In this application, we request continuation of MH57881, """"""""Genetic Association in Schizophrenia and Other Disorders"""""""". In our previous aims, we targeted the development of statistical methods for fine-mapping and mapping variants affecting liability to simple and complex disease. Much of the proposed work focused on haplotypes. Our emphasis was apparently timely given the current interest in haplotypes and their use for finding genetic variants affecting liability to disease. During the next five years, we propose to continue some of the methodological work on haplotypes, especially in three areas: (1) association analysis guided by the evolutionary relationships among haplotypes; (2) methods to cluster haplotypes before testing hypotheses, which serves to reduce the dimension of the test and potentially increase its power; and, (3) complementing the HapMap project, develop statistical methods to define the multilocus structure of linkage disequilibrium within regions of the human genome, as well as developing related statistics. Two new thrusts include approaches to analyze large, complex models, such as those that arise when genetic variants in different genes affect liability (liability alleles), potentially through their interaction and the use of admixture mapping to find liability alleles. Regarding complex models, we plan to extend False Discovery Rate (FDR) methods to the setting of multiple, dependent variables and multistage FDR. Multistage FDR will be evaluated as a tool for finding gene-gene interactions in which """"""""main effects"""""""" of the genes are also detectable. To find gene-gene interactions without detectable main effects, we propose refinements of model selection. For admixture mapping, we take the view that the properties of the likelihood are incompletely understood. Therefore we propose to study its properties. We also propose refinement to models for admixture mapping that account for uncertainty in ancestral allele distributions and dependent markers.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH057881-07
Application #
6748939
Study Section
Mammalian Genetics Study Section (MGN)
Program Officer
Lehner, Thomas
Project Start
1998-07-01
Project End
2008-06-30
Budget Start
2004-07-01
Budget End
2005-06-30
Support Year
7
Fiscal Year
2004
Total Cost
$385,766
Indirect Cost
Name
University of Pittsburgh
Department
Psychiatry
Type
Schools of Medicine
DUNS #
004514360
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
Gaugler, Trent; Klei, Lambertus; Sanders, Stephan J et al. (2014) Most genetic risk for autism resides with common variation. Nat Genet 46:881-5
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
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