This project aims to develop a series of novel approaches to phenotyping drug use and abuse. The general scheme is to develop statistical models from theory, implement them in user friendly software, and examine their statistical properties. Those models that perform sufficiently well will be applied to one or more sets of data to bring new insight into the assessment of substance use. The first goal is to extend of models for factorial invariance, which form the basis of testing for differences between groups. The primary extension will be to allow testing of invariance not merely between distinct groups, but also within groups that vary with respect to continuous variables such as age. This approach will be applied to confirmatory factor analysis, tc latent class analysis, and to models that represent mixtures of both factors and latent classes, and will be able to handle binary, ordinal and continuous observed variables. The method should prove valuable in assessing whether substance abuse patterns in the population represent continuous variation in liability or whether distinct groups exist. The second goal is to extend models for regime switching in the context of growth curve and other factor mixture models.
This aim i s intended to provide a better model for data that involve onset and offset of substance use, and to assist in uncovering heterogeneity. Third, we will develop methods for the analysis of certain forms of partially anonymized data such as those involving randomized response. These methods will be compared for their performance at detecting relationships with predictors, sequelae and correlates of partially randomized data, including resemblance between relatives and outcomes. All model development will be designed to permit the analysis and exploitation of data collected from relatives, and will include models for data on genetic markers, for both linkage and association studies. Applied data analyses will yield substantive results, guide model development, and test for robustness. An array of cross-sectional, longitudinal, and genetically informative datasets will be assembled and analyzed.

Agency
National Institute of Health (NIH)
Institute
National Institute on Drug Abuse (NIDA)
Type
Method to Extend Research in Time (MERIT) Award (R37)
Project #
5R37DA018673-09
Application #
8305141
Study Section
Special Emphasis Panel (NSS)
Program Officer
Weinberg, Naimah Z
Project Start
2004-09-30
Project End
2014-07-31
Budget Start
2012-08-01
Budget End
2013-07-31
Support Year
9
Fiscal Year
2012
Total Cost
$434,684
Indirect Cost
Name
Virginia Commonwealth University
Department
Psychiatry
Type
Schools of Medicine
DUNS #
105300446
City
Richmond
State
VA
Country
United States
Zip Code
23298
Verhulst, B; Neale, M C; Kendler, K S (2015) The heritability of alcohol use disorders: a meta-analysis of twin and adoption studies. Psychol Med 45:1061-72
Vink, Jacqueline M; Hottenga, Jouke Jan; de Geus, Eco J C et al. (2014) Polygenic risk scores for smoking: predictors for alcohol and cannabis use? Addiction 109:1141-51
van Beek, Jenny H D A; de Moor, Marleen H M; Geels, Lot M et al. (2014) The association of alcohol intake with ?-glutamyl transferase (GGT) levels: evidence for correlated genetic effects. Drug Alcohol Depend 134:99-105
Verhulst, Brad; Eaves, Lindon J; Neale, Michael C (2014) Moderating the covariance between family member's substance use behavior. Behav Genet 44:337-46
Kubarych, Thomas S; Kendler, Kenneth S; Aggen, Steven H et al. (2014) Comparing factor, class, and mixture models of cannabis initiation and DSM cannabis use disorder criteria, including craving, in the Brisbane longitudinal twin study. Twin Res Hum Genet 17:89-98
Aliev, Fazil; Latendresse, Shawn J; Bacanu, Silviu-Alin et al. (2014) Testing for measured gene-environment interaction: problems with the use of cross-product terms and a regression model reparameterization solution. Behav Genet 44:165-81
Neale, Michael C (2014) Latent classiness and other mixtures. Behav Genet 44:205-11
Treur, Jorien L; Boomsma, Dorret I; Lubke, Gitta H et al. (2014) The predictive value of smoking expectancy and the heritability of its accuracy. Nicotine Tob Res 16:359-68
Eyler, Lisa T; Vuoksimaa, Eero; Panizzon, Matthew S et al. (2014) Conceptual and data-based investigation of genetic influences and brain asymmetry: a twin study of multiple structural phenotypes. J Cogn Neurosci 26:1100-17
van Dongen, Jenny; Willemsen, Gonneke; Chen, Wei-Min et al. (2013) Heritability of metabolic syndrome traits in a large population-based sample. J Lipid Res 54:2914-23

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