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Abstract Details

Activity Number: 522
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract - #305446
Title: Multivariate Statistical Methods Used for Lymphoblastoid Cell Lines Drug Response Shows Promise in Pharmacogenomics Gene Discovery
Author(s): Chad C Brown*+ and Tammy Havener and Ronald Krauss and Marisa Wong-Medina and Kevin Long and Howard McLeod and Alison Anne Motsinger-Reif
Companies: North Carolina State University and The University of North Carolina at Chapel Hill and Children's Hospital Oakland Research Institute and Children's Hospital Oakland Research Institute and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and North Carolina State University
Address: 2311 Stinson Drive, Raleigh, NC, 27695-8203,
Keywords: cytotoxicity ; lymphoblastoid ; pharmacogenomics ; GWAS ; MANOVA ; cancer

Cytotoxicity assays of immortalized lymphoblastoid cell lines (LCLs) are a promising new in vitro approach in pharmacogenomics discovery research. Previous studies employing LCLs in gene mapping have used univariate association methods, inadequately capturing the true differences in nonlinear response profiles between genotypes. Simulation studies based on real data have shown increased power and robustness for multivariate ANOVA (MANCOVA) in detecting these differences for a wide variety of biological signals. The software program MAGWAS was developed to implement MANCOVA analysis in genome-wide association studies (GWAS) in a computationally efficient manner using common data formats. MAGWAS was applied to a GWAS of 520 LCLs exposed to six concentrations of the cancer drug temozolomide. Significant (P< 10e-15) association was found for genetic loci within the gene coding for methyl guanine methyl transferase (MGMT), an enzyme which has been previously associated with temozolomide efficacy in cancer patients (Hegi, M. E. et. all 2005). This result is a proof of concept that LCLs can be used to identify genes that are valuable in the prediction of clinical efficacy.

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