Large-Sample Genomic Data Mining for Quantitative Traits in U.S. Holstein Cows.

Journal of data mining in genomics & proteomics Pub Date : 2024-01-01 Epub Date: 2024-05-13
Yang Da, Dzianis Prakapenka, Zuoxiang Liang
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Abstract

The U.S. Holstein cattle have unprecedentedly large samples for genomic evaluation with genotypes of Single Nucleotide Polymorphism (SNP) markers and phenotypic observations of dairy quantitative traits. Such large samples provided unprecedented opportunities for the discovery of genetic variants and mechanisms affecting quantitative traits in Holstein cattle. Recent studies using the Holstein large samples on finding genetic variants affecting quantitative traits included a fat percentage study and two studies on reproductive traits. The fat percentage study confirmed that a chromosome region interacted with all chromosomes and the reproductive studies detected sharply negative homozygous recessive genotypes that were recommended for heifer culling. These novel findings provided examples showing the power of large-sample genomic mining for quantitative traits.

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美国荷斯坦奶牛数量性状的大样本基因组数据挖掘。
美国荷斯坦牛拥有前所未有的大样本,可通过单核苷酸多态性(SNP)标记的基因型和乳制品数量性状的表型观察进行基因组评估。这种大样本为发现影响荷斯坦牛数量性状的遗传变异和机制提供了前所未有的机会。最近,利用荷斯坦大样本发现影响数量性状的遗传变异的研究包括一项脂肪率研究和两项繁殖性状研究。脂肪率研究证实,一个染色体区域与所有染色体都有相互作用,而繁殖研究则发现了急剧阴性的同源隐性基因型,建议对这些基因型进行小母牛淘汰。这些新发现举例说明了大样本基因组挖掘对数量性状的作用。
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Large-Sample Genomic Data Mining for Quantitative Traits in U.S. Holstein Cows.
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