Assessing association between protein truncating variants and quantitative traits

Manuel A Rivas,Matthew J Neville,Mark I Mccarthy,Peter Donnelly,Loukas Moutsianas,Kyle J Gaulton,Matti Pirinen,Cecilia M Lindgren,Fredrik Karpe

doi:10.1093/bioinformatics/btt409

Abstract

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/mambaContact: donnelly@well.ox.ac.uk

Highlights

Advances in DNA sequencing and customized exome arrays are quickly transforming the landscape of genomic studies of diseases and related quantitative traits (Bonnefond et al, 2012; Jonsson et al, 2012; Momozawa et al, 2011; Nejentsev et al, 2009; Rivas et al, 2011)
We denote by Y1, . . . , YN the standardized quantitative trait values of the individuals and assume that the trait values Y1, . . . , Yn correspond to the carriers of Protein truncating variants (PTVs) and the values Ynþ1, . . . , YN correspond to the noncarriers of PTVs
Analogous issues arise in Genome-wide association studies (GWAS) for quantitative traits where individuals with extreme trait values can contribute to a strong signal at sequence and exomesingle nucleotide polymorphism (SNP) where they are called homozygous for a rare variant

Summary

Introduction

Advances in DNA sequencing and customized exome arrays are quickly transforming the landscape of genomic studies of diseases and related quantitative traits (Bonnefond et al, 2012; Jonsson et al, 2012; Momozawa et al, 2011; Nejentsev et al, 2009; Rivas et al, 2011). An alternative would be to combine variants across all genes within a pathway, but this adds several complications, not least because of the currently rather imprecise knowledge of biological pathways. The ideal approach would be to combine only those variants that affect the trait of interest. This is, difficult in practice because these will not be known in advance. One approximation would be to include only the variants with functional effects Even this is challenging with current limited knowledge of the function of coding variants in the human genome: commonly used predictors of the function of non-synonymous variants (Adzhubei et al, 2010; Kumar et al, 2009) can often be unreliable (Flanagan et al, 2010)

Methods

Results

Discussion

Conclusion