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While the co-morbidity between metabolic and psychiatric behaviors is well-established, the mechanisms are poorly understood, and exposure to early life adversity (ELA) is a common developmental risk factor. ELA is associated with altered insulin sensitivity and poor behavioral inhibition throughout life, which seems to contribute to the development of metabolic and psychiatric disturbances in the long term. We hypothesize that a genetic background associated with higher fasting insulin interacts with ELA to influence the development of executive functions (e.g., impulsivity in young children). We calculated the polygenic risk scores (PRSs) from the genome-wide association study (GWAS) of fasting insulin at different thresholds and identified the subset of single nucleotide polymorphisms (SNPs) that best predicted peripheral insulin levels in children from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort [ N = 467; p t – initial = 0.24 (10,296 SNPs), p t – refined = 0.05 (57 SNPs)]. We then calculated the refined PRS (rPRS) for fasting insulin at this specific threshold in the children from the Maternal Adversity, Vulnerability and Neurodevelopment (MAVAN) cohort and investigated its interaction effect with adversity on an impulsivity task applied at 36 months. We found a significant effect of interaction between fasting insulin rPRS and adversity exposure predicting impulsivity measured by the Snack Delay Task at 36 months [β = −0.329, p = 0.024], such that higher PRS [β = −0.551, p = 0.009] was linked to more impulsivity in individuals exposed to more adversity. Enrichment analysis (MetaCore TM ) of the SNPs that compose the fasting insulin rPRS at this threshold was significant for certain nervous system development processes including dopamine D2 receptor signaling. Additional enrichment analysis (FUMA) of the genes mapped from the SNPs in the fasting insulin rPRS showed enrichment with the accelerated cognitive decline GWAS. Therefore, the genetic background associated with risk for adult higher fasting insulin moderates the impact of early adversity on childhood impulsivity.

Synesthesia is a neurologic trait in which specific inducers, such as sounds, automatically elicit additional idiosyncratic percepts, such as color (thus “colored hearing”). One explanation for this trait—and the one tested here—is that synesthesia results from unusually weak pruning of cortical synaptic hyperconnectivity during early perceptual development. We tested the prediction from this hypothesis that synesthetes would be superior at making discriminations from nonnative categories that are normally weakened by experience-dependent pruning during a critical period early in development—namely, discrimination among nonnative phonemes (Hindi retroflex /d̪a/ and dental /ɖa/), among chimpanzee faces, and among inverted human faces. Like the superiority of 6-mo-old infants over older infants, the synesthetic groups were significantly better than control groups at making all the nonnative discriminations across five samples and three testing sites. The consistent superiority of the synesthetic groups in making discriminations that are normally eliminated during infancy suggests that residual cortical connectivity in synesthesia supports changes in perception that extend beyond the specific synesthetic percepts, consistent with the incomplete pruning hypothesis.

All languages employ certain phonetic contrasts when distinguishing words. Infant speech perception is rapidly attuned to these contrasts before many words are learned, thus phonetic attunement is thought to proceed independently of lexical and referential knowledge. Here, evidence to the contrary is provided. Ninety-eight 9-month-old English-learning infants were trained to perceive a non-native Cantonese tone contrast. Two object–tone audiovisual pairings were consistently presented, which highlighted the target contrast (Object A with Tone X; Object B with Tone Y). Tone discrimination was then assessed. Results showed improved tone discrimination if object–tone pairings were perceived as being referential word labels, although this effect was modulated by vocabulary size. Results suggest how lexical and referential knowledge could play a role in phonetic attunement.

