Behavioral Genetics Research Articles

Social behavior and aging: A fly model.

The field of behavioral genetics has recently begun to explore the effect of age on social behaviors. Such studies are particularly important, as certain neuropsychiatric disorders with abnormal social interactions, like autism and schizophrenia, have been linked to older parents. Appropriate social interaction can also have a positive impact on longevity, and is associated with successful aging in humans. Currently, there are few genetic models for understanding the effect of aging on social behavior and its potential transgenerational inheritance. The fly is emerging as a powerful model for identifying the basic molecular mechanisms underlying neurological and neuropsychiatric disorders. In this review, we discuss these recent advancements, with a focus on how studies in Drosophila melanogaster have provided insight into the effect of aging on aspects of social behavior, including across generations.

The Ankyrin Repeat and Kinase Domain Containing 1 Gene Polymorphism (ANKK1Taq1A) and Personality Traits in Addicted Subjects.

The Taq1A polymorphism located in the ANKK1 gene is one of the most widely studied polymorphisms in regards to the genetics of behavior and addiction. The aim of our study was to analyze this polymorphism with regard to personality characteristics and anxiety measured by means of the Personality Inventory—(NEO Five-Factor Inventory—NEO—FFI) and the State-Trait Anxiety Inventory (STAI) in polysubstance addicted subjects. The study group consisted of 600 male volunteers, including 299 addicted subjects and 301 controls. Psychiatrists recruited members for both groups. Addiction was diagnosed in the case group. In the control group mental illness was excluded. The same psychometric test and genotyping using the real-time PCR (polymerase chain reaction) method was performed for both groups. The results were investigated by means of multivariate analysis of the main effects Multi-factor ANOVA. Significantly higher scores on the scale of STAI state and Neuroticism and Openness traits, as well as lower scores on the scales of Extraversion, Agreeability, and Conscientiousness, were found in the case group subjects, compared to the controls. Differences in frequency of genotypes and alleles of Taq1A polymorphism between the studied groups were not found. Multi-factor ANOVA of addicted subjects and control subjects and the ANKK1 Taq1A variant interaction approximated the statistical significance for the STAI state. The main effects ANOVA of both subjects’ groups were found for the STAI state and trait, the Neuroticism scale, the Extraversion scale, and the Agreeability scale. The ANKK1 Taq1A main effects approximated the statistical significance of the STAI trait. Our study shows not only differences in personality traits between addicted and non-addicted subjects, but also the possible impact of ANKK1 on given traits and on addiction itself.

Public Understanding of Behavioral Genetics: Integrating Heuristic Thinking, Motivated Reasoning and Planned Social Change Theories for Better Communication Strategies.

The field of behavioral genetics is experiencing a revolution following the development of genome-wide association studies and the availability of large datasets from international consortia. This rapid change could increase the existing gaps between basic research, translation, and public understanding of science. In the present work, we aim to synthesize key explanations of how public understanding of socio-scientific issues develop. We propose that integrating dual-process, motivated reasoning, and change management theories will increase the extent to which we understand, and can change, how people respond to findings from behavior genetics.

Precision neuroradiology: mapping the nodes and networks that link genes to behaviour.

What is the future of neuroradiology in the era of precision medicine? As with any big change, this transformation in medicine presents both challenges and opportunities, and to flourish in this new environment we will have to adapt. It is difficult to predict exactly how neuroradiology will evolve in this shifting landscape, but there will be changes in both what we image and what we do. In terms of imaging, we will need to move beyond simply imaging brain anatomy and toward imaging function, both at the molecular and circuit level. In terms of what we do, we will need to move from the periphery of the clinical enterprise toward its center, with a new emphasis on integrating imaging with genetic and clinical data to form a comprehensive picture of the patient that can be used to direct further testing and care.The payoff is that these changes will align neuroradiology with the emerging field of precision psychiatry, which promises to replace symptom-based diagnosis and trial-and-error treatment of psychiatric disorders with diagnoses based on quantifiable genetic, imaging, physiologic, and behavioural criteria and therapies targeted to the particular pathophysiology of individual patients. Here we review some of the recent developments in behavioural genetics and neuroscience that are laying the foundation for precision psychiatry. By no means comprehensive, our goal is to introduce some of the perspectives and techniques that are likely to be relevant to the precision neuroradiologist of the future.

