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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\u2019s 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We employ a non-parametric model that allows for clustering of the genes in tandem with a regression model for marker-level covariates, and demonstrate how incorporating these additional characteristics can improve power. We further demonstrate that our non-local alternative model gives symmetric rates of convergence for the null and alternative hypotheses, whereas commonly used local alternative models have asymptotic rates that favor the alternative hypothesis over the null. We demonstrate the robustness and flexibility of our structured and non-local model for different data generating scenarios and signal-to-noise ratios. We apply our Bayesian GWAS method to single nucleotide polymorphisms data collected from a pool of Alzheimer\u2019s disease and cognitively normal patients from the Alzheimer\u2019s Database Neuroimaging Initiative.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>R code to perform the SNLPs method is available at https:\/\/github.com\/lockEF\/BayesianScreening.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz518","type":"journal-article","created":{"date-parts":[[2019,6,18]],"date-time":"2019-06-18T11:10:36Z","timestamp":1560856236000},"page":"17-25","source":"Crossref","is-referenced-by-count":2,"title":["Bayesian GWAS with Structured and Non-Local Priors"],"prefix":"10.1093","volume":"36","author":[{"given":"Adam","family":"Kaplan","sequence":"first","affiliation":[{"name":"Division of Biostatistics, School of Public Health, University of Minnesota , Minneapolis, MN 55455, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4663-2356","authenticated-orcid":false,"given":"Eric F","family":"Lock","sequence":"additional","affiliation":[{"name":"Division of Biostatistics, School of Public Health, University of Minnesota , Minneapolis, MN 55455, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Fiecas","sequence":"additional","affiliation":[{"name":"Division of Biostatistics, School of Public Health, University of Minnesota , Minneapolis, MN 55455, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"for the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2019,6,22]]},"reference":[{"key":"2023020108351617900_btz518-B1","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1080\/01621459.1993.10476321","article-title":"Bayesian analysis of binary and polychotomous response data","volume":"88","author":"Albert","year":"1993","journal-title":"J. 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