Background Polygenic risk scores (PRS) describe the genomic contribution to complex phenotypes and consistently account for a larger proportion of variance in outcome than single nucleotide polymorphisms (SNPs) alone. However, there is little consensus on the optimal data input for generating PRS, and existing approaches largely preclude the use of imputed posterior probabilities and strand-ambiguous SNPs i.e., A/T or C/G polymorphisms. Our ability to predict complex traits that arise from the additive effects of a large number of SNPs would likely benefit from a more inclusive approach. Results We developed PRS-on-Spark (PRSoS), a software implemented in Apache Spark and Python that accommodates different data inputs and strand-ambiguous SNPs to calculate PRS. We compared performance between PRSoS and an existing software (PRSice v1.25) for generating PRS for major depressive disorder using a community cohort ( N  = 264). We found PRSoS to perform faster than PRSice v1.25 when PRS were generated for a large number of SNPs (~ 17 million SNPs; t  = 42.865, p  = 5.43E-04). We also show that the use of imputed posterior probabilities and the inclusion of strand-ambiguous SNPs increase the proportion of variance explained by a PRS for major depressive disorder (from 4.3% to 4.8%). Conclusions PRSoS provides the user with the ability to generate PRS using an inclusive and efficient approach that considers a larger number of SNPs than conventional approaches. We show that a PRS for major depressive disorder that includes strand-ambiguous SNPs, calculated using PRSoS, accounts for the largest proportion of variance in symptoms of depression in a community cohort, demonstrating the utility of this approach. The availability of this software will help users develop more informative PRS for a variety of complex phenotypes.

Prenatal adversity shapes child neurodevelopment and risk for later mental health problems. The quality of the early care environment can buffer some of the negative effects of prenatal adversity on child development. Retrospective studies, in adult samples, highlight epigenetic modifications as sentinel markers of the quality of the early care environment; however, comparable data from pediatric cohorts are lacking. Participants were drawn from the Maternal Adversity Vulnerability and Neurodevelopment (MAVAN) study, a longitudinal cohort with measures of infant attachment, infant development, and child mental health. Children provided buccal epithelial samples (mean age = 6.99, SD = 1.33 years, n = 226), which were used for analyses of genome-wide DNA methylation and genetic variation. We used a series of linear models to describe the association between infant attachment and (a) measures of child outcome and (b) DNA methylation across the genome. Paired genetic data was used to determine the genetic contribution to DNA methylation at attachment-associated sites. Infant attachment style was associated with infant cognitive development (Mental Development Index) and behavior (Behavior Rating Scale) assessed with the Bayley Scales of Infant Development at 36 months. Infant attachment style moderated the effects of prenatal adversity on Behavior Rating Scale scores at 36 months. Infant attachment was also significantly associated with a principal component that accounted for 11.9% of the variation in genome-wide DNA methylation. These effects were most apparent when comparing children with a secure versus a disorganized attachment style and most pronounced in females. The availability of paired genetic data revealed that DNA methylation at approximately half of all infant attachment-associated sites was best explained by considering both infant attachment and child genetic variation. This study provides further evidence that infant attachment can buffer some of the negative effects of early adversity on measures of infant behavior. We also highlight the interplay between infant attachment and child genotype in shaping variation in DNA methylation. Such findings provide preliminary evidence for a molecular signature of infant attachment and may help inform attachment-focused early intervention programs.

Maternal antenatal depression strongly influences child mental health but with considerable inter-individual variation that is, in part, linked to genotype. The challenge is to effectively capture the genotypic influence. We outline a novel approach to describe genomic susceptibility to maternal antenatal depression focusing on child emotional/behavioral difficulties. Two cohorts provided measures of maternal depression, child genetic variation, and child mental health symptoms. We constructed a conventional polygenic risk score (PRS) for attention-deficit/hyperactivity disorder (ADHD) (PRSADHD) that significantly moderated the association between maternal antenatal depression and internalizing problems at 60 months (p = 2.94 × 10−4, R2 = .18). We then constructed an interaction PRS (xPRS) based on a subset of those single nucleotide polymorphisms from the PRSADHD that most accounted for the moderation of the association between maternal antenatal depression and child outcome. The interaction between maternal antenatal depression and this xPRS accounted for a larger proportion of the variance in child emotional/behavioral problems than models based on any PRSADHD (p = 5.50 × 10−9, R2 = .27), with similar findings in the replication cohort. The xPRS was significantly enriched for genes involved in neuronal development and synaptic function. Our study illustrates a novel approach to the study of genotypic moderation on the impact of maternal antenatal depression on child mental health and highlights the utility of the xPRS approach. These findings advance our understanding of individual differences in the developmental origins of mental health.

