Yen‐Chen Anne Feng

This paper improves genetic discovery and polygenic risk prediction for respiratory diseases by analyzing multiple traits and ancestries, identifying novel loci and developing a new polygenic risk score method.

This study identifies shared and distinct ultra-rare genetic risk factors for epilepsy subtypes using whole-exome sequencing of 20,979 patients and 33,444 controls.

This paper introduces SuSiEx, a cross-population fine-mapping method that improves resolution and power by integrating data across ancestries, leading to more accurate identification of causal variants in complex traits and diseases.

This study uses EHR data to estimate TRD probability and identifies two genome-wide significant loci linked to metabolic traits, suggesting shared biology with related conditions.

The paper identifies 979 novel genetic loci for 36 quantitative traits by analyzing data from the Taiwan Biobank, Biobank Japan, and UK Biobank, highlighting the value of diverse populations in GWAS.

This study evaluates polygenic risk score methods across diverse biobanks and ancestries, finding that European reference panels often yield better performance, while PRS-CS outperforms P+T for high-heritability traits.

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

The paper introduces the Taiwan Biobank, a large-scale biobank with genetic and phenotypic data from 150,000 individuals, highlighting its population structure and potential for genetic research.

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 paper develops a trans-ancestry polygenic risk score for type 2 diabetes and validates it across diverse populations to improve equitable risk prediction.

This paper introduces PRS-CSx, a method that improves polygenic prediction across ancestrally diverse populations by integrating GWAS summary statistics and sharing genetic effects.

The paper analyzes ultra-rare variants in epilepsy patients to identify gene-set enrichment patterns and differential impacts in neuronal gene categories.

The paper introduces methods to clean and analyze clinical lab data linked to EHRs, finding polygenic scores associated with lipid levels and coronary artery disease biomarkers.

The paper investigates the heritability of age of first occurrence for complex disorders using GWAS data from UK Biobank and FinnGen, finding genetic contributions and correlations with disease susceptibility.

This study identifies ultra-rare genetic variants associated with different epilepsy types using whole-exome sequencing in 17,634 individuals.

This paper introduces PRS-CS, a Bayesian polygenic prediction method that improves accuracy over existing approaches for complex diseases and traits.

Cross-disorder meta-analysis of eight psychiatric GWAS (232,964 cases, 494,162 controls) reports 109 loci shared by two or more disorders and three groups of correlated disorders.