Genome wide association studies using sequence-resolution data potentially allow mapping of causal variants directly, though such variants may still be difficult to discern from non-functional, equivalently associated variants in strong or perfect linkage disequilibrium. Further, conducting association analyses with millions of sequence variants presents computational challenges, particularly for resource intensive Bayesian approaches. To attempt to reduce the causative variant search space and conduct computationally feasible, Bayesian genome-wide association analyses with sequence-derived variant sets, we used an annotation-based filtering approach to focus only on high-priority protein-coding variants. We annotated whole genome sequence data with RefSeq and Ensembl gene models, and then identified all candidate non-synonymous and non-sense variants. We then verified the validity of transcript annotations for these variants by examining RNA-seq data from various tissues, resulting in a set of 55,935 protein-coding variants. BayesB association mapping was performed with priors determined in BayesCPi for eight milk production phenotypes. Quantitative trait loci (QTL) for known causal variants were detected (BTA 6, 14 and 20), as well as a number of uncharacterized loci (BTA 3, 12, 14, 15, 29). We herein report the top 10 QTL for milk protein percent and pleiotropic effects in a variety of milk production traits.

Kelsey Burborough, Chad Harland, Carol Charlier, Russell Snell, Richard Spelman, Mathew Littlejohn

Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Electronic Poster Session - Molecular Genetics 2, , 421, 2018
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