多祖先精细定位在变异异质性与 LD 不匹配下的系统基准评测
Systematic benchmarking of multi-ancestry fine-mapping under variant heterogeneity and LD mismatch
研究系统评测了九种多祖先精细定位方法，包括显式建模跨祖先 LD 与效应共享的联合概率模型，以及汇总各祖先独立结果的单变量整合方法。作者用基于 UK Biobank 基因型数据构建的模拟数据评估精细定位准确性、稳健性和计算效率，并在 All of Us Research Program 中做真实数据验证。结果显示，联合建模对跨人群共享的因果变异有更高的检验功效和分辨率；事后整合则在因果变异仅存在于部分人群时更稳健，因为它能容纳变异可用性的差异。当 GWAS 汇总统计与外部 LD 参考面板存在系统性偏差时，各方法普遍功效不足。
来源：Briefings in Bioinformatics · doi.org
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.