Statistical Methods For Gene/environment Interaction And Genetic Susceptibility

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Our earlier approach to analyzing case-parents data for multiplicative gene-environment interaction leads to valid inference for a causative SNP under two crucial assumptions. The first assumption is that, conditional on parents' genotypes, the genotype distributions of children reflect Mendelian assortment. The second is that, conditional on parents' genotypes, a child's genotype and exposure are independent. Recently, we have been considering a study design that involves one affected and one unaffected offspring and their parents. We call this structure a tetrad. Our proposal is to genotype the affected offspring and the parents and to collect exposure information from both offspring under that idea that we could test genetic and gene-environment interaction effects using the embedded case-parent-triad design and we could study exposure using the embedded sibling-pair design. In studying this design, we also found that, for matched designs with one affected and two or more unaffected siblings, treating the sibship as a nuclear family with missing parents is more efficient than traditional conditional logistic regression, and it allows inclusion of unmatched subjects as well. We are studying the application of specimen pooling to DNA from case-parent triads when the genotyping assay counts the number of variant alleles in a pooled specimen. Our procedure partitions a sample of triads into small subsets of, say, two triads each, and, for each subset, constructs three pooled DNA specimens: one each from mothers, from fathers, and from offspring. Treating the individual genotypes that comprise each pool as missing data, our log-linear-modeling approach uses the expectation-maximization algorithm to estimate relative risk parameters for inherited alleles, maternal alleles, or parent-of-origin effects, something other DNA pooling approaches cannot do. We see little loss of power compared to genotyping individuals when genotypes are measured without error, but power declines as genotyping error rates increase. For sufficiently accurate assays, our approach promises to reduce genotyping costs with minimal loss of power. Genome-wide association studies usually focus on variation in autosomal genes; however, less studied genetic mechanisms may also influence risk for complex disease. We are studying three mechanisms that can induce differential risk in maternal versus paternal lineages of affected individuals. These include mitochondrial variants (inherited from the mother), imprinted genes (where the effect of variants depends on the parent of origin), and maternal genes (that influence risk through modulation of the prenatal environment). Asymmetries in risk can be measured by inter-lineage relative risks or relative odds. We define the relative risks of interest; and, under certain simplifying assumptions, we derived algebraic expressions for the inter-lineage relative risks predicated on commonly used risk models. We find that careful analyses of family history data provide clues about whether these mechanisms contribute to risk but dissecting which mechanism may be operating requires genotype data on family members. (see also Z01 ES040007 BB; PI Clare Weinberg.)