Tzu‐Ting Chen

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.

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 improves genetic scores for adiposity traits in East Asian populations by using trans-ancestry meta-analysis, increasing variance explained compared to European-derived scores.