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This article is part of the supplement: Genetic Analysis Workshop 13: Analysis of Longitudinal Family Data for Complex Diseases and Related Risk Factors

Open Access Proceedings

Analysis of gene × environment interactions in sibships using mixed models

Jill S Barnholtz-Sloan1*, Laila M Poisson2, Steven W Coon2, Gary A Chase2 and Benjamin A Rybicki2

Author Affiliations

1 Department of Internal Medicine (Oncology), Wayne State University School of Medicine and Karmanos Cancer Institute, 110 East Warren, Detroit, Michigan, USA

2 Department of Biostatistics and Research Epidemiology, Henry Ford Health Sciences Center, One Ford Place, Detroit, Michigan, USA

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BMC Genetics 2003, 4(Suppl 1):S18  doi:10.1186/1471-2156-4-S1-S18

Published: 31 December 2003

Abstract

Background

Gene × environment models are widely used to assess genetic and environmental risks and their association with a phenotype of interest for many complex diseases. Mixed generalized linear models were used to assess gene × environment interactions with respect to systolic blood pressure on sibships adjusting for repeated measures and hierarchical nesting structures. A data set containing 410 sibships from the Framingham Heart Study offspring cohort (part of the Genetic Analysis Workshop 13 data) was used for all analyses. Three mixed gene × environment models, all adjusting for repeated measurement and varying levels of nesting, were compared for precision of estimates: 1) all sibships with adjustment for two levels of nesting (sibs within sibships and sibs within pedigrees), 2) all sibships with adjustment for one level of nesting (sibs within sibships), and 3) 100 data sets containing random draws of one sibship per extended pedigree adjusting for one level of nesting.

Results

The main effects were: gender, baseline age, body mass index (BMI), hypertensive treatment, cigarettes per day, grams of alcohol per day, and marker GATA48G07A. The interaction fixed effects were: baseline age by gender, baseline age by cigarettes per day, baseline age by hypertensive treatment, baseline age by BMI, hypertensive treatment by BMI, and baseline age by marker GATA48G07A. The estimates for all three nesting techniques were not widely discrepant, but precision of estimates and determination of significant effects did change with the change in adjustment for nesting.

Conclusion

Our results show the importance of the adjustment for all levels of hierarchical nesting of sibs in the presence of repeated measures.