Seunggeun Lee

Rare variant effect estimation and polygenic risk prediction

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

Preference-Based Fine-Tuning of Genomic Sequence Models for Personal Expression Prediction with Data Augmentation

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: an R package for transfer learning of polygenic risk scores with pseudovalidation

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

Large-scale evaluation of proteomic and polygenic risk scores reveals complementary contributions to incident disease prediction

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: A Tool for Interpretable and Explainable Polygenic Risk Score

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

Proteome-Wide Association Studies for Blood Lipids and Comparison with Transcriptome-Wide Association Studies

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: Scalable and Accurate Meta-Analysis for Rare Variants

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

Investigation of Genetic Variants and Causal Biomarkers Associated with Brain Aging

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.

The Construction of Multi-ethnic Polygenic Risk Score using Transfer Learning

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.

Genome-wide study on 72,298 Korean individuals in Korean biobank data for 76 traits identifies hundreds of 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.

Set-based rare variant association tests for biobank scale sequencing data sets

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.

Incorporating family disease history and controlling case-control imbalance for population based genetic association studies

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.

Recovery of trait heritability from whole genome sequence data

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

On Cross-ancestry Cancer Polygenic Risk Scores

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.

Utility of family history in disease prediction in the era of polygenic scores

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.

Efficient mixed model approach for large-scale genome-wide association studies of ordinal categorical phenotypes

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.

A powerful subset-based gene-set analysis method identifies novel associations and improves interpretation in UK Biobank

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

An efficient and accurate frailty model approach for genome-wide survival association analysis controlling for population structure and relatedness in large-scale biobanks

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: a humaN-disEase phenoType MAp GEnerator for the Visualization of PheWAS

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

Scalable generalized linear mixed model for region-based association tests in large biobanks and cohorts

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.

Fast and robust ancestry prediction using principal component analysis

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

UK-Biobank Whole Exome Sequence Binary Phenome Analysis with Robust Region-based Rare Variant Test

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: Multiple phenotype meta-analysis for region-based association test

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

Technical Note: Efficient and accurate estimation of genotype odds ratios in biobank-based unbalanced case-control studies

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.

Sex-specific and pleiotropic effects underlying kidney function identified from GWAS meta-analysis

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

Loss-of-function genomic variants with impact on liver-related blood traits highlight potential therapeutic targets for cardiovascular disease

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.

Efficient variant set mixed model association tests for continuous and binary traits in large-scale whole genome sequencing studies

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.

Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies

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: General framework to test multiple phenotype associations of rare variants

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.

Genome-wide association study of 1 million people identifies 111 loci for atrial fibrillation

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