Study design options in evaluating gene-environment interactions: practical considerations for a planned case-control study of pediatric leukemia.
BACKGROUND
We compare methodological approaches for evaluating gene-environment interaction using a planned study of pediatric leukemia as a practical example. We considered three design options: a full case-control study (Option I), a case-only study (Option II), and a partial case-control study (Option III), in which information on controls is limited to environmental exposure only.


METHODS
For each design option we determined its ability to measure the main effects of environmental factor E and genetic factor G, and the interaction between E and G. Using the leukemia study example, we calculated sample sizes required to detect and odds ratio (OR) of 2.0 for E alone, an OR of 10 for G alone and an interaction G x E of 3.


RESULTS
Option I allows measuring both main effects and interaction, but requires a total sample size of 1,500 cases and 1,500 controls. Option II allows measuring only interaction, but requires just 121 cases. Option III allows calculating the main effect of E, and interaction, but not the main effect of G, and requires a total of 156 cases and 133 controls.


CONCLUSIONS
In this case, the partial case-control study (Option III) appears to be more efficient with respect to its ability to answer the research questions for the amount of resources required. The design options considered in this example are not limited to observational epidemiology and may be applicable in studies of pharmacogenomics, survivorship, and other areas of pediatric ALL research.
Goodman
Michael
M
Department of Epidemiology Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, USA. goodm2@sph.emory.edu
Dana Flanders
W
W
D017668:Age of Onset; D016022:Case-Control Studies; D002648:Child; D003043:Cocarcinogenesis; D035061:Control Groups; D004781:Environmental Exposure; D005787:Gene Frequency; D052138:Genes, Neoplasm; D020022:Genetic Predisposition to Disease; D005838:Genotype; D006801:Humans; D007107:Immune System / Q000254:growth & development / Q000276:immunology; D007239:Infections / Q000453:epidemiology / Q000276:immunology; D016015:Logistic Models; D016131:Lymphocyte Subsets / Q000276:immunology; D008954:Models, Biological; D015514:Oncogene Proteins, Fusion / Q000235:genetics* / Q000502:physiology; D018579:Patient Selection; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000453:epidemiology / Q000209:etiology* / Q000235:genetics; D012107:Research Design / Q000592:standards / Q000706:statistics & numerical data*; D012306:Risk; D012307:Risk Factors; D018401:Sample Size; D012984:Software
D015514:Oncogene Proteins, Fusion