Assessing association between protein truncating variants and quantitative traits

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/mamba

Fig. 1.

(a) Prior and sampling distribution for the SEM: (i) distribution of the trait values under the null model, ; (ii) distribution of the trait values under the alternative model, ; and (iii) 50:50 mixture of two normal distributions as prior for . (b) Prior and sampling distribution for the GEM: (i) distribution of the trait values under the null model; (ii) under the alternative model, trait values are grouped around and ; and (iii) priors for and

Fig. 2.

Plots of mean trait value for PTV carriers (X-axis) and BF from SEM () (Y-axis), for different values of n (written next to each point) the number of PTV carriers, for a fixed P-value of 0.001. The dashed line shows the density of the prior (restricted to positive values) on mean effect size under SEM (not on scale)

Fig. 3.

(a) Protein and transcript locations of APOC3 mutations, including predicted impact of splice variant on transcript splicing. Transcript diagram demonstrates that variant c.IVS2 + 1G >A will disrupt proper splicing of the second exon and create a new spliced mRNA with exon 1 and exon 3 joining because of proper recognition of splice sequence in the donor site of exon 1 and acceptor site of exon 3. Genomic codon position is shown for the stop-gain mutation, i.e. g.55C >T. (b) Prior, likelihood and posterior of mean trait value after combining data from the Oxford Biobank study and GoT2D study. The shaded histogram in each panel represents the distribution of trait values for the relevant PTV carriers