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PubMed2026

Elevated water levels drive greenhouse gas mitigation in the riparian zone profile.

Wetlands are critical for climate regulation, with their hyporheic zone serving as sensitive interfaces for groundwater-soil-atmosphere exchange. These zones are active hotspots for carbon-nitrogen cycling and greenhouse gas (GHG) emissions (CO2, CH4, N2O), yet the impact of water level fluctuations on these emissions and their microbial drivers in freshwater wetlands remains poorly understood. This study investigated the spatiotemporal dynamics of GHG emissions and carbon-nitrogen coupling processes along riparian soil profiles of Baiyangdian Lake during water level fluctuations. Employing static chamber measurements, microcosms, quantitative PCR, Metagenome-Assembled genome (MAG) analyses, and Structural Equation Modeling (SEM), we observed that GHG emissions were significantly affected by water level fluctuations. Specifically, CO2 and N2O fluxes, as well as CO2 production potential were significantly lower at high-water-level conditions. Water level also emerged as a key driver of microbial community structure, with Methylococcaceae and Methanosarcinaceae as key regulators of CH4 emission, and Anaeromyxobacteraceae as central to N2O dynamics. A high-quality Methylomirabilales-like MAG, possessing the complete pathway for coupled nitrate reduction and methane oxidation, was identified. Its abundance negatively correlated with water level, suggesting that these C-N coupling bacteria contribute to reducing GHG emissions. This study provides crucial theoretical insights and identifies microbial targets for mitigating wetland GHG emission through hydrological management.

Greenhouse Gases↗

PubMed2026

Social disconnection integrates genetic and proteomic risks in suicidal ideation and depression.

Suicidal ideation (SI) and major depressive disorder (MDD) are complex psychiatric conditions arising from the interplay of genetic liability, molecular processes, and psychosocial factors. While these dimensions have been extensively studied in isolation, their joint contribution to SI and MDD remains unclear. This study integrates multi-modal data to elucidate these synergistic effects and develop robust models for individual-level risk stratification. Leveraging longitudinal multi-modal data from 13,085 UK Biobank participants, we integrated genomic, proteomic, and social connection profiles. We developed interpretable risk scores using a rigorous supervised machine learning framework encompassing diverse linear and ensemble classifiers. Permutation importance was employed to quantify feature contributions and derive transparent, weighted risk metrics across diverse classifiers. These scores were validated through association, interaction, and mediation analyses. Social connection-based risk scores significantly differentiated cases and controls across the two suicidal ideation phenotypes at 2017 and 2023 with cross-sectional analyses (AUCs: 0.70 - 0.73), outperforming proteomic-only models. Functional dimensions of social connection emerged as the most informative predictors. Longitudinal analyses revealed that social risk scores at baseline predicted suicidal ideation onset six years later, independent of demographic covariates. Interaction analyses demonstrated that polygenic risk for suicide attempt significantly interacted with both social and proteomic risk features in relation to depression. Structural equation models further confirmed that social disconnection acts as a key mediator linking genetic predisposition to MDD and SI. Social disconnection is a critical risk factor mediating the impact of genetic vulnerability on psychiatric outcomes. Integrating social, genetic, and molecular data supports a multilevel framework for risk stratification and highlights the potential of socially oriented interventions to mitigate biological risk.

Humans↗

PubMed1984

Construct validity of heroin abuse estimators.

The construct validity of two methodologically unrelated procedures for estimating the prevalence of heroin abuse was studied using a covariance structure model-fitting approach. A factor analytic estimation procedure and a log-linear-based capture-recapture method of estimation were simultaneously applied to 24 major metropolitan areas of the nation. A test of the construct validity of these procedures for estimating the prevalence of heroin abuse was carried out in the context of structural equation models where the results of the two methods were compared directly.

Data Collection↗

PubMed1992

Personality, life events and coping in the oldest-old.

This paper compares older adults in their sixties, eighties, and 100s on personality, experience of life events, and coping. A secondary goal was to test a structural model of adaptation. Participants (165) filled out a personality inventory, life-event lists, and coping and mental health measures. Results revealed differences in personality: centenarians scored higher on dominance, suspiciousness, and imagination. While centenarians scored lower on active behavioral coping than other age groups, they used cognitive strategies when coping with health and family events. Results from the structural equation model indicated that extraversion and anxiety predicted morale and mental health.

