In this application, we request continuation of MH057881, "Genetic Association in Schizophrenia and Other Disorders". In our previous aims, covering the last nine years, we targeted the development of statistical methods for identifying genetic variants affecting liability to simple and complex disease. Specifically we have developed and refined methods for the following problems: for population-based samples, to control for population substructure;to detect association with phenotype from a set of adjacent loci, including haplotype information;to use evolution in association analysis;to differentiate between haplotype distributions from affected and unaffected individuals;to understand the linkage disequilibrium (LD) structure throughout the human genome;and to discover disease loci from high dimensional statistical models for complex disease. During the next five years, we propose to continue some of the methodological work on high dimensional statistical models, especially those related to genome- wide association (GWA) analysis. In fact GWA analysis will be the target for four aims. In the context of GWA, we plan to continue research on effective methods to explore large genetic (and environmental) models to identify factors conferring risk for disease;continue research on weighted hypothesis-testing as a means to introduce critical biological and genetic information into GWA;develop methods to use a wide variety of databases for GWA analyses;and build a Bayesian model to find copy number variation by combining genetic information, such as Mendelian errors, with molecular features such as image intensity. Our other thrusts are applicable to GWA and the broader field of association studies: methods to use multiple, related phenotypes and multivariate analyses to identify risk variants;and to develop models useful for identifying rare or uncommon variants that impact risk for phenotypes, as well as integrating those effects of those for common variants. As has been true for our last two funding periods, our theoretical work will be guided by real data from the evolving field of human genetics. Some complex diseases are common in the population, such as major depression and heart disease;and the causes of complex disease are often quite obscure, such as the rarer psychiatric diseases. Thus determining the genetic variants underlying these diseases could be a major step toward improving the health and well being of mankind. To accomplish this goal, researchers need the right tools;our research group seeks to develop the required statistical tools.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Genomics, Computational Biology and Technology Study Section (GCAT)
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Addington, Anjene M
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University of Pittsburgh
Schools of Medicine
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Chen, Kehui; Lei, Jing (2015) Localized Functional Principal Component Analysis. J Am Stat Assoc 110:1266-1275
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