Preterm birth accounts for 75% of all perinatal deaths in the U.S. and can result in substantial morbidity in surviving infants. Despite a revolution in our appreciation of the genetic, molecular, cellular, and physiologic underpinnings of human biology, there has been little improvement in the rate of preterm birth over the last decade. The major goal of our application is to develop a theoretical framework and an analytic strategy for describing the complex interplay of genetic, physiological, and environmental factors in the mother and fetus that are related to preterm birth. Preterm birth is a """"""""complex trait"""""""" in that it has a multifactorial etiology that cannot be explained by independent variations in single genetic or environmental factors. Rather, a complex gestational trait like preterm birth involves an enormous array of interacting genes with multiple variable sites in both the mother and fetus, as well as interacting environmental factors that combine to produce the observed outcome. Standard statistical genetic models to predict quantitative phenotypic differences cannot accommodate the large number of variables in such systems. As such, descriptions of the genetic architecture of preterm birth require the development of novel theoretical models and analytic tools that consider the combinatorial effects of these many variable sites in the context of multiple environmental factors. In the proposed work, we will model the genetic architecture of the maternal-fetal neuroendocrine system, a prototypical physiological system that plays a central role in parturition. This model can be used to analyze the interrelationships among the genetic and environmental variables involved in any physiological system that links genetic and environmental causes to interindividual variation in the risk for preterm birth. This objective will be met through the following three specific aims: ? ? 1. Build a model bioinformatics resource describing the etiological components of the maternal-fetal neuroendocrine pathway. ? ? 2. Develop a generalized theoretical model describing the interactions among the genetic and environmental components of the maternal-fetal neuroendocrine system and their relationships to preterm birth. ? ? 3. Develop a statistical method for estimating the effect of maternal and fetal genes, gene-gene interactions, and gene-environment interactions on preterm birth in population based studies. ? ? This application is designed as a training vehicle for Dr. Vinod Misra to build upon his prior skills and experience in mathematical modeling in the physical sciences and to develop his expertise in a new field of study addressing the important problem of preterm birth. The grant provides support for both didactic and research training at the University of Michigan, a major research institution with a strong tradition in statistical genetics and genetic epidemiology. The educational climate and resources of the University, the commitment of his Department, and the expertise of his mentor will prepare Dr. Misra for a career as an independent investigator. ? ?
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