PT  - JOURNAL ARTICLE
AU  - Sanni E. Ruotsalainen
AU  - Juulia J. Partanen
AU  - Anna Cichonska
AU  - Jake Lin
AU  - Christian Benner
AU  - Ida Surakka
AU  - FinnGen
AU  - Mary Pat Reeve
AU  - Priit Palta
AU  - Marko Salmi
AU  - Sirpa Jalkanen
AU  - Ari Ahola-Olli
AU  - Aarno Palotie
AU  - Veikko Salomaa
AU  - Mark J. Daly
AU  - Matti Pirinen
AU  - Samuli Ripatti
AU  - Jukka Koskela
TI  - An expanded analysis framework for multivariate GWAS connects inflammatory biomarkers to functional variants and disease
AID  - 10.1101/867267
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 867267
4099  - http://biorxiv.org/content/early/2019/12/06/867267.short
4100  - http://biorxiv.org/content/early/2019/12/06/867267.full
AB  - Multivariate methods are known to increase the statistical power of association detection, but they have lacked essential follow-up analysis tools necessary for understanding 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 putative causal 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 putative causal 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.