Explore the vast range of titles available.

Humans vary substantially in their willingness to take risks. In a combined sample of over 1 million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. Across all GWAS, we identified hundreds of associated loci, including 99 loci associated with general risk tolerance. We report evidence of substantial shared genetic influences across risk tolerance and the risky behaviors: 46 of the 99 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is genetically correlated ( r ̂ g  ~ 0.25 to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near SNPs associated with general risk tolerance are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We found no evidence of enrichment for genes previously hypothesized to relate to risk tolerance. A genetic study identifies hundreds of loci associated with risk tolerance and risky behaviors, finds evidence of substantial shared genetic influences across these phenotypes, and implicates genes involved in neurotransmission.

Abstract Caenorhabditis elegans’ behavioral states, like those of other animals, are shaped by its immediate environment, its past experiences, and by internal factors. We here review the literature on C. elegans behavioral states and their regulation. We discuss dwelling and roaming, local and global search, mate finding, sleep, and the interaction between internal metabolic states and behavior.

Preferences are fundamental building blocks in all models of economic and political behavior. We study a new sample of comprehensively genotyped subjects with data on economic and political preferences and educational attainment. We use dense single nucleotide polymorphism (SNP) data to estimate the proportion of variation in these traits explained by common SNPs and to conduct genome-wide association study (GWAS) and prediction analyses. The pattern of results is consistent with findings for other complex traits. First, the estimated fraction of phenotypic variation that could, in principle, be explained by dense SNP arrays is around one-half of the narrow heritability estimated using twin and family samples. The molecular-genetic-based heritability estimates, therefore, partially corroborate evidence of significant heritability from behavior genetic studies. Second, our analyses suggest that these traits have a polygenic architecture, with the heritable variation explained by many genes with small effects. Our results suggest that most published genetic association studies with economic and political traits are dramatically underpowered, which implies a high false discovery rate. These results convey a cautionary message for whether, how, and how soon molecular genetic data can contribute to, and potentially transform, research in social science. We propose some constructive responses to the inferential challenges posed by the small explanatory power of individual SNPs.

A chemically defined diet for Drosophila melanogaster is described. It should enable a variety of behavioral, metabolic and fitness studies where controlled nutrition is important. A critical requirement for research using model organisms is a well-defined and consistent diet. There is currently no complete chemically defined (holidic) diet available for Drosophila melanogaster . We describe a holidic medium that is equal in performance to an oligidic diet optimized for adult fecundity and lifespan. This holidic diet supports development over multiple generations but at a reduced rate. Over 7 years of experiments, the holidic diet yielded more consistent experimental outcomes than did oligidic food for egg laying by females. Nutrients and drugs were more available to flies in holidic medium and, similar to dietary restriction on oligidic food, amino acid dilution increased fly lifespan. We used this holidic medium to investigate amino acid–specific effects on food-choice behavior and report that folic acid from the microbiota is sufficient for Drosophila development.

For many multivariate twin models, the numerical Type I error rates are lower than theoretically expected rates using a likelihood ratio test (LRT), which implies that the significance threshold for statistical hypothesis tests is more conservative than most twin researchers realize. This makes the numerical Type II error rates higher than theoretically expected. Furthermore, the discrepancy between the observed and expected error rates increases as more variables are included in the analysis and can have profound implications for hypothesis testing and statistical inference. In two simulation studies, we examine the Type I error rates for the Cholesky decomposition and Correlated Factors models. Both show markedly lower than nominal Type I error rates under the null hypothesis, a discrepancy that increases with the number of variables in the model. In addition, we observe slightly biased parameter estimates for the Cholesky decomposition and Correlated Factors models. By contrast, if the variance–covariance matrices for variance components are estimated directly (without constraints), the numerical Type I error rates are consistent with theoretical expectations and there is no bias in the parameter estimates regardless of the number of variables analyzed. We call this the direct symmetric approach. It appears that each model-implied boundary, whether explicit or implicit, increases the discrepancy between the numerical and theoretical Type I error rates by truncating the sampling distributions of the variance components and inducing bias in the parameters. The direct symmetric approach has several advantages over other multivariate twin models as it corrects the Type I error rate and parameter bias issues, is easy to implement in current software, and has fewer optimization problems. Implications for past and future research, and potential limitations associated with direct estimation of genetic and environmental covariance matrices are discussed.

