Genomic imprinting and maternal effect are involved in many complex human diseases but have long been neglected in association studies. In this dissertation, we propose two likelihood approaches for detecting imprinting and maternal effects (LIME) simultaneously in family-based association studies. Since these two effects could cause similar parent-of-origin patterns in binary disease traits, it is important to incorporate both of them into the modeling to avoid lurking effects. Statistical methods that are developed to detect one of them while assuming the absence of the other will report false positives when the assumption is violated.
Our first LIME approach (LIME-ped) is designed for general pedigrees with missing genotypes from prospective family-based association studies. LIME-ped formulates the probability of familial genotypes by introducing a novel concept called "conditional mating type" between marry-in founders and their non-founder spouses, and models the penetrance using a logit link. To deal with missing genotypes, LIME-ped enumerates possible unobserved genotypes and sums over the likelihoods of all compatible familial genotypes conditional on observed genotypes. Our simulation study demonstrates that: (1) LIME-ped has the correct type I error rate for testing for imprinting when maternal effect is present, or vice versa; (2) applying LIME-ped to pedigrees fully utilizes the data and achieves higher power than trimming down the pedigrees to nuclear families; (3) "filling in" the unobserved genotypes conditional on the genotypes of relatives augments the total information, leading to higher power for LIME-ped than simply excluding individuals with missing genotypes.
The second LIME approach (LIME-mix) is designed for case-parent/control-parent triads studies. Since biological fathers are often hard to recruit in family-based studies, we also allow for case-mother/control-mother pairs arising from the triads with missing fathers. The approach is referred to as "LIME-mix", since the real data is a mixed sample of triads and pairs. We assume multiplicative relative risks due to variant allele effect, genomic imprinting, and maternal effect; and analytically derive the partial likelihood of the family counts with particular genotype combinations. LIME-mix can be applied to either rare or common diseases without restriction on the disease prevalence. Since the partial likelihood does not involve any nuisance parameters about mating types, LIME-mix makes no assumptions on the mating type frequencies, such as allelic exchangeability and mating symmetry, the latter of which is a necessary assumption universally made in many existing imprinting and/or maternal effects detection methods. The robustness of the proposed LIME-mix approach compared to two existing methods is demonstrated via simulation.
The LIME approaches are applied to nuclear families, general pedigrees and case-control families from the Framingham Heart Study data. Several SNPs that have variant allele, imprinting and/or maternal effects are identified.