An ACE in the hole: Twin family models for applied behavioral genetics research

Genetic and environmental influence on resting state networks in young male and female adults: a cartographer mapping study

We propose a unique, minimal assumption, approach based on variance analyses (compared with standard approaches) to investigate genetic influence on individual differences on the functional connectivity of the brain using 65 monozygotic and 65 dizygotic healthy young adult twin pairs' low-frequency oscillation resting state functional Magnetic Resonance Imaging (fMRI) data from the Human Connectome Project. Overall, we found high number of genetically-influenced functional (GIF) connections involving posterior to posterior brain regions (occipital/temporal/parietal) implicated in low-level processes such as vision, perception, motion, categorization, dorsal/ventral stream visuospatial, and long-term memory processes, as well as high number across midline brain regions (cingulate) implicated in attentional processes, and emotional responses to pain. We found low number of GIF connections involving anterior to anterior/posterior brain regions (frontofrontal > frontoparietal, frontotemporal, frontooccipital) implicated in high-level processes such as working memory, reasoning, emotional judgment, language, and action planning. We found very low number of GIF connections involving subcortical/noncortical networks such as basal ganglia, thalamus, brainstem, and cerebellum. In terms of sex-specific individual differences, individual differences in males were more genetically influenced while individual differences in females were more environmentally influenced in terms of the interplay of interactions of Task positive networks (brain regions involved in various task-oriented processes and attending to and interacting with environment), extended Default Mode Network (a central brain hub for various processes such as internal monitoring, rumination, and evaluation of self and others), primary sensorimotor systems (vision, audition, somatosensory, and motor systems), and subcortical/noncortical networks. There were >8.5-19.1 times more GIF connections in males than females. These preliminary (young adult cohort-specific) findings suggest that individual differences in the resting state brain may be more genetically influenced in males and more environmentally influenced in females; furthermore, standard approaches may suggest that it is more substantially nonadditive genetics, rather than additive genetics, which contribute to the differences in sex-specific individual differences based on this young adult (male and female) specific cohort. Finally, considering the preliminary cohort-specific results, based on standard approaches, environmental influences on individual differences may be substantially greater than that of genetics, for either sex, frontally and brain-wide. [Correction added on 10 May 2023, after first online publication: added: functional Magnetic Resonance Imaging. Added: individual differences in, twice. Added statement between furthermore … based on standard approaches.].

Concomitant Coronary Artery Disease in Identical Twins: Case Report and Systematic Literature Review

Coronary artery disease (CAD) is multifactorial and strongly affected by genetic, epigenetic and environmental factors. Several studies have reported development of concomitant CAD in identical twins. We report a case in which a pair of Caucasian male monozygotic twins presented almost concomitantly with acute coronary syndrome (ACS) and had concordant coronary anatomy and identical site of occlusion. We performed a systematic literature review of PubMed, Web Of Science and Scopus databases from inception until 28 February 2023 of case reports/case series reporting the concomitant development of CAD in monozygotic twins. We found 25 eligible case reports with a total of 31 monozygotic twin pairs (including the case from our center) suffering from CAD and presenting (most of them simultaneously) with ACS (mean age of presentation: 45 ± 12 years, males: 81%). Coronary angiograms demonstrated lesion and anatomy concordance in 77% and 79% of the twin pairs, respectively. Screening for disease-related genetic mutations was performed in six twin pairs leading to the identification of five CAD-related genetic polymorphisms. This is the first systematic literature review of studies reporting identical twin pairs suffering from CAD. In summary, there is high concordance of coronary anatomy and clinical presentation between monozygotic twins. Future monozygotic twin studies-unbiased by age effects-can provide insights into CAD heritability being able to disentangle the traditional dyad of genetic and environmental factors and investigate the within-pair epigenetic drift.

