Speaker: Dr Mingxuan Cai (CUHK) 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. The major challenges of fine-mapping arise from the homogeneous LD patterns and unadjusted confounding bias in GWAS samples, leading to sub-optimal power and false positives. Here, 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. About the speaker: Dr Cai is an Assistant Professor at Department of Biostatistics, City University of Hong Kong. He obtained his PhD degree from The Hong Kong University of Science and Technology in 2022. His broad area of interest lies in statistical machine learning and data science with applications in genetics and genomics data analysis.