bioRxiv preprint doi: https://doi.org/10.1101/257162; this version posted July 23, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 Bayesian model comparison for rare variant association studies Guhan Ram Venkataraman1, Christopher DeBoever1, Yosuke Tanigawa1, Matthew Aguirre1, Alexander G. Ioannidis1, Hakhamanesh Mostafavi1, Chris C. A. Spencer2, Timothy Poterba3, Carlos D. Bustamante1,4, Mark J. Daly3,5, Matti Pirinen6,7,8*, Manuel A. Rivas1* 1 Department of Biomedical Data Science, Stanford University, Stanford, CA, USA 2 Genomics plc, Oxford, UK 3 Broad Institute of MIT and Harvard, Cambridge, MA, USA 4 Department of Genetics, Stanford University, CA, USA 5 Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA 6 Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland 7 Department of Public Health, University of Helsinki, Helsinki, Finland 8 Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland *
[email protected] *
[email protected] Abstract 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 Bayesian model comparison approach that we refer to as MRP (Multiple Rare-variants and Phenotypes) for rare-variant association studies that considers correlation, scale, and direction of genetic effects across a group of genetic variants, phenotypes, and studies.