Heritability Estimation of Cognitive Phenotypes in the ABCD Study® Using Mixed Models

Twin and family studies have historically aimed to partition phenotypic variance into components corresponding to additive genetic effects (A), common environment (C), and unique environment (E). Here we present the ACE Model and several extensions in the Adolescent Brain Cognitive Development℠ Study (ABCD Study®), employed using the new Fast Efficient Mixed Effects Analysis (FEMA) package. In the twin sub-sample (n = 924; 462 twin pairs), heritability estimates were similar to those reported by prior studies for height (twin heritability = 0.86) and cognition (twin heritability between 0.00 and 0.61), respectively. Incorporating SNP-derived genetic relatedness and using the full ABCD Study® sample (n = 9,742) led to narrower confidence intervals for all parameter estimates. By leveraging the sparse clustering method used by FEMA to handle genetic relatedness only for participants within families, we were able to take advantage of the diverse distribution of genetic relatedness within the ABCD Study® sample.

The Heritability of Kidney Function using an Older Australian Twin Population

Introduction

Twin studies are unique population models which estimate observed rather than inferred genetic components of complex traits. Nonmonogenic chronic kidney disease (CKD) is a complex disease process with strong genetic and environmental influences, amenable to twin studies. We aimed to assess the heritability of CKD using twin analysis and modeling within Older Australian Twin Study (OATS) data.

Methods

OATS had 109 dizygotic (DZ) and 126 monozygotic (MZ) twin pairs with paired serum creatinine levels. Heritability of kidney function as estimated glomerular filtration rate (eGFR CKD Epidemiology Collaboration [CKD-EPI]) was modeled using the ACE model to estimate additive heritability (A), common (C), and unique (E) environmental factors. Intratwin pair analysis using mixed effects logistic regression allowed analysis of variation in eGFR from established CKD risk factors.

Results

The median age was 69.71 (interquartile range 78.4–83.0) years, with 65% female, and a mean CKD-EPI of 82.8 ml/min (SD 6.7). The unadjusted ACE model determined kidney function to be 33% genetically determined (A), 18% shared genetic-environmental (C), and 49% because of unique environment (E). This remained unchanged when adjusted for age, hypertension, and sex. Hypertension was associated with eGFR; however, intertwin variance in hypertension did not explain variance in eGFR. Two or more hypertension medications were associated with decreased eGFR (P = 0.009).

Conclusion

This study estimates observed heritability at 33%, notably higher than inferred heritability in genome-wide association study (GWAS) (7.1%–18%). Epigenetics and other genomic phenomena may explain this heritability gap. Difference in antihypertension medications explains part of unique environmental exposures, though discordance in hypertension and diabetes does not.

Heritability of Coronary Artery Disease: Insights From a Classical Twin Study

BACKGROUND

Genetics have a strong influence on calcified atherosclerotic plaques; however, data regarding the heritability of noncalcified plaque volume are scarce. We aimed to evaluate genetic versus environmental influences on calcium (coronary artery calcification) score, noncalcified and calcified plaque volumes by coronary computed tomography angiography in adult twin pairs without known coronary artery disease.

METHODS

In the prospective BUDAPEST-GLOBAL (Burden of Atherosclerotic Plaques Study in Twins-Genetic Loci and the Burden of Atherosclerotic Lesions) classical twin study, we analyzed twin pairs without known coronary artery disease. All twins underwent coronary computed tomography angiography to assess coronary atherosclerotic plaque volumes. Structural equation models were used to quantify the contribution of additive genetic, common environmental, and unique environmental components to plaque volumes adjusted for age, gender, or atherosclerotic cardiovascular disease risk estimate and statin use.

RESULTS

We included 196 twins (mean age±SD, 56±9 years, 63.3% females), 120 monozygotic and 76 same-gender dizygotic pairs. Using structural equation models, noncalcified plaque volume was predominantly determined by environmental factors (common environment, 63% [95% CI, 56%-67%], unique environment, 37% [95% CI, 33%-44%]), while coronary artery calcification score and calcified plaque volumes had a relatively strong genetic heritability (additive genetic, 58% [95% CI, 50%-66%]; unique environmental, 42% [95% CI, 34%-50%] and additive genetic, 78% [95% CI, 73%-80%]; unique environmental, 22% [95% CI, 20%-27%]), respectively.

CONCLUSIONS

Noncalcified plaque volume is mainly influenced by shared environmental factors, whereas coronary artery calcification score and calcified plaque volume are more determined by genetics. These findings emphasize the importance of early lifestyle interventions in preventing coronary plaque formation.

REGISTRATION

URL: https://www.

