Systematic benchmarking of multi-ancestry fine-mapping under variant heterogeneity and LD mismatch.
- 1Department of Biological Sciences, Purdue University, 915 Mitch Daniels Boulevard, West Lafayette, IN 47907, United States.
- 2Department of Statistics, Purdue University, 150 N. University Street, West Lafayette, IN 47907, United States.
Abstract
Increasingly diverse, multi-ancestry genome-wide association studies (GWASs) provide unprecedented opportunities to better understand the genetic architecture of complex traits. In particular, differences in linkage disequilibrium (LD) patterns across populations can be leveraged to improve the resolution and reliability of statistical fine-mapping. However, cross-ancestry effect heterogeneity, population-specific variant support, and LD mismatches pose substantial challenges, motivating a systematic evaluation of multi-ancestry fine-mapping methods to provide practical guidance for analyses across diverse populations. We conducted a comprehensive benchmarking of nine methods, spanning joint probabilistic models that explicitly model cross-ancestry LD and effect sharing, and univariate-based integration approaches that aggregate ancestry-specific results. We evaluated fine-mapping accuracy, robustness, and computational efficiency using realistic simulations derived from UK Biobank genotype data, with real-data validation in the All of Us Research Program. Our study revealed that joint modeling approaches achieve improved power and resolution for causal variants shared across populations. In comparison, post-hoc integration is more robust in recovering causal variants present in only a subset of populations by accommodating differences in variant availability. Moreover, methods are generally underpowered in the presence of systematic discrepancies between GWAS summary statistics and external LD reference panels. Motivated by these complementary performance profiles, we propose the multi-ancestry fine-mapping decision strategy, which avoids a priori method selection by adaptively selecting approaches based on locus-level evidence of ancestry-specific variants. Together, our benchmarking and decision strategy offer practical guidance for robust multi-ancestry fine-mapping and highlight the importance of joint modeling that effectively accounts for ancestry-specific variant availability and LD mismatch.
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BibTeX
@article{chen2026systematic,
title = {{Systematic benchmarking of multi-ancestry fine-mapping under variant heterogeneity and LD mismatch.}},
author = {Guangxin Chen and Peristera Paschou and Boran Gao},
journal = {Briefings in bioinformatics},
year = {2026},
month = sep,
doi = {10.1093/bib/bbag574},
url = {https://europepmc.org/article/MED/42894387},
note = {CC BY},
}
RIS
TY - JOUR
TI - Systematic benchmarking of multi-ancestry fine-mapping under variant heterogeneity and LD mismatch.
AU - Guangxin Chen
AU - Peristera Paschou
AU - Boran Gao
PY - 2026
DA - 2026/09/01
JO - Briefings in bioinformatics
DO - 10.1093/bib/bbag574
UR - https://europepmc.org/article/MED/42894387
AB - Increasingly diverse, multi-ancestry genome-wide association studies (GWASs) provide unprecedented opportunities to better understand the genetic architecture of complex traits. In particular, differences in linkage disequilibrium (LD) patterns across populations can be leveraged to improve the resolution and reliability of statistical fine-mapping. However, cross-ancestry effect heterogeneity, population-specific variant support, and LD mismatches pose substantial challenges, motivating a systematic evaluation of multi-ancestry fine-mapping methods to provide practical guidance for analyses across diverse populations. We conducted a comprehensive benchmarking of nine methods, spanning joint probabilistic models that explicitly model cross-ancestry LD and effect sharing, and univariate-based integration approaches that aggregate ancestry-specific results. We evaluated fine-mapping accuracy, robustness, and computational efficiency using realistic simulations derived from UK Biobank genotype data, with real-data validation in the All of Us Research Program. Our study revealed that joint modeling approaches achieve improved power and resolution for causal variants shared across populations. In comparison, post-hoc integration is more robust in recovering causal variants present in only a subset of populations by accommodating differences in variant availability. Moreover, methods are generally underpowered in the presence of systematic discrepancies between GWAS summary statistics and external LD reference panels. Motivated by these complementary performance profiles, we propose the multi-ancestry fine-mapping decision strategy, which avoids a priori method selection by adaptively selecting approaches based on locus-level evidence of ancestry-specific variants. Together, our benchmarking and decision strategy offer practical guidance for robust multi-ancestry fine-mapping and highlight the importance of joint modeling that effectively accounts for ancestry-specific variant availability and LD mismatch.
SN - 1467-5463
SN - 1477-4054
ER -