XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias

Mingxuan Cai (), Zhiwei Wang, Jiashun Xiao, Xianghong Hu, Gang Chen and Can Yang ()
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Mingxuan Cai: City University of Hong Kong
Zhiwei Wang: Guangzhou HKUST Fok Ying Tung Research Institute
Jiashun Xiao: Shenzhen Research Institute of Big Data
Xianghong Hu: Guangzhou HKUST Fok Ying Tung Research Institute
Gang Chen: Central South University
Can Yang: Guangzhou HKUST Fok Ying Tung Research Institute

Nature Communications, 2023, vol. 14, issue 1, 1-17

Abstract: Abstract Fine-mapping prioritizes risk variants identified by genome-wide association studies (GWASs), serving as a critical step to uncover biological mechanisms underlying complex traits. However, several major challenges still remain for existing fine-mapping methods. First, the strong linkage disequilibrium among variants can limit the statistical power and resolution of fine-mapping. Second, it is computationally expensive to simultaneously search for multiple causal variants. Third, the confounding bias hidden in GWAS summary statistics can produce spurious signals. To address these challenges, we develop a statistical method for cross-population fine-mapping (XMAP) by leveraging genetic diversity and accounting for confounding bias. By using cross-population GWAS summary statistics from global biobanks and genomic consortia, we show that XMAP can achieve greater statistical power, better control of false positive rate, and substantially higher computational efficiency for identifying multiple causal signals, compared to existing methods. Importantly, we show that the output of XMAP can be integrated with single-cell datasets, which greatly improves the interpretation of putative causal variants in their cellular context at single-cell resolution.

Date: 2023
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DOI: 10.1038/s41467-023-42614-7

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