CLINICALTRIALS

gov; Unique identifier: NCT01738828.

Where the genome meets the connectome: Understanding how genes shape human brain connectivity

The integration of modern neuroimaging methods with genetically informative designs and data can shed light on the molecular mechanisms underlying the structural and functional organization of the human connectome. Here, we review studies that have investigated the genetic basis of human brain network structure and function through three complementary frameworks: (1) the quantification of phenotypic heritability through classical twin designs; (2) the identification of specific DNA variants linked to phenotypic variation through association and related studies; and (3) the analysis of correlations between spatial variations in imaging phenotypes and gene expression profiles through the integration of neuroimaging and transcriptional atlas data. We consider the basic foundations, strengths, limitations, and discoveries associated with each approach. We present converging evidence to indicate that anatomical connectivity is under stronger genetic influence than functional connectivity and that genetic influences are not uniformly distributed throughout the brain, with phenotypic variation in certain regions and connections being under stronger genetic control than others. We also consider how the combination of imaging and genetics can be used to understand the ways in which genes may drive brain dysfunction in different clinical disorders.

Multi-polygenic Analysis of Nicotine Dependence in Individuals of European Ancestry

INTRODUCTION

Heritability estimates of nicotine dependence (ND) range from 40-70%, but discovery GWAS of ND are underpowered and have limited predictive utility. In this work, we leverage genetically correlated traits and diseases to increase the accuracy of polygenic risk prediction.

METHODS

We employed a multi-trait model using summary statistic-based best linear unbiased predictors (SBLUP) of genetic correlates of DSM-IV diagnosis of ND in 6,394 individuals of European Ancestry (prevalence = 45.3%, %female = 46.8%, µage = 40.08 [s.d. = 10.43]) and 3,061 individuals from a nationally-representative sample with Fagerström Test for Nicotine Dependence symptom count (FTND; 51.32% female, mean age = 28.9 [s.d. = 1.70]). Polygenic predictors were derived from GWASs known to be phenotypically and genetically correlated with ND (i.e., Cigarettes per Day (CPD), the Alcohol Use Disorders Identification Test (AUDIT-Consumption and AUDIT-Problems), Neuroticism, Depression, Schizophrenia, Educational Attainment, Body Mass Index (BMI), and Self-Perceived Risk-Taking); including Height as a negative control. Analyses controlled for age, gender, study site, and the first 10 ancestral principal components.

RESULTS

The multi-trait model accounted for 3.6% of the total trait variance in DSM-IV ND. Educational Attainment (β=-0.125; 95% confidence interval (CI): [-0.149,-0.101]), CPD (0.071 [0.047,0.095]), and Self-Perceived Risk-Taking (0.051 [0.026,0.075]) were the most robust predictors. PGS effects on FTND were limited.

CONCLUSIONS

Risk for ND is not only polygenic, but also pleiotropic. Polygenic effects on ND that are accessible by these traits are limited in size and act additively to explain risk.

Heritability of overlapping impulsivity and compulsivity dimensional phenotypes

Impulsivity and compulsivity are traits relevant to a range of mental health problems and have traditionally been conceptualised as distinct constructs. Here, we reconceptualised impulsivity and compulsivity as partially overlapping phenotypes using a bifactor modelling approach and estimated heritability for their shared and unique phenotypic variance within a classical twin design. Adult twin pairs (N = 173) completed self-report questionnaires measuring psychological processes related to impulsivity and compulsivity. We fitted variance components models to three uncorrelated phenotypic dimensions: a general impulsive-compulsive dimension; and two narrower phenotypes related to impulsivity and obsessiveness.There was evidence of moderate heritability for impulsivity (A² = 0.33), modest additive genetic or common environmental effects for obsessiveness (A² = 0.25; C² = 0.23), and moderate effects of common environment (C² = 0.36) for the general dimension, This general impulsive-compulsive phenotype may reflect a quantitative liability to related mental health disorders that indexes exposure to potentially modifiable environmental risk factors.

