Zucker Roei, Kelman Guy, Linial Michal
The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.
The Jerusalem Center for Personalized Computational Medicine, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9112102, Israel.
Nucleic Acids Res. 2025 Jan 6;53(D1):D1132-D1143. doi: 10.1093/nar/gkae1125.
The Proteome-Wide Association Study (PWAS) is a protein-based genetic association approach designed to complement traditional variant-based methods like GWAS. PWAS operates in two stages: first, machine learning models predict the impact of genetic variants on protein-coding genes, generating effect scores. These scores are then aggregated into a gene-damaging score for each individual. This score is then used in case-control statistical tests to significantly link to specific phenotypes. PWAS Hub (v1.2) is a user-friendly platform that facilitates the exploration of gene-disease associations using clinical and genetic data from the UK Biobank (UKB), encompassing 500k individuals. PWAS Hub reports on 819 diseases and phenotypes determined by PheCode and ICD-10 clinical codes, each with a minimum of 400 affected individuals. PWAS-derived gene associations were reported for 72% of the tested phenotypes. The PWAS Hub also analyzes gene associations separately for males and females, considering sex-specific genetic effects, inheritance patterns (dominant and recessive), and gene pleiotropy. We illustrated the utility of the PWAS Hub for primary (essential) hypertension (I10), type 2 diabetes mellitus (E11), and specified haematuria (R31) that showed sex-dependent genetic signals. The PWAS Hub, available at pwas.huji.ac.il, is a valuable resource for studying genetic contributions to common diseases and sex-specific effects.
全蛋白质组关联研究(PWAS)是一种基于蛋白质的基因关联方法,旨在补充像全基因组关联研究(GWAS)这样的传统基于变异体的方法。PWAS分两个阶段进行:首先,机器学习模型预测基因变异对蛋白质编码基因的影响,生成效应分数。然后将这些分数汇总为每个个体的基因损伤分数。然后将该分数用于病例对照统计测试,以显著关联到特定表型。PWAS Hub(v1.2)是一个用户友好的平台,它利用来自英国生物银行(UKB)的临床和遗传数据促进对基因-疾病关联的探索,该数据库涵盖50万名个体。PWAS Hub报告了由PheCode和ICD-10临床代码确定的819种疾病和表型,每种疾病和表型至少有400名受影响个体。对于72%的测试表型报告了PWAS衍生的基因关联。PWAS Hub还分别分析男性和女性的基因关联,考虑性别特异性遗传效应、遗传模式(显性和隐性)以及基因多效性。我们展示了PWAS Hub在原发性(特发性)高血压(I10)、2型糖尿病(E11)和特定血尿(R31)方面的效用,这些疾病显示出性别依赖性遗传信号。可在pwas.huji.ac.il上获取的PWAS Hub是研究常见疾病的遗传贡献和性别特异性效应的宝贵资源。