Ancestry Composition: A Novel, Efficient Pipeline for Ancestry Deconvolution

BioRxiv : the Preprint Server for Biology
Eric Y DurandJ. Michael Macpherson


Ancestry deconvolution, the task of identifying the ancestral origin of chromosomal segments in admixed individuals, has important implications, from mapping disease genes to identifying candidate loci under natural selection. To date, however, most existing methods for ancestry deconvolution are typically limited to two or three ancestral populations, and cannot resolve contributions from populations related at a sub-continental scale. We describe Ancestry Composition, a modular three-stage pipeline that efficiently and accurately identifies the ancestral origin of chromosomal segments in admixed individuals. It assumes the genotype data have been phased. In the first stage, a support vector machine classifier assigns tentative ancestry labels to short local phased genomic regions. In the second stage, an autoregressive pair hidden Markov model simultaneously corrects phasing errors and produces reconciled local ancestry estimates and confidence scores based on the tentative ancestry labels. In the third stage, confidence estimates are recalibrated using isotonic regression. We compiled a reference panel of almost 10,000 individuals of homogeneous ancestry, derived from a combination of several publicly available datasets an...Continue Reading

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