ACE Model Research Articles

Psychometric Modelling of Longitudinal Genetically Informative Twin Data.

The often-used A(C)E model that decomposes phenotypic variance into parts due to additive genetic and environmental influences can be extended to a longitudinal model when the trait has been assessed at multiple occasions. This enables inference about the nature (e.g., genetic or environmental) of the covariance among the different measurement points. In the case that the measurement of the phenotype relies on self-report data (e.g., questionnaire data), often, aggregated scores (e.g., sum–scores) are used as a proxy for the phenotype. However, earlier research based on the univariate ACE model that concerns a single measurement occasion has shown that this can lead to an underestimation of heritability and that instead, one should prefer to model the raw item data by integrating an explicit measurement model into the analysis. This has, however, not been translated to the more complex longitudinal case. In this paper, we first present a latent state twin A(C)E model that combines the genetic twin model with an item response theory (IRT) model as well as its specification in a Bayesian framework. Two simulation studies were conducted to investigate 1) how large the bias is when sum–scores are used in the longitudinal A(C)E model and 2) if using the latent twin model can overcome the potential bias. Results of the first simulation study (e.g., AE model) demonstrated that using a sum–score approach leads to underestimated heritability estimates and biased covariance estimates. Surprisingly, the IRT approach also lead to bias, but to a much lesser degree. The amount of bias increased in the second simulation study (e.g., ACE model) under both frameworks, with the IRT approach still being the less biased approach. Since the bias was less severe under the IRT approach than under the sum–score approach and due to other advantages of latent variable modelling, we still advise researcher to adopt the IRT approach. We further illustrate differences between the traditional sum–score approach and the latent state twin A(C)E model by analyzing data of a two-wave twin study, consisting of the answers of 8,016 twins on a scale developed to measure social attitudes related to conservatism.

Cell-free DNA levels of twins and sibling pairs indicate individuality and possible use as a personalized biomarker

Cell-free DNA (cfDNA) in the human blood circulation has been under investigation since its initial observation in 1948. Plasma cfDNA is known to be significantly elevated in diseased people. Due to possible variation in the population, evaluating cfDNA as a non-invasive biomarker at disease onset alone may not be sensitive enough to accurately diagnose diseases, particularly early stage cancers on a personal level. To understand the factors that define the cfDNA levels on the personal level and for better use as a non-invasive biomarker, we isolated cfDNA from the plasma of healthy individuals with varying degrees of genetic and/or environmental similarities (monozygotic twins, dizygotic twins, sibling pairs, and unrelated individuals) as well as from patients with varying stages of breast and ovarian cancer undergoing treatment. Cell-free DNA levels were quantified by a fluorometer (ng/ml) and/or real-time PCR (copies/ml). The associations between individuals with various degrees of genetic and/or environmental similarities and their plasma cfDNA levels were evaluated. The ACE model (A = additive genetic, C = common environment, and E = specific environmental factors) was used to determine the proportion of each factor on the cfDNA levels. We found a high correlation (r = 0.77; p < 0.0001) in plasma cfDNA levels between monozygotic twins (n = 39). However, the correlation was gradually reduced to moderate (r = 0.47; p = 0.016) between dizygotic twins (n = 13) and low correlation (r = 0.28; p = 0.043) between sibling pairs (n = 26). The ACE model analysis showed that the plasma cfDNA level of a given healthy individual is influenced both by genetic and the environmental components in similar proportions (53% and 47%, respectively; A = 53%, C = 22.5%, E = 24.5%). Moreover, while age had no effect, gender significantly influenced the individual’s plasma cfDNA level. As expected, cfDNA levels were significantly higher in both breast (n = 26) (p<0.0001) and ovarian (n = 64) (p<0.0001) cancer patients compared to the healthy individuals. Our study demonstrated that both genome and environmental factors modulate the individual’s cfDNA level suggesting that its diagnostic sensitivity may be improved only if the person’s cfDNA level is known prior to disease presentation.

Responding to a 100-Year-Old Challenge from Fisher: A Biometrical Analysis of Adult Height in the NLSY Data Using Only Cousin Pairs.

