The Carolina Center for Exploratory Genetic Analysis will establish an interdisciplinary infrastructure for the efficient identification of the complex genetic traits underlying human diseases, based on clinical studies, population studies, and model systems. The planning stage for this center will build a collaborative community of investigators and deploy prototype infrastructure, driven by the quantitative analysis of relationships among genotypes and clinical or experimental phenotypes in three contexts: family linkage studies (susceptibility to alcoholic addiction), expression profile studies (breast cancer) and public health studies (atherosclerosis risk in communities). This effort will rely on the combined expertise of three complementary groups of UNC scientists: (a) experimental geneticists, (b) quantitative experts in statistics and biostatistics, and (c) computer scientists with expertise in algorithm development, software construction, and high-performance computing. To reduce the barriers between data providers and data analyzers, we will organize a series of intensive, specialized workshops, colloquia and intramural meetings. ? ? We believe the next major breakthroughs in our understanding of biology and disease will come from the integrated analysis of genetic data and its expression as phenotypes. To accommodate the diverse, multi-investigator data bases necessary to answer such questions, we will develop a prototype, extensible data model and provide access to data via a portal constructed using the Open Grid Computing Environment toolkit. To advance the practice of integrated analysis we will incorporate new methods under development into the data analysis workflow. These include new techniques in linkage analysis (oligogenic analysis, multivariate linkage analysis, epistasis, and genotype by environment interaction), subspace clustering, and association analysis (quantitative trait nucleotide analysis). For the interactive use of these computationally intensive techniques, we will explore new visualizations and develop high-performance implementations. ? ?

Agency
National Institute of Health (NIH)
Institute
National Center for Research Resources (NCRR)
Type
Exploratory Grants (P20)
Project #
5P20RR020751-03
Application #
7093549
Study Section
Special Emphasis Panel (ZRR1-BT-B (01))
Program Officer
Farber, Gregory K
Project Start
2004-09-22
Project End
2009-07-31
Budget Start
2006-08-01
Budget End
2009-07-31
Support Year
3
Fiscal Year
2006
Total Cost
$546,286
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Genetics
Type
Schools of Medicine
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Webb, Amy; Lind, Penelope A; Kalmijn, Jelger et al. (2011) The investigation into CYP2E1 in relation to the level of response to alcohol through a combination of linkage and association analysis. Alcohol Clin Exp Res 35:10-8
Gizer, Ian R; Ehlers, Cindy L; Vieten, Cassandra et al. (2011) Linkage scan of alcohol dependence in the UCSF Family Alcoholism Study. Drug Alcohol Depend 113:125-32
Li, Chun; Li, Mingyao; Lange, Ethan M et al. (2008) Prioritized subset analysis: improving power in genome-wide association studies. Hum Hered 65:129-41
Wright, Fred A; Huang, Hanwen; Guan, Xiaojun et al. (2007) Simulating association studies: a data-based resampling method for candidate regions or whole genome scans. Bioinformatics 23:2581-8
Zhang, Shuxing; Wei, Linyi; Bastow, Ken et al. (2007) Antitumor agents 252. Application of validated QSAR models to database mining: discovery of novel tylophorine derivatives as potential anticancer agents. J Comput Aided Mol Des 21:97-112
Tropsha, Alexander; Golbraikh, Alexander (2007) Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr Pharm Des 13:3494-504
Perkins, Diana O; Jeffries, Clark D; Jarskog, L Fredrik et al. (2007) microRNA expression in the prefrontal cortex of individuals with schizophrenia and schizoaffective disorder. Genome Biol 8:R27
Jeffries, Clark; Perkins, Diana O; Jarstfer, Michael (2006) Systematic discovery of the grammar of translational inhibition by RNA hairpins. J Theor Biol 241:205-15
Hemminger, Bradley M; Saelim, Billy; Sullivan, Patrick F (2006) TAMAL: an integrated approach to choosing SNPs for genetic studies of human complex traits. Bioinformatics 22:626-7