Yixuan Wu

This paper introduces a statistical framework to optimize and benchmark polygenic risk scores using GWAS summary statistics, showing improved performance over existing methods.

This study identifies risks of false positives in ML-assisted GWAS and introduces POP-GWAS, a framework that provides valid inference for GWAS on ML-imputed phenotypes, applied to BMD data to find 89 novel loci.

This study introduces PIGEON, a statistical framework for gene-environment interaction analysis using summary statistics, showing its superiority in multiple analytic settings.