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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Arch Gen Psychiatry. Author manuscript; available in PMC Apr 10, 2012.
Published in final edited form as:
PMCID: PMC3322461
NIHMSID: NIHMS367020
Higher-Order Genetic and Environmental Structure of Prevalent Forms of Child and Adolescent Psychopathology
Benjamin B. Lahey, Carol A. Van Hulle, Amber L. Singh, Irwin D. Waldman, and Paul J. Rathouz
Benjamin B. Lahey, Department of Health Studies, University of Chicago, Chicago, IL;
Corresponding Author: Benjamin B. Lahey, Department of Health Studies (MC 2007), University of Chicago, Chicago, IL 60637. blahey/at/uchicago.edu.
Context
It is necessary to understand the etiologic structure of child and adolescent psychopathology to advance theory and guide future research.
Objective
To test alternative models of the higher-order structure of etiologic influences on 11 dimensions of child and adolescent psychopathology using confirmatory factor analyses of genetic and environmental covariances.
Design
Representative sample of twins.
Participants
1,571 pairs of 9-17 year old twins.
Setting
Home interviews.
Main Outcome Measures
Structured assessments of psychopathology using adult caregivers and youth as informants.
Results
The best-fitting genetic model revealed that most genetic factors nonspecifically influence risk for either all 11 symptom dimensions or for dimensions of psychopathology within one of two broad domains. With some notable exceptions, dimension-specific genetic influences accounted for modest amounts of variance.
Conclusions
To inform theory and guide molecular genetic studies, an etiologic model is offered in which three patterns of pleiotropy are hypothesized to be the principal modes of genetic risk transmission for common forms of child and adolescent psychopathology. Some common environmental influences were found, but consistent with a “generalist genes, specialist environments” model, there was little sharing of environmental influences. This implies that prevalent dimensions of child and adolescent psychopathology mostly share their genetic liabilities but are differentiated by nonshared experiences.
All common dimensions of psychopathology in children and adolescents are positively correlated to varying degrees, often quite substantially.1-3 Like others,4, 5 we posit that the correlated nature of psychopathology reflects the underlying etiologic structure of psychopathology. In particular, Kendler hypothesized that many genes pleiotropically influence risk for multiple mental disorders.6 This hypothesis is supported by studies of adult twins showing that major depressive disorder (MDD) and generalized anxiety disorder (GAD) are substantially influenced by common genes, as are antisocial behavior and multiple forms of substance abuse.7, 8 Perhaps most importantly, a twin study of categorical mental disorders in adults identified two broad genetic factors accounting for most of the genetic variance in the mental disorders that loaded on them: An internalizing (anxiety disorders and depression) and an externalizing (conduct problems, antisocial personality disorder, and substance use disorders) factor.8
Less comprehensive studies have been conducted of child and adolescent psychopathology, but consistent with the apparent importance of pleiotropy in adult disorders, bivariate analyses of child and adolescent twins indicate substantial shared genetic influences on several pairs of mental disorders. Furthermore, a broader study of the externalizing domain in older adolescents generally confirmed findings on adults.9, 10
The present analyses test the hypothesis that psychopathology dimensions in children and adolescents are highly correlated at the phenotypic level largely because they are correlated at the genotypic level. These analyses are based on the premise that the underlying latent dimensions of prevalent mental disorders are continuous rather than discrete taxa. That is, like others, 11-14 we assume that the diagnostic thresholds for prevalent mental disorders represent conventional cut-points on symptom continua designed to help clinicians make informed dichotomous treatment decisions rather than reflecting qualitatively distinct states of mental health.
The models of the causal structure of child and adolescent psychopathology tested here are partly derived from previous findings that two higher-order “internalizing” and “externalizing” factors account for most of the phenotypic correlations among common psychopathology dimensions in children, adolescents, and adults.3, 15-17 Furthermore, based on the sizable phenotypic correlations between the latent internalizing and externalizing factors in these studies, we also tested the novel hypothesis that the phenotypic correlations between the internalizing and externalizing disorders partly reflect their loadings on a factor reflecting general risk for psychopathology. We present the results of separate confirmatory factor analyses (CFA) of genetic and environmental covariances among 11 prevalent psychopathology dimensions in a representative sample of twin pairs to compare these alternative models of the structure of etiologic influences.
