Elucidating the role of negative parenting in the genetic v. environmental influences on adult psychopathic traits.

BACKGROUND: Psychopathic traits involve interpersonal manipulation, callous affect, erratic lifestyle, and antisocial behavior. Though adult psychopathic traits emerge from both genetic and environmental risk, no studies have examined etiologic associations between adult psychopathic traits and experiences of parenting in childhood, or the extent to which parenting practices may impact the heritability of adult psychopathic traits using a genetically-informed design. METHODS: In total, 1842 adult twins from the community reported their current psychopathic traits and experiences of negative parenting during childhood. We fit bivariate genetic models to the data, decomposing the variance within, and the covariance between, psychopathic traits and perceived negative parenting into their genetic and environmental components. We then fit a genotype × environment interaction model to evaluate whether negative parenting moderated the etiology of psychopathic traits. RESULTS: Psychopathic traits were moderately heritable with substantial non-shared environmental influences. There were significant associations between perceived negative parenting and three of four psychopathy facets (interpersonal manipulation, erratic lifestyle, antisocial tendencies, but not callous affect). These associations were attributable to a common non-shared environmental pathway and not to overlapping genetic effects. Additionally, we found that primarily shared environmental influences were stronger on psychopathic traits for individuals with a history of greater negative parenting. CONCLUSIONS: Utilizing a genetically-informed design, we found that both genetic and non-shared environmental factors contribute to the emergence of psychopathic traits. Moreover, perceptions of negative parenting emerged as a clear environmental influence on the development of interpersonal, lifestyle, and antisocial features of psychopathy.

Effects of Genetic Relatedness of Kin Pairs on Univariate ACE Model Performance.

The current study explored the impact of genetic relatedness differences (ΔH) and sample size on the performance of nonclassical ACE models, with a focus on same-sex and opposite-sex twin groups. The ACE model is a statistical model that posits that additive genetic factors (A), common environmental factors (C), and specific (or nonshared) environmental factors plus measurement error (E) account for individual differences in a phenotype. By extending Visscher's (2004) least squares paradigm and conducting simulations, we illustrated how genetic relatedness of same-sex twins (HSS) influences the statistical power of additive genetic estimates (A), AIC-based model performance, and the frequency of negative estimates. We found that larger HSS and increased sample sizes were positively associated with increased power to detect additive genetic components and improved model performance, and reduction of negative estimates. We also found that the common solution of fixing the common environment correlation for sex-limited effects to .95 caused slightly worse model performance under most circumstances. Further, negative estimates were shown to be possible and were not always indicative of a failed model, but rather, they sometimes pointed to low power or model misspecification. Researchers using kin pairs with ΔH less than .5 should carefully consider performance implications and conduct comprehensive power analyses. Our findings provide valuable insights and practical guidelines for those working with nontwin kin pairs or situations where zygosity is unavailable, as well as areas for future research.

Using Multimodel Inference/Model Averaging to Model Causes of Covariation Between Variables in Twins.

OBJECTIVE: To explore and apply multimodel inference to test the relative contributions of latent genetic, environmental and direct causal factors to the covariation between two variables with data from the classical twin design by estimating model-averaged parameters. METHODS: Behavior genetics is concerned with understanding the causes of variation in phenotypes and the causes of covariation between two or more phenotypes. Two variables may correlate as a result of genetic, shared environmental or unique environmental factors contributing to variation in both variables. Two variables may also correlate because one or both directly cause variation in the other. Furthermore, covariation may result from any combination of these sources, leading to 25 different identified structural equation models. OpenMx was used to fit all these models to account for covariation between two variables collected in twins. Multimodel inference and model averaging were used to summarize the key sources of covariation, and estimate the magnitude of these causes of covariance. Extensions of these models to test heterogeneity by sex are discussed. RESULTS: We illustrate the application of multimodel inference by fitting a comprehensive set of bivariate models to twin data from the Virginia Twin Study of Psychiatric and Substance Use Disorders. Analyses of body mass index and tobacco consumption data show sufficient power to reject distinct models, and to estimate the contribution of each of the five potential sources of covariation, irrespective of selecting the best fitting model. Discrimination between models on sample size, type of variable (continuous versus binary or ordinal measures) and the effect size of sources of variance and covariance. CONCLUSIONS: We introduce multimodel inference and model averaging approaches to the behavior genetics community, in the context of testing models for the causes of covariation between traits in term of genetic, environmental and causal explanations.

