Assessing the Effects of Imprinting and Maternal Genotypes on Complex Genetic Traits

Conference paper
Part of the Lecture Notes in Statistics book series (LNS, volume 215)


A susceptibility variant may affect a trait not only through sequence variation, but also through parental origin, and even through combination with the maternal genotype. Although associations have been established for more than one thousand five hundred Single Nucleotide Polymorphisms (SNPs) and over two hundred diseases through genome-wide association studies, imprinting and maternal genotype effects (collectively referred to as parent-of-origin effects) have largely not been taken into account. The ignorance of parent-of-origin effects may have adversely contributed to “missing heritability”; thus, attempts have been made to incorporate these two epigenetic factors when assessing the effect of a genetic variant on a complex trait. In this review, we will discuss the difference between retrospective and prospective studies in genetic analysis and indicate how this difference may influence the choice of methods for assessing parent-of-origin effects on the risk of complex genetic traits. We will provide expositions on several specific study designs and their associated analysis methods, including the case-parent triad design and designs that include control samples, such as the case-parent triads/control-parent triads design. Most available methods are for retrospective studies, but a handful of methods applicable to extended pedigrees from prospective studies also exist. Although log-linear or logistic models are frequently used to factor in parent-of-origin effects, we review non-parametric approaches as well for detecting imprinting effects. We further discuss implications of various assumptions made in the modeling to avoid overparameterization. In summary, a model factoring in epigenetically modulated gene variant effects is expected to be of greater value in risk assessment and prediction if such epigenetic factors indeed play a role in the etiology of the disease.



This work was supported in part by the National Science Foundation grant DMS-1208968. The author would like to thank Dr. Lynn Friedman for her valuable comments on an earlier version of the manuscript.


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© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of StatisticsThe Ohio State UniversityColumbusUSA

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