Participants
The Tennessee Twin Study (TTS)3 is representative of 6-17-year-old twins born in Tennessee and living in one of the state's five metropolitan statistical areas (MSAs) in 2000-2001. These MSAs include the 28 urban, suburban, and rural counties surrounding the cities of Nashville, Memphis, Knoxville, Chattanooga, and Bristol. The Tennessee Department of Health identified 7,794 birth records representing all twin pairs born in Tennessee in the eligible age range and used external databases to locate families (2,431 twin pairs were eliminated because they lived outside the five MSAs). A random sample was selected from the remaining families, stratified by age and 35 geographic subareas, proportional to the number of listed families in each subarea. Of 4,012 selected households, 3,592 (89.5%) were located and screened, with 2,646 of the screened families being eligible (neither twin was autistic or psychotic, both twins co-resided with the adult caretaker ≥50% time during the past 6 months, the twins and caretaker spoke English, and the twins were at least 6 years old at the time of the interview and no more than 17 years old at the time of screening. Twelve families were ineligible on the basis of language, indicating little bias. Biological mothers, biological fathers, stepmothers, and grandmothers were eligible to be interviewed as the adult caretaker. Interviews were completed with 2,063 adult caretakers (90.8% biological mothers) with a 70% response rate. When the caretaker was interviewed, both twins were interviewed 98% of the time. The caretaker classified 71% of the twins as Non-Hispanic white, 24% African American, 2% Hispanic, and 3% other groups.
Measures
Caretakers were interviewed about all 6-17 year olds and 9-17 year olds were directly interviewed separately using the Child and Adolescent Psychopathology Scale (CAPS).2 All participants are administered the same items addressing ADHD, ODD, CD, MDD, GAD, separation anxiety disorder (SAD), agoraphobia, social phobia, specific phobia, and obsessive-compulsive disorder (OCD) symptoms. The GAD symptom of difficulty controlling worrying was judged to be too difficult for respondents to report and was not included. CAPS items covering DSM-IV symptoms are based on the “stem questions” of the Diagnostic Interview Schedule for Children (DISC-IV).18 Rather than asking DISC “contingent questions” to address frequency, duration, and severity, respondents are asked to think about how well each stem question describes the youth's emotion or behavior, how often it occurred, and how serious it was during the last 12 months using a four-point response scale: “1. Not at all, 2. Just a little, 3. Pretty much, 4. Very much.” Like the DISC, multiple items addressing different aspects of compound symptoms were combined by taking the highest score of the combined items. Consistent with DSM-IV, 9 of 24 CAPS items that define MDD symptoms refer to changes in functioning (“more/less than usual”) during the last year, as do 3 of 11 items defining GAD symptoms.Items were randomized within the CAPS and administered in two counterbalanced orders to control order effects. For the 9% of youth who had taken psychoactive medication during the last year, the respondent was asked to rate the youth when not taking medication.
In a previous study,2 participants were administered a second CAPS interview 7-14 days later. Test-retest intra-class correlations (ICCs) mean ratings of each DSM-IV symptom dimension reported by caretakers were: CD = .89; ODD = .80; inattention = .89; hyperactivityimpulsivity = .88; MDD = .82; GAD = .80; SAD = .76; social phobia = .65; specific phobia = .84; agoraphobia = .77; and OCD = .73. Test-retest ICCs for youth reports were: CD = .78; MDD = .69; GAD = .65; SAD = .68; social phobia = .62; specific phobia = .83; agoraphobia = .70; and OCD = .67. Robust correlations between symptom dimensions and functional impairment indicated external validity for both versions of the CAPS.2 Although such dimensional psychopathology scores are strongly correlated with categorical diagnoses,19-21 they focus on global ratings of symptom severity rather than diagnostic criteria such as duration, clustering, and age of onset.
Because caretaker and youth reports of symptoms are modestly correlated, many researchers obtain complementary information from multiple informants.22 Indeed, several studies indicate that the use of multiple informants improves the validity of assessments of psychopathology in children and adolescents, but the optimal informants are different at different ages and for different disorders. Children ≥ 9 years of age are reliable and valid informants on anxiety, depression, and conduct disorder (CD), but not on ODD and ADHD.23-25 Based on these considerations, parent and youth reports of symptoms of symptoms of anxiety disorders, depression, and CD were combined using the standard method of taking the higher rating of each symptom from the adult caretaker or youth.26 Only caretaker ratings defined ODD and ADHD. The present analyses were limited to 1,571 pairs of 9-17 year old twins in which both informants were interviewed. To ensure that combining parent and youth reports did not bias findings, the primary models were refitted using only parent ratings of all symptom dimensions for 6-17 year old twins with qualitatively identical results (available upon request).