Testing Pleiotropy vs. Separate QTL in Multiparental Populations

The high mapping resolution of multiparental populations, combined with technology to measure tens of thousands of phenotypes, presents a need for quantitative methods to enhance understanding of the genetic architecture of complex traits. When multiple traits map to a common genomic region, knowledge of the number of distinct loci provides important insight into the underlying mechanism and can assist planning for subsequent experiments. We extend the method of Jiang and Zeng (1995), for testing pleiotropy with a pair of traits, to the case of more than two alleles. We also incorporate polygenic random effects to account for population structure. We use a parametric bootstrap to determine statistical significance. We apply our methods to a behavioral genetics data set from Diversity Outbred mice. Our methods have been incorporated into the R package qtl2pleio.

An Extended Twin-pedigree Study Of Voluntary Exercise Behavior In The Netherlands

PURPOSE: In the current research we investigated the heritability of voluntary regular exercise behavior using extended pedigrees, this allowed us to estimate the contribution of shared household effects in the presence of non-additive genetic effects, in contrast to much of the earlier work based on twin data. In addition, rather than assessing the total volume of exercise behavior as a unitary construct we have separated this across three domains: time spent on (1) any voluntary exercise and sports, (2) solitary exercise and sports, or (3) team-based exercise and sports. METHODS: For the participants in the Netherlands Twin Register (NTR) we constructed the extended pedigrees which specify all relations among nuclear and larger twin families in the register. A total of 253,015 subjects from 58,645 families were linked to each other, to the degree that we had information on the relations among participants. For 56,161 adolescent and adult NTR participants in 20,897 families data were available on harmonized scores for total weekly MET hours, and the six domains. We analyzed these data in the Mendel software package to estimate the contributions of additive and non-additive genetic factors. RESULTS: The estimated broad-sense heritability of total weekly MET hours spent on (1) any voluntary exercise and sports was 41% (26% additive genetic effects (A), and 15% non-additive genetic effects (D)). A shared household effect explained 24% and unique environmental factors explained the remaining 35% of the variance. For weekly MET hours spent on team-based exercise non-additive (dominance) genetic factors (28%) were a larger contributor compared to additive genetic factors (19%), while in solitary exercise these results were reversed (12% and 22% respectively). CONCLUSIONS: In concordance with previous literature, our results suggest that exercise is a heritable trait, however our varying results in the various domains of exercise suggest that, at least in behavioral genetics, splitting exercise over domains rather than treating it as a unitary construct may be preferable.

Reference Trait Analysis Reveals Correlations Between Gene Expression and Quantitative Traits in Disjoint Samples.

Systems genetic analysis of complex traits involves the integrated analysis of genetic, genomic, and disease-related measures. However, these data are often collected separately across multiple study populations, rendering direct correlation of molecular features to complex traits impossible. Recent transcriptome-wide association studies (TWAS) have harnessed gene expression quantitative trait loci (eQTL) to associate unmeasured gene expression with a complex trait in genotyped individuals, but this approach relies primarily on strong eQTL. We propose a simple and powerful alternative strategy for correlating independently obtained sets of complex traits and molecular features. In contrast to TWAS, our approach gains precision by correlating complex traits through a common set of continuous phenotypes instead of genetic predictors, and can identify transcript-trait correlations for which the regulation is not genetic. In our approach, a set of multiple quantitative "reference" traits is measured across all individuals, while measures of the complex trait of interest and transcriptional profiles are obtained in disjoint subsamples. A conventional multivariate statistical method, canonical correlation analysis, is used to relate the reference traits and traits of interest to identify gene expression correlates. We evaluate power and sample size requirements of this methodology, as well as performance relative to other methods, via extensive simulation and analysis of a behavioral genetics experiment in 258 Diversity Outbred mice involving two independent sets of anxiety-related behaviors and hippocampal gene expression. After splitting the data set and hiding one set of anxiety-related traits in half the samples, we identified transcripts correlated with the hidden traits using the other set of anxiety-related traits and exploiting the highest canonical correlation (R = 0.69) between the trait data sets. We demonstrate that this approach outperforms TWAS in identifying associated transcripts. Together, these results demonstrate the validity, reliability, and power of reference trait analysis for identifying relations between complex traits and their molecular substrates.

Beyond nature vs. nurture in expertise research – comment on Baker & Wattie

The field of expertise is mired in a nature vs. nurture debate. Despite what we now know from behavioral genetics research about the underpinnings of human behavior, some expertise theorists continue to deny or downplay the importance of genetic factors (“innate talent”) in expert performance. In this commentary, we argue that this viewpoint is neither defensible nor productive. Our argument is based on two observations. First, there are always limits on human performance, even among individuals who have engaged in long periods of intensive training. Second, grounded in a neurobiological system that has evolved through natural selection, variation across people in phenotypes reflecting these limits will have a genetic component. We comment on directions for future research to advance the field of expertise.

