The proposed research education program, has three objectives: 1) to provide innovative education in statistical genetics methods for addictive behaviors to graduate students and postdoctoral fellows;2) to involve and mentor them in the development and application of new statistical methods and computational models relevant to analyze the vast and ever increasing genetic data being generated from emerging high throughput technologies;and 3) to apply such methods to critical questions in the genetics of addiction. We have focused on two overarching and inter-related addictive disorders with which our Program leaders and mentors have significant expertise: nicotine and opiate dependence. We offer a rich data infrastructure of ongoing and completed studies pertinent to the genetics of select addictive behaviors. We propose to develop curriculum-based methods that provide new knowledge and opportunities for graduate students and postdoctoral fellows to work with faculty mentors on research projects focusing on statistical genetics and computational modeling approaches to nicotine and prescription opioid addiction. We will select two pre-doctoral students and one postdoctoral fellow in the first year. Each subsequent year, we will increase the number of participants and at the end of 5 years we will have appointed 5 predoctoral students and 5 postdoctoral fellows each for a maximum of five years. Mentors have been identified from the faculty of collaborating institutions where participants will have their primary appointments including The University of Texas M. D. Anderson Cancer Center, UT Health Science Center and School of Public Health, Baylor College of Medicine, and Rice University. Additional mentors who are leaders in addiction and statistical genetics also serve on the Program Advisory Committee from the National Institutes of Alcohol Abuse and Alcoholism, Rockefeller University, and Texas A&M. These mentors have agreed to join the Program for their expertise and experience in addiction research, statistical genetics and computational models, behavioral science, and genetic epidemiology. The significance of this research education program then is multi-fold. In addition to teaching students to become statistical genetic leaders with a focus in addiction research, it is anticipated that robust new statistical methodologies will be tested and validated and finally that novel genetic targets will be identified. The identification of novel genetic risk factors and their interaction with the environment can contribute to understanding the genetic etiology of nicotine and opiate addiction and help to identify individuals at highest risk as well as to develop targeted therapies on the basis of their personal exposure patterns and their genetic risk profiles. In summary, changing patterns of tobacco use and growing illicit use of prescription pain killers, particularly by children, make the significance of educating these new scientists and launching them in research paramount.

Public Health Relevance

This research education program for doctoral students and postdoctoral fellows will provide mentored training in statistical genetics methods applied to addictive disorders focused on nicotine and opiate dependence. The trainees and their faculty mentors will develop and apply new statistical methods and computational models relevant to analyze the vast and ever increasing genetic data being generated from emerging high throughput technologies;and they will apply such methods to critical questions in the genetics of addiction.

Agency
National Institute of Health (NIH)
Institute
National Institute on Drug Abuse (NIDA)
Type
Education Projects (R25)
Project #
5R25DA026120-05
Application #
8646903
Study Section
Special Emphasis Panel (ZDA1-MXS-M (10))
Program Officer
Babecki, Beth
Project Start
2010-08-01
Project End
2015-03-31
Budget Start
2014-04-01
Budget End
2015-03-31
Support Year
5
Fiscal Year
2014
Total Cost
$285,272
Indirect Cost
$17,493
Name
University of Texas MD Anderson Cancer Center
Department
None
Type
Schools of Medicine
DUNS #
800772139
City
Houston
State
TX
Country
United States
Zip Code
77030
Shorter, Daryl; Nielsen, David A; Hamon, Sara C et al. (2016) The α-1 adrenoceptor (ADRA1A) genotype moderates the magnitude of acute cocaine-induced subjective effects in cocaine-dependent individuals. Pharmacogenet Genomics 26:428-35
Reyes-Gibby, Cielito C; Wang, Jian; Silvas, Mary Rose T et al. (2016) Genome-wide association study suggests common variants within RP11-634B7.4 gene influencing severe pre-treatment pain in head and neck cancer patients. Sci Rep 6:34206
Zhu, Xuan; Wang, Jian; Peng, Bo et al. (2016) Empirical estimation of sequencing error rates using smoothing splines. BMC Bioinformatics 17:177
Azadeh, Shabnam; Hobbs, Brian P; Ma, Liangsuo et al. (2016) Integrative Bayesian analysis of neuroimaging-genetic data with application to cocaine dependence. Neuroimage 125:813-24
Reyes-Gibby, Cielito C; Wang, Jian; Silvas, Mary Rose T et al. (2016) MAPK1/ERK2 as novel target genes for pain in head and neck cancer patients. BMC Genet 17:40
Dai, Tianjiao; Shete, Sanjay (2016) Time-varying SMART design and data analysis methods for evaluating adaptive intervention effects. BMC Med Res Methodol 16:112
Nieto, Steven J; Patriquin, Michelle A; Nielsen, David A et al. (2016) Don't worry; be informed about the epigenetics of anxiety. Pharmacol Biochem Behav 146-147:60-72
Talluri, Rajesh; Shete, Sanjay (2016) Using the weighted area under the net benefit curve for decision curve analysis. BMC Med Inform Decis Mak 16:94
Pan, Wei; Chen, Yue-Ming; Wei, Peng (2015) Testing for polygenic effects in genome-wide association studies. Genet Epidemiol 39:306-16
Talluri, Rajesh; Shete, Sanjay (2015) Evaluating methods for modeling epistasis networks with application to head and neck cancer. Cancer Inform 14:17-23

Showing the most recent 10 out of 33 publications