In this application, we request extension of MERIT award R37MH057881, ?Genetic Association in Schizophrenia and Other Disorders?. In our previous aims, covering the last twenty years, we targeted the development of statistical methods for identifying genetic variants affecting liability to mental disorders. At each cycle we introduced novel statistical methods and evaluated existing methods to extract association signal from genetic data. Here we propose to push the field forward again, but with a different goal in mind: we plan to develop methods to understand how genetic variation influences risk for mental disorders, what neurobiological mechanisms are perturbed, and how such variation can be used to predict those at risk. Because we believe that a large portion of risk to mental disorders is due to perturbation of gene expression and coexpression networks in specific cell types, our first aim will be to develop models to describe tissue?level and cell?level transcriptomes and how they differ by case?control status. Next, we hypothesize that neurobiological mechanisms of risk can best be identified by studying ?community?level? behavior, which could be at the level of coexpressed risk genes or case? control differences in neural circuits connecting brain regions. Thus, in Aim 2, we will develop methods to detect communities in static or dynamic systems and relate them to case?control status. Finally we believe a key to prevention of mental disorders is to first identifying those at risk.
In Aim 3, we will develop methods for prediction of risk that account for fine?scale ancestry and relatedness on the genomic level. We expect that Aims 1?2 will yield key insights into the etiology of mental disorders by modeling core features that determine risk from the genetic and neurobiological perspectives, while Aim 3 lays a foundation for prevention by improving prediction of those at risk. As has been true for our last four funding periods, our theoretical work will be guided by real data from the evolving field of human genetics and transcriptomics. We are well positioned to move between theory and data because we have a diverse team of investigators lead by the PI (Devlin) and subcontract PI (Roeder) who have decades of experience in the statistical genetics field. 4 What individuals will work on the project? Effort (monts) 3.6 2.4 1 6 1 6 Role PI Co?investigator Co?investigator Technician Technician Technician Degree PhD PhD PhD PhD PhD PhD DOB 10/1954 03/1959 11/1982 SSN 2807 1876 6455 Name Devlin, Bernie Roeder, Kathryn Lei, Jing Klei, Lambertus Doman, John TBH Post?Doc Key Yes Yes Yes No No No Commons ID devlinbj roeder jinglei 5
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