 20% 93.9M 0s\n",
            "  4500K .......... .......... .......... .......... .......... 20% 82.8M 0s\n",
            "  4550K .......... .......... .......... .......... .......... 20% 87.8M 0s\n",
            "  4600K .......... .......... .......... .......... .......... 21% 47.2M 0s\n",
            "  4650K .......... .......... .......... .......... .......... 21% 76.1M 0s\n",
            "  4700K .......... .......... .......... .......... .......... 21%  102M 0s\n",
            "  4750K .......... .......... .......... .......... .......... 21% 90.0M 0s\n",
            "  4800K .......... .......... .......... .......... .......... 21%  103M 0s\n",
            "  4850K .......... .......... .......... .......... .......... 22% 84.6M 0s\n",
            "  4900K .......... .......... .......... .......... .......... 22% 66.1M 0s\n",
            "  4950K .......... .......... .......... .......... .......... 22%  104M 0s\n",
            "  5000K .......... .......... .......... .......... .......... 22% 78.6M 0s\n",
            "  5050K .......... .......... .......... .......... .......... 23%  202M 0s\n",
            "  5100K .......... .......... .......... .......... .......... 23%  221M 0s\n",
            "  5150K .......... .......... .......... .......... .......... 23%  242M 0s\n",
            "  5200K .......... .......... .......... .......... .......... 23%  217M 0s\n",
            "  5250K .......... .......... .......... .......... .......... 24%  247M 0s\n",
            "  5300K .......... .......... .......... .......... .......... 24%  190M 0s\n",
            "  5350K .......... .......... .......... .......... .......... 24%  236M 0s\n",
            "  5400K .......... .......... .......... .......... .......... 24%  204M 0s\n",
            "  5450K .......... .......... .......... .......... .......... 24%  191M 0s\n",
            "  5500K .......... .......... .......... .......... .......... 25%  228M 0s\n",
            "  5550K .......... .......... .......... .......... .......... 25%  229M 0s\n",
            "  5600K .......... .......... .......... .......... .......... 25%  217M 0s\n",
            "  5650K .......... .......... .......... .......... .......... 25%  145M 0s\n",
            "  5700K .......... .......... .......... .......... .......... 26%  211M 0s\n",
            "  5750K .......... .......... .......... .......... .......... 26%  202M 0s\n",
            "  5800K .......... .......... .......... .......... .......... 26%  242M 0s\n",
            "  5850K .......... .......... .......... .......... .......... 26%  208M 0s\n",
            "  5900K .......... .......... .......... .......... .......... 26%  250M 0s\n",
            "  5950K .......... .......... .......... .......... .......... 27%  207M 0s\n",
            "  6000K .......... .......... .......... .......... .......... 27%  242M 0s\n",
            "  6050K .......... .......... .......... .......... .......... 27%  180M 0s\n",
            "  6100K .......... .......... .......... .......... .......... 27%  246M 0s\n",
            "  6150K .......... .......... .......... .......... .......... 28%  233M 0s\n",
            "  6200K .......... .......... .......... .......... .......... 28%  234M 0s\n",
            "  6250K .......... .......... .......... .......... .......... 28% 89.1M 0s\n",
            "  6300K .......... .......... .......... .......... .......... 28%  110M 0s\n",
            "  6350K .......... .......... .......... .......... .......... 28%  104M 0s\n",
            "  6400K .......... .......... .......... .......... .......... 29%  144M 0s\n",
            "  6450K .......... .......... .......... .......... .......... 29%  205M 0s\n",
            "  6500K .......... .......... .......... .......... .......... 29%  226M 0s\n",
            "  6550K .......... .......... .......... .......... .......... 29%  146M 0s\n",
            "  6600K .......... .......... .......... .......... .......... 30%  197M 0s\n",
            "  6650K .......... .......... .......... .......... .......... 30%  217M 0s\n",
            "  6700K .......... .......... .......... .......... .......... 30%  248M 0s\n",
            "  6750K .......... .......... .......... .......... .......... 30%  210M 0s\n",
            "  6800K .......... .......... .......... .......... .......... 31%  184M 0s\n",
            "  6850K .......... .......... .......... .......... .......... 31%  193M 0s\n",
            "  6900K .......... .......... .......... .......... .......... 31%  208M 0s\n",
            "  6950K .......... .......... .......... .......... .......... 31%  238M 0s\n",
            "  7000K .......... .......... .......... .......... .......... 31%  244M 0s\n",
            "  7050K .......... .......... .......... .......... .......... 32%  199M 0s\n",
            "  7100K .......... .......... .......... .......... .......... 32%  255M 0s\n",
            "  7150K .......... .......... .......... .......... .......... 32%  223M 0s\n",
            "  7200K .......... .......... .......... .......... .......... 32%  191M 0s\n",
            "  7250K .......... .......... .......... .......... .......... 33% 31.1M 0s\n",
            "  7300K .......... .......... .......... .......... .......... 33%  144M 0s\n",
            "  7350K .......... .......... .......... .......... .......... 33%  251M 0s\n",
            "  7400K .......... .......... .......... .......... .......... 33%  227M 0s\n",
            "  7450K .......... .......... .......... .......... .......... 33%  261M 0s\n",
            "  7500K .......... .......... .......... .......... .......... 34%  213M 0s\n",
            "  7550K .......... .......... .......... .......... .......... 34%  216M 0s\n",
            "  7600K .......... .......... .......... .......... .......... 34%  197M 0s\n",
            "  7650K .......... .......... .......... .......... .......... 34%  237M 0s\n",
            "  7700K .......... .......... .......... .......... .......... 35%  173M 0s\n",
            "  7750K .......... .......... .......... .......... .......... 35%  229M 0s\n",
            "  7800K .......... .......... .......... .......... .......... 35%  203M 0s\n",
            "  7850K .......... .......... .......... .......... .......... 35%  259M 0s\n",
            "  7900K .......... .......... .......... .......... .......... 36%  188M 0s\n",
            "  7950K .......... .......... .......... .......... .......... 36%  193M 0s\n",
            "  8000K .......... .......... .......... .......... .......... 36%  226M 0s\n",
            "  8050K .......... .......... .......... .......... .......... 36%  195M 0s\n",
            "  8100K .......... .......... .......... .......... .......... 36%  247M 0s\n",
            "  8150K .......... .......... .......... .......... .......... 37%  237M 0s\n",
            "  8200K .......... .......... .......... .......... .......... 37%  187M 0s\n",
            "  8250K .......... .......... .......... .......... .......... 37%  220M 0s\n",
            "  8300K .......... .......... .......... .......... .......... 37%  205M 0s\n",
            "  8350K .......... .......... .......... .......... .......... 38%  201M 0s\n",
            "  8400K .......... .......... .......... .......... .......... 38%  243M 0s\n",
            "  8450K .......... .......... .......... .......... .......... 38%  226M 0s\n",
            "  8500K .......... .......... .......... .......... .......... 38%  249M 0s\n",
            "  8550K .......... .......... .......... .......... .......... 38%  197M 0s\n",
            "  8600K .......... .......... .......... .......... .......... 39%  214M 0s\n",
            "  8650K .......... .......... .......... .......... .......... 39%  208M 0s\n",
            "  8700K .......... .......... .......... .......... .......... 39%  201M 0s\n",
            "  8750K .......... .......... .......... .......... .......... 39%  245M 0s\n",
            "  8800K .......... .......... .......... .......... .......... 40%  201M 0s\n",
            "  8850K .......... .......... .......... .......... .......... 40%  186M 0s\n",
            "  8900K .......... .......... .......... .......... .......... 40%  210M 0s\n",
            "  8950K .......... .......... .......... .......... .......... 40%  170M 0s\n",
            "  9000K .......... .......... .......... .......... .......... 40%  220M 0s\n",
            "  9050K .......... .......... .......... .......... .......... 41%  213M 0s\n",
            "  9100K .......... .......... .......... .......... .......... 41%  235M 0s\n",
            "  9150K .......... .......... .......... .......... .......... 41%  206M 0s\n",
            "  9200K .......... .......... .......... .......... .......... 41%  243M 0s\n",
            "  9250K .......... .......... .......... .......... .......... 42%  196M 0s\n",
            "  9300K .......... .......... .......... .......... .......... 42%  210M 0s\n",
            "  9350K .......... .......... .......... .......... .......... 42% 31.9M 0s\n",
            "  9400K .......... .......... .......... .......... .......... 42%  126M 0s\n",
            "  9450K .......... .......... .......... .......... .......... 43%  116M 0s\n",
            "  9500K .......... .......... .......... .......... .......... 43% 74.5M 0s\n",
            "  9550K .......... .......... .......... .......... .......... 43% 97.8M 0s\n",
            "  9600K .......... .......... .......... .......... .......... 43%  183M 0s\n",
            "  9650K .......... .......... .......... .......... .......... 43%  218M 0s\n",
            "  9700K .......... .......... .......... .......... .......... 44%  193M 0s\n",
            "  9750K .......... .......... .......... .......... .......... 44% 47.3M 0s\n",
            "  9800K .......... .......... .......... .......... .......... 44%  123M 0s\n",
            "  9850K .......... .......... .......... .......... .......... 44%  159M 0s\n",
            "  9900K .......... .......... .......... .......... .......... 45%  227M 0s\n",
            "  9950K .......... .......... .......... .......... .......... 45%  238M 0s\n",
            " 10000K .......... .......... .......... .......... .......... 45%  215M 0s\n",
            " 10050K .......... .......... .......... .......... .......... 45%  105M 0s\n",
            " 10100K .......... .......... .......... .......... .......... 45%  127M 0s\n",
            " 10150K .......... .......... .......... .......... .......... 46%  194M 0s\n",
            " 10200K .......... .......... .......... .......... .......... 46%  199M 0s\n",
            " 10250K .......... .......... .......... .......... .......... 46%  228M 0s\n",
            " 10300K .......... .......... .......... .......... .......... 46%  199M 0s\n",
            " 10350K .......... .......... .......... .......... .......... 47%  208M 0s\n",
            " 10400K .......... .......... .......... .......... .......... 47%  177M 0s\n",
            " 10450K .......... .......... .......... .......... .......... 47%  205M 0s\n",
            " 10500K .......... .......... .......... .......... .......... 47%  188M 0s\n",
            " 10550K .......... .......... .......... .......... .......... 48%  254M 0s\n",
            " 10600K .......... .......... .......... .......... .......... 48%  223M 0s\n",
            " 10650K .......... .......... .......... .......... .......... 48%  218M 0s\n",
            " 10700K .......... .......... .......... .......... .......... 48%  119M 0s\n",
            " 10750K .......... .......... .......... .......... .......... 48%  151M 0s\n",
            " 10800K .......... .......... .......... .......... .......... 49%  195M 0s\n",
            " 10850K .......... .......... .......... .......... .......... 49%  214M 0s\n",
            " 10900K .......... .......... .......... .......... .......... 49%  204M 0s\n",
            " 10950K .......... .......... .......... .......... .......... 49%  217M 0s\n",
            " 11000K .......... .......... .......... .......... .......... 50%  210M 0s\n",
            " 11050K .......... .......... .......... .......... .......... 50%  207M 0s\n",
            " 11100K .......... .......... .......... .......... .......... 50%  220M 0s\n",
            " 11150K .......... .......... .......... .......... .......... 50%  226M 0s\n",
            " 11200K .......... .......... .......... .......... .......... 50%  225M 0s\n",
            " 11250K .......... .......... .......... .......... .......... 51%  194M 0s\n",
            " 11300K .......... .......... .......... .......... .......... 51%  216M 0s\n",
            " 11350K .......... .......... .......... .......... .......... 51%  223M 0s\n",
            " 11400K .......... .......... .......... .......... .......... 51%  215M 0s\n",
            " 11450K .......... .......... .......... .......... .......... 52%  198M 0s\n",
            " 11500K .......... .......... .......... .......... .......... 52%  237M 0s\n",
            " 11550K .......... .......... .......... .......... .......... 52%  245M 0s\n",
            " 11600K .......... .......... .......... .......... .......... 52%  184M 0s\n",
            " 11650K .......... .......... .......... .......... .......... 52%  195M 0s\n",
            " 11700K .......... .......... .......... .......... .......... 53%  220M 0s\n",
            " 11750K .......... .......... .......... .......... .......... 53%  244M 0s\n",
            " 11800K .......... .......... .......... .......... .......... 53%  247M 0s\n",
            " 11850K .......... .......... .......... .......... .......... 53%  172M 0s\n",
            " 11900K .......... .......... .......... .......... .......... 54%  225M 0s\n",
            " 11950K .......... .......... .......... .......... .......... 54%  207M 0s\n",
            " 12000K .......... .......... .......... .......... .......... 54%  204M 0s\n",
            " 12050K .......... .......... .......... .......... .......... 54%  224M 0s\n",
            " 12100K .......... .......... .......... .......... .......... 55%  232M 0s\n",
            " 12150K .......... .......... .......... .......... .......... 55%  193M 0s\n",
            " 12200K .......... .......... .......... .......... .......... 55%  245M 0s\n",
            " 12250K .......... .......... .......... .......... .......... 55%  247M 0s\n",
            " 12300K .......... .......... .......... .......... .......... 55%  173M 0s\n",
            " 12350K .......... .......... .......... .......... .......... 56%  175M 0s\n",
            " 12400K .......... .......... .......... .......... .......... 56%  234M 0s\n",
            " 12450K .......... .......... .......... .......... .......... 56%  239M 0s\n",
            " 12500K .......... .......... .......... .......... .......... 56%  224M 0s\n",
            " 12550K .......... .......... .......... .......... .......... 57%  169M 0s\n",
            " 12600K .......... .......... .......... .......... .......... 57%  196M 0s\n",
            " 12650K .......... .......... .......... .......... .......... 57%  227M 0s\n",
            " 12700K .......... .......... .......... .......... .......... 57%  195M 0s\n",
            " 12750K .......... .......... .......... .......... .......... 57%  199M 0s\n",
            " 12800K .......... .......... .......... .......... .......... 58%  242M 0s\n",
            " 12850K .......... .......... .......... .......... .......... 58%  174M 0s\n",
            " 12900K .......... .......... .......... .......... .......... 58%  188M 0s\n",
            " 12950K .......... .......... .......... .......... .......... 58%  221M 0s\n",
            " 13000K .......... .......... .......... .......... .......... 59%  200M 0s\n",
            " 13050K .......... .......... .......... .......... .......... 59%  220M 0s\n",
            " 13100K .......... .......... .......... .......... .......... 59%  225M 0s\n",
            " 13150K .......... .......... .......... .......... .......... 59%  213M 0s\n",
            " 13200K .......... .......... .......... .......... .......... 60% 97.1M 0s\n",
            " 13250K .......... .......... .......... .......... .......... 60%  313M 0s\n",
            " 13300K .......... .......... .......... .......... .......... 60%  170M 0s\n",
            " 13350K .......... .......... .......... .......... .......... 60%  112M 0s\n",
            " 13400K .......... .......... .......... .......... .......... 60%  162M 0s\n",
            " 13450K .......... .......... .......... .......... .......... 61%  178M 0s\n",
            " 13500K .......... .......... .......... .......... .......... 61%  207M 0s\n",
            " 13550K .......... .......... .......... .......... .......... 61%  203M 0s\n",
            " 13600K .......... .......... .......... .......... .......... 61%  200M 0s\n",
            " 13650K .......... .......... .......... .......... .......... 62%  187M 0s\n",
            " 13700K .......... .......... .......... .......... .......... 62%  178M 0s\n",
            " 13750K .......... .......... .......... .......... .......... 62%  204M 0s\n",
            " 13800K .......... .......... .......... .......... .......... 62%  214M 0s\n",
            " 13850K .......... .......... .......... .......... .......... 62%  201M 0s\n",
            " 13900K .......... .......... .......... .......... .......... 63%  236M 0s\n",
            " 13950K .......... .......... .......... .......... .......... 63%  164M 0s\n",
            " 14000K .......... .......... .......... .......... .......... 63%  244M 0s\n",
            " 14050K .......... .......... .......... .......... .......... 63%  227M 0s\n",
            " 14100K .......... .......... .......... .......... .......... 64%  234M 0s\n",
            " 14150K .......... .......... .......... .......... .......... 64%  207M 0s\n",
            " 14200K .......... .......... .......... .......... .......... 64%  250M 0s\n",
            " 14250K .......... .......... .......... .......... .......... 64%  244M 0s\n",
            " 14300K .......... .......... .......... .......... .......... 64%  184M 0s\n",
            " 14350K .......... .......... .......... .......... .......... 65%  205M 0s\n",
            " 14400K .......... .......... .......... .......... .......... 65% 90.8M 0s\n",
            " 14450K .......... .......... .......... .......... .......... 65%  183M 0s\n",
            " 14500K .......... .......... .......... .......... .......... 65%  213M 0s\n",
            " 14550K .......... .......... .......... .......... .......... 66%  216M 0s\n",
            " 14600K .......... .......... .......... .......... .......... 66%  232M 0s\n",
            " 14650K .......... .......... .......... .......... .......... 66%  213M 0s\n",
            " 14700K .......... .......... .......... .......... .......... 66%  128M 0s\n",
            " 14750K .......... .......... .......... .......... .......... 67%  158M 0s\n",
            " 14800K .......... .......... .......... .......... .......... 67%  166M 0s\n",
            " 14850K .......... .......... .......... .......... .......... 67%  199M 0s\n",
            " 14900K .......... .......... .......... .......... .......... 67%  201M 0s\n",
            " 14950K .......... .......... .......... .......... .......... 67%  230M 0s\n",
            " 15000K .......... .......... .......... .......... .......... 68%  176M 0s\n",
            " 15050K .......... .......... .......... .......... .......... 68%  202M 0s\n",
            " 15100K .......... .......... .......... .......... .......... 68%  170M 0s\n",
            " 15150K .......... .......... .......... .......... .......... 68%  236M 0s\n",
            " 15200K .......... .......... .......... .......... .......... 69%  212M 0s\n",
            " 15250K .......... .......... .......... .......... .......... 69%  240M 0s\n",
            " 15300K .......... .......... .......... .......... .......... 69%  221M 0s\n",
            " 15350K .......... .......... .......... .......... .......... 69%  228M 0s\n",
            " 15400K .......... .......... .......... .......... .......... 69%  165M 0s\n",
            " 15450K .......... .......... .......... .......... .......... 70%  187M 0s\n",
            " 15500K .......... .......... .......... .......... .......... 70%  234M 0s\n",
            " 15550K .......... .......... .......... .......... .......... 70%  240M 0s\n",
            " 15600K .......... .......... .......... .......... .......... 70%  195M 0s\n",
            " 15650K .......... .......... .......... .......... .......... 71%  214M 0s\n",
            " 15700K .......... .......... .......... .......... .......... 71%  182M 0s\n",
            " 15750K .......... .......... .......... .......... .......... 71%  207M 0s\n",
            " 15800K .......... .......... .......... .......... .......... 71%  237M 0s\n",
