American Journal of Epidemiology Advance Access originally published online on July 23, 2008
American Journal of Epidemiology 2008 168(5):541-547; doi:10.1093/aje/kwn149
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ORIGINAL CONTRIBUTIONS |
Making the Most of Case-Mother/Control-Mother Studies
1 Biostatistics Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC
2 Departments of Endocrinology; Epidemiology, Biostatistics and HTA; and Human Genetics, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
Correspondence to Dr. Clarice R. Weinberg, Biostatistics Branch, National Institute of Environmental Health Sciences, Mail Drop A3-03 101/A315, Research Triangle Park, NC 27709 (e-mail: weinber2{at}niehs.nih.gov).
Received for publication January 30, 2008. Accepted for publication May 8, 2008.
The prenatal environment plays an important role in many conditions, particularly those with onset early in life, such as childhood cancers and birth defects. Because both maternal and fetal genotypes can influence risk, investigators sometimes use a case-mother/control-mother design, with mother-offspring pairs as the unit of analysis, to study genetic factors. Risk models should account for both the maternal genotype and the correlated fetal genotype to avoid confounding. The usual logistic regression analysis, however, fails to fully exploit the fact that these are mothers and offspring. Consider an autosomal, diallelic locus, which could be related to disease susceptibility either directly or through linkage with a polymorphic causal locus. Three nested levels of assumptions are often natural and plausible. The first level simply assumes Mendelian inheritance. The second further assumes parental mating symmetry for the studied locus in the source population. The third additionally assumes parental allelic exchangeability. Those assumptions imply certain nonlinear constraints; the authors enforce those constraints by using Poisson regression together with the expectation-maximization algorithm. Calculations reveal that improvements in efficiency over the usual logistic analysis can be substantial, even if only the Mendelian assumption is honored. Benefits are even more marked if, as is typical, information on genotype is missing for some individuals.
case-control studies; genetics; linear models; polymorphism, single nucleotide; risk
Abbreviations: EM, expectation-maximization
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