Bayesian GWAS with Structured and Non-Local Priors
Abstract
The flexibility of a Bayesian framework is promising for GWAS, but current approaches can benefit from more informative prior models. We introduce a novel Bayesian approach to GWAS, called Structured and Non-Local Priors (SNLPs) GWAS, that improves over existing methods in two important ways. First, we describe a model that allows for a marker’s gene-parent membership and other characteristics to influence its probability of association with an outcome. Second, we describe a non-local alternative model for differential minor allele rates at each marker, in which the null and alternative hypotheses have no common support.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 22, 2019
- Source ID
- 10.1093/bioinformatics/btz518
Entities
People
- Adam Kaplan
- Eric F Lock
- For The Alzheimer’s Disease Neuroimaging Initiative*
- Mark Fiecas
Organizations
- Alzheimer's Drug Discovery Foundation
- BioClinica
- Bristol-Myers Squibb
- Canadian Institutes of Health Research
- Chiron Corporation
- Foundation for the National Institutes of Health
- Hoffmann-La Roche
- Johnson & Johnson Pharmaceutical Research and Development
- Merck & Co.
- Meso Scale Diagnostics (United States)
- National Institute of Biomedical Imaging and Bioengineering
- National Institute on Aging
- National Institutes of Health
- Northern California Institute for Research and Education
- Takeda Pharmaceutical Company
- United States Department of Defense
- University of Minnesota