            " 15850K .......... .......... .......... .......... .......... 72%  182M 0s\n",
            " 15900K .......... .......... .......... .......... .......... 72%  239M 0s\n",
            " 15950K .......... .......... .......... .......... .......... 72%  239M 0s\n",
            " 16000K .......... .......... .......... .......... .......... 72%  184M 0s\n",
            " 16050K .......... .......... .......... .......... .......... 72%  164M 0s\n",
            " 16100K .......... .......... .......... .......... .......... 73%  221M 0s\n",
            " 16150K .......... .......... .......... .......... .......... 73%  247M 0s\n",
            " 16200K .......... .......... .......... .......... .......... 73%  251M 0s\n",
            " 16250K .......... .......... .......... .......... .......... 73%  213M 0s\n",
            " 16300K .......... .......... .......... .......... .......... 74%  201M 0s\n",
            " 16350K .......... .......... .......... .......... .......... 74%  217M 0s\n",
            " 16400K .......... .......... .......... .......... .......... 74%  239M 0s\n",
            " 16450K .......... .......... .......... .......... .......... 74%  176M 0s\n",
            " 16500K .......... .......... .......... .......... .......... 74%  219M 0s\n",
            " 16550K .......... .......... .......... .......... .......... 75%  249M 0s\n",
            " 16600K .......... .......... .......... .......... .......... 75%  220M 0s\n",
            " 16650K .......... .......... .......... .......... .......... 75%  173M 0s\n",
            " 16700K .......... .......... .......... .......... .......... 75%  248M 0s\n",
            " 16750K .......... .......... .......... .......... .......... 76%  171M 0s\n",
            " 16800K .......... .......... .......... .......... .......... 76%  225M 0s\n",
            " 16850K .......... .......... .......... .......... .......... 76%  248M 0s\n",
            " 16900K .......... .......... .......... .......... .......... 76%  218M 0s\n",
            " 16950K .......... .......... .......... .......... .......... 76%  221M 0s\n",
            " 17000K .......... .......... .......... .......... .......... 77%  191M 0s\n",
            " 17050K .......... .......... .......... .......... .......... 77% 91.2M 0s\n",
            " 17100K .......... .......... .......... .......... .......... 77% 49.5M 0s\n",
            " 17150K .......... .......... .......... .......... .......... 77%  182M 0s\n",
            " 17200K .......... .......... .......... .......... .......... 78%  213M 0s\n",
            " 17250K .......... .......... .......... .......... .......... 78% 82.7M 0s\n",
            " 17300K .......... .......... .......... .......... .......... 78%  174M 0s\n",
            " 17350K .......... .......... .......... .......... .......... 78%  416M 0s\n",
            " 17400K .......... .......... .......... .......... .......... 79%  352M 0s\n",
            " 17450K .......... .......... .......... .......... .......... 79%  324M 0s\n",
            " 17500K .......... .......... .......... .......... .......... 79%  395M 0s\n",
            " 17550K .......... .......... .......... .......... .......... 79%  526M 0s\n",
            " 17600K .......... .......... .......... .......... .......... 79%  480M 0s\n",
            " 17650K .......... .......... .......... .......... .......... 80%  252M 0s\n",
            " 17700K .......... .......... .......... .......... .......... 80%  359M 0s\n",
            " 17750K .......... .......... .......... .......... .......... 80%  579M 0s\n",
            " 17800K .......... .......... .......... .......... .......... 80%  478M 0s\n",
            " 17850K .......... .......... .......... .......... .......... 81%  546M 0s\n",
            " 17900K .......... .......... .......... .......... .......... 81%  234M 0s\n",
            " 17950K .......... .......... .......... .......... .......... 81%  480M 0s\n",
            " 18000K .......... .......... .......... .......... .......... 81%  415M 0s\n",
            " 18050K .......... .......... .......... .......... .......... 81%  431M 0s\n",
            " 18100K .......... .......... .......... .......... .......... 82%  571M 0s\n",
            " 18150K .......... .......... .......... .......... .......... 82%  554M 0s\n",
            " 18200K .......... .......... .......... .......... .......... 82%  511M 0s\n",
            " 18250K .......... .......... .......... .......... .......... 82%  471M 0s\n",
            " 18300K .......... .......... .......... .......... .......... 83%  555M 0s\n",
            " 18350K .......... .......... .......... .......... .......... 83%  573M 0s\n",
            " 18400K .......... .......... .......... .......... .......... 83%  543M 0s\n",
            " 18450K .......... .......... .......... .......... .......... 83%  473M 0s\n",
            " 18500K .......... .......... .......... .......... .......... 84%  534M 0s\n",
            " 18550K .......... .......... .......... .......... .......... 84%  168M 0s\n",
            " 18600K .......... .......... .......... .......... .......... 84%  453M 0s\n",
            " 18650K .......... .......... .......... .......... .......... 84%  373M 0s\n",
            " 18700K .......... .......... .......... .......... .......... 84%  366M 0s\n",
            " 18750K .......... .......... .......... .......... .......... 85%  327M 0s\n",
            " 18800K .......... .......... .......... .......... .......... 85%  423M 0s\n",
            " 18850K .......... .......... .......... .......... .......... 85%  535M 0s\n",
            " 18900K .......... .......... .......... .......... .......... 85% 84.6M 0s\n",
            " 18950K .......... .......... .......... .......... .......... 86%  477M 0s\n",
            " 19000K .......... .......... .......... .......... .......... 86%  559M 0s\n",
            " 19050K .......... .......... .......... .......... .......... 86%  463M 0s\n",
            " 19100K .......... .......... .......... .......... .......... 86%  433M 0s\n",
            " 19150K .......... .......... .......... .......... .......... 86%  403M 0s\n",
            " 19200K .......... .......... .......... .......... .......... 87%  501M 0s\n",
            " 19250K .......... .......... .......... .......... .......... 87%  514M 0s\n",
            " 19300K .......... .......... .......... .......... .......... 87%  513M 0s\n",
            " 19350K .......... .......... .......... .......... .......... 87%  506M 0s\n",
            " 19400K .......... .......... .......... .......... .......... 88%  565M 0s\n",
            " 19450K .......... .......... .......... .......... .......... 88%  577M 0s\n",
            " 19500K .......... .......... .......... .......... .......... 88%  449M 0s\n",
            " 19550K .......... .......... .......... .......... .......... 88%  516M 0s\n",
            " 19600K .......... .......... .......... .......... .......... 88%  541M 0s\n",
            " 19650K .......... .......... .......... .......... .......... 89%  438M 0s\n",
            " 19700K .......... .......... .......... .......... .......... 89%  474M 0s\n",
            " 19750K .......... .......... .......... .......... .......... 89%  558M 0s\n",
            " 19800K .......... .......... .......... .......... .......... 89%  526M 0s\n",
            " 19850K .......... .......... .......... .......... .......... 90%  456M 0s\n",
            " 19900K .......... .......... .......... .......... .......... 90%  542M 0s\n",
            " 19950K .......... .......... .......... .......... .......... 90%  558M 0s\n",
            " 20000K .......... .......... .......... .......... .......... 90%  575M 0s\n",
            " 20050K .......... .......... .......... .......... .......... 91%  550M 0s\n",
            " 20100K .......... .......... .......... .......... .......... 91% 66.1M 0s\n",
            " 20150K .......... .......... .......... .......... .......... 91% 88.3M 0s\n",
            " 20200K .......... .......... .......... .......... .......... 91% 91.7M 0s\n",
            " 20250K .......... .......... .......... .......... .......... 91% 95.0M 0s\n",
            " 20300K .......... .......... .......... .......... .......... 92% 93.3M 0s\n",
            " 20350K .......... .......... .......... .......... .......... 92% 78.9M 0s\n",
            " 20400K .......... .......... .......... .......... .......... 92% 96.5M 0s\n",
            " 20450K .......... .......... .......... .......... .......... 92% 95.2M 0s\n",
            " 20500K .......... .......... .......... .......... .......... 93% 98.8M 0s\n",
            " 20550K .......... .......... .......... .......... .......... 93% 84.7M 0s\n",
            " 20600K .......... .......... .......... .......... .......... 93% 97.8M 0s\n",
            " 20650K .......... .......... .......... .......... .......... 93% 89.5M 0s\n",
            " 20700K .......... .......... .......... .......... .......... 93%  102M 0s\n",
            " 20750K .......... .......... .......... .......... .......... 94% 92.7M 0s\n",
            " 20800K .......... .......... .......... .......... .......... 94%  101M 0s\n",
            " 20850K .......... .......... .......... .......... .......... 94% 86.6M 0s\n",
            " 20900K .......... .......... .......... .......... .......... 94%  352M 0s\n",
            " 20950K .......... .......... .......... .......... .......... 95%  237M 0s\n",
            " 21000K .......... .......... .......... .......... .......... 95%  230M 0s\n",
            " 21050K .......... .......... .......... .......... .......... 95%  213M 0s\n",
            " 21100K .......... .......... .......... .......... .......... 95%  200M 0s\n",
            " 21150K .......... .......... .......... .......... .......... 96%  244M 0s\n",
            " 21200K .......... .......... .......... .......... .......... 96% 32.1M 0s\n",
            " 21250K .......... .......... .......... .......... .......... 96% 81.3M 0s\n",
            " 21300K .......... .......... .......... .......... .......... 96% 77.3M 0s\n",
            " 21350K .......... .......... .......... .......... .......... 96% 95.8M 0s\n",
            " 21400K .......... .......... .......... .......... .......... 97%  104M 0s\n",
            " 21450K .......... .......... .......... .......... .......... 97% 99.0M 0s\n",
            " 21500K .......... .......... .......... .......... .......... 97%  193M 0s\n",
            " 21550K .......... .......... .......... .......... .......... 97%  319M 0s\n",
            " 21600K .......... .......... .......... .......... .......... 98%  440M 0s\n",
            " 21650K .......... .......... .......... .......... .......... 98%  503M 0s\n",
            " 21700K .......... .......... .......... .......... .......... 98%  523M 0s\n",
            " 21750K .......... .......... .......... .......... .......... 98%  531M 0s\n",
            " 21800K .......... .......... .......... .......... .......... 98%  482M 0s\n",
            " 21850K .......... .......... .......... .......... .......... 99%  568M 0s\n",
            " 21900K .......... .......... .......... .......... .......... 99%  579M 0s\n",
            " 21950K .......... .......... .......... .......... .......... 99%  326M 0s\n",
            " 22000K .......... .......... .......... .......... .......... 99%  522M 0s\n",
            " 22050K .......... .......... .........                       100%  554M=0.1s\n",
            "\n",
            "2023-08-18 00:49:48 (150 MB/s) - ‘data/base/Height.gwas.txt.gz’ saved [22609185/22609185]\n",
            "\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1uhJR_3sn7RA8U5iYQbcmTp6vFdQiF4F2\n",
            "To: /content/data/target/EUR.zip\n",
            "\r  0%|          | 0.00/38.4M [00:00<?, ?B/s]\r 12%|█▏        | 4.72M/38.4M [00:00<00:00, 42.7MB/s]\r 25%|██▍       | 9.44M/38.4M [00:00<00:00, 37.3MB/s]\r 45%|████▌     | 17.3M/38.4M [00:00<00:00, 50.5MB/s]\r 67%|██████▋   | 25.7M/38.4M [00:00<00:00, 50.2MB/s]\r 89%|████████▊ | 34.1M/38.4M [00:00<00:00, 58.2MB/s]\r100%|██████████| 38.4M/38.4M [00:00<00:00, 58.0MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Decompress the target data\n",
        "!unzip data/target/EUR.zip -d data/target"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xtcohBHokP8i",
        "outputId": "df1ada94-257b-4b39-c4cd-3914f4a7b64b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  data/target/EUR.zip\n",
            "  inflating: data/target/EUR.bed     \n",
            "  inflating: data/target/EUR.bim     \n",
            "  inflating: data/target/EUR.cov     \n",
            "  inflating: data/target/EUR.fam     \n",
            "  inflating: data/target/EUR.height  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Brief look into the data"
      ],
      "metadata": {
        "id": "4hZVuIK0nJX-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Base data**"
      ],
      "metadata": {
        "id": "qAt1ZhzZkWzs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Base data (GWAS), consisting of summary statistics of genotype-phenotype associations at genetic variants (hereafter SNPs) genome-wide, typically made available online in text format by the investigators who performed the GWAS.\n",
        "1. **CHR:** Chromosome Number. Indicates the specific chromosome where the SNP is located, ranging from chromosomes 1-22, and including X, Y, and MT (if sequenced). In the context of PLINK and many other genomics tools:\n",
        "    * X chromosome is typically coded as 23.\n",
        "    * Y chromosome is coded as 24.\n",
        "    * MT or mitochondrial genome is coded as 25.\n",
        "2. **BP:** Base pair position of the SNP on the chromosome.\n",
        "3. **SNP:** Identifier for the SNP (usually the rsID from the dbSNP database).\n",
        "4. **A1:** The allele used for testing (often the minor allele).\n",
        "5. **A2:** The other allele (often the major allele).\n",
        "6. **N:** Sample size or the number of individuals used in the analysis.\n",
        "7. **SE:** Standard error of the association test statistic.\n",
        "8. **P:** P-value from the association test. A lower P-value suggests that the SNP might be more strongly associated with the trait of interest.\n",
        "9. **OR:** Odds ratio. It represents the odds of the trait occurring given the A1 allele compared to the odds of the trait occurring given the A2 allele. An OR > 1 suggests the A1 allele is a risk factor for the trait, whereas an OR < 1 suggests the A1 allele might be protective.\n",
        "10. **INFO:** This typically represents imputation quality. A value close to 1 indicates high imputation quality, while values far from 1 might suggest lower confidence in the imputed genotypes for that SNP.\n",
        "11. **MAF:** Minor Allele Frequency. It's the frequency of the A1 allele in the sample.\n",
        "\n"
      ],
      "metadata": {
        "id": "OrNkNKKmkkA2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!zcat data/base/Height.gwas.txt.gz | head"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zuNUPWlJZ9Np",
        "outputId": "61c91ee9-46fa-4e7f-d7f7-368338edfa7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CHR\tBP\tSNP\tA1\tA2\tN\tSE\tP\tOR\tINFO\tMAF\n",
            "1\t756604\trs3131962\tA\tG\t388028\t0.00301666\t0.483171\t0.997886915712657\t0.890557941364774\t0.369389592764921\n",
            "1\t768448\trs12562034\tA\tG\t388028\t0.00329472\t0.834808\t1.00068731609353\t0.895893511351165\t0.336845754096289\n",
            "1\t779322\trs4040617\tG\tA\t388028\t0.00303344\t0.42897\t0.997603556067569\t0.897508290615237\t0.377368010940814\n",
            "1\t801536\trs79373928\tG\tT\t388028\t0.00841324\t0.808999\t1.00203569922793\t0.908962856432993\t0.483212245374095\n",
            "1\t808631\trs11240779\tG\tA\t388028\t0.00242821\t0.590265\t1.00130832511154\t0.893212523690488\t0.450409558999587\n",
            "1\t809876\trs57181708\tG\tA\t388028\t0.00336785\t0.71475\t1.00123165786833\t0.923557624081969\t0.499743932656759\n",
            "1\t835499\trs4422948\tG\tA\t388028\t0.0023758\t0.710884\t0.999119752645202\t0.906437735120596\t0.481016005816168\n",
            "1\t838555\trs4970383\tA\tC\t388028\t0.00235773\t0.150993\t0.996619945289758\t0.907716506801574\t0.327164029672754\n",
            "1\t840753\trs4970382\tC\tT\t388028\t0.00207377\t0.199967\t0.99734567895614\t0.914602590137255\t0.498936220426316\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Target data**"
      ],
      "metadata": {
        "id": "I51bPFdlmShb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Consisting of genotypes, and usually also phenotypes, in individuals from a sample to which the researchers performing the PRS analysis have access (often not publicly available), which should be independent of the GWAS sample.\n",
        "* **FID/FAMID:** Family Identifier. This tells us which family a particular sample comes from. It's like a unique code given to each family.\n",
        "* **IID:** Individual Identifier. This is a unique code given to each individual or sample. Even if two samples come from the same family, their IIDs will be different.\n",
        "* **Height/Trait:** This represents the height of the individual linked to the IID. It's a measurement of how tall they are."
      ],
      "metadata": {
        "id": "jtLnKocYmsEI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!cat data/target/EUR.height | head"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tQZv0r4gnF0e",
        "outputId": "3ad8dfc8-47f7-42b0-9620-3bba0505f3f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FID\tIID\tHeight\n",
            "HG00096\tHG00096\t169.132168767547\n",
            "HG00097\tHG00097\t171.256258630279\n",
            "HG00099\tHG00099\t171.534379938588\n",
            "HG00101\tHG00101\t169.850176470551\n",
            "HG00102\tHG00102\t172.788360878389\n",
            "HG00103\tHG00103\t169.862973824923\n",
            "HG00105\tHG00105\t168.939248611414\n",
            "HG00107\tHG00107\t168.972346393861\n",
            "HG00108\tHG00108\t171.311736719186\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Import data\n",
        "pheno_height <- read.table(\"data/target/EUR.height\", header=T)\n",
        "\n",
        "p <- ggplot(pheno_height, aes(x=Height)) +\n",
        "    geom_density(color=\"darkblue\", fill=\"lightblue\")\n",
        "p\n",
        "\n",
        "# Add mean line\n",
        "p + geom_vline(aes(xintercept=mean(Height)),\n",
        "               color=\"red\", linetype=\"dashed\", size=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "H2Dzy7C-lVzu",
        "outputId": "6eb80e7f-354f-400c-e5bf-89fbbb949f7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# POLYGENIC RISK SCORES"
      ],
      "metadata": {
        "id": "_TsevKSBZurE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "Xj_u7rBhIYVf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Polygenic Risk Scores (PRS) are a method used to quantify an individual's genetic risk for a particular trait or disease. The process involves summing up the effects of many genetic variants, each of which has a small effect on the trait or disease, to create a single score that captures an individual's overall genetic predisposition."
      ],
      "metadata": {
        "id": "UllpBRWqZyQn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. **Large GWAS to Identify Associated Alleles:**\n",
        "    * Conduct a genome-wide association study (GWAS) on a large sample.\n",
        "    * Analyze millions of SNPs to identify which ones are associated with the trait or disease of interest.\n",
        "    * The goal is to find genetic variants that are more common in individuals with the trait or disease compared to those without.\n",
        "2. **Develop PRS Model:**\n",
        "    * Using the results from the GWAS, develop a Polygenic Risk Score (PRS) model.\n",
        "    * The PRS is calculated by summing up the number of risk alleles an individual has, each weighted by the effect size (usually the beta coefficient) from the GWAS results.\n",
        "    * This model aims to predict an individual's risk based on the combination of multiple genetic variants.\n",
        "3. **Calculate Individual PRS in Target Sample:**\n",
        "    * For a new individual or sample, calculate their PRS using their genotypic data and the PRS model developed in step 2.\n",
        "    * This provides a score that represents their genetic risk for the trait or disease.\n",
        "4. **Stratify Target Sample by PRS:**\n",
        "    * Once you have PRS for individuals in a sample, you can stratify or group them based on their scores.\n",
        "    * For example, individuals can be grouped into categories like low, medium, and high risk based on their PRS.\n",
        "    * This stratification can then be used in further analyses, interventions, or personalized medical strategies."
