This proposal for a Mentored Research Scientist Award is designed to provide the candidate with the necessary training and enhanced research experience to transition into an independent drug dependence genetic statistician/genetic epidemiologist. The proposed training, in the areas of drug dependence epidemiology, psychiatric genetics, and kernel-based statistical interaction analysis will complement the candidate's previous training and existing skills, and provide a solid foundation for the candidate to become an independent investigator. This training will be advised by a multi-discipline mentoring team with extensive experience and complementary skills. The Primary Mentor, Dr. Jim Anthony, and Co-Mentor, Dr. Naomi Breslau, will advise the candidate's training in drug dependence epidemiology, with guided learning experiences via consultants on pathophysiology, etiology, clinical diagnosis, and therapeutics. Co-Mentor, Dr. Karen Friderici, and Collaborator, Dr. Laura Bierut, will supervise the candidate's training in psychiatric genetics Co-Mentor, Daniel Schaid, will advise the candidate's training in kernel-based statistical interaction analysis. The long term goal is to identify gene-gene/gene-environment (G-G/G-E) interactions that account for tobacco/nicotine dependence and related phenotypes (NDRP), and then to evaluate their role in NDRP prediction and prevention, and in personalized treatment. The research proposed in this application is to improve the detection of NDRP-related G-G/G-E interactions by employing innovative statistical genetic approaches. In the proposed research, non-parametric kernel-based statistical genetic approaches will be developed and applied to NDRP-associated genes (e.g., nAChRs sub-unit genes) and environmental determinants (e.g., childhood abuse) for (a) detecting high-order interactions, (b) discovering interactions among low-marginal-effect genetic and environmental determinants, and (c) identifying common and unique interactions accounting for NDRP and alcohol dependence and related phenotypes. An existing case-control genome-wide association study dataset from the Study of Addiction: Genetics and Environment will be used for the proposed G-G/G-E interaction analysis. The novel G-G/G-E interaction findings will likely provide new insights into the underlying NDRP pathophysiological and etiological processes, as well as have significant implications for better NDRP prediction and prevention/treatment strategies. The training and research experience gained from the proposed study will serve as the groundwork for an independent research program, including a new R01 proposal to be submitted during the second year of the K01 award interval, in order to strengthen and refine current methods for NDRP-related G-G/G-E interaction research.

Public Health Relevance

The proposed career development and research plan will initiate a discovery process focused on the tobacco/nicotine dependence processes that account for substantial global health burdens in the form of tobacco-attributable morbidity and mortality. Novel biostatistical approaches will be developed and applied to an existing large-scale genome-wide association study datasets to search gene-gene and gene-environment interactions predisposing to nicotine dependence, and if successful, the research will lead to the discovery of novel gene-gene and gene-environment interactions, suitable for follow-up in nicotine dependence prediction and prevention investigations, and possibly in personalized treatment.

Agency
National Institute of Health (NIH)
Institute
National Institute on Drug Abuse (NIDA)
Type
Research Scientist Development Award - Research & Training (K01)
Project #
1K01DA033346-01A1
Application #
8443232
Study Section
Human Development Research Subcommittee (NIDA)
Program Officer
Rutter, Joni
Project Start
2013-02-15
Project End
2018-01-31
Budget Start
2013-02-15
Budget End
2014-01-31
Support Year
1
Fiscal Year
2013
Total Cost
$175,176
Indirect Cost
$12,435
Name
Michigan State University
Department
Public Health & Prev Medicine
Type
Schools of Medicine
DUNS #
193247145
City
East Lansing
State
MI
Country
United States
Zip Code
48824
Wen, Yalu; He, Zihuai; Li, Ming et al. (2016) Risk Prediction Modeling of Sequencing Data Using a Forward Random Field Method. Sci Rep 6:21120
Li, Ming; Li, Jingyun; He, Zihuai et al. (2016) Testing Allele Transmission of an SNP Set Using a Family-Based Generalized Genetic Random Field Method. Genet Epidemiol 40:341-51
Li, Ming; Li, Jingyun; Wei, Changshuai et al. (2016) A Three-Way Interaction among Maternal and Fetal Variants Contributing to Congenital Heart Defects. Ann Hum Genet 80:20-31
Wei, Changshuai; Elston, Robert C; Lu, Qing (2016) A weighted U statistic for association analyses considering genetic heterogeneity. Stat Med 35:2802-14
Vsevolozhskaya, Olga A; Zaykin, Dmitri V; Barondess, David A et al. (2016) Uncovering Local Trends in Genetic Effects of Multiple Phenotypes via Functional Linear Models. Genet Epidemiol 40:210-21
Li, Ming; He, Zihuai; Schaid, Daniel J et al. (2015) A powerful nonparametric statistical framework for family-based association analyses. Genetics 200:69-78
Wen, Yalu; Lu, Qing (2015) Risk prediction models for oral clefts allowing for phenotypic heterogeneity. Front Genet 6:264
Li, Ming; Gardiner, Joseph C; Breslau, Naomi et al. (2014) A non-parametric approach for detecting gene-gene interactions associated with age-at-onset outcomes. BMC Genet 15:79
Vsevolozhskaya, Olga A; Zaykin, Dmitri V; Greenwood, Mark C et al. (2014) Functional analysis of variance for association studies. PLoS One 9:e105074
Sun, Xiangqing; Lu, Qing; Mukherjee, Shubhabrata et al. (2014) Analysis pipeline for the epistasis search - statistical versus biological filtering. Front Genet 5:106

Showing the most recent 10 out of 17 publications