我们介绍了全蛋白质组关联研究(PWAS),一种检测由蛋白质功能改变介导的基因-表型关联的新方法。PWAS聚合共同影响蛋白质编码基因的所有变体的信号,并使用机器学习和概率模型评估它们对蛋白质功能的总体影响。随后,它测试该基因是否在个体之间表现出与感兴趣的表型相关的功能变异性。PWAS可以捕捉复杂的遗传力模式,包括隐性遗传。与GWAS和其他现有方法的比较证明了其恢复致病蛋白质编码基因并突出新关联的能力。PWAS可作为命令行工具使用。
We introduce Proteome-Wide Association
Study (PWAS), a new method for detecting gene-phenotype associations mediated by protein function alterations. PWAS aggregates the signal of all variants jointly affecting a protein-coding gene and assesses their overall impact on the protein\'s function using machine learning and probabilistic models. Subsequently, it tests whether the gene exhibits functional variability between individuals that correlates with the phenotype of interest. PWAS can capture complex modes of heritability, including recessive inheritance. A comparison with GWAS and other existing methods proves its capacity to recover causal protein-coding genes and highlight new associations. PWAS is available as a command-line tool.