Ancestry inference using reference labeled clusters of haplotypes.

Autor: Wang Y; AncestryDNA, San Francisco, CA, 94107, USA., Song S; AncestryDNA, San Francisco, CA, 94107, USA., Schraiber JG; AncestryDNA, San Francisco, CA, 94107, USA., Sedghifar A; AncestryDNA, San Francisco, CA, 94107, USA., Byrnes JK; AncestryDNA, San Francisco, CA, 94107, USA., Turissini DA; AncestryDNA, San Francisco, CA, 94107, USA., Hong EL; AncestryDNA, San Francisco, CA, 94107, USA., Ball CA; AncestryDNA, San Francisco, CA, 94107, USA., Noto K; AncestryDNA, San Francisco, CA, 94107, USA. knoto@ancestry.com.
Jazyk: angličtina
Zdroj: BMC bioinformatics [BMC Bioinformatics] 2021 Sep 25; Vol. 22 (1), pp. 459. Date of Electronic Publication: 2021 Sep 25.
DOI: 10.1186/s12859-021-04350-x
Abstrakt: 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.
(© 2021. The Author(s).)
Databáze: MEDLINE
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