Abstract

Genome-wide high-density molecular markers (e.g., SNPs) are increasingly being incorporated into animal and plant breeding programs to speed up genetic improvement through genomic prediction. The same statistical models can also be used for genome-wide association studies. Bayesian multiple-regression methods are widely used in genomic prediction with complete genomic data (all phenotyped individuals in the analysis are genotyped). These methods have been extended to accommodate incomplete genomic data (some phenotyped animals not genotyped), simultaneously using all available pedigree, phenotypic and genomic information (“single-step Bayesian methods). We have developed a well-documented software tool called JWAS (acronym for ""Julia Whole-genome Analysis Software”) in a relatively new scientific programming language, Julia, which approaches the computing speed of compiled languages such as C, C++ or Fortran, but has the benefits of dynamic languages such as R or Python.

Hao Cheng, Rohan Fernando, Dorian Garrick

Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Methods and Tools - Software, , 859, 2018
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