Parent‐of‐origin‐environment interactions in case‐parent triads with or without independent controls

Abstract

With case–parent triad data, one can frequently deduce parent of origin of the child's alleles. This allows a parent‐of‐origin (PoO) effect to be estimated as the ratio of relative risks associated with the alleles inherited from the mother and the father, respectively. A possible cause of PoO effects is DNA methylation, leading to genomic imprinting. Because environmental exposures may influence methylation patterns, gene–environment interaction studies should be extended to allow for interactions between PoO effects and environmental exposures (i.e., PoOxE). One should thus search for loci where the environmental exposure modifies the PoO effect.We have developed an extensive framework to analyze PoOxE effects in genome‐wide association studies (GWAS), based on complete or incomplete case–parent triads with or without independent control triads. The interaction approach is based on analyzing triads in each exposure stratum using maximum likelihood estimation in a log‐linear model. Interactions are then tested applying a Wald‐based posttest of parameters across strata. Our framework includes a complete setup for power calculations. We have implemented the models in the R software package Haplin.To illustrate our PoOxE test, we applied the new methodology to top hits from our previous GWAS, assessing whether smoking during the periconceptional period modifies PoO effects on cleft palate only.

Design efficiency in genetic association studies

“…Interactions between genetic effects and categorical exposure variables are incorporated into the log‐linear framework of Haplin by fitting the log‐linear model separately for each exposure stratum. A Wald test is then applied to detect whether the relative risk estimates differ significantly across exposure levels . The genetic effect in question might be a regular autosomal, PoO, or maternal effect.…”

“…We have previously developed an extensive framework for genetic epidemiological analyses of binary traits based on log‐linear modeling, implemented in the R package Haplin . Haplin includes a complete setup for power and sample size calculation, which is useful in study planning and in interpreting findings from a genome‐wide association study (GWAS).…”

“…It implements a full maximum‐likelihood model for estimation and calculates explicit estimates of relative risks with asymptotic standard errors (SEs) and confidence intervals. Haplin enables the estimation of regular autosomal effects, PoO effects, and maternal effects, as well as interactions between genetic effects and categorical or ordinal exposure variables . It allows for parallel processing of analyses as well as data structure for handling GWAS data.…”

“…The log‐linear model in Haplin assumes Mendelian transmission, Hardy‐Weinberg equilibrium (HWE), and random mating, although moderate deviations from HWE are unlikely to cause bias . A detailed description of the underlying model is provided in several of our previous publications . For applications of Haplin to GWAS data, readers are referred to some of our previous publications …”

“…The full hybrid design comprises complete pairs of case‐parent triads and control‐parent triads, but truncated versions may include case‐parent triads supplemented by control‐mother dyads or case‐mother dyads supplemented by control‐mother dyads . Analysis methods such as log‐linear and multinomial modeling approaches are particularly appealing as they can readily be adapted to accommodate the broad spectrum of various hybrid designs as well as a wide array of causal scenarios and genetic effects . As an example, they can easily be extended to include the maternal‐fetal genotype incompatibility test .…”