Adaptation, Psychological↗

PubMed2026

Unraveling causal links between chronic rhinosinusitis and peripheral artery diseases: insights from genetic correlations through genome-wide association studies.

OBJECTIVES: Chronic Rhinosinusitis (CRS) shares epidemiological links with Cardiovascular Diseases (CVDs), however, their shared genetic basis remains unclear. We hypothesized that pleiotropic genetic variants underlie CRS-CVDs links via distinct biological pathways. METHODS: Using large-scale GWAS data from European-ancestry individuals, we assessed global and local genetic correlations. We applied Genomic Structural Equation Modeling (Genomic SEM) to dissect shared genetic architecture, performed bidirectional Mendelian Randomization (MR) to infer causality, and conducted cis-eQTL colocalization to identify shared genetic signals. Finally, in vitro endothelial models (HUVECs) validated the functional dynamics of candidate genes under CRS-mimicking inflammatory stress. RESULTS: CRS showed significant genetic correlations with multiple CVDs. Genomic SEM revealed a latent factor structuring shared genetic risk through three pathways: artery diseases, myocardial diseases, and heart failure. Local genetic correlations identified significant local genetic correlations specifically between CRS and Peripheral Atherosclerosis (PAS)/Peripheral Artery Disease (PAD) specifically within the chr6: 31.57‒33.24 Mb locus. MR demonstrated causal effects of CRS on PAD (OR = 1.23, p = 0.022) and PAS (OR = 1.21, p = 0.011), but not vice versa. Genetically predicted HLA-DRB1, APOM, and COL11A2 expression conferred protection, while HLA-DQA2 increased risk. Crucially, in vitro validation corroborated these pathogenic trajectories, inflammatory stress significantly downregulated the protective APOM and upregulated the risk-associated HLA-DQA2 alongside pro-atherogenic VCAM-1, while HLA-DRB1 exhibited a compensatory upregulation (p < 0.05). CONCLUSION: CRS shares global genetic liability with CVDs, structured through three primary etiological pathways. Causal effects of CRS on peripheral artery diseases are mediated by immune and lipid-related genes within the chr6 locus, revealing divergent pleiotropic mechanisms. Our integrated genetic and in vitro evidence provides a mechanistic framework wherein chronic mucosal inflammation contributes to systemic endothelial vulnerability, thereby highlighting candidate targets for mechanism-directed therapy.

Humans↗

PubMedDate not supplied

A practical and theoretical guide to measurement invariance in aging research.

We describe mathematical and statistical models for factor invariance. We demonstrate that factor invariance is a condition of measurement invariance. In any study of change (as over age) measurement invariance is necessary for valid inference and interpretation. Two important forms of factorial invariance are distinguished: "configural" and "metric". Tests for factorial invariance and the range of tests from strong to weak are illustrated with multiple group factor and structural equation modeling analyses (with programs such as LISREL, COSAN, and RAM). The tests are for models of the organization and age changes of intellectual abilities. The models are derived from current theory of fluid (Gf) and crystallized (Gc) abilities. The models are made manifest with measurements of the WAIS-R in the standardization sample. Although this is a methodological paper, the key issues and major principles and conclusions are presented in basic English, devoid of technical details and obscure notation. Conceptual principles of multivariate methods of data analysis are presented in terms of substantive issues of importance for the science of the psychology of aging.

Aged↗

PubMed1995

The factorial structure and stability of the McGill Pain Questionnaire in patients experiencing oral mucositis following bone marrow transplantation.

The McGill Pain Questionnaire (MPQ) (Melzack 1975) is an important assessment tool for multidimensional pain measurement in both clinical practice and research. Despite widespread acceptance, empirical analyses have not consistently verified the 3 a-priori factors that guided the subclass construction of the Pain Rating Index (PRI) of the MPQ. This study compared the a-priori model with 2 qualitatively different factor models in 191 patients with oral mucositis pain at 3 days and 10 days following bone marrow transplantation. A semantic model defined by Sensory Action, Sensory Evaluation, and Affective Evaluation factors of subclass descriptor content fit better than the a-priori model and a model positing a single general pain factor. The 3 semantic PRI factors were highly intercorrelated, with the sensory factors correlating more highly with an independent visual analogue (VAS) pain scale. Standardized factor regression coefficients between the two occasions of measurement ranged between 0.4 and 0.5. Mean factor change was greatest for Sensory Evaluation and lowest for Affective Evaluation. All analyses were conducted with the LISREL 7 structural equation modeling program. Although the factor analyses indicated an unambiguous ranking of PRI models according to statistical criteria, these theoretical results generalize poorly to simple scores formed by direct addition of the PRI subclasses. Summary scores can only approximate the unobserved factors and cannot retain the fine discriminations revealed by the theoretical factors. Psychometric considerations suggest that a single PRI total score will yield better practical measurement than any scoring rules based on multiple factors.

