Behavior Genetics最新文献

We investigate natural selection on polygenic scores in the contemporary US, using the Health and Retirement Study. Across three generations, scores which correlate negatively (positively) with education are selected for (against). However, results only partially support the economic theory of fertility as an explanation for natural selection. The theory predicts that selection coefficients should be stronger among low-income, less educated, unmarried and younger parents, but these predictions are only half borne out: coefficients are larger only among low-income parents and unmarried parents. We also estimate effect sizes corrected for noise in the polygenic scores. Selection for some health traits is similar in magnitude to that for cognitive traits.

Retraining retired racehorses for various purposes can help correct behavioral issues. However, ensuring efficiency and preventing accidents present global challenges. Based on the hypothesis that a simple personality assessment could help address these challenges, the present study aimed to identify genetic markers associated with personality. Eight genes were selected from 18 personality-related candidate genes that are orthologs of human personality genes, and their association with personality was verified based on actual behavior. A total of 169 Thoroughbred horses were assessed for their tractability (questionnaire concerning tractability in 14 types of situations and 3 types of impressions) during the training process. Personality factors were extracted from the data using principal component analysis and analyzed for their association with single nucleotide variants as non-synonymous substitutions in the target genes. Three genes, CDH13, SLC6A4, and MAOA, demonstrated significant associations based on simple linear regression, marking the identification of these genes for the first time as contributors to temperament in Thoroughbred horses. All these genes, as well as the previously identified HTR1A, are involved in the serotonin neurotransmitter system, suggesting that the tractability of horses may be correlated with their social personality. Assessing the genotypes of these genes before retraining is expected to prevent problems in the development of a racehorse's second career and shorten the training period through individual customization of training methods, thereby improving racehorse welfare.

Structural equation models (SEMs) involving feedback loops may offer advantages over standard instrumental variables estimators in terms of modelling causal effects in the presence of bidirectional relationships. In the following note, we show that in the case of a single "exposure" and "outcome" variable, modelling relationships using a SEM with a simple bidirectional linear feedback loop offers no advantage over traditional instrumental variables estimators in terms of consistency (i.e. both approaches yield consistent estimates of the causal effect, provided that causal estimates are obtained in both directions). In the case of finite samples, traditional IV estimators and SEM exhibited similar power across many of the conditions we examined, although which method performed best depended on the residual correlation between variables and the strength of the instruments. In particular, the power of SEM was insensitive to the residual correlation between variables, whereas the power of the Wald estimator/2SLS improved (deteriorated) relative to SEM as the magnitude of the residual correlation increased (decreased) assuming a positive causal effect of the exposure on the outcome. The power of SEM improved relative to the Wald estimator/2SLS as the instruments explained more residual variance in the "outcome" variable.

Subjective health ratings are associated with dementia risk such that those who rate their health more poorly have increased risk for dementia. The genetic and environmental mechanisms underlying this association are unclear, as prior research cannot rule out whether the association is due to genetic confounds. The current study addresses this gap in two samples of twins, one from Sweden (N = 548) and one from Denmark (N = 4,373). Using genetically-informed, bivariate regression models, we assessed whether additive genetic effects explained the association between subjective health and dementia risk as indexed by a latent variable proxy measure. Age at intake, sex, education, depressive symptomatology, and follow-up time between subjective health and dementia risk assessments were included as covariates. Results indicate that genetic variance and other sources of confounding accounted for the majority of the effect of subjective health ratings on dementia risk. After adjusting for genetic confounding and other covariates, a small correlation was observed between subjective health and latent dementia risk in the Danish sample (r_E = - .09, p < .05). The results provide further support for the genetic association between subjective health and dementia risk, and also suggest that subjective ratings of health measures may be useful for predicting dementia risk.

Haseman-Elston regression (HE-reg) has been known as a classic tool for detecting an additive genetic variance component. However, in this study we find that HE-reg can capture GxE under certain conditions, so we derive and reinterpret the analytical solution of HE-reg. In the presence of GxE, it leads to a natural discrepancy between linkage and association results, the latter of which is not able to capture GxE if the environment is unknown. Considering linkage and association as symmetric designs, we investigate how the symmetry can and cannot hold in the absence and presence of GxE, and consequently we propose a pair of statistical tests, Symmetry Test I and Symmetry Test II, both of which can be tested using summary statistics. Test statistics, and their statistical power issues are also investigated for Symmetry Tests I and II. Increasing the number of sib pairs is important to improve statistical power for detecting GxE.

