Cortical surface area measures appear to be functionally relevant and distinct in etiology, devel... more Cortical surface area measures appear to be functionally relevant and distinct in etiology, development, and behavioral correlates compared with other size characteristics, such as cortical thickness. Little is known about genetic and environmental influences on individual differences in regional surface area in humans. Using a large sample of adult twins, we determined relative contributions of genes and environment on variations in regional cortical surface area as measured by magnetic resonance imaging before and after adjustment for genetic and environmental influences shared with total cortical surface area. We found high heritability for total surface area and, before adjustment, moderate heritability for regional surface areas. Compared with other lobes, heritability was higher for frontal lobe and lower for medial temporal lobe. After adjustment for total surface area, regionally specific genetic influences were substantially reduced, although still significant in most regions. Unlike other lobes, left frontal heritability remained high after adjustment. Thus, global and regionally specific genetic factors both influence cortical surface areas. These findings are broadly consistent with results from animal studies regarding the evolution and development of cortical patterning and may guide future research into specific environmental and genetic determinants of variation among humans in the surface area of particular regions.
Magnetic resonance imaging data are being used in statistical models to predicted brain ageing (P... more Magnetic resonance imaging data are being used in statistical models to predicted brain ageing (PBA) and as biomarkers for neurodegenerative diseases such as Alzheimer's Disease. Despite their increasing application, the genetic and environmental etiology of global PBA indices is unknown. Likewise, the degree to which genetic influences in PBA are longitudinally stable and how PBA changes over time are also unknown. We analyzed data from 734 men from the Vietnam Era Twin Study of Aging with repeated MRI assessments between the ages 51-72 years. Biometrical genetic analyses "twin models" revealed significant and highly correlated estimates of additive genetic heritability ranging from 59 to 75%. Multivariate longitudinal modeling revealed that covariation between PBA at different timepoints could be explained by a single latent factor with 73% heritability. Our results suggest that genetic influences on PBA are detectable in midlife or earlier, are longitudinally very stable, and are largely explained by common genetic influences.
International Journal of Eating Disorders, Oct 3, 2012
Objective-Current research on the etiology of thin-ideal internalization focuses on psychosocial ... more Objective-Current research on the etiology of thin-ideal internalization focuses on psychosocial influences (e.g., media exposure). The possibility that genetic influences also account for variance in thin-ideal internalization has never been directly examined. This study used a twin design to estimate genetic effects on thin-ideal internalization and examine if environmental influences are primarily shared or nonshared in origin. Method-Participants were 343 post-pubertal female twins (ages 12-22; M=17.61) from the Michigan State University Twin Registry. Thin-ideal internalization was assessed using the Sociocultural Attitudes toward Appearance Questionniare-3. Results-Twin modeling suggested significant additive genetic and nonshared environmental influences on thin-ideal internalization. Shared environmental influences were small and nonsignificant. Discussion-Although prior research focused on psychosocial factors, genetic influences on thin-ideal internalization were significant and moderate in magnitude. Research is needed to investigate possible interplay between genetic and nonshared environmental factors in the development of thin-ideal internalization.
For many multivariate twin models, the numerical Type I error rates are lower than theoretically ... more For many multivariate twin models, the numerical Type I error rates are lower than theoretically expected rates using a likelihood ratio test (LRT), which implies that the significance threshold for statistical hypothesis tests is more conservative than most twin researchers realize. This makes the numerical Type II error rates higher than theoretically expected. Furthermore, the discrepancy between the observed and expected error rates increases as more variables are included in the analysis and can have profound implications for hypothesis testing and statistical inference. In two simulation studies, we examine the Type I error rates for the Cholesky decomposition and Correlated Factors models. Both show markedly lower than nominal Type I error rates under the null hypothesis, a discrepancy that increases with the number of variables in the model. In addition, we observe slightly biased parameter estimates for the Cholesky decomposition and Correlated Factors models. By contrast, if the variance-covariance matrices for variance components are estimated directly (without constraints), the numerical Type I error rates are consistent with theoretical expectations and there is no bias in the parameter estimates regardless of the number of variables analyzed. We call this the Direct Symmetric approach. It appears that each model-implied boundary, whether explicit or implicit, increases the discrepancy between the numerical and theoretical Type I error rates by truncating the sampling distributions of the variance components and inducing bias in the parameters. The Direct Symmetric approach has several advantages over other multivariate twin models as it corrects the Type I error rate and Compliance with Ethical Standards Conflict of interest Brad Verhulst, Elizabeth Prom-Wormley, Matthew C Keller, Sarah Medland, and Michael C. Neale declare that they have no conflict of interest. Statement of Human and Animal Rights This article does not contain any studies with human participants or animals performed by any of the authors.
