README

2024-11-05

Allelic Series

This package implements gene-level rare variant association tests targeting allelic series: genes where increasingly deleterious mutations have increasingly large phenotypic effects. The main COding-variant Allelic Series Test (COAST) operates on the benign missense variants (BMVs), deleterious missense variants (DMVs), and protein truncating variants (PTVs) within a gene. COAST uses a set of adjustable weights that tailor the test towards rejecting the null for genes where the average magnitude of phenotypic effect increases monotonically from BMVs to DMVs to PTVs. Such genes are of candidate therapeutic interest due to the existence of a dose-response relationship between gene functionality and phenotypic impact. See McCaw ZR, O’Dushlaine C, Somineni H, Bereket M, Klein C, Karaletsos T, Casale FP, Koller D, Soare TW. (2023) “An allelic-series rare-variant association test for candidate-gene discovery” doi:10.1016/j.ajhg.2023.07.001.

Installation

install.packages("AllelicSeries")

library(AllelicSeries)

Example data

set.seed(101)
n <- 100
data <- DGP(
  n = n,
  snps = 300,
  beta = c(1, 2, 3) / sqrt(n),
)

The example data are a list with the following components:

• anno: An snps by 1 annotation vector coded as 0 for benign missense variants (BMVs), 1 for deleterious missense variants (DMVs), and 2 for protein truncating variants (PTVs). Note that the values of (0, 1, 2) simply identify different categories of variants; weights other than these can be set when performing the association test.

• covar: An n by 6 covariate matrix including an intercept int, and covariates representing age, sex, and 3 genetic PCs (pc1, pc2, pc3).

• geno: An n by snps genotype matrix with additive coding and minor allele frequencies between 0.5% and 1.0%.

• pheno: An n by 1 phenotype vector.

Note: Scaling beta by 1 / sqrt(n) makes the power invariant to the sample size n.

COding-variant Allelic Series Test

results <- COAST(
  anno = data$anno,
  covar = data$covar,
  geno = data$geno,
  pheno = data$pheno,
  weights = c(1, 2, 3)
)

The function COAST performs the coding-variant allelic series test. The required inputs are the annotation vector, a covariate matrix, the per-variant genotype matrix, and the phenotype vector.

• The function assumes 3 annotation categories, coded as: 0, 1, 2. The length of anno should match the number of columns of the genotype matrix geno.

• If unspecified, covar will default to an intercept vector (i.e. a vector of 1s). If covar is provided, an intercept should be included manually, if desired.

• weights encodes the relative importance of BMVs, DMVs, and PTVs. The example weights of c(1, 2, 3) target a genetic architecture where effect sizes increase with increasing deleteriousness: BMVs have an effect of 1, DMVs have an effect of 2, and PTVs have an effect of 3. Weights of c(1, 1, 1) target instead a genetic architecture where all variant types have equivalent expected magnitudes.

show(results)

## Counts:
##   anno alleles variants carriers
## 1    0     287      163       96
## 2    1     162      109       82
## 3    2      61       28       45
## 
## 
## P-values:
##           test   type     pval
## 1     baseline burden 3.11e-26
## 2          ind burden 1.32e-09
## 3    max_count burden 3.08e-10
## 4      max_ind burden 5.37e-09
## 5    sum_count burden 1.66e-20
## 6      sum_ind burden 2.55e-11
## 7 allelic_skat   skat 2.66e-07
## 8         omni   omni 3.74e-25

By default, the output of COAST includes a data.frame of counts showing the number of alleles, variants, and carriers in each class that contributed to the test, and a data.frame of p-values, with the omni test denoting the final, overall p-value. The counts data.frame is accessed via:

results@Counts

##   anno alleles variants carriers
## 1    0     287      163       96
## 2    1     162      109       82
## 3    2      61       28       45

and the p-values data.frame via:

results@Pvals

##           test   type         pval
## 1     baseline burden 3.112702e-26
## 2          ind burden 1.322084e-09
## 3    max_count burden 3.076876e-10
## 4      max_ind burden 5.374363e-09
## 5    sum_count burden 1.661854e-20
## 6      sum_ind burden 2.554417e-11
## 7 allelic_skat   skat 2.658137e-07
## 8         omni   omni 3.735235e-25

To return the omnibus p-value only, specify return_omni_only = TRUE when calling COAST.

