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| PMID |
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| TITLE |
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| Estimating two-stage models for genetic influences on alcohol, tobacco or drug use initiation and dependence vulnerability in twin and family data. |
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| ABSTRACT |
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| Genetic research on risk of alcohol, tobacco or drug dependence must make allowance for the partial overlap of risk-factors for initiation of use, and risk-factors for dependence or other outcomes in users. Except in the extreme cases where genetic and environmental risk-factors for initiation and dependence overlap completely or are uncorrelated, there is no consensus about how best to estimate the magnitude of genetic or environmental correlations between Initiation and Dependence in twin and family data. We explore by computer simulation the biases to estimates of genetic and environmental parameters caused by model misspecification when Initiation can only be defined as a binary variable. For plausible simulated parameter values, the two-stage genetic models that we consider yield estimates of genetic and environmental variances for Dependence that, although biased, are not very discrepant from the true values. However, estimates of genetic (or environmental) correlations between Initiation and Dependence may be seriously biased, and may differ markedly under different two-stage models. Such estimates may have little credibility unless external data favor selection of one particular model. These problems can be avoided if Initiation can be assessed as a multiple-category variable (e.g. never versus early-onset versus later onset user), with at least two categories measurable in users at risk for dependence. Under these conditions, under certain distributional assumptions, recovery of simulated genetic and environmental correlations becomes possible. Illustrative application of the model to Australian twin data on smoking confirmed substantial heritability of smoking persistence (42%) with minimal overlap with genetic influences on initiation. |
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| DATE PUBLISHED |
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| HISTORY |
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| PUBSTATUS |
PUBSTATUSDATE |
| pubmed |
2002/04/05 10:00 |
| medline |
2002/08/16 10:01 |
| entrez |
2002/04/05 10:00 |
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| AUTHORS |
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| NAME |
COLLECTIVENAME |
LASTNAME |
FORENAME |
INITIALS |
AFFILIATION |
AFFILIATIONINFO |
| Heath AC |
|
Heath |
Andrew C |
AC |
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Department of Psychiatry, Washington University School of Medicine, St Louis, Missouri, USA. |
| Martin NG |
|
Martin |
Nicholas G |
NG |
|
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| Lynskey MT |
|
Lynskey |
Michael T |
MT |
|
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| Todorov AA |
|
Todorov |
Alexandre A |
AA |
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| Madden PA |
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Madden |
Pamela A F |
PA |
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| INVESTIGATORS |
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| JOURNAL |
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| VOLUME: 5 |
| ISSUE: 2 |
| TITLE: Twin research : the official journal of the International Society for Twin Studies |
| ISOABBREVIATION: Twin Res |
| YEAR: 2002 |
| MONTH: Apr |
| DAY: |
| MEDLINEDATE: |
| SEASON: |
| CITEDMEDIUM: Print |
| ISSN: 1369-0523 |
| ISSNTYPE: Print |
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| MEDLINE JOURNAL |
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| MEDLINETA: Twin Res |
| COUNTRY: Australia |
| ISSNLINKING: 1369-0523 |
| NLMUNIQUEID: 9815819 |
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| PUBLICATION TYPE |
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| PUBLICATIONTYPE TEXT |
| Journal Article |
| Research Support, U.S. Gov't, P.H.S. |
| Review |
| Twin Study |
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| COMMENTS AND CORRECTIONS |
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| GRANTS |
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| GRANTID |
AGENCY |
COUNTRY |
| P01-CA75581 |
NCI NIH HHS |
United States |
| P50-AA11998 |
NIAAA NIH HHS |
United States |
| R01-AA09022 |
NIAAA NIH HHS |
United States |
| R01-AA10248 |
NIAAA NIH HHS |
United States |
| R01-DA12540 |
NIDA NIH HHS |
United States |
| R01-DA12854 |
NIDA NIH HHS |
United States |
| R37-AA07728 |
NIAAA NIH HHS |
United States |
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| GENERAL NOTE |
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| KEYWORDS |
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| MESH HEADINGS |
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| DESCRIPTORNAME |
QUALIFIERNAME |
| Australia |
epidemiology |
| Cohort Studies |
epidemiology |
| Computer Simulation |
epidemiology |
| Dependency (Psychology) |
epidemiology |
| Diseases in Twins |
genetics |
| Environment |
genetics |
| Female |
genetics |
| Genetic Predisposition to Disease |
genetics |
| Humans |
genetics |
| Male |
genetics |
| Models, Genetic |
genetics |
| Multivariate Analysis |
genetics |
| Substance-Related Disorders |
psychology |
| Twins |
psychology |
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| SUPPLEMENTARY MESH |
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| GENE SYMBOLS |
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| CHEMICALS |
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| OTHER ID's |
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