In 1918, Fisher suggested that his research team had consistently found inflated cousin correlations. He also commented that because a cousin sample with minimal selection bias was not available the cause of the inflation could not be addressed, leaving this inflation as a challenge still to be solved. In the National Longitudinal Survey of Youth (the NLSY79, the NLSY97, and the NLSY-Children/Young Adult datasets), there are thousands of available cousin pairs. Those in the NLSYC/YA are obtained approximately without selection. In this paper, we address Fisher's challenge using these data. Further, we also evaluate the possibility of fitting ACE models using only cousin pairs, including full cousins, half-cousins, and quarter-cousins. To have any chance at success in such a restricted kinship domain requires an available and highly-reliable phenotype; we use adult height in our analysis. Results provide a possible answer to Fisher's challenge, and demonstrate the potential for using cousin pairs in a stand-alone analysis (as well as in combination with other biometrical designs).

Perceived Stress and Sleep Quality in Midlife and Later: Controlling for Genetic and Environmental Influences

ABSTRACT Objective/Background Stress is a strong predictor for poor sleep quality. However, little is known about the mechanism of this association or the respective contribution of genetic and environmental factors. This study aims to investigate general distress as a mediator and cognitive/emotional control as a moderator in the stress–sleep relationship and estimate the influence of gene and environment in this mechanism using a national representative sample. Participants 1,255 middle-aged and elderly Americans and a subset of 296 twins. Methods The Pittsburgh Sleep Quality Index, the Perceived Stress Scale, the Mood and Anxiety Symptom Questionnaire and the Self-Control Scale assessed sleep quality, perceived stress, general distress, and emotional/cognitive control. Structural equation modeling was used to test the mediating and moderating effect. ACE models on MZ and DZ twins were used to separate the contribution of genetic and environmental factors. Finally, a co-twin design was nested in the SEM to control for the genetic and familial confounds in the stress–sleep relationship. Results General distress mediated the relationship between stress and sleep while emotional/cognitive control buffered the impact of stress on general distress. 7.69% of the variance in sleep quality was explained by genetic and familial factors and 8.26% was explained by individual-specific factors. Emotional/cognitive control only moderated the individual-specific association between stress and sleep. Conclusions Gene/family factors and individual factors explained an equivalent proportion of the stress–sleep relationship. The genetic and familial association between stress and sleep is more robust, whereas the individual-specific association can be buffered by regulation strategies.

Shared genetic influences on adolescent body mass index and brain structure: A voxel-based morphometry study in twins

BackgroundPrevious research has demonstrated significant relationships between obesity and brain structure. Both phenotypes are heritable, but it is not known whether they are influenced by common genetic factors. We investigated the genetic etiology of the relationship between individual variability in brain morphology and BMIz using structural MRI in adolescent twins. MethodThe sample (n = 258) consisted of 54 monozygotic and 75 dizygotic twin pairs (mean(SD) age = 13.61(0.505), BMIz = 0.608(1.013). Brain structure (volume and density of gray and white matter) was assessed using VBM. Significant voxelwise heritability of brain structure was established using the Accelerated Permutation inference for ACE models (APACE) program, with structural heritability varying from 15 to 97%, depending on region. Bivariate heritability analyses were carried out comparing additive genetic and unique environment models with and without shared genetics on BMIz and the voxels showing significant heritability in the APACE analyses. ResultsBMIz was positively related to gray matter volume in the brainstem and thalamus and negatively related to gray matter volume in the bilateral uncus and medial orbitofrontal cortex, gray matter density in the cerebellum, prefrontal lobe, temporal lobe, and limbic system, and white matter density in the brainstem. Bivariate heritability analyses showed that BMIz and brain structure share ∼1/3 of their genes and that ∼95% of the phenotypic correlation between BMIz and brain structure is due to shared additive genetic influences. These regions included areas related to decision-making, motivation, liking vs. wanting, taste, interoception, reward processing/learning, caloric evaluation, and inhibition. ConclusionThese results suggested genetic factors are responsible for the relationship between BMIz and heritable BMIz related brain structure in areas related to eating behavior.

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.

Mixed States: Modelling and Management.