Gene–Environment Correlation in Humans: Lessons from Psychology for Quantitative Genetics

Evolutionary biologists have long been aware that the effects of genes can reach beyond the boundary of the individual, that is, the phenotypic effects of genes can alter the environment. Yet, we rarely apply a quantitative genetics approach to understand the causes and consequences of genetic variation in the ways that individuals choose and manipulate their environments, particularly in wild populations. Here, I aim to stimulate research in this area by reviewing empirical examples of such processes from the psychology literature. Indeed, psychology researchers have been actively investigating genetic variation in the environments that individuals experience—a phenomenon termed “gene–environment correlation” (rGE)—since the 1970s. rGE emerges from genetic variation in individuals’ behavior and personality traits, which in turn affects the environments that they experience. I highlight concepts and examples from this literature, emphasizing the relevance to quantitative geneticists working on wild, nonhuman organisms. I point out fruitful areas of crossover between these disciplines, including how quantitative geneticists can test ideas about rGE in wild populations.

Variations of cingulate sulcal organization and link with cognitive performance

The sulcal morphology of the human medial frontal cortex has received marked interest because of (1) its remarkable link with the functional organization of this region, and (2) observations that deviations from 'normal' sulcal morphological variability correlate with the prevalence of some psychiatric disorders, cognitive abilities, or personality traits. Unfortunately, background studies on environmental or genetic factors influencing the ontogenesis of the sulcal organization in this region are critically lacking. We analysed the sulcal morphological organization in this region in twins and non-twin siblings, as well as in control subjects for a total of 599 subjects from the Human Connectome Project. The data first confirm significant biases in the presence of paracingulate sulci in left vs right hemispheres in the whole population (twin: p < 2.4.10-9; non-twin: p < 2.10-6) demonstrating a clear general laterality in human subjects. Second, measures of similarity between siblings and estimations of heritability suggest significant environmental factors, in particular in-womb environment, and weak additive genetic factors influencing the presence of a paracingulate sulcus. Finally, we found that relationships between sulcal organization and performance in cognitive, motor, and affective tests depend on the twin status (Twins versus Non-twins). These results provide important new insights to the issue of the significance of sulcal organization in the human medial frontal cortex.

Modeling the Covariance Structure of Complex Datasets Using Cognitive Models: An Application to Individual Differences and the Heritability of Cognitive Ability

Understanding individual differences in cognitive performance is an important part of understanding how variations in underlying cognitive processes can result in variations in task performance. However, the exploration of individual differences in the components of the decision process-such as cognitive processing speed, response caution, and motor execution speed-in previous research has been limited. Here, we assess the heritability of the components of the decision process, with heritability having been a common aspect of individual differences research within other areas of cognition. Importantly, a limitation of previous work on cognitive heritability is the underlying assumption that variability in response times solely reflects variability in the speed of cognitive processing. This assumption has been problematic in other domains, due to the confounding effects of caution and motor execution speed on observed response times. We extend a cognitive model of decision-making to account for relatedness structure in a twin study paradigm. This approach can separately quantify different contributions to the heritability of response time. Using data from the Human Connectome Project, we find strong evidence for the heritability of response caution, and more ambiguous evidence for the heritability of cognitive processing speed and motor execution speed. Our study suggests that the assumption made in previous studies-that the heritability of cognitive ability is based on cognitive processing speed-may be incorrect. More generally, our methodology provides a useful avenue for future research in complex data that aims to analyze cognitive traits across different sources of related data, whether the relation is between people, tasks, experimental phases, or methods of measurement.

ACEt: An R Package for Estimating Dynamic Heritability and Comparing Twin Models

Estimating dynamic effects of age on the genetic and environmental variance components in twin studies may contribute to the investigation of gene-environment interactions, and may provide more insights into more accurate and powerful estimation of heritability. Existing parametric models for estimating dynamic variance components suffer from various drawbacks such as limitation of predefined functions. We present ACEt, an R package for fast estimating dynamic variance components and heritability that may change with respect to age or other moderators. Building on the twin models using penalized splines, ACEt provides a unified framework to incorporate a class of ACE models, in which each component can be modeled independently and is not limited by a linear or quadratic function. We demonstrate that ACEt is robust against misspecification of the number of spline knots, and offers a refined resolution of dynamic behavior of the genetic and environmental components and thus a detailed estimation of age-specific heritability. Moreover, we develop resampling methods for testing twin models with different variance functions including splines, log-linearity and constancy, which can be easily employed to verify various model assumptions. We evaluated the type I error rate and statistical power of the proposed hypothesis testing procedures under various scenarios using simulated datasets. Potential numerical issues and computational cost were also assessed through simulations. We applied the ACEt package to a Finnish twin cohort to investigate age-specific heritability of body mass index and height. Our results show that the age-specific variance components of these two traits exhibited substantially different patterns despite of comparable estimates of heritability. In summary, the ACEt R package offers a useful tool for the exploration of age-dependent heritability and model comparison in twin studies.