While many studies have reported that individual differences in personality traits are geneticall... more While many studies have reported that individual differences in personality traits are genetically influenced, the neurobiological bases mediating these influences have not yet been well characterized. To advance understanding concerning the pathway from genetic variation to personality, here we examined whether measures of heritable variation in neuroanatomical size in candidate regions (amygdala and medial orbitofrontal cortex) were associated with heritable effects on personality. A sample of 486 middle-aged (mean = 55 years) male twins (complete MZ pairs = 120; complete DZ pairs = 84) underwent structural brain scans and also completed measures of two core domains of personality: positive and negative emotionality. After adjusting for estimated intracranial volume, significant phenotypic (r p) and genetic (r g) correlations were observed between left amygdala volume and positive emotionality (r p = .16, p < .01; r g = .23, p <. 05, respectively). In addition, after adjusting for mean cortical thickness, genetic and nonsharedenvironmental correlations (r e) between left medial orbitofrontal cortex thickness and negative emotionality were also observed (r g = .34, p < .01; r e = −.19, p < .05, respectively). These findings support a model positing that heritable bases of personality are, at least in part, mediated through *
Mendelian Randomization (MR) is an important approach to modelling causality in nonexperimental s... more Mendelian Randomization (MR) is an important approach to modelling causality in nonexperimental settings. MR uses genetic instruments to test causal relationships between exposures and outcomes of interest. Individual genetic variants have small effects, and so, when used as instruments, render MR liable to weak instrument bias. Polygenic scores have the advantage of larger effects, but may be characterized by direct pleiotropy, which violates a central assumption of MR. We developed the MR-DoC twin model by integrating MR with the Direction of Causation twin model. This model allows us to test pleiotropy directly. We considered the issue of parameter identification, and given identification, we conducted extensive power calculations. MR-DoC allows one to test causal hypotheses and to obtain unbiased estimates of the causal effect given pleiotropic instruments (polygenic scores), while controlling for genetic and environmental influences common to the outcome and exposure. Furthermore, MR-DoC in twins has appreciably greater statistical power than a standard MR analysis applied to singletons, if the unshared environmental effects on the exposure and the outcome are uncorrelated. Generally, power increases with: 1) decreasing residual exposure-outcome correlation, and 2) decreasing heritability of the exposure variable. MR-DoC allows one to employ strong instrumental variables (polygenic scores, possibly pleiotropic), guarding against weak instrument bias and increasing the power to detect causal effects. Our approach will enhance and extend MR's range of applications, and increase the value of the large cohorts collected at twin registries as they correctly detect causation and estimate effect sizes even in the presence of pleiotropy.
We present a procedure to simultaneously fit a genetic covariance structure model and a regressio... more We present a procedure to simultaneously fit a genetic covariance structure model and a regression model to multivariate data from mono-and dizygotic twin pairs to test for the prediction of a dependent trait by multiple correlated predictors. We applied the model to aggressive behavior as an outcome trait and investigated the prediction of aggression from inattention (InA) and hyperactivity (HA) in two age groups. Predictions were examined in twins with an average age of 10 years (11,345 pairs), and in adult twins with an average age of 30 years (7433 pairs). All phenotypes were assessed by the same, but ageappropriate, instruments in children and adults. Because of the different genetic architecture of aggression, InA and HA, a model was fitted to these data that specified additive and non-additive genetic factors (A and D) plus common and unique environmental (C and E) influences. Given appropriate identifying constraints, this ADCE model is identified in trivariate data. We obtained different results for the prediction of aggression in children, where HA was the more important predictor, and in adults, where InA was the more important predictor. In children, about 36% of the total aggression variance was explained by the genetic and environmental components of HA and InA. Most of this was explained by the genetic components of HA and InA, i.e., 29.7%, with 22.6% due to the genetic component of HA. In adults, about 21% of the aggression variance was explained. Most was this was again explained by the genetic components of InA and HA (16.2%), with 8.6% due to the genetic component of InA.
Although prior studies have demonstrated that genetic factors play the dominant role in the patte... more Although prior studies have demonstrated that genetic factors play the dominant role in the patterning of the pediatric brain, it remains unclear how these patterns change over time. Using 1748 longitudinal anatomic MRI scans from 792 healthy twins and siblings, we quantified how genetically mediated interregional associations change over time via multivariate longitudinal structural equation modeling. These analyses found that genetic correlations for both lobar volumes and cortical thickness are dynamic, with relatively static effects on surface area. While genetic correlations for lobar volumes decrease over childhood and adolescence, in general they increase for cortical thickness in the second decade of life. Quantification of how genetic factors influence maturational coupling improves our understanding of typical neurodevelopment and informs future molecular genetic analyses.
