scholarly journals Statistical Power and the Classical Twin Design

2020 ◽  
Vol 23 (2) ◽  
pp. 87-89
Author(s):  
Pak C. Sham ◽  
Shaun M. Purcell ◽  
Stacey S. Cherny ◽  
Michael C. Neale ◽  
Benjamin M. Neale
Keyword(s):  
Structural Equation ◽  
Statistical Power ◽  
Behavior Genetics ◽  
Sample Size Calculation ◽  
Twin Studies ◽  
Equation Modeling ◽  
Twin Design ◽  
Sufficient Power ◽  
Chi Squared ◽  
Classical Twin Design

AbstractDr Nick Martin has made enormous contributions to the field of behavior genetics over the past 50 years. Of his many seminal papers that have had a profound impact, we focus on his early work on the power of twin studies. He was among the first to recognize the importance of sample size calculation before conducting a study to ensure sufficient power to detect the effects of interest. The elegant approach he developed, based on the noncentral chi-squared distribution, has been adopted by subsequent researchers for other genetic study designs, and today remains a standard tool for power calculations in structural equation modeling and other areas of statistical analysis. The present brief article discusses the main aspects of his seminal paper, and how it led to subsequent developments, by him and others, as the field of behavior genetics evolved into the present era.

Nature, Nurture, and Ethnocentrism in the Minnesota Twin Study

2012 ◽  
Vol 15 (1) ◽  
pp. 71-73 ◽  
Author(s):  
Byron D'Andra Orey ◽  
Hyung Park
Keyword(s):  
Environmental Effects ◽  
Structural Equation ◽  
Equation Modeling ◽  
Genetic Inheritance ◽  
Twin Design ◽  
Ace Model ◽  
Classical Twin Design ◽  
And Behavior ◽  
Attitudes And Behavior ◽  
Additive Genetic Effects

The preponderance of research on the study of ethnocentrism has primarily attributed such attitudes to learned behavior. The research here advances the argument that both socialization and genetic inheritance contribute to the development of ethnocentric attitudes and behavior. This analysis employs the Minnesota Twins Political Survey data consisting of 596 complete twin pairs. Using the classical twin design, we employed structural equation modeling to model the covariance of twins in regards to additive genetic effects, shared environmental effects, and unique environmental effects (i.e., the classic ACE model). The findings reveal that genetic inheritance is significant in explaining the variance in genetic attitudes. Specifically, genetic inheritance explains 18% of the variance, with the overwhelming 82% being explained by the unique environment.

scholarly journals Genomics of ageing in twins

2014 ◽  
Vol 73 (4) ◽  
pp. 526-531 ◽  
Author(s):  
Massimo Mangino
Keyword(s):  
Molecular Mechanisms ◽  
Twin Studies ◽  
Later Life ◽  
Twin Design ◽  
The Past ◽  
Human Ageing ◽  
Classical Twin Design ◽  
Environmental Component ◽  
Early Life Events ◽  
Generation Sequencing

Ageing is a complex multifactorial process, reflecting the progression of all degenerative pathways within an organism. Due to the increase of life expectancy, in recent years, there is a pressing need to identify early-life events and risk factors that determine health outcomes in later life. So far, genetic variation only explains ~20–25 % of the variability of human survival to age 80+. This clearly implies that other factors (environmental, epigenetic and lifestyle) contribute to lifespan and the rate of healthy ageing within an individual. Twin studies in the past two decades proved to be a very powerful tool to discriminate the genetic from the environmental component. The aim of this review is to describe the basic concepts of the twin study design and to report some of the latest studies in which high-throughput technologies (e.g. genome/epigenome-wide assay, next generation sequencing, MS metabolic profiling) combined with the classical twin design have been applied to the analysis of novel ‘omics’ to further understand the molecular mechanisms of human ageing.

scholarly journals Gene-Environment Interplay in Twin Models

2013 ◽  
Vol 21 (3) ◽  
pp. 368-389 ◽  
Author(s):  
Brad Verhulst ◽  
Peter K. Hatemi
Keyword(s):  
Genetic Similarity ◽  
Behavior Genetics ◽  
Empirical Studies ◽  
Environment Interaction ◽  
Twin Design ◽  
Gene Environment Interaction ◽  
Twin Model ◽  
Gene Environment ◽  
Twin Models ◽  
Classical Twin Design

In this article, we respond to Shultziner's critique that argues that identical twins are more alike not because of genetic similarity, but because they select into more similar environments and respond to stimuli in comparable ways, and that these effects bias twin model estimates to such an extent that they are invalid. The essay further argues that the theory and methods that undergird twin models, as well as the empirical studies which rely upon them, are unaware of these potential biases. We correct this and other misunderstandings in the essay and find that gene-environment (GE) interplay is a well-articulated concept in behavior genetics and political science, operationalized as gene-environment correlation and gene-environment interaction. Both are incorporated into interpretations of the classical twin design (CTD) and estimated in numerous empirical studies through extensions of the CTD. We then conduct simulations to quantify the influence of GE interplay on estimates from the CTD. Due to the criticism's mischaracterization of the CTD and GE interplay, combined with the absence of any empirical evidence to counter what is presented in the extant literature and this article, we conclude that the critique does not enhance our understanding of the processes that drive political traits, genetic or otherwise.

