Danny S. Park

The paper defines novel COVID-19 phenotypes and shows they reveal distinct genetic associations and protective effects compared to traditional definitions.

The paper identifies a rare variant that lowers ACE2 expression and reduces COVID-19 risk, and develops a genetic risk score predicting severe disease.

This study shows that using admixed populations in GWAS increases statistical power and improves cross-population polygenic score accuracy compared to single-ancestry populations.

This study links genomic data with EHRs to identify health outcomes enriched in sub-populations, revealing both known and novel associations.

The paper develops a statistical test to detect polygenic epistasis by analyzing genotype-by-ancestry interactions in mice and yeast, finding significant associations across multiple phenotypes.

This paper introduces a reference-free method for generating polygenic risk scores (PRS) that avoids over-fitting and computational inefficiencies, and demonstrates applications in pleiotropy detection and mixed-model association testing.

The paper introduces ADGLM to model population variance structure, showing it biases genetic association studies and identifies novel genotype-variance associations.

This paper challenges claims that GCTA cannot reliably estimate heritability, showing errors in the analysis that invalidate those claims.