Meta-Analysis of Quantitative Trait Association and Mapping Studies using Parametric and Non-Parametric Models

dc.contributor.author Wu, Xiao-Lin
dc.contributor.author Reecy, James
dc.contributor.author Gianola, Daniel
dc.contributor.author Hu, Zhi-Liang
dc.contributor.author Reecy, James
dc.contributor.department Animal Science
dc.date 2018-02-17T04:07:50.000
dc.date.accessioned 2020-06-29T23:38:28Z
dc.date.available 2020-06-29T23:38:28Z
dc.date.copyright Sat Jan 01 00:00:00 UTC 2011
dc.date.issued 2011-10-01
dc.description.abstract <p>Meta-analysis is an important method for integration of information from multiple studies. In quantitative trait association and mapping experiments, combining results from several studies allows greater statistical power for detection of causal loci and more precise estimation of their effects, and thus can yield stronger conclusions than individual studies. Various meta-analysis methods have been proposed for synthesizing information from multiple candidate gene studies and QTL mapping experiments, but there are several questions and challenges associated with these methods. For example, meta-analytic fixed-effect models assume homogeneity of outcomes from individual studies, which may not always be true. Whereas random-effect models takes into account the heterogeneity among studies they typically assume a normal distribution of study-specific outcomes. However in reality, the observed distribution pattern tends to be multi-modal, suggesting a mixture whose underlying components are not directly observable. In this paper, we examine several existing parametric meta-analysis methods, and propose the use of a non-parametric model with a Dirichlet process prior (DPP), which relaxes the normality assumptions about study- specific outcomes. With a DPP model, the posterior distribution of outcomes is discrete, reflecting a clustering property that may have biological implications. Features of these methods were illustrated and compared using both simulation data and real QTL data extracted from the Animal QTLdb (http://www.animalgenome.org/cgi-bin/QTLdb/index). The meta analysis of reported average daily body weight gain (ADG) QTL suggested that there could be from six to eight distinct ADG QTL on swine chromosome 1.</p>
dc.description.comments <p>This article is from <em>Journal of Biometrics & Biostatistics</em> S1 (2011): 001, doi:<a href="http://dx.doi.org/10.4172/2155-6180.S1-001" target="_blank">10.4172/2155-6180.S1-001</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/ans_pubs/139/
dc.identifier.articleid 1144
dc.identifier.contextkey 7765886
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ans_pubs/139
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/9537
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/ans_pubs/139/2011_Reecy_MetaAnalysisQuantitative.pdf|||Fri Jan 14 20:03:40 UTC 2022
dc.source.uri 10.4172/2155-6180.S1-001
dc.subject.disciplines Agriculture
dc.subject.disciplines Animal Sciences
dc.subject.disciplines Genetics
dc.subject.disciplines Meat Science
dc.subject.keywords Average daily gain
dc.subject.keywords Dirichlet process prior
dc.subject.keywords Meta-analysis
dc.subject.keywords Markov chain Monte Carlo
dc.subject.keywords Non-parametric models
dc.subject.keywords Quantitative trait loci (QTL)
dc.subject.keywords Swine
dc.title Meta-Analysis of Quantitative Trait Association and Mapping Studies using Parametric and Non-Parametric Models
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication fb994cd9-94d5-4370-94ab-f33934c4cd6f
relation.isOrgUnitOfPublication 85ecce08-311a-441b-9c4d-ee2a3569506f
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