In this application, we request continuation of MH057881, """"""""Genetic Association in Schizophrenia and Other Disorders"""""""". In our previous aims, covering the last fourteen years, we targeted the development of statistical methods for identifying genetic variants affecting liability to simple and complex disease. Specifically we have developed novel methods to control for population substructure and to fine-map risk variants, targeted methods to characterize linkage disequilibrium (LD) and use LD for association analysis, explored false discovery rate (FDR) procedures for genetic analysis, especially in the setting of high dimensional models, and developed robust methods for common variant associations with disease via genome-wide association. During the next five years, we propose to pursue methods related to association of rare variants with disease status, an area currently of keen interest to human genetics. Next Generation Sequencing (NGS) data have only recently become economically feasible to generate and application of the technology for genetic analysis of complex diseases is underway. As expected NGS data are noisy and signals for association emanating from it are often modest. Nonetheless it is reasonable to expect these data will enhance our understanding of the genetic etiology of complex diseases, but we need good tools to dissect the data. The overarching aim for this R01 is to develop novel statistical methods and evaluate existing methods to extract association signal from NGS data, with particular emphasis on data from disorders affecting mental health. Motivating this proposal we have recently shown the utility of de novo events - mutations in children not found in parents - for identification of genes involved in risk for autism. A portion of our research will build on his foundational observation and how to integrate de novo and inherited variation. Moreover, to garner additional power, we propose to develop methods to incorporate biological information, such as the nature of sequence variation (loss-of-function, missense, and silent) and gene networks, with the distribution of rare variation in subjects to identify disease genes. As has been true for our last three funding periods, our theoretical work will be guided by real data from the evolving field of human genetics. 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.

Public Health Relevance

Whether diseases are common in the population, such as major depression and heart disease, or relatively uncommon, such as psychiatric disorders, their genetic causes are often obscure. Yet 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 develops statistical tools to identify risk genes, especially those affecting mental health.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Method to Extend Research in Time (MERIT) Award (R37)
Project #
5R37MH057881-17
Application #
8653604
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Addington, Anjene M
Project Start
1998-07-01
Project End
2018-05-31
Budget Start
2014-06-01
Budget End
2015-05-31
Support Year
17
Fiscal Year
2014
Total Cost
$423,889
Indirect Cost
$70,004
Name
University of Pittsburgh
Department
Psychiatry
Type
Schools of Medicine
DUNS #
004514360
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213
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Agrawal, A; Chou, Y-L; Carey, C E et al. (2017) Genome-wide association study identifies a novel locus for cannabis dependence. Mol Psychiatry :
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Cotney, Justin; Muhle, Rebecca A; Sanders, Stephan J et al. (2015) The autism-associated chromatin modifier CHD8 regulates other autism risk genes during human neurodevelopment. Nat Commun 6:6404
Sanders, Stephan J; He, Xin; Willsey, A Jeremy et al. (2015) Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci. Neuron 87:1215-1233

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