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Meta-Analysis of Quantitative Trait Association and Mapping Studies using Parametric and Non-Parametric Models | OMICS International | Abstract
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Research Article

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

Xiao-Lin Wu1*, Daniel Gianola1,2, Zhi-Liang Hu3 and James M. Reecy3

1Department of Dairy Science, Department of Animal Sciences, University of Wisconsin, Madison, WI 53719, USA

2Department of Biostatistics and Medical Bioinformatics, University of Wisconsin, Madison, WI 53706, USA

3Department of Animal Science, Center for Integrated Animal Genomics, Iowa State University, Ames, IA 50011-3150, USA

*Corresponding Author:
Xiao-Lin Wu
1675 Observatory Docter
Department of Dairy Science
University of Wisconsin
Madison, WI 53706, USA
Tel: (608) 263-7824
Fax: (608) 263-9412
E-mail: [email protected]

Received date: June 15, 2011; Accepted date: July 14, 2011; Published date: October 30, 2011

Citation: Wu XL, Gianola D, Hu ZL, Reecy JM (2011) Meta-Analysis of Quantitative Trait Association and Mapping Studies using Parametric and Non-Parametric Models. J Biomet Biostat S1:001 doi:10.4172/2155-6180.S1-001

Copyright: © 2011 Wu XL, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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 ( 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