About The Hastings Center and the Columbia University Center for Research on Ethical, Legal & Social Implications of Psychiatric, Neurologic & Behavioral Genetics

About The Hastings Center and the Columbia University Center for Research on Ethical, Legal & Social Implications of Psychiatric, Neurologic & Behavioral Genetics

The Social Science Blues.

At the dawn of the new century, Robert Plomin was gloomy. As he recounts in Blueprint: How DNA Makes Us Who We Are, attempts to find the DNA responsible for the heritability of behavior failed. Month after month, journals would report new findings of specific genes for behavioral phenotypes, but they never replicated. One amazing genomic methodology after another was developed in biological genetics and applied to medicine, where it succeeded, and then to human behavior, where it failed. This was the moment of Plomin's despair. He had, with great intellectual courage, staked his reputation on the existence of actionable scientific knowledge of the DNA-based genesis of twin-based heritability. But Blueprint is hardly the product of a gloomy author. Quite the opposite: it is a declaration of victory of nature over nurture, a celebration of the vindication of Plomin as a scientist and of behavior genetics as a field of study. What happened between 2000 and 2019 to brighten Plomin's outlook so radically? Were the genes for schizophrenia and intelligence finally discovered? Are we at last on our way to understanding why, at a biological level, all differences in human behavior are substantially heritable? Alas, no. What happened is that Robert Plomin gave up on the search for individual genes that explain heritability and decided to be satisfied with much less.

Across the great divide: pluralism and the hunt for missing heritability

Genetic explanation of complex human behavior presents an excellent test case for pluralism. Although philosophers agree that successful scientific investigation of behavior is pluralistic, there remains disagreement regarding integration and elimination—is the plurality of approaches here to stay, or merely a waystation on the road to monism? In this paper we introduce an issue taken very seriously by scientists yet mostly ignored by philosophers—the missing heritability problem—and assess its implications for disagreement among pluralists. We argue that the missing heritability problem, which isn’t going anywhere any time soon, implies that pluralism in behavior genetics is both practically ineliminative and theoretically non-integrative.

Behavior Genetics and Agent Responsibility

Recent evidence from psychological science and genetics suggests that genetic influences underlie all behavior as well as the most worrisome social inequalities. This may be considered to call into question traditional conceptions of agency and agent responsibility. They could be thought to be undermined if gene-environment transactions were sufficiently potent in influencing behaviors. Here we identify the theoretical parameters that require investigation and the conceptual challenges to agent responsibility that arise from research in behavior genetics. We (i) introduce the empirical basis of the discussion, (ii) identify the particular questions that arise from considering the connection between behavior genetics and agent responsibility in the context of the legal system, (iii) bring into focus the general challenges to agent responsibility, and (iv) outline a potential resolution.

Is Apostasy Heritable? A Behavior Genetics Study.

The present study explores whether genetic factors explain variation in the levels of apostasy - defined as a disengagement from religious belief, identity and/or practice - in a US-based sample during the transition from adolescence to early adulthood. I posit that genetic factors at least partially explain the variance of three measures of apostasy: disengagement from religious institutions, cessation of prayer and religious disaffiliation. I argue that genetic factors associated with risk-taking behaviors, externalizing behaviors and/or correlates of apostasy may all influence the likelihood of becoming an apostate during the transition from adolescence to early adulthood in the USA. Results reveal that genetic factors explain approximately 34% of the variance in cessation of prayer and 75% of the variance in religious disaffiliation. However, genetic factors do not influence disengagement from religious institutions. This study advances our knowledge of the etiology of apostasy and highlights the need to incorporate genetic data into social scientific research.

Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important

Combining neuroimaging and clinical information for diagnosis, as for example behavioral tasks and genetics characteristics, is potentially beneficial but presents challenges in terms of finding the best data representation for the different sources of information. Their simple combination usually does not provide an improvement if compared with using the best source alone. In this paper, we proposed a framework based on a recent multiple kernel learning algorithm called EasyMKL and we investigated the benefits of this approach for diagnosing two different mental health diseases. The well known Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset tackling the Alzheimer Disease (AD) patients versus healthy controls classification task, and a second dataset tackling the task of classifying an heterogeneous group of depressed patients versus healthy controls. We used EasyMKL to combine a huge amount of basic kernels alongside a feature selection methodology, pursuing an optimal and sparse solution to facilitate interpretability. Our results show that the proposed approach, called EasyMKLFS, outperforms baselines (e.g. SVM and SimpleMKL), state-of-the-art random forests (RF) and feature selection (FS) methods.