Our current conceptualisation of mixed states, defined as co-occurring manic and depressive symptoms, is unlikely to advance our knowledge or inform clinical practice. Episodes of mixed states can no longer be coded in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), and the 'mixed features specifier' fails to capture the most common mixed state presentations. This reflects a lack of understanding of both the importance of mixed states and their underlying pathophysiology. Indeed, research into the nature of mixed states is scarce and uninformative, and most clinical practice guidelines fail to provide advice regarding their management. In this paper, we proffer a reconceptualisation of mixed states that provides a framework for informing clinical practice and research. It is based on the ACE model, which deconstructs mood disorders into three domains of symptoms: activity, cognition, and emotion. Symptoms within each domain vary independently over time and in different directions (towards either excitation or inhibition). By deconstructing mood disorders into component domains, mixed states can be explained as the product of different domains varying 'out of sync'. In most cases, the aetiology of mixed states is unknown. Alongside such idiopathic mixed states, we describe three potential causes of mixed states that are important to consider when formulating management: transitions, ultradian cycling, and treatment-emergent affective switches. In addition to providing guidance on the identification of various kinds of mixed states, we discuss practical strategies for their management, including the monitoring of ACE domains and functioning, to inform the use of psychoeducation and lifestyle changes, psychotherapy, pharmacology, and electroconvulsive therapy.

Umx: Twin and Path-Based Structural Equation Modeling in R.

Structural equation modeling (SEM) is an important research tool, both for path-based model specification (common in the social sciences) and also for matrix-based models (in heavy use in behavior genetics). We developed umx to give more immediate access, relatively concise syntax and helpful defaults for users in these two broad disciplines. umx supports development, modification and comparison of models, as well as both graphical and tabular outputs. The second major focus of umx, behavior genetic models, is supported via functions implementing standard multigroup twin models. These functions support raw and covariance data, including joint ordinal data, and give solutions for ACE models, including support for covariates, common- and independent-pathway models, and gene × environment interaction models. A tutorial site and question forum are also available.

Genetic and environmental influences on structural brain measures in twins with autism spectrum disorder

Atypical growth patterns of the brain have been previously reported in autism spectrum disorder (ASD) but these alterations are heterogeneous across individuals, which may be associated with the variable effects of genetic and environmental influences on brain development. Monozygotic (MZ) and dizygotic (DZ) twin pairs with and without ASD (aged 6–15 years) were recruited to participate in this study. T1-weighted MRIs (n = 164) were processed with FreeSurfer to evaluate structural brain measures. Intra-class correlations were examined within twin pairs and compared across diagnostic groups. ACE modeling was also completed. Structural brain measures, including cerebral and cerebellar gray matter (GM) and white matter (WM) volume, surface area, and cortical thickness, were primarily influenced by genetic factors in TD twins; however, mean curvature appeared to be primarily influenced by environmental factors. Similarly, genetic factors accounted for the majority of variation in brain size in twins with ASD, potentially to a larger extent regarding curvature and subcortical GM; however, there were also more environmental contributions in twins with ASD on some structural brain measures, such that cortical thickness and cerebellar WM volume were primarily influenced by environmental factors. These findings indicate potential neurobiological outcomes of the genetic and environmental risk factors that have been previously associated with ASD and, although preliminary, may help account for some of the previously outlined neurobiological heterogeneity across affected individuals. This is especially relevant regarding the role of genetic and environmental factors in the development of ASD, in which certain brain structures may be more sensitive to specific influences.

Genetic and environmental influences on third molar root mineralization

ObjectiveTo assess the genetic and environmental influences on the variability of human third molar (M3) root mineralization stages via a twin study. DesignThe study sample consisted of 162 same-sex twins (66 dizygotic and 96 monozygotic, mean age: 17.9 years) with normal growth and development. Panoramic radiographs were evaluated to assess root mineralization stages of the upper and lower third molars, using the method of Demirijian et al. (1973). Zygosity was established using 15 specific DNA markers. ResultsFemales developed their third molar roots earlier in life than males. Monozygotic twins (MZ) demonstrated higher intra-pair correlations for M3 root development stages than did DZ twins. An ACE model with additive genes, as well as common and specific environmental factors, provided the best explanation of variation in M3 root development. The mineralization of M3 roots showed highly additive genetic determination, varying from 60 to 63%, whereas a common environment contributed from 25 to 27%, and a specific environment only accounted 14% of the total variation, at most. ConclusionsA considerable proportion of the total variability of the third molar root mineralization rate can be attributed to additive genetic effects, while common and specific environmental effects have a smaller, yet significant, impact.

Heritability and Genome-Wide Association Study of Plasma Cholesterol in Chinese Adult Twins.

