Genes, behavior, and behavior genetics

Review of the geometrical developments in GEANT4-DNA: From a biological perspective

GEANT4-DNA is an expansion of the widely utilised GEANT4 Monte Carlo toolkit. This extension focuses on modelling the physical, chemical, and biological stages of ionising radiation for radiobiological applications at cellular and DNA level interactions. To date, review papers on GEANT4-DNA focus solely on evaluating a selection of the latest developments with a greater focus on mechanistic developments rather than progress in biologically specific geometries. In this work, an overview of biological analysis and biological geometries that have been developed are discussed, highlighting the latest developments and future possible development avenues for GEANT4-DNA for this application. An overview of the biological organisation levels, namely DNA, cellular, and population levels, and how GEANT4-DNA models the physical, chemical, and biological processes are also described. This review emphasises the need for persistent development of specific biological geometry accompanied by personalised DNA damage analysis parameters dependent on the biological processes considered within a specific model. It also provides an in-depth understanding of the advances at all the biological organisation levels (DNA, cellular, and population) and the use of co-operative platforms developed to model alongside GEANT4 to provide further detailed geometries and or biological damage analysis. The developments presented have been analytically discussed along with their key findings and prospects for GEANT4-DNA. Finally, a perspective on future necessary developments is portrayed since many of the advancements in the biological analysis and biological geometries discussed have not been exploited to their full potential. The development of GEANT4-DNA, using the advances discussed in this review, provides a favourable method for the evaluation of biological damage comparable to radiobiological studies.

Minimally invasive brain injections for viral-mediated transgenesis: New tools for behavioral genetics in sticklebacks

Behavioral genetics in non-model organisms is currently gated by technological limitations. However, with the growing availability of genome editing and functional genomic tools, complex behavioral traits such as social behavior can now be explored in diverse organisms. Here we present a minimally invasive neurosurgical procedure for a classic behavioral, ecological and evolutionary system: threespine stickleback (Gasterosteus aculeatus). Direct brain injection enables viral-mediated transgenesis and pharmaceutical delivery which bypasses the blood-brain barrier. This method is flexible, fast, and amenable to statistically powerful within-subject experimental designs, making it well-suited for use in genetically diverse animals such as those collected from natural populations. Developing this minimally invasive neurosurgical protocol required 1) refining the anesthesia process, 2) building a custom surgical rig, and 3) determining the normal recovery pattern allowing us to clearly identify warning signs of failure to thrive. Our custom-built surgical rig (publicly available) and optimized anesthetization methods resulted in high (90%) survival rates and quick behavioral recovery. Using this method, we detected changes in aggression from the overexpression of either of two different genes, arginine vasopressin (AVP) and monoamine oxidase (MAOA), in outbred animals in less than one month. We successfully used multiple promoters to drive expression, allowing for tailored expression profiles through time. In addition, we demonstrate that widely available mammalian plasmids work with this method, lowering the barrier of entry to the technique. By using repeated measures of behavior on the same fish before and after transfection, we were able to drastically reduce the necessary sample size needed to detect significant changes in behavior, making this a viable approach for examining genetic mechanisms underlying complex social behaviors.

Association of the Sp1 binding site and -1997 promoter variations in COL1A1 with osteoporosis risk: The application of meta-analysis and bioinformatics approaches offers a new perspective for future research

As a complex disease, osteoporosis is influenced by several genetic markers. Many studies have examined the link between the Sp1 binding site +1245 G > T (rs1800012) and -1997 G > T (rs1107946) variations in the COL1A1 gene with osteoporosis risk. However, the findings of these studies have been contradictory; therefore, we performed a meta-analysis to aggregate additional information and obtain increased statistical power to more efficiently estimate this correlation. A meta-analysis was conducted with studies published between 1991-2020 that were identified by a systematic electronic search of the Scopus and Clarivate Analytics databases. Studies with bone mineral density (BMD) data and complete genotypes of the single-nucleotide variations (SNVs) for the overall and postmenopausal female population were included in this meta-analysis and analyzed using the R metaphor package. A relationship between rs1800012 and significantly decreased BMD values at the lumbar spine and femoral neck was found in individuals carrying the "ss" versus the "SS" genotype in the overall population according to a random effects model (p < 0.0001). Similar results were also found in the postmenopausal female population (p = 0.003 and 0.0002, respectively). Such findings might be an indication of increased osteoporosis risk in both studied groups in individuals with the "ss" genotype. Although no association was identified between the -1997 G > T and low BMD in the overall population, those individuals with the "GT" genotype showed a higher level of BMD than those with "GG" in the subgroup analysis (p = 0.007). To determine which transcription factor (TF) might bind to the -1997 G > T in COL1A1, 45 TFs were identified based on bioinformatics predictions. According to the GSE35958 microarray dataset, 16 of 45 TFs showed differential expression profiles in osteoporotic human mesenchymal stem cells relative to normal samples from elderly donors. By identifying candidate TFs for the -1997 G > T site, our study offers a new perspective for future research.

Estimating the heritability of psychological measures in the Human Connectome Project dataset

The Human Connectome Project (HCP) is a large structural and functional MRI dataset with a rich array of behavioral and genotypic measures, as well as a biologically verified family structure. This makes it a valuable resource for investigating questions about individual differences, including questions about heritability. While its MRI data have been analyzed extensively in this regard, to our knowledge a comprehensive estimation of the heritability of the behavioral dataset has never been conducted. Using a set of behavioral measures of personality, emotion and cognition, we show that it is possible to re-identify the same individual across two testing times (fingerprinting), and to identify identical twins significantly above chance. Standard heritability estimates of 37 behavioral measures were derived from twin correlations, and machine-learning models (univariate linear model, Ridge classifier and Random Forest model) were trained to classify monozygotic twins and dizygotic twins. Correlations between the standard heritability metric and each set of model weights ranged from 0.36 to 0.7, and questionnaire-based and task-based measures did not differ significantly in their heritability. We further explored the heritability of a smaller number of latent factors extracted from the 37 measures and repeated the heritability estimation; in this case, the correlations between the standard heritability and each set of model weights were lower, ranging from 0.05 to 0.43. One specific discrepancy arose for the general intelligence factor, which all models assigned high importance, but the standard heritability calculation did not. We present a thorough investigation of the heritabilities of the behavioral measures in the HCP as a resource for other investigators, and illustrate the utility of machine-learning methods for qualitative characterization of the differential heritability across diverse measures.

Understanding the effects of education through the lens of biology

Early educational interventions aim to close gaps in achievement levels between children. However, early interventions do not eliminate individual differences in populations and the effects of early interventions often fade-out over time, despite changes of the mean of the population immediately following the intervention. Here, we discuss biological factors that help to better understand why early educational interventions do not eliminate achievement gaps. Children experience and respond to educational interventions differently. These stable individual differences are a consequence of biological mechanisms that support the interplay between genetic predispositions and the embedding of experience into our biology. Accordingly, we argue that it is not plausible to conceptualize the goals of educational interventions as both a shifting of the mean and a narrowing of the distribution of a particular measure of educational attainment assumed to be of utmost importance (such as a standardized test score). Instead of aiming to equalize the performance of students, the key goal of educational interventions should be to maximize potential at the individual level and consider a kaleidoscope of educational outcomes across which individuals vary. Additionally, in place of employing short-term interventions in the hope of achieving long-term gains, educational interventions need to be sustained throughout development and their long-term, rather than short-term, efficacy be evaluated. In summary, this paper highlights how biological research is valuable for driving a re-evaluation of how educational success across development can be conceptualized and thus what policy implications may be drawn.