Seongwon Hwang

This study uses genetic data to show that HMGCR inhibition affects coronary artery disease and type 2 diabetes through different causal pathways, with BMI and LDL-C playing distinct roles.

The paper benchmarks four colocalization methods using protein QTL data to assess their reliability and consistency in detecting shared genetic causes.

This paper introduces a framework to infer gene regulatory networks (GRNs) during tissue morphogenesis by integrating cell movement and gene expression data.

This study examines how ancestry and environmental factors influence de novo mutation rates and spectra in a large whole-genome sequenced cohort, finding ancestry-related effects and a link between tobacco smoke exposure and increased mutation rates.

The paper estimates SNP heritability of de novo mutations in offspring using the 100k Genomes Project cohort, finding paternal but not maternal genetic contributions.

The paper shows cell rearrangement during mesoderm elongation generates gene expression patterns through temporal Wnt and FGF exposure.

This paper trains a CNN to stage zebrafish tail buds with small image datasets, achieving high accuracy without large data.

The paper develops a convolutional neural network to predict causal noncoding variants with regulatory function in GWAS data, identifying variants in active regulatory regions and associated genes for psychiatric and autoimmune diseases.