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Bayesian model comparison for rare variant association studies

View ORCID ProfileGuhan Ram Venkataraman, View ORCID ProfileChristopher DeBoever, View ORCID ProfileYosuke Tanigawa, View ORCID ProfileMatthew Aguirre, View ORCID ProfileAlexander G. Ioannidis, View ORCID ProfileHakhamanesh Mostafavi, Chris C. A. Spencer, View ORCID ProfileTimothy Poterba, View ORCID ProfileCarlos D. Bustamante, View ORCID ProfileMark J. Daly, View ORCID ProfileMatti Pirinen, View ORCID ProfileManuel A. Rivas
doi: https://doi.org/10.1101/257162
Guhan Ram Venkataraman
1Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
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Christopher DeBoever
1Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
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  • For correspondence: cdeboeve@stanford.edu
Yosuke Tanigawa
1Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
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Matthew Aguirre
1Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
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Alexander G. Ioannidis
1Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
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Hakhamanesh Mostafavi
1Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
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Chris C. A. Spencer
2Genomics plc, Oxford, UK
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Timothy Poterba
3Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Carlos D. Bustamante
1Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
4Department of Genetics, Stanford University, CA, USA
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Mark J. Daly
3Broad Institute of MIT and Harvard, Cambridge, MA, USA
5Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
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Matti Pirinen
6Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
7Department of Public Health, University of Helsinki, Helsinki, Finland
8Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
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Manuel A. Rivas
1Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
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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. The approach requires only summary statistic data. To demonstrate the efficacy of MRP, we apply our method to exome sequencing data (N = 184,698) across 2,019 traits from the UK Biobank, aggregating signals in genes. MRP demonstrates an ability to recover previously-verified signals such as associations between PCSK9 and LDL cholesterol levels. We additionally find MRP effective in conducting meta-analyses in exome data. Notable non-biomarker findings include associations between MC1R and red hair color and skin color, IL17RA and monocyte count, IQGAP2 and mean platelet volume, and JAK2 and platelet count and crit (mass). Finally, we apply MRP in a multi-phenotype setting; after clustering the 35 biomarker phenotypes based on genetic correlation estimates into four clusters, we find that joint analysis of these phenotypes results in substantial power gains for gene-trait associations, such as in TNFRSF13B in one of the clusters containing diabetes and lipid-related traits. Overall, we show that the MRP model comparison approach is able to improve upon useful features from widely-used meta-analysis approaches for rare variant association analyses and prioritize protective modifiers of disease risk.

Competing Interest Statement

M.A.R. is on the SAB of 54Gene, Related Sciences and scientific founder of Broadwing Bio and has advised BioMarin, Third Rock Ventures and MazeTx. C.D.B. is the Owner and President of C.D.B. Consulting, LTD. and also a Director at EdenRoc Sciences, LLC and Etalon DX, founder of Arc Bio LLC (formerly IdentifyGenomics LLC and BigData Bio LLC), and an SAB member of Imprimed, FaunaBio, Columbia Care, and Digitalis Ventures. He is also a Venture Partner at F-Prime Capital Partners. M.J.D. is a founder of MazeTx.

Footnotes

  • ↵* matti.pirinen{at}helsinki.fi, mrivas{at}stanford.edu

  • This revision has been updated to include results from new exome data, a productionization of the python package, and a visualization tool in the Global Biobank Engine. We include an Acknowledgements and Author Contributions statement as well.

  • https://biobankengine.stanford.edu/RIVAS_HG38/mrpgene/all

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted July 24, 2021.
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Bayesian model comparison for rare variant association studies
Guhan Ram Venkataraman, Christopher DeBoever, Yosuke Tanigawa, Matthew Aguirre, Alexander G. Ioannidis, Hakhamanesh Mostafavi, Chris C. A. Spencer, Timothy Poterba, Carlos D. Bustamante, Mark J. Daly, Matti Pirinen, Manuel A. Rivas
bioRxiv 257162; doi: https://doi.org/10.1101/257162
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Bayesian model comparison for rare variant association studies
Guhan Ram Venkataraman, Christopher DeBoever, Yosuke Tanigawa, Matthew Aguirre, Alexander G. Ioannidis, Hakhamanesh Mostafavi, Chris C. A. Spencer, Timothy Poterba, Carlos D. Bustamante, Mark J. Daly, Matti Pirinen, Manuel A. Rivas
bioRxiv 257162; doi: https://doi.org/10.1101/257162

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