Yungil Kim

OTTERS: A powerful TWAS framework leveraging summary-level reference data

OTTERS is a TWAS framework that uses summary-level eQTL reference data to estimate eQTL weights and perform omnibus TWAS, showing improved power and applicability.

Population-level variation of enhancer expression identifies novel disease mechanisms in the human brain

The paper identifies population-level variation in enhancer expression in the human brain and links enhancer eQTL to schizophrenia heritability.

Unraveling the polygenic architecture of complex traits using blood eQTL meta-analysis

The paper uses blood eQTL meta-analysis to explore the polygenic architecture of complex traits, finding that trans-eQTLs are more informative for identifying causal genes than cis-eQTLs.

Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions

This paper performs a large-scale eQTL meta-analysis across cerebral cortical and cerebellar brain regions, identifying distinct eQTL patterns and demonstrating improved power over existing studies.

Functional annotation of rare structural variation in the human brain

The paper functionally annotates rare structural variants in the human brain by integrating SV calling with RNA-seq data to assess their regulatory effects and prioritize pathogenic deletions.

Integrative transcriptome imputation reveals tissue-specific and shared biological mechanisms mediating susceptibility to complex traits

This study integrates transcriptome data with genetic variation to identify gene-trait associations and uncover biological mechanisms underlying complex traits in humans and mice.

Mendelian Randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits

This paper introduces TWMR, a Mendelian Randomization method that identifies gene-trait associations by integrating GWAS and eQTL data, revealing 3,913 novel associations across 43 human phenotypes.

Novel susceptibility loci and genetic regulation mechanisms for type 2 diabetes

This study identifies 139 common and 4 rare genetic variants associated with type 2 diabetes through GWAS and integrates gene expression and epigenomic data to highlight potential functional genes and regulatory mechanisms.

Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood

The paper identifies gene targets for brain-related traits using transcriptomic and methylomic data from blood, finding strong correlations between genetic effects in brain and blood tissues.

Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics

The paper develops S-PrediXcan to compute gene expression associations from GWAS summary data and applies it to 44 GTEx tissues and 100+ phenotypes, finding tissue-specific effects and colocalized associations.

Co-expression networks reveal the tissue-specific regulation of transcription and splicing

The paper constructs co-expression networks that integrate transcription and splicing regulation across tissues, identifying shared and tissue-specific regulatory hubs and genetic variants impacting gene expression.

The impact of structural variation on human gene expression

This study identifies structural variants (SVs) as significant contributors to gene expression, finding they are more frequent causal variants than previously estimated and have larger effect sizes.

The impact of rare variation on gene expression across tissues

The paper identifies rare variants contributing to extreme gene expression changes across tissues and introduces RIVER, a Bayesian method to prioritize functional variants.