An expanded analysis framework for multivariate GWAS connects inflammatory biomarkers to functional variants and disease

Abstract

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.

Fig. 1. Study workflow.

The novel LCP-GWAS method that enables follow-up analyses such as fine-mapping for multivariate GWAS is illustrated in the violet panel on the right.

Fig. 2. Power comparison between multivariate and univariate methods.

Red and blue dots represent genetic variants reaching genome-wide significance only by the multivariate (metaCCA) or univariate method, respectively. Black dots reach the genome-wide significance threshold by both methods and gray dots do not by either method. Respective numbers are reported in the accompanying table.

Fig. 3. Manhattan plot of the multivariate GWAS results on 12 inflammatory biomarkers.

Gene names colored in orange represent associations only detected by the multivariate method while black are detected by both multivariate and univariate methods. 13 genome-wide significant nonsynonymous and splice-region variants are denoted with diamonds.