Two-Stage Bayesian Approach for GWAS With Known Genealogy

e-Archivo Repository

Show simple item record

dc.contributor.author Armero, Carmen
dc.contributor.author Cabras, Stefano
dc.contributor.author Castellanos, María Eugenia
dc.contributor.author Quirós, Alicia
dc.date.accessioned 2021-11-24T17:32:45Z
dc.date.available 2021-11-24T17:32:45Z
dc.date.issued 2019-01-02
dc.identifier.bibliographicCitation Armero, C., Cabras, S., Castellanos, M. E., & Quirós, A. (2018). Two-Stage Bayesian Approach for GWAS With Known Genealogy. Journal of Computational and Graphical Statistics, 28 (1), pp. 197-204
dc.identifier.issn 1061-8600
dc.identifier.uri http://hdl.handle.net/10016/33675
dc.description.abstract Genome-wide association studies (GWAS) aim to assess relationships between single nucleotide polymorphisms (SNPs) and diseases. They are one of the most popular problems in genetics, and have some peculiarities given the large number of SNPs compared to the number of subjects in the study. Individuals might not be independent, especially in animal breeding studies or genetic diseases in isolated populations with highly inbred individuals. We propose a family-based GWAS model in a two-stage approach comprising a dimension reduction and a subsequent model selection. The first stage, in which the genetic relatedness between the subjects is taken into account, selects the promising SNPs. The second stage uses Bayes factors for comparison among all candidate models and a random search strategy for exploring the space of all the regression models in a fully Bayesian approach. A simulation study shows that our approach is superior to Bayesian lasso for model selection in this setting. We also illustrate its performance in a study on Beta-thalassemia disorder in an isolated population from Sardinia. Supplementary Material describing the implementation of the method proposed in this article is available online.
dc.description.sponsorship This article was partially funded byMTM2016-77501-P research grant and ECO2012-38442, RYC-2012-11455 projects from the Spanish Ministry of Economy and Competitiveness, and CRP-59903 from Regione Autonoma della Sardegna (Italy).
dc.language.iso eng
dc.publisher Taylor&Francis
dc.rights © Taylor&Francis
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Bayes factor
dc.subject.other Beta-thalassemia disorder
dc.subject.other Gaussian Markov random field
dc.subject.other Kinship coefficient
dc.subject.other Model selection
dc.subject.other Robust prior distribution
dc.subject.other Genome-wide association
dc.subject.other Variable-selection
dc.subject.other Beta-thalassemia
dc.subject.other Regression
dc.subject.other Regularization
dc.subject.other Population
dc.subject.other Phenotype
dc.subject.other Models
dc.title Two-Stage Bayesian Approach for GWAS With Known Genealogy
dc.type article
dc.subject.eciencia Estadística
dc.identifier.doi 10.1080/10618600.2018.1483828
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. ECO2012-38442
dc.relation.projectID Gobierno de España. RYC-2012-11455
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 197
dc.identifier.publicationissue 1
dc.identifier.publicationlastpage 204
dc.identifier.publicationtitle JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
dc.identifier.publicationvolume 28
dc.identifier.uxxi AR/0000023668
dc.contributor.funder Ministerio de Economía y Competitividad (España)
 Find Full text

Files in this item

*Click on file's image for preview. (Embargoed files's preview is not supported)


The following license files are associated with this item:

This item appears in the following Collection(s)

Show simple item record