Statistical Analyses
Mean ratings of the 11 psychopathology dimensions were residualized on age, age-squared, and age × sex. Univariate biometric models were used to decompose observed phenotypic variance into variance attributable to additive genetic factors (A), dominance genetic factors (D), environmental factors shared by the twins (C), and environmental factors not shared by the twins plus measurement error (E), and to account for sibling interaction/rater bias (S).27 A correlated factors multivariate biometric model27 was used to estimate both the variances for genetic and environment components for each psychopathology phenotype and the 11 × 11 covariance matrices among the genetic, and among the environmental, components of variance.28 Model estimation for the 11 psychopathology dimensions was based on the observed 22 × 22 phenotypic covariance matrices for the 11 phenotypes across the two co-twins for each type of twin pair.
We then separately tested alternative models of the underlying structure of the genetic and environmental covariances using a series of hierarchical CFAs. We tested models with one through four factors for each of A and E. In models for A, no factor structure or other restrictions were imposed on E, allowing the E components of each phenotypic dimension to freely correlate. Similar models were tested for E, allowing the A components to correlate freely.
We used standard covariance structure model estimation procedures in Mplus 5.1 29 for both biometric and hierarchical biometric-CFA models. Such procedures will be maximum likelihood if the raw data are multivariate normal. We compared nested models with the Satorra-Bentler scaled-difference (Δ) χ2 test, however, as that test is valid for large samples even if the data are skewed.30 We also used information-theoretic indices to compare the fit of alternative etiologic models underlying the observed phenotypic covariance matrices. Under normality, a Bayesian Information Criterion (BIC)31 can be computed for both biometric and CFA models, comparing each model to a “saturated” model in which each manifest variable has its own latent dimension. BIC includes a penalty for the number of parameters in the model, so that the alternative model with the lower BIC is preferred in the sense of balancing model parsimony with fidelity to the data in representing the observed covariances among the variables. The standardized root mean square residual (SRMR)32 quantifies the standardized difference between the observed predicted covariances, with 0 indicating a perfect fit and values < .08 conventionally indicating good fit.The root mean square error of approximation (RMSEA)33 estimates the discrepancy between the index model and the true population covariance matrix of the variables. Smaller values of RMSEA indicate better fit, with values less than 0.05 conventionally indicating close fits.33
Univariate Biometric Models
For each phenotype, alternative univariate models containing A, E, and either C, D, or S were compared. Because C, D, and S are estimated using the same information, they cannot be included in the same model. The best fitting model for each phenotype was chosen on the basis of BIC (Table 1). For CD, inattention, and hyperactivity-impulsivity, AE+S models had the lowest BICs, although estimates of S were small (CD = -.08, hyperactivity-impulsivity = -.11, and inattention= -.15). Negative estimates of S are interpreted as reflecting biases due to raters contrasting twin behavior or competition between twins.34
Table 1
Table 1
Fit statistics for the alternative univariate biometric models for each observed dimension of child and adolescent psychopathology based on combined caretaker and youth reports.
Multivariate Biometric Models
Genetic and nonshared environmental contributions to variance in the 11 manifest psychopathology dimensions, and the correlations among the genetic and nonshared environmental contributions to these 11 phenotypes derived from the correlated factors model, are presented in Table 2. The contributions of shared environmental influences (c2) on each psychopathology dimension could be set to 0 without loss of fit, with the exception of GAD, MDD, SAD and OCD. Estimates of c2 for these phenotypes were: MDD = .05, GAD = .03, SAD = .23, and OCD = .21. All six correlations among these C components were significant, ranging from .11 between GAD and OCD to .93 between MDD and GAD (available upon request); C for these six phenotypes was allowed to correlate freely. All multivariate models included S for inattention, hyperactivity-impulsivity, and CD, but because they do not reflect genetic or environmental influences, they were not allowed to correlate.
Table 2
Table 2
Estimated proportions of variance in combined adult caretaker- and youth-reported dimensions of child and adolescent psychopathology attributable to either additive genetic (a2) or nonshared environmental (e2) influences (in bold in the first column and (more ...)
The correlations in Table 2 quantify the extent to which the 11 psychopathology dimensions are associated due to common genetic and common nonshared environmental influences on each pair of dimensions. All 11 psychopathology dimensions were heritable (a2) and substantial genetic correlations accounted for most of the phenotypic correlations among the psychopathology dimensions (Table 2, below the diagonal). In contrast, nonshared environmental influences (e2) were substantial, but correlations among these influences for the 11 psychopathology dimensions were substantially smaller (Table 2, above the diagonal).
