1. An expanded analysis framework for multivariate GWAS connects inflammatory biomarkers to functional variants and disease
- Author
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Priit Palta, Mark J. Daly, Ida Surakka, Veikko Salomaa, Anna Cichonska, Ari Ahola-Olli, Sanni Ruotsalainen, Mary Pat Reeve, Marko Salmi, Juulia Partanen, Samuli Ripatti, Jukka Koskela, Jake Lin, Aarno Palotie, Sirpa Jalkanen, Matti Pirinen, Christian Benner, Samuli Olli Ripatti / Principal Investigator, Complex Disease Genetics, Institute for Molecular Medicine Finland, Statistical and population genetics, Genomics of Neurological and Neuropsychiatric Disorders, Centre of Excellence in Complex Disease Genetics, Aarno Palotie / Principal Investigator, Research Programs Unit, Helsinki Institute of Life Science HiLIFE, Department of Mathematics and Statistics, Helsinki Institute for Information Technology, Biostatistics Helsinki, Department of Public Health, University Management, Doctoral Programme in Social Sciences, and HUS Helsinki and Uusimaa Hospital District
- Subjects
EXPRESSION ,Male ,Multivariate statistics ,GROWTH-FACTOR ,Multivariate analysis ,Genomics ,Genome-wide association study ,Computational biology ,Disease ,Biology ,ANGIOGENESIS ,Article ,03 medical and health sciences ,0302 clinical medicine ,Linear regression ,Serpin E2 ,Genetics ,Humans ,RECURRENT ,GENOME-WIDE ASSOCIATION ,Genetics (clinical) ,030304 developmental biology ,Aged ,0303 health sciences ,IMPROVED IMPUTATION ACCURACY ,Univariate ,1184 Genetics, developmental biology, physiology ,Genetic Variation ,PDGF ,Middle Aged ,HYPERTROPHIC SCARS ,Phenotype ,Canonical Correlation Analysis ,MENINGIOMAS ,Cytokines ,Female ,Canonical correlation ,PHASE-II TRIAL ,030217 neurology & neurosurgery ,Biomarkers ,Genome-Wide Association Study - 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
- Published
- 2019