Heteropaternal Siblings Misclassified as Dizygotic Twins: A Potential Biasing Factor for Heritability Estimates?

Heteropaternal superfecundation (HP) occurs when two or more ova are fertilized by sperm from separate males. The resulting siblings are genetically equivalent to half-siblings and share, on average, 25% of their inherited genetic material. In the absence of genetic testing HP siblings could be treated as dizygotic (DZ) twins in behavioral genetic analyses and bias heritability estimates in phenotypic decomposition models. However, the extent to which such misclassification could affect calculated estimates of heritability is currently unknown. Employing simulation analyses, the current study assessed the potential biasing impact across a variety of conditions varying by proportions of DZ twins, sample sizes, and low, moderate, and high levels of genetic and environmental contribution to phenotypic variance. Overall, the results indicated that misclassified HP siblings had minimal impact on estimates of heritability. Nonetheless, greater attention should be paid to the identification of HP siblings within existing and future twin datasets.

Determining Zygosity in Infant Twins - Revisiting the Questionnaire Approach.

Accurate zygosity determination is a fundamental step in twin research. Although DNA-based testing is the gold standard for determining zygosity, collecting biological samples is not feasible in all research settings or all families. Previous work has demonstrated the feasibility of zygosity estimation based on questionnaire (physical similarity) data in older twins, but the extent to which this is also a reliable approach in infancy is less well established. Here, we report the accuracy of different questionnaire-based zygosity determination approaches (traditional and machine learning) in 5.5 month-old twins. The participant cohort comprised 284 infant twin pairs (128 dizygotic and 156 monozygotic) who participated in the Babytwins Study Sweden (BATSS). Manual scoring based on an established technique validated in older twins accurately predicted 90.49% of the zygosities with a sensitivity of 91.65% and specificity of 89.06%. The machine learning approach improved the prediction accuracy to 93.10%, with a sensitivity of 91.30% and specificity of 94.29%. Additionally, we quantified the systematic impact of zygosity misclassification on estimates of genetic and environmental influences using simulation-based sensitivity analysis on a separate data set to show the implication of our machine learning accuracy gain. In conclusion, our study demonstrates the feasibility of determining zygosity in very young infant twins using a questionnaire with four items and builds a scalable machine learning model with better metrics, thus a viable alternative to DNA tests in large-scale infant twin studies.

Accelerated estimation and permutation inference for ACE modeling.

There are a wealth of tools for fitting linear models at each location in the brain in neuroimaging analysis, and a wealth of genetic tools for estimating heritability for a small number of phenotypes. But there remains a need for computationally efficient neuroimaging genetic tools that can conduct analyses at the brain-wide scale. Here we present a simple method for heritability estimation on twins that replaces a variance component model-which requires iterative optimisation-with a (noniterative) linear regression model, by transforming data to squared twin-pair differences. We demonstrate that the method has comparable bias, mean squared error, false positive risk, and power to best practice maximum-likelihood-based methods, while requiring a small fraction of the computation time. Combined with permutation, we call this approach "Accelerated Permutation Inference for the ACE Model (APACE)" where ACE refers to the additive genetic (A) effects, and common (C), and unique (E) environmental influences on the trait. We show how the use of spatial statistics like cluster size can dramatically improve power, and illustrate the method on a heritability analysis of an fMRI working memory dataset.

Causality and Pleiotropy in the Association Between Bullying Victimization in Adolescence and Depressive Episodes in Adulthood.

Children and adolescents who are victims or perpetrators of bullying victimization are at elevated risk for maladjustment problems, concurrently and in the long run. Previous studies suggest that this correlation is partly explained by genetic influence. However, whether the genetic correlation is independent of a causal effect of victimization on maladjustment remains unclear. Using data from 2,510 females from the TwinsUK registry, we applied an innovative extension of the Cholesky decomposition to investigate to what extent the association between victimization in adolescence and self-reported depressive episodes in adulthood is caused by shared genetic effects (pleiotropy), and to what extent it is due to a phenotypic causal relationship. We find that around 60% of the association between victimization and self-reported depressive episodes is due to a causal effect of victimization on depressive episodes, and 40% is due to pleiotropic effects. These findings underline the importance of integrating genetic information into social science research and demonstrate a neat strategy to elucidate causal mechanisms in the absence of experimental designs.

Estimating Heritability of Prostate Cancer-Specific Survival Using Population-Based Registers.