      ],
      "metadata": {
        "id": "kgybmwPjZ0XD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. QUALITY CONTROL"
      ],
      "metadata": {
        "id": "67uKGcS7o-SY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "0dhuVZkuaW78"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## A. QC of Base Data"
      ],
      "metadata": {
        "id": "ST8IqLYhpU51"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Read the base data file**"
      ],
      "metadata": {
        "id": "O6d0ivyUps2b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!zcat data/base/Height.gwas.txt.gz | head"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jfRboyJzpCck",
        "outputId": "4243ba40-51e8-4b25-87f2-d3d86920bc49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CHR\tBP\tSNP\tA1\tA2\tN\tSE\tP\tOR\tINFO\tMAF\n",
            "1\t756604\trs3131962\tA\tG\t388028\t0.00301666\t0.483171\t0.997886915712657\t0.890557941364774\t0.369389592764921\n",
            "1\t768448\trs12562034\tA\tG\t388028\t0.00329472\t0.834808\t1.00068731609353\t0.895893511351165\t0.336845754096289\n",
            "1\t779322\trs4040617\tG\tA\t388028\t0.00303344\t0.42897\t0.997603556067569\t0.897508290615237\t0.377368010940814\n",
            "1\t801536\trs79373928\tG\tT\t388028\t0.00841324\t0.808999\t1.00203569922793\t0.908962856432993\t0.483212245374095\n",
            "1\t808631\trs11240779\tG\tA\t388028\t0.00242821\t0.590265\t1.00130832511154\t0.893212523690488\t0.450409558999587\n",
            "1\t809876\trs57181708\tG\tA\t388028\t0.00336785\t0.71475\t1.00123165786833\t0.923557624081969\t0.499743932656759\n",
            "1\t835499\trs4422948\tG\tA\t388028\t0.0023758\t0.710884\t0.999119752645202\t0.906437735120596\t0.481016005816168\n",
            "1\t838555\trs4970383\tA\tC\t388028\t0.00235773\t0.150993\t0.996619945289758\t0.907716506801574\t0.327164029672754\n",
            "1\t840753\trs4970382\tC\tT\t388028\t0.00207377\t0.199967\t0.99734567895614\t0.914602590137255\t0.498936220426316\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Base data (GWAS), consisting of summary statistics of genotype-phenotype associations at genetic variants (hereafter SNPs) genome-wide, typically made available online in text format by the investigators who performed the GWAS.\n",
        "1. **CHR:** Chromosome Number. Indicates the specific chromosome where the SNP is located, ranging from chromosomes 1-22, and including X, Y, and MT (if sequenced). In the context of PLINK and many other genomics tools:\n",
        "    * X chromosome is typically coded as 23.\n",
        "    * Y chromosome is coded as 24.\n",
        "    * MT or mitochondrial genome is coded as 25.\n",
        "2. **BP:** Base pair position of the SNP on the chromosome.\n",
        "3. **SNP:** Identifier for the SNP (usually the rsID from the dbSNP database).\n",
        "4. **A1**: The effect allele of the SNP used for testing (often the minor allele).\n",
        "5. **A2**: The non-effect allele of the SNP (often the major allele).\n",
        "6. **N**: Number of samples used to obtain the effect size estimate.\n",
        "7. **SE**: The standard error (SE) of the effect size esimate.\n",
        "8. **P**: The P-value of association between the SNP genotypes and the base phenotype. A lower P-value suggests that the SNP might be more strongly associated with the trait of interest.\n",
        "9. **OR**: Odds ratio. It represents the odds of the trait occurring given the A1 allele compared to the odds of the trait occurring given the A2 allele. An OR > 1 suggests the A1 allele is a risk factor for the trait, whereas an OR < 1 suggests the A1 allele might be protective.\n",
        "10. **INFO**: This typically represents imputation quality. A value close to 1 indicates high imputation quality, while values far from 1 might suggest lower confidence in the imputed genotypes for that SNP.\n",
        "11. **MAF**: The minor allele frequency (MAF) of the SNP. It's the frequency of the A1 allele in the sample.\n",
        "\n"
      ],
      "metadata": {
        "id": "CsdxCbcxp1IL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### QC checklist: Base data"
      ],
      "metadata": {
        "id": "wCtq67WOqaJ3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**i. Heritability check**"
      ],
      "metadata": {
        "id": "njgFmE_oqdDj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Recommend that PRS analyses are performed on base data with a chip-heritability estimate \th^2 SNP > 0.05\n"
      ],
      "metadata": {
        "id": "T4xiUJY2rHcG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ii. Effect allele**"
      ],
      "metadata": {
        "id": "HULkSxVCrJ1M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is important to know which allele is the effect allele and which is the non-effect allele for PRS association results to be in the correct direction."
      ],
      "metadata": {
        "id": "Ba0pOW73r1gJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**iii. Genome build**"
      ],
      "metadata": {
        "id": "eFVtBqJTsIzZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- hg19\n",
        "- hg38\n",
        "- ..."
      ],
      "metadata": {
        "id": "oQ23LPCjsQ4h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**iv. Standard GWAS QC**"
      ],
      "metadata": {
        "id": "t2D58i4ys_Ex"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "mkdir QC\n",
        "mkdir -p QC/base\n",
        "mkdir -p QC/target"
      ],
      "metadata": {
        "id": "x1157QrTtfiZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "\n",
        "# Filter by MAF, INFO\n",
        "## MAF > 0.01 & INFO > 0.8\n",
        "\n",
        "zcat data/base/Height.gwas.txt.gz |\\\n",
        "awk 'NR==1 || ($11 > 0.01) && ($10 > 0.8) {print}' |\\\n",
        "gzip  > QC/base/Height.gz"
      ],
      "metadata": {
        "id": "stLBv_TFtEDQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**v. Mismatching SNPs**"
      ],
      "metadata": {
        "id": "o1ZE0z94tDKg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**vi. Duplicates SNPs**"
      ],
      "metadata": {
        "id": "Dr8JMdv7t7Zv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "# Remove duplicate SNPs\n",
        "\n",
        "zcat QC/base/Height.gz |\\\n",
        "awk '{seen[$3]++; if(seen[$3]==1){ print}}' |\\\n",
        "gzip - > QC/base/Height.nodup.gz"
      ],
      "metadata": {
        "id": "0v3SfhUnuALK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**vii. Ambigous SNPs**"
      ],
      "metadata": {
        "id": "V3u8aiu-uMJ5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "\n",
        "zcat QC/base/Height.nodup.gz |\\\n",
        "awk '!( ($4==\"A\" && $5==\"T\") || \\\n",
        "        ($4==\"T\" && $5==\"A\") || \\\n",
        "        ($4==\"G\" && $5==\"C\") || \\\n",
        "        ($4==\"C\" && $5==\"G\")) {print}' |\\\n",
        "    gzip > QC/base/Height.QC.gz"
      ],
      "metadata": {
        "id": "8Z40oXZOuQIZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**vii. Sex chromosomes**"
      ],
      "metadata": {
        "id": "z6Ajs0Wy-pqa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**viii. Relatedness**"
      ],
      "metadata": {
        "id": "ISFCkOCK-u2A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## B. QC of Target Data"
      ],
      "metadata": {
        "id": "dqxyIUW9-2iW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### QC checklist: Target data"
      ],
      "metadata": {
        "id": "z3zhbLgA_Fxc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**i. Sample size**"
      ],
      "metadata": {
        "id": "Xgapxpj8_Opi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The target data suitable to perform PRS analyses should have at least 100 individuals. The sample size of our data here is 503 individuals."
      ],
      "metadata": {
        "id": "5IoBDadnADYM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "# Check the sample size\n",
        "cat data/target/EUR.fam | wc -l"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q18b88OR--mV",
        "outputId": "6eca30ea-6466-4e9f-e19c-1a45e5a87686"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "503\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ii. Genome build**"
      ],
      "metadata": {
        "id": "DeIcvAfrAgWZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**iii. Standard GWAS QC**"
      ],
      "metadata": {
        "id": "qGNbDW1KAjbh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "\n",
        "plink \\\n",
        "    --bfile data/target/EUR \\\n",
        "    --maf 0.01 \\\n",
        "    --hwe 1e-6 \\\n",
        "    --geno 0.01 \\\n",
        "    --mind 0.01 \\\n",
        "    --write-snplist \\\n",
        "    --make-just-fam \\\n",
        "    --out QC/target/EUR.QC"
      ],
      "metadata": {
        "id": "LmSa4IixBEGm",
        "outputId": "83c124dd-a35a-47f4-f865-3661fa0a4a3b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PLINK v1.90b6.21 64-bit (19 Oct 2020)          www.cog-genomics.org/plink/1.9/\n",
            "(C) 2005-2020 Shaun Purcell, Christopher Chang   GNU General Public License v3\n",
            "Logging to QC/target/EUR.QC.log.\n",
            "Options in effect:\n",
            "  --bfile data/target/EUR\n",
            "  --geno 0.01\n",
            "  --hwe 1e-6\n",
            "  --maf 0.01\n",
            "  --make-just-fam\n",
            "  --mind 0.01\n",
            "  --out QC/target/EUR.QC\n",
            "  --write-snplist\n",
            "\n",
            "12982 MB RAM detected; reserving 6491 MB for main workspace.\n",
            "551892 variants loaded from .bim file.\n",
            "503 people (240 males, 263 females) loaded from .fam.\n",
            "14 people removed due to missing genotype data (--mind).\n",
            "IDs written to QC/target/EUR.QC.irem .\n",
            "Using 1 thread (no multithreaded calculations invoked).\n",
            "Before main variant filters, 489 founders and 0 nonfounders present.\n",
            "Calculating allele frequencies... 0%\b\b1%\b\b2%\b\b3%\b\b4%\b\b5%\b\b6%\b\b7%\b\b8%\b\b9%\b\b10%\b\b\b11%\b\b\b12%\b\b\b13%\b\b\b14%\b\b\b15%\b\b\b16%\b\b\b17%\b\b\b18%\b\b\b19%\b\b\b20%\b\b\b21%\b\b\b22%\b\b\b23%\b\b\b24%\b\b\b25%\b\b\b26%\b\b\b27%\b\b\b28%\b\b\b29%\b\b\b30%\b\b\b31%\b\b\b32%\b\b\b33%\b\b\b34%\b\b\b35%\b\b\b36%\b\b\b37%\b\b\b38%\b\b\b39%\b\b\b40%\b\b\b41%\b\b\b42%\b\b\b43%\b\b\b44%\b\b\b45%\b\b\b46%\b\b\b47%\b\b\b48%\b\b\b49%\b\b\b50%\b\b\b51%\b\b\b52%\b\b\b53%\b\b\b54%\b\b\b55%\b\b\b56%\b\b\b57%\b\b\b58%\b\b\b59%\b\b\b60%\b\b\b61%\b\b\b62%\b\b\b63%\b\b\b64%\b\b\b65%\b\b\b66%\b\b\b67%\b\b\b68%\b\b\b69%\b\b\b70%\b\b\b71%\b\b\b72%\b\b\b73%\b\b\b74%\b\b\b75%\b\b\b76%\b\b\b77%\b\b\b78%\b\b\b79%\b\b\b80%\b\b\b81%\b\b\b82%\b\b\b83%\b\b\b84%\b\b\b85%\b\b\b86%\b\b\b87%\b\b\b88%\b\b\b89%\b\b\b90%\b\b\b91%\b\b\b92%\b\b\b93%\b\b\b94%\b\b\b95%\b\b\b96%\b\b\b97%\b\b\b98%\b\b\b99%\b\b\b\b done.\n",
            "Total genotyping rate in remaining samples is 0.999816.\n",
            "5353 variants removed due to missing genotype data (--geno).\n",
            "--hwe: 944 variants removed due to Hardy-Weinberg exact test.\n",
            "5061 variants removed due to minor allele threshold(s)\n",
            "(--maf/--max-maf/--mac/--max-mac).\n",
            "540534 variants and 489 people pass filters and QC.\n",
            "Note: No phenotypes present.\n",
            "List of variant IDs written to QC/target/EUR.QC.snplist .\n",
            "--make-just-fam to QC/target/EUR.QC.fam ... done.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning: 1806 het. haploid genotypes present (see QC/target/EUR.QC.hh ); many\n",
            "commands treat these as missing.\n",
            "Warning: --hwe observation counts vary by more than 10%, due to the X\n",
            "chromosome.  You may want to use a less stringent --hwe p-value threshold for X\n",
            "chromosome variants.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Screenshot from 2023-08-16 13-26-15.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "EGP57SBWBgqc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Some files are needed in the QC/target folder for the below processing:"
      ],
      "metadata": {
        "id": "UbYN3mwuD9uN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "\n",
        "cp data/target/EUR* QC/target/"
      ],
      "metadata": {
        "id": "7XSPvSEADm2z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Samples with extreme heterozygosity are typically removed prior to downstream analyses.\n",
        "\n",
        "This will generate two files 1) EUR.QC.prune.in and 2) EUR.QC.prune.out. All SNPs within EUR.QC.prune.in have a pairwise r2\\<0.25"
      ],
      "metadata": {
        "id": "5AZJcQA5CR9h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "# Perform pruning to remove highly correlated SNPs\n",
        "\n",
        "plink \\\n",
        "    --bfile QC/target/EUR \\\n",
        "    --keep QC/target/EUR.QC.fam \\\n",
        "    --extract QC/target/EUR.QC.snplist \\\n",
        "    --indep-pairwise 200 50 0.25 \\\n",
        "    --out QC/target/EUR.QC"
      ],
      "metadata": {
        "id": "KU0qzFxtCXBL",
        "outputId": "00df9ea1-3a2e-43ac-8859-68331ad9543e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PLINK v1.90b6.21 64-bit (19 Oct 2020)          www.cog-genomics.org/plink/1.9/\n",
            "(C) 2005-2020 Shaun Purcell, Christopher Chang   GNU General Public License v3\n",
            "Logging to QC/target/EUR.QC.log.\n",
            "Options in effect:\n",
            "  --bfile QC/target/EUR\n",
            "  --extract QC/target/EUR.QC.snplist\n",
            "  --indep-pairwise 200 50 0.25\n",
            "  --keep QC/target/EUR.QC.fam\n",
            "  --out QC/target/EUR.QC\n",
            "\n",
            "12982 MB RAM detected; reserving 6491 MB for main workspace.\n",
            "551892 variants loaded from .bim file.\n",
            "503 people (240 males, 263 females) loaded from .fam.\n",
            "--extract: 540534 variants remaining.\n",
            "--keep: 489 people remaining.\n",
            "Using 1 thread (no multithreaded calculations invoked).\n",
            "Before main variant filters, 489 founders and 0 nonfounders present.\n",
            "Calculating allele frequencies... 0%\b\b1%\b\b2%\b\b3%\b\b4%\b\b5%\b\b6%\b\b7%\b\b8%\b\b9%\b\b10%\b\b\b11%\b\b\b12%\b\b\b13%\b\b\b14%\b\b\b15%\b\b\b16%\b\b\b17%\b\b\b18%\b\b\b19%\b\b\b20%\b\b\b21%\b\b\b22%\b\b\b23%\b\b\b24%\b\b\b25%\b\b\b26%\b\b\b27%\b\b\b28%\b\b\b29%\b\b\b30%\b\b\b31%\b\b\b32%\b\b\b33%\b\b\b34%\b\b\b35%\b\b\b36%\b\b\b37%\b\b\b38%\b\b\b39%\b\b\b40%\b\b\b41%\b\b\b42%\b\b\b43%\b\b\b44%\b\b\b45%\b\b\b46%\b\b\b47%\b\b\b48%\b\b\b49%\b\b\b50%\b\b\b51%\b\b\b52%\b\b\b53%\b\b\b54%\b\b\b55%\b\b\b56%\b\b\b57%\b\b\b58%\b\b\b59%\b\b\b60%\b\b\b61%\b\b\b62%\b\b\b63%\b\b\b64%\b\b\b65%\b\b\b66%\b\b\b67%\b\b\b68%\b\b\b69%\b\b\b70%\b\b\b71%\b\b\b72%\b\b\b73%\b\b\b74%\b\b\b75%\b\b\b76%\b\b\b77%\b\b\b78%\b\b\b79%\b\b\b80%\b\b\b81%\b\b\b82%\b\b\b83%\b\b\b84%\b\b\b85%\b\b\b86%\b\b\b87%\b\b\b88%\b\b\b89%\b\b\b90%\b\b\b91%\b\b\b92%\b\b\b93%\b\b\b94%\b\b\b95%\b\b\b96%\b\b\b97%\b\b\b98%\b\b\b99%\b\b\b\b done.\n",
            "Total genotyping rate in remaining samples is 0.999919.\n",
            "540534 variants and 489 people pass filters and QC.\n",
