Gene-environment interplay in early life cognitive development

Children's differences in early life cognitive development are driven by the interplay of genetic and environmental factors, but identifying replicable gene-environment interactions (GxE) has proven difficult. We systematically tested GxE effects in the prediction of cognitive development from 2 to 4 years, using polygenic scores (PGS) for years spent in education and 39 measures of the home and neighborhood environment. Data came from up to 6973 unrelated individuals from the Twins Early Development Study (TEDS), a UK population-representative cohort. The environmental measures accounted together for 20.6% of the variance in cognitive development, while the PGS accounted for 0.5% (p < .001). We observed substantial gene-environment correlations but found no conclusive evidence for GxE effects. While associations between PGS and cognitive development were weak, genetic and environmental factors had direct and additive (i.e., main effects) rather than interactive influences on early life cognitive development

Thriving at university: Designing a coaching psychology programme to promote wellbeing and resilience among undergraduate students

Wellbeing concerns increased during the Covid-19 pandemic, with university students being a population deserving special attention. Considering this, and the positive impact that coaching psychology can make in promoting wellbeing and resilience, we designed Time to Thrive (version 2022), a coaching psychology programme for undergraduate students based on an integrative model of cognitive-behavioural solution-focused coaching, positive psychology coaching and principles of neuroscience. This paper presents 1) the coaching psychology model used as a framework to structure the content of the programme. The model, called EMERALD, is based on the following domains for wellbeing and resilience: Emotions, Meaning and Engagement, Relationships, Achievements, Living better and Driving change; and 2) the co-creation approach that we used to design the programme and its upcoming evaluation, and a thematic analysis of semi-structured interviews with students after a pilot run. Voluntary response sampling was used to recruit first-year undergraduate students across the different faculties of the university. Preliminary results (N=6) indicate that the acceptability of Time to Thrive is high among undergraduate students. Participants commented on perceived outcomes from engaging with the programme, topic content, the virtual learning environment resources and materials, interaction with other students, structure and timeline, and recruitment strategies for increasing the visibility of Time to Thrive and reaching students more broadly. By presenting EMERALD and the initial results of this student-centred project, we hope to contribute to the work of other colleagues aiming to develop similar coaching psychology strategies to promote student wellbeing and resilience at universities and other educational settings

Capture Hi-C identifies the chromatin interactome of colorectal cancer risk loci

Multiple regulatory elements distant from their targets on the linear genome can influence the expression of a single gene through chromatin looping. Chromosome conformation capture implemented in Hi-C allows for genome-wide agnostic characterization of chromatin contacts. However, detection of functional enhancer–promoter interactions is precluded by its effective resolution that is determined by both restriction fragmentation and sensitivity of the experiment. Here we develop a capture Hi-C (cHi-C) approach to allow an agnostic characterization of these physical interactions on a genome-wide scale. Single-nucleotide polymorphisms associated with complex diseases often reside within regulatory elements and exert effects through long-range regulation of gene expression. Applying this cHi-C approach to 14 colorectal cancer risk loci allows us to identify key long-range chromatin interactions in cis and trans involving these loci

Genetic predisposition to chronic lymphocytic leukemia is mediated by a BMF super-enhancer polymorphism

Chronic lymphocytic leukemia (CLL) is an adult B cell malignancy. Genome-wide association studies show that variation at 15q15.1 influences CLL risk. We deciphered the causal variant at 15q15.1 and the mechanism by which it influences tumorigenesis. We imputed all possible genotypes across the locus and then mapped highly associated SNPs to areas of chromatin accessibility, evolutionary conservation, and transcription factor binding. SNP rs539846 C>A, the most highly associated variant (p = 1.42 × 10(-13), odds ratio = 1.35), localizes to a super-enhancer defined by extensive histone H3 lysine 27 acetylation in intron 3 of B cell lymphoma 2 (BCL2)-modifying factor (BMF). The rs539846-A risk allele alters a conserved RELA-binding motif, disrupts RELA binding, and is associated with decreased BMF expression in CLL. These findings are consistent with rs539846 influencing CLL susceptibility through differential RELA binding, with direct modulation of BMF expression impacting on anti-apoptotic BCL2, a hallmark of oncogenic dependency in CLL

Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder

\ua9 2015 The Authors. This is an open access article under the CC BY-NC-ND license. Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk

Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyper-activity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.Peer reviewe

Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4.

International audienceWe conducted a combined genome-wide association study (GWAS) of 7,481 individuals with bipolar disorder (cases) and 9,250 controls as part of the Psychiatric GWAS Consortium. Our replication study tested 34 SNPs in 4,496 independent cases with bipolar disorder and 42,422 independent controls and found that 18 of 34 SNPs had P < 0.05, with 31 of 34 SNPs having signals with the same direction of effect (P = 3.8 × 10(-7)). An analysis of all 11,974 bipolar disorder cases and 51,792 controls confirmed genome-wide significant evidence of association for CACNA1C and identified a new intronic variant in ODZ4. We identified a pathway comprised of subunits of calcium channels enriched in bipolar disorder association intervals. Finally, a combined GWAS analysis of schizophrenia and bipolar disorder yielded strong association evidence for SNPs in CACNA1C and in the region of NEK4-ITIH1-ITIH3-ITIH4. Our replication results imply that increasing sample sizes in bipolar disorder will confirm many additional loci

Bipolar multiplex families have an increased burden of common risk variants for psychiatric disorders.

Multiplex families with a high prevalence of a psychiatric disorder are often examined to identify rare genetic variants with large effect sizes. In the present study, we analysed whether the risk for bipolar disorder (BD) in BD multiplex families is influenced by common genetic variants. Furthermore, we investigated whether this risk is conferred mainly by BD-specific risk variants or by variants also associated with the susceptibility to schizophrenia or major depression. In total, 395 individuals from 33 Andalusian BD multiplex families (166 BD, 78 major depressive disorder, 151 unaffected) as well as 438 subjects from an independent, BD case/control cohort (161 unrelated BD, 277 unrelated controls) were analysed. Polygenic risk scores (PRS) for BD, schizophrenia (SCZ), and major depression were calculated and compared between the cohorts. Both the familial BD cases and unaffected family members had higher PRS for all three psychiatric disorders than the independent controls, with BD and SCZ being significant after correction for multiple testing, suggesting a high baseline risk for several psychiatric disorders in the families. Moreover, familial BD cases showed significantly higher BD PRS than unaffected family members and unrelated BD cases. A plausible hypothesis is that, in multiplex families with a general increase in risk for psychiatric disease, BD development is attributable to a high burden of common variants that confer a specific risk for BD. The present analyses demonstrated that common genetic risk variants for psychiatric disorders are likely to contribute to the high incidence of affective psychiatric disorders in the multiplex families. However, the PRS explained only part of the observed phenotypic variance, and rare variants might have also contributed to disease development

Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.National Institutes of HealthVoRSUNY DownstatePsychiatry and Behavioral SciencesInstitute for Genomics in HealthN/