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metaCCA: Summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

Anna Cichonska, Juho Rousu, Pekka Marttinen, Antti J Kangas, Pasi Soininen, Terho Lehtimäki, Olli T Raitakari, Marjo-Riitta Järvelin, Veikko Salomaa, Mika Ala-Korpela, Samuli Ripatti, Matti Pirinen
doi: https://doi.org/10.1101/022665
Anna Cichonska
1Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
2Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland
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Juho Rousu
2Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland
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Pekka Marttinen
2Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland
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Antti J Kangas
3Computational Medicine, Institute of Health Sciences, University of Oulu and Oulu University Hospital, Oulu, Finland
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Pasi Soininen
3Computational Medicine, Institute of Health Sciences, University of Oulu and Oulu University Hospital, Oulu, Finland
4NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
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Terho Lehtimäki
5Department of Clinical Chemistry, Fimlab Laboratories, University of Tampere School of Medicine, Tampere, Finland
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Olli T Raitakari
6Department of Clinical Physiology and Nuclear Medicine, University of Turku and Turku University Hospital, Turku, Finland
7Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku and Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
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Marjo-Riitta Järvelin
8Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment & Health, School of Public Health, Imperial College London, London, United Kingdom
9Centre for Life Course Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
10Biocenter Oulu, University of Oulu, Oulu, Finland
11Unit of Primary Care, Oulu University Hospital, Oulu, Finland
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Veikko Salomaa
12National Institute for Health and Welfare, Helsinki, Finland
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Mika Ala-Korpela
3Computational Medicine, Institute of Health Sciences, University of Oulu and Oulu University Hospital, Oulu, Finland
4NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
13Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
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Samuli Ripatti
1Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
14Public Health, University of Helsinki, Helsinki, Finland
15Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom
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Matti Pirinen
1Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
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Abstract

A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analysing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests.

We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.

Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies.

Code is available at https://github.com/aalto-ics-kepaco.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted July 16, 2015.
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metaCCA: Summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis
Anna Cichonska, Juho Rousu, Pekka Marttinen, Antti J Kangas, Pasi Soininen, Terho Lehtimäki, Olli T Raitakari, Marjo-Riitta Järvelin, Veikko Salomaa, Mika Ala-Korpela, Samuli Ripatti, Matti Pirinen
bioRxiv 022665; doi: https://doi.org/10.1101/022665
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metaCCA: Summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis
Anna Cichonska, Juho Rousu, Pekka Marttinen, Antti J Kangas, Pasi Soininen, Terho Lehtimäki, Olli T Raitakari, Marjo-Riitta Järvelin, Veikko Salomaa, Mika Ala-Korpela, Samuli Ripatti, Matti Pirinen
bioRxiv 022665; doi: https://doi.org/10.1101/022665

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