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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #347558

Research Project: Improving Dairy Animals by Increasing Accuracy of Genomic Prediction, Evaluating New Traits, and Redefining Selection Goals

Location: Animal Genomics and Improvement Laboratory

Title: GWAS and fine-mapping of 35 production, reproduction and conformation traits with imputed sequences of 27K Holstein bulls

item JIANG, JICAI - University Of Maryland
item Vanraden, Paul
item Cole, John
item DA, YANG - University Of Minnesota
item MA, LI - University Of Maryland

Submitted to: Plant and Animal Genome Conference
Publication Type: Abstract Only
Publication Acceptance Date: 12/8/2017
Publication Date: 12/19/2017
Citation: Jiang, J., Van Raden, P.M., Cole, J.B., Da, Y., Ma, L. 2017. GWAS and fine-mapping of 35 production, reproduction and conformation traits with imputed sequences of 27K Holstein bulls. Plant and Animal Genome Conference. San Diego, CA, Jan. 13-17. abstr. P0484.

Interpretive Summary:

Technical Abstract: Fine-mapping of causal variants is becoming feasible for complex traits in livestock GWAS, as an increasing number of animals are sequenced. Imputation has been routinely applied to ascertain sequence variants in large genotyped populations based on small reference populations of sequenced animals. Using the run5 data from the 1000 Bull Genomes Project, we imputed three million sequence variants for 27,000 Holstein bulls after QC edits and LD pruning. These bulls were selected to have highly reliable PTAs for 35 production, reproduction, and body conformation traits. We first performed whole-genome single-marker scans for the 35 traits using a mixed-model based association test. The single-trait association statistics were then used in meta-analyses of three groups of traits, production, reproduction and body conformation, respectively. Two-Mb-long candidate genomic regions were selected based on the meta-analysis results and used in fine-mapping studies. We implemented a state-of-art fine-mapping procedure using a Variational Bayesian method that can assign a posterior probability of causality to each variant and for each independent association signal to generate a minimum set of associated variants whose total posterior probability of causality exceeds a threshold (e.g., 95%). Our fine-mapping identified 355 candidate genes for production traits, 258 for reproduction traits, and 369 for body conformation traits, respectively, including some previously reported causal variants, e.g., Chr6:38027010 in ABCG2 for production traits and Chr7:93244933 in ARRDC3 for reproduction and body conformation traits. The candidate variant list may facilitate follow-up functional validation and expand our understanding of complex traits in dairy cattle. Additionally, our method can be readily applied to other species where large-scale sequence genotypes are available.