Developmental behavioral genetics research on school achievement is missing vulnerable children, to our detriment

Gene–environment processes tell us how genes and environments work together to influence children in schools. One type of gene–environment process that has been extensively studied using behavioral genetics methods is a gene-by-environment interaction. A gene-by-environment interaction shows us when the effect of your context differs depending on your genes, or vice versa, when the effect of your genes differs depending on your context. Developmental behavioral geneticists interested in children’s school achievement have examined many different contexts within the gene-by-environment interaction model, including contexts measured from within children’s home and school environments. However, this work has been overwhelmingly focused on White children, leaving us with non-inclusive scientific evidence. This can lead to detrimental outcomes when we overgeneralize this non-inclusive scientific evidence to racialized groups. We conclude with a call to include racialized children in more research samples.

CyberGenomics: Application of Behavioral Genetics in Cybersecurity

Cybersecurity (CS) is a contemporary field for research and applied study of a range of aspects from across multiple disciplines. A cybersecurity expert has an in-depth knowledge of technology but is often also recognized for the ability to view technology in a non-standard way. This paper explores how CS specialists are both a combination of professional computing-based skills and genetically encoded traits. Almost every human behavioral trait is a result of many genome variants in action altogether with environmental factors. The review focuses on contextualizing the behavior genetics aspects in the application of cybersecurity. It reconsiders methods that help to identify aspects of human behavior from the genetic information. And stress is an illustrative factor to start the discussion within the community on what methodology should be used in an ethical way to approach those questions. CS positions are considered stressful due to the complexity of the domain and the social impact it can have in cases of failure. An individual risk profile could be created combining known genome variants linked to a trait of particular behavior using a special biostatistical approach such as a polygenic score. These revised advancements bring challenging possibilities in the applications of human behavior genetics and CS.

Recurrence eigenvalues of movements from brain signals

The ability to characterize muscle activities or skilled movements controlled by signals from neurons in the motor cortex of the brain has many useful implications, ranging from biomedical perspectives to brain–computer interfaces. This paper presents the method of recurrence eigenvalues for differentiating moving patterns in non-mammalian and human models. The non-mammalian models of Caenorhabditis elegans have been studied for gaining insights into behavioral genetics and discovery of human disease genes. Systematic probing of the movement of these worms is known to be useful for these purposes. Study of dynamics of normal and mutant worms is important in behavioral genetic and neuroscience. However, methods for quantifying complexity of worm movement using time series are still not well explored. Neurodegenerative diseases adversely affect gait and mobility. There is a need to accurately quantify gait dynamics of these diseases and differentiate them from the healthy control to better understand their pathophysiology that may lead to more effective therapeutic interventions. This paper attempts to explore the potential application of the method for determining the largest eigenvalues of convolutional fuzzy recurrence plots of time series for measuring the complexity of moving patterns of Caenorhabditis elegans and neurodegenerative disease subjects. Results obtained from analyses demonstrate that the largest recurrence eigenvalues can differentiate phenotypes of behavioral dynamics between wild type and mutant strains of Caenorhabditis elegans; and walking patterns among healthy control subjects and patients with Parkinson’s disease, Huntington’s disease, or amyotrophic lateral sclerosis.

Are you ready for the genetic revolution in education?

Recent findings in behavioral genetics and technological advances have the potential to alter education administration in ways that were inconceivable just a decade ago. Specifically, new understandings about the heritability of educational outcomes and the ability to calculate polygenic scores that predict likely student outcomes could change how educators identify students who are eligible for specific services. Russell Warne explains where the science currently stands, describes the benefits and possible drawbacks of using genetic technologies, and suggests policies for applying these technologies in schools.

Influence of genetic factors and institutional environment on entrepreneurial activity: evidence from a twin study in Italy

Genetic factors influence entrepreneurial activity, but we know little about how genetic factors influence entrepreneurial activity when the institutional environment is favorable. Two theories from behavioral genetics explain the gene–environment interaction. One theory argues that a favorable environment favors the development of genetic factors and their influence. An alternative theory posits that unfavorable environment triggers the development of genetic factors and their influence. I test these two competing theories with a twin study based in Italy. I compare the influence of genetic factors in provinces where the institutional environment favors entrepreneurial activity with provinces where the institutional environment is unfavorable to entrepreneurial activity. I found consistent evidence that genetic factors exert a larger influence in favorable institutional environments, suggesting that favorable institutional environments complement genetic factors.