scholarly journals Twin Studies on Obsessive–Compulsive Disorder: A Review

2005 ◽  
Vol 8 (5) ◽  
pp. 450-458 ◽  
Author(s):  
Daniël S. van Grootheest ◽  
Daniëlle C. Cath ◽  
Aartjan T. Beekman ◽  
Dorret I. Boomsma
Keyword(s):  
Obsessive Compulsive Disorder ◽  
Structural Equation ◽  
Twin Studies ◽  
Conclusive Evidence ◽  
Equation Modeling ◽  
Dimensional Approach ◽  
Obsessive Compulsive ◽  
Twin Research ◽  
Compulsive Disorder ◽  
Genetic And Environmental Factors

AbstractGenetic factors have historically been thought of as important in the development of obsessive–compulsive disorder (OCD). For the estimation of the relative importance of genetic and environmental factors, twin studies are an obvious approach. Twin studies of OCD have a long history, starting in 1929. In this review, over 70 years of twin research of OCD is presented, using four different approaches that represent the steps in the twin research of OCD from past to present. These steps include (1) case-studies of twins with OCD from the old literature; (2) twin studies of OCD using Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria; (3) twin studies of OCD using a dimensional approach, comparing resemblances in monozygotic and dizygotic twins; and (4) twin studies of OCD using a dimensional approach, analyzing the data with Structural Equation Modeling. It is concluded that only the studies using the last method have convincingly shown that, in children, obsessive–compulsive (OC) symptoms are heritable, with genetic influences in the range of 45% to 65%. In adults, studies are suggestive for a genetic influence on OC symptoms, ranging from 27% to 47%, but a large twin study using a bio- metrical approach with continuous data is still needed to provide conclusive evidence. Strategies for future twin studies of OCD are discussed.

A twin study of genetic and environmental influences on tobacco initiation, regular tobacco use and nicotine dependence

2004 ◽  
Vol 34 (7) ◽  
pp. 1251-1261 ◽  
Author(s):  
HERMINE H. MAES ◽  
PATRICK F. SULLIVAN ◽  
CYNTHIA M. BULIK ◽  
MICHAEL C. NEALE ◽  
CAROL A. PRESCOTT ◽  
...  
Keyword(s):  
Nicotine Dependence ◽  
Tobacco Use ◽  
Structural Equation ◽  
Genetic Factors ◽  
Smoking Behavior ◽  
Twin Studies ◽  
Population Based ◽  
Equation Modeling ◽  
Opposite Sex ◽  
Tobacco Initiation

Background. Numerous twin studies have reported significant genetic contributions to the variability of tobacco initiation (TI), while fewer studies have shown similar results for the persistence of smoking behavior, or nicotine dependence (ND). As the development of ND requires regular tobacco use (RTU) which in turn requires TI, a conditional approach is necessary.Method. We used structural equation modeling of multi-step conditional processes to examine the relationship between genetic and environmental risk factors for TI, RTU and ND. The tobacco variables were assessed by personal interview in female, male and opposite-sex twin pairs from the population-based Virginia Twin Registry.Results. The results suggested that the liabilities to TI, RTU and ND were correlated. Over 80% of the variance in liability to TI and RTU were shared, and a smaller proportion was shared between RTU and ND. The heritabilities were estimated at 75%, 80% and 60% respectively for TI, RTU and ND. The variance specific to liability to RTU was entirely accounted for by additive genetic factors. Only a modest part of the heritability in liability of ND was due to genetic factors specific to ND. Shared environmental factors were not significant. No sex differences were found for the sources of variation or causal paths, but prevalences were significantly greater in males versus females.Conclusions. This study showed significant overlap in the contribution of genetic factors to individual differences in TI, RTU and ND. Furthermore, there was evidence for significant additional genetic factors specific to RTU and ND.

scholarly journals umx: Twin and Path-based Structural Equation Modeling in R

2017 ◽  
Author(s):  
Timothy C Bates ◽  
Hermine H Maes ◽  
Michael C Neale
Keyword(s):  
Structural Equation Modeling ◽  
Ordinal Data ◽  
Structural Equation ◽  
Behavior Genetics ◽  
Model Specification ◽  
Equation Modeling ◽  
Environment Interaction ◽  
The Social ◽  
Twin Models ◽  
Pathway Models

Structural equation modeling (SEM) is an important research tool, both for path-based model specification, common in the social sciences, and also matrix-based models in heavy use in behavior genetics. We developed umx to give more immediate access, concise syntax and helpful defaults for users in these two broad disciplines. umx supports development, modification, and comparison of models, as well as both graphical and tabular output. The second major focus of umx, behavior genetic models, is supported via functions implementing standard multi-group twin models. These functions support raw and covariance data, including joint ordinal data, and give solutions for ACE models including support for covariates, common- and independent-Pathway models, and Gene \(\times\) Environment interaction models. A tutorial site and question forum are also available.