            "Note: No phenotypes present.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 20214 variants from chromosome 1, leaving 21127.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 20984 variants from chromosome 2, leaving 20879.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 17463 variants from chromosome 3, leaving 17812.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 16344 variants from chromosome 4, leaving 16690.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 15027 variants from chromosome 5, leaving 15981.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r31%\r32%\r33%\r34%\r35%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 20731 variants from chromosome 6, leaving 15475.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r84%\r85%\r86%\r87%\r88%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 14046 variants from chromosome 7, leaving 14606.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r96%\r97%\r98%\r99%\rPruned 13289 variants from chromosome 8, leaving 13667.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 10723 variants from chromosome 9, leaving 11951.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r21%\r22%\r23%\r24%\r25%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 12722 variants from chromosome 10, leaving 13383.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 13053 variants from chromosome 11, leaving 12666.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r82%\r83%\r84%\r85%\r86%\r87%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 11654 variants from chromosome 12, leaving 12500.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 8429 variants from chromosome 13, leaving 9609.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 8072 variants from chromosome 14, leaving 8791.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 7978 variants from chromosome 15, leaving 8470.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 8808 variants from chromosome 16, leaving 9500.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 8032 variants from chromosome 17, leaving 8724.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 7204 variants from chromosome 18, leaving 8560.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 7014 variants from chromosome 19, leaving 6726.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 6341 variants from chromosome 20, leaving 7508.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r97%\r98%\r99%\rPruned 3629 variants from chromosome 21, leaving 4280.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 4085 variants from chromosome 22, leaving 4323.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 16235 variants from chromosome 23, leaving 5229.\n",
            "Pruning complete.  272077 of 540534 variants removed.\n",
            "Writing...\rMarker lists written to QC/target/EUR.QC.prune.in and\n",
            "QC/target/EUR.QC.prune.out .\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning: 1273 het. haploid genotypes present (see QC/target/EUR.QC.hh ); many\n",
            "commands treat these as missing.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Heterozygosity rates can then be computed using plink:"
      ],
      "metadata": {
        "id": "WWtha2ruFRFw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "\n",
        "plink \\\n",
        "    --bfile QC/target/EUR \\\n",
        "    --extract QC/target/EUR.QC.prune.in \\\n",
        "    --keep QC/target/EUR.QC.fam \\\n",
        "    --het \\\n",
        "    --out QC/target/EUR.QC"
      ],
      "metadata": {
        "id": "ZdtERELwFRoO",
        "outputId": "4ff4a6cd-3555-449d-ccb2-fada42a44f13",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PLINK v1.90b6.21 64-bit (19 Oct 2020)          www.cog-genomics.org/plink/1.9/\n",
            "(C) 2005-2020 Shaun Purcell, Christopher Chang   GNU General Public License v3\n",
            "Logging to QC/target/EUR.QC.log.\n",
            "Options in effect:\n",
            "  --bfile QC/target/EUR\n",
            "  --extract QC/target/EUR.QC.prune.in\n",
            "  --het\n",
            "  --keep QC/target/EUR.QC.fam\n",
            "  --out QC/target/EUR.QC\n",
            "\n",
            "12982 MB RAM detected; reserving 6491 MB for main workspace.\n",
            "551892 variants loaded from .bim file.\n",
            "503 people (240 males, 263 females) loaded from .fam.\n",
            "--extract: 268457 variants remaining.\n",
            "--keep: 489 people remaining.\n",
            "Using 1 thread (no multithreaded calculations invoked).\n",
            "Before main variant filters, 489 founders and 0 nonfounders present.\n",
            "Calculating allele frequencies... 0%\b\b1%\b\b2%\b\b3%\b\b4%\b\b5%\b\b6%\b\b7%\b\b8%\b\b9%\b\b10%\b\b\b11%\b\b\b12%\b\b\b13%\b\b\b14%\b\b\b15%\b\b\b16%\b\b\b17%\b\b\b18%\b\b\b19%\b\b\b20%\b\b\b21%\b\b\b22%\b\b\b23%\b\b\b24%\b\b\b25%\b\b\b26%\b\b\b27%\b\b\b28%\b\b\b29%\b\b\b30%\b\b\b31%\b\b\b32%\b\b\b33%\b\b\b34%\b\b\b35%\b\b\b36%\b\b\b37%\b\b\b38%\b\b\b39%\b\b\b40%\b\b\b41%\b\b\b42%\b\b\b43%\b\b\b44%\b\b\b45%\b\b\b46%\b\b\b47%\b\b\b48%\b\b\b49%\b\b\b50%\b\b\b51%\b\b\b52%\b\b\b53%\b\b\b54%\b\b\b55%\b\b\b56%\b\b\b57%\b\b\b58%\b\b\b59%\b\b\b60%\b\b\b61%\b\b\b62%\b\b\b63%\b\b\b64%\b\b\b65%\b\b\b66%\b\b\b67%\b\b\b68%\b\b\b69%\b\b\b70%\b\b\b71%\b\b\b72%\b\b\b73%\b\b\b74%\b\b\b75%\b\b\b76%\b\b\b77%\b\b\b78%\b\b\b79%\b\b\b80%\b\b\b81%\b\b\b82%\b\b\b83%\b\b\b84%\b\b\b85%\b\b\b86%\b\b\b87%\b\b\b88%\b\b\b89%\b\b\b90%\b\b\b91%\b\b\b92%\b\b\b93%\b\b\b94%\b\b\b95%\b\b\b96%\b\b\b97%\b\b\b98%\b\b\b99%\b\b\b\b done.\n",
            "Total genotyping rate in remaining samples is 0.999958.\n",
            "268457 variants and 489 people pass filters and QC.\n",
            "Note: No phenotypes present.\n",
            "--het: 263228 variants scanned, report written to QC/target/EUR.QC.het .\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning: 558 het. haploid genotypes present (see QC/target/EUR.QC.hh ); many\n",
            "commands treat these as missing.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "\n",
        "cat QC/target/EUR.QC.het | head"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6RgtRsNOGKX5",
        "outputId": "5a2c9f98-f37f-4237-8b72-9c5948374fba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      FID       IID       O(HOM)       E(HOM)        N(NM)            F\n",
            "  HG00096   HG00096       215057    2.148e+05       263228     0.005196\n",
            "  HG00097   HG00097       214695    2.148e+05       263228     -0.00228\n",
            "  HG00099   HG00099       214846    2.148e+05       263228    0.0008384\n",
            "  HG00101   HG00101       214569    2.148e+05       263228    -0.004882\n",
            "  HG00102   HG00102       215187    2.148e+05       263228     0.007881\n",
            "  HG00103   HG00103       215264    2.148e+05       263228     0.009471\n",
            "  HG00105   HG00105       214725    2.148e+05       263228     -0.00166\n",
            "  HG00107   HG00107       214943    2.148e+05       263228     0.002842\n",
            "  HG00108   HG00108       214855    2.148e+05       263228     0.001024\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Read in the EUR.het file, specify it has header\n",
        "dat <- read.table(\"QC/target/EUR.QC.het\", header=T)\n",
        "\n",
        "# Calculate the mean\n",
        "m <- mean(dat$F)\n",
        "\n",
        "# Calculate the SD\n",
        "s <- sd(dat$F)\n",
        "\n",
        "# Get any samples with F coefficient within 3 SD of the population mean\n",
        "valid <- subset(dat, F <= m+3*s & F >= m-3*s)\n",
        "\n",
        "# print FID and IID for valid samples\n",
        "write.table(valid[,c(1,2)], \"QC/target/EUR.valid.sample\", quote=F, row.names=F)"
      ],
      "metadata": {
        "id": "Ml4fijL-GWcv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "\n",
        "head(valid)"
      ],
      "metadata": {
        "id": "SMWRN1OSHxCA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**iv. Ambiguous SNPs**"
      ],
      "metadata": {
        "id": "Lrh8kV3wIV6Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**v. Mismatching SNPs**"
      ],
      "metadata": {
        "id": "UlTusXYFIdJ8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Load the bim file, the summary statistic and the QC SNP list into R"
      ],
      "metadata": {
        "id": "j9vmsqzULDSX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Read in bim file\n",
        "bim <- read.table(\"data/target/EUR.bim\")\n",
        "colnames(bim) <- c(\"CHR\", \"SNP\", \"CM\", \"BP\", \"B.A1\", \"B.A2\")\n",
        "\n",
        "# Read in QCed SNPs\n",
        "qc <- read.table(\"QC/target/EUR.QC.snplist\", header = F, stringsAsFactors = F)\n",
        "\n",
        "# Read in the GWAS data\n",
        "height <-\n",
        "    read.table(gzfile(\"QC/base/Height.QC.gz\"),\n",
        "            header = T,\n",
        "            stringsAsFactors = F,\n",
        "            sep=\"\\t\")\n",
        "\n",
        "# Change all alleles to upper case for easy comparison\n",
        "height$A1 <- toupper(height$A1)\n",
        "height$A2 <- toupper(height$A2)\n",
        "bim$B.A1 <- toupper(bim$B.A1)\n",
        "bim$B.A2 <- toupper(bim$B.A2)"
      ],
      "metadata": {
        "id": "GX91k6Y8Ig0D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "head(bim)"
      ],
      "metadata": {
        "id": "aUeqrU8gLIt_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. Identify SNPs that require strand flipping"
      ],
      "metadata": {
        "id": "6f9oTOv8LbUm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "\n",
        "# Merge summary statistic with target\n",
        "info <- merge(bim, height, by = c(\"SNP\", \"CHR\", \"BP\"))\n",
        "\n",
        "# Filter QCed SNPs\n",
        "info <- info[info$SNP %in% qc$V1,]\n",
        "\n",
        "# Function for finding the complementary allele\n",
        "complement <- function(x) {\n",
        "    switch (\n",
        "        x,\n",
        "        \"A\" = \"T\",\n",
        "        \"C\" = \"G\",\n",
        "        \"T\" = \"A\",\n",
        "        \"G\" = \"C\",\n",
        "        return(NA)\n",
        "    )\n",
        "}\n",
        "\n",
        "# Get SNPs that have the same alleles across base and target\n",
        "info.match <- subset(info, A1 == B.A1 & A2 == B.A2)\n",
        "\n",
        "# Identify SNPs that are complementary between base and target\n",
        "info$C.A1 <- sapply(info$B.A1, complement)\n",
        "info$C.A2 <- sapply(info$B.A2, complement)\n",
        "info.complement <- subset(info, A1 == C.A1 & A2 == C.A2)\n",
        "\n",
        "# Update the complementary alleles in the bim file\n",
        "# This allow us to match the allele in subsequent analysis\n",
        "complement.snps <- bim$SNP %in% info.complement$SNP\n",
        "bim[complement.snps,]$B.A1 <-\n",
        "    sapply(bim[complement.snps,]$B.A1, complement)\n",
        "bim[complement.snps,]$B.A2 <-\n",
        "    sapply(bim[complement.snps,]$B.A2, complement)"
      ],
      "metadata": {
        "id": "VN7XgnKkLdh5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. Identify SNPs that require recoding in the target (to ensure the coding allele in the target data is the effective allele in the base summary statistic)"
      ],
      "metadata": {
        "id": "nub7L1zZMDKn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# identify SNPs that need recoding\n",
        "info.recode <- subset(info, A1 == B.A2 & A2 == B.A1)\n",
        "\n",
        "# Update the recode SNPs\n",
        "recode.snps <- bim$SNP %in% info.recode$SNP\n",
        "tmp <- bim[recode.snps,]$B.A1\n",
        "bim[recode.snps,]$B.A1 <- bim[recode.snps,]$B.A2\n",
        "bim[recode.snps,]$B.A2 <- tmp\n",
        "\n",
        "# identify SNPs that need recoding & complement\n",
        "info.crecode <- subset(info, A1 == C.A2 & A2 == C.A1)\n",
        "\n",
        "# Update the recode + strand flip SNPs\n",
        "com.snps <- bim$SNP %in% info.crecode$SNP\n",
        "tmp <- bim[com.snps,]$B.A1\n",
        "bim[com.snps,]$B.A1 <- as.character(sapply(bim[com.snps,]$B.A2, complement))\n",
        "bim[com.snps,]$B.A2 <- as.character(sapply(tmp, complement))\n",
        "\n",
        "# Output updated bim file\n",
        "write.table(\n",
        "    bim[,c(\"SNP\", \"B.A1\")],\n",
        "    \"QC/target/EUR.a1\",\n",
        "    quote = F,\n",
        "    row.names = F,\n",
        "    col.names = F,\n",
        "    sep=\"\\t\"\n",
        ")"
      ],
      "metadata": {
        "id": "bJMKxU7FMFNw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cat QC/target/EUR.a1 | head"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8TZZby0RMrbA",
        "outputId": "9ff3f481-ac21-4589-8c23-d4aa36acc0a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rs3131962\tA\n",
            "rs12562034\t0\n",
            "rs4040617\tG\n",
            "rs79373928\tG\n",
            "rs11240779\tG\n",
            "rs57181708\tG\n",
            "rs4422948\tG\n",
            "rs4970383\tA\n",
            "rs4970382\tC\n",
            "rs950122\tC\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4. Identify SNPs that have different allele in base and target (usually due to difference in genome build or Indel)"
      ],
      "metadata": {
        "id": "D6s4IREQNl3h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "mismatch <-\n",
        "    bim$SNP[!(bim$SNP %in% info.match$SNP |\n",
        "                bim$SNP %in% info.complement$SNP |\n",
        "                bim$SNP %in% info.recode$SNP |\n",
        "                bim$SNP %in% info.crecode$SNP)]\n",
        "write.table(\n",
        "    mismatch,\n",
        "    \"QC/target/EUR.mismatch\",\n",
        "    quote = F,\n",
        "    row.names = F,\n",
        "    col.names = F\n",
        ")"
      ],
      "metadata": {
        "id": "OYIdKytyNq-H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "head(mismatch)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H2VFNJzEOGBw",
        "outputId": "aaab61ab-0ecc-4fb3-8df3-2638570b06e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1] \"rs12562034\"  \"rs950122\"    \"rs112164716\" \"rs41285810\"  \"rs3829740\"  \n",
            "[6] \"rs138288952\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**vi. Duplicate SNPs**"
      ],
      "metadata": {
        "id": "lUyiAl_QOWg_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**vii. Sex Chromosomes**"
      ],
      "metadata": {
        "id": "yAumx1F9Olb3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "\n",
        "plink \\\n",
        "    --bfile QC/target/EUR \\\n",
        "    --extract QC/target/EUR.QC.prune.in \\\n",
        "    --keep QC/target/EUR.valid.sample \\\n",
        "    --check-sex \\\n",
        "    --out QC/target/EUR.QC"
      ],
      "metadata": {
        "id": "8ZS7hvIVOdhu",
        "outputId": "46d4a41e-04fd-42be-d756-5a93ac59f729",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PLINK v1.90b6.21 64-bit (19 Oct 2020)          www.cog-genomics.org/plink/1.9/\n",
            "(C) 2005-2020 Shaun Purcell, Christopher Chang   GNU General Public License v3\n",
            "Logging to QC/target/EUR.QC.log.\n",
            "Options in effect:\n",
            "  --bfile QC/target/EUR\n",
            "  --check-sex\n",
            "  --extract QC/target/EUR.QC.prune.in\n",
            "  --keep QC/target/EUR.valid.sample\n",
            "  --out QC/target/EUR.QC\n",
            "\n",
            "12982 MB RAM detected; reserving 6491 MB for main workspace.\n",
            "551892 variants loaded from .bim file.\n",
            "503 people (240 males, 263 females) loaded from .fam.\n",
            "--extract: 268457 variants remaining.\n",
            "--keep: 487 people remaining.\n",
            "Using 1 thread (no multithreaded calculations invoked).\n",
            "Before main variant filters, 487 founders and 0 nonfounders present.\n",
            "Calculating allele frequencies... 0%\b\b1%\b\b2%\b\b3%\b\b4%\b\b5%\b\b6%\b\b7%\b\b8%\b\b9%\b\b10%\b\b\b11%\b\b\b12%\b\b\b13%\b\b\b14%\b\b\b15%\b\b\b16%\b\b\b17%\b\b\b18%\b\b\b19%\b\b\b20%\b\b\b21%\b\b\b22%\b\b\b23%\b\b\b24%\b\b\b25%\b\b\b26%\b\b\b27%\b\b\b28%\b\b\b29%\b\b\b30%\b\b\b31%\b\b\b32%\b\b\b33%\b\b\b34%\b\b\b35%\b\b\b36%\b\b\b37%\b\b\b38%\b\b\b39%\b\b\b40%\b\b\b41%\b\b\b42%\b\b\b43%\b\b\b44%\b\b\b45%\b\b\b46%\b\b\b47%\b\b\b48%\b\b\b49%\b\b\b50%\b\b\b51%\b\b\b52%\b\b\b53%\b\b\b54%\b\b\b55%\b\b\b56%\b\b\b57%\b\b\b58%\b\b\b59%\b\b\b60%\b\b\b61%\b\b\b62%\b\b\b63%\b\b\b64%\b\b\b65%\b\b\b66%\b\b\b67%\b\b\b68%\b\b\b69%\b\b\b70%\b\b\b71%\b\b\b72%\b\b\b73%\b\b\b74%\b\b\b75%\b\b\b76%\b\b\b77%\b\b\b78%\b\b\b79%\b\b\b80%\b\b\b81%\b\b\b82%\b\b\b83%\b\b\b84%\b\b\b85%\b\b\b86%\b\b\b87%\b\b\b88%\b\b\b89%\b\b\b90%\b\b\b91%\b\b\b92%\b\b\b93%\b\b\b94%\b\b\b95%\b\b\b96%\b\b\b97%\b\b\b98%\b\b\b99%\b\b\b\b done.\n",
            "Total genotyping rate in remaining samples is 0.999958.\n",
            "268457 variants and 487 people pass filters and QC.\n",
            "Note: No phenotypes present.\n",
            "--check-sex: 5229 Xchr and 0 Ychr variant(s) scanned, 4 problems detected.\n",
            "Report written to QC/target/EUR.QC.sexcheck .\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning: 558 het. haploid genotypes present (see QC/target/EUR.QC.hh ); many\n",
            "commands treat these as missing.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This will generate a file called EUR.QC.sexcheck containing the F-statistics for each individual. Individuals are typically called as being biologically male if the F-statistic is > 0.8 and biologically female if F < 0.2."
