Modeling maternal-offspring gene-gene interactions: the extended-MFG test

• Published in: Genetic epidemiology 2010-07, Vol.34 (5), p.512-521
• Main Authors: Childs, Erica J., Palmer, Christina G.S., Lange, Kenneth, Sinsheimer, Janet S.
• Format: Article
• Language: English
• Subjects: Alleles; Computer Simulation; family based association; Family Health; Female; gene by environment; Genetic Predisposition to Disease; Genetic Testing; Genotype; Humans; Likelihood Functions; maternal-fetal interaction; Models, Genetic; Models, Statistical; Mothers; Nuclear Family; Pedigree; pedigree likelihood; Pregnancy; Risk Factors
• ISSN: 0741-0395; 1098-2272; 1098-2272
• DOI: 10.1002/gepi.20508

Maternal‐fetal genotype (MFG) incompatibility is an interaction between the genes of a mother and offspring at a particular locus that adversely affects the developing fetus, thereby increasing susceptibility to disease. Statistical methods for examining MFG incompatibility as a disease risk factor have been developed for nuclear families. Because families collected as part of a study can be large and complex, containing multiple generations and marriage loops, we create the Extended‐MFG (EMFG) Test, a model‐based likelihood approach, to allow for arbitrary family structures. We modify the MFG test by replacing the nuclear‐family based “mating type” approach with Ott's representation of a pedigree likelihood and calculating MFG incompatibility along with the Mendelian transmission probability. In order to allow for extension to arbitrary family structures, we make a slightly more stringent assumption of random mating with respect to the locus of interest. Simulations show that the EMFG test has appropriate type‐I error rate, power, and precise parameter estimation when random mating holds. Our simulations and real data example illustrate that the chief advantages of the EMFG test over the earlier nuclear family version of the MFG test are improved accuracy of parameter estimation and power gains in the presence of missing genotypes. Genet. Epidemiol. 34: 512–521, 2010.© 2010 Wiley‐Liss, Inc.