Adolescent↗

PubMed2026

Integrated bioinformatics and SEM analysis reveal GPAM as a key mediator of fibrosis in NAFLD with metabolic dysfunction.

Nonalcoholic fatty liver disease (NAFLD) is a complex condition influenced by metabolic and genetic factors, yet the shared genetic architecture underlying its progression remains poorly understood. The aim of this study was to employ genomic structural equation modeling (GSEM) to elucidate the genetic architecture linking NAFLD with key metabolic traits-including insulin resistance, body mass index (BMI), hemoglobin A1c (HbA1c), and liver fibrosis using summary statistics from large-scale genome-wide association studies. By harmonizing 2.18 million variants across five genome-wide association studies (GWAS) datasets, we identified 134 genome-wide significant loci that mapped to 24 genes. GSEM revealed a latent genetic structure composed of two distinct dimensions: a metabolic regulation factor primarily driven by insulin resistance, BMI, and HbA1c; and a structural pathology factor specifically associated with liver fibrosis. These factors explained 65.5% and 78.1% of the genetic variance in BMI and fibrosis, respectively, with minimal correlation (rg = 0:07), indicating their genetic distinctness. Additionally, integrating Mendelian randomization with liver transcriptome profiling, we characterized how the 24 genes contribute to disease and identified mitochondrial glycerol-3-phosphate acyltransferase (GPAM) as the key gene that causally links lipid metabolism to fibrogenesis. In conclusion, we present the first genetically grounded mechanism for the progression of NAFLD to fibrosis. This mechanism encompasssses genetic variants, dysregulated gene expression, metabolic disturbances, and the processes involved in fibrotic remodeling. This research establishes a genetic framework for understanding the pathogenesis of NAFLD and highlights novel therapeutic targets for intervention.

Non-alcoholic Fatty Liver Disease↗

PubMedDate not supplied

Multidimensional causal model of dental caries development in low-income preschool children.

Despite the decline in the incidence of dental caries in the United States over the past several years, the condition remains a significant problem for the nation's poor children. Efforts to identify the factors responsible for caries development in samples of children of low socioeconomic status have primarily focused on a limited number of variables, and those have been predominantly biological (mutans streptococci, for example). Resulting models of caries development have usually shown good sensitivity but poor specificity. They have had limited implications for treatment. In an effort to produce a comprehensive model of caries development, 184 low-income preschool children were clinically assessed for mutans streptococci and for decayed, missing, or filled surfaces of deciduous teeth twice, first at age 4 years (baseline) and again a year later (year 1 assessment). As the clinical assessments were being done, caretakers were being interviewed to obtain data from five domains: demographics, social status, dental health behaviors, cognitive factors such as self-efficacy (self-confidence) and controllability, and perceived life stress. Data were analyzed using a structural equations modeling approach in which variables from all domains, plus baseline decayed missing and filled surfaces and baseline mutants, were used together to create a model of caries development in the year 1 assessment. Results confirmed earlier work that suggested that caries development at a 1-year followup was strongly dependent on earlier caries development. Early caries development in this sample was determined in part by mutans levels and by dental health behaviors. These behaviors themselves were accounted for partly by a cognitive factor. The results support the advantages of employing multidimensional models and provide some direction for intervention to reduce caries incidence.

Causality↗

PubMed2026

From fear to empowerment: the impact of employees AI awareness on workplace well-being - a new insight from the JD-R model.

