Price Alkes L, Tandon Arti, Patterson Nick, Barnes Kathleen C, Rafaels Nicholas, Ruczinski Ingo, Beaty Terri H, Mathias Rasika, Reich David, Myers Simon
Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA.
PLoS Genet. 2009 Jun;5(6):e1000519. doi: 10.1371/journal.pgen.1000519. Epub 2009 Jun 19.
Identifying the ancestry of chromosomal segments of distinct ancestry has a wide range of applications from disease mapping to learning about history. Most methods require the use of unlinked markers; but, using all markers from genome-wide scanning arrays, it should in principle be possible to infer the ancestry of even very small segments with exquisite accuracy. We describe a method, HAPMIX, which employs an explicit population genetic model to perform such local ancestry inference based on fine-scale variation data. We show that HAPMIX outperforms other methods, and we explore its utility for inferring ancestry, learning about ancestral populations, and inferring dates of admixture. We validate the method empirically by applying it to populations that have experienced recent and ancient admixture: 935 African Americans from the United States and 29 Mozabites from North Africa. HAPMIX will be of particular utility for mapping disease genes in recently admixed populations, as its accurate estimates of local ancestry permit admixture and case-control association signals to be combined, enabling more powerful tests of association than with either signal alone.
识别具有不同祖先的染色体片段的祖先信息，在从疾病定位到了解历史等广泛领域都有应用。大多数方法需要使用不连锁的标记；但是，利用全基因组扫描阵列中的所有标记，原则上应该能够以极高的精度推断出哪怕非常小的片段的祖先信息。我们描述了一种名为HAPMIX的方法，它采用明确的群体遗传模型，基于精细尺度的变异数据进行这种局部祖先推断。我们表明HAPMIX优于其他方法，并探讨了它在推断祖先信息、了解祖先群体以及推断混合日期方面的效用。我们通过将其应用于经历了近期和古代混合的群体来实证验证该方法：来自美国的935名非裔美国人和来自北非的29名莫扎比特人。HAPMIX对于在近期混合群体中定位疾病基因将特别有用，因为其对局部祖先的准确估计允许将混合和病例对照关联信号结合起来，从而能够进行比单独使用任何一种信号更强大的关联测试。
