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Opportunities and challenges of local ancestry in genetic association analyses.

Sun Quan, Q Horimoto, Andrea R V R ARVR et al.

40185073 PubMed ID
12 Authors
2025-04-03 Published
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Chapter I

Publication Details

Comprehensive information about this research publication

Authors

SQ
Sun Quan
QH
Q Horimoto
AR
Andrea R V R ARVR
CB
Chen Brian
BO
B Ockerman
FF
Frank F
MK
Mohlke Karen L
KB
KL Blue
EE
Elizabeth E
RL
Raffield Laura M
LL
LM Li
YY
Yun Y
Chapter II

Abstract

Summary of the research findings

Recently, admixed populations make up an increasing percentage of the US and global populations, and the admixture is not uniform over space or time or across genomes. Therefore, it becomes indispensable to evaluate local ancestry in addition to global ancestry to improve genetic epidemiological studies. Recent advances in representing human genome diversity, coupled with large-scale whole-genome sequencing initiatives and improved tools for local ancestry inference, have enabled studies to demonstrate that incorporating local ancestry information enhances both genetic association analyses and polygenic risk predictions. Along with the opportunities that local ancestry provides, there exist challenges preventing its full usage in genetic analyses. In this review, we first summarize methods for local ancestry inference and illustrate how local ancestry can be utilized in various analyses, including admixture mapping, association testing, and polygenic risk score construction. In addition, we discuss current challenges in research involving local ancestry, both in terms of the inference itself and its role in genetic association studies. We further pinpoint some future study directions and methodology development opportunities to help more effectively incorporate local ancestry in genetic analyses. It is worth the effort to pursue those future directions and address these analytical challenges because the appropriate use of local ancestry estimates could help mitigate inequality in genomic medicine and improve our understanding of health and disease outcomes.

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