多变量适应性缩改善了跨种群转录组预测和在代表性不足的人群中的关联研究
Daniel S Araujo1, Chris Nguyen2, Xiaowei Hu3
1Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA.
HGG advances
|October 23, 2023
概括
由于遗传差异,转录组预测模型在不同种群中表现不佳. 使用与MASHR类似的共享调控效应的方法,显著改善了跨种群转录全基因组关联研究 (TWAS) 和遗传发现.
科学领域:
- 基因组学就是基因组学.
- 统计遗传学 统计遗传学
- 生物信息学是一种生物信息学.
背景情况:
- 在欧洲血统数据上训练的转录组预测模型在其他种群中显示精度降低.
- 遗传变异,包括链接不平衡模式和等位基因频率,在不同种群中存在差异,影响模型性能.
- 利用跨种群的共享监管效应是增强跨种群转录组预测的潜在策略.
研究的目的:
- 在人口匹配和跨人口场景中评估不同转录组预测模型的准确性.
- 评估这些模型在多民族转录组广泛关联研究 (TWASs) 中的适用性.
- 确定改善不同种群的遗传发现的方法.
主要方法:
- 开发了使用弹性网,JTI,矩阵eQTL,MASHR和TIGAR的转录组预测模型.
- 在人口匹配和跨人口设置中测试了样本外预测准确性.
- 综合模型与PAGE和PanUKBB的多民族GWAS总结统计数据用于TWAS分析.
主要成果:
- 在人口匹配和跨人口比较中,MASHR模型显示出优越或同等的转录组预测准确性.
- 在PAGE和PanUKBB数据集中,MASHR模型在多民族TWAS中产生了更多的复制发现.
- 通过使用MASHR的TWAS分析确定了以前已知的和新的遗传位点.
结论:
- 该研究强调了采用来自不同人群的效果大小估计方法来改善TWAS的重要性.
- MASHR模型显示了增强多民族和代表性不足的人口中的遗传发现的前景.
- 改善跨种群预测准确度对于公平的基因组研究至关重要.
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