Toward incorporating genetic risk scores into symptom networks of psychosis.

Psychosis spectrum disorder is a heterogeneous, multifactorial clinical phenotype, known to have a high heritability, only a minor portion of which can be explained by molecular measures of genetic variation. This study proposes that the identification of genetic variation underlying psychotic disorder may have suffered due to issues in the psychometric conceptualization of the phenotype. Here we aim to open a new line of research into the genetics of mental disorders by explicitly incorporating genes into symptom networks. Specifically, we investigate whether links between a polygenic risk score (PRS) for schizophrenia and measures of psychosis proneness can be identified in a network model. We analyzed data from n = 2180 subjects (controls, patients diagnosed with a non-affective psychotic disorder, and the first-degree relatives of the patients). A network structure was computed to examine associations between the 42 symptoms of the Community Assessment of Psychic Experiences (CAPE) and the PRS for schizophrenia. The resulting network shows that the PRS is directly connected to the spectrum of positive and depressive symptoms, with the items conspiracy and no future being more often located on predictive pathways from PRS to other symptoms. To our knowledge, the current exploratory study provides a first application of the network framework to the field of behavior genetics research. This allows for a novel outlook on the investigation of the relations between genome-wide association study-based PRSs and symptoms of mental disorders, by focusing on the dependencies among variables.

Nature vs. nurture is nonsense: On the necessity of an integrated genetic, social, developmental, and personality psychology

The field of behavioural genetics unambiguously demonstrates that heritable individual differences exist and are important in explaining human behaviour. Despite this, some psychological perspectives ignore this research. If we wish to comprehensively understand the impact of parenting, the environment, or any social factor, however, we must engage with genetics. In this article, I review research that reveals that genes affect not only our personalities, but the way that we understand and react to the social world. Studies further reveal that notable life events are in part explained by genetic variance. I detail how this could be the case through active, evocative, and passive genetic correlations, and go on to argue that all complex psychological traits are likely the result of multifaceted gene by environment interactions. A mistaken belief that genetic influence implies genetic essentialism, and is therefore tantamount to prejudice, is raised as possible reason why heritability is often ignored in the social sciences. The article concludes with practical suggestions for how we can embrace behavioural genetics as our methods struggle to match the divine complexity of human existence.

The Problem of Non-Shared Environment in Behavioral Genetics.

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.

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.

Developing individual differences in primate behavior: the role of genes, environment, and their interplay

As is the case for humans, it has long been thought that nonhuman primates can be described in terms of their personality. Scientific observations that support this view include the presence of individual differences in social behavior and that they are relatively stable throughout life. Consequently, individuals are constrained in their behavioral flexibility when dealing with various environmental challenges. Still, the variation among individuals during development suggests that the environment influences how primates behave. Research in fields including psychology, behavior genetics, and behavioral ecology have tried to identify the mechanisms responsible for this interplay of behavioral stability and change. In this review, we integrate theories and findings from research on humans and nonhuman primates that highlight how and to what extent genetic and environmental contributions shape the development of social behavior. To do so, we first provide an overview and define what is meant by mean-level and rank-order change of behavior. We then review explanations of behavioral stability and change, focusing on the role of genetic effects, how environmental circumstances influence behavioral variation throughout development, and how genetic and environmental influences may interact to produce this variation. Finally, we point to future research directions that could help us to further understand the development of social behavior in primates from within a behavior genetics framework.

A Major Limitation of the Direction of Causation Model: Non-Shared Environmental Confounding.

Determining (1) the direction of causation and (2) the size of causal effects between two constructs is a central challenge of the scientific study of humans. In the early 1990s, researchers in behavioral genetics invented what was termed the direction of causation (DoC) model to address exactly these two concerns. The model claims that for any two traits whose mode of inheritance is sufficiently different, the direction of causation can be ascertained using a sufficiently large genetically informative sample. Using a series of simulation studies, we demonstrate a major challenge to the DoC model, namely that it is extremely sensitive to even tiny amounts of non-shared confounding. Even under ideal conditions for the DoC model (a large sample, N = 10,000), a large causal relationship (e.g., a causal correlation of .50) with very different modes of inheritance between the two traits (e.g., a pure AE model for one trait and a pure CE model for another trait) and a modest degree (correlation of .10) of non-shared confounding between the two traits results in the choice of the wrong causal models and estimating the wrong causal effects.