TY - JOUR T1 - A scalable pipeline for local ancestry inference using tens of thousands of reference haplotypes JF - bioRxiv DO - 10.1101/2021.01.19.427308 SP - 2021.01.19.427308 AU - Eric Y. Durand AU - Chuong B. Do AU - Peter R. Wilton AU - Joanna L. Mountain AU - Adam Auton AU - G. David Poznik AU - J. Michael Macpherson Y1 - 2021/01/01 UR - http://biorxiv.org/content/early/2021/01/20/2021.01.19.427308.abstract N2 - Ancestry deconvolution is the task of identifying the ancestral origins of chromosomal segments of admixed individuals. It has important applications, from mapping disease genes to identifying loci potentially under natural selection. However, most existing methods are limited to a small number of ancestral populations and are unsuitable for large-scale applications.In this article, we describe Ancestry Composition, a modular pipeline for accurate and efficient ancestry deconvolution. In the first stage, a string-kernel support-vector-machines classifier assigns provisional ancestry labels to short statistically phased genomic segments. In the second stage, an autoregressive pair hidden Markov model corrects phasing errors, smooths local ancestry estimates, and computes confidence scores.Using publicly available datasets and more than 12,000 individuals from the customer database of the personal genetics company, 23andMe, Inc., we have constructed a reference panel containing more than 14,000 unrelated individuals of unadmixed ancestry. We used principal components analysis (PCA) and uniform manifold approximation and projection (UMAP) to identify genetic clusters and define 45 distinct reference populations upon which to train our method. In cross-validation experiments, Ancestry Composition achieves high precision and recall.Competing Interest StatementPR Wilton, JL Mountain, A Auton, GD Poznik, and JM Macpherson are current employees of 23andMe, Inc. EY Durand and CB Do are former employees of 23andMe, Inc. ER -