The hippocampus expresses a large number of androgen receptors; therefore, in men it is potential... more The hippocampus expresses a large number of androgen receptors; therefore, in men it is potentially vulnerable to the gradual age-related decline of testosterone levels. In the present study we sought to elucidate the nature of the relationship between testosterone and hippocampal volume in a sample of middle-aged male twins (average age 55.8 years). We found no evidence for a correlation between testosterone level and hippocampal volume, as well as no indication of shared genetic influences. However, a significant moderating effect of testosterone on the genetic
International Journal of Eating Disorders, Jun 25, 2014
Objective-Mean-levels of thin-ideal internalization increase during adolescence and pubertal deve... more Objective-Mean-levels of thin-ideal internalization increase during adolescence and pubertal development, but it is unknown whether these phenotypic changes correspond to developmental changes in etiological (i.e., genetic and environmental) risk. Given the limited knowledge on risk for thin-ideal internalization, research is needed to guide the identification of specific types of risk factors during critical developmental periods. The present twin study examined genetic and environmental influences on thin-ideal internalization across adolescent and pubertal development. Method-Participants were 1,064 female twins (ages 8-25 years) from the Michigan State University Twin Registry. Thin-ideal internalization and pubertal development were assessed using self-report questionnaires. Twin moderation models were used to examine if age and/or pubertal development moderate genetic and environmental influences on thin-ideal internalization. Results-Phenotypic analyses indicated significant increases in thin-ideal internalization across age and pubertal development. Twin models suggested no significant differences in etiologic effects across development. Nonshared environmental influences were most important in the etiology of thin-ideal internalization, with genetic, shared environmental, and nonshared environmental accounting for approximately 8%, 15%, and 72%, respectively, of the total variance.
Background-A clearer understanding of the etiological overlap between DSM-IV personality disorder... more Background-A clearer understanding of the etiological overlap between DSM-IV personality disorders (PDs) and alcohol use (AU) and alcohol use disorder (AUD) is needed. To our knowledge, no study has modeled the association between all 10 DSM-IV PDs and lifetime AU and AUD. The aim of the present study is to identify which PDs are most strongly associated with ^S upplementary material can be found by accessing the online version of this paper at http://dx.doi.org and by entering doi:...
The genetics of cortical arealization in youth is not well understood. In this study, we use a ge... more The genetics of cortical arealization in youth is not well understood. In this study, we use a genetically informative sample of 677 typically developing children and adolescents (mean age 12.72 years), high-resolution MRI, and quantitative genetic methodology to address several fundamental questions on the genetics of cerebral surface area. We estimate that Ͼ85% of the phenotypic variance in total brain surface area in youth is attributable to additive genetic factors. We also observed pronounced regional variability in the genetic influences on surface area, with the most heritable areas seen in primary visual and visual association cortex. A shared global genetic factor strongly influenced large areas of the frontal and temporal cortex, mirroring regions that are the most evolutionarily novel in humans relative to other primates. In contrast to studies on older populations, we observed statistically significant genetic correlations between measures of surface area and cortical thickness (r G ϭ 0.63), suggestive of overlapping genetic influences between these endophenotypes early in life. Finally, we identified strong and highly asymmetric genetically mediated associations between Full-Scale Intelligence Quotient and left perisylvian surface area, particularly receptive language centers. Our findings suggest that spatially complex and temporally dynamic genetic factors are influencing cerebral surface area in our species.
Dr Nick Martin has made enormous contributions to the field of behavior genetics over the past 50... more Dr 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.
The classical twin model can be reparametrized as an equivalent multilevel model. The multilevel ... more The classical twin model can be reparametrized as an equivalent multilevel model. The multilevel parameterization has underexplored advantages, such as the possibility to include higher-level clustering variables in which lower levels are nested. When this higher-level clustering is not modeled, its variance is captured by the common environmental variance component. In this paper we illustrate the application of a 3-level multilevel model to twin data by analyzing the regional clustering of 7-year-old children's height in the Netherlands. Our findings show that 1.8%, of the phenotypic variance in children's height is attributable to regional clustering, which is 7% of the variance explained by between-family or common environmental components. Since regional clustering may represent ancestry, we also investigate the effect of region after correcting for genetic principal components, in a subsample of participants with genome-wide SNP data. After correction, region no longer explained variation in height. Our results suggest that the phenotypic variance explained by region might represent ancestry effects on height.
The assumption in the twin model that genotypic and environmental variables are uncorrelated is p... more The assumption in the twin model that genotypic and environmental variables are uncorrelated is primarily made to ensure parameter identification, not because researchers necessarily think that these variables are uncorrelated. Although the biasing effects of such correlations are well understood, a method to estimate these parameters in the twin model would be useful. Here we explore the possibility of relaxing this assumption by adding polygenic scores to the (univariate) twin model. We demonstrate that this extension renders the additive genetic (A)-common environmental (C) covariance (σ AC) identified. We study the statistical power to reject σ AC = 0 in the ACE model and present the results of simulations.
Pathological changes in Alzheimer's disease (AD) begin decades before dementia onset. Because loc... more Pathological changes in Alzheimer's disease (AD) begin decades before dementia onset. Because locus coeruleus tau pathology is the earliest occurring AD pathology, targeting indicators of locus coeruleus (dys)function may improve midlife screening for earlier identification of AD risk. Pupillary responses during cognitive tasks are driven by the locus coeruleus and index cognitive effort. Several findings suggest task-associated pupillary response as an early marker of AD risk. Requiring greater effort suggests being closer to one's compensatory capacity, and adults with mild cognitive impairment (MCI) have greater pupil dilation during digit span tasks than cognitively normal individuals, despite equivalent task performance. Higher AD polygenic risk scores (AD-PRSs) are associated with increased odds of MCI and tau positivity. We hypothesized that AD-PRSs would be associated with pupillary responses in cognitively normal middle-aged adults. We demonstrated that pupillary responses during digit span tasks were heritable (h 2 =.30-.36) in 1119 men ages 56-66. We then examined associations between AD-PRSs and pupillary responses in a cognitively normal subset who all had comparable span capacities (n=539). Higher AD-PRSs were associated with greater pupil dilation/effort in a high (9-digit recall) cognitive load condition; Cohen's d=.36 for the upper versus lower quartile of the AD-PRS distribution. Results held up after controlling for APOE genotype. The results support pupillary response-and by inference, locus coeruleus dysfunction-as a genetically-mediated biomarker of early MCI/AD risk. In some studies, cognition predicted disease progression earlier than biomarkers. Pupillary responses might improve screening and early identification of genetically at-risk individuals even before cognitive performance declines.
