评估和改善多基因分数的健康公平性和公平性
bioRxiv : the preprint server for biology
|October 4, 2023
概括
联合-拉索改善了多基因分数 (PGS) 预测的准确性和跨祖先的公平性. 这种新方法提高了遗传风险预测模型的可移植性,减少了不同人群中的偏见.
科学领域:
- 遗传学 是一个遗传学.
- 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:
- 多基因分数 (PGS) 预测表型值,但往往缺乏跨祖先的可移植性.
- 像Lassosum这样的现有方法虽然有效,但在全基因组关联研究 (GWAS) 中代表性不足的祖先上应用时表现不佳.
- 这种与祖先相关的预测偏见可能会导致临床应用中的公平性问题.
结论:
- 联合Lassosum增强了遗传预测模型的可移植性,减轻了非欧洲人群中的偏差.
- 这种方法可以帮助减少在临床环境中与PGS相关的与公平性相关的伤害.
- 虽然通用可移植性仍然具有挑战性,但联合拉索索在公平的遗传风险预测方面取得了重大进展.
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