Sensitive Detection of Chromosomal Segments of Distinct Ancestry in Admixed Populations

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.Author Summary: The genomes of individuals from admixed populations consist of chromosomal segments of distinct ancestry. For example, the genomes of African American individuals contain segments of both African and European ancestry, so that a specific location in the genome may inherit 0, 1, or 2 copies of European ancestry. Inferring an individual's local ancestry, their number of copies of each ancestry at each location in the genome, has important applications in disease mapping and in understanding human history. Here we describe HAPMIX, a method that analyzes data from dense genotyping chips to infer local ancestry with very high precision. An important feature of HAPMIX is that it makes use of data from haplotypes (blocks of nearby markers), which are more informative for ancestry than individual markers. Our simulations demonstrate the utility of HAPMIX for local ancestry inference, and empirical applications to African American and Mozabite data sets uncover important aspects of the history of these populations.