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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 #321073
Title: Explorations in genome-wide association studies and network analyses with dairy cattle fertility traits

Author
item PARKER GADDIS, KRISTEN - University Of Florida
item Null, Daniel
item Cole, John

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/15/2016
Publication Date: 8/1/2016
Citation: Parker Gaddis, K.L., Null, D.J., Cole, J.B. 2016. Explorations in genome-wide association studies and network analyses with dairy cattle fertility traits. Journal of Dairy Science. 99(8):6420-6435.

Interpretive Summary: Improving fertility continues to be a challenge for dairy herds across the United States. This research identified genes associated with fertility in dairy cattle using several approaches. Genome-wide association studies were used to identify genes associated with fertility, as well as to compare results between differing genotyping chip densities, reference populations, and dependent variables. A gene interaction network was also developed. These results can aid in further investigating the complex nature of fertility.

Technical Abstract: Unfavorable genetic correlations between production and fertility traits are well documented. Genetic selection for fertility traits is slow, however, due to low heritabilities. Identification of single nucleotide polymorphisms (SNP) involved in reproduction has improved the reliability of genomic estimates for these low heritability traits. High-density marker panels can increase the power of resultant genome-wide association studies (GWAS) by providing increased coverage and stronger linkage disequilibrium between markers and causal variants. Research in systems biology and network approaches also hold promise for complex traits. The objective of this study was to identify SNP and gene networks associated with three fertility traits in dairy cattle, daughter pregnancy rate (DPR), heifer conception rate (HCR), and cow conception rate (CCR), using several different approaches. Deregressed predicted transmitting abilities were available for approximately 60,000 bulls and cows sampled from the National Dairy Database with high-density genotypes. Of those, 1,732 bulls and 375 cows had been genotyped with the Illumina BovineHD Genotyping BeadChip. The remaining animals were genotyped with various chips of lower density that were imputed to high-density. Univariate and trivariate analyses with both medium- and high-density marker panels were performed for DPR, HCR, and CCR using GEMMA (version 0.94). Analyses were conducted using bulls only, cows only, and a sample of both bulls and cows. The partial correlation and information theory (PCIT) algorithm was utilized to develop gene interaction networks. Numerous putatively associated genes were identified with GWAS at both marker densities. Little overlap in associated genes could be found between GWAS using different reference populations. The PCIT algorithm was able to identify several genes that were not identified by ordinary GWAS. The results obtained herein will aid in further dissecting the complex biology underlying fertility traits in dairy cattle, while also providing insight into the nuances of GWAS.