Samuel Sungil Kim

The paper integrates GWAS with scATAC-seq and scRNA-seq data to identify disease-critical fetal and adult brain cell types for multiple traits.

The paper introduces LDSPEC to estimate correlations between causal disease effects of proximal SNPs, finding significant correlations that vary with distance, allele frequency, LD, and functional annotations, implicating stabilizing selection.

The paper links SNPs to genes using functionally informed strategies, revealing enhancer-related and master-regulator genes contribute to autoimmune disease heritability.

The paper introduces GCSC regression to identify gene sets enriched for disease heritability by leveraging gene co-regulation in TWAS data, finding immune-related associations in Alzheimer's disease.

The paper develops a method to identify population-specific causal effect sizes in functionally important regions using trans-ethnic genetic correlations.

The paper assesses and improves Mendelian disease-derived pathogenicity scores for common disease using stratified LD score regression and a gradient boosting framework.

This study evaluates how well deep learning annotations predict disease heritability and finds they provide limited unique information beyond existing functional annotations.

The paper improves deep learning SNP annotations for complex diseases by integrating them with SNP-to-gene linking strategies and gradient boosting, showing increased informativeness for autoimmune diseases.

LDpred-funct adds baseline-LD functional priors to polygenic scores: average prediction R2 0.144 across 21 UK Biobank traits, a 4.6% relative gain over SBayesR.

The paper assesses gene network connectivity's role in disease heritability using LD score regression and finds that network annotations are enriched but largely explained by regulatory annotations.