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LANTERN: Leveraging Local Ancestry Tracts to Enhance Rare-Variant Aggregate Association Testing.

Yu Wang, Bjoernar Tuftin, Laura M Raffield et al.

42094175 PubMed ID
8 Authors
2026-04-27 Published
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Chapter I

Publication Details

Comprehensive information about this research publication

Authors

YW
Yu Wang
BT
Bjoernar Tuftin
LM
Laura M Raffield
BH
Bertha Hidalgo
SL
Sarah L Kerns
AT
Andrew T DeWan
SM
Suzanne M Leal
PL
Paul L Auer
Chapter II

Abstract

Summary of the research findings

Individuals with admixed ancestry comprise a significant proportion of populations of the Americas. Statistical methods have been developed to specifically leverage local ancestry inference to enhance the power and interpretability of genome-wide association studies in admixed populations. However, no such methods currently exist to test for rare-variant aggregate associations. Here we present LANTERN (Leveraging local ANcestry Tracts to Enhance Rare variaNt aggregate associations), a method that infers the alleles that lie on each ancestral haplotype and conducts rare-variant aggregate association testing in a generalized linear mixed model framework. Through simulation studies we demonstrated that LANTERN achieves proper control of Type 1 error while boosting power to detect associations when causal alleles predominately lie on one ancestral haplotype. Using data from a cohort of African American participants from the Jackson Heart Study, LANTERN identified two genes known to be involved in red-blood cell (RBC) biology when local ancestry information was incorporated. Specifically, a burden of rare alleles on European ancestral haplotypes in EPO was associated with both hemoglobin levels (HGB) and RBC counts, whereas a burden of rare alleles on African ancestral haplotypes in EPB42 was associated with HGB and RBC. In summary, LANTERN (i) allows for the identification of ancestry-specific rare-variant associations; and (ii) enhances rare-variant association signals compared to an analysis that ignores local ancestry. LANTERN is implemented in R and is freely available on GitHub.

Chapter III

AI-Generated Summary

AI-generated by DNAGENICS

Independent AI summary of ancestry and genetic findings from the published study

Important: This summary is AI-generated by DNAGENICS for informational purposes only. It was not created by, affiliated with, or endorsed by the researchers behind the original publication, and is based solely on that published research. It may contain errors or omissions. DNAGENICS disclaims all liability for any inaccuracies or consequences arising from use of this information. Verify all information against the original publication. This is not professional scientific review or medical advice.

Summary

Key Findings

Ancestry Insights

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Historical Context