PURPOSE: The primary purpose of the study was to explore the impact of health workers' awareness of artificial intelligence (AI) on their workplace well-being, addressing a critical gap in the literature. By examining this relationship through the lens of the Job demands-resources (JD-R) model, the study aimed to provide insights into how health workers' perceptions of AI integration in their jobs and careers could influence their informal learning behaviour and, consequently, their overall well-being in the workplace. The study's findings could inform strategies for supporting healthcare workers during technological transformations. DESIGN/METHODOLOGY/APPROACH: The study employed a quantitative research design using a survey methodology to collect data from 420 health workers across 10 hospitals in Ghana that have adopted AI technologies. The study was analysed using OLS and structural equation modelling. FINDINGS: The study findings revealed that health workers' AI awareness positively impacts their informal learning behaviour at the workplace. Again, informal learning behaviour positively impacts health workers' workplace well-being. Moreover, informal learning behaviour mediates the relationship between health workers' AI awareness and workplace wellbeing. Furthermore, employee learning orientation was found to strengthen the effect of AI awareness on informal learning behaviour. RESEARCH LIMITATIONS/IMPLICATIONS: While the study provides valuable insights, it is important to acknowledge its limitations. The study was conducted in a specific context (Ghanaian hospitals adopting AI), which may limit the generalizability of the findings to other healthcare settings or industries. Self-reported data from the questionnaires may be subject to response biases, and the study did not account for potential confounding factors that could influence the relationships between the variables. PRACTICAL IMPLICATIONS: The study offers practical implications for healthcare organizations navigating the digital transformation era. By understanding the positive impact of health workers' AI awareness on their informal learning behaviour and well-being, organizations can prioritize initiatives that foster a learning-oriented culture and provide opportunities for informal learning. This could include implementing mentorship programs, encouraging knowledge-sharing among employees and offering training and development resources to help workers adapt to AI-driven changes. Additionally, the findings highlight the importance of promoting employee learning orientation, which can enhance the effectiveness of such initiatives. ORIGINALITY/VALUE: The study contributes to the existing literature by addressing a relatively unexplored area - the impact of AI awareness on healthcare workers' well-being. While previous research has focused on the potential job displacement effects of AI, this study takes a unique perspective by examining how health workers' perceptions of AI integration can shape their informal learning behaviour and, subsequently, their workplace well-being. By drawing on the JD-R model and incorporating employee learning orientation as a moderator, the study offers a novel theoretical framework for understanding the implications of AI adoption in healthcare organizations.

Humans↗

PubMed2026

Innovation-related perception as a key driver of alternative protein acceptance: evidence from an early-stage model for cultivated meat and algae-/microalgae-based alternative protein products in Italy.

Alternative proteins are increasingly considered part of the transition toward more sustainable food systems, yet their diffusion depends critically on consumer acceptance. This study investigates the early-stage acceptance of two alternative protein categories in Italy-cultivated meat and algae-/microalgae-based alternative protein products. Focusing on the first three phases of acceptance, the analysis examines how innovation-related perception (IRP) shapes consumer perceived value (CPV), consumer perceived risk (CPR), and subsequent affective (AFF), cognitive (COG), and conative (CON) responses. Data were collected through an online survey administered to 238 Italian respondents and analysed using partial least squares structural equation modelling (PLS-SEM). The results show that IRP is the main upstream driver of early-stage acceptance in both product domains: more favourable perceptions strongly increase perceived value and reduce perceived risk. In turn, CPV exerts a much stronger influence than CPR on both affective and cognitive attitudes. A tentative cross-model comparison suggests only a descriptive variation in the final transition toward conative acceptance: affective and cognitive responses were both significant in the two models, with a relatively larger affective coefficient for cultivated meat and more balanced coefficients for algae-/microalgae-based products. Overall, the findings support a process-based interpretation of alternative protein acceptance and highlight the central role of innovation-related perception in shaping early consumer responses. These results provide relevant implications for communication strategies, product positioning, and policy actions aimed at improving the acceptability of alternative proteins in food cultures characterised by strong culinary traditions.

Italy↗

PubMed2026

Long-term petroleum pollution alters soil microbial communities via electron transfer capacity: Evidence from a 35-year chronosequence.

