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Paterson","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Parameter Expanded Algorithms for Bayesian Latent Variable Modeling of Genetic Pleiotropy Data"}],"publisher":"Taylor \u0026 Francis","container":{},"publicationYear":2016,"subjects":[{"subject":"Genetics"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Biotechnology"},{"subject":"Environmental Sciences not elsewhere classified"},{"subject":"Chemical Sciences not elsewhere classified"},{"subject":"Immunology"},{"subject":"FOS: Clinical medicine","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Biological Sciences not elsewhere classified"},{"subject":"Information Systems not elsewhere classified"},{"subject":"Mathematical Sciences not elsewhere classified"}],"contributors":[],"dates":[{"date":"2016-05-10","dateType":"Created"},{"date":"2019-08-19","dateType":"Updated"},{"date":"2016","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"Collection","resourceType":"Collection","resourceTypeGeneral":"Collection"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Motivated by genetic association studies of pleiotropy, we propose a Bayesian latent variable approach to jointly study multiple outcomes. 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