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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 #346695

Research Project: Improving Genetic Predictions in Dairy Animals Using Phenotypic and Genomic Information

Location: Animal Genomics and Improvement Laboratory

Title: Genome-wide association study and gene network analysis of fertility, retained placenta, and metritis in US Holstein cattle

Author
item Cole, John
item Parker Gaddis, Kristen - Council On Dairy Cattle Breeding
item Null, Daniel
item Maltecca, Christian - University Of North Carolina
item Clay, John - Dairy Records Management Systems(DRMS)

Submitted to: World Congress of Genetics Applied in Livestock Production
Publication Type: Proceedings
Publication Acceptance Date: 2/7/2018
Publication Date: 2/7/2018
Citation: Cole, J.B., Parker Gaddis, K.L., Null, D.J., Maltecca, C., Clay, J.S. 2018. Genome-wide association study and gene network analysis of fertility, retained placenta, and metritis in US Holstein cattle. World Congress of Genetics Applied in Livestock Production. Auckland, New Zealand, Feb. 11–16, Vol. Biol.–Dis. Resist. 1, p. 171.

Interpretive Summary:

Technical Abstract: The objectives of this research were to identify genes, genomic regions, and gene networks associated with three measures of fertility (daughter pregnancy rate, DPR; heifer conception rate, HCR; and cow conception rate, CCR) and two measures of reproductive health (metritis, METR; and retained placenta, RETP) in US Holstein cows using producer-reported data. A five-trait mixed model analysis was used to perform a genome-wide association study (GWAS) to identify significant SNP located within 25 kbp of genes in bull and cow predictor populations. Gene ontology (GO) and medical subject heading (MeSH) analyses were used to identify pathways and processed over-represented compared to a background set of all annotated Bos taurus genes. An adaptive weight matrix was used to identify significant associations among genes. GWAS results identified different sets of SNP in the two predictor populations, with the SNP of largest effect affecting protein processing, cell-cell signaling, sex differentiation, and embryonic development. Significant GO and MeSH terms also differed between predictor populations, but terms associated with reproductive processes were identified in both cases. The degree of nodes in the network analysis did not deviate from expectations, but fertility-related terms also were identified, but several of the most-connected genes were associated with male or female fertility and embryo size and morphology in mice or humans, most notably ITPR1, SETB1, LMNB1, NEO1, and DGKA. None of the 100 SNP explaining the most variance in the GWAS were among the most connected genes in the networks. While this study identified genes and interactions among them clearly related to fertility, no obvious associations with peripartum reproductive health were found. A more powerful experimental design, such as a case-control study, may be needed to identify relationships among fertility and reproductive tract health.