The use of multiple thresholds in determining the mode of transmission of semi‐continuous traits*

Reich, Theodore; James, J. W.; Morris, C. A.

doi:10.1111/j.1469-1809.1972.tb00767.x

Cited by 361 publications

(179 citation statements)

References 13 publications

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“…For genetic analysis of the dichotomous data (drug use, abuse/dependence), tetrachoric or liability correlation coefficients were calculated (Neale and Cardon, 1992). Liability to drug abuse/ dependence is an estimate of the magnitude of prevalence of the diagnosis among co-twins of affected probands compared to prevalence in the general population (Reich et al, 1972). Under the influence of multiple genes in combination with multiple environmental factors, liability is normally distributed.…”

Section: Discussionmentioning

confidence: 99%

Genetic and environmental influences on drug use and abuse/dependence in male and female twins

Bree

Johnson²,

Neale

et al. 1998

Drug and Alcohol Dependence

212

127

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Twins were recruited through alcohol and drug treatment programs. With structural equation modeling, genetic and environmental estimates were obtained for use and DSM-III abuse/dependence of sedatives, opioids, cocaine, stimulants, and cannabis as well as any illicit drug. Analyses were conducted separately for males and females. Models included thresholds based on population prevalence of use or abuse/dependence and ever having been in treatment. Genetic influences were found for most measures. They were generally stronger for males than females and for clinical diagnoses of abuse/dependence compared to use. Common environmental influences played a greater role in use than abuse/dependence.

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Section: Discussionmentioning

confidence: 99%

Genetic and environmental influences on drug use and abuse/dependence in male and female twins

Bree

Johnson²,

Neale

et al. 1998

Drug and Alcohol Dependence

212

127

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“…By adopting an ordinal model-fitting approach we specified a threshold to separate 'normality' from 'pathology' upon the traits under study. This approach assumes phenotypic discreteness (or semi-continuity) of traits imposed upon of the same normally distributed underlying multi-factorial continuum of risk [39]. As such, it is the closest approximation to the classical medical distinction between affected and unaffected subjects while assuming a multi-factorial underlying liability, whereby several environmental and genetic determinants add to each other to influence the manifestation of an illness once a critical threshold is reached (see Fig.…”

Section: Model Fitting Analysesmentioning

confidence: 99%

Genetic and environmental influences upon the CBCL/6-18 DSM-oriented scales: similarities and differences across three different computational approaches and two age ranges

Spatola

Rende

Battaglia

2010

Eur Child Adolesc Psychiatry

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To cite this version:Chiara A. M. Spatola, Richard Rende, Marco Battaglia. Genetic and environmental influences upon the CBCL/6-18 DSM-oriented scales: similarities and differences across three different computational approaches and two age ranges. European child & adolescent psychiatry, 2010, 19 (8), pp.647-658. <10.1007/s00787-010-0102-z>. ORIGINAL CONTRIBUTIONGenetic and environmental influences upon the CBCL/6-18 DSM-oriented scales: similarities and differences across three different computational approaches and two age ranges Abstract Inasmuch as the newly established DSM-oriented CBCL/6-18 scales are to be increasingly employed to assess clinical/high-risk populations, it becomes important to explore their aetiology both within the normal-and the extreme range of variation in general population samples and to compare the results obtained in different age groups. We investigated by the Quantitative Maximum Likelihood, the De Fries-Fulker, and the Ordinal Maximum Likelihood methods the genetic and environmental influences upon the five DSM-oriented CBCL/6-18 scales in 796 twins aged 8-17 years belonging to the general population-based Italian Twin Registry. When children were analysed together regardless of age, most best-fitting solutions yielded genetic and non-shared environmental factors as the sole influences for DSMoriented CBCL/6-18 behaviours, both for the normal and the extreme variations. When analyses were conducted separately for two age groups, shared environmental influences emerged consistently for Affective and Anxiety Problems in children aged 8-11. Oppositional-Defiant, Attention Deficit/Hyperactivity, and Conduct Problems appeared-with few exceptions-influenced only by genetic and non-shared environmental factors in both age groups, according to all three computational approaches. The De Fries-Fulker method appeared to be more sensitive in detecting shared environmental effects. Analysing the same set of data with different analytic approaches leads to better-balanced views on the aetiology of psychopathological behaviours in the developmental years.

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“…The observed discontinuous phenotype is therefore conceptualized as the visible manifestation of some underlying, quantitative physiological trait (Falconer, 1989). In principle any number of phenotypic classes can be subsumed in a liability threshold model (Reich et al 1972(Reich et al , 1979Gianola & Foulley, 1983;Morton et al 1991), but diseases involving two phenotypic states and a single liability threshold are typical in human genetic studies-individuals whose liability is below the threshold value are 'unaffected', and those whose liability exceeds the threshold value exhibit the 'affected' condition.…”

Section: Liability and Thresholdmentioning

confidence: 99%

Power of Variance Component Linkage Analysis—II. Discrete Traits

Williams¹,

Blangero²

2004

Annals of Human Genetics

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SummaryWe determine the power of variance component linkage analysis in the case of discrete, dichotomous traits analyzed under a classical liability threshold model. For simplicity we consider randomly ascertained samples and an additive model of variation incorporating a QTL, residual additive genetic factors, and individual-specific random environmental effects. We derive an expression for the power of variance component linkage analysis in arbitrary relative pairs, and compare the power of discrete and quantitative trait linkage analysis in the specific case of sibpairs. The predicted sample sizes required in linkage analysis of sibpairs are confirmed by analysis of simulated data. Unlike the affected-sibpair method, the power of discrete trait variance component analysis increases with trait prevalence. The relative efficiency of a discrete trait for linkage analysis increases with population trait prevalence, but does not exceed about 40% and is typically much less.

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The use of multiple thresholds in determining the mode of transmission of semi‐continuous traits*

Cited by 361 publications

References 13 publications

Genetic and environmental influences on drug use and abuse/dependence in male and female twins

Genetic and environmental influences on drug use and abuse/dependence in male and female twins

Genetic and environmental influences upon the CBCL/6-18 DSM-oriented scales: similarities and differences across three different computational approaches and two age ranges

Power of Variance Component Linkage Analysis—II. Discrete Traits

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