A1233
Title: A statistical framework for fine-mapping by leveraging genetic diversity and accounting for confounding bias
Authors: Mingxuan Cai - City University of Hong Kong (Hong Kong) [presenting]
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 fine-mapping's statistical power and resolution. Second, it is computationally expensive to search for multiple causal variants simultaneously. Third, the confounding bias hidden in GWAS summary statistics can produce spurious signals. To address these challenges, a statistical method is developed for cross-population fine-mapping (XMAP) by leveraging genetic diversity and accounting for confounding bias. Using cross-population GWAS summary statistics from global biobanks and genomic consortia shows 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, it is shown that the output of XMAP can be integrated with single-cell datasets, greatly improving the interpretation of putative causal variants in their cellular context at single-cell resolution.
