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

机译:奶牛育性性状全基因组关联研究和网络分析的探索

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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. 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.
机译:这项研究的目的是使用不同的方法,鉴定与奶牛-女儿怀孕率,小母牛受胎率和母牛受胎率的三种生育性状相关的单核苷酸多态性和基因网络。从国家奶牛数据库中以高密度基因型采样的大约24,000头荷斯坦公牛和36,000头荷斯坦牛可获得递减的预测传播能力。在其中的1,732头公牛和375头母牛已通过Illumina BovineHD基因分型BeadChip(Illumina Inc.,圣地亚哥,加利福尼亚)进行了基因分型。剩余的动物用各种低密度的芯片进行基因分型,这些低密度的芯片被推算为高密度。使用GEMMA对中(60,671个标记)和高密度(312,614个标记)面板进行单变量和三变量全基因组关联研究(GWAS),以进行女儿怀孕率,小母牛受胎率和母牛受胎率的研究。仅使用公牛,仅母牛以及公牛和母牛的样本进行分析。使用偏相关和信息论算法开发基因相互作用网络。进一步研究了最重要的标记,以鉴定推定相关的基因。在仅使用公牛,仅母牛以及公牛和母牛组合的不同参考种群的GWAS之间,相关基因几乎没有重叠。偏相关和信息论算法能够识别出普通GWAS无法识别的几个基因。本文获得的结果将有助于进一步剖析奶牛育性特征下的复杂生物学,同时也提供对GWAS细微差别的洞察力。

著录项

  • 来源
    《Journal of dairy science》 |2016年第8期|6420-6435|共16页
  • 作者单位

    Department of Animal Sciences, University of Florida, Gainesville 32611;

    Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350;

    Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350;

  • 收录信息 美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    fertility; genome-wide association; high-density genotypes; network analysis;

    机译:生育能力全基因组关联;高密度基因型网络分析;
  • 入库时间 2022-08-17 23:23:22

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