Wen-Chung Lee1, Chin-Hao Chang. 1. Graduate Institute of Epidemiology, College of Public Health, National Taiwan University, Taipei. wenchung@ha.mc.ntu.edu.tw
Abstract
BACKGROUND: Assessing joint genetic and environmental contributions to disease risk is the central issue in many genetic epidemiological studies. To characterise the effects of a gene, the case-control study may suffer from the problem of population stratification bias. For a late onset disease, recruiting control subjects into case-parents and case-sibling studies may be difficult. METHODS: Two novel approaches to analysing case-spouse data are introduced: the 1:1 case-counterfactual-control analysis (genotype swapping between the case and their spouse) and the 1:5 case-counterfactual-controls analysis (allele swapping). RESULTS: Both can be implemented using statistical packages that allow matched analysis (the conditional logistic regression) to yield valid estimates of the genotype relative risk, the gene-environment interaction parameter, the gene-sex interaction parameter, and the gene-environment-sex three factor interaction parameter (if desired), if certain assumptions are fulfilled. CONCLUSION: Because of the ease in recruiting subjects, and in collecting and analysing data, this approach makes a convenient tool for gene characterisation.
BACKGROUND: Assessing joint genetic and environmental contributions to disease risk is the central issue in many genetic epidemiological studies. To characterise the effects of a gene, the case-control study may suffer from the problem of population stratification bias. For a late onset disease, recruiting control subjects into case-parents and case-sibling studies may be difficult. METHODS: Two novel approaches to analysing case-spouse data are introduced: the 1:1 case-counterfactual-control analysis (genotype swapping between the case and their spouse) and the 1:5 case-counterfactual-controls analysis (allele swapping). RESULTS: Both can be implemented using statistical packages that allow matched analysis (the conditional logistic regression) to yield valid estimates of the genotype relative risk, the gene-environment interaction parameter, the gene-sex interaction parameter, and the gene-environment-sex three factor interaction parameter (if desired), if certain assumptions are fulfilled. CONCLUSION: Because of the ease in recruiting subjects, and in collecting and analysing data, this approach makes a convenient tool for gene characterisation.
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