Excessive internet use has been linked to psychopathology. Therefore, understanding the genetic a... more Excessive internet use has been linked to psychopathology. Therefore, understanding the genetic and environmental risks underpinning internet use and their relation to psychopathology is important. This study aims to explore the genetic and environmental etiology of internet use measures and their associations with internalizing disorders and substance use disorders. The sample included 2,059 monozygotic (MZ) and dizygotic (DZ) young adult twins from the Brisbane Longitudinal Twin Study (BLTS). Younger participants reported more frequent internet use, while women were more likely to use the internet for interpersonal communication. Familial aggregation in 'frequency of internet use' was entirely explained by additive genetic factors accounting for 41% of the variance. Familial aggregation in 'frequency of use after 11 pm', 'using the internet to contact peers', and 'using the internet primarily to access social networking sites' was attributable to varying combinations of additive genetic and shared environmental factors. In terms of psychopathology, there were no significant associations between internet use measures and major depression (MD), but there were positive significant associations between 'frequency of internet use' and 'frequency of use after 11 pm' with social phobia (SP). 'Using the internet to contact peers' was positively associated with alcohol abuse, whereas 'using the internet to contact peers' and 'using the internet primarily to access social networking sites' were negatively associated with cannabis use disorders and nicotine symptoms. Individual differences in internet use can be attributable to varying degrees of genetic and environmental risks. Despite some significant associations of small effect, variation in internet use appears mostly unrelated to psychopathology.
Acta geneticae medicae et gemellologiae, Apr 1, 1984
Three questionnaires measuring altruistic tendencies were completed by 573 adult twin pairs from ... more Three questionnaires measuring altruistic tendencies were completed by 573 adult twin pairs from the University of London Institute of Psychiatry Volunteer Twin Register. The questionnaires consisted of a 20-item Self-Report Altruism Scale, a 33-item Empathy Scale, and a 16-item Nurturance Scale, all of which had previously been shown to have construct validity. For the three scales, the intra-class correlations for the 296 MZ pairs were 0.53, 0.54, and 0.49, and for the 179 same-sex DZ pairs were 0.25,020, and 0.14, giving rough estimates of broad heritability of 56%, 68%, and 72%, respectively. Maximum-likelihood model-fitting revealed about 50% of the variance on each scale to be associated with genetic effects, virtually 0% to be due to the twins' common environment, and the remaining 50% to be due to each twins' specific environment and/or error associated with the test.
Objective-The authors sought to clarify the structure of the genetic and environmental risk facto... more Objective-The authors sought to clarify the structure of the genetic and environmental risk factors for 22 DSM-IV disorders: 12 common axis I disorders and all 10 axis II disorders. Method-The authors examined syndromal and subsyndromal axis I diagnoses and five categories reflecting number of endorsed criteria for axis II disorders in 2,111 personally interviewed young adult members of the Norwegian Institute of Public Health Twin Panel. Results-Four correlated genetic factors were identified: axis I internalizing, axis II internalizing, axis I externalizing, and axis II externalizing. Factors 1 and 2 and factors 3 and 4 were moderately correlated, supporting the importance of the internalizing-externalizing distinction. Five disorders had substantial loadings on two factors: borderline personality disorder (factors 3 and 4), somatoform disorder (factors 1 and 2), paranoid and dependent personality disorders (factors 2 and 4), and eating disorders (factors 1 and 4). Three correlated environmental factors were identified: axis II disorders, axis I internalizing disorders, and externalizing disorders versus anxiety disorders. Conclusions-Common axis I and II psychiatric disorders have a coherent underlying genetic structure that reflects two major dimensions: internalizing versus externalizing, and axis I versus axis II. The underlying structure of environmental influences is quite different. The organization of common psychiatric disorders into coherent groups results largely from genetic, not environmental, factors. These results should be interpreted in the context of unavoidable limitations of current statistical methods applied to this number of diagnostic categories. Psychiatric disorders are clinical-historical constructs whose etiology and pathophysiology are largely unknown, and hence most psychiatric nosologies, including DSM-IV (1) and ICD-10 (2), arrange disorders into categories primarily on the basis of clinical similarities. Our field has long hoped for an etiologically based classification of psychiatric disorders. Of the possible organizing principles for such an approach, familial/genetic factors have frequently been emphasized (3-5).