Petroleum pollution poses a serious threat to soil ecosystems, especially in areas surrounding oil wells, where contamination should not be overlooked. Through a 35-year longitudinal study of soils surrounding oil wells, we demonstrate that petroleum hydrocarbons accumulate predominantly in the top 10 cm of soil, reducing the electron acceptor capacity (EAC) by 61.59 % (from 12.68 to 4.87 μmole-/gC) and decreasing the electron transfer capacity (ETC) by 43 %. Structural equation modeling identified ETC as the critical mediator of microbial community shifts, with EAC playing a pivotal role in sustaining redox processes. Notably, hydrocarbon accumulation triggered a microbial succession: The abundance of Actinomycetota (including genera Rhodococcus, Arthrobacter, and Rubrobacter) showed the most significant fluctuations within 2 years, while Pseudomonadota (genera Methylobacter, Thiobacillus, and Pseudomonas), which were dominant in uncontaminated soils, decreased markedly during this period. This transition coincided with peak microbial dysbiosis (microbial dysbiosis index in 2022 reached 31.41 times that of controls). Within two to four years following mild petroleum stress, the bacterial community established a new structural configuration, revealing a crucial window for ecological recovery. The coupling between ETC reduction and microbial succession highlights the pivotal role of electron flux in soil recovery. Our findings establish a mechanistic framework for ETC-targeted restoration strategies to enhance bioremediation in petroleum-contaminated soils.

Soil Microbiology↗

PubMed1985

Organizational stress in the hospital: development of a model for diagnosis and prediction.

A model of organizational stress in the hospital was developed and tested. The model utilized measures of organizational climate, supervisory practices, and work group relations as predictors of the amount of role conflict and ambiguity that nurses perceived in providing patient care. Role conflict and ambiguity were treated as variables that intervene between organizational variables and the level of stress that the nursing staff experienced. Nursing stress was viewed as a direct cause of job dissatisfaction and as an indirect cause of absenteeism among the nursing staff. Data from 158 registered nurses, licensed practical nurses, and nursing assistants on seven nursing units in a 1,160-bed private teaching hospital were used to estimate the parameters of a structural equation model. The model was used to predict the results of a survey feedback project designed to change the supervisory style used on the units. Pre- and posttest data from four surgical units were used to validate the model. The findings suggest that, as predicted, supervisory practices that led to more open expression of views and joint problem solving resulted in reduced role conflict, ambiguity, and stress; increased job satisfaction; and lower levels of absenteeism among the nursing staff.

Absenteeism↗

PubMed1991

Differentiation of early adolescent predictors of drug use versus abuse: a developmental risk-factor model.

Many psychosocial factors are associated with adolescent drug use, though most have not been tested as true predictors of drug use in prospective studies. Studies to date have also not differentiated predictors of drug use from abuse and have not addressed differential effects for specific substances. To address these concerns, we expanded the multiple risk-factor approach using 2-year longitudinal data from a sample of seventh graders. Frequencies of use for alcohol, cigarettes, marijuana, cocaine, and hard drugs were assessed at Time 1 and Time 2 and used to reflect latent constructs of polydrug use. From a set of 29 risk factors, unique predictors of any substance were separated conceptually according to whether they most related to initiation/experimental or problem/heavy drug use and were then summed into two-unit weighted indexes at each time. Distribution-free structural equation models were used to accommodate the nonnormal distributions of the illicit drug use measures. The problem risk index was strongly correlated with polydrug use at Time 1 and increased polydrug use at Time 2. Several specific relationships between risk and drug use across time also were noted.

Adolescent↗

PubMed1994

Psychosocial, behavioral, and medical outcomes in children with epilepsy: a developmental risk factor model using longitudinal data.

OBJECTIVE: We studied factors predicting the risk of adverse long-term psychosocial, behavioral, and medical outcomes in children with epilepsy. METHODS: Children (N = 157, 4.5 to 13 years) were enrolled in a prospective longitudinal study when first seen. Potential subjects were excluded if they were moderately or severely mentally retarded, had motor or sensory handicaps interfering with testing, or did not speak either English or Spanish. MEASURES: To develop risk predictors, we collected information regarding the child's medical and seizure history, cognitive functioning, and behavior problems, and family functioning. Children and their families were followed for a minimum of 18 months, then underwent reassessment of medical status, parent's attitudes toward epilepsy, and the child's behavioral and cognitive functioning. Data were analyzed by confirmatory factor analysis to develop baseline factors (Sociocultural Risk, Seizure Risk, and Behavior Problems) and outcome factors (Medical/Seizure Problems, Parent's Negative Attitudes Toward Epilepsy, and Behavior Problems), followed by structural equation modeling to determine across-time causal effects. Eighty-eight subjects completed all baseline and outcome measures. RESULTS: Among significant across-time effects, Medical Outcome was predicted by Seizure Risk. An increased number of stressful life events predicted better Medical Outcome. Low acculturation increased Parent's Negative Attitudes and was associated with increased Behavior Problems at baseline. Behavior Problems were stable across time. It is interesting that IQ did not affect any of the outcomes, although its effect may have been mediated through other baseline measures. CONCLUSIONS: Seizure history was the best predictor of ongoing medical difficulties, whereas the most important causes of ongoing parental anxiety and negative attitudes toward epilepsy were sociocultural. Variation in medical or attitudinal outcomes was not influenced by either the child's IQ or reported behavioral problems. These findings suggest that to alter attitudes toward epilepsy, programs should be tailored to the sociocultural background of the family. Studies of quality of life of children with epilepsy should include appropriate sociocultural measures.