BACKGROUND: There is a strong genetic component in prostate cancer development with an estimated heritability of 58%. In addition, recent epidemiological assessments show a familial component in prostate cancer-specific survival, which could be due to either common genetics or environment. In this study we sought to estimate the heritability of prostate cancer-specific survival by studying brothers and father-son pairs in Sweden. METHODS: We used linkage records from three Swedish national registers: the Multi-Generation Register, the Cancer Register, and the Cause of Death Register. One thousand seven hundred twenty-eight brother pairs and 6,444 father-son pairs, where both family members were diagnosed with prostate cancer, were followed for prostate cancer mortality. By assuming that (i) brothers on average share 50% of their segregating alleles and 100% environment and (ii) fathers and sons share 50% of their segregating alleles and no environment, we implemented a model including influences of additive genetics (heritability), shared environment and non-shared environment for survival data. A conditional likelihood estimation procedure was developed to fit the model. Data simulation was applied to validate model assumptions. RESULTS: In a model that adjusted for age at diagnosis and calendar period, the estimated heritability of prostate cancer-specific survival was 0.10 (95% CI = 0.00-0.20) that was borderline significantly different from zero (P = 0.057). The shared environment component was not significantly different from zero with a point estimate of 0.00 (95% CI = 0.00-0.13). Simulation studies and sensitivity analysis revealed that the estimated heritability component was robust, whereas the shared environmental component may be underestimated. CONCLUSIONS: Heritability of prostate cancer-specific survival is considerably lower than for prostate cancer incidence. This supports a hypothesis that susceptibility of disease and progression of disease are separate mechanisms that involve different genes. Further assessment of the genetic basis of prostate cancer-specific survival is warranted. Prostate 77:900-907, 2017. © 2017 Wiley Periodicals, Inc.

Patient outcomes related to receiving care on a dedicated Acute Care for Elders (ACE) unit versus with an ACE order set.

BACKGROUND: The Acute Care for Elders (ACE) unit model of care aims to reduce common complications of hospitalization in older adults through early involvement of allied health providers, changes to the care environment, elder-friendly care protocols, and proactive discharge planning. Our hospital established a dedicated 28-bed medical ACE unit. Because of capacity limitations, the number of eligible older medical patients often exceeds the available number of beds. Thus, some ACE unit-eligible patients are instead admitted to other medical or surgical units for their medical care. These "bed-spaced" ACE patients receive care by the same general internists and ACE order set that ACE unit patients are cared under. We sought to compare the health outcomes of ACE-designated patients admitted to the ACE unit versus bed-spaced peers cared for using a protocolized ACE order set. METHODS: 3046 ACE-designated patient admissions were analyzed (1499 ACE unit and 1547 bed-spaced). The primary outcomes examined were discharge disposition and in-hospital mortality. Univariate and multivariate comparisons were performed. Propensity matching was used to adjust for case mix in a post-hoc analysis. RESULTS: The mean age of participants was 83.5 years for ACE unit patients and 82.6 for bedspaced patients. In adjusted models, ACE unit patients were more likely to be discharged home (OR 1.28 [1.08-1.50], p = 0.003). In an unadjusted analysis, patients admitted to ACE unit were less likely to die in hospital, but this finding did not persist after adjustment for case mix. CONCLUSION: Care of older adults delivered on a dedicated ACE unit increases the likelihood of discharge to home when compared to care delivered with an ACE order set alone for general internal medicine patients.

Comparing Phenotypic, Genetic, and Environmental Associations between Personality and Loneliness.

As a strong risk factor for mortality, individual differences in loneliness are of clear public health significance. Four of the Big Five traits have emerged as cross-sectional correlates, but the etiology of these links is unclear, as are relations with more specific personality facets. Thus, we estimated phenotypic, genetic, and environmental associations between loneliness and both broader and narrower personality dimensions. Traits that indexed Negative Emotionality (e.g., Neuroticism, Stress Reactivity, Alienation) and low Positive Emotionality (e.g., low Extraversion, low Well-Being) had the strongest associations with loneliness, though low Conscientiousness, low Agreeableness, and high Aggression were also implicated. These associations were explained by both genetic (0.30<|rg|<0.80) and unique environmental (0.10<|re|<0.35) influences, consistent with an etiology of loneliness involving several personality domains.

Differences in the heritability of craniofacial skeletal and dental characteristics between twin pairs with skeletal Class I and II malocclusions.