Multivariate Tests of Alternative Hypotheses for Genetic Structure
Because our theoretical interest is in what the patterns of higher-order genetic and environmental correlations among the psychopathology phenotypes in Table 2 reveal about taxonomy and etiology, the 11-dimension correlated factors model is not informative on this issue; it allows the genetic and environmental influences on the 11 psychopathology dimensions to correlate freely without imposing any higher-order structure on the correlations. Therefore, a series of models were estimated to compare alternative hypotheses regarding theoretically informative patterns of correlated genetic and environmental influences.
Model 1
Most previous molecular genetic studies have sought to identify risk polymorphisms for one mental disorder at a time, reflecting the implicit extreme assumption that each mental disorder has mostly disorder-specific genetic risks. Therefore, Model 1 in Table 3 reflects the extreme view that each dimension of psychopathology has only dimension-specific genetic influences which are uncorrelated with genetic influences on any of the other dimensions.
Table 3
Table 3
Fit statistics for alternative hierarchical multivariate models of additive genetic influences.
Model 2
This alternative model, which fit better than Model 1, tests the hypothesis that all pleiotropic sharing of genetic influences by the psychopathology dimensions is through a single higher order factor.
Model 3
This tested the hypothesis that genetic components of the six anxiety disorders and MDD load on a higher-order internalizing factor and genetic components of the four disruptive behavior disorders load on a higher-order externalizing factor. Model 3 fit significantly better than Model 2 (Table 3), consistent with the hypothesis5 that at least some genetic factors nonspecifically influence risk for psychopathology dimensions within each of the internalizing and externalizing domains. Based on the results of an earlier phenotypic CFA of the present sample,3 we tested an untabled submodel of Model 3 in which MDD and GAD were allowed to load on both the higher-order internalizing and externalizing genetic factors, but the fit (BIC = 5347) did not improve over Model 3 (Δχ2 = 2.9, DF = 2, P = .23).
Models 4 and 5
Importantly, the estimated correlation between the latent internalizing and externalizing genetic factors in Model 3 was r=.89, suggesting that many of the same genetic factors influence variability in the psychopathology dimensions in both the internalizing and externalizing domains. Based on this revealing observation, Model 4 was formulated to test the addition of a third higher-order genetic factor on which all psychopathology dimensions loaded, termed “global psychopathology.” Correlations among the global, internalizing, and externalizing factors were set to 0 in Model 4. As reported in Table 3, Model 4 fit significantly better than the two-factor model (i.e., Model 3), suggesting that, in addition to the higher order internalizing and externalizing factors, there is a global genetic factor that influences variability in all 11 psychopathology dimensions. Model 5, which allowed the internalizing and externalizing factors to correlate, fit significantly better than Model 4.
The proportions of genetic variance in each dimension of psychopathology explained by each higher-order factor and by unique genetic influences in Model 5 are shown in Figure 1. For eight of the 11 symptom dimensions, ≥ 68% of their genetic variance was accounted for by various combinations of the three higher-order genetic factors, with a small to modest proportion of their genetic variance being unique to each of these eight dimensions. Approximately half (52-54%) of the additive genetic variance of the remaining four symptom dimensions was unique to that dimension, with the other half shared with the higher-order genetic factors.
Figure 1
Figure 1
Proportions of genetic variance in combined caretaker- and youth-reported dimensions of child and adolescent psychopathology in Model 5 (Table 3) associated with three higher-order latent genetic factors and unique to each specific dimension of psychopathology (more ...)
Model 6
We also tested a four-factor model including a separate fourth factor for MDD and GAD only that did not fit as well as Model 5.
Multivariate Tests of Alternative Hypotheses for Environmental Structure
A parallel set of analyses was conducted for the E covariance structure in Table 2, allowing A to correlate freely. As reported in see Table 4, a 3 higher order factor model like that for A fit best, but as shown in Figure 2, for 8 of the 11 psychopathology phenotypes the majority of E was unique (not shared with other dimensions). Exceptions are ODD, which shares half of its E variance with the higher-order factors, and MDD and GAD, which are the only two dimensions with high loadings on the global factor.
Table 4
Table 4
Fit statistics for alternative hierarchical multivariate models of non-shared environmental influences.
