A Variational Bayes Discrete Mixture Test for Rare Variant Association |
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| Authors: | Benjamin A. Logsdon James Y. Dai Paul L. Auer Jill M. Johnsen Santhi K. Ganesh Nicholas L. Smith James G. Wilson Russell P. Tracy Leslie A. Lange Shuo Jiao Stephen S. Rich Guillaume Lettre Christopher S. Carlson Rebecca D. Jackson Christopher J. O'Donnell Mark M. Wurfel Deborah A. Nickerson Hua Tang Alexander P. Reiner Charles Kooperberg |
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| Affiliation: | 1. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America;2. Department of Genome Science, University of Washington, Seattle, Washington, United States of America;3. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America;4. School of Public Health, University of Wisconsin‐Milwaukee, Milwaukee, Wisconsin, United States of America;5. Research Institute, Puget Sound Blood Center, Seattle, Washington, United States of America;6. Department of Medicine, University of Washington, Seattle, Washington, United States of America;7. Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America;8. Department of Epidemiology, University of Washington, Seattle, Washington, United States of America;9. Group Health Research Institute, Group Health Cooperative, Seattle, Washington, United States of America;10. Seattle Epidemiologic Research and Information Center, VA Office of Research and Development, Seattle, Washington, United States of America;11. Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi, United States of America;12. Department of Pathology and Biochemistry, University of Vermont College of Medicine, Burlington, Vermont, United States of America;13. Departments of Epidemiology, Genetics and Biostatistics, Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, North Carolina, United States of America;14. Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America;15. Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America;16. Montreal Heart Institute and Départment de Médecine, Université de Montréal, Montréal, Quebec, Canada;17. Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, Ohio, United States of America;18. National Heart, Lung, and Blood Institute Center for Population Studies, The Framingham Heart Study, Framingham, Massachusetts, United States of America;19. Division of Pulmonary and Critical Care Medicine, University of Washington, Seattle, Washington, United States of America;20. Department of Statistics and Department of Genetics, Stanford University, Stanford, California, United States of America;21. Full authorship banner included in the Supporting Information |
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| Abstract: | ![]()
Recently, many statistical methods have been proposed to test for associations between rare genetic variants and complex traits. Most of these methods test for association by aggregating genetic variations within a predefined region, such as a gene. Although there is evidence that “aggregate” tests are more powerful than the single marker test, these tests generally ignore neutral variants and therefore are unable to identify specific variants driving the association with phenotype. We propose a novel aggregate rare‐variant test that explicitly models a fraction of variants as neutral, tests associations at the gene‐level, and infers the rare‐variants driving the association. Simulations show that in the practical scenario where there are many variants within a given region of the genome with only a fraction causal our approach has greater power compared to other popular tests such as the Sequence Kernel Association Test (SKAT), the Weighted Sum Statistic (WSS), and the collapsing method of Morris and Zeggini (MZ). Our algorithm leverages a fast variational Bayes approximate inference methodology to scale to exome‐wide analyses, a significant computational advantage over exact inference model selection methodologies. To demonstrate the efficacy of our methodology we test for associations between von Willebrand Factor (VWF) levels and VWF missense rare‐variants imputed from the National Heart, Lung, and Blood Institute's Exome Sequencing project into 2,487 African Americans within the VWF gene. Our method suggests that a relatively small fraction (～10%) of the imputed rare missense variants within VWF are strongly associated with lower VWF levels in African Americans. |
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| Keywords: | Exome sequencing study approximate inference von Willebrand Factor genetics |
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