Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan.
Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA.
Commun Biol. 2022 Nov 3;5(1):1175. doi: 10.1038/s42003-022-04168-0.
To explore the complex genetic architecture of common diseases and traits, we conducted comprehensive PheWAS of ten diseases and 34 quantitative traits in the community-based Taiwan Biobank (TWB). We identified 995 significantly associated loci with 135 novel loci specific to Taiwanese population. Further analyses highlighted the genetic pleiotropy of loci related to complex disease and associated quantitative traits. Extensive analysis on glycaemic phenotypes (T2D, fasting glucose and HbA) was performed and identified 115 significant loci with four novel genetic variants (HACL1, RAD21, ASH1L and GAK). Transcriptomics data also strengthen the relevancy of the findings to metabolic disorders, thus contributing to better understanding of pathogenesis. In addition, genetic risk scores are constructed and validated for absolute risks prediction of T2D in Taiwanese population. In conclusion, our data-driven approach without a priori hypothesis is useful for novel gene discovery and validation on top of disease risk prediction for unique non-European population.
为了探索常见疾病和特征的复杂遗传结构，我们对基于社区的台湾生物库（TWB）中的十种疾病和三十四种定量特征进行了全面的表型全基因组关联分析（PheWAS）。我们鉴定了 995 个与 135 个新的特定于台湾人群的新型位点显著相关的基因座。进一步的分析强调了与复杂疾病和相关定量特征相关的基因座的遗传多效性。对血糖表型（T2D、空腹血糖和 HbA）进行了广泛的分析，鉴定了 115 个具有四个新的遗传变异（HACL1、RAD21、ASH1L 和 GAK）的显著基因座。转录组学数据也证实了这些发现与代谢紊乱的相关性，从而有助于更好地理解发病机制。此外，还构建和验证了用于预测台湾人群 T2D 绝对风险的遗传风险评分。总之，我们这种无需先验假设的基于数据驱动的方法，对于独特的非欧洲人群的新型基因发现和验证以及疾病风险预测是非常有用的。
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