Cortical surface area measures appear to be functionally relevant and distinct in etiology, devel... more Cortical surface area measures appear to be functionally relevant and distinct in etiology, development, and behavioral correlates compared with other size characteristics, such as cortical thickness. Little is known about genetic and environmental influences on individual differences in regional surface area in humans. Using a large sample of adult twins, we determined relative contributions of genes and environment on variations in regional cortical surface area as measured by magnetic resonance imaging before and after adjustment for genetic and environmental influences shared with total cortical surface area. We found high heritability for total surface area and, before adjustment, moderate heritability for regional surface areas. Compared with other lobes, heritability was higher for frontal lobe and lower for medial temporal lobe. After adjustment for total surface area, regionally specific genetic influences were substantially reduced, although still significant in most regions. Unlike other lobes, left frontal heritability remained high after adjustment. Thus, global and regionally specific genetic factors both influence cortical surface areas. These findings are broadly consistent with results from animal studies regarding the evolution and development of cortical patterning and may guide future research into specific environmental and genetic determinants of variation among humans in the surface area of particular regions.
Magnetic resonance imaging data are being used in statistical models to predicted brain ageing (P... more Magnetic resonance imaging data are being used in statistical models to predicted brain ageing (PBA) and as biomarkers for neurodegenerative diseases such as Alzheimer's Disease. Despite their increasing application, the genetic and environmental etiology of global PBA indices is unknown. Likewise, the degree to which genetic influences in PBA are longitudinally stable and how PBA changes over time are also unknown. We analyzed data from 734 men from the Vietnam Era Twin Study of Aging with repeated MRI assessments between the ages 51-72 years. Biometrical genetic analyses "twin models" revealed significant and highly correlated estimates of additive genetic heritability ranging from 59 to 75%. Multivariate longitudinal modeling revealed that covariation between PBA at different timepoints could be explained by a single latent factor with 73% heritability. Our results suggest that genetic influences on PBA are detectable in midlife or earlier, are longitudinally very stable, and are largely explained by common genetic influences.
International Journal of Eating Disorders, Oct 3, 2012
Objective-Current research on the etiology of thin-ideal internalization focuses on psychosocial ... more Objective-Current research on the etiology of thin-ideal internalization focuses on psychosocial influences (e.g., media exposure). The possibility that genetic influences also account for variance in thin-ideal internalization has never been directly examined. This study used a twin design to estimate genetic effects on thin-ideal internalization and examine if environmental influences are primarily shared or nonshared in origin. Method-Participants were 343 post-pubertal female twins (ages 12-22; M=17.61) from the Michigan State University Twin Registry. Thin-ideal internalization was assessed using the Sociocultural Attitudes toward Appearance Questionniare-3. Results-Twin modeling suggested significant additive genetic and nonshared environmental influences on thin-ideal internalization. Shared environmental influences were small and nonsignificant. Discussion-Although prior research focused on psychosocial factors, genetic influences on thin-ideal internalization were significant and moderate in magnitude. Research is needed to investigate possible interplay between genetic and nonshared environmental factors in the development of thin-ideal internalization.
For many multivariate twin models, the numerical Type I error rates are lower than theoretically ... more For many multivariate twin models, the numerical Type I error rates are lower than theoretically expected rates using a likelihood ratio test (LRT), which implies that the significance threshold for statistical hypothesis tests is more conservative than most twin researchers realize. This makes the numerical Type II error rates higher than theoretically expected. Furthermore, the discrepancy between the observed and expected error rates increases as more variables are included in the analysis and can have profound implications for hypothesis testing and statistical inference. In two simulation studies, we examine the Type I error rates for the Cholesky decomposition and Correlated Factors models. Both show markedly lower than nominal Type I error rates under the null hypothesis, a discrepancy that increases with the number of variables in the model. In addition, we observe slightly biased parameter estimates for the Cholesky decomposition and Correlated Factors models. By contrast, if the variance-covariance matrices for variance components are estimated directly (without constraints), the numerical Type I error rates are consistent with theoretical expectations and there is no bias in the parameter estimates regardless of the number of variables analyzed. We call this the Direct Symmetric approach. It appears that each model-implied boundary, whether explicit or implicit, increases the discrepancy between the numerical and theoretical Type I error rates by truncating the sampling distributions of the variance components and inducing bias in the parameters. The Direct Symmetric approach has several advantages over other multivariate twin models as it corrects the Type I error rate and Compliance with Ethical Standards Conflict of interest Brad Verhulst, Elizabeth Prom-Wormley, Matthew C Keller, Sarah Medland, and Michael C. Neale declare that they have no conflict of interest. Statement of Human and Animal Rights This article does not contain any studies with human participants or animals performed by any of the authors.
