Population-level association between disorder phenotype and either alleles directly affecting the disorder (causal association) or marker alleles flanking the disorder locus (linkage disequilibrium) has enjoyed recent recognition for its potential in the analysis of simple and complex genetic disorders. These associations may be particularly important for the analysis of psychiatric disorders, such as schizophrenia, which have complex etiology and have resisted standard pedigree-based methods. For simple disorders, linkage disequilibrium has been used to refine disease gene location after the gene has been crudely localized using standard methods. But the utility of causal association and linkage disequilibrium for the genetic analysis of complex disorders will depend upon the level of complexity and, for linkage disequilibrium, the evolutionary history of the population and genes involved. All of these factors are largely unknown. Empirical results, however, suggest the association analyses can in some instances be very effective at pinpointing loci affecting phenotypic expression. Analytic methods for genetic association data are immature. This project will develop and refine statistical methods to analyze these data, thereby helping to pinpoint genes involved in the etiology of complex disorders. We plan to apply these methods to data on three psychiatric disorders, namely schizophrenia, attention deficit/ hyperactivity disorder, and Alzheimer's disease, as well as to data on simple genetic disorders.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH057881-02
Application #
2891048
Study Section
Mammalian Genetics Study Section (MGN)
Program Officer
Moldin, Steven Owen
Project Start
1998-07-01
Project End
2003-06-30
Budget Start
1999-07-01
Budget End
2000-06-30
Support Year
2
Fiscal Year
1999
Total Cost
Indirect Cost
Name
University of Pittsburgh
Department
Psychiatry
Type
Schools of Medicine
DUNS #
053785812
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213
Agrawal, A; Chou, Y-L; Carey, C E et al. (2018) Genome-wide association study identifies a novel locus for cannabis dependence. Mol Psychiatry 23:1293-1302
DeMichele-Sweet, M A A; Weamer, E A; Klei, L et al. (2018) Genetic risk for schizophrenia and psychosis in Alzheimer disease. Mol Psychiatry 23:963-972
Bodea, Corneliu A; Neale, Benjamin M; Ripke, Stephan et al. (2016) A Method to Exploit the Structure of Genetic Ancestry Space to Enhance Case-Control Studies. Am J Hum Genet 98:857-868
Chen, Kehui; Lei, Jing (2015) Localized Functional Principal Component Analysis. J Am Stat Assoc 110:1266-1275
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
Samocha, Kaitlin E; Robinson, Elise B; Sanders, Stephan J et al. (2014) A framework for the interpretation of de novo mutation in human disease. Nat Genet 46:944-50
Zhao, Tuo; Roeder, Kathryn; Liu, Han (2014) Positive Semidefinite Rank-based Correlation Matrix Estimation with Application to Semiparametric Graph Estimation. J Comput Graph Stat 23:895-922
Gaugler, Trent; Klei, Lambertus; Sanders, Stephan J et al. (2014) Most genetic risk for autism resides with common variation. Nat Genet 46:881-5
Liu, Li; Sabo, Aniko; Neale, Benjamin M et al. (2013) Analysis of rare, exonic variation amongst subjects with autism spectrum disorders and population controls. PLoS Genet 9:e1003443
Lim, Elaine T; Raychaudhuri, Soumya; Sanders, Stephan J et al. (2013) Rare complete knockouts in humans: population distribution and significant role in autism spectrum disorders. Neuron 77:235-42

Showing the most recent 10 out of 97 publications