Statistical Bioinformatics Seminar: Dr Mingxuan Cai (CUHK)

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.