      ],
      "metadata": {
        "id": "tLYMp1bbO5uH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Read in file\n",
        "valid <- read.table(\"QC/target/EUR.valid.sample\", header=T)\n",
        "dat <- read.table(\"QC/target/EUR.QC.sexcheck\", header=T)\n",
        "valid <- subset(dat, STATUS==\"OK\" & FID %in% valid$FID)\n",
        "write.table(valid[,c(\"FID\", \"IID\")], \"QC/target/EUR.QC.valid\", row.names=F, col.names=F, sep=\"\\t\", quote=F)"
      ],
      "metadata": {
        "id": "lAJmC6Z5O9bv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**viii. Sample overlap**"
      ],
      "metadata": {
        "id": "f_l0Xs-0POwH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ix. Relatedness**"
      ],
      "metadata": {
        "id": "PvJVOPF6PTdH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "\n",
        "plink \\\n",
        "    --bfile QC/target/EUR \\\n",
        "    --extract QC/target/EUR.QC.prune.in \\\n",
        "    --keep QC/target/EUR.QC.valid \\\n",
        "    --rel-cutoff 0.125 \\\n",
        "    --out QC/target/EUR.QC"
      ],
      "metadata": {
        "id": "1ACPexeMPWXn",
        "outputId": "ffaa33bf-8df2-43e0-d5d7-b09a44ce276f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PLINK v1.90b6.21 64-bit (19 Oct 2020)          www.cog-genomics.org/plink/1.9/\n",
            "(C) 2005-2020 Shaun Purcell, Christopher Chang   GNU General Public License v3\n",
            "Logging to QC/target/EUR.QC.log.\n",
            "Options in effect:\n",
            "  --bfile QC/target/EUR\n",
            "  --extract QC/target/EUR.QC.prune.in\n",
            "  --keep QC/target/EUR.QC.valid\n",
            "  --out QC/target/EUR.QC\n",
            "  --rel-cutoff 0.125\n",
            "\n",
            "12982 MB RAM detected; reserving 6491 MB for main workspace.\n",
            "551892 variants loaded from .bim file.\n",
            "503 people (240 males, 263 females) loaded from .fam.\n",
            "--extract: 268457 variants remaining.\n",
            "--keep: 483 people remaining.\n",
            "Using up to 2 threads (change this with --threads).\n",
            "Before main variant filters, 483 founders and 0 nonfounders present.\n",
            "Calculating allele frequencies... 0%\b\b1%\b\b2%\b\b3%\b\b4%\b\b5%\b\b6%\b\b7%\b\b8%\b\b9%\b\b10%\b\b\b11%\b\b\b12%\b\b\b13%\b\b\b14%\b\b\b15%\b\b\b16%\b\b\b17%\b\b\b18%\b\b\b19%\b\b\b20%\b\b\b21%\b\b\b22%\b\b\b23%\b\b\b24%\b\b\b25%\b\b\b26%\b\b\b27%\b\b\b28%\b\b\b29%\b\b\b30%\b\b\b31%\b\b\b32%\b\b\b33%\b\b\b34%\b\b\b35%\b\b\b36%\b\b\b37%\b\b\b38%\b\b\b39%\b\b\b40%\b\b\b41%\b\b\b42%\b\b\b43%\b\b\b44%\b\b\b45%\b\b\b46%\b\b\b47%\b\b\b48%\b\b\b49%\b\b\b50%\b\b\b51%\b\b\b52%\b\b\b53%\b\b\b54%\b\b\b55%\b\b\b56%\b\b\b57%\b\b\b58%\b\b\b59%\b\b\b60%\b\b\b61%\b\b\b62%\b\b\b63%\b\b\b64%\b\b\b65%\b\b\b66%\b\b\b67%\b\b\b68%\b\b\b69%\b\b\b70%\b\b\b71%\b\b\b72%\b\b\b73%\b\b\b74%\b\b\b75%\b\b\b76%\b\b\b77%\b\b\b78%\b\b\b79%\b\b\b80%\b\b\b81%\b\b\b82%\b\b\b83%\b\b\b84%\b\b\b85%\b\b\b86%\b\b\b87%\b\b\b88%\b\b\b89%\b\b\b90%\b\b\b91%\b\b\b92%\b\b\b93%\b\b\b94%\b\b\b95%\b\b\b96%\b\b\b97%\b\b\b98%\b\b\b99%\b\b\b\b done.\n",
            "Total genotyping rate in remaining samples is 0.999957.\n",
            "268457 variants and 483 people pass filters and QC (before --rel-cutoff).\n",
            "Note: No phenotypes present.\n",
            "Excluding 5229 variants on non-autosomes from relationship matrix calc.\n",
            "\r60 markers complete.\r120 markers complete.\r180 markers complete.\r240 markers complete.\r300 markers complete.\r360 markers complete.\r420 markers complete.\r480 markers complete.\r540 markers complete.\r600 markers complete.\r660 markers complete.\r720 markers complete.\r780 markers complete.\r840 markers complete.\r900 markers complete.\r960 markers complete.\r1020 markers complete.\r1080 markers complete.\r1140 markers complete.\r1200 markers complete.\r1260 markers complete.\r1320 markers complete.\r1380 markers complete.\r1440 markers complete.\r1500 markers complete.\r1560 markers complete.\r1620 markers complete.\r1680 markers complete.\r1740 markers complete.\r1800 markers complete.\r1860 markers complete.\r1920 markers complete.\r1980 markers complete.\r2040 markers complete.\r2100 markers complete.\r2160 markers complete.\r2220 markers complete.\r2280 markers complete.\r2340 markers complete.\r2400 markers complete.\r2460 markers complete.\r2520 markers 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markers complete.\r5100 markers complete.\r5160 markers complete.\r5220 markers complete.\r5280 markers complete.\r5340 markers complete.\r5400 markers complete.\r5460 markers complete.\r5520 markers complete.\r5580 markers complete.\r5640 markers complete.\r5700 markers complete.\r5760 markers complete.\r5820 markers complete.\r5880 markers complete.\r5940 markers complete.\r6000 markers complete.\r6060 markers complete.\r6120 markers complete.\r6180 markers complete.\r6240 markers complete.\r6300 markers complete.\r6360 markers complete.\r6420 markers complete.\r6480 markers complete.\r6540 markers complete.\r6600 markers complete.\r6660 markers complete.\r6720 markers complete.\r6780 markers complete.\r6840 markers complete.\r6900 markers complete.\r6960 markers complete.\r7020 markers complete.\r7080 markers complete.\r7140 markers complete.\r7200 markers complete.\r7260 markers complete.\r7320 markers complete.\r7380 markers complete.\r7440 markers complete.\r7500 markers complete.\r7560 markers complete.\r7620 markers complete.\r7680 markers complete.\r7740 markers complete.\r7800 markers complete.\r7860 markers complete.\r7920 markers complete.\r7980 markers complete.\r8040 markers complete.\r8100 markers complete.\r8160 markers complete.\r8220 markers complete.\r8280 markers complete.\r8340 markers complete.\r8400 markers complete.\r8460 markers complete.\r8520 markers complete.\r8580 markers complete.\r8640 markers complete.\r8700 markers complete.\r8760 markers complete.\r8820 markers complete.\r8880 markers complete.\r8940 markers complete.\r9000 markers complete.\r9060 markers complete.\r9120 markers complete.\r9180 markers complete.\r9240 markers complete.\r9300 markers complete.\r9360 markers complete.\r9420 markers complete.\r9480 markers complete.\r9540 markers complete.\r9600 markers complete.\r9660 markers complete.\r9720 markers complete.\r9780 markers complete.\r9840 markers complete.\r9900 markers complete.\r9960 markers complete.\r10020 markers complete.\r10080 markers complete.\r10140 markers complete.\r10200 markers complete.\r10260 markers complete.\r10320 markers complete.\r10380 markers complete.\r10440 markers complete.\r10500 markers complete.\r10560 markers complete.\r10620 markers complete.\r10680 markers complete.\r10740 markers complete.\r10800 markers complete.\r10860 markers complete.\r10920 markers complete.\r10980 markers complete.\r11040 markers complete.\r11100 markers complete.\r11160 markers complete.\r11220 markers complete.\r11280 markers complete.\r11340 markers complete.\r11400 markers complete.\r11460 markers complete.\r11520 markers complete.\r11580 markers complete.\r11640 markers complete.\r11700 markers complete.\r11760 markers complete.\r11820 markers complete.\r11880 markers complete.\r11940 markers complete.\r12000 markers complete.\r12060 markers complete.\r12120 markers complete.\r12180 markers complete.\r12240 markers complete.\r12300 markers complete.\r12360 markers complete.\r12420 markers complete.\r12480 markers complete.\r12540 markers complete.\r12600 markers complete.\r12660 markers complete.\r12720 markers complete.\r12780 markers complete.\r12840 markers complete.\r12900 markers complete.\r12960 markers complete.\r13020 markers complete.\r13080 markers complete.\r13140 markers complete.\r13200 markers complete.\r13260 markers complete.\r13320 markers complete.\r13380 markers complete.\r13440 markers complete.\r13500 markers complete.\r13560 markers complete.\r13620 markers complete.\r13680 markers complete.\r13740 markers complete.\r13800 markers complete.\r13860 markers complete.\r13920 markers complete.\r13980 markers complete.\r14040 markers complete.\r14100 markers complete.\r14160 markers complete.\r14220 markers complete.\r14280 markers complete.\r14340 markers complete.\r14400 markers complete.\r14460 markers complete.\r14520 markers complete.\r14580 markers complete.\r14640 markers complete.\r14700 markers complete.\r14760 markers complete.\r14820 markers complete.\r14880 markers complete.\r14940 markers complete.\r15000 markers complete.\r15060 markers complete.\r15120 markers complete.\r15180 markers complete.\r15240 markers complete.\r15300 markers complete.\r15360 markers complete.\r15420 markers complete.\r15480 markers complete.\r15540 markers complete.\r15600 markers complete.\r15660 markers complete.\r15720 markers complete.\r15780 markers complete.\r15840 markers complete.\r15900 markers complete.\r15960 markers complete.\r16020 markers complete.\r16080 markers complete.\r16140 markers complete.\r16200 markers complete.\r16260 markers complete.\r16320 markers complete.\r16380 markers complete.\r16440 markers complete.\r16500 markers complete.\r16560 markers complete.\r16620 markers complete.\r16680 markers complete.\r16740 markers complete.\r16800 markers complete.\r16860 markers complete.\r16920 markers complete.\r16980 markers complete.\r17040 markers complete.\r17100 markers complete.\r17160 markers complete.\r17220 markers complete.\r17280 markers complete.\r17340 markers complete.\r17400 markers complete.\r17460 markers complete.\r17520 markers complete.\r17580 markers complete.\r17640 markers complete.\r17700 markers complete.\r17760 markers complete.\r17820 markers complete.\r17880 markers complete.\r17940 markers complete.\r18000 markers complete.\r18060 markers complete.\r18120 markers complete.\r18180 markers complete.\r18240 markers complete.\r18300 markers complete.\r18360 markers complete.\r18420 markers complete.\r18480 markers complete.\r18540 markers complete.\r18600 markers complete.\r18660 markers complete.\r18720 markers complete.\r18780 markers complete.\r18840 markers complete.\r18900 markers complete.\r18960 markers complete.\r19020 markers complete.\r19080 markers complete.\r19140 markers complete.\r19200 markers complete.\r19260 markers complete.\r19320 markers complete.\r19380 markers complete.\r19440 markers complete.\r19500 markers complete.\r19560 markers complete.\r19620 markers complete.\r19680 markers complete.\r19740 markers complete.\r19800 markers complete.\r19860 markers complete.\r19920 markers complete.\r19980 markers complete.\r20040 markers complete.\r20100 markers complete.\r20160 markers complete.\r20220 markers complete.\r20280 markers complete.\r20340 markers complete.\r20400 markers complete.\r20460 markers complete.\r20520 markers complete.\r20580 markers complete.\r20640 markers complete.\r20700 markers complete.\r20760 markers complete.\r20820 markers complete.\r20880 markers complete.\r20940 markers complete.\r21000 markers complete.\r21060 markers complete.\r21120 markers complete.\r21180 markers complete.\r21240 markers complete.\r21300 markers complete.\r21360 markers complete.\r21420 markers complete.\r21480 markers complete.\r21540 markers complete.\r21600 markers complete.\r21660 markers complete.\r21720 markers complete.\r21780 markers complete.\r21840 markers complete.\r21900 markers complete.\r21960 markers complete.\r22020 markers complete.\r22080 markers complete.\r22140 markers complete.\r22200 markers complete.\r22260 markers complete.\r22320 markers complete.\r22380 markers complete.\r22440 markers complete.\r22500 markers complete.\r22560 markers complete.\r22620 markers complete.\r22680 markers complete.\r22740 markers complete.\r22800 markers complete.\r22860 markers complete.\r22920 markers complete.\r22980 markers complete.\r23040 markers complete.\r23100 markers complete.\r23160 markers complete.\r23220 markers complete.\r23280 markers complete.\r23340 markers complete.\r23400 markers complete.\r23460 markers complete.\r23520 markers complete.\r23580 markers complete.\r23640 markers complete.\r23700 markers complete.\r23760 markers complete.\r23820 markers complete.\r23880 markers complete.\r23940 markers complete.\r24000 markers complete.\r24060 markers complete.\r24120 markers complete.\r24180 markers complete.\r24240 markers complete.\r24300 markers complete.\r24360 markers complete.\r24420 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markers complete.\r31680 markers complete.\r31740 markers complete.\r31800 markers complete.\r31860 markers complete.\r31920 markers complete.\r31980 markers complete.\r32040 markers complete.\r32100 markers complete.\r32160 markers complete.\r32220 markers complete.\r32280 markers complete.\r32340 markers complete.\r32400 markers complete.\r32460 markers complete.\r32520 markers complete.\r32580 markers complete.\r32640 markers complete.\r32700 markers complete.\r32760 markers complete.\r32820 markers complete.\r32880 markers complete.\r32940 markers complete.\r33000 markers complete.\r33060 markers complete.\r33120 markers complete.\r33180 markers complete.\r33240 markers complete.\r33300 markers complete.\r33360 markers complete.\r33420 markers complete.\r33480 markers complete.\r33540 markers complete.\r33600 markers complete.\r33660 markers complete.\r33720 markers complete.\r33780 markers complete.\r33840 markers complete.\r33900 markers complete.\r33960 markers complete.\r34020 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markers complete.\r41280 markers complete.\r41340 markers complete.\r41400 markers complete.\r41460 markers complete.\r41520 markers complete.\r41580 markers complete.\r41640 markers complete.\r41700 markers complete.\r41760 markers complete.\r41820 markers complete.\r41880 markers complete.\r41940 markers complete.\r42000 markers complete.\r42060 markers complete.\r42120 markers complete.\r42180 markers complete.\r42240 markers complete.\r42300 markers complete.\r42360 markers complete.\r42420 markers complete.\r42480 markers complete.\r42540 markers complete.\r42600 markers complete.\r42660 markers complete.\r42720 markers complete.\r42780 markers complete.\r42840 markers complete.\r42900 markers complete.\r42960 markers complete.\r43020 markers complete.\r43080 markers complete.\r43140 markers complete.\r43200 markers complete.\r43260 markers complete.\r43320 markers complete.\r43380 markers complete.\r43440 markers complete.\r43500 markers complete.\r43560 markers complete.\r43620 markers complete.\r43680 markers complete.\r43740 markers complete.\r43800 markers complete.\r43860 markers complete.\r43920 markers complete.\r43980 markers complete.\r44040 markers complete.\r44100 markers complete.\r44160 markers complete.\r44220 markers complete.\r44280 markers complete.\r44340 markers complete.\r44400 markers complete.\r44460 markers complete.\r44520 markers complete.\r44580 markers complete.\r44640 markers complete.\r44700 markers complete.\r44760 markers complete.\r44820 markers complete.\r44880 markers complete.\r44940 markers complete.\r45000 markers complete.\r45060 markers complete.\r45120 markers complete.\r45180 markers complete.\r45240 markers complete.\r45300 markers complete.\r45360 markers complete.\r45420 markers complete.\r45480 markers complete.\r45540 markers complete.\r45600 markers complete.\r45660 markers complete.\r45720 markers complete.\r45780 markers complete.\r45840 markers complete.\r45900 markers complete.\r45960 markers complete.\r46020 markers complete.\r46080 markers complete.\r46140 markers complete.\r46200 markers complete.\r46260 markers complete.\r46320 markers complete.\r46380 markers complete.\r46440 markers complete.\r46500 markers complete.\r46560 markers complete.\r46620 markers complete.\r46680 markers complete.\r46740 markers complete.\r46800 markers complete.\r46860 markers complete.\r46920 markers complete.\r46980 markers complete.\r47040 markers complete.\r47100 markers complete.\r47160 markers complete.\r47220 markers complete.\r47280 markers complete.\r47340 markers complete.\r47400 markers complete.\r47460 markers complete.\r47520 markers complete.\r47580 markers complete.\r47640 markers complete.\r47700 markers complete.\r47760 markers complete.\r47820 markers complete.\r47880 markers complete.\r47940 markers complete.\r48000 markers complete.\r48060 markers complete.\r48120 markers complete.\r48180 markers complete.\r48240 markers complete.\r48300 markers complete.\r48360 markers complete.\r48420 markers complete.\r48480 markers complete.\r48540 markers complete.\r48600 markers complete.\r48660 markers complete.\r48720 markers complete.\r48780 markers complete.\r48840 markers complete.\r48900 markers complete.\r48960 markers complete.\r49020 markers complete.\r49080 markers complete.\r49140 markers complete.\r49200 markers complete.\r49260 markers complete.\r49320 markers complete.\r49380 markers complete.\r49440 markers complete.\r49500 markers complete.\r49560 markers complete.\r49620 markers complete.\r49680 markers complete.\r49740 markers complete.\r49800 markers complete.\r49860 markers complete.\r49920 markers complete.\r49980 markers complete.\r50040 markers complete.\r50100 markers complete.\r50160 markers complete.\r50220 markers complete.\r50280 markers complete.\r50340 markers complete.\r50400 markers complete.\r50460 markers complete.\r50520 markers complete.\r50580 markers complete.\r50640 markers complete.\r50700 markers complete.\r50760 markers complete.\r50820 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markers complete.\r53280 markers complete.\r53340 markers complete.\r53400 markers complete.\r53460 markers complete.\r53520 markers complete.\r53580 markers complete.\r53640 markers complete.\r53700 markers complete.\r53760 markers complete.\r53820 markers complete.\r53880 markers complete.\r53940 markers complete.\r54000 markers complete.\r54060 markers complete.\r54120 markers complete.\r54180 markers complete.\r54240 markers complete.\r54300 markers complete.\r54360 markers complete.\r54420 markers complete.\r54480 markers complete.\r54540 markers complete.\r54600 markers complete.\r54660 markers complete.\r54720 markers complete.\r54780 markers complete.\r54840 markers complete.\r54900 markers complete.\r54960 markers complete.\r55020 markers complete.\r55080 markers complete.\r55140 markers complete.\r55200 markers complete.\r55260 markers complete.\r55320 markers complete.\r55380 markers complete.\r55440 markers complete.\r55500 markers complete.\r55560 markers complete.\r55620 markers complete.\r55680 markers complete.\r55740 markers complete.\r55800 markers complete.\r55860 markers complete.\r55920 markers complete.\r55980 markers complete.\r56040 markers complete.\r56100 markers complete.\r56160 markers complete.\r56220 markers complete.\r56280 markers complete.\r56340 markers complete.\r56400 markers complete.