Sensitive Detection of Chromosomal Segments of Distinct Ancestry in Admixed Populations

TLDR

Identifying the ancestry of chromosomal segments has wide applications from disease mapping to historical insights, yet most methods rely on unlinked markers, and using all genome‑wide array markers could in principle enable highly accurate inference of even very small segments. The authors describe HAPMIX, a method that employs an explicit population‑genetic model to perform local ancestry inference based on fine‑scale variation data. The method is validated empirically on 935 African Americans and 29 Mozabites, populations that have experienced recent and ancient admixture. HAPMIX outperforms existing methods and enables accurate inference of ancestry, admixture dates, and ancestral populations, proving especially useful for mapping disease genes in recently admixed populations by combining local ancestry with case‑control signals for more powerful association tests.

Abstract

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.