Genetics and Organizational Behavior

Articles on the genetics of complex human behaviors and psychological traits provided in past volumes of journals published by Annual Reviews tended to adopt a pathological perspective and focused heavily on the disorders of human affect and behaviors. In our review, we expand our focus to the more general, nonclinical population, and in particular on the advances in the understanding of the genetics of attitudes and behaviors in work settings. We review the recent and emerging literature using a behavioral genetics approach to examine the influence of genetics on a wide array of important constructs in organizational behavior (OB) research and provide unique theoretical insights offered by this approach. We discuss practical implications and future research directions from a broad person-environment interactionist perspective by taking a genetics approach.

Estimating Modifying Effect of Age on Genetic and Environmental Variance Components in Twin Models

Twin studies have been adopted for decades to disentangle the relative genetic and environmental contributions for a wide range of traits. However, heritability estimation based on the classical twin models does not take into account dynamic behavior of the variance components over age. Varying variance of the genetic component over age can imply the existence of gene-environment (G×E) interactions that general genome-wide association studies (GWAS) fail to capture, which may lead to the inconsistency of heritability estimates between twin design and GWAS. Existing parametricG×Einteraction models for twin studies are limited by assuming a linear or quadratic form of the variance curves with respect to a moderator that can, however, be overly restricted in reality. Here we propose spline-based approaches to explore the variance curves of the genetic and environmental components. We choose the additive genetic, common, and unique environmental variance components (ACE) model as the starting point. We treat the component variances as variance functions with respect to age modeled by B-splines or P-splines. We develop an empirical Bayes method to estimate the variance curves together with their confidence bands and provide an R package for public use. Our simulations demonstrate that the proposed methods accurately capture dynamic behavior of the component variances in terms of mean square errors with a data set of >10,000 twin pairs. Using the proposed methods as an alternative and major extension to the classical twin models, our analyses with a large-scale Finnish twin data set (19,510 MZ twins and 27,312 DZ same-sex twins) discover that the variances of the A, C, and E components for body mass index (BMI) change substantially across life span in different patterns and the heritability of BMI drops to ∼50% after middle age. The results further indicate that the decline of heritability is due to increasing unique environmental variance, which provides more insights into age-specific heritability of BMI and evidence ofG×Einteractions. These findings highlight the fundamental importance and implication of the proposed models in facilitating twin studies to investigate the heritability specific to age and other modifying factors.

Long term conservation of human metabolic phenotypes and link to heritability

Changes in an individual's human metabolic phenotype (metabotype) over time can be indicative of disorder-related modifications. Studies covering several months to a few years have shown that metabolic profiles are often specific for an individual. This "metabolic individuality" and detected changes may contribute to personalized approaches in human health care. However, it is not clear whether such individual metabotypes persist over longer time periods. Here we investigate the conservation of metabotypes characterized by 212 different metabolites of 818 participants from the Cooperative Health Research in the Region of Augsburg; Germany population, taken within a 7-year time interval. For replication, we used paired samples from 83 non-related individuals from the TwinsUK study. Results indicated that over 40 % of all study participants could be uniquely identified after 7 years based on their metabolic profiles alone. Moreover, 95 % of the study participants showed a high degree of metabotype conservation (>70 %) whereas the remaining 5 % displayed major changes in their metabolic profiles over time. These latter individuals were likely to have undergone important biochemical changes between the two time points. We further show that metabolite conservation was positively associated with heritability (rank correlation 0.74), although there were some notable exceptions. Our results suggest that monitoring changes in metabotypes over several years can trace changes in health status and may provide indications for disease onset. Moreover, our study findings provide a general reference for metabotype conservation over longer time periods that can be used in biomarker discovery studies.