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

被引:51
|
作者
Gaddis, K. L. Parker [1 ]
Null, D. J. [2 ]
Cole, J. B. [2 ]
机构
[1] Univ Florida, Dept Anim Sci, Gainesville, FL 32611 USA
[2] ARS, Anim Genom & Improvement Lab, USDA, Beltsville, MD 20705 USA
基金
美国农业部; 美国食品与农业研究所;
关键词
fertility; genome-wide association; high-density genotypes; network analysis; EMBRYONIC-DEVELOPMENT; GENETIC EVALUATION; HOLSTEIN; BOVINE; COW; HEALTH; REPRODUCTION; ENDOMETRIUM; EXPRESSION; MUTATIONS;
D O I
10.3168/jds.2015-10444
中图分类号
S8 [畜牧、 动物医学、狩猎、蚕、蜂];
学科分类号
0905 ;
摘要
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.
引用
收藏
页码:6420 / 6435
页数:16
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