While many studies have reported that individual differences in personality traits are geneticall... more While many studies have reported that individual differences in personality traits are genetically influenced, the neurobiological bases mediating these influences have not yet been well characterized. To advance understanding concerning the pathway from genetic variation to personality, here we examined whether measures of heritable variation in neuroanatomical size in candidate regions (amygdala and medial orbitofrontal cortex) were associated with heritable effects on personality. A sample of 486 middle-aged (mean = 55 years) male twins (complete MZ pairs = 120; complete DZ pairs = 84) underwent structural brain scans and also completed measures of two core domains of personality: positive and negative emotionality. After adjusting for estimated intracranial volume, significant phenotypic (r p) and genetic (r g) correlations were observed between left amygdala volume and positive emotionality (r p = .16, p < .01; r g = .23, p <. 05, respectively). In addition, after adjusting for mean cortical thickness, genetic and nonsharedenvironmental correlations (r e) between left medial orbitofrontal cortex thickness and negative emotionality were also observed (r g = .34, p < .01; r e = −.19, p < .05, respectively). These findings support a model positing that heritable bases of personality are, at least in part, mediated through *
Mendelian Randomization (MR) is an important approach to modelling causality in nonexperimental s... more Mendelian Randomization (MR) is an important approach to modelling causality in nonexperimental settings. MR uses genetic instruments to test causal relationships between exposures and outcomes of interest. Individual genetic variants have small effects, and so, when used as instruments, render MR liable to weak instrument bias. Polygenic scores have the advantage of larger effects, but may be characterized by direct pleiotropy, which violates a central assumption of MR. We developed the MR-DoC twin model by integrating MR with the Direction of Causation twin model. This model allows us to test pleiotropy directly. We considered the issue of parameter identification, and given identification, we conducted extensive power calculations. MR-DoC allows one to test causal hypotheses and to obtain unbiased estimates of the causal effect given pleiotropic instruments (polygenic scores), while controlling for genetic and environmental influences common to the outcome and exposure. Furthermore, MR-DoC in twins has appreciably greater statistical power than a standard MR analysis applied to singletons, if the unshared environmental effects on the exposure and the outcome are uncorrelated. Generally, power increases with: 1) decreasing residual exposure-outcome correlation, and 2) decreasing heritability of the exposure variable. MR-DoC allows one to employ strong instrumental variables (polygenic scores, possibly pleiotropic), guarding against weak instrument bias and increasing the power to detect causal effects. Our approach will enhance and extend MR's range of applications, and increase the value of the large cohorts collected at twin registries as they correctly detect causation and estimate effect sizes even in the presence of pleiotropy.
We present a procedure to simultaneously fit a genetic covariance structure model and a regressio... more We present a procedure to simultaneously fit a genetic covariance structure model and a regression model to multivariate data from mono-and dizygotic twin pairs to test for the prediction of a dependent trait by multiple correlated predictors. We applied the model to aggressive behavior as an outcome trait and investigated the prediction of aggression from inattention (InA) and hyperactivity (HA) in two age groups. Predictions were examined in twins with an average age of 10 years (11,345 pairs), and in adult twins with an average age of 30 years (7433 pairs). All phenotypes were assessed by the same, but ageappropriate, instruments in children and adults. Because of the different genetic architecture of aggression, InA and HA, a model was fitted to these data that specified additive and non-additive genetic factors (A and D) plus common and unique environmental (C and E) influences. Given appropriate identifying constraints, this ADCE model is identified in trivariate data. We obtained different results for the prediction of aggression in children, where HA was the more important predictor, and in adults, where InA was the more important predictor. In children, about 36% of the total aggression variance was explained by the genetic and environmental components of HA and InA. Most of this was explained by the genetic components of HA and InA, i.e., 29.7%, with 22.6% due to the genetic component of HA. In adults, about 21% of the aggression variance was explained. Most was this was again explained by the genetic components of InA and HA (16.2%), with 8.6% due to the genetic component of InA.
Although prior studies have demonstrated that genetic factors play the dominant role in the patte... more Although prior studies have demonstrated that genetic factors play the dominant role in the patterning of the pediatric brain, it remains unclear how these patterns change over time. Using 1748 longitudinal anatomic MRI scans from 792 healthy twins and siblings, we quantified how genetically mediated interregional associations change over time via multivariate longitudinal structural equation modeling. These analyses found that genetic correlations for both lobar volumes and cortical thickness are dynamic, with relatively static effects on surface area. While genetic correlations for lobar volumes decrease over childhood and adolescence, in general they increase for cortical thickness in the second decade of life. Quantification of how genetic factors influence maturational coupling improves our understanding of typical neurodevelopment and informs future molecular genetic analyses.