Acculturation↗

PubMed1983

Path analysis in genetic epidemiology: a critique.

Path analysis, a form of general linear structural equation models, is used in studies of human genetics data to discern genetic, environmental, and cultural factors contributing to familial resemblance. It postulates a set of linear and additive parametric relationships between phenotypes and genetic and cultural variables and then essentially uses the assumption of multivariate normality to estimate and perform tests of hypothesis on parameters. Such an approach has been advocated for the analysis of genetic epidemiological data by D. C. Rao, N. Morton, C. R. Cloninger, L. J. Eaves, and W. E. Nance, among others. This paper reviews and evaluates the formulations, assumptions, methodological procedures, interpretations, and applications of path analysis. To give perspective, we begin with a discussion of path analysis as it occurs in the form of general linear causal models in several disciplines of the social sciences. Several specific path analysis models applied to lipoprotein concentrations, IQ, and twin data are then reviewed to keep the presentation self-contained. The bulk of the critical discussion that follows is directed toward the following four facets of path analysis: (1) coherence of model specification and applicability to data; (2) plausibility of modeling assumptions; (3) interpretability and utility of the model; and (4) validity of statistical and computational procedures. In the concluding section, a brief discussion of the problem of appropriate model selection is presented, followed by a number of suggestions of essentially model-free alternative methods of use in the treatment of complex structured data such as occurs in genetic epidemiology.

Asian↗

PubMed1995

Genetic, environmental, and phenotypic links between body mass index and blood pressure among women.

Greater relative weight is associated with higher blood pressure, but the reasons are unknown. The inability of current technology to induce sustained weight loss among overweight persons precludes experimental tests of whether this association is causal. We evaluated the degree to which the covariation between body mass index (BMI; kg/m2) and blood pressure (BP) among women is due to pleiotropic genetic factors, environmental factors, or phenotypic causation. The sample included 75 monozygotic (MZ) and 39 dizygotic (DZ) pairs of adult female twins. "BP" was calculated as the unit-weighted mean of systolic and diastolic. Data were analyzed through structural equation modeling. A model was specified stipulating that additive genetic effects (A) and unique environmental effects (E) each contributed to the covariance between BMI and BP, thus allowing for both pleiotropic and unique environmental influences on the covariance between BMI and BP. Dropping the pleiotropic influences significantly worsened the model (chi 2 = 4.62, df = 1, P = .032), suggesting significant pleiotropic effects. Dropping the environmental influences on the cross-phenotype covariance did not significantly worsen the model (chi 2 = 1.42, df = 1, P = .233). This indicates no significant effect of the environment on the covariance between BMI and BP. Finally, a model of phenotypic causation in which BMI directly influenced BP was fitted. This model provided the best single parameter explanation of the BP-BMI covariation. These data suggest that, among women, regardless of the source of variation, changes in BMI should lead to long-standing changes in BP.

Adult↗

PubMed1992

Genetic and environmental contributions to the variance of body height in a sample of first and second degree relatives.

Height was measured in a health screening of the population in Nord-Trøndelag, Norway. Correlations were computed for 24,281 pairs of spouses, 43,613 pairs of parents and offspring, 19,168 pairs of siblings, 1,318 pairs of grandparents and grandchildren, 1,218 cognate avuncular pairs, 849 noncognate avuncular pairs, 175 pairs of same-sexed twins, and smaller groups of other types of relatives. Fitting of structural equation models showed proportions of additive genetic variance of approximately 0.8 for both sexes and small sex-specific effects that probably reflect genetic dominance or environmental sibling effects. The correlations between parents and offspring were significantly lower in old than young cohorts, seeming to imply some kind of interaction effect between genes and environment.

Age Factors↗