alexa Gene-environment Interaction Studies with Measurement Error Application in the Complex Diseases in the Newfoundland Populat ion: Environment and Genetics Study
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Research Article

Gene-environment Interaction Studies with Measurement Error Application in the Complex Diseases in the Newfoundland Populat ion: Environment and Genetics Study

Taraneh Abarin*

Department of Mathematics and Statistics, Memorial University, Canada

*Corresponding Author:
Taraneh Abarin
Department of Mathematics and Statistics
Memorial University
St. John's, NL, Canada
Tel: (709) 864-8733
Fax:(709) 864-3010
E-mail: [email protected]

Received Date: July 29, 2013; Accepted Date: August 26, 2013; Published Date: August 30, 2013

Citation: Abarin T (2013) Gene-environment Interaction Studies with Measurement Error Application in the Complex Diseases in the Newfoundland Population: Environment and Genetics Study. J Biomet Biostat 4:173. doi:10.4172/2155-6180.1000173

Copyright: © 2013 Abarin T. 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.

Abstract

Newfoundland and Labrador NL has had the highest percentage of overweight/obese residents in Canada since 2007. This complex trait is determined by multiple genetic and environmental factors that interact with one another. The existing studies examine such factors under the assumption that they are measured accurately. However, error- prone environmental and genetic factors are unavoidable. The impact of ignoring these errors varies from bias to false results in detecting associations. Motivated by COD-ING study, we present methodologies to estimate model parameters, while accounting for measurement error and misclassification. We applied bias-corrected methods for three separate studies: candidate-gene association study and two gene-environment interaction models, where both environmental and genetic factors are subject to error. Our results based on simulation studies show that the proposed methodologies perform quite satisfactory.

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