学习多祖先多基因分数的最佳策略在特征上有所不同
Brieuc Lehmann1, Maxine Mackintosh2,3, Gil McVean4
1Department of Statistical Science, University College London, London, UK. b.lehmann@ucl.ac.uk.
Nature communications
|July 7, 2023
概括
多基因分数 (PGS) 在不同人群中训练时表现更好. 从代表性不足的群体收集有针对性的数据对于提高遗传预测准确性和减少多基因分数表现差异至关重要.
科学领域:
- 遗传学 是一个遗传学.
- 生物信息学是一种生物信息学.
- 人口健康 人口健康
背景情况:
- 多基因分数 (PGS) 总结了全基因组的遗传倾向,但通常来自欧洲祖先样本.
- 这种祖先偏见导致非欧洲人群的预测准确性降低,突出显示出严重的健康差异.
研究的目的:
- 调查样本大小和祖先组成对不同种群的PGS表现的影响.
- 探索在多祖先队伍中最大限度地提高PGS性能的方法.
主要方法:
- 利用英国生物库的数据来评估15个特征的PGS性能.
- 分析了不同训练集祖先组成和样本大小对不同祖先测试集预测准确性的不同训练集祖先组成和样本大小对预测准确性的影响.
主要成果:
- 对于某些特征,在较小的非洲血统数据集上训练的PGS在非洲血统个体上测试时,在较大的欧洲血统数据集上训练的PGS表现优于非洲血统个体.
- 在英国生物银行 (UK Biobank) 内的其他少数民族祖先群体中也观察到类似的趋势.
结论:
- 这些发现强调了对代表性不足的祖先群体进行包容性,有针对性的数据收集的关键需求.
- 解决这些数据缺口对于减轻多基因分数准确性和实用性的现有差异至关重要.
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