Benchmarking statistical methods for analyzing parent–child dyads in genetic association studies

Abstract

Genetic association studies of child health outcomes often employ family‐based study designs. One of the most popular family‐based designs is the case–parent trio design that considers the smallest possible nuclear family consisting of two parents and their affected child. This trio design is particularly advantageous for studying relatively rare disorders because it is less prone to type 1 error inflation due to population stratification compared to population‐based study designs (e.g., case–control studies). However, obtaining genetic data from both parents is difficult, from a practical perspective, and many large studies predominantly measure genetic variants in mother–child dyads. While some statistical methods for analyzing parent‐child dyad data (most commonly involving mother–child pairs) exist, it is not clear if they provide the same advantage as trio methods in protecting against population stratification, or if a specific dyad design (e.g., case–mother dyads vs. case–mother/control–mother dyads) is more advantageous. In this article, we review existing statistical methods for analyzing genome‐wide marker data on dyads and perform extensive simulation experiments to benchmark their type I errors and statistical power under different scenarios. We extend our evaluation to existing methods for analyzing a combination of case–parent trios and dyads together. We apply these methods on genotyped and imputed data from multiethnic mother–child pairs only, case–parent trios only or combinations of both dyads and trios from the Gene, Environment Association Studies consortium (GENEVA), where each family was ascertained through a child affected by nonsyndromic cleft lip with or without cleft palate. Results from the GENEVA study corroborate the findings from our simulation experiments. Finally, we provide recommendations for using statistical genetic association methods for dyads.

Methods and Software to Analyze Gene-Environment Interactions under a Case-Mother-Control-Mother Design with Partially Missing Child Genotype

“…In this package, mating type frequencies are parameterized with two μ parameters per stratum representing stratum-specific log-ratios over a reference mating type frequency captured by stratum-specific intercepts in γ. Alternative software packages implement this retrospective likelihood with a single stratum: EMIM [10] offers a numerically efficient implementation and a variety of mating type frequency parameterizations with between one and nine parameters per stratum, while the LEM package [11] presents difficulties to run in current computing environments [12] . EMIM and LEM are not as convenient as Haplin for stratifying on a categorical covariate, as scripts to perform the stratification and a test of trend of the estimates across strata would have to be written.…”