Malakhov Mykhaylo M, Pan Wei
Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
medRxiv. 2024 Dec 13:2024.10.02.24314813. doi: 10.1101/2024.10.02.24314813.
Transcriptome- and proteome-wide association studies (TWAS/PWAS) have proven successful in prioritizing genes and proteins whose genetically regulated expression modulates disease risk, but they ignore potential co-expression and interaction effects. To address this limitation, we introduce the co-expression-wide association study (COWAS) method, which can identify pairs of genes or proteins whose genetically regulated co-expression is associated with complex traits. COWAS first trains models to predict expression and co-expression conditional on genetic variation, and then tests for association between imputed co-expression and the trait of interest while also accounting for direct effects from each exposure. We applied our method to plasma proteomic concentrations from the UK Biobank, identifying dozens of interacting protein pairs associated with cholesterol levels, Alzheimer's disease, and Parkinson's disease. Notably, our results demonstrate that co-expression between proteins may affect complex traits even if neither protein is detected to influence the trait when considered on its own. We also show how COWAS can help disentangle direct and interaction effects, providing a richer picture of the molecular networks that mediate genetic effects on disease outcomes.
全转录组和蛋白质组关联研究（TWAS/PWAS）已成功地对基因和蛋白质进行了优先级排序，这些基因和蛋白质的基因调控表达会调节疾病风险，但它们忽略了潜在的共表达和相互作用效应。为了解决这一局限性，我们引入了共表达全关联研究（COWAS）方法，该方法可以识别基因或蛋白质对，其基因调控的共表达与复杂性状相关。COWAS首先训练模型以根据遗传变异预测表达和共表达，然后测试估算的共表达与感兴趣性状之间的关联，同时还考虑每种暴露的直接效应。我们将我们的方法应用于英国生物银行的血浆蛋白质组浓度，识别出数十对与胆固醇水平、阿尔茨海默病和帕金森病相关的相互作用蛋白质对。值得注意的是，我们的结果表明，即使单独考虑时没有检测到任何一种蛋白质会影响该性状，蛋白质之间的共表达也可能影响复杂性状。我们还展示了COWAS如何有助于区分直接效应和相互作用效应，从而更全面地描绘介导基因对疾病结局影响的分子网络。
