Year: 2013
Type: article
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
Source: Bioinformatics
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $2,304
Awards: WTCCC2 core activities., Statistical methods development and analysis of genomic data in health and disease., Next-Generation Disease Association Analyses: Low pass Sequencing and High Density SNP genotyping for type 2 diabetes ., WTCCC2 core activities, Characterising causal alleles for common disease., G3244737412, An investigation of genes in key beta-cell pathways following the type 2 diabetes WTCCC genome wide association study., G328833407, Identification and functional analysis of susceptibility genes in multi- factorial diseases, G3673804865, Genetics and genomics of central obesity., Statistical methods development and analysis of genomic data in health and disease., G5594960057, Low-Pass Sequencing and High-Density SNP Genotyping for Type 2 Diabetes, G6728599356, Understanding the genetic basis of common human diseases: core funding for the Wellcome Trust Centre for Human Genetics., WTCCC2 core activities., The Impact of Human Gene Knockouts in Type 2 Diabetes and Related Traits, G7650667963, Understanding the genetic basis of common human diseases: core funding for the Wellcome Trust Centre for Human Genetics., G8975541179, The genetics of weight, BMI, adiposity and obesity
OpenAlex ID: W2110090977