\r56460 markers complete.\r56520 markers complete.\r56580 markers complete.\r56640 markers complete.\r56700 markers complete.\r56760 markers complete.\r56820 markers complete.\r56880 markers complete.\r56940 markers complete.\r57000 markers complete.\r57060 markers complete.\r57120 markers complete.\r57180 markers complete.\r57240 markers complete.\r57300 markers complete.\r57360 markers complete.\r57420 markers complete.\r57480 markers complete.\r57540 markers complete.\r57600 markers complete.\r57660 markers complete.\r57720 markers complete.\r57780 markers complete.\r57840 markers complete.\r57900 markers complete.\r57960 markers complete.\r58020 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markers complete.\r62880 markers complete.\r62940 markers complete.\r63000 markers complete.\r63060 markers complete.\r63120 markers complete.\r63180 markers complete.\r63240 markers complete.\r63300 markers complete.\r63360 markers complete.\r63420 markers complete.\r63480 markers complete.\r63540 markers complete.\r63600 markers complete.\r63660 markers complete.\r63720 markers complete.\r63780 markers complete.\r63840 markers complete.\r63900 markers complete.\r63960 markers complete.\r64020 markers complete.\r64080 markers complete.\r64140 markers complete.\r64200 markers complete.\r64260 markers complete.\r64320 markers complete.\r64380 markers complete.\r64440 markers complete.\r64500 markers complete.\r64560 markers complete.\r64620 markers complete.\r64680 markers complete.\r64740 markers complete.\r64800 markers complete.\r64860 markers complete.\r64920 markers complete.\r64980 markers complete.\r65040 markers complete.\r65100 markers complete.\r65160 markers complete.\r65220 markers complete.\r65280 markers complete.\r65340 markers complete.\r65400 markers complete.\r65460 markers complete.\r65520 markers complete.\r65580 markers complete.\r65640 markers complete.\r65700 markers complete.\r65760 markers complete.\r65820 markers complete.\r65880 markers complete.\r65940 markers complete.\r66000 markers complete.\r66060 markers complete.\r66120 markers complete.\r66180 markers complete.\r66240 markers complete.\r66300 markers complete.\r66360 markers complete.\r66420 markers complete.\r66480 markers complete.\r66540 markers complete.\r66600 markers complete.\r66660 markers complete.\r66720 markers complete.\r66780 markers complete.\r66840 markers complete.\r66900 markers complete.\r66960 markers complete.\r67020 markers complete.\r67080 markers complete.\r67140 markers complete.\r67200 markers complete.\r67260 markers complete.\r67320 markers complete.\r67380 markers complete.\r67440 markers complete.\r67500 markers complete.\r67560 markers complete.\r67620 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complete.\r210660 markers complete.\r210720 markers complete.\r210780 markers complete.\r210840 markers complete.\r210900 markers complete.\r210960 markers complete.\r211020 markers complete.\r211080 markers complete.\r211140 markers complete.\r211200 markers complete.\r211260 markers complete.\r211320 markers complete.\r211380 markers complete.\r211440 markers complete.\r211500 markers complete.\r211560 markers complete.\r211620 markers complete.\r211680 markers complete.\r211740 markers complete.\r211800 markers complete.\r211860 markers complete.\r211920 markers complete.\r211980 markers complete.\r212040 markers complete.\r212100 markers complete.\r212160 markers complete.\r212220 markers complete.\r212280 markers complete.\r212340 markers complete.\r212400 markers complete.\r212460 markers complete.\r212520 markers complete.\r212580 markers complete.\r212640 markers complete.\r212700 markers complete.\r212760 markers complete.\r212820 markers complete.\r212880 markers 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complete.\r217500 markers complete.\r217560 markers complete.\r217620 markers complete.\r217680 markers complete.\r217740 markers complete.\r217800 markers complete.\r217860 markers complete.\r217920 markers complete.\r217980 markers complete.\r218040 markers complete.\r218100 markers complete.\r218160 markers complete.\r218220 markers complete.\r218280 markers complete.\r218340 markers complete.\r218400 markers complete.\r218460 markers complete.\r218520 markers complete.\r218580 markers complete.\r218640 markers complete.\r218700 markers complete.\r218760 markers complete.\r218820 markers complete.\r218880 markers complete.\r218940 markers complete.\r219000 markers complete.\r219060 markers complete.\r219120 markers complete.\r219180 markers complete.\r219240 markers complete.\r219300 markers complete.\r219360 markers complete.\r219420 markers complete.\r219480 markers complete.\r219540 markers complete.\r219600 markers complete.\r219660 markers complete.\r219720 markers complete.\r219780 markers complete.\r219840 markers complete.\r219900 markers complete.\r219960 markers complete.\r220020 markers complete.\r220080 markers complete.\r220140 markers complete.\r220200 markers complete.\r220260 markers complete.\r220320 markers complete.\r220380 markers complete.\r220440 markers complete.\r220500 markers complete.\r220560 markers complete.\r220620 markers complete.\r220680 markers complete.\r220740 markers complete.\r220800 markers complete.\r220860 markers complete.\r220920 markers complete.\r220980 markers complete.\r221040 markers complete.\r221100 markers complete.\r221160 markers complete.\r221220 markers complete.\r221280 markers complete.\r221340 markers complete.\r221400 markers complete.\r221460 markers complete.\r221520 markers complete.\r221580 markers complete.\r221640 markers complete.\r221700 markers complete.\r221760 markers complete.\r221820 markers complete.\r221880 markers complete.\r221940 markers complete.\r222000 markers complete.\r222060 markers complete.\r222120 markers complete.\r222180 markers complete.\r222240 markers complete.\r222300 markers complete.\r222360 markers complete.\r222420 markers complete.\r222480 markers complete.\r222540 markers complete.\r222600 markers complete.\r222660 markers complete.\r222720 markers complete.\r222780 markers complete.\r222840 markers complete.\r222900 markers complete.\r222960 markers complete.\r223020 markers complete.\r223080 markers complete.\r223140 markers complete.\r223200 markers complete.\r223260 markers complete.\r223320 markers complete.\r223380 markers complete.\r223440 markers complete.\r223500 markers complete.\r223560 markers complete.\r223620 markers complete.\r223680 markers complete.\r223740 markers complete.\r223800 markers complete.\r223860 markers complete.\r223920 markers complete.\r223980 markers complete.\r224040 markers complete.\r224100 markers complete.\r224160 markers complete.\r224220 markers complete.\r224280 markers complete.\r224340 markers complete.\r224400 markers complete.\r224460 markers complete.\r224520 markers complete.\r224580 markers complete.\r224640 markers complete.\r224700 markers complete.\r224760 markers complete.\r224820 markers complete.\r224880 markers complete.\r224940 markers complete.\r225000 markers complete.\r225060 markers complete.\r225120 markers complete.\r225180 markers complete.\r225240 markers complete.\r225300 markers complete.\r225360 markers complete.\r225420 markers complete.\r225480 markers complete.\r225540 markers complete.\r225600 markers complete.\r225660 markers complete.\r225720 markers complete.\r225780 markers complete.\r225840 markers complete.\r225900 markers complete.\r225960 markers complete.\r226020 markers complete.\r226080 markers complete.\r226140 markers complete.\r226200 markers complete.\r226260 markers complete.\r226320 markers complete.\r226380 markers complete.\r226440 markers complete.\r226500 markers complete.\r226560 markers complete.\r226620 markers complete.\r226680 markers complete.\r226740 markers complete.\r226800 markers complete.\r226860 markers complete.\r226920 markers complete.\r226980 markers complete.\r227040 markers complete.\r227100 markers complete.\r227160 markers complete.\r227220 markers complete.\r227280 markers complete.\r227340 markers complete.\r227400 markers complete.\r227460 markers complete.\r227520 markers complete.\r227580 markers complete.\r227640 markers complete.\r227700 markers complete.\r227760 markers complete.\r227820 markers complete.\r227880 markers complete.\r227940 markers complete.\r228000 markers complete.\r228060 markers complete.\r228120 markers complete.\r228180 markers complete.\r228240 markers complete.\r228300 markers complete.\r228360 markers complete.\r228420 markers complete.\r228480 markers complete.\r228540 markers complete.\r228600 markers complete.\r228660 markers complete.\r228720 markers complete.\r228780 markers complete.\r228840 markers complete.\r228900 markers complete.\r228960 markers complete.\r229020 markers complete.\r229080 markers complete.\r229140 markers complete.\r229200 markers complete.\r229260 markers complete.\r229320 markers complete.\r229380 markers complete.\r229440 markers complete.\r229500 markers complete.\r229560 markers complete.\r229620 markers complete.\r229680 markers complete.\r229740 markers complete.\r229800 markers complete.\r229860 markers complete.\r229920 markers complete.\r229980 markers complete.\r230040 markers complete.\r230100 markers complete.\r230160 markers complete.\r230220 markers complete.\r230280 markers complete.\r230340 markers complete.\r230400 markers complete.\r230460 markers complete.\r230520 markers complete.\r230580 markers complete.\r230640 markers complete.\r230700 markers complete.\r230760 markers complete.\r230820 markers complete.\r230880 markers complete.\r230940 markers complete.\r231000 markers complete.\r231060 markers complete.\r231120 markers complete.\r231180 markers complete.\r231240 markers complete.\r231300 markers complete.\r231360 markers complete.\r231420 markers complete.\r231480 markers complete.\r231540 markers complete.\r231600 markers complete.\r231660 markers complete.\r231720 markers complete.\r231780 markers complete.\r231840 markers complete.\r231900 markers complete.\r231960 markers complete.\r232020 markers complete.\r232080 markers complete.\r232140 markers complete.\r232200 markers complete.\r232260 markers complete.\r232320 markers complete.\r232380 markers complete.\r232440 markers complete.\r232500 markers complete.\r232560 markers complete.\r232620 markers complete.\r232680 markers complete.\r232740 markers complete.\r232800 markers complete.\r232860 markers complete.\r232920 markers complete.\r232980 markers complete.\r233040 markers complete.\r233100 markers complete.\r233160 markers complete.\r233220 markers complete.\r233280 markers complete.\r233340 markers complete.\r233400 markers complete.\r233460 markers complete.\r233520 markers complete.\r233580 markers complete.\r233640 markers complete.\r233700 markers complete.\r233760 markers complete.\r233820 markers complete.\r233880 markers complete.\r233940 markers complete.\r234000 markers complete.\r234060 markers complete.\r234120 markers complete.\r234180 markers complete.\r234240 markers complete.\r234300 markers complete.\r234360 markers complete.\r234420 markers complete.\r234480 markers complete.\r234540 markers complete.\r234600 markers complete.\r234660 markers complete.\r234720 markers complete.\r234780 markers complete.\r234840 markers complete.\r234900 markers complete.\r234960 markers complete.\r235020 markers complete.\r235080 markers complete.\r235140 markers complete.\r235200 markers complete.\r235260 markers complete.\r235320 markers complete.\r235380 markers complete.\r235440 markers complete.\r235500 markers complete.\r235560 markers complete.\r235620 markers complete.\r235680 markers complete.\r235740 markers complete.\r235800 markers complete.\r235860 markers complete.\r235920 markers complete.\r235980 markers complete.\r236040 markers complete.\r236100 markers complete.\r236160 markers complete.\r236220 markers complete.\r236280 markers complete.\r236340 markers complete.\r236400 markers complete.\r236460 markers complete.\r236520 markers complete.\r236580 markers complete.\r236640 markers complete.\r236700 markers complete.\r236760 markers complete.\r236820 markers complete.\r236880 markers complete.\r236940 markers complete.\r237000 markers complete.\r237060 markers complete.\r237120 markers complete.\r237180 markers complete.\r237240 markers complete.\r237300 markers complete.\r237360 markers complete.\r237420 markers complete.\r237480 markers complete.\r237540 markers complete.\r237600 markers complete.\r237660 markers complete.\r237720 markers complete.\r237780 markers complete.\r237840 markers complete.\r237900 markers complete.\r237960 markers complete.\r238020 markers complete.\r238080 markers complete.\r238140 markers complete.\r238200 markers complete.\r238260 markers complete.\r238320 markers complete.\r238380 markers complete.\r238440 markers complete.\r238500 markers complete.\r238560 markers complete.\r238620 markers complete.\r238680 markers complete.\r238740 markers complete.\r238800 markers complete.\r238860 markers complete.\r238920 markers complete.\r238980 markers complete.\r239040 markers complete.\r239100 markers complete.\r239160 markers complete.\r239220 markers complete.\r239280 markers complete.\r239340 markers complete.\r239400 markers complete.\r239460 markers complete.\r239520 markers complete.\r239580 markers complete.\r239640 markers complete.\r239700 markers complete.\r239760 markers complete.\r239820 markers complete.\r239880 markers complete.\r239940 markers complete.\r240000 markers complete.\r240060 markers complete.\r240120 markers complete.\r240180 markers complete.\r240240 markers complete.\r240300 markers complete.\r240360 markers complete.\r240420 markers complete.\r240480 markers complete.\r240540 markers complete.\r240600 markers complete.\r240660 markers complete.\r240720 markers complete.\r240780 markers complete.\r240840 markers complete.\r240900 markers complete.\r240960 markers complete.\r241020 markers complete.\r241080 markers complete.\r241140 markers complete.\r241200 markers complete.\r241260 markers complete.\r241320 markers complete.\r241380 markers complete.\r241440 markers complete.\r241500 markers complete.\r241560 markers complete.\r241620 markers complete.\r241680 markers complete.\r241740 markers complete.\r241800 markers complete.\r241860 markers complete.\r241920 markers complete.\r241980 markers complete.\r242040 markers complete.\r242100 markers complete.\r242160 markers complete.\r242220 markers complete.\r242280 markers complete.\r242340 markers complete.\r242400 markers complete.\r242460 markers complete.\r242520 markers complete.\r242580 markers complete.\r242640 markers complete.\r242700 markers complete.\r242760 markers complete.\r242820 markers complete.\r242880 markers complete.\r242940 markers complete.\r243000 markers complete.\r243060 markers complete.\r243120 markers complete.\r243180 markers complete.\r243240 markers complete.\r243300 markers complete.\r243360 markers complete.\r243420 markers complete.\r243480 markers complete.\r243540 markers complete.\r243600 markers complete.\r243660 markers complete.\r243720 markers complete.\r243780 markers complete.\r243840 markers complete.\r243900 markers complete.\r243960 markers complete.\r244020 markers complete.\r244080 markers complete.\r244140 markers complete.\r244200 markers complete.\r244260 markers complete.\r244320 markers complete.\r244380 markers complete.\r244440 markers complete.\r244500 markers complete.\r244560 markers complete.\r244620 markers complete.\r244680 markers complete.\r244740 markers complete.\r244800 markers complete.\r244860 markers complete.\r244920 markers complete.\r244980 markers complete.\r245040 markers complete.\r245100 markers complete.\r245160 markers complete.\r245220 markers complete.\r245280 markers complete.\r245340 markers complete.\r245400 markers complete.\r245460 markers complete.\r245520 markers complete.\r245580 markers complete.\r245640 markers complete.\r245700 markers complete.\r245760 markers complete.\r245820 markers complete.\r245880 markers complete.\r245940 markers complete.\r246000 markers complete.\r246060 markers complete.\r246120 markers complete.\r246180 markers complete.\r246240 markers complete.\r246300 markers complete.\r246360 markers complete.\r246420 markers complete.\r246480 markers complete.\r246540 markers complete.\r246600 markers complete.\r246660 markers complete.\r246720 markers complete.\r246780 markers complete.\r246840 markers complete.\r246900 markers complete.\r246960 markers complete.\r247020 markers complete.\r247080 markers complete.\r247140 markers complete.\r247200 markers complete.\r247260 markers complete.\r247320 markers complete.\r247380 markers complete.\r247440 markers complete.\r247500 markers complete.\r247560 markers complete.\r247620 markers complete.\r247680 markers complete.\r247740 markers complete.\r247800 markers complete.\r247860 markers complete.\r247920 markers complete.\r247980 markers complete.\r248040 markers complete.\r248100 markers complete.\r248160 markers complete.\r248220 markers complete.\r248280 markers complete.\r248340 markers complete.\r248400 markers complete.\r248460 markers complete.\r248520 markers complete.\r248580 markers complete.\r248640 markers complete.\r248700 markers complete.\r248760 markers complete.\r248820 markers complete.\r248880 markers complete.\r248940 markers complete.\r249000 markers complete.\r249060 markers complete.\r249120 markers complete.\r249180 markers complete.\r249240 markers complete.\r249300 markers complete.\r249360 markers complete.\r249420 markers complete.\r249480 markers complete.\r249540 markers complete.\r249600 markers complete.\r249660 markers complete.\r249720 markers complete.\r249780 markers complete.\r249840 markers complete.\r249900 markers complete.\r249960 markers complete.\r250020 markers complete.\r250080 markers complete.\r250140 markers complete.\r250200 markers complete.\r250260 markers complete.\r250320 markers complete.\r250380 markers complete.\r250440 markers complete.\r250500 markers complete.\r250560 markers complete.\r250620 markers complete.\r250680 markers complete.\r250740 markers complete.\r250800 markers complete.\r250860 markers complete.\r250920 markers complete.\r250980 markers complete.\r251040 markers complete.\r251100 markers complete.\r251160 markers complete.\r251220 markers complete.\r251280 markers complete.\r251340 markers complete.\r251400 markers complete.\r251460 markers complete.\r251520 markers complete.\r251580 markers complete.\r251640 markers complete.\r251700 markers complete.\r251760 markers complete.\r251820 markers complete.\r251880 markers complete.\r251940 markers complete.\r252000 markers complete.\r252060 markers complete.\r252120 markers complete.\r252180 markers complete.\r252240 markers complete.\r252300 markers complete.\r252360 markers complete.\r252420 markers complete.\r252480 markers complete.