Visible phenotypes based on locomotion and posture have played a critical role in understanding the molecular basis of behavior and development in Caenorhabditis elegans and other model organisms. However, it is not known whether these human-defined features capture the most important aspects of behavior for phenotypic comparison or whether they are sufficient to discover new behaviors. Here we show that four basic shapes, or eigenworms, previously described for wild-type worms, also capture mutant shapes, and that this representation can be used to build a dictionary of repetitive behavioral motifs in an unbiased way. By measuring the distance between each individual's behavior and the elements in the motif dictionary, we create a fingerprint that can be used to compare mutants to wild type and to each other. This analysis has revealed phenotypes not previously detected by real-time observation and has allowed clustering of mutants into related groups. Behavioral motifs provide a compact and intuitive representation of behavioral phenotypes.

The classical twin model can be reparametrized as an equivalent multilevel model. The multilevel parameterization has underexplored advantages, such as the possibility to include higher-level clustering variables in which lower levels are nested. When this higher-level clustering is not modeled, its variance is captured by the common environmental variance component. In this paper we illustrate the application of a 3-level multilevel model to twin data by analyzing the regional clustering of 7-year-old children’s height in the Netherlands. Our findings show that 1.8%, of the phenotypic variance in children’s height is attributable to regional clustering, which is 7% of the variance explained by between-family or common environmental components. Since regional clustering may represent ancestry, we also investigate the effect of region after correcting for genetic principal components, in a subsample of participants with genome-wide SNP data. After correction, region no longer explained variation in height. Our results suggest that the phenotypic variance explained by region might represent ancestry effects on height.

The role of non-shared environment (NSE) in the development of psychological traits is usually comparable with that of the genotype. However, no specific factors of NSE with significant impact on such traits have been discovered so far. We propose that the current failures in understanding the origin of NSE are at least partly due to the fact that behavioral genetics has left out one of the key sources of phenotypic variation. This source is the intrinsic stochasticity of molecular processes underlying individual development. At the critical stages of ontogeny, even minor fluctuations in gene expression or gene-product functioning can remarkably affect the phenotype; this role is experimentally proved in multiple model organisms. In the present paper, several mechanisms of molecular stochasticity, which could affect the development of psychological traits, are discussed. We propose to distinguish external NSE (any external differences) and internal NSE (intrinsic molecular stochasticity). Available data indicate that the impact of external NSE is likely to be low, which makes the presumptive role of internal NSE rather decisive. If our assumption is true, the paradigm of behavioral genetics should be revised, and comprehensive analysis of molecular stochasticity during individual development is strongly required.

Recent research has demonstrated the suitability of adult zebrafish to model some aspects of complex behaviour. Studies of reward behaviour, learning and memory, aggression, anxiety and sleep strongly suggest that conserved regulatory processes underlie behaviour in zebrafish and mammals. The isolation and molecular analysis of zebrafish behavioural mutants is now starting, allowing the identification of novel behavioural control genes. As a result of this, studies of adult zebrafish are now helping to uncover the genetic pathways and neural circuits that control vertebrate behaviour.

This review summarizes neurobiological and genetic findings in behavioural addictions, draws parallels with findings pertaining to substance use disorders, and offers suggestions for future research. Articles concerning brain function, neurotransmitter activity, and family history and (or) genetic findings for behavioural addictions involving gambling, Internet use, video game playing, shopping, kleptomania, and sexual activity were reviewed. Behavioural addictions involve dysfunction in several brain regions, particularly the frontal cortex and striatum. Findings from imaging studies incorporating cognitive tasks have arguably been more consistent than cue-induction studies. Early results suggest white and grey matter differences. Neurochemical findings suggest roles for dopaminergic and serotonergic systems, but results from clinical trials seem more equivocal. While limited, family history and genetic data support heritability for pathological gambling and that people with behavioural addictions are more likely to have a close family member with some form of psychopathology. Parallels exist between neurobiological and genetic and family history findings in substance and nonsubstance addictions, suggesting that compulsive engagement in these behaviours may constitute addictions. To date, findings are limited, particularly for shopping, kleptomania, and sexual behaviour. Genetic understandings are at an early stage. Future research directions are offered.