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\"Educational environments interact with children's unique genetic profiles, leading to wide individual differences in learning ability, motivation, and achievement in different academic subjects - even when children study with the same teacher, attend the same school and follow the same curriculum. This book considers how education can benefit from the recent progress in genetically informative research. The book provides new insights into the origins of individual differences in education traits such as cognitive abilities and disabilities; motivation and personality; behavioural and emotional problems; social functioning; well-being, and academic achievement. Written and edited by international interdisciplinary experts, this book will be of interest to teachers, parents, educational and developmental psychologists, policy makers and researchers in different fields working on educationally-relevant issues. \"-- Provided by publisher.

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.

In the context of current concerns about replication in psychological science, we describe 10 findings from behavioral genetic research that have replicated robustly. These are \"big\" findings, both in terms of effect size and potential impact on psychological science, such as linearly increasing heritability of intelligence from infancy (20%) through adulthood (60%). Four of our top 10 findings involve the environment, discoveries that could have been found only with genetically sensitive research designs. We also consider reasons specific to behavioral genetics that might explain why these findings replicate.

Significance Differences among children in educational achievement are highly heritable from the early school years until the end of compulsory education at age 16, when UK students are assessed nationwide with standard achievement tests [General Certificate of Secondary Education (GCSE)]. Genetic research has shown that intelligence makes a major contribution to the heritability of educational achievement. However, we show that other broad domains of behavior such as personality and psychopathology also account for genetic influence on GCSE scores beyond that predicted by intelligence. Together with intelligence, these domains account for 75% of the heritability of GCSE scores. These results underline the importance of genetics in educational achievement and its correlates. The results also support the trend in education toward personalized learning. Because educational achievement at the end of compulsory schooling represents a major tipping point in life, understanding its causes and correlates is important for individual children, their families, and society. Here we identify the general ingredients of educational achievement using a multivariate design that goes beyond intelligence to consider a wide range of predictors, such as self-efficacy, personality, and behavior problems, to assess their independent and joint contributions to educational achievement. We use a genetically sensitive design to address the question of why educational achievement is so highly heritable. We focus on the results of a United Kingdom-wide examination, the General Certificate of Secondary Education (GCSE), which is administered at the end of compulsory education at age 16. GCSE scores were obtained for 13,306 twins at age 16, whom we also assessed contemporaneously on 83 scales that were condensed to nine broad psychological domains, including intelligence, self-efficacy, personality, well-being, and behavior problems. The mean of GCSE core subjects (English, mathematics, science) is more heritable (62%) than the nine predictor domains (35–58%). Each of the domains correlates significantly with GCSE results, and these correlations are largely mediated genetically. The main finding is that, although intelligence accounts for more of the heritability of GCSE than any other single domain, the other domains collectively account for about as much GCSE heritability as intelligence. Together with intelligence, these domains account for 75% of the heritability of GCSE. We conclude that the high heritability of educational achievement reflects many genetically influenced traits, not just intelligence.

The assumption in the personality and politics literature is that a person's personality motivates them to develop certain political attitudes later in life. This assumption is founded on the simple correlation between the two constructs and the observation that personality traits are genetically influenced and develop in infancy, whereas political preferences develop later in life. Work in psychology, behavioral genetics, and recently political science, however, has demonstrated that political preferences also develop in childhood and are equally influenced by genetic factors. These findings cast doubt on the assumed causal relationship between personality and politics. Here we test the causal relationship between personality traits and political attitudes using a direction of causation structural model on a genetically informative sample. The results suggest that personality traits do not cause people to develop political attitudes; rather, the correlation between the two is a function of an innate common underlying genetic factor.

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.

ObjectivesSpeech and language impairments are core features of the neurodevelopmental genetic condition Kleefstra syndrome. Communication has not been systematically examined to guide intervention recommendations. We define the speech, language and cognitive phenotypic spectrum in a large cohort of individuals with Kleefstra syndrome.Method103 individuals with Kleefstra syndrome (40 males, median age 9.5 years, range 1–43 years) with pathogenic variants (52 9q34.3 deletions, 50 intragenic variants, 1 balanced translocation) were included. Speech, language and non-verbal communication were assessed. Cognitive, health and neurodevelopmental data were obtained.ResultsThe cognitive spectrum ranged from average intelligence (12/79, 15%) to severe intellectual disability (12/79, 15%). Language ability also ranged from average (10/90, 11%) to severely impaired (53/90, 59%). Speech disorders occurred in 48/49 (98%) verbal individuals and even occurred alongside average language and cognition. Developmental regression occurred in 11/80 (14%) individuals across motor, language and psychosocial domains. Communication aids, such as sign and speech-generating devices, were crucial for 61/103 (59%) individuals including those who were minimally verbal, had a speech disorder or following regression.ConclusionsThe speech, language and cognitive profile of Kleefstra syndrome is broad, ranging from severe impairment to average ability. Genotype and age do not explain the phenotypic variability. Early access to communication aids may improve communication and quality of life.

BackgroundGenetics is an important contributor to autism spectrum disorder (ASD). Clinical guidelines endorse genetic testing in the medical workup of ASD, particularly tests that use whole genome sequencing (WGS) technology. While the clinical utility of genetic testing in ASD is demonstrated, the breadth of impact of results can depend on the variant and/or gene being reported.MethodsWe reviewed research results returned to families enrolled in our ASD WGS study between 2012 and 2023. For significant results, we grouped the outcome of each genetic finding into three outcome categories: (1) genetic diagnosis, (2) counselling benefits and (3) support to family.ResultsOut of 202 families who received genome sequencing results, 100 had at least one clinically relevant finding related to ASD. With detailed examples, we show that all significant results led to a genetic diagnosis and counselling benefits.ConclusionOur findings show the relevance of genome sequencing in ASD and provide illustrative examples of how the information can be used.

A century after the first twin and adoption studies of behavior in the 1920s, this review looks back on the journey and celebrates milestones in behavioral genetic research. After a whistle-stop tour of early quantitative genetic research and the parallel journey of molecular genetics, the travelogue focuses on the last fifty years. Just as quantitative genetic discoveries were beginning to slow down in the 1990s, molecular genetics made it possible to assess DNA variation directly. From a rocky start with candidate gene association research, by 2005 the technological advance of DNA microarrays enabled genome-wide association studies, which have successfully identified some of the DNA variants that contribute to the ubiquitous heritability of behavioral traits. The ability to aggregate the effects of thousands of DNA variants in polygenic scores has created a DNA revolution in the behavioral sciences by making it possible to use DNA to predict individual differences in behavior from early in life.

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.