Yingchang Lu

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

The paper demonstrates that TOPMed imputation improves the identification of rare variants associated with type 2 diabetes in Latino populations, leading to novel associations and better polygenic scores.

The paper estimates heritability for height and BMI using whole-genome sequence data and finds that rare variants contribute significantly to the missing heritability.

The paper investigates the pathogenicity of the SLC26A4 p.C565Y variant using genetically modified mice, finding it non-pathogenic in mice despite being reported as pathogenic in humans.

The paper identifies genetic variants and transcripts associated with glycemic traits by analyzing exome-array data from 144,600 individuals, highlighting tissue-specific roles in glucose regulation.

This study identifies 15 common and 9 low-frequency protein-coding variants associated with body fat distribution, implicating novel genes related to lipid homeostasis and type 2 diabetes.

This study identifies 93 genetic loci associated with kidney function across four ancestry groups and highlights causal effects on kidney-related diseases.

The paper assesses if genetic risk for atrial fibrillation explains cardioembolic stroke risk using genome-wide data from the Stroke Genetics Network.

The paper conducts a GWAS in non-European populations, identifies new genetic loci, and highlights the importance of diverse cohorts for understanding complex traits.

This study identifies 16 causal coding variants in type 2 diabetes using fine-mapping and shows that one third of signals are false leads.

This study identifies 6 new blood pressure loci and 48 genes involved in BP regulation using GWAS and gene expression data from blood and kidney tissues.