This project aims to develop and apply novel statistical approaches to address key challenges in the etiology of substance use, abuse and dependence. These are: high-density data; genotype by environment interplay; measurement; onset and offset; and comorbidity. Recent advances in genomic and other -omic technologies, neuroimaging, and near-continuous assessments of environment, location and behavior are creating unprecedented opportunities to understand the interplay between genetic and environmental risk factors for substance use behaviors. The project will develop new statistical models and computationally efficient software and methods to permit data mining of substance use phenotypes, genotypes and environmental measures. These models include those for: detecting GxE interaction in the presence of variable measurement precision; incorporating genetic marker and other high-density data into structural equation models to distinguish direct from indirect effects; models for gene-environment interplay, especially niche-selection; mixture distribution models of comorbidity; alternative models for symptoms co-occurrence; and regime switching to better characterize alternative pathways to outcomes. The new methods and models will be applied to datasets including measured genotypes and environmental risk factors collected in the United States, The Netherlands and Australia by project team members, The new models and methods will be disseminated freely and greatly increase the value of existing datasets and those being assembled with novel technologies.

Public Health Relevance

Substance use abuse and dependence are especially complex, with genetic and environmental risk factors, with highly variable patterns of onset, offset and comorbidity with psychiatric disorders, cardiovascular disease and cancer. This project will develop statistical methods and software to improve the prediction of drug use, abuse and dependence, and its treatment and prevention.

Agency
National Institute of Health (NIH)
Institute
National Institute on Drug Abuse (NIDA)
Type
Research Project (R01)
Project #
5R01DA018673-16
Application #
9636526
Study Section
Behavioral Genetics and Epidemiology Study Section (BGES)
Program Officer
Weinberg, Naimah Z
Project Start
2004-09-30
Project End
2021-01-31
Budget Start
2019-02-01
Budget End
2021-01-31
Support Year
16
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Virginia Commonwealth University
Department
Psychiatry
Type
Schools of Medicine
DUNS #
105300446
City
Richmond
State
VA
Country
United States
Zip Code
23298
Baker, Jessica H; Johnson, Nicole K; Munn-Chernoff, Melissa A et al. (2018) Illicit Drug Use, Cigarette Smoking, and Eating Disorder Symptoms: Associations in an Adolescent Twin Sample. J Stud Alcohol Drugs 79:720-724
Boker, Steven M; Martin, Mike (2018) A Conversation between Theory, Methods, and Data. Multivariate Behav Res :1-14
Gillespie, Nathan A; Aggen, Steven H; Neale, Michael C et al. (2018) Associations between personality disorders and cannabis use and cannabis use disorder: a population-based twin study. Addiction 113:1488-1498
Pritikin, Joshua N; Brick, Timothy R; Neale, Michael C (2018) Multivariate normal maximum likelihood with both ordinal and continuous variables, and data missing at random. Behav Res Methods 50:490-500
Gillespie, Nathan A; Aggen, Steven H; Gentry, Amanda E et al. (2018) Testing Genetic and Environmental Associations Between Personality Disorders and Cocaine Use: A Population-Based Twin Study. Twin Res Hum Genet 21:24-32
Moulder, Robert G; Boker, Steven M; Ramseyer, Fabian et al. (2018) Determining synchrony between behavioral time series: An application of surrogate data generation for establishing falsifiable null-hypotheses. Psychol Methods 23:757-773
Hancock, D B; Guo, Y; Reginsson, G W et al. (2018) Genome-wide association study across European and African American ancestries identifies a SNP in DNMT3B contributing to nicotine dependence. Mol Psychiatry 23:1-9
Minic?, Camelia C; Dolan, Conor V; Boomsma, Dorret I et al. (2018) Extending Causality Tests with Genetic Instruments: An Integration of Mendelian Randomization with the Classical Twin Design. Behav Genet :
Bartels, Meike; Hendriks, Anne; Mauri, Matteo et al. (2018) Childhood aggression and the co-occurrence of behavioural and emotional problems: results across ages 3-16 years from multiple raters in six cohorts in the EU-ACTION project. Eur Child Adolesc Psychiatry 27:1105-1121
Minic?, Camelia C; Verweij, Karin J H; van der Most, Peter J et al. (2018) Genome-wide association meta-analysis of age at first cannabis use. Addiction 113:2073-2086

Showing the most recent 10 out of 174 publications