University of Oulu

Kaakinen et al. MARV: a tool for genome-wide multi- phenotype analysis of rare variants BMC Bioinformatics (2017) 18:110 DOI 10.1186/s12859-017-1530-2

MARV : a tool for genome-wide multi-phenotype analysis of rare variants

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Author: Kaakinen, Marika1; Mägi, Reedik2; Fischer, Krista2;
Organizations: 1Department of Genomics of Common Disease, Imperial College London
2Estonian Genome Center, University of Tartu
3Neuroepidemiology and Ageing (NEA) Research Unit, Imperial College London
4Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London
5Center for Life Course Health Research, University of Oulu
6Unit of Primary Care, Oulu University Hospital
7Biocenter Oulu, University of Oulu
8Department of Biostatistics, University of Liverpool
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe201704276269
Language: English
Published: Springer Nature, 2017
Publish Date: 2017-04-27
Description:

Abstract

Background: Genome-wide association studies have enabled identification of thousands of loci for hundreds of traits. Yet, for most human traits a substantial part of the estimated heritability is unexplained. This and recent advances in technology to produce high-dimensional data cost-effectively have led to method development beyond standard common variant analysis, including single-phenotype rare variant and multi-phenotype common variant analysis, with the latter increasing power for locus discovery and providing suggestions of pleiotropic effects. However, there are currently no optimal methods and tools for the combined analysis of rare variants and multiple phenotypes.

Results: We propose a user-friendly software tool MARV for Multi-phenotype Analysis of Rare Variants. The tool is based on a method that collapses rare variants within a genomic region and models the proportion of minor alleles in the rare variants on a linear combination of multiple phenotypes. MARV provides analyses of all phenotype combinations within one run and calculates the Bayesian Information Criterion to facilitate model selection. The running time increases with the size of the genetic data while the number of phenotypes to analyse has little effect both on running time and required memory. We illustrate the use of MARV with analysis of triglycerides (TG), fasting insulin (FI) and waist-to-hip ratio (WHR) in 4,721 individuals from the Northern Finland Birth Cohort 1966. The analysis suggests novel multi-phenotype effects for these metabolic traits at APOA5 and ZNF259, and at ZNF259 provides stronger support for association (PTG+FI = 1.8 × 10⁻⁹) than observed in single phenotype rare variant analyses (PTG = 6.5 × 10⁻⁸ and PFI = 0.27).

Conclusions: MARV is a computationally efficient, flexible and user-friendly software tool allowing rapid identification of rare variant effects on multiple phenotypes, thus paving the way for novel discoveries and insights into biology of complex traits.

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Series: BMC bioinformatics
ISSN: 1471-2105
ISSN-E: 1471-2105
ISSN-L: 1471-2105
Volume: 18
Issue: 110
Pages: 1 - 8
DOI: 10.1186/s12859-017-1530-2
OADOI: https://oadoi.org/10.1186/s12859-017-1530-2
Type of Publication: A1 Journal article – refereed
Field of Science: 3142 Public health care science, environmental and occupational health
1184 Genetics, developmental biology, physiology
Subjects:
Funding: This work used the computing resources of the UK MEDical BIOinformatics partnership - aggregation, integration, visualisation and analysis of large, complex data (UK MED-BIO) which is supported by the Medical Research Council [grant number MR/L01632X/1]; the Imperial College High Performance Computing Service, URL: http://www.imperial.ac.uk/admin-services/ict/self-service/research-support/hpc/. MK is funded by the European Commission under the Marie Curie Intra-European Fellowship (project MARVEL (WPGA-P48951)). APM is a Wellcome Trust Senior Fellow in Basic Biomedical Science (WT098017). IP was in part funded by the Elsie Widdowson Fellowship. NFBC1966 received financial support from University of Oulu Grant no. 65354, Oulu University Hospital Grant no. 2/97, 8/97, Ministry of Health and Social Affairs Grant no. 23/251/97, 160/97, 190/97, National Institute for Health and Welfare, Helsinki Grant no. 54121, Regional Institute of Occupational Health, Oulu, Finland Grant no. 50621, 54231.
Dataset Reference: The MARV software tool is freely available at URL: https://github.com/ImperialStatGen/MARV. The Northern Finland Birth Cohort data which were used for the application of the developed tool are available upon collaboration and formal data request only, please see http://www.oulu.fi/nfbc/node/18136. The results from the case study are available as Additional files 1, 2, 3 and 4.
Copyright information: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.