Evidence map›Paper›PMID 40471844›Full record

ArticleG3 (Bethesda, Md.)2025

Phase-free local ancestry inference mitigates the impact of switch errors on phase-based methods.

Siddharth Avadhanam, Amy L Williams

Abstract read
In one paragraph

Article in G3 (Bethesda, Md.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Siddharth AvadhanamDepartment of Computational Biology, Cornell University, Ithaca, NY 14853, USA.ORCID 0000-0002-2764-9876
Amy L WilliamsDepartment of Computational Biology, Cornell University, Ithaca, NY 14853, USA.ORCID 0000-0003-0737-0533

Funding

Diverse ancestry biobank to map biomedical traits and elucidate health disparitieU01HG007417 · NHGRI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI LOOS, RUTH JF · 2013 to 2017
$3.5M
Scalable methods for the characterization and analysis of families in large genomic datasetsR35GM133805 · NIGMS · CORNELL UNIVERSITY · PI WILLIAMS, AMY LYNNE · 2019 to 2023
$1.6M
NHGRI NIH HHS U01 HG007417NIGMS NIH HHS R35 GM133805NIH HHS R35 GM133805
6 · The paper itself

Abstract

Local ancestry inference is an indispensable component of a variety of analyses in medical and population genetics, from admixture mapping to characterizing demographic history. However, the accuracy of local ancestry inference depends on a number of factors such as phase quality (for phase-based local ancestry inference methods) and time since admixture. Here, we present an empirical analysis of four local ancestry inference methods using simulated individuals of mixed African and European ancestry, examining the impact of variable phase quality and a range of demographic scenarios. We find that regardless of phasing options, calls from local ancestry inference methods that operate on unphased genotypes (phase-free local ancestry inference) have 2.6-4.6% higher Pearson correlation with the ground truth than methods that operate on phased genotypes (phase-based local ancestry inference). Applying the TRACTOR phase correction algorithm led to modest improvements in phase-based local ancestry inference, but despite this, the Pearson correlation of phase-free local ancestry inference remains 2.4-3.8% higher than phase-corrected phase-based approaches (considering the best-performing methods in each category). Further, analyzing perfectly phased data yields accuracies for the phase-based local ancestry inference methods that are only slightly inferior to those of HAPMIX. Phase-free and phase-based local ancestry inference accuracy differences can dramatically impact downstream analyses: estimates of the time since admixture using phase-based local ancestry inference tracts are upwardly biased by ≈10 generations using our highest quality statistically phased data but have virtually no bias using phase-free local ancestry inference calls. This study underscores the strong dependence of phase-based local ancestry inference accuracy on phase quality and highlights the merits of local ancestry inference approaches that analyze unphased genetic data.

Indexed as

African PeopleEuropean PeopleGenetics, PopulationAlgorithmsGenotypeHumansModels, GeneticPolymorphism, Single Nucleotideadmixtureancestryhaplotypeslocal ancestry inferencephase

Identifiers

PMID40471844
PMCPMC12341942

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.