title: MARV: a tool for genome-wide multi-phenotype analysis of rare variants.
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 × 10-8and 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.
authors.affiliation: Department of Genomics of Common Disease, Imperial College London, London, W12 0NN, UK. m.kaakinen@imperial.ac.uk.
authors.forename: Marika
authors.identifier: http://orcid.org/0000-0002-9228-0462
authors.initials: M
authors.lastname: Kaakinen
authors.affiliation: Estonian Genome Center, University of Tartu, Tartu, 51010, Estonia.
authors.forename: Reedik
authors.initials: R
authors.lastname: Mägi
authors.affiliation: Estonian Genome Center, University of Tartu, Tartu, 51010, Estonia.
authors.forename: Krista
authors.initials: K
authors.lastname: Fischer
authors.affiliation: Department of Genomics of Common Disease, Imperial College London, London, W12 0NN, UK.|Neuroepidemiology and Ageing (NEA) Research Unit, Imperial College London, London, W6 8RP, UK.
authors.forename: Jani
authors.initials: J
authors.lastname: Heikkinen
authors.affiliation: Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, W2 1PG, UK.|Center for Life Course Health Research, University of Oulu, 90014, Oulu, Finland.|Unit of Primary Care, Oulu University Hospital, 90220, Oulu, Finland.|Biocenter Oulu, University of Oulu, 90014, Oulu, Finland.
authors.forename: Marjo-Riitta
authors.initials: MR
authors.lastname: Järvelin
authors.affiliation: Department of Biostatistics, University of Liverpool, Liverpool, L69 3BX, UK.
authors.forename: Andrew P
authors.initials: AP
authors.lastname: Morris
authors.affiliation: Department of Genomics of Common Disease, Imperial College London, London, W12 0NN, UK. i.prokopenko@imperial.ac.uk.
authors.forename: Inga
authors.initials: I
authors.lastname: Prokopenko
mesh_terms: D019295:Computational Biology / Q000379:methods*; D014644:Genetic Variation / Q000235:genetics*; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006801:Humans; D010641:Phenotype; D012984:Software
keywords: High-dimensional data; Multi-phenotype analysis; Rare variant analysis