BIOINFORMATICS TO IMPLEMENT GENOMIC SELECTION (BIGS)
Genetics, Breeding, & Animal Health
2011 Annual Report
1a.Objectives (from AD-416)
This research and development project will develop analytical software for Bayesian analysis of genomic information and deliver it within an integrated bioinformatics infrastructure that will enable genomic evaluation using high-throughput SNP genotyping technology in livestock. We will implement these methodologies across a range of economic traits in beef cattle, first using existing genomic and phenotypic records from the U.S. Meat Animal Resarch Center and ultimately enabling researchers to submit data sets for analysis via a web interface. Collectively, we will deliver immediate and on-going opportunities in beef cattle for livestock managers, public researchers and industry geneticists to exploit genomic evaluation.
1b.Approach (from AD-416)
We will enhance existing methods for predicting missing genotypes to applications involving haplotypes and web-enable this software. We will extend theory-based single-trait Bayesian methods for additive effects to practical circumstances that can estimate allelic or haplotypic effects in the context of multiple traits, maternally influenced traits, and categorically expressed traits, to capitalize on both additive and non-additive effects. All these analytical tools will be made available via web interface. Our activities will be motivated and focused on genomic evaluation of beef cattle. Implementation of this research will fully support goals of the 2005-2010 USDA Strategic Plan, by enhancing international competitiveness of the U.S beef cattle and other economically-relevant agricultural industries through practical access to the latest advances in molecular genetic technology.
Progress of the BIGS project was monitored via email and telephone contact with the project directors, and access to the BIGS web site and source code repository. GenSel analysis of USMARC GPE and 2000 Bull Project data for growth and carcass traits was completed on the BIGS system, and genomic predictions trained by both GPE and 2000 Bulls provided to cooperators. Procedures to infer missing genotypes from known parent and progeny genotypes according to Mendelian inheritance rules were incorporated into GenSel software. The process of developing a platform-independent version of GenoProb software, which uses complex pedigrees with incomplete marker data to compute probabilities of complete marker genotypes, is underway. Proprietary Microsoft libraries and database dependencies are being replaced with open-source equivalents, with thorough testing of each modification of the complex program to ensure integrity of the computations.