Human Heredity

Original Paper

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Accounting for Disease Model Uncertainty in Mapping Heterogeneous Traits – A Bayesian Model Averaging Approach

Biswas S.a · Papachristou C.b

Author affiliations

aDepartment of Biostatistics, School of Public Health, University of North Texas Health Science Center, Fort Worth, Tex., and bDepartment of Mathemetics, Physics, and Statistics, University of the Sciences in Philadelphia, Philadelphia, Pa., USA

Corresponding Author

Swati Biswas, PhD

Department of Biostatistics, School of Public Health

University of North Texas Health Science Center

3500 Camp Bowie Blvd., Fort Worth, TX 76107-2699 (USA)

Tel. +1 817 735 5442, Fax +1 817 735 2314, E-Mail swati.biswas@unthsc.edu

Keywords: Linkage analysisLocus heterogeneityMarkov chain Monte CarloReversible jumpAlzheimer’s disease

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Hum Hered 2010;69:242–253
https://doi.org/10.1159/000298285

Abstract

Background: Locus heterogeneity, wherein a disease can be caused in different individuals by different genes and/or environmental factors, is a ubiquitous feature of complex traits. A Bayesian approach has been proposed to account for variable rates of heterogeneity across families in a parametric linkage analysis setup [Biswas and Lin: J Am Stat Assoc 2006;101:1341–1351]. As with any parametric approach, its application requires specification of the disease model, which limits its practical utility. Methods: We address this limitation by proposing a Bayesian model averaging (BMA) approach. We consider a finite number of disease models and treat the model as an unknown parameter. In practice, we use simple single-locus disease models as various categories for model. Results: Our simulations as well as analysis of Genetic Analysis Workshop 13 simulated data show that BMA retains at least 80% of the power that is obtained by analyzing under the true disease model. The coverage probability of interval for disease gene is maintained around the nominal level. Finally, we apply BMA to a Late-Onset Alzheimer’s Disease dataset and find evidence for linkage on chromosomes 19, 9, and 21. Conclusion: We conclude that the BMA approach utilizing simple single-locus models for averaging is effective for mapping heterogeneous traits.

© 2010 S. Karger AG, Basel



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Article / Publication Details

First-Page Preview
Abstract of Original Paper

Received: September 22, 2009
Accepted: January 19, 2010
Published online: March 26, 2010
Issue release date: April 2010

Number of Print Pages: 12
Number of Figures: 3
Number of Tables: 5

ISSN: 0001-5652 (Print)
eISSN: 1423-0062 (Online)

For additional information: https://www.karger.com/HHE

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2010, Vol.69, No. 4

April 2010

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