Young Jin Kim

The paper demonstrates that long-read sequencing detects more mitochondrial genome variants than short-read sequencing or microarrays in a Korean population.

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

This study profiles pharmacogenomic variants in 14,490 Koreans using a population-specific array, showing high concordance and highlighting ancestry-specific pharmacogenomic diversity.

This study identifies SAMM50 rs3761472 as a risk factor for MASLD by showing it causes mitochondrial dysfunction and metabolic impairments in mice.

This study identifies independent genetic contributions of HLA missense variants and C4 copy number variations to SLE risk using a new East Asian MHC imputation panel.

This study identifies 1,289 genetic signals for type 2 diabetes in 2.5 million individuals, revealing distinct clusters linked to vascular complications and highlighting ancestry-specific genetic risk factors.

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 study identifies 242 genetic loci for glycaemic traits using trans-ancestry meta-analysis, finding 99 novel loci and improved fine-mapping resolution compared to single-ancestry analyses.

The paper analyzes multi-nucleotide variants (MNVs) in 125,748 exomes and 15,708 genomes, identifying their distribution, mutational origins, and functional implications.

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