\r252540 markers complete.\r252600 markers complete.\r252660 markers complete.\r252720 markers complete.\r252780 markers complete.\r252840 markers complete.\r252900 markers complete.\r252960 markers complete.\r253020 markers complete.\r253080 markers complete.\r253140 markers complete.\r253200 markers complete.\r253260 markers complete.\r253320 markers complete.\r253380 markers complete.\r253440 markers complete.\r253500 markers complete.\r253560 markers complete.\r253620 markers complete.\r253680 markers complete.\r253740 markers complete.\r253800 markers complete.\r253860 markers complete.\r253920 markers complete.\r253980 markers complete.\r254040 markers complete.\r254100 markers complete.\r254160 markers complete.\r254220 markers complete.\r254280 markers complete.\r254340 markers complete.\r254400 markers complete.\r254460 markers complete.\r254520 markers complete.\r254580 markers complete.\r254640 markers complete.\r254700 markers complete.\r254760 markers complete.\r254820 markers complete.\r254880 markers complete.\r254940 markers complete.\r255000 markers complete.\r255060 markers complete.\r255120 markers complete.\r255180 markers complete.\r255240 markers complete.\r255300 markers complete.\r255360 markers complete.\r255420 markers complete.\r255480 markers complete.\r255540 markers complete.\r255600 markers complete.\r255660 markers complete.\r255720 markers complete.\r255780 markers complete.\r255840 markers complete.\r255900 markers complete.\r255960 markers complete.\r256020 markers complete.\r256080 markers complete.\r256140 markers complete.\r256200 markers complete.\r256260 markers complete.\r256320 markers complete.\r256380 markers complete.\r256440 markers complete.\r256500 markers complete.\r256560 markers complete.\r256620 markers complete.\r256680 markers complete.\r256740 markers complete.\r256800 markers complete.\r256860 markers complete.\r256920 markers complete.\r256980 markers complete.\r257040 markers complete.\r257100 markers complete.\r257160 markers complete.\r257220 markers complete.\r257280 markers complete.\r257340 markers complete.\r257400 markers complete.\r257460 markers complete.\r257520 markers complete.\r257580 markers complete.\r257640 markers complete.\r257700 markers complete.\r257760 markers complete.\r257820 markers complete.\r257880 markers complete.\r257940 markers complete.\r258000 markers complete.\r258060 markers complete.\r258120 markers complete.\r258180 markers complete.\r258240 markers complete.\r258300 markers complete.\r258360 markers complete.\r258420 markers complete.\r258480 markers complete.\r258540 markers complete.\r258600 markers complete.\r258660 markers complete.\r258720 markers complete.\r258780 markers complete.\r258840 markers complete.\r258900 markers complete.\r258960 markers complete.\r259020 markers complete.\r259080 markers complete.\r259140 markers complete.\r259200 markers complete.\r259260 markers complete.\r259320 markers complete.\r259380 markers complete.\r259440 markers complete.\r259500 markers complete.\r259560 markers complete.\r259620 markers complete.\r259680 markers complete.\r259740 markers complete.\r259800 markers complete.\r259860 markers complete.\r259920 markers complete.\r259980 markers complete.\r260040 markers complete.\r260100 markers complete.\r260160 markers complete.\r260220 markers complete.\r260280 markers complete.\r260340 markers complete.\r260400 markers complete.\r260460 markers complete.\r260520 markers complete.\r260580 markers complete.\r260640 markers complete.\r260700 markers complete.\r260760 markers complete.\r260820 markers complete.\r260880 markers complete.\r260940 markers complete.\r261000 markers complete.\r261060 markers complete.\r261120 markers complete.\r261180 markers complete.\r261240 markers complete.\r261300 markers complete.\r261360 markers complete.\r261420 markers complete.\r261480 markers complete.\r261540 markers complete.\r261600 markers complete.\r261660 markers complete.\r261720 markers complete.\r261780 markers complete.\r261840 markers complete.\r261900 markers complete.\r261960 markers complete.\r262020 markers complete.\r262080 markers complete.\r262140 markers complete.\r262200 markers complete.\r262260 markers complete.\r262320 markers complete.\r262380 markers complete.\r262440 markers complete.\r262500 markers complete.\r262560 markers complete.\r262620 markers complete.\r262680 markers complete.\r262740 markers complete.\r262800 markers complete.\r262860 markers complete.\r262920 markers complete.\r262980 markers complete.\r263040 markers complete.\r263100 markers complete.\r263160 markers complete.\r263220 markers complete.\r263228 markers complete.\rRelationship matrix calculation complete.\n",
            "0 people excluded by --rel-cutoff.\n",
            "Remaining sample IDs written to QC/target/EUR.QC.rel.id .\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Generate final QC'ed target data file"
      ],
      "metadata": {
        "id": "Ikhrjgy5Pno5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "\n",
        "plink \\\n",
        "    --bfile QC/target/EUR \\\n",
        "    --make-bed \\\n",
        "    --keep QC/target/EUR.QC.rel.id \\\n",
        "    --out QC/target/EUR.QC \\\n",
        "    --extract QC/target/EUR.QC.snplist \\\n",
        "    --exclude QC/target/EUR.mismatch \\\n",
        "    --a1-allele QC/target/EUR.a1"
      ],
      "metadata": {
        "id": "26JeLKElPsl3",
        "outputId": "c79d326a-5fcb-4ec9-937d-88a29c04423b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PLINK v1.90b6.21 64-bit (19 Oct 2020)          www.cog-genomics.org/plink/1.9/\n",
            "(C) 2005-2020 Shaun Purcell, Christopher Chang   GNU General Public License v3\n",
            "Logging to QC/target/EUR.QC.log.\n",
            "Options in effect:\n",
            "  --a1-allele QC/target/EUR.a1\n",
            "  --bfile QC/target/EUR\n",
            "  --exclude QC/target/EUR.mismatch\n",
            "  --extract QC/target/EUR.QC.snplist\n",
            "  --keep QC/target/EUR.QC.rel.id\n",
            "  --make-bed\n",
            "  --out QC/target/EUR.QC\n",
            "\n",
            "12982 MB RAM detected; reserving 6491 MB for main workspace.\n",
            "551892 variants loaded from .bim file.\n",
            "503 people (240 males, 263 females) loaded from .fam.\n",
            "--extract: 540534 variants remaining.\n",
            "--exclude: 489805 variants remaining.\n",
            "--keep: 483 people remaining.\n",
            "Using 1 thread (no multithreaded calculations invoked).\n",
            "Before main variant filters, 483 founders and 0 nonfounders present.\n",
            "Calculating allele frequencies... 0%\b\b1%\b\b2%\b\b3%\b\b4%\b\b5%\b\b6%\b\b7%\b\b8%\b\b9%\b\b10%\b\b\b11%\b\b\b12%\b\b\b13%\b\b\b14%\b\b\b15%\b\b\b16%\b\b\b17%\b\b\b18%\b\b\b19%\b\b\b20%\b\b\b21%\b\b\b22%\b\b\b23%\b\b\b24%\b\b\b25%\b\b\b26%\b\b\b27%\b\b\b28%\b\b\b29%\b\b\b30%\b\b\b31%\b\b\b32%\b\b\b33%\b\b\b34%\b\b\b35%\b\b\b36%\b\b\b37%\b\b\b38%\b\b\b39%\b\b\b40%\b\b\b41%\b\b\b42%\b\b\b43%\b\b\b44%\b\b\b45%\b\b\b46%\b\b\b47%\b\b\b48%\b\b\b49%\b\b\b50%\b\b\b51%\b\b\b52%\b\b\b53%\b\b\b54%\b\b\b55%\b\b\b56%\b\b\b57%\b\b\b58%\b\b\b59%\b\b\b60%\b\b\b61%\b\b\b62%\b\b\b63%\b\b\b64%\b\b\b65%\b\b\b66%\b\b\b67%\b\b\b68%\b\b\b69%\b\b\b70%\b\b\b71%\b\b\b72%\b\b\b73%\b\b\b74%\b\b\b75%\b\b\b76%\b\b\b77%\b\b\b78%\b\b\b79%\b\b\b80%\b\b\b81%\b\b\b82%\b\b\b83%\b\b\b84%\b\b\b85%\b\b\b86%\b\b\b87%\b\b\b88%\b\b\b89%\b\b\b90%\b\b\b91%\b\b\b92%\b\b\b93%\b\b\b94%\b\b\b95%\b\b\b96%\b\b\b97%\b\b\b98%\b\b\b99%\b\b\b\b done.\n",
            "--a1-allele: 489805 assignments made.\n",
            "489805 variants and 483 people pass filters and QC.\n",
            "Note: No phenotypes present.\n",
            "--make-bed to QC/target/EUR.QC.bed + QC/target/EUR.QC.bim +\n",
            "QC/target/EUR.QC.fam ... 0%\b\b1%\b\b2%\b\b3%\b\b4%\b\b5%\b\b6%\b\b7%\b\b8%\b\b9%\b\b10%\b\b\b11%\b\b\b12%\b\b\b13%\b\b\b14%\b\b\b15%\b\b\b16%\b\b\b17%\b\b\b18%\b\b\b19%\b\b\b20%\b\b\b21%\b\b\b22%\b\b\b23%\b\b\b24%\b\b\b25%\b\b\b26%\b\b\b27%\b\b\b28%\b\b\b29%\b\b\b30%\b\b\b31%\b\b\b32%\b\b\b33%\b\b\b34%\b\b\b35%\b\b\b36%\b\b\b37%\b\b\b38%\b\b\b39%\b\b\b40%\b\b\b41%\b\b\b42%\b\b\b43%\b\b\b44%\b\b\b45%\b\b\b46%\b\b\b47%\b\b\b48%\b\b\b49%\b\b\b50%\b\b\b51%\b\b\b52%\b\b\b53%\b\b\b54%\b\b\b55%\b\b\b56%\b\b\b57%\b\b\b58%\b\b\b59%\b\b\b60%\b\b\b61%\b\b\b62%\b\b\b63%\b\b\b64%\b\b\b65%\b\b\b66%\b\b\b67%\b\b\b68%\b\b\b69%\b\b\b70%\b\b\b71%\b\b\b72%\b\b\b73%\b\b\b74%\b\b\b75%\b\b\b76%\b\b\b77%\b\b\b78%\b\b\b79%\b\b\b80%\b\b\b81%\b\b\b82%\b\b\b83%\b\b\b84%\b\b\b85%\b\b\b86%\b\b\b87%\b\b\b88%\b\b\b89%\b\b\b90%\b\b\b91%\b\b\b92%\b\b\b93%\b\b\b94%\b\b\b95%\b\b\b96%\b\b\b97%\b\b\b98%\b\b\b99%\b\b\bdone.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LVB55UzNQ-JG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Calculating and analysing PRS using PLINK"
      ],
      "metadata": {
        "id": "f-OQG0hiRJV1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir PRS"
      ],
      "metadata": {
        "id": "QFEnjLNzR3gF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Update Effect Size"
      ],
      "metadata": {
        "id": "XJzpVFM0RQkl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "\n",
        "dat <- read.table(gzfile(\"QC/base/Height.QC.gz\"), header=T)\n",
        "dat$BETA <- log(dat$OR)\n",
        "write.table(dat, \"PRS/Height.QC.Transformed\", quote=F, row.names=F)"
      ],
      "metadata": {
        "id": "bWxQs47eRuNd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Clumping"
      ],
      "metadata": {
        "id": "siWd3RhtTLhj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This will generate EUR.clumped, containing the index SNPs after clumping is performed."
      ],
      "metadata": {
        "id": "tgTtyKMzVsAa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "\n",
        "plink \\\n",
        "    --bfile QC/target/EUR.QC \\\n",
        "    --clump-p1 1 \\\n",
        "    --clump-r2 0.1 \\\n",
        "    --clump-kb 250 \\\n",
        "    --clump PRS/Height.QC.Transformed \\\n",
        "    --clump-snp-field SNP \\\n",
        "    --clump-field P \\\n",
        "    --out PRS/EUR"
      ],
      "metadata": {
        "id": "UKWIQvjsTOLs",
        "outputId": "2497b88e-e59a-48f8-f109-2fbb2ae8779e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PLINK v1.90b6.21 64-bit (19 Oct 2020)          www.cog-genomics.org/plink/1.9/\n",
            "(C) 2005-2020 Shaun Purcell, Christopher Chang   GNU General Public License v3\n",
            "Logging to PRS/EUR.log.\n",
            "Options in effect:\n",
            "  --bfile QC/target/EUR.QC\n",
            "  --clump PRS/Height.QC.Transformed\n",
            "  --clump-field P\n",
            "  --clump-kb 250\n",
            "  --clump-p1 1\n",
            "  --clump-r2 0.1\n",
            "  --clump-snp-field SNP\n",
            "  --out PRS/EUR\n",
            "\n",
            "12982 MB RAM detected; reserving 6491 MB for main workspace.\n",
            "489805 variants loaded from .bim file.\n",
            "483 people (232 males, 251 females) loaded from .fam.\n",
            "Using 1 thread (no multithreaded calculations invoked).\n",
            "Before main variant filters, 483 founders and 0 nonfounders present.\n",
            "Calculating allele frequencies... 0%\b\b1%\b\b2%\b\b3%\b\b4%\b\b5%\b\b6%\b\b7%\b\b8%\b\b9%\b\b10%\b\b\b11%\b\b\b12%\b\b\b13%\b\b\b14%\b\b\b15%\b\b\b16%\b\b\b17%\b\b\b18%\b\b\b19%\b\b\b20%\b\b\b21%\b\b\b22%\b\b\b23%\b\b\b24%\b\b\b25%\b\b\b26%\b\b\b27%\b\b\b28%\b\b\b29%\b\b\b30%\b\b\b31%\b\b\b32%\b\b\b33%\b\b\b34%\b\b\b35%\b\b\b36%\b\b\b37%\b\b\b38%\b\b\b39%\b\b\b40%\b\b\b41%\b\b\b42%\b\b\b43%\b\b\b44%\b\b\b45%\b\b\b46%\b\b\b47%\b\b\b48%\b\b\b49%\b\b\b50%\b\b\b51%\b\b\b52%\b\b\b53%\b\b\b54%\b\b\b55%\b\b\b56%\b\b\b57%\b\b\b58%\b\b\b59%\b\b\b60%\b\b\b61%\b\b\b62%\b\b\b63%\b\b\b64%\b\b\b65%\b\b\b66%\b\b\b67%\b\b\b68%\b\b\b69%\b\b\b70%\b\b\b71%\b\b\b72%\b\b\b73%\b\b\b74%\b\b\b75%\b\b\b76%\b\b\b77%\b\b\b78%\b\b\b79%\b\b\b80%\b\b\b81%\b\b\b82%\b\b\b83%\b\b\b84%\b\b\b85%\b\b\b86%\b\b\b87%\b\b\b88%\b\b\b89%\b\b\b90%\b\b\b91%\b\b\b92%\b\b\b93%\b\b\b94%\b\b\b95%\b\b\b96%\b\b\b97%\b\b\b98%\b\b\b99%\b\b\b\b done.\n",
            "489805 variants and 483 people pass filters and QC.\n",
            "Note: No phenotypes present.\n",
            "--clump: 193758 clumps formed from 489805 top variants.\n",
            "Results written to PRS/EUR.clumped .\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning: 'rs1076829' is missing from the main dataset, and is a top variant.\n",
            "Warning: 'rs3129818' is missing from the main dataset, and is a top variant.\n",
            "Warning: 'rs3118359' is missing from the main dataset, and is a top variant.\n",
            "9809 more top variant IDs missing; see log file.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can extract the index SNP ID by performing the following command:"
      ],
      "metadata": {
        "id": "MSLonrgTVyLT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "\n",
        "awk 'NR!=1{print $3}' PRS/EUR.clumped >  PRS/EUR.valid.snp"
      ],
      "metadata": {
        "id": "3khKt1brV1k0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Generate PRS"
      ],
      "metadata": {
        "id": "8upAO8weZocv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "plink provides a convenient function --score and --q-score-range for calculating polygenic scores."
      ],
      "metadata": {
        "id": "UeZPpZ8wZqr8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. The base data file: Height.QC.Transformed"
      ],
      "metadata": {
        "id": "OYyh5MTEdPEv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. A file containing SNP IDs and their corresponding P-values $3 because SNP ID is located in the third column; $8 because the P-value is located in the eighth column"
      ],
      "metadata": {
        "id": "fI3STmqZdTRX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "awk '{print $3,$8}' PRS/Height.QC.Transformed > PRS/SNP.pvalue"
      ],
      "metadata": {
        "id": "TPMVIIU3ZlCY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. A file containing the different P-value thresholds for inclusion of SNPs in the PRS. Here calculate PRS corresponding to a few thresholds for illustration purposes:"
      ],
      "metadata": {
        "id": "altuBqWcZtCI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "\n",
        "echo \"0.001 0 0.001\" > PRS/range_list\n",
        "echo \"0.05 0 0.05\" >> PRS/range_list\n",
        "echo \"0.1 0 0.1\" >> PRS/range_list\n",
        "echo \"0.2 0 0.2\" >> PRS/range_list\n",
        "echo \"0.3 0 0.3\" >> PRS/range_list\n",
        "echo \"0.4 0 0.4\" >> PRS/range_list\n",
        "echo \"0.5 0 0.5\" >> PRS/range_list"
      ],
      "metadata": {
        "id": "lmrwfb_Hd-Td"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can then calculate the PRS with the following plink command:"
      ],
      "metadata": {
        "id": "eWidNd8QeMrf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "\n",
        "plink \\\n",
        "    --bfile QC/target/EUR.QC \\\n",
        "    --score PRS/Height.QC.Transformed 3 4 12 header \\\n",
        "    --q-score-range PRS/range_list PRS/SNP.pvalue \\\n",
        "    --extract PRS/EUR.valid.snp \\\n",
        "    --out PRS/EUR"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v4_iu_gzeMRf",
        "outputId": "0aa34bfd-08bf-4279-99d1-84348d115f46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PLINK v1.90b6.21 64-bit (19 Oct 2020)          www.cog-genomics.org/plink/1.9/\n",
            "(C) 2005-2020 Shaun Purcell, Christopher Chang   GNU General Public License v3\n",
            "Logging to PRS/EUR.log.\n",
            "Options in effect:\n",
            "  --bfile QC/target/EUR.QC\n",
            "  --extract PRS/EUR.valid.snp\n",
            "  --out PRS/EUR\n",
            "  --q-score-range PRS/range_list PRS/SNP.pvalue\n",
            "  --score PRS/Height.QC.Transformed 3 4 12 header\n",
            "\n",
            "12982 MB RAM detected; reserving 6491 MB for main workspace.\n",
            "489805 variants loaded from .bim file.\n",
            "483 people (232 males, 251 females) loaded from .fam.\n",
            "--extract: 193758 variants remaining.\n",
            "Using 1 thread (no multithreaded calculations invoked).\n",
            "Before main variant filters, 483 founders and 0 nonfounders present.\n",
            "Calculating allele frequencies... 0%\b\b1%\b\b2%\b\b3%\b\b4%\b\b5%\b\b6%\b\b7%\b\b8%\b\b9%\b\b10%\b\b\b11%\b\b\b12%\b\b\b13%\b\b\b14%\b\b\b15%\b\b\b16%\b\b\b17%\b\b\b18%\b\b\b19%\b\b\b20%\b\b\b21%\b\b\b22%\b\b\b23%\b\b\b24%\b\b\b25%\b\b\b26%\b\b\b27%\b\b\b28%\b\b\b29%\b\b\b30%\b\b\b31%\b\b\b32%\b\b\b33%\b\b\b34%\b\b\b35%\b\b\b36%\b\b\b37%\b\b\b38%\b\b\b39%\b\b\b40%\b\b\b41%\b\b\b42%\b\b\b43%\b\b\b44%\b\b\b45%\b\b\b46%\b\b\b47%\b\b\b48%\b\b\b49%\b\b\b50%\b\b\b51%\b\b\b52%\b\b\b53%\b\b\b54%\b\b\b55%\b\b\b56%\b\b\b57%\b\b\b58%\b\b\b59%\b\b\b60%\b\b\b61%\b\b\b62%\b\b\b63%\b\b\b64%\b\b\b65%\b\b\b66%\b\b\b67%\b\b\b68%\b\b\b69%\b\b\b70%\b\b\b71%\b\b\b72%\b\b\b73%\b\b\b74%\b\b\b75%\b\b\b76%\b\b\b77%\b\b\b78%\b\b\b79%\b\b\b80%\b\b\b81%\b\b\b82%\b\b\b83%\b\b\b84%\b\b\b85%\b\b\b86%\b\b\b87%\b\b\b88%\b\b\b89%\b\b\b90%\b\b\b91%\b\b\b92%\b\b\b93%\b\b\b94%\b\b\b95%\b\b\b96%\b\b\b97%\b\b\b98%\b\b\b99%\b\b\b\b done.\n",
            "193758 variants and 483 people pass filters and QC.\n",
            "Note: No phenotypes present.\n",
            "--score: 193758 valid predictors loaded.\n",
            "--score: 7 ranges processed.\n",
            "Results written to PRS/EUR.*.profile.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning: 305859 lines skipped in --score file (305859 due to variant ID\n",
            "mismatch, 0 due to allele code mismatch); see PRS/EUR.nopred for details.\n",
            "Warning: 305860 lines skipped in --q-score-range data file.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The above command and range_list will generate 7 files:\n",
        "\n",
        "* EUR.0.5.profile\n",
        "* EUR.0.4.profile\n",
        "* EUR.0.3.profile\n",
        "* EUR.0.2.profile\n",
        "* EUR.0.1.profile\n",
        "* EUR.0.05.profile\n",
        "* EUR.0.001.profile"
      ],
      "metadata": {
        "id": "KVPsB3Wse1mr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "NRoWlOWpfHiU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Accounting for Population Stratification\n"
      ],
      "metadata": {
        "id": "tTugSbVPfIqV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Population structure is the principal source of confounding in GWAS and is usually accounted for by incorporating principal components (PCs) as covariates. We can incorporate PCs into our PRS analysis to account for population stratification."
