Soo Heon Kwak

The paper conducts a multi-ancestry, trans-generational GWAS meta-analysis of gestational diabetes and glycemic traits, identifying novel loci and highlighting limited pregnancy-specific genetic effects.

This paper develops and validates multi-ancestry polygenic risk scores for type 2 diabetes prediction and complications across diverse populations.

This paper conducts a multi-ancestry time-to-event GWAS to identify genetic variants associated with incident cardiovascular disease in people with type 2 diabetes, finding three novel loci and confirming known CAD variants.

This paper clusters T2D genetic loci into ten groups using bNMF, identifying distinct biological mechanisms and clinical associations.

This study identifies 277 T2D loci using diverse populations and improves fine-mapping to assess causal genes and mechanisms, enhancing clinical translation.

This study identifies 35 genes associated with circulating lipid levels using rare coding variants from 170,000 exomes across multiple ancestries.

The paper evaluates penetrance and variable expressivity of monogenic metabolic conditions using exome data from 77,184 individuals, finding that monogenic variants have larger effect sizes than polygenic scores but low penetrance.

This paper identifies 56 new type 2 diabetes loci in 433,540 East Asian individuals through a meta-analysis of GWAS data.

This study identifies rare genetic variants associated with type 2 diabetes using exome sequencing of 20,791 cases and 24,440 controls, and provides a Bayesian framework to improve variant prioritization.

This paper develops a method integrating family history with genetic data to improve type 2 diabetes risk prediction, showing family history adds significant predictive value beyond genetic variants.