Genome-wide association studies (GWAS) are often underpowered due to small effect sizes of common single nucleotide polymorphisms (SNPs) on phenotypes and extreme multiple testing thresholds. The most common approach for increasing statistical power is to increase sample size. We propose an alternative strategy of redefining case-control outcomes into ordinal case-subthreshold-asymptomatic variables. While maintaining the clinical case threshold, we subdivide controls into two groups: individuals who are symptomatic but do not meet the clinical criteria for diagnosis (subthreshold) and individuals who are effectively asymptomatic. We conducted a simulation study to examine the impact of effect size, minor allele frequency, population prevalence, and the prevalence of the subthreshold group on statistical power to detect genetic associations in three scenarios: a standard case-control, an ordinal, and a case-asymptomatic control analysis. Our results suggest the ordinal model consistently provides the greatest statistical power while the case-control model the least. Power in the case-asymptomatic control model reflects the case-control or ordinal model depending on the population prevalence and size of the subthreshold category. We then analyzed a major depression phenotype from the UK Biobank to corroborate our simulation results. Overall, the ordinal model improves statistical power in GWAS consistent with increasing the sample size by approximately 10%.

The attachment and caregiving domains maintain proximity and care-giving behavior between parents and offspring, in a way that has been argued to shape people's mental models of how relationships work, resulting in secure, anxious or avoidant interpersonal styles in adulthood. Several theorists have suggested that the attachment system is closely connected to orientations and behaviors in social and political domains, which should be grounded in the same set of familial experiences as are the different attachment styles. We use a sample of Norwegian twins (N = 1987) to assess the genetic and environmental relationship between attachment, trust, altruism, right-wing authoritarianism (RWA), and social dominance orientation (SDO). Results indicate no shared environmental overlap between attachment and ideology, nor even between the attachment styles or between the ideological traits, challenging conventional wisdom in developmental, social, and political psychology. Rather, evidence supports two functionally distinct systems, one for navigating intimate relationships (attachment) and one for navigating social hierarchies (RWA/SDO), with genetic overlap between traits within each system, and two distinct genetic linkages to trust and altruism. This is counter-posed to theoretical perspectives that link attachment, ideology, and interpersonal orientations through early relational experiences.

Brain-imaging-genetic analysis is an emerging field of research that aims at aggregating data from neuroimaging modalities, which characterize brain structure or function, and genetic data, which capture the structure and function of the genome, to explain or predict normal (or abnormal) brain performance. Brain-imaging-genetic studies offer great potential for understanding complex brain-related diseases/disorders of genetic etiology. Still, a combined brain-wide genome-wide analysis is difficult to perform as typical datasets fuse multiple modalities, each with high dimensionality, unique correlational landscapes, and often low statistical signal-to-noise ratios. In this review, we outline the progress in brain-imaging-genetic methodologies starting from early massive univariate to current deep learning approaches, highlighting each approach's strengths and weaknesses and elongating it with the field's development. We conclude by discussing selected remaining challenges and prospects for the field.

There is a negative association between intelligence and psychopathology. We analyzed data on intelligence and psychopathology to assess this association in seven-year-old Dutch twin pairs (ranging from 616 to 14,150 depending on the phenotype) and estimated the degree to which genetic and environmental factors common to intelligence and psychopathology explain the association. Secondly, we examined whether genetic and environmental effects on psychopathology are moderated by intelligence. We found that intelligence, as assessed by psychometric IQ tests, correlated negatively with childhood psychopathology, as assessed by the DSM-oriented scales of the Child Behavior Check List (CBCL). The correlations ranged between - .09 and - .15 and were mainly explained by common genetic factors. Intelligence moderated genetic and environmental effects on anxiety and negative affect, but not those on ADHD, ODD, and autism. The heritability of anxiety and negative affect was greatest in individuals with below-average intelligence. We discuss mechanisms through which this effect could arise, and we end with some recommendations for future research.

Although research shows a strong positive association between perceived stress and loneliness, the genetic and environmental etiology underlying their association remains unknown. People with a genetic predisposition to perceived stress, for example, may be more prone to feeling lonely and vice versa. Conversely, unique factors in people's lives may explain differences in perceived stress levels that, in turn, affect feelings of loneliness. We tested whether genetic factors, environmental factors, or both account for the association between perceived stress and loneliness. Participants were 3,066 individual twins (n_Female = 2,154, 70.3%) from the Washington State Twin Registry who completed a survey during April-May, 2020. Structural equation modeling was used to analyze the item-level perceived stress and loneliness measures. The correlation between latent perceived stress and latent loneliness was .68. Genetic and nonshared environmental variance components underlying perceived stress accounted for 3.71% and 23.26% of the total variance in loneliness, respectively. The genetic correlation between loneliness and perceived stress was .45 and did not differ significantly between men and women. The nonshared environmental correlation was .54 and also did not differ between men and women. Findings suggest that holding constant the strong genetic association between perceived stress and loneliness, unique life experiences underlying people's perceived stress account for individual differences in loneliness.