The hippocampus expresses a large number of androgen receptors; therefore, in men it is potential... more The hippocampus expresses a large number of androgen receptors; therefore, in men it is potentially vulnerable to the gradual age-related decline of testosterone levels. In the present study we sought to elucidate the nature of the relationship between testosterone and hippocampal volume in a sample of middle-aged male twins (average age 55.8 years). We found no evidence for a correlation between testosterone level and hippocampal volume, as well as no indication of shared genetic influences. However, a significant moderating effect of testosterone on the genetic
International Journal of Eating Disorders, Jun 25, 2014
Objective-Mean-levels of thin-ideal internalization increase during adolescence and pubertal deve... more Objective-Mean-levels of thin-ideal internalization increase during adolescence and pubertal development, but it is unknown whether these phenotypic changes correspond to developmental changes in etiological (i.e., genetic and environmental) risk. Given the limited knowledge on risk for thin-ideal internalization, research is needed to guide the identification of specific types of risk factors during critical developmental periods. The present twin study examined genetic and environmental influences on thin-ideal internalization across adolescent and pubertal development. Method-Participants were 1,064 female twins (ages 8-25 years) from the Michigan State University Twin Registry. Thin-ideal internalization and pubertal development were assessed using self-report questionnaires. Twin moderation models were used to examine if age and/or pubertal development moderate genetic and environmental influences on thin-ideal internalization. Results-Phenotypic analyses indicated significant increases in thin-ideal internalization across age and pubertal development. Twin models suggested no significant differences in etiologic effects across development. Nonshared environmental influences were most important in the etiology of thin-ideal internalization, with genetic, shared environmental, and nonshared environmental accounting for approximately 8%, 15%, and 72%, respectively, of the total variance.
Background-A clearer understanding of the etiological overlap between DSM-IV personality disorder... more Background-A clearer understanding of the etiological overlap between DSM-IV personality disorders (PDs) and alcohol use (AU) and alcohol use disorder (AUD) is needed. To our knowledge, no study has modeled the association between all 10 DSM-IV PDs and lifetime AU and AUD. The aim of the present study is to identify which PDs are most strongly associated with ^S upplementary material can be found by accessing the online version of this paper at http://dx.doi.org and by entering doi:...
The genetics of cortical arealization in youth is not well understood. In this study, we use a ge... more The genetics of cortical arealization in youth is not well understood. In this study, we use a genetically informative sample of 677 typically developing children and adolescents (mean age 12.72 years), high-resolution MRI, and quantitative genetic methodology to address several fundamental questions on the genetics of cerebral surface area. We estimate that Ͼ85% of the phenotypic variance in total brain surface area in youth is attributable to additive genetic factors. We also observed pronounced regional variability in the genetic influences on surface area, with the most heritable areas seen in primary visual and visual association cortex. A shared global genetic factor strongly influenced large areas of the frontal and temporal cortex, mirroring regions that are the most evolutionarily novel in humans relative to other primates. In contrast to studies on older populations, we observed statistically significant genetic correlations between measures of surface area and cortical thickness (r G ϭ 0.63), suggestive of overlapping genetic influences between these endophenotypes early in life. Finally, we identified strong and highly asymmetric genetically mediated associations between Full-Scale Intelligence Quotient and left perisylvian surface area, particularly receptive language centers. Our findings suggest that spatially complex and temporally dynamic genetic factors are influencing cerebral surface area in our species.
Dr Nick Martin has made enormous contributions to the field of behavior genetics over the past 50... more Dr 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.
The classical twin model can be reparametrized as an equivalent multilevel model. The multilevel ... more The classical twin model can be reparametrized as an equivalent multilevel model. The multilevel parameterization has underexplored advantages, such as the possibility to include higher-level clustering variables in which lower levels are nested. When this higher-level clustering is not modeled, its variance is captured by the common environmental variance component. In this paper we illustrate the application of a 3-level multilevel model to twin data by analyzing the regional clustering of 7-year-old children's height in the Netherlands. Our findings show that 1.8%, of the phenotypic variance in children's height is attributable to regional clustering, which is 7% of the variance explained by between-family or common environmental components. Since regional clustering may represent ancestry, we also investigate the effect of region after correcting for genetic principal components, in a subsample of participants with genome-wide SNP data. After correction, region no longer explained variation in height. Our results suggest that the phenotypic variance explained by region might represent ancestry effects on height.
The assumption in the twin model that genotypic and environmental variables are uncorrelated is p... more The assumption in the twin model that genotypic and environmental variables are uncorrelated is primarily made to ensure parameter identification, not because researchers necessarily think that these variables are uncorrelated. Although the biasing effects of such correlations are well understood, a method to estimate these parameters in the twin model would be useful. Here we explore the possibility of relaxing this assumption by adding polygenic scores to the (univariate) twin model. We demonstrate that this extension renders the additive genetic (A)-common environmental (C) covariance (σ AC) identified. We study the statistical power to reject σ AC = 0 in the ACE model and present the results of simulations.
Pathological changes in Alzheimer's disease (AD) begin decades before dementia onset. Because loc... more Pathological changes in Alzheimer's disease (AD) begin decades before dementia onset. Because locus coeruleus tau pathology is the earliest occurring AD pathology, targeting indicators of locus coeruleus (dys)function may improve midlife screening for earlier identification of AD risk. Pupillary responses during cognitive tasks are driven by the locus coeruleus and index cognitive effort. Several findings suggest task-associated pupillary response as an early marker of AD risk. Requiring greater effort suggests being closer to one's compensatory capacity, and adults with mild cognitive impairment (MCI) have greater pupil dilation during digit span tasks than cognitively normal individuals, despite equivalent task performance. Higher AD polygenic risk scores (AD-PRSs) are associated with increased odds of MCI and tau positivity. We hypothesized that AD-PRSs would be associated with pupillary responses in cognitively normal middle-aged adults. We demonstrated that pupillary responses during digit span tasks were heritable (h 2 =.30-.36) in 1119 men ages 56-66. We then examined associations between AD-PRSs and pupillary responses in a cognitively normal subset who all had comparable span capacities (n=539). Higher AD-PRSs were associated with greater pupil dilation/effort in a high (9-digit recall) cognitive load condition; Cohen's d=.36 for the upper versus lower quartile of the AD-PRS distribution. Results held up after controlling for APOE genotype. The results support pupillary response-and by inference, locus coeruleus dysfunction-as a genetically-mediated biomarker of early MCI/AD risk. In some studies, cognition predicted disease progression earlier than biomarkers. Pupillary responses might improve screening and early identification of genetically at-risk individuals even before cognitive performance declines.
