Sun Quan, Horimoto Andrea R V R, Chen Brian, Ockerman Frank, Mohlke Karen L, Blue Elizabeth, Raffield Laura M, Li Yun
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Center for Computational and Genomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Am J Hum Genet. 2025 Apr 3;112(4):727-740. doi: 10.1016/j.ajhg.2025.03.004.
Recently, admixed populations make up an increasing percentage of the US and global populations, and the admixture is not uniform over space or time or across genomes. Therefore, it becomes indispensable to evaluate local ancestry in addition to global ancestry to improve genetic epidemiological studies. Recent advances in representing human genome diversity, coupled with large-scale whole-genome sequencing initiatives and improved tools for local ancestry inference, have enabled studies to demonstrate that incorporating local ancestry information enhances both genetic association analyses and polygenic risk predictions. Along with the opportunities that local ancestry provides, there exist challenges preventing its full usage in genetic analyses. In this review, we first summarize methods for local ancestry inference and illustrate how local ancestry can be utilized in various analyses, including admixture mapping, association testing, and polygenic risk score construction. In addition, we discuss current challenges in research involving local ancestry, both in terms of the inference itself and its role in genetic association studies. We further pinpoint some future study directions and methodology development opportunities to help more effectively incorporate local ancestry in genetic analyses. It is worth the effort to pursue those future directions and address these analytical challenges because the appropriate use of local ancestry estimates could help mitigate inequality in genomic medicine and improve our understanding of health and disease outcomes.
近年来,混合群体在美国和全球人口中所占的比例越来越大,而且这种混合在空间、时间或整个基因组上并不均匀。因此,除了评估全基因组祖源外,评估局部祖源对于改进遗传流行病学研究变得不可或缺。在表示人类基因组多样性方面的最新进展,加上大规模全基因组测序计划以及用于局部祖源推断的改进工具,使得研究能够证明纳入局部祖源信息可增强遗传关联分析和多基因风险预测。伴随着局部祖源带来的机遇,也存在一些阻碍其在遗传分析中充分应用的挑战。在这篇综述中,我们首先总结局部祖源推断的方法,并说明局部祖源如何用于各种分析,包括混合映射、关联测试和多基因风险评分构建。此外,我们讨论了涉及局部祖源的研究当前面临的挑战,包括推断本身及其在遗传关联研究中的作用。我们进一步指出一些未来的研究方向和方法开发机会,以帮助更有效地将局部祖源纳入遗传分析。努力探索这些未来方向并应对这些分析挑战是值得的,因为适当地使用局部祖源估计有助于减轻基因组医学中的不平等现象,并增进我们对健康和疾病结果的理解。