      ],
      "metadata": {
        "id": "1CBKZs-efoqE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Again, we can calculate the PCs using `plink`:"
      ],
      "metadata": {
        "id": "eNyIV4jefsav"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "\n",
        "# First, we need to perform prunning\n",
        "plink \\\n",
        "    --bfile QC/target/EUR.QC \\\n",
        "    --indep-pairwise 200 50 0.25 \\\n",
        "    --out PRS/EUR\n",
        "\n",
        "# Then we calculate the first 6 PCs\n",
        "plink \\\n",
        "    --bfile QC/target/EUR.QC \\\n",
        "    --extract PRS/EUR.prune.in \\\n",
        "    --pca 6 \\\n",
        "    --out PRS/EUR"
      ],
      "metadata": {
        "id": "xfmuwQOkfSWI",
        "outputId": "5b8a08b5-0409-4b96-edea-9c062812016d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PLINK v1.90b6.21 64-bit (19 Oct 2020)          www.cog-genomics.org/plink/1.9/\n",
            "(C) 2005-2020 Shaun Purcell, Christopher Chang   GNU General Public License v3\n",
            "Logging to PRS/EUR.log.\n",
            "Options in effect:\n",
            "  --bfile QC/target/EUR.QC\n",
            "  --indep-pairwise 200 50 0.25\n",
            "  --out PRS/EUR\n",
            "\n",
            "12982 MB RAM detected; reserving 6491 MB for main workspace.\n",
            "489805 variants loaded from .bim file.\n",
            "483 people (232 males, 251 females) loaded from .fam.\n",
            "Using 1 thread (no multithreaded calculations invoked).\n",
            "Before main variant filters, 483 founders and 0 nonfounders present.\n",
            "Calculating allele frequencies... 0%\b\b1%\b\b2%\b\b3%\b\b4%\b\b5%\b\b6%\b\b7%\b\b8%\b\b9%\b\b10%\b\b\b11%\b\b\b12%\b\b\b13%\b\b\b14%\b\b\b15%\b\b\b16%\b\b\b17%\b\b\b18%\b\b\b19%\b\b\b20%\b\b\b21%\b\b\b22%\b\b\b23%\b\b\b24%\b\b\b25%\b\b\b26%\b\b\b27%\b\b\b28%\b\b\b29%\b\b\b30%\b\b\b31%\b\b\b32%\b\b\b33%\b\b\b34%\b\b\b35%\b\b\b36%\b\b\b37%\b\b\b38%\b\b\b39%\b\b\b40%\b\b\b41%\b\b\b42%\b\b\b43%\b\b\b44%\b\b\b45%\b\b\b46%\b\b\b47%\b\b\b48%\b\b\b49%\b\b\b50%\b\b\b51%\b\b\b52%\b\b\b53%\b\b\b54%\b\b\b55%\b\b\b56%\b\b\b57%\b\b\b58%\b\b\b59%\b\b\b60%\b\b\b61%\b\b\b62%\b\b\b63%\b\b\b64%\b\b\b65%\b\b\b66%\b\b\b67%\b\b\b68%\b\b\b69%\b\b\b70%\b\b\b71%\b\b\b72%\b\b\b73%\b\b\b74%\b\b\b75%\b\b\b76%\b\b\b77%\b\b\b78%\b\b\b79%\b\b\b80%\b\b\b81%\b\b\b82%\b\b\b83%\b\b\b84%\b\b\b85%\b\b\b86%\b\b\b87%\b\b\b88%\b\b\b89%\b\b\b90%\b\b\b91%\b\b\b92%\b\b\b93%\b\b\b94%\b\b\b95%\b\b\b96%\b\b\b97%\b\b\b98%\b\b\b99%\b\b\b\b done.\n",
            "489805 variants and 483 people pass filters and QC.\n",
            "Note: No phenotypes present.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 18635 variants from chromosome 1, leaving 20449.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 19319 variants from chromosome 2, leaving 20251.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 16145 variants from chromosome 3, leaving 17177.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 15167 variants from chromosome 4, leaving 16141.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 13994 variants from chromosome 5, leaving 15446.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 19041 variants from chromosome 6, leaving 14943.\n",
            "\r1%\r2%\r3%\r4%\r4%\r5%\r6%\r7%\r8%\r9%\r9%\r10%\r11%\r12%\r13%\r14%\r14%\r15%\r16%\r17%\r18%\r19%\r19%\r20%\r21%\r22%\r23%\r24%\r24%\r25%\r26%\r27%\r28%\r29%\r29%\r30%\r31%\r32%\r33%\r34%\r34%\r35%\r36%\r37%\r38%\r39%\r39%\r40%\r41%\r42%\r43%\r44%\r44%\r45%\r46%\r47%\r48%\r49%\r49%\r50%\r51%\r52%\r53%\r54%\r54%\r55%\r56%\r57%\r58%\r59%\r59%\r60%\r61%\r62%\r63%\r64%\r64%\r65%\r66%\r67%\r68%\r69%\r69%\r70%\r71%\r72%\r73%\r74%\r74%\r75%\r76%\r77%\r78%\r79%\r79%\r80%\r81%\r82%\r83%\r84%\r84%\r85%\r86%\r87%\r88%\r89%\r89%\r90%\r91%\r92%\r93%\r94%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 12882 variants from chromosome 7, leaving 14119.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 12195 variants from chromosome 8, leaving 13100.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 9844 variants from chromosome 9, leaving 11496.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r98%\r99%\rPruned 11825 variants from chromosome 10, leaving 12923.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 11986 variants from chromosome 11, leaving 12286.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r97%\r98%\r99%\rPruned 10802 variants from chromosome 12, leaving 12107.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 7824 variants from chromosome 13, leaving 9296.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r65%\r66%\r67%\r68%\r69%\r70%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 7508 variants from chromosome 14, leaving 8478.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 7251 variants from chromosome 15, leaving 8203.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 7900 variants from chromosome 16, leaving 9012.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 7344 variants from chromosome 17, leaving 8410.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 6654 variants from chromosome 18, leaving 8298.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 6347 variants from chromosome 19, leaving 6472.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 5862 variants from chromosome 20, leaving 7282.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 3373 variants from chromosome 21, leaving 4124.\n",
            "\r1%\r2%\r3%\r4%\r5%\r6%\r7%\r8%\r9%\r10%\r11%\r12%\r13%\r14%\r15%\r16%\r17%\r18%\r19%\r20%\r21%\r22%\r23%\r24%\r25%\r26%\r27%\r28%\r29%\r30%\r31%\r32%\r33%\r34%\r35%\r36%\r37%\r38%\r39%\r40%\r41%\r42%\r43%\r44%\r45%\r46%\r47%\r48%\r49%\r50%\r51%\r52%\r53%\r54%\r55%\r56%\r57%\r58%\r59%\r60%\r61%\r62%\r63%\r64%\r65%\r66%\r67%\r68%\r69%\r70%\r71%\r72%\r73%\r74%\r75%\r76%\r77%\r78%\r79%\r80%\r81%\r82%\r83%\r84%\r85%\r86%\r87%\r88%\r89%\r90%\r91%\r92%\r93%\r94%\r95%\r96%\r97%\r98%\r99%\rPruned 3720 variants from chromosome 22, leaving 4174.\n",
            "Pruning complete.  235618 of 489805 variants removed.\n",
            "Writing...\rMarker lists written to PRS/EUR.prune.in and PRS/EUR.prune.out .\n",
            "PLINK v1.90b6.21 64-bit (19 Oct 2020)          www.cog-genomics.org/plink/1.9/\n",
            "(C) 2005-2020 Shaun Purcell, Christopher Chang   GNU General Public License v3\n",
            "Logging to PRS/EUR.log.\n",
            "Options in effect:\n",
            "  --bfile QC/target/EUR.QC\n",
            "  --extract PRS/EUR.prune.in\n",
            "  --out PRS/EUR\n",
            "  --pca 6\n",
            "\n",
            "12982 MB RAM detected; reserving 6491 MB for main workspace.\n",
            "489805 variants loaded from .bim file.\n",
            "483 people (232 males, 251 females) loaded from .fam.\n",
            "--extract: 254187 variants remaining.\n",
            "Using up to 2 threads (change this with --threads).\n",
            "Before main variant filters, 483 founders and 0 nonfounders present.\n",
            "Calculating allele frequencies... 0%\b\b1%\b\b2%\b\b3%\b\b4%\b\b5%\b\b6%\b\b7%\b\b8%\b\b9%\b\b10%\b\b\b11%\b\b\b12%\b\b\b13%\b\b\b14%\b\b\b15%\b\b\b16%\b\b\b17%\b\b\b18%\b\b\b19%\b\b\b20%\b\b\b21%\b\b\b22%\b\b\b23%\b\b\b24%\b\b\b25%\b\b\b26%\b\b\b27%\b\b\b28%\b\b\b29%\b\b\b30%\b\b\b31%\b\b\b32%\b\b\b33%\b\b\b34%\b\b\b35%\b\b\b36%\b\b\b37%\b\b\b38%\b\b\b39%\b\b\b40%\b\b\b41%\b\b\b42%\b\b\b43%\b\b\b44%\b\b\b45%\b\b\b46%\b\b\b47%\b\b\b48%\b\b\b49%\b\b\b50%\b\b\b51%\b\b\b52%\b\b\b53%\b\b\b54%\b\b\b55%\b\b\b56%\b\b\b57%\b\b\b58%\b\b\b59%\b\b\b60%\b\b\b61%\b\b\b62%\b\b\b63%\b\b\b64%\b\b\b65%\b\b\b66%\b\b\b67%\b\b\b68%\b\b\b69%\b\b\b70%\b\b\b71%\b\b\b72%\b\b\b73%\b\b\b74%\b\b\b75%\b\b\b76%\b\b\b77%\b\b\b78%\b\b\b79%\b\b\b80%\b\b\b81%\b\b\b82%\b\b\b83%\b\b\b84%\b\b\b85%\b\b\b86%\b\b\b87%\b\b\b88%\b\b\b89%\b\b\b90%\b\b\b91%\b\b\b92%\b\b\b93%\b\b\b94%\b\b\b95%\b\b\b96%\b\b\b97%\b\b\b98%\b\b\b99%\b\b\b\b done.\n",
            "254187 variants and 483 people pass filters and QC.\n",
            "Note: No phenotypes present.\n",
            "\r60 markers complete.\r120 markers complete.\r180 markers complete.\r240 markers complete.\r300 markers complete.\r360 markers complete.\r420 markers complete.\r480 markers complete.\r540 markers complete.\r600 markers complete.\r660 markers complete.\r720 markers complete.\r780 markers complete.\r840 markers complete.\r900 markers complete.\r960 markers complete.\r1020 markers complete.\r1080 markers complete.\r1140 markers complete.\r1200 markers complete.\r1260 markers complete.\r1320 markers complete.\r1380 markers complete.\r1440 markers complete.\r1500 markers complete.\r1560 markers complete.\r1620 markers complete.\r1680 markers complete.\r1740 markers complete.\r1800 markers complete.\r1860 markers complete.\r1920 markers complete.\r1980 markers complete.\r2040 markers complete.\r2100 markers complete.\r2160 markers complete.\r2220 markers complete.\r2280 markers complete.\r2340 markers complete.\r2400 markers complete.\r2460 markers complete.\r2520 markers 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complete.\r253980 markers complete.\r254040 markers complete.\r254100 markers complete.\r254160 markers complete.\r254187 markers complete.\rRelationship matrix calculation complete.\n",
            "[extracting eigenvalues and eigenvectors]\r--pca: Results saved to PRS/EUR.eigenval and PRS/EUR.eigenvec .\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Finding the \"best-fit\" PRS"
      ],
      "metadata": {
        "id": "U_J2EikZf-EI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "\n",
        "p.threshold <- c(0.001,0.05,0.1,0.2,0.3,0.4,0.5)\n",
        "\n",
        "# Read in the phenotype file\n",
        "phenotype <- read.table(\"data/target/EUR.height\", header=T)\n",
        "\n",
        "# Read in the PCs\n",
        "pcs <- read.table(\"PRS/EUR.eigenvec\", header=F)\n",
        "\n",
        "# The default output from plink does not include a header\n",
        "# To make things simple, we will add the appropriate headers\n",
        "# (1:6 because there are 6 PCs)\n",
        "colnames(pcs) <- c(\"FID\", \"IID\", paste0(\"PC\",1:6))\n",
        "\n",
        "# Read in the covariates (here, it is sex)\n",
        "covariate <- read.table(\"data/target/EUR.cov\", header=T)\n",
        "\n",
        "# Now merge the files\n",
        "pheno <- merge(merge(phenotype, covariate, by=c(\"FID\", \"IID\")), pcs, by=c(\"FID\",\"IID\"))\n",
        "\n",
        "# We can then calculate the null model (model with PRS) using a linear regression\n",
        "# (as height is quantitative)\n",
        "null.model <- lm(Height~., data=pheno[,!colnames(pheno)%in%c(\"FID\",\"IID\")])\n",
        "\n",
        "# And the R2 of the null model is\n",
        "null.r2 <- summary(null.model)$r.squared\n",
        "prs.result <- NULL\n",
        "for(i in p.threshold){\n",
        "    # Go through each p-value threshold\n",
        "    prs <- read.table(paste0(\"PRS/EUR.\",i,\".profile\"), header=T)\n",
        "    # Merge the prs with the phenotype matrix\n",
        "    # We only want the FID, IID and PRS from the PRS file, therefore we only select the\n",
        "    # relevant columns\n",
        "    pheno.prs <- merge(pheno, prs[,c(\"FID\",\"IID\", \"SCORE\")], by=c(\"FID\", \"IID\"))\n",
        "    # Now perform a linear regression on Height with PRS and the covariates\n",
        "    # ignoring the FID and IID from our model\n",
        "    model <- lm(Height~., data=pheno.prs[,!colnames(pheno.prs)%in%c(\"FID\",\"IID\")])\n",
        "    # model R2 is obtained as\n",
        "    model.r2 <- summary(model)$r.squared\n",
        "    # R2 of PRS is simply calculated as the model R2 minus the null R2\n",
        "    prs.r2 <- model.r2-null.r2\n",
        "    # We can also obtain the coeffcient and p-value of association of PRS as follow\n",
        "    prs.coef <- summary(model)$coeff[\"SCORE\",]\n",
        "    prs.beta <- as.numeric(prs.coef[1])\n",
        "    prs.se <- as.numeric(prs.coef[2])\n",
        "    prs.p <- as.numeric(prs.coef[4])\n",
        "    # We can then store the results\n",
        "    prs.result <- rbind(prs.result, data.frame(Threshold=i, R2=prs.r2, P=prs.p, BETA=prs.beta,SE=prs.se))\n",
        "}\n",
        "# Best result is:\n",
        "prs.result[which.max(prs.result$R2),]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_QKKUVUuf-WT",
        "outputId": "c0aab319-8eb7-4cd3-ec15-f774a599821e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Threshold        R2           P     BETA       SE\n",
            "5       0.3 0.1612372 2.77407e-25 45316.19 4107.777\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "P-value 0.3 threshold generates the \"best-fit\" PRS"
      ],
      "metadata": {
        "id": "PCd6ncv0wh_o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "head(data.frame(prs.result))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nh4bDe7FraND",
        "outputId": "3c12ea41-a1f1-4a8b-f535-ed86a0391223"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Threshold         R2            P      BETA        SE\n",
            "1     0.001 0.09138941 2.541697e-14  6709.307  852.5751\n",
            "2     0.050 0.14513031 1.178621e-22 22854.938 2212.1540\n",
            "3     0.100 0.15228704 8.158574e-24 29534.299 2774.7686\n",
            "4     0.200 0.15510537 2.827468e-24 37982.152 3527.8667\n",
            "5     0.300 0.16123724 2.774070e-25 45316.189 4107.7767\n",
            "6     0.400 0.16010540 4.265358e-25 50452.947 4593.7776\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Visualizing PRS Results"
      ],
      "metadata": {
        "id": "KbPpm8SIhF7r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plotting the Results"
      ],
      "metadata": {
        "id": "DEJpCwSohJG0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will be using prs.result variable, which was generated in the previous section"
      ],
      "metadata": {
        "id": "IQd02AYQhM8m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "install.packages(\"ggplot2\")\n",
        "library(ggplot2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LFDZjYt4hTS0",
        "outputId": "b82a64ab-910f-46fd-edd0-228c7390bd40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
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            "\n",
            "R[write to console]: \n",
            "R[write to console]: The downloaded source packages are in\n",
            "\t‘/tmp/Rtmp2qfIEU/downloaded_packages’\n",
            "R[write to console]: \n",
            "R[write to console]: \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# generate a pretty format for p-value output\n",
        "prs.result$print.p <- round(prs.result$P, digits = 3)\n",
        "prs.result$print.p[!is.na(prs.result$print.p) &\n",
        "                    prs.result$print.p == 0] <-\n",
        "    format(prs.result$P[!is.na(prs.result$print.p) &\n",
        "                            prs.result$print.p == 0], digits = 2)\n",
        "prs.result$print.p <- sub(\"e\", \"*x*10^\", prs.result$print.p)\n",
        "\n",
        "# Initialize ggplot, requiring the threshold as the x-axis (use factor so that it is uniformly distributed)\n",
        "ggplot(data = prs.result, aes(x = factor(Threshold), y = R2)) +\n",
        "    # Specify that we want to print p-value on top of the bars\n",
        "    geom_text(\n",
        "        aes(label = paste(print.p)),\n",
        "        vjust = -1.5,\n",
        "        hjust = 0,\n",
        "        angle = 45,\n",
        "        cex = 4,\n",
        "        parse = T\n",
        "    )  +\n",
        "    # Specify the range of the plot, *1.25 to provide enough space for the p-values\n",
        "    scale_y_continuous(limits = c(0, max(prs.result$R2) * 1.25)) +\n",
        "    # Specify the axis labels\n",
        "    xlab(expression(italic(P) - value ~ threshold ~ (italic(P)[T]))) +\n",
        "    ylab(expression(paste(\"PRS model fit:  \", R ^ 2))) +\n",
        "    # Draw a bar plot\n",
        "    geom_bar(aes(fill = -log10(P)), stat = \"identity\") +\n",
        "    # Specify the colors\n",
        "    scale_fill_gradient2(\n",
        "        low = \"dodgerblue\",\n",
        "        high = \"firebrick\",\n",
        "        mid = \"dodgerblue\",\n",
        "        midpoint = 1e-4,\n",
        "        name = bquote(atop(-log[10] ~ model, italic(P) - value),)\n",
        "    ) +\n",
        "\n",
        "    # Some beautification of the plot\n",
        "    theme_classic() + theme(\n",
        "        axis.title = element_text(face = \"bold\", size = 18),\n",
        "        axis.text = element_text(size = 14),\n",
        "        legend.title = element_text(face = \"bold\", size =\n",
        "                                        18),\n",
        "        legend.text = element_text(size = 14),\n",
        "        axis.text.x = element_text(angle = 45, hjust =\n",
        "                                    1)\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "5lX9gjheiBmm",
        "outputId": "4ae0389d-4fac-4916-9a4d-c9ac8b4799de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%R\n",
        "# Read in the files\n",
        "prs <- read.table(\"PRS/EUR.0.3.profile\", header=T)\n",
        "height <- read.table(\"data/target/EUR.height\", header=T)\n",
        "sex <- read.table(\"data/target/EUR.cov\", header=T)\n",
        "\n",
        "# Rename the sex\n",
        "sex$Sex <- as.factor(sex$Sex)\n",
        "levels(sex$Sex) <- c(\"Male\", \"Female\")\n",
        "\n",
        "# Merge the files\n",
        "dat <- merge(merge(prs, height), sex)\n",
        "\n",
        "# Start plotting\n",
        "ggplot(dat, aes(x=SCORE, y=Height, color=Sex))+\n",
        "    geom_point()+\n",
        "    theme_classic()+\n",
        "    labs(x=\"Polygenic Score\", y=\"Height\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "FCHALzUDipua",
        "outputId": "81cf8491-9398-4657-8c5b-bfb742d0439f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}