Format

Send to

Choose Destination
Bioinformatics. 2016 Jul 1;32(13):1981-9. doi: 10.1093/bioinformatics/btw052. Epub 2016 Feb 19.

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

Author information

1
Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland, Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.
2
Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.
3
Computational Medicine, University of Oulu, Oulu University Hospital and Biocenter Oulu, Oulu, Finland.
4
Computational Medicine, University of Oulu, Oulu University Hospital and Biocenter Oulu, Oulu, Finland, NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland.
5
Department of Clinical Chemistry, Fimlab Laboratories, University of Tampere School of Medicine, Tampere, Finland.
6
Department of Clinical Physiology and Nuclear Medicine, University of Turku and Turku University Hospital, Turku, Finland, Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku and Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland.
7
Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment & Health, School of Public Health, Imperial College London, London, UK, Centre for Life Course Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland, Biocenter Oulu, University of Oulu, Oulu, Finland, Unit of Primary Care, Oulu University Hospital, Oulu, Finland.
8
National Institute for Health and Welfare, Helsinki, Finland.
9
Computational Medicine, University of Oulu, Oulu University Hospital and Biocenter Oulu, Oulu, Finland, NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland, Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
10
Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland, Public Health, University of Helsinki, Helsinki, Finland and Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK.
11
Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland.

Abstract

MOTIVATION:

A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing 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.

RESULTS:

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.

AVAILABILITY AND IMPLEMENTATION:

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

CONTACTS:

anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
27153689
PMCID:
PMC4920109
DOI:
10.1093/bioinformatics/btw052
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Silverchair Information Systems Icon for PubMed Central
Loading ...
Support Center