Figure 2
Figure 2
Proportions of non-shared environmental variance in combined caretaker- and youth-reported dimensions of child and adolescent psychopathology in Model 5 (Table 4) associated with three higher-order latent nonshared environmental factors and unique to (more ...)
The results of the present analyses have important implications both for understanding the nature of child and adolescent psychopathology and for the design of future studies of etiology and pathophysiology.
Structure of Genetic Influences
The present findings support hypotheses derived from studies of adult twins based on categorical diagnoses of mental disorders5, 8 that two sets of pleiotropic genetic factors nonspecifically influence risk for all internalizing or all externalizing psychopathology dimensions during childhood and adolescence. Furthermore, the present findings are consistent with the new hypothesis that a set of highly pleiotropic genetic influences are globally associated to varying degrees with risk for all 11 prevalent forms of child and adolescent psychopathology. Furthermore, all psychopathology dimensions also are influenced by unique genetic factors with varying, but usually small magnitudes of effect. It is informative that some psychopathology dimensions had slightly more unique than shared genetic variance, but the estimates of unique genetic variance never exceed 55% of the total genetic variance for any dimension of psychopathology.
These findings strongly suggest that most additive genetic factors associated with variation in the common psychopathology dimensions in children and adolescents are not specific to each individual dimension of psychopathology. Rather, the three patterns of pleiotropy identified in these analyses appear to be the principal modes of genetic risk transmission for most of the 11 dimensions of child and adolescent psychopathology.
The present findings based on sharing of etiologic influences were not consistent with the hypothesis based on studies of mental disorders in adults that anxiety disorders and depression can be parsed into two correlated higher-order “distress” (MDD, dysthymia, GAD, and post-traumatic stress disorder) and “fears” (specific phobia, agoraphobia, social phobia, and panic disorder) domains.17, 35, 36 It is possible that separate higher-order fears and distress factors would have been identified in the present study had symptoms of post-traumatic stress disorder been assessed, but it also is possible that the distinction between the distress and fears domains only emerges in adulthood. Still, it is important to note that internalizing factor in Figures 1 and and22 might be considered to be a “fears” factors because MDD and GAD did not load on it in the best-fitting models.
Structure of Environmental Influences
As shown in Table 1 and Figure 2, nonshared environmental influences jointly influenced multiple correlated psychopathology dimensions to a modest extent, but the majority of the nonshared environmental variance was unique for most dimensions. Exceptions are ODD, which was found to share half of its nonshared environmental variance with higher-order factors, and MDD and GAD, which had the highest loadings on the global E factor and small unique E variances. This suggests that nonshared environmental influences on MDD and GAD are mostly not unique to them in this age range.
Shared environmental influences were mostly near or at zero, with the exception of modest C for SAD and OCD. Correlations in C across dimensions were sometimes large, but the absolute magnitude of C that these disorders shared was small.
Taken together, these findings indicate that phenotypic correlations among dimensions of child and adolescent psychopathology are primarily, but not entirely, due to correlated additive genetic influences. This does not rule out the possibility of gene-environment correlations and interactions as sources of shared etiologic influences. This is because gene-environment correlations and gene-environment interactions with C are folded into estimates of A in biometric models, whereas gene-environment interactions with E are included in estimates of E.37, 38 Nonetheless, the present findings on child and adolescent psychopathology are generally consistent with Plomin's “generalist genes, specialist environments” hypothesis for cognitive abilities.39, 40 That is, pleiotropic genetic factors tend to promote correlations among phenotypes, whereas environments tend to promote their differentiation.
Implications for Taxonomy and Neurobiology
If the emerging view that common forms of psychopathology are best treated as dimensional phenomena which are dichotomized using data-based but conventional thresholds to facilitate treatment decisions is supported, the present findings have important implications for taxonomy. They support an etiologic explanation for the observation that child and adolescent psychopathology is phenotypically organized within the higher-order internalizing and externalizing domains.3, 15 As with adult psychopathology,8 we hypothesize that dimensions of child and adolescent psychopathology within both the internalizing and externalizing domains are highly correlated largely because they share genetic influences. Furthermore, the present findings suggest an etiologic explanation for the well-documented, but largely ignored, robust correlation between the internalizing and externalizing domains of psychopathology.2, 3, 15 That is, the broad construct of “psychopathology” may have a physical reality in the sense that all 11 psychopathology dimensions were found to share some of the same genetic influences, albeit to varying extents.
