Bucholz, Kathleen K

Grant, Julia D

Scherrer, Jeffrey F

Xian, Hong

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Washington University School of MedicineDigital Commons@BeckerPosters 2005: Alcoholism and Comorbidity2005The magnitude of genetic and environmentalinfluences common to comorbid alcoholism andmarijuana problem use is stable across diagnosticphenotypesJeffrey F. ScherrerWashington University School of Medicine in St. LouisHong XianWashington University School of Medicine in St. LouisJulia D. GrantWashington University School of Medicine in St. LouisKathleen K. BucholzWashington University School of Medicine in St. LouisFollow this and additional works at: http://digitalcommons.wustl.edu/guzeposter2005Part of the Medicine and Health Sciences CommonsThis Poster is brought to you for free and open access by the 2005: Alcoholism and Comorbidity at Digital Commons@Becker. It has been accepted forinclusion in Posters by an authorized administrator of Digital Commons@Becker. For more information, please contact engeszer@wustl.edu.Recommended CitationScherrer, Jeffrey F.; Xian, Hong; Grant, Julia D.; and Bucholz, Kathleen K., "The magnitude of genetic and environmental influencescommon to comorbid alcoholism and marijuana problem use is stable across diagnostic phenotypes" (2005). Posters. Paper 11 SamuelB. Guze Symposium on Alcoholism.http://digitalcommons.wustl.edu/guzeposter2005/11Jeffrey F. Scherrer1,2, Hong Xian2,3, Julia D. Grant1, Kathleen K. Bucholz11 Dept. of Psychiatry, Washington University School of Medicine, St. Louis, MO2 Veterans Administration Medical Center- Research Service, St. Louis, MO.3 Dept Internal Med., Div. of Gen. Med. Sciences, Washington University School of Medicine, St. Louis, MOThe Magnitude of Genetic and Environmental Influences Common to Co-morbid Alcoholism and Marijuana Problem Use is Stable Across Diagnostic Phenotypes INTRODUCTION Alcohol Dependence (AD) and Marijuana Dependence (MJD) are oftenco-morbid disorders Previous analyses of Vietnam Era Twin Registry (VETR) data hassuggested common genetic risk at the symptom level diagnoses Past research has not investigated if the common genetic andenvironmental risks are similar for varying diagnostic phenotypes Does the genetic architecture underlying alcohol and marijuanaproblem use vary by diagnostic phenotype?METHODS Subjects and data collection• Male-Male twin pair members of the Vietnam Era Twin (VET) Registry• Both twins served during Vietnam Era (1965-1975)• 6,711 twins answered questions about alcohol and marijuana use in 1992 administration of the Diagnostic Interview Schedule (DIS)• Eligibility criteria for current study-Participated in 1992 telephone admin. of DIS-Data available from both members of a twin pair-Data complete for alcohol and marijuana Alcohol and Marijuana Phenotypes dependence only abuse/dependence severity (unaffected, abuse only, dependence only)- Genetic model fitting- Bivariate Cholesky models- Lisrel and MX softwareAAlcoholC E A C EMarijuanaRESULTS• Prevalence of alcohol and marijuana phenotypesAlcohol abuse only 19.3%Alcohol dependence only 35.2%Alcohol abuse/dependence 54.5%Marijuana abuse only 0.6%Marijuana dependence only 6.6%Marijuana abuse/dependence 7.2%Distribution of Co-morbid Alcohol and Marijuana Abuse and DependenceUnaffected Abuse DependenceUnaffected 299898.2%60.2%481.6%Abuse 121994.1%70.5%705.4%Dependence 201085.1%271.1%32613.8%MarijuanaAlcoholBivariate Model Fitting Results: Variance Component Estimates for Full and Best Fitting Models1. Severity phenotype: full modelAlcohol unaffected/abuse/depCannabis unaffected/abuse/dependenceModel FitCommon Specific AICa2 c2 e2 a2 c2 e2 a2 c2 e2 -7.6143.8% 9.0% 47.2% 14.8% 8.0% 5.3% 17.5% 21.6% 32.9%2. Severity phenotype: best reduced modelAlcohol unaffected/abuse/depCannabis unaffected/abuse/dependenceModel FitCommon Specific AICa2 c2 e2 a2 c2 e2 a2 c2 e2 -10.4954.0% -- 46.0% 23.2% -- 4.5% -- 36.7% 35.6%1. Abuse/dependence phenotype: full modelAlcohol abuse/dependenceCannabis abuse/dependenceModel FitCommon Specific AICa2 c2 e2 a2 c2 e2 a2 c2 e2 -7.6140.3% 12.8% 46.8% 27.0% 1.0% 4.7% 4.1% 29.7% 33.7%2. Abuse/dependence phenotype: best reduced modelAlcohol abuse/dependenceCannabis abuse/dependenceModel FitCommon Specific AICa2 c2 e2 a2 c2 e2 a2 c2 e2 -10.8954.8% -- 45.2% 25.1% -- 4.4% -- 35.5% 35.0%1. Dependence phenotype: full modelAlcohol dependenceCannabis abuse/dependenceModel FitCommon Specific AICa2 c2 e2 a2 c2 e2 a2 c2 e2 -4.6651.6% 3.7% 44.7% 8.6% 0% 6.7% 23.7% 29.5% 31.6%2. Dependence phenotype: best reduced modelAlcohol dependenceCannabis abuse/dependenceModel FitCommon Specific AICa2 c2 e2 a2 c2 e2 a2 c2 e2 -8.0655.7% -- 44.3% 20.5% -- 5.2% -- 38.7% 35.5%* AIC: Akaike Information Criterion – measure of parsimonyPath diagram for best fitting dependence modelAAlcoholE C EMarijuana55.7%(50.2-60.8)20.5%(12.9-20.9)44.3%(50.2-60.8)5.2%(1.7-10.8)35.5%(27-43.3)38.7%(29.0-47.3)CONCLUSIONS Most co-morbidity accounted for by marijuana dependence among subjects with alcohol dependence Genetic factors account for 20% and unique environmental factors account for 5% of marijuana dependence co-morbid with alcohol   dependence The genetic architecture was similar across phenotypes studied Further research should consider diagnostic phenotypes outside limitations of DSM that may be derived from factor analyses or latent class analysesPrimary Funding Sources: NIAAA grants AA11998, NIDA grants DA14363, DA00272  and Department of Veterans Affairs Health Services, Research and Development Service and the Cooperative Studies Program (Study 992)

The magnitude of genetic and environmental influences common to comorbid alcoholism and marijuana problem use is stable across diagnostic phenotypes

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The magnitude of genetic and environmental influences common to comorbid alcoholism and marijuana problem use is stable across diagnostic phenotypes

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