scholarly journals umx: Twin and Path-based Structural Equation Modeling in R

2017 ◽  
Author(s):  
Timothy C Bates ◽  
Hermine H Maes ◽  
Michael C Neale
Keyword(s):  
Structural Equation Modeling ◽  
Ordinal Data ◽  
Structural Equation ◽  
Behavior Genetics ◽  
Model Specification ◽  
Equation Modeling ◽  
Environment Interaction ◽  
The Social ◽  
Twin Models ◽  
Pathway Models

Structural equation modeling (SEM) is an important research tool, both for path-based model specification, common in the social sciences, and also matrix-based models in heavy use in behavior genetics. We developed umx to give more immediate access, concise syntax and helpful defaults for users in these two broad disciplines. umx supports development, modification, and comparison of models, as well as both graphical and tabular output. The second major focus of umx, behavior genetic models, is supported via functions implementing standard multi-group twin models. These functions support raw and covariance data, including joint ordinal data, and give solutions for ACE models including support for covariates, common- and independent-Pathway models, and Gene \(\times\) Environment interaction models. A tutorial site and question forum are also available.

Heritabilities of Lipids in Young European American and African American Twins

2005 ◽  
Vol 8 (5) ◽  
pp. 492-498 ◽  
Author(s):  
Anastasia Iliadou ◽  
Harold Snieder ◽  
Xiaoling Wang ◽  
Frank A. Treiber ◽  
Catherine L. Davis
Keyword(s):  
African American ◽  
Ethnic Differences ◽  
Environmental Effects ◽  
Structural Equation ◽  
Density Lipoprotein ◽  
Twin Studies ◽  
Equation Modeling ◽  
Parameter Estimates ◽  
European American ◽  
Eastern United States

AbstractTwin studies of lipids have almost exclusively involved Caucasians. People of African descent are known to show a healthier lipid profile, but relatively little is known about ethnic differences in genetic and environmental influences on lipids. One hundred and six African American (AA) and 106 European American (EA) twins (30 singletons and 91 complete pairs: 49 monozygotic, 21 dizygotic and 21 opposite-sex) from the south-eastern United States were studied (mean age 17.9 ± 3.2 years; 79% fasting). Lipids were assayed with the Cholestech LDX system. Analyses were adjusted for fasting status. Generalized estimating equations were used to test for the effects of sex and ethnicity on means, controlling for the dependence within twin pairs. Structural equation modeling was used to estimate genetic and environmental effects on each lipid variable. Females showed higher high-density lipoprotein (HDL) values than males (p< .001) and AAs showed higher HDL values than EAs (p< .001). EA males had higher triglyceride values than other groups (p= .02). All parameter estimates could be set equal across sex. Parameter estimates for total cholesterol, triglycerides and HDL could be set equal across ethnicity. The best fitting model for low- density lipoprotein (LDL) showed higher heritability in AAs (.92) than EAs (.69). Heritabilities ranged from 69% to 92%, with remaining variation explained by nonshared environmental effects. Adjustment for body mass index had virtually no effect on the heritability estimates. In this first twin study on lipids to include AAs, no ethnic differences in heritability were found except for LDL, where AAs exhibited higher estimates.

scholarly journals A Test of the Equal Environment Assumption (EEA) in Multivariate Twin Studies

2006 ◽  
Vol 9 (3) ◽  
pp. 403-411 ◽  
Author(s):  
Eske M. Derks ◽  
Conor V. Dolan ◽  
Dorret I. Boomsma
Keyword(s):  
Spatial Ability ◽  
Environmental Effects ◽  
Statistical Power ◽  
Model Identification ◽  
Twin Studies ◽  
Parameter Estimates ◽  
Sufficient Information ◽  
Phenotypic Data ◽  
Twin Design ◽  
Dizygotic Twins

AbstractIn the classic twin design, estimation of genetic and environmental effects is based on the assumption that environmental influences are shared to the same extent by monozygotic and dizygotic twins (equal environment assumption, EEA). We explore the conditions in which the EEA can be tested based on multivariate phenotypic data. We focus on the test whether the correlation between shared environmental factors in dizygotic twins (rC) is less than 1. First, model identification was investigated analytically in Maple and Mx. Second, statistical power was examined in Mx. Third, the amount of bias caused by violation of the EEA was evaluated. Finally, applications to empirical data concern spatial ability in adolescents and aggression in children. Bivariate and trivariate models include several instances in which the EEA can be tested. The number of twin pairs that is needed to detect violation of the EEA with a statistical power of .80 (α = .05) varied between 508 and 3576 pairs for the situations considered. The bias in parameter estimates, given misspecification, ranged from 5% to 34% for additive genetic effects, and from 4% to 34% for shared environmental effects. Estimates of the nonshared environmental effects were not biased. The EEA was not violated for spatial ability or aggression. Multivariate data provide sufficient information to test the validity of the EEA. The number of twin pairs that is needed is no greater than the number typically available in most twin registries. The analysis of spatial ability and aggression indicated no detectable violation of the EEA.

Sign in / Sign up

Export Citation Format

Share Document