Excessive internet use has been linked to psychopathology. Therefore, understanding the genetic a... more Excessive internet use has been linked to psychopathology. Therefore, understanding the genetic and environmental risks underpinning internet use and their relation to psychopathology is important. This study aims to explore the genetic and environmental etiology of internet use measures and their associations with internalizing disorders and substance use disorders. The sample included 2,059 monozygotic (MZ) and dizygotic (DZ) young adult twins from the Brisbane Longitudinal Twin Study (BLTS). Younger participants reported more frequent internet use, while women were more likely to use the internet for interpersonal communication. Familial aggregation in 'frequency of internet use' was entirely explained by additive genetic factors accounting for 41% of the variance. Familial aggregation in 'frequency of use after 11 pm', 'using the internet to contact peers', and 'using the internet primarily to access social networking sites' was attributable to varying combinations of additive genetic and shared environmental factors. In terms of psychopathology, there were no significant associations between internet use measures and major depression (MD), but there were positive significant associations between 'frequency of internet use' and 'frequency of use after 11 pm' with social phobia (SP). 'Using the internet to contact peers' was positively associated with alcohol abuse, whereas 'using the internet to contact peers' and 'using the internet primarily to access social networking sites' were negatively associated with cannabis use disorders and nicotine symptoms. Individual differences in internet use can be attributable to varying degrees of genetic and environmental risks. Despite some significant associations of small effect, variation in internet use appears mostly unrelated to psychopathology.
Acta geneticae medicae et gemellologiae, Apr 1, 1984
Three questionnaires measuring altruistic tendencies were completed by 573 adult twin pairs from ... more Three questionnaires measuring altruistic tendencies were completed by 573 adult twin pairs from the University of London Institute of Psychiatry Volunteer Twin Register. The questionnaires consisted of a 20-item Self-Report Altruism Scale, a 33-item Empathy Scale, and a 16-item Nurturance Scale, all of which had previously been shown to have construct validity. For the three scales, the intra-class correlations for the 296 MZ pairs were 0.53, 0.54, and 0.49, and for the 179 same-sex DZ pairs were 0.25,020, and 0.14, giving rough estimates of broad heritability of 56%, 68%, and 72%, respectively. Maximum-likelihood model-fitting revealed about 50% of the variance on each scale to be associated with genetic effects, virtually 0% to be due to the twins' common environment, and the remaining 50% to be due to each twins' specific environment and/or error associated with the test.
Objective-The authors sought to clarify the structure of the genetic and environmental risk facto... more Objective-The authors sought to clarify the structure of the genetic and environmental risk factors for 22 DSM-IV disorders: 12 common axis I disorders and all 10 axis II disorders. Method-The authors examined syndromal and subsyndromal axis I diagnoses and five categories reflecting number of endorsed criteria for axis II disorders in 2,111 personally interviewed young adult members of the Norwegian Institute of Public Health Twin Panel. Results-Four correlated genetic factors were identified: axis I internalizing, axis II internalizing, axis I externalizing, and axis II externalizing. Factors 1 and 2 and factors 3 and 4 were moderately correlated, supporting the importance of the internalizing-externalizing distinction. Five disorders had substantial loadings on two factors: borderline personality disorder (factors 3 and 4), somatoform disorder (factors 1 and 2), paranoid and dependent personality disorders (factors 2 and 4), and eating disorders (factors 1 and 4). Three correlated environmental factors were identified: axis II disorders, axis I internalizing disorders, and externalizing disorders versus anxiety disorders. Conclusions-Common axis I and II psychiatric disorders have a coherent underlying genetic structure that reflects two major dimensions: internalizing versus externalizing, and axis I versus axis II. The underlying structure of environmental influences is quite different. The organization of common psychiatric disorders into coherent groups results largely from genetic, not environmental, factors. These results should be interpreted in the context of unavoidable limitations of current statistical methods applied to this number of diagnostic categories. Psychiatric disorders are clinical-historical constructs whose etiology and pathophysiology are largely unknown, and hence most psychiatric nosologies, including DSM-IV (1) and ICD-10 (2), arrange disorders into categories primarily on the basis of clinical similarities. Our field has long hoped for an etiologically based classification of psychiatric disorders. Of the possible organizing principles for such an approach, familial/genetic factors have frequently been emphasized (3-5).
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Papers by Michael Neale