Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.
Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA.
Nat Commun. 2019 Sep 6;10(1):4064. doi: 10.1038/s41467-019-11953-9.
Population-based biobanks with genomic and dense phenotype data provide opportunities for generating effective therapeutic hypotheses and understanding the genomic role in disease predisposition. To characterize latent components of genetic associations, we apply truncated singular value decomposition (DeGAs) to matrices of summary statistics derived from genome-wide association analyses across 2,138 phenotypes measured in 337,199 White British individuals in the UK Biobank study. We systematically identify key components of genetic associations and the contributions of variants, genes, and phenotypes to each component. As an illustration of the utility of the approach to inform downstream experiments, we report putative loss of function variants, rs114285050 (GPR151) and rs150090666 (PDE3B), that substantially contribute to obesity-related traits and experimentally demonstrate the role of these genes in adipocyte biology. Our approach to dissect components of genetic associations across the human phenome will accelerate biomedical hypothesis generation by providing insights on previously unexplored latent structures.
基于人群的生物库拥有基因组和密集表型数据，为生成有效的治疗假说和理解基因组在疾病易感性中的作用提供了机会。为了描述遗传关联的潜在成分，我们将截断奇异值分解（DeGAs）应用于从英国生物库研究中 337199 名白种英国人的 2138 种表型的全基因组关联分析中得出的汇总统计数据矩阵。我们系统地确定了遗传关联的关键成分，以及变体、基因和表型对每个成分的贡献。作为该方法用于为下游实验提供信息的实用性的说明，我们报告了可能的功能丧失变体 rs114285050（GPR151）和 rs150090666（PDE3B），它们对肥胖相关特征有很大贡献，并通过实验证明了这些基因在脂肪细胞生物学中的作用。我们在人类表型中剖析遗传关联成分的方法将通过提供对以前未探索的潜在结构的见解，加速生物医学假说的产生。
Zotero 插件