Dyslipidemia represents a strong and independent risk factor for cardiovascular disease. Plasma cholesterol, such as total cholesterol (TC), low density lipoprotein cholesterol (LDL-C), and high density lipoprotein cholesterol (HDL-C), is the common indicator of diagnosing dyslipidemia. Here based on 382 Chinese twin pairs, we explored the magnitude of genetic impact on TC, HDL-C, LDL-C variation and further searched for genetic susceptibility loci for them using genome-wide association study (GWAS). The ACE model was the best fit model with additive genetic parameter (A) accounting for 26.6%, common or shared environmental parameter (C) accounting for 47.8%, unique/non-shared environmental parameter (E) accounting for 25.6% for the variance in HDL-C. The AE model was the best fit model for TC (A: 61.4%; E: 38.6%) and LDL-C (A: 65.5%; E: 34.5%). While no SNPs reached the genome-wide significance level (P < 5 × 10−8), 8, 14, 9 SNPs exceeded the suggestive significance level (P < 1 × 10−5) for TC, HDL-C, LDL-C, respectively. The promising genetic regions for TC, HDL-C, LDL-C were on chromosome 11 around rs7107698, chromosome 5 around rs12518218, chromosome 2 around rs10490120, respectively. Gene-based analysis found 1038, 1033 and 1090 genes nominally associated with TC, HDL-C, LDL-C (P < 0.05), especially FAF1, KLKB1 for TC, KLKB1 for HDL-C, and NTRK1, FAF1, SNTB2 for LDL-C, respectively. The number of common related genes among TC, HDL-C and LDL-C was 71, including FAF1, KLKB1, etc. Pathway enrichment analysis discovered known related pathways-zinc transporters, metal ion SLC transporters for TC, cell adhesion molecules CAMs, IL-6 signaling for HDL, FC epsilon RI signaling pathway, NFAT pathway for LDL, respectively. In conclusion, the TC and LDL-C level is moderately heritable and the HDL-C level is lowly heritable in Chinese population. The genomic loci, functional genes and pathways are identified to account for the heritability of plasma cholesterol level. Our findings provide important insights into plasma cholesterol molecular physiology and expect future research to replicate and validate our results.

Modelling mood disorders: An ACE solution?

The treatment of mood disorders remains sub-optimal. A major reason for this is our lack of understanding of the underlying pathophysiology of depression and bipolar disorder. A core problem is the lack of specificity of our current diagnoses. This paper discusses the history of this problem and posits a solution in the form of a more sophisticated model. The authors review the notable historical works that laid the foundations of mood disorder nosology; discuss the more recent influences that shaped modern diagnoses; and examine the evidence that mood disorders are characterised by multidimensional and longitudinal symptom profiles. The ACE model considers mood disorders as a combination of symptoms across three domains: Activity, Cognition, and Emotion; that vary over time. This multidimensional and longitudinal perspective is consistent with the prevalence of complex clinical presentations, such as mixed states, and highlights the importance of recurrence in mood disorders. The ACE model encourages researchers to characterise patients from a number of equally important perspectives and, by doing so, add specificity to the treatment of mood disorders.

Role of Genetic Susceptibility in the Development of Bronchopulmonary Dysplasia

To assess heritable contributions to bronchopulmonary dysplasia (BPD) risk in a twin cohort restricted to gestational age at birth <29 weeks. A total of 250 twin pairs (192 dichorionic, 58 monochorionic) born <29 weeks gestational age with known BPD status were identified. Three statistical methods applicable to twin cohorts (χ2 test, intraclass correlations [ICCs], and ACE modeling [additive genetic or A, common environmental or C, and unique environmental or E components]) were applied. Heritability was estimated as percent variability from A. Identical methods were applied to a subcohort defined by zygosity and to an independent validation cohort. χ2 analyses comparing whether neither, 1, or both of monochorionic (23, 19, 16) and dichorionic (88, 56, 48) twin pairs developed BPD revealed no difference. Although there was similarity in BPD outcome within both monochorionic and dichorionic twin pairs by ICC (monochorionic ICC = 0.34, 95% CI [0.08, 0.55]; dichorionic ICC = 0.39, 95% CI [0.25, 0.51]), monochorionic twins were not more likely than dichorionic twins to have the same outcome (P = .70). ACE modeling revealed no contribution of heritability to BPD risk (% A = 0.0%, 95% CI [0.0%, 43.1%]). Validation and zygosity based cohort results were similar. Our analysis suggests that heritability is not a major contributor to BPD risk in preterm infants <29 weeks gestational age.

