Association of Genetic Risks with Autism Spectrum Disorder and Early Neurodevelopmental Delays among Children without Intellectual Disability

IMPORTANCE Autism spectrum disorder (ASD) is highly heritable, and modest contributions of common genetic variants to ASD have been reported. However, the association of genetic risks derived from common risk variants with ASD traits in children from the general population is not clear, and the association of these genetic risks with neurodevelopment in infants has not been well understood. OBJECTIVE To test whether a polygenic risk score (PRS) for ASD is associated with neurodevelