Kisung Nam

The paper introduces CASTIE, a method for context-dependent eQTL mapping in single-cell data, identifying regulatory variants missed by static models and linking them to disease traits.

This paper introduces RareEffect, a method for estimating rare variant effects and improving polygenic risk prediction using UK Biobank whole exome sequencing data.

This study identifies 22 novel gene-sleep duration interaction loci for blood pressure traits across 811,405 individuals, highlighting distinct genetic influences of short and long sleep on cardiovascular health.

The paper uses ENCODE-derived regulatory elements to analyze rare variants in WGS data, identifying 1,987 significant associations across 100 traits with high replication rates.

Meta-SAIGE improves rare variant meta-analysis by controlling type I error and enhancing computational efficiency, identifying 237 associations across 83 disease phenotypes.

This study investigates genetic variants and causal biomarkers associated with brain aging using multimodal data from UK Biobank, identifying key brain regions and immune-related causal factors.

This paper shows that meta-analysis fine-mapping is often miscalibrated due to cohort heterogeneity and introduces a QC method to identify suspicious loci.

Meta-analysis of harmonized GWAS from up to 18 biobanks (six ancestry groups) across 14 endpoints replicated 320 known loci and identified 188 apparently novel loci.

This study performs GWAS on 76 traits in 72,298 Koreans, identifying 2,237 loci, including 117 novel associations, and demonstrates improved polygenic risk scores through meta-analysis with Biobank Japan data.

The paper introduces a method to improve genetic association studies by incorporating family disease history and addressing case-control imbalance, showing improved power and calibration.