Wei‐Min Chen

The paper introduces SECRET, a method to develop polygenic scores for traits with limited data, showing improved prediction power over existing methods.

This paper develops ancestry-specific and combined HLA-focused genetic risk scores for type 1 diabetes and evaluates their performance across diverse populations.

The paper reports whole-genome association analyses of sleep-disordered breathing traits in the NHLBI TOPMed program, identifying rare and common variant associations.

The paper uses whole genome sequencing to identify genetic loci associated with platelet aggregation, including novel variants in RGS18, SVEP1, and PEAR1.

This study identifies causal variants, cells, genes, and drug targets for type 1 diabetes using large-scale genetic and functional genomic analyses.

This study analyzes the genetic structure of Gullah African Americans, finding higher African ancestry and lower European admixture compared to non-Gullah African Americans.

The paper identifies 302 new genetic signals for type 1 diabetes and autoimmune thyroid disease by combining effect size and significance, showing that only 28% of genome-wide significant signals have large effects.

This paper introduces within-family Mendelian randomization methods to reduce biases from family structure and shows they yield different results compared to population-based analyses.

This study identifies 16 genome-wide significant loci associated with diabetic kidney disease, including a protective variant in the COL4A3 gene.

This study fine-maps RA and T1D loci outside the MHC, identifying causal variants and confirming their functional impact through allele-specific binding and enhancer activity.