Seunggeun Lee

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

The paper improves gene expression prediction from DNA sequences by fine-tuning genomic models with synthetic data and preference-based objectives, outperforming existing methods on the GEUVADIS dataset.

PTL-PRS improves polygenic risk score prediction in non-European populations using pseudovalidation and software optimizations without requiring individual-level data.

The paper evaluates proteomic and polygenic risk scores for disease prediction, finding proteomic scores outperform polygenic scores in most traits and show temporal responsiveness.

XPRS decomposes polygenic risk scores into gene and SNP contributions using SHAP values and provides visualization tools to enhance interpretability and clinical communication.

The paper conducts proteome-wide and transcriptome-wide association studies for blood lipids, finding high-level consistency between proteome and transcriptome-lipid associations across tissues.

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 introduces TL-PRS, a transfer learning method for constructing polygenic risk scores across ancestries, achieving up to 42% average relative improvement in South Asian and African ancestry groups.

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 improves rare variant association testing by introducing SAIGE-GENE+ which reduces type I error and computational costs, identifying 551 gene-phenotype associations in UK Biobank WES 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.

The paper estimates heritability for height and BMI using whole-genome sequence data and finds that rare variants contribute significantly to the missing heritability.

This study evaluates the utility of breast and prostate cancer polygenic risk scores across different ancestry groups using UK Biobank data, showing significant enrichments in cancer cases within top PRS percentiles.

The paper evaluates family history and polygenic scores as predictors for coronary artery disease and type 2 diabetes using the Trondelag Health Study and UK Biobank.

The paper introduces POLMM, a mixed model for ordinal categorical phenotypes in GWAS, showing it is computationally efficient, controls type I error, and identifies more significant variants than existing methods.

GAUSS identifies gene-set-phenotype associations using GWAS summary statistics, improving power and interpretability in UK Biobank data.

The paper introduces a frailty model for genome-wide survival analysis in large biobanks, handling population structure and relatedness, and demonstrates its performance on real-world datasets.

NETMAGE generates interactive phenotype networks from PheWAS data to visualize shared genetic associations and identify pleiotropy, aiding in drug target discovery.

SAIGE-GENE is a scalable generalized linear mixed model for region-based association tests in large cohorts, efficiently handling over 400,000 samples with controlled type I error rates.

This paper introduces two methods to improve ancestry prediction in GWAS by reducing bias and computational cost compared to existing approaches.

The paper develops a region-based rare variant test for unbalanced binary phenotypes, showing improved type I error control and scalability, and identifies 10 rare variant associations in UK Biobank whole exome data.

Meta-MultiSKAT improves power for region-based genetic association tests by meta-analyzing multiple correlated phenotypes using summary statistics and variance component tests.

The paper introduces a scalable method to accurately estimate genotype odds ratios in unbalanced biobank-based case-control studies, improving computational efficiency compared to existing methods.

This study identifies 147 genetic loci associated with eGFR, including 53 novel ones, and finds sex-specific effects on kidney function.

The paper identifies loss-of-function variants affecting liver-related blood traits that reduce CVD risk without increasing metabolic disease risk, highlighting potential therapeutic targets.

The paper introduces SMMAT, a variant-set mixed model association test for continuous and binary traits in large-scale WGS studies, showing it controls type I error rates and applies to population structured and related samples.

This paper introduces SAIGE, a method that improves the accuracy of genetic association tests in unbalanced case-control studies by using saddlepoint approximation, and demonstrates its effectiveness on UK Biobank data.

Multi-SKAT improves power to detect rare variant associations with multiple phenotypes while maintaining type I error rate using a general framework with kernel matrices and random effects.

This study identifies 111 genetic loci and 163 risk variants associated with atrial fibrillation using a GWAS of over 1 million individuals.