Bayesian model comparison for rare variant association studies of multiple phenotypes
Chris C. A. Spencer,
Carlos D. Bustamante,
Mark J Daly,
Manuel A. Rivas
Posted 31 Jan 2018
bioRxiv DOI: 10.1101/257162
Posted 31 Jan 2018
Whole genome sequencing studies applied to large populations or biobanks with extensive phenotyping raise new analytic challenges. The need to consider many variants at a locus or group of genes simultaneously and the potential to study many correlated phenotypes with shared genetic architecture provide opportunities for discovery and inference that are not addressed by the traditional one variant-one phenotype association study. Here we introduce a model comparison approach we refer to as MRP for rare variant association studies that considers correlation, scale, and location of genetic effects across a group of genetic variants, phenotypes, and studies. We consider the use of summary statistic data to apply univariate and multivariate gene-based meta-analysis models for identifying rare variant associations with an emphasis on protective protein-truncating variants that can expedite drug discovery. Through simulation studies, we demonstrate that the proposed model comparison approach can improve ability to detect rare variant association signals. We also apply the model to two groups of phenotypes from the UK Biobank: 1) asthma diagnosis (43,626 cases), eosinophil counts, forced expiratory volume, and forced vital capacity; and 2) glaucoma diagnosis (5,863 cases), intra-ocular pressure, and corneal resistance factor. We are able to recover known associations such as the protective association between rs146597587 in IL33 and asthma (log10 Bayes Factor = 29.4). We also find evidence for novel protective associations between rare variants in ANGPTL7 and glaucoma (log10 Bayes Factor = 13.1). Overall, we show that the MRP model comparison approach is able to retain and improve upon useful features from widely-used meta-analysis approaches for rare variant association analyses and prioritize protective modifiers of disease risk.
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