{"data":{"id":"10.6084/m9.figshare.c.2081276","type":"dois","attributes":{"doi":"10.6084/m9.figshare.c.2081276","prefix":"10.6084","suffix":"m9.figshare.c.2081276","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Jiang, Bo","givenName":"Bo","familyName":"Jiang","affiliation":[],"nameIdentifiers":[]},{"name":"S. Liu, Jun","givenName":"Jun","familyName":"S. Liu","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Bayesian Partition Models for Identifying Expression Quantitative Trait Loci"}],"publisher":"Taylor \u0026 Francis","container":{},"publicationYear":2015,"subjects":[{"subject":"Genetics"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Biological Sciences not elsewhere classified"},{"subject":"Mathematical Sciences not elsewhere classified"},{"subject":"Infectious Diseases"},{"subject":"FOS: Health sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2015-12-05","dateType":"Created"},{"date":"2019-08-19","dateType":"Updated"},{"date":"2015","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":"Expression quantitative trait loci (eQTLs) are genomic locations associated with changes of expression levels of certain genes. By assaying gene expressions and genetic variations simultaneously on a genome-wide scale, scientists wish to discover genomic loci responsible for expression variations of a set of genes. The task can be viewed as a multivariate regression problem with variable selection on both responses (gene expression) and covariates (genetic variations), including also multi-way interactions among covariates. Instead of learning a predictive model of quantitative trait given combinations of genetic markers, we adopt an inverse modeling perspective to model the distribution of genetic markers conditional on gene expression traits. A particular strength of our method is its ability to detect interactive effects of genetic variations with high power even when their marginal effects are weak, addressing a key weakness of many existing eQTL mapping methods. Furthermore, we introduce a hierarchical model to capture the dependence structure among correlated genes. Through simulation studies and a real data example in yeast, we demonstrate how our Bayesian hierarchical partition model achieves a significantly improved power in detecting eQTLs compared to existing methods. Supplementary materials for this article are available online.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://tandf.figshare.com/collections/Bayesian_Partition_Models_for_Identifying_Expression_Quantitative_Trait_Loci/2081276","contentUrl":null,"metadataVersion":3,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"viewsOverTime":[],"downloadCount":0,"downloadsOverTime":[],"referenceCount":0,"citationCount":0,"citationsOverTime":[],"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2015-12-05T17:55:46.000Z","registered":"2015-12-05T17:55:47.000Z","published":"2015","updated":"2024-03-21T08:45:35.000Z"},"relationships":{"client":{"data":{"id":"figshare.ars","type":"clients"}},"provider":{"data":{"id":"otjm","type":"providers"}},"media":{"data":{"id":"10.6084/m9.figshare.c.2081276","type":"media"}},"references":{"data":[]},"citations":{"data":[]},"parts":{"data":[]},"partOf":{"data":[]},"versions":{"data":[]},"versionOf":{"data":[]}}}}