title: Sensitive detection of chromosomal segments of distinct ancestry in admixed populations.
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.
authors: Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA.
authors: Alkes L
authors: AL
authors: Price
authors: Arti
authors: A
authors: Tandon
authors: Nick
authors: N
authors: Patterson
authors: Kathleen C
authors: KC
authors: Barnes
authors: Nicholas
authors: N
authors: Rafaels
authors: Ingo
authors: I
authors: Ruczinski
authors: Terri H
authors: TH
authors: Beaty
authors: Rasika
authors: R
authors: Mathias
authors: David
authors: D
authors: Reich
authors: Simon
authors: S
authors: Myers
mesh_terms: D001741:Black or African American / Q000235:genetics; D044383:Black People / Q000235:genetics; D002877:Chromosomes, Human / Q000235:genetics*; D004194:Disease / Q000235:genetics; D019143:Evolution, Molecular; D005828:Genetics, Population / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D012984:Software