Multivariate methods are known to increase the statistical power to detect associations in the case of shared genetic basis between phenotypes. They have, however, lacked essential analytic tools to follow-up and understand the biology underlying these associations. We developed a novel computational workflow for multivariate GWAS follow-up analyses, including fine-mapping and identification of the subset of traits driving associations (driver traits). Many follow-up tools require univariate regression coefficients which are lacking from multivariate results. Our method overcomes this problem by using Canonical Correlation Analysis to turn each multivariate association into its optimal univariate Linear Combination Phenotype (LCP). This enables an LCP-GWAS, which in turn generates the statistics required for follow-up analyses. We implemented our method on 12 highly correlated inflammatory biomarkers in a Finnish population-based study. Altogether, we identified 11 associations, four of which (F5, ABO, C1orf140 and PDGFRB) were not detected by biomarker-specific analyses. Fine-mapping identified 19 signals within the 11 loci and driver trait analysis determined the traits contributing to the associations. A phenome-wide association study on the 19 representative variants from the signals in 176,899 individuals from the FinnGen study revealed 53 disease associations (p < 1 × 10^–4). Several reported pQTLs in the 11 loci provided orthogonal evidence for the biologically relevant functions of the representative variants. Our novel multivariate analysis workflow provides a powerful addition to standard univariate GWAS analyses by enabling multivariate GWAS follow-up and thus promoting the advancement of powerful multivariate methods in genomics.

在表型之间共享遗传基础的情况下，已知多变量方法可以增加检测关联的统计能力。然而，他们缺乏必要的分析工具来跟进和了解这些关联背后的生物学。我们为多变量 GWAS 后续分析开发了一种新的计算工作流程，包括精细映射和识别驱动关联的特征子集（驱动程序特征）。许多后续工具需要单变量回归系数，而多变量结果缺乏这些回归系数。我们的方法通过使用典型相关分析将每个多变量关联转化为其最佳单变量线性组合表型 (LCP) 来克服这个问题。这启用了 LCP-GWAS，进而生成后续分析所需的统计数据。我们在芬兰基于人群的研究中对 12 个高度相关的炎症生物标志物实施了我们的方法。总共，我们确定了 11 个关联，其中 4 个 (生物标志物特异性分析未检测到F5、ABO、C1orf140和PDGFRB 。精细映射确定了 11 个基因座内的 19 个信号，驱动特征分析确定了促成关联的特征。对来自 FinnGen 研究的 176,899 个人的信号中的 19 个代表性变体进行的全表型关联研究揭示了 53 种疾病关联（p  < 1 × 10 ^–4）。11 个基因座中的几个报告的 pQTL 为代表性变体的生物学相关功能提供了正交证据。我们新颖的多变量分析工作流程通过启用多变量 GWAS 跟进，为标准单变量 GWAS 分析提供了强大的补充，从而促进了基因组学中强大的多变量方法的进步。