将GWAS与基因连接起来:采用牛数据的综合性多omics方法
Mohammad Ghoreishifar1,2, Iona M Macleod3,4, Tuan Nguyen3
1Agriculture Victoria Research, AgriBio Centre for AgriBioscience, Bundoora, VIC, 3083, Australia. mohammad.ghoreishifar@agriculture.vic.gov.au.
BMC genomics
|January 15, 2026
概括
这项研究整合了多个omics数据,确定了20个可能导致牛乳糖百分比的因果基因. 这些基因通过全基因组关联研究 (GWAS) 和基因表达分析确定,对于乳腺功能至关重要.
科学领域:
- 动物基因组学 动物基因组学
- 量化遗传学 量化遗传学
- 生物信息学是一种生物信息学.
背景情况:
- 全基因组关联研究 (GWAS) 识别复杂特征的遗传位置,但难以确定因果变异和目标基因.
- 多omics数据集成提供了一个强大的策略来克服这些挑战.
研究的目的:
- 通过使用多品种数据集和多omics方法,确定乳牛乳糖百分比 (LP) 的因果基因.
- 利用基因组和转录组数据将遗传变异与基因表达和表型特征联系起来.
主要方法:
- 利用了大量的多品种数据集 (> 81,000 头牛) 与牛奶LP表型和归因序列基因型.
- 应用贝叶斯R用于SNP效应估计和预测局部基因组繁殖值 (GEBVs).
- 雇员遗传分数 (GSOR) 和基于窗口的同地化测试,使用GWAS总结统计数据.
主要成果:
- 通过使用GSOR在乳腺组织中与局部GEBV相关的711个显著基因 (FDR ≤0.1) 被确定.
- 在GWAS信号和GSOR识别的基因之间发现了30个显著的局部化窗口,涉及34个候选基因.
- 突出显示了20个富含"跨膜运输"GO术语的基因,与乳糖生产生理学相关.
结论:
- 20个已识别的基因是乳糖百分比的潜在因果基因的强有力的候选者,得到乳腺表达,GEBV关联,GWAS同位化和功能丰富的支持.
- 证明了整合GWAS,基因表达和功能数据的有效性,用于复杂特征中的因果基因发现.
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