Robust omnibus test

In the case that all variants have comparable magnitudes, standard SKAT-O test will have more power than an allelic series test with weights c(1, 2, 3). This is because the generative weighting scheme is in fact c(1, 1, 1). To perform a robust test that is powerful for detecting allelic series but as powerful as standard SKAT-O when all variants have similar magnitudes, we can incorporate standard SKAT-O in the allelic series omnibus test. To do this, specify include_orig_skato_all = TRUE when calling COAST. Another option is to incorporate standard SKAT-O incorporating PTVs only: include_orig_skato_ptv = TRUE.

results <- COAST(
  anno = data$anno,
  covar = data$covar,
  geno = data$geno,
  pheno = data$pheno,
  include_orig_skato_all = TRUE,
  include_orig_skato_ptv = TRUE,
  weights = c(1, 2, 3)
)
show(results)

## Counts:
##   anno alleles variants carriers
## 1    0     287      163       96
## 2    1     162      109       82
## 3    2      61       28       45
## 
## 
## P-values:
##             test   type     pval
## 1       baseline burden 3.11e-26
## 2            ind burden 1.32e-09
## 3      max_count burden 3.08e-10
## 4        max_ind burden 5.37e-09
## 5      sum_count burden 1.66e-20
## 6        sum_ind burden 2.55e-11
## 7   allelic_skat   skat 2.66e-07
## 8  orig_skat_all   skat 1.55e-05
## 9  orig_skat_ptv   skat 6.63e-08
## 10          omni   omni 3.74e-25

COAST from Summary Statistics

Summary statistics calculation

The function CalcSumstats calculates summary statistics starting either from:

• The direct output of DGP, or
• An annotation vector anno, covariate matrix covar, genotype matrix geno, and phenotype vector pheno, formatted as provided by DGP.

data <- DGP(n = 1e3)
sumstats <- CalcSumstats(data = data)

The output sumstats is a list containing:

• anno, the (snps x 1) annotation vector.
• ld, a (snps x snps) LD (genotype correlation) matrix.
• maf, a (snps x 1) minor allele frequency vector.
• sumstats, a (snps x 4) data.frame including the effect size beta, standard error se, and p-value p.

head(sumstats$sumstats)

##   anno       beta        se            p
## 1    0 -0.1133459 0.7749020 8.837072e-01
## 2    0 -0.2249223 1.0606390 8.320578e-01
## 3    0 -0.4892382 1.0083429 6.275413e-01
## 4    2  5.7698852 0.7777121 1.179629e-13
## 5    1  2.3879472 0.6786403 4.336290e-04
## 6    2  6.6218967 0.8581600 1.196717e-14

Running COAST from summary statistics

COASTSS is the main function for running the coding-variant allelic series test from summary statistics. The necessary inputs are the annotation vector anno, the effect size vector beta, and the standard error vector se. The test will run without the LD matrix ld or the minor alleles frequencies maf vector. However, to do so, it assumes the variants are in linkage equilibrium (i.e. ld is the identity matrix) and that the minor allele frequencies are zero. These assumptions are at best approximations, and providing ld and maf (or estimates) is always preferred.

results <- COASTSS(
  anno = sumstats$anno,
  beta = sumstats$sumstats$beta,
  se = sumstats$sumstats$se,
  maf = sumstats$maf,
  ld = sumstats$ld
)
show(results)

## P-values:
##           test   type      pval
## 1     baseline burden 1.19e-215
## 2    sum_count burden 8.88e-165
## 3 allelic_skat   skat 3.18e-297
## 4         omni   omni 6.37e-297

In comparing the outputs of the summary statistics based test to those of the individual level data test, several differences are noteworthy:

• Not all components of COAST could be included in COASTSS. In particular, the max tests cannot be obtained starting from standard summary statistics. In addition, by convention, summary statistics are generated from count rather than indicator genotypes. If available, COASTSS can be applied to summary statistics generated from indicator genotypes.

• Several approximations are required in order to perform the coding-variant allelic series test with summary statistics. As such, the p-values obtained from COASTSS and COAST will not be identical, even when starting from the same data. Nonetheless, the operating characteristics of COASTSS (and the original COAST) have been validated through extensive simulation studies.

Appendix

Loading genotypes

The genio and rbgen packages may be used to load PLINK and BGEN genotypes in R, respectively. Moreover, PLINK enables conversion between these file types.