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Shili Lin, Professor, Department of Statistics, Ohio State University
Title: Partial Likelihood for Detecting the Effects of Two Epigenetic Factors on Complex Diseases
Abstract: Genomic imprinting and maternal effects are two epigenetic factors that have been increasingly explored for their roles in the etiology of complex diseases. In particular, statistical methods have been proposed to detect imprinting and maternal effects simultaneously based on either a case-parent triads design or a case-mother/control-mother pairs design. However, most existing methods are full-likelihood based and have to make strong assumptions concerning mating type probabilities (nuisance parameters) to avoid overparametrization. In this talk I will describe a partial Likelihood methodology for detecting Imprinting and Maternal Effects simultaneously (LIME). This method is applicable to data that augment the two popular study designs by combining them and including control-parent triads, so that the data may contain a mixture of case-parent/control-parent triads and case-mother/control-mother pairs. Data from additional siblings may be included as well. By matching case families with control families of the same structure and stratifying according to the familial genotypes, we are able to derive a partial likelihood that is free of the nuisance parameters. This renders unnecessary and unrealistic assumptions and leads to a robust procedure without sacrificing power. Our simulation study demonstrates that LIME has correct type I error rate, little bias and reasonable power under a variety of settings. Based on the asymptotic properties of LIME, we further investigate several study designs based on their expected information and make recommendations on how to design an efficient experiment for detecting imprinting and maternal effects.
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