In sequencing studies of common diseases and quantitative traits, power to test rare and low frequency variants individually is weak. To improve power, a common approach is to combine statistical evidence from several genetic variants in a region. Major challenges are how to do the combining and which statistical framework to use. General approaches for testing association between rare variants and quantitative traits include aggregating genotypes and trait values, referred to as 'collapsing', or using a score-based variance component test. However, little attention has been paid to alternative models tailored for protein truncating variants. Recent studies have highlighted the important role that protein truncating variants, commonly referred to as 'loss of function' variants, may have on disease susceptibility and quantitative levels of biomarkers. We propose a Bayesian modelling framework for the analysis of protein truncating variants and quantitative traits.Our simulation results show that our models have an advantage over the commonly used methods. We apply our models to sequence and exome-array data and discover strong evidence of association between low plasma triglyceride levels and protein truncating variants at APOC3 (Apolipoprotein C3).Software is available from http://www.well.ox.ac.uk/~rivas/mamba

Original publication

DOI

10.1093/bioinformatics/btt409

Type

Journal article

Journal

Bioinformatics (Oxford, England)

Publication Date

10/2013

Volume

29

Pages

2419 - 2426

Addresses

Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK, Institute for Molecular Medicine Finland, University of Helsinki, Helsinki 00290, Finland, Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, Oxford OX3 7LJ, UK, NIHR Oxford Biomedical Research Centre, OUH Trust, Oxford OX3 7LE, UK and Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.

Keywords

GoT2D Consortium, Humans, Diabetes Mellitus, Type 2, Triglycerides, Bayes Theorem, Genotype, Phenotype, Mutation, Quantitative Trait Loci, Genome, Human, Models, Genetic, Internet, Software Design, Apolipoprotein C-III, Exome