We study the dynamics of a spatially structured model of worldwide epidemics and formulate predictions for arrival times of the disease at any city in the network. The model is comprised of a system of ordinary differential equations describing a meta-population SIR compartmental model defined on a network where each node represents a city and edges represent flight paths connecting cities. Making use of the linear determinacy of the system, we consider spreading speeds and arrival times in the system linearized about the unstable disease free state and compare these to arrival times in the nonlinear system. Two predictions are presented. The first is based upon expansion of the heat kernel for the linearized system. The second assumes that the dominant transmission pathway between any two cities can be approximated by a one dimensional lattice or homogeneous tree and gives a uniform prediction for arrival times independent of specific network features. We test these predictions on a real network describing worldwide airline traffic.

Importance: Activation of brain insulin receptors occurs on mesocorticolimbic regions, modulating reward sensitivity and inhibitory control. Variations in the functioning of this mechanism likely associate with individual differences in the risk for related psychopathologies (attention-deficit hyperactivity disorder, addiction), an idea that agrees with the high co-morbidity between insulin resistant states and psychiatric conditions. While genetic studies comprise an interesting tool to explore neurobiological mechanisms in community samples, the conventional genome-wide association studies and polygenic risk score methodologies completely ignore the fact that genes operate in networks, and code for precise biological functions in specific tissues. Objective: We propose a novel, biologically informed genetic score reflecting the mesocorticolimbic insulin receptor-related gene network, and investigate if it predicts dopamine-related psychopathology (impulsivity and addiction) in community samples. Design: Birth cohort (Maternal Adversity, Vulnerability and Neurodevelopment, MAVAN) and adult cohort (Study of Addiction, Genes and Environment, SAGE). Setting: General community. Participants: 212 4-year-old children (MAVAN), and 1626 adults (SAGE). Exposure: The biologically informed, mesocorticolimbic specific, insulin receptor polygenic score was created based on levels of co-expression with the insulin receptor in striatum and prefrontal cortex, and calculated in the two samples using the genotype data (Psychip/Psycharray). Main outcome: childhood impulsivity in the Information Sampling task, and risk for early addiction onset. Results: The insulin receptor polygenic score showed improved prediction of childhood impulsivity in boys and risk for early addiction onset in males in comparison to conventional polygenic risk scores for attention-deficit hyperactivity disorder or addiction. Conclusions and relevance: This novel genomic approach reveals insulin action as a relevant biological process involved in the risk for dopamine-related psychopathology.

Motivation: Polygenic risk scores describe the genomic contribution to complex phenotypes and consistently account for a larger proportion of the variance than single nucleotide polymorphisms alone. However, there is little consensus on the optimal data input for generating polygenic risk scores and existing approaches largely preclude the use of imputed posterior probabilities and strand-ambiguous SNPs. Results: We developed PRS-on-Spark (PRSoS) a polygenic risk score software implemented in Apache Spark and Python that accommodates a variety of data input (e.g., observed genotypes, imputed genotypes or imputed dosage data) and strand-ambiguous SNPs. We show that PRSoS is flexible and efficient, accommodates strand-ambiguous SNP and computes polygenic risk scores at a range of p-value thresholds more quickly than existing software (PRSice). We also show that the use of imputed posterior probabilities and the inclusion of strand ambiguous SNPs increase the proportion of variance explained by a polygenic risk scores for major depression. Availability and Implementation: PRSoS is written in Apache Spark and Python and is freely available (see https://github.com/MeaneyLab/PRSoS).

Conventional polygenic scores derived from genome-wide association studies do not reflect gene networks that code for biological functions. We present an alternative approach creating a biologically-informed polygenic score based on the insulin receptor (IR) gene networks in the mesocorticolimbic system and hippocampus that regulate reward sensitivity/inhibitory control and memory, respectively. Across multiple samples (n = 4300) our biologically-informed IR-PRS score showed better prediction of child impulsivity and cognitive performance, as well as risk for early addiction onset and Alzheimer’s disease in comparison to conventional polygenic scores for ADHD, addiction and dementia. This novel, biologically-informed approach enables the use of genomic datasets to probe relevant biological processes involved in neural function and disorders. A polygenic score based on genes co-expressed with the insulin receptor predicts childhood behavior and adult disease.