Yen-Feng Lin

Sep 10, 2024 | pericles.pericles-prod.literatumonline.com | Yu-Cheng Hsu |Mei-Hsin Su |Chia-Yen Chen |Yen-Feng Lin

Conflicts of Interest Chia-Yen Chen is an employee of Biogen. The remaining authors declare no conflicts of interest. Supporting Information Filename Description ajmgb33007-sup-0001-Tables.docxWord 2007 document , 27.7 KB Table S1. References , , , et al. 2016. “Population Structure of Han Chinese in the Modern Taiwanese Population Based on 10,000 Participants in the Taiwan Biobank Project.” Human Molecular Genetics 25, no. 24: 5321–5331.

Aug 26, 2024 | nature.com | Kai Yuan |Antonio F Pardiñas |Mingrui Yu |Tzu-Ting Chen |Max Lam |Mark Daly | +5 more

AbstractGenome-wide association studies (GWAS) of human complex traits or diseases often implicate genetic loci that span hundreds or thousands of genetic variants, many of which have similar statistical significance. While statistical fine-mapping in individuals of European ancestry has made important discoveries, cross-population fine-mapping has the potential to improve power and resolution by capitalizing on the genomic diversity across ancestries.

Jan 4, 2024 | nature.com | Tzu-Ting Chen |Jaeyoung Kim |Max Lam |Sang-Hyuk Jung |Soyeon Kim |Injeong Shim | +9 more

AbstractEducational attainment (EduYears), a heritable trait often used as a proxy for cognitive ability, is associated with various health and social outcomes. Previous genome-wide association studies (GWASs) on EduYears have been focused on samples of European (EUR) genetic ancestries. Here we present the first large-scale GWAS of EduYears in people of East Asian (EAS) ancestry (n = 176,400) and conduct a cross-ancestry meta-analysis with EduYears GWAS in people of EUR ancestry (n = 766,345).

Sep 18, 2023 | nature.com | Adrian Campos |Shinichi Namba |Julia Sidorenko |Huanwei Wang |Seunggeun Lee |Yen-Feng Lin | +3 more

AbstractGenome-wide association studies (GWASs) have been mostly conducted in populations of European ancestry, which currently limits the transferability of their findings to other populations. Here, we show, through theory, simulations and applications to real data, that adjustment of GWAS analyses for polygenic scores (PGSs) increases the statistical power for discovery across all ancestries.