MOTIVATION: 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. RESULTS: 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). AVAILABILITY: Software is available from http://www.well.ox.ac.uk/~rivas/mamba

Original publication

DOI

10.1093/bioinformatics/btt409

Type

Journal article

Journal

Bioinformatics

Publication Date

01/10/2013

Volume

29

Pages

2419 - 2426

Keywords

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