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CGIAR C++ functions

Lifecycle: experimental

This package has been developed primarily to store optimal cross selection functions that heavily rely in Rcpp and for installation purposes has been separated from other base packages like cgiarBase or cgiarPipeline. Any Rcpp dependent function should be stored in this package.

Scope

The current scope is limited to enable Biometrical Genetics methods recommended by the Quantitative Genetics community of the CGIAR [Excellence in Breeding (EiB) and collaborator centers, now Accelerated Breeding Initiative (ABI)] to standardize the methods and key performance indicators (KPIs) used across the CGIAR for decision making. For our collaborators, please keep this in mind when submitting pull requests for new methods since these may not be accepted if the are not under the current recommended approaches (see contribution process below).

The target audience is the breeders and geneticists from the CGIAR but this is a package that runs behind the scenes of the bioflow interface together with cgiarPipeline.

Roles and responsabilities

In this project we have two different roles

Maintainers: Have the permission and responsibility to commit, push, and merge changes.

Contributor: Have the permission and responsibility to commit, push using forked versions.

The current list of maintainers and contributors is the following:

Mainteiners: Khaled Al-Sham’aa, Ibnou Dieng, Eduardo Covarrubias, Angela Pacheco

Contributor: Bert De Boeck, Raul Eizaguirre, Abhishek Rathore, Roma Das, Keith Gardner, Fernando Toledo, Juan Burgueno, Aubin Amagnide, Luis Delgado Munoz, Christian Cadena, Christian Werner, Sergio Cruz, Alaine Gulles, Justine Bonifacio.

Installation

You can install the development version of cgiarOcs like this:

# devtools::install_github("Breeding-Analytics/cgiarOcs")

You can clone the GitHub repository in your computer if you would like to add something or make a change as

# git clone https://github.com/Breeding-Analytics/cgiarOcs.git

Contribution process

In this collaborative development model, anyone can fork an existing repository and push changes to their personal fork. You do not need permission from the original repository to push to your own fork. The changes can be merged into the original repository by the project maintainer. When you open a pull request proposing changes from your user-owned fork to a branch in the source (upstream) repository, you can allow anyone with push access to the upstream repository to make changes to your pull request. This model is popular with open-source projects as it reduces the amount of friction for new contributors and allows people to work independently without upfront coordination.

For more details, please check the following guidelines: https://docs.github.com/en/get-started/exploring-projects-on-github/contributing-to-a-project

Contributing an R function or pipeline?

If you wish to contribute a function that can be used across modules (e.g., a function to summarize data) please make your pull request to the cgiarBase repository.

If you wish to contribute a pipeline in the form of an R function, make sure that it can use the data structure proposed (an example can be found calling data(example) in any of the packages) and submit your new pipeline function to the cgiarPipeline repository.

Any function with use of the Rcpp package as core should be stored in cgiarOcs.

If you wish to contribute a shiny module please make sure that it can use the data structure proposed and make your pull request to the bioflow repository.

How is a contribution review and accepted?

Priority issues and functionalities will be posted in the confluence space for internal and external collaborators interested in contributing (link).

The submitted functions will be tested using a sample of multiple datasets collected by the different centers to ensure that new functions and interfaces perform well across a variety of scenarios.

Types of contributions that will be accepted (check list)

Only contributions that met the following criteria will be accepted:

  1. Functions that contribute to the main goal of bioflow (see scope section above).

  2. Functions that enable methods approved by the Quantitative Genetics community lead by the Accelerated Breeding Initiative (before EiB).

  3. Functions that are proven better than existing methods, robust enough to handle the variability of data encountered across the CGIAR (use sample datasets to test your own function).

Open an issue before submitting a pull-request

For more information about GitHub Issues and Pull Request (PR) templates, please check the following link: https://github.blog/2016-02-17-issue-and-pull-request-templates/

References

Bernardo Rex. 2010. Breeding for quantitative traits in plants. Second edition. Stemma Press. 390 pp.

Gilmour et al. 1995. Average Information REML: An efficient algorithm for variance parameter estimation in linear mixed models. Biometrics 51(4):1440-1450.

Kang et al. 2008. Efficient control of population structure in model organism association mapping. Genetics 178:1709-1723.

Lee, D.-J., Durban, M., and Eilers, P.H.C. (2013). Efficient two-dimensional smoothing with P-spline ANOVA mixed models and nested bases. Computational Statistics and Data Analysis, 61, 22 - 37.

Lee et al. 2015. MTG2: An efficient algorithm for multivariate linear mixed model analysis based on genomic information. Cold Spring Harbor. doi: http://dx.doi.org/10.1101/027201.

Maier et al. 2015. Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder. Am J Hum Genet; 96(2):283-294.

Rodriguez-Alvarez, Maria Xose, et al. Correcting for spatial heterogeneity in plant breeding experiments with P-splines. Spatial Statistics 23 (2018): 52-71.

Searle. 1993. Applying the EM algorithm to calculating ML and REML estimates of variance components. Paper invited for the 1993 American Statistical Association Meeting, San Francisco.

Yu et al. 2006. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Genetics 38:203-208.

Tunnicliffe W. 1989. On the use of marginal likelihood in time series model estimation. JRSS 51(1):15-27.

Zhang et al. 2010. Mixed linear model approach adapted for genome-wide association studies. Nat. Genet. 42:355-360.

Jensen, J., Mantysaari, E. A., Madsen, P., and Thompson, R. (1997). Residual maximum likelihood estimation of (co) variance components in multivariate mixed linear models using average information. Journal of the Indian Society of Agricultural Statistics, 49, 215-236.

Covarrubias-Pazaran G. Genome assisted prediction of quantitative traits using the R package sommer. PLoS ONE 2016, 11(6): doi:10.1371/journal.pone.0156744