Explorations in genome-wide association studies and network analyses with dairy cattle fertility traits

The objective of this study was to identify single nucleotide polymorphisms and gene networks associated with 3 fertility traits in dairy cattle—daughter pregnancy rate, heifer conception rate, and cow conception rate—using different approaches. Deregressed predicted transmitting abilities were avai...

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Veröffentlicht in:Journal of dairy science 2016-08, Vol.99 (8), p.6420-6435
Hauptverfasser: Parker Gaddis, K.L., Null, D.J., Cole, J.B.
Format: Artikel
Sprache:eng
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Zusammenfassung:The objective of this study was to identify single nucleotide polymorphisms and gene networks associated with 3 fertility traits in dairy cattle—daughter pregnancy rate, heifer conception rate, and cow conception rate—using different approaches. Deregressed predicted transmitting abilities were available for approximately 24,000 Holstein bulls and 36,000 Holstein 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 (Illumina Inc., San Diego, CA). The remaining animals were genotyped with various chips of lower density that were imputed to high density. Univariate and trivariate genome-wide association studies (GWAS) with both medium- (60,671 markers) and high-density (312,614 markers) panels were performed for daughter pregnancy rate, heifer conception rate, and cow conception rate using GEMMA (version 0.94; http://www.xzlab.org/software.html). Analyses were conducted using bulls only, cows only, and a sample of both bulls and cows. The partial correlation and information theory algorithm was used to develop gene interaction networks. The most significant markers were further investigated to identify putatively associated genes. Little overlap in associated genes could be found between GWAS using different reference populations of bulls only, cows only, and combined bulls and cows. The partial correlation and information theory 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.
ISSN:0022-0302
1525-3198
DOI:10.3168/jds.2015-10444