OBJECTIVE: To investigate differences in the heritability of skeletodental characteristics between twin pairs with skeletal Class I and Class II malocclusions. METHODS: Forty Korean adult twin pairs were divided into Class I (C-I) group (0° ≤ angle between point A, nasion, and point B [ANB]) ≤ 4°; mean age, 40.7 years) and Class II (C-II) group (ANB > 4°; mean age, 43.0 years). Each group comprised 14 monozygotic and 6 dizygotic twin pairs. Thirty-three cephalometric variables were measured using lateral cephalograms and were categorized as the anteroposterior, vertical, dental, mandible, and cranial base characteristics. The ACE model was used to calculate heritability (A > 0.7, high heritability). Thereafter, principal component analysis (PCA) was performed. RESULTS: Twin pairs in C-I group exhibited high heritability values in the facial anteroposterior characteristics, inclination of the maxillary and mandibular incisors, mandibular body length, and cranial base angles. Twin pairs in C-II group showed high heritability values in vertical facial height, ramus height, effective mandibular length, and cranial base length. PCA extracted eight components with 88.3% in the C-I group and seven components with 91.0% cumulative explanation in the C-II group. CONCLUSIONS: Differences in the heritability of skeletodental characteristics between twin pairs with skeletal Class I and II malocclusions might provide valuable information for growth prediction and treatment planning.

Patient reported outcomes in an elder-friendly surgical environment: Prospective, controlled before-after study.

INTRODUCTION: The Acute Care for the Elderly (ACE) model has demonstrated clinical benefit, but there is little evidence regarding quality of life after discharge. The Elder-friendly Approaches to the Surgical Environment (EASE) study was conducted to assess implementation of an ACE unit on an acute surgical service. Improved clinical and economic outcomes have been demonstrated, but post-discharge patient reported outcomes have not yet been reported. METHODS: Prospective, concurrently controlled, before-after study at two tertiary care hospitals in Alberta, Canada. The SF-12, EQ-5D, Canadian Malnutrition Screening Tool (CMST) and patient satisfaction were collected from elderly (≥ 65 years old) patients, 6 weeks and 6 months after discharge from an acute care surgical service. A difference-in-difference (DID) method was used to analyze between-site effects. RESULTS: At six weeks, patient satisfaction was high at 68%-86%, with significant improvement Pre-to Post-EASE at the control site (p < 0.001), but not the intervention site (p = 0.06). For the intervention site, within-site adjusted pre-post effects were nonsignificant for all patient reported outcomes [EQ-Index Score ß coefficient (SE): 0.042 (0.022); EQ-Visual Analog Scale: 0.10 (2.14); SF-12 Physical Component Score: -0.57 (0.84); SF-12 Mental Component Score: 1.17 (0.84); CMST Score: -0.39 (0.34)]. DID analyses were also non significant for all outcomes except for SF-12 Mental Component Score (p < 0.001). CONCLUSION: The clinically and economically beneficial EASE interventions do not appear to compromise quality of life, risk for malnutrition, or patient satisfaction in the post-discharge period. Further research with larger sample size is needed with comparisons to pre-intervention and the early post-discharge period.

ACE Model for Older Adults in ED.

The emergency department (ED) is uniquely positioned to improve care for older adults and affect patient outcome trajectories. The Mount Sinai Hospital ED cares for 15,000+ patients >65 years old annually. From 2012 to 2015, emergency care in a dedicated Geriatric Emergency Department (GED) replicated an Acute Care for Elderly (ACE) model, with focused assessments on common geriatric syndromes and daily comprehensive interdisciplinary team (IDT) meetings for high-risk patients. The IDT, comprised of an emergency physician, geriatrician, transitional care nurse (TCN) or geriatric nurse practitioner (NP), ED nurse, social worker (SW), pharmacist (RX), and physical therapist (PT), developed comprehensive care plans for vulnerable older adults at high risk for morbidity, ED revisit, functional decline, or potentially avoidable hospital admission. Patients were identified using the Identification of Seniors at Risk (ISAR) screen, followed by geriatric assessments to assist in the evaluation of elders in the ED. On average, 38 patients per day were evaluated by the IDT with approximately 30% of these patients formally discussed during IDT rounds. Input from the IDT about functional and cognitive, psychosocial, home safety, and pharmacological assessments influenced decisions on hospital admission, care transitions, access to community based resources, and medication management. This paper describes the role of a Geriatric Emergency Medicine interdisciplinary team as an innovative ACE model of care for older adults who present to the ED.