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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 #351513

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: Big data genomic investigation of dairy fertility and related traits with imputed sequences of 24K 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: American Dairy Science Association Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 3/8/2018
Publication Date: 6/24/2018
Citation: Jiang, J., Van Raden, P.M., Cole, J.B., Da, Y., Ma, L. 2018. Big data genomic investigation of dairy fertility and related traits with imputed sequences of 24K Holstein bulls [abstract]. Journal of Dairy Science. 101(Suppl. 2):328(abstr. 277).

Interpretive Summary:

Technical Abstract: Imputation has been routinely applied to ascertain sequence variants in large genotyped populations based on reference populations of sequenced animals. With the implementation of the 1000 Bull Genomes Project and increasing numbers of animals sequenced, fine-mapping of causal variants is becoming feasible for complex traits in cattle. Using the 1000 Bull Genomes data, we imputed three million selected sequence variants to 27,000 Holstein bulls after quality control edits and LD pruning. These bulls were selected to have highly reliable breeding values (PTAs) for 35 production, reproduction, and body conformation traits. We first performed whole-genome single-marker scan for the 35 traits using the mixed-model based association test in MMAP ( The single-trait association statistics were then merged in multi-trait analyses of three groups of traits, production, reproduction, and body conformation, respectively. Two-Mb long candidate genomic regions were selected based on the multi-trait analyses and used in fine-mapping studies. We implemented a state-of-art fine-mapping procedure with a Bayesian method that can assign a posterior probability of causality to each variant and for each independent association signal generate a minimum set of associated variants whose total posterior probability of causality exceeds a threshold (e.g. 95%). Our fine-mapping identified 36 candidate genes for production traits, 48 for reproduction traits, and 29 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.