JWAS: Julia implementation of Whole-genome Analyses Software

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. JWAS is a well-documented software platform based on Julia and an interactive Jupyter notebook for analyses of general univariate and multivariate Bayesian mixed effects models. These models are especially useful for, but not limited to, routine single-trait and multi-trait genomic prediction and genome-wide association studies using either complete or incomplete genomic data ("single-step" methods). Currently, JWAS provides broad scope of analyses, e.g., a wide collection of Bayesian methods for whole-genome analyses, including shrinkage estimation and variable selection methods. We believe the friendly user interface and fast computing speed of JWAS will provide power and convenience for users in both industry and academia to analyse large datasets. Further, as a well-documented open-source software tool, JWAS will also be used by a group of active community members, who will contribute to the source code and help maintain the project. Junior scientists can understand and learn the methodologies for whole-genome analyses by using JWAS and reading the tutorials and source code. JWAS is available with source code and documentation at http://QTL.rocks.