Limitations and Future Directions
The present findings are based on a standard method of combining symptom reports from multiple informants, but additional psychometric research is needed to compare alternative methods of doing so. Although the present findings were replicated using the parent informant only, other methods of combining reports from multiple informants could have yielded different results. Therefore, the paucity of knowledge on the optimal way to combine data from multiple informants is a limitation of the present study that would similarly limit other studies of child and adolescent psychopathology at present.
It is important to attempt to disconfirm the present hypotheses regarding the higher-order genetic structure of child and adolescent psychopathology in future studies for at least three reasons. First, consistent with previous recommendations,6 this hypothesis implies that rather than searching for genetic polymorphisms associated with psychopathology one disorder at a time, it should be far more informative to simultaneously search for networks of pleiotropic genetic factors associated with multiple forms of psychopathology (and for the dimension-specific genetic and environmental factors uniquely associated with each dimension of psychopathology). This would require measuring multiple phenotypes in each study (across both internalizing and externalizing domains) and conducting genetic association analyses that squarely address the likelihood of widespread pleiotropy. Although the field will eventually discover which genetic variants are associated with multiple psychopathology dimensions even if we mostly study mental disorders one at a time, the present hypothesis imposes a testable model on the data that should make gene discovery more efficient and revealing about the biological nature of psychopathology.
Second, there is evidence from the study of somatic characteristics that pleiotropic genes generally have stronger effects than other genes.41 This also could be true of genes that are pleiotropically associated with multiple psychopathology dimensions. Because the hypothesized global psychopathology genetic factor is related to many psychopathology dimensions (regardless of which symptoms are exhibited at any point in time), the association between each pleiotropic gene variant with higher-order psychopathology dimensions may be stronger than the association of the same variant with lower-order psychopathology dimensions. Indeed, there is evidence that the heritability of the higher-order latent externalizing factor could be almost twice that of lower-order dimensions.9, 10 Therefore, the likelihood of detecting gene variants associated with higher-order psychopathology dimensions may be greater than for lower-order dimensions.
Third, because gene variants associated with the higher-order phenotypes simultaneously influence multiple psychopathology dimensions, they increase risk for comorbid mental health problems. Therefore, it is particularly important to discover pleiotropic psychopathology risk genes because comorbid mental disorders are associated with more distress, functional impairment, persistence, and mental health service use than single disorders.42
It will be important to test the present etiologic hypothesis at the molecular level. In the present analyses, the genetic influences on each psychopathology dimension were latent, inferred from the analyses of twin pairs. If such participants are genotyped, however, it should be possible to directly estimate A from the measured polymorphisms, although the genotyping and statistical methodologies for doing so are still under development.43 In principle, A for each psychopathology dimension could be estimated from the sums of the varying magnitudes of association of all polymorphisms with that phenotype. The variance-covariance matrix of the A components for all psychopathology dimensions could then be computed and subjected to CFA to determine if the same higher-order factor structure fits the genetic data based on measured polymorphisms.
If the present hypothesis is supported at the molecular level, it would likely force a foundational shift in how the neurobiology of common forms of psychopathology is conceptualized. Genetic polymorphisms influence risk for psychopathology by encoding protein components of neurons and other relevant biological systems through a chain of processes. If many genetic polymorphisms are pleiotropically associated with variation in all psychopathology dimensions (and within the internalizing and externalizing domains) that would almost certainly mean that those correlated forms of psychopathology share many aspects of their genetically influenced pathophysiology. That is, in contrast to the dominant paradigm in which forms of psychopathology are studied singly as if each were neurobiologically unique, the present genetic hypothesis implies that patterns of dysfunction in neurobiological systems may be related to risk for multiple psychopathology dimensions, likely through transactions with the environment. In turn, this implies that neurobiological studies should consider both multiple neurobiological systems and multiple forms of psychopathology at the same time to identify both the common and dimension-specific mechanisms underlying psychopathology.
ACKNOWLEDGEMENTS
This study was supported by grant R01 MH59111 to Benjamin Lahey and R21 MH086099 to Paul Rathouz. The authors have no financial disclosures. Dr. Lahey had full access to all data and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Contributor Information
Benjamin B. Lahey, Department of Health Studies, University of Chicago, Chicago, IL.
Carol A. Van Hulle, Waisman Center, University of Wisconsin.
Amber L. Singh, Department of Psychological and Brain Sciences, Indiana University.
Irwin D. Waldman, Department of Psychology, Emory University.
Paul J. Rathouz, Department of Health Studies, University of Chicago, Chicago, IL.
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