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The Chemistry of the Brain
Published in Gail S. Anderson, Biological Influences on Criminal Behavior, 2019
In many of the cases we have discussed, although various factors such as hormones, neurotransmitters, brain injury, and genes can provide risk factors for antisocial behavior, there are also many protective factors that reduce or eliminate the risk. One well-accepted protective factor is that of academic success in school. Failing or dropping out of high school has been linked to a plethora of unfavorable outcomes, such as substance abuse, antisocial behavior, and delinquency.46 Students who succeed academically have much more favorable outcomes. Although most studies have linked environmental factors such as family status and socioeconomic status (SES) with success in school, recent research is also considering the genetic underpinnings of academic success. A variety of studies looking at different samples of students have reliably shown that around 50% of the variance in academic achievement is under genetic control.46 Of course, as in all behavioral genetics, behaviors are under the control of many genes, and each one makes only a small contribution to such complex traits as behaviors. Moreover, academic success is not a behavior or function on its own, so in itself, it is not under genetic or other types of control but is dependent on other factors that do impact success, such as memory, information retention, motivation, reward response, speed of information processing, and attention.46 As the dopaminergic system is heavily involved in all these factors, it has been suggested that it plays a role in educational success.46
Patterns of Inheritance: Mendelian and Non-Mendelian
Published in Merlin G. Butler, F. John Meaney, Genetics of Developmental Disabilities, 2019
Merlin G. Butler, Michael Begleiter, Shannon Lillis, Molly Lund, F. John Meaney
More detailed methods to detect the influence of genes in multifactorial traits and diseases are presented in Chapter 21 on Behavioral Genetics and Developmental Disabilities. In addition, Chapter 19 applies both genetic and epidemiological methods to the detection of genetic and environmental factors contributing to the cause of autism and mental retardation. A review and perspective concerning genetic and environmental factors in mental retardation is also provided in Meaney (16). Although we have not dealt with the topic herein, research on multifactorial conditions will increasingly be focused on the search for evidence of gene–environment interactions. For example, Dyer-Friedman et al. (17) have provided a model approach to the study of genetic and environmental factors that predict cognitive outcomes such as IQ in fragile X syndrome. The search for the interactions among genes and among genes, environmental exposures, and interventions is very likely to emerge as a major focus of future research in developmental disabilities.
Managing Patient Information
Published in Gary Seay, Susana Nuccetelli, Engaging Bioethics, 2017
Thus neither genetic determinism nor environmental determinism can capture the facts about the complex relations between hereditary disease, genetics, and the environment. Only genetics can, by developing branches concerned with studying those relations scientifically. Consider behavioral genetics, the branch devoted to studying behavioral variations in animals, including humans, resulting from complex interactions between genes and environment. It tells us that it is neither true that our mind is a ‘blank slate’ at birth (so that conditioning is the only cause of mental illness), nor that mental disorders are encoded in our genes in a straightforward way (so that it can be known by genetics whether a person will develop, for example, bipolar disorder). The truth lies in complex interactions between genes and the environment that are not yet fully understood.
Environmental Sensitivity in Adults: Psychometric Properties of the Japanese Version of the Highly Sensitive Person Scale 10-Item Version
Published in Journal of Personality Assessment, 2023
Shuhei Iimura, Kosuke Yano, Yukiko Ishii
As has been suggested by influential developmental theories such as the bioecological model of human development (Bronfenbrenner & Morris, 2006), from birth to death, humans are affected by a wide range of environmental influences, and we undergo neurophysiological and psychosocial development through dynamic interactions with these environments. In this sense, humans are social beings that cannot be separated from their environment. However, it is important to note that individual differences in sensitivity to environmental influences can be observed (e.g., Aron et al., 2012; Belsky & Pluess, 2009; Boyce & Ellis, 2005; Ellis et al., 2011; Monroe & Simons, 1991; Pluess & Belsky, 2013). Some individuals are more likely to be susceptible to both positive and negative experiences than others. Currently, such individual differences in sensitivity to the environment are being empirically studied from the perspectives of developmental psychology, neurophysiology, behavioral genetics, molecular genetics, and personality psychology within the integrated framework of Environmental Sensitivity Theory (Greven et al., 2019; Pluess, 2015). In this paper, we discuss our development of a brief Japanese version of the Highly Sensitive Person (HSP) scale to measure individual differences in sensitivity in adults (Aron & Aron, 1997; Takahashi, 2016), and provide new information on its psychometric properties.
Comparative behavioral genetics: the Yamamoto approach
Published in Journal of Neurogenetics, 2019
This special issue of the Journal of Neurogenetics celebrates the achievements and unique contributions of Daisuke Yamamoto on the occasion of his lab relocation from Tohoku University, where he spent nearly 13 years (2005–2018). Daisuke’s profound and persistent scientific interest is sexual behavior of insects, Drosophila melanogaster in particular. His characteristic strategy amply illustrated an effective multi-layered approach: identification of genes and neural circuits that shape or control behavior and how they adapt and contribute to mating behaviors in different species. We here call this unique approach “comparative behavioral genetics.”
Transcending boundaries: from quantitative genetics to single genes
Published in Journal of Neurogenetics, 2021
Jeffrey S. Dason, Ina Anreiter, Chun-Fang Wu
Marla is most well known for her discovery of two allelic variants, rovers and sitters, that differ in larval foraging behaviour (Sokolowski, 1980). To understand how and why these variants existed, she developed a new subdiscipline of behavioural genetics, one that explores the mechanistic and evolutionary significance of behavioural variation. She and her trainees would show that a gene that encodes a cGMP-dependent kinase was primarily responsible for differences in larval foraging behaviour between rovers and sitters (de Belle, Hilliker, & Sokolowski, 1989; Osborne et al., 1997). Marla and her group would name this gene foraging and go on to discover that it affects a number of behaviours and physiological processes (reviewed in Anreiter & Sokolowski, 2019), including adult foraging behaviour (Pereira & Sokolowski, 1993; Anreiter, Kramer, & Sokolowski, 2017), learning and memory (Kaun, Hendel, Gerber, & Sokolowski, 2007, Mery, Belay, So, Sokolowski, & Kawecki, 2007), sleep (Donlea et al., 2012), nociception (Dason et al., 2020), stress responses (Dawson-Scully, Armstrong, Kent, Robertson, & Sokolowski, 2007; Dawson-Scully et al., 2010) and neurotransmission (Renger, Yao, Sokolowski, & Wu, 1999; Dason, Allen, Vasquez, & Sokolowski, 2019). foraging‘s role in behaviour is conserved in organisms ranging from flies, honey bees and ants to humans (Ben-Shahar, Robichon, Sokolowski, & Robinson, 2002; Lucas & Sokolowski, 2009; Struk et al., 2019). In recent years, Marla and her trainees have studied the gene structure of foraging in detail and linked many of these phenotypes to specific promoters (Allen, Anreiter, Neville, & Sokolowski, 2017; Anreiter et al., 2017; Allen, Anreiter, Vesterberg, Douglas, & Sokolowski, 2018; Dason et al., 2020).