Ancestry inference using reference labeled clusters of haplotypes

Abstract

Background: We present ARCHes, a fast and accurate haplotype-based approach for inferring an individual's ancestry composition. Our approach works by modeling haplotype diversity from a large, admixed cohort of hundreds of thousands, then annotating those models with population information from reference panels of known ancestry.

Results: The running time of ARCHes does not depend on the size of a reference panel because training and testing are separate processes, and the inferred population-annotated haplotype models can be written to disk and reused to label large test sets in parallel (in our experiments, it averages less than one minute to assign ancestry from 32 populations using 10 CPU). We test ARCHes on public data from the 1000 Genomes Project and the Human Genome Diversity Project (HGDP) as well as simulated examples of known admixture.

Conclusions: Our results demonstrate that ARCHes outperforms RFMix at correctly assigning both global and local ancestry at finer population scales regardless of the amount of population admixture.

Keywords: ARCHes; Ancestry inference; HMM; Haplotype modeling; Local ancestry; RFMix.

Fig. 1

Boxplot of the estimated ancestry proportions for single-origin individuals from each testing population comparing ARCHes and RFMix

Fig. 2

Precision/Recall for each population calculated from estimated ancestry proportions of simulated admixed individuals with ancestry from a pair of neighboring population

Fig. 3

Illustration of annotating haplotype-cluster model representing one genomic window with D SNPs (in our experiments D is about 75–80, about 3-4 cM). Each box illustrates the expected proportion of haplotypes in all the genotypes of different populations that include a certain model state at a certain level

Fig. 4

Illustration of genome wide HMM where each window has a series of emitting states, which corresponds to a population assignment (p,q) with 1 ≤ p ≤ q ≤ K