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IJE Advance Access originally published online on August 5, 2005
International Journal of Epidemiology 2005 34(5):1077-1079; doi:10.1093/ije/dyi156
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Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2005; all rights reserved.

Commentary

Commentary: Models for longitudinal family data

W James Gauderman* and David V Conti

Department of Preventive Medicine, University of Southern California School of Medicine, 1540 Alcazar Street, CHP-200, Los Angeles, CA 90089-9011, USA

* Corresponding author. E-mail: jimg@usc.edu

The first 10% of the full text of this article appears below.

Cohort studies will become increasingly important in understanding the aetiology of complex human traits.1 While the longstanding approach of analysing cross-sectional data to identify genetic and/or environmental factors for disease or quantitative traits has resulted in some success, there have been many inconclusive results and far too few replications. There are recognized explanations that are often put forth for this, including low power and heterogeneity across study samples. However, a reason that is not often cited is that a single cross-sectional examination of data may not capture the essential aetiological mechanisms. For example, a specific variant genotype might cause an increase in a trait value that cumulates as a person ages. That is, a specific gene may affect the trajectory of the trait over time. Thus, two studies, one . . . [Full Text of this Article]


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