The differential impact of adverse childhood experiences in the development of pre-diabetes in a longitudinal cohort of US adults

BackgroundACEs have a dose-response relationship with diabetes. The relationship between ACEs and pre-diabetes is not well known and may represent an effective area for prevention efforts. MethodsData from 1054 participants from two waves of the longitudinal MIDUS study were used. Multivariate general linear regression models assessed the relationship between ACEs and biomarker outcomes. Correlation tests and mediation models investigated the relationship between ACE and pre-diabetes. ResultsIndividuals reporting ACEs were statistically significantly more likely to have higher BMI (1.13 (0.34–1.92)), higher waist circumference (2.74 (0.72–4.76)), elevated blood fasting insulin levels (2.36 (0.71–4.02)) and higher insulin resistance (HOMA-IR (0.57 (0.08–1.06)). BMI/waist circumference and insulin resistance did not maintain independent relationships with ACEs once HOMA-IR was included in the dichotomized ACE model (p = 0.05 and p = 0.06, respectively), suggesting the relationship between BMI and ACEs may be mediated by insulin resistance. ConclusionsThese results represent one of the first studies to examine the differential impact of ACEs on a diverse set of clinical pre-diabetes measures. Findings suggest sexual and physical abuse, and financial strain during childhood are important factors associated with higher risk for pre-diabetes, and should be considered during intervention development.

The Impact of Genetics and Environmental Factors on the Position of the Upper Third Molars

The purpose of this study was to estimate the importance of heredity in the position of the upper third molars. Panoramic radiographs and lateral cephalograms of same-gender twins were analyzed. The determination of zygosity was performed by means of DNA tests with polymerase chain reaction for the amplification of short tandem repeats and 15 specific DNA markers. Data were estimated by the relative influence of additive genetic factors (A), nonadditive genetic factors (D), the common or shared environment (C), and unique environmental factors (E). The study sample consisted of 212 twins: 80 dizygotic and 132 monozygotic twins. The genetic analysis showed that the best-fitting model for the size of the molars and their angulations was AE (additive genetic factors and unique environmental factors), in which the additive genetic factors had up to 84% influence and specific environment had up to 40%.Therefore, the ACE (additive genetic factors, common or shared environment, and unique environmental factors) model showed higher significance for the tooth eruption level. The heritability estimates were up to 59%, specific environment contributed up to 16%, and common environment reached 30%. Genetic factors play a key role in the position of the upper third molars.

AGENT‐BASED MACROECONOMICS AND DYNAMIC STOCHASTIC GENERAL EQUILIBRIUM MODELS: WHERE DO WE GO FROM HERE?

AbstractAgent‐based computational economics (ACE) has been used for tackling major research questions in macroeconomics for at least two decades. This growing field positions itself as an alternative to dynamic stochastic general equilibrium (DSGE) models. In this paper, we provide a much needed review and synthesis of this literature and recent attempts to incorporate insights from ACE into DSGE models. We first review the arguments raised against DSGE in the macroeconomic ACE (macro ACE) literature, and then review existing macro ACE models, their explanatory power and empirical performance. We then turn to the literature on behavioural New Keynesian models that attempts to synthesize these two approaches to macroeconomic modelling by incorporating insights of ACE into DSGE modelling. Finally, we provide a thorough description of the internally rational New Keynesian model, and discuss how this promising line of research can progress.

T10. HERITABILITY OF AMYGDALA ACTIVITY AND ITS GENOME WIDE ASSOCIATION WITH THE SCHIZOPHRENIA RISK LOCUS OF MIR137

BackgroundIt is well known that heritability plays a prominent role in risk for schizophrenia, and that this brain disorder is crucially characterized by emotional symptoms. Less known is how heritability shapes brain activity during emotion processing and whether this brain phenotype is also associated with genetic variation increasing risk for schizophrenia. Here, we implemented a multi-step, data-driven approach in order to assess the relevance of the link between heritability, genetic variation, and schizophrenia for brain activity during emotion processing.MethodsWe investigated three samples of healthy individuals and one sample of schizophrenia (SCZ) patients: i) 28 healthy twin pairs (16 monozygotic and 12 dizygotic twin pairs); ii) 289 unrelated healthy participants (genome-wide association study - GWAS -discovery sample); iii) 90 unrelated healthy participants (replication sample); iv) 40 SCZ patients. During fMRI, participants approached or avoided threatening angry faces (explicit emotion processing). Intra-class correlations (ICC) between twin pairs and ACE models (A: additive genetics; C: common environment; E: unique environment) were used to identify regions of interest (ROIs) with heritable functional activity. Then, we extracted BOLD signal from these ROIs and conducted a GWAS on 565,137 single nucleotide polymorphisms (SNPs) (selected with the following criteria: minor allele frequency>0.15, Hardy–Weinberg equilibrium<0.001, linkage disequilibrium pruning r2>0.9) using robust linear models of allelic dosage corrected for multiple comparisons (Gao et al. 2008 Genetic Epidemiology). Finally, we assessed the effect of surviving SNPs in the replication sample of healthy individuals as well as in the sample of SCZ patients.ResultsIn healthy twins, we identified bilateral amygdala as the brain region with the highest heritability during explicit emotion processing as evaluated with our task (ICC=.79; h2=0.54; p<.001). The subsequent GWAS in healthy non-twins indicated that bilateral amygdala activity during the task was associated with a polymorphism close to miR-137 (rs1198575) (p=1.5 × 10–7), with the C allele corresponding to lower activity than the t allele. A similar effect was found in the replication sample (p=.01) and in patients with SCZ (p=.03).DiscussionOur data-driven approach revealed that amygdala activity as evaluated with our task is heritable. Furthermore, our results indicate that a polymorphism in miR-137 has genome wide association with amygdala response during emotion processing which is also replicated in two independent samples of healthy subjects and of patients with schizophrenia. Previous findings indicated that this polymorphism has genome-wide association with schizophrenia (Ripke et al. 2014). Other results reveal that miR-137 is a key regulatory neuronal factor linked to SCZ and involved in emotion processing (Cosgrove et al., 2017). Our findings are consistent with these previous findings and further highlight a crucial role for miR-137 in emotion processing and SCZ (Anticevic et al., 2012 Schizophr Bull).

Frequency-specific genetic influence on inferior parietal lobule activation commonly observed during action observation and execution

Brain activity relating to recognition of action varies among subjects. These differences have been hypothesised to originate from genetic and environmental factors although the extent of their effect remains unclear. Effects of these factors on brain activity during action recognition were evaluated by comparing magnetoencephalography (MEG) signals in twins. MEG signals of 20 pairs of elderly monozygotic twins and 11 pairs of elderly dizygotic twins were recorded while they observed finger movements and copied them. Beamformer and group statistical analyses were performed to evaluate spatiotemporal differences in cortical activities. Significant event-related desynchronisation (ERD) of the β band (13–25 Hz) at the left inferior parietal lobule (IPL) was observed for both action observation and execution. Moreover, β-band ERD at the left IPL during action observation was significantly better correlated among monozygotic twins compared to unrelated pairs (Z-test, p = 0.027). β-band ERD heritability at the left IPL was 67% in an ACE model. These results demonstrate that β-band ERD at the IPL, which is commonly observed during action recognition and execution, is affected by genetic rather than environmental factors. The effect of genetic factors on the cortical activity of action recognition may depend on anatomical location and frequency characteristics.

Want to make me emotional? The influence of emotional advertisements on women\u2019s consumption behavior

A wide difference of opinion exists about the content and composition of emotions. Advertising may influence an audience and their buying decisions about products and services. The objective of this study is to better conceptualize how women emotionally respond to emotional advertisements (EAs). The variant views are integrated into an ACE model, composed of subordinate levels of emotions (E), celebrity endorsements (C), and appeal drivers (A). This empirical study examines women’s emotional response using data from 240 Chinese women respondents. The study participants were invited to develop ACE mix based advertisements and fill out questionnaires. PLS-SEM analysis, a novel approach in ACE advertisement development and its applicability to consumer behavior, was used. The results show that showbiz celebrities expressing the emotion of happiness with music and color make the most effective ACE mix to influence the consumption behavior of women. The results are significantly mediated by attention levels and are widely applicable in the burgeoning advertising industry. The study also calls for further research with different ACE mixes in different contexts and on different audiences. It also opens doors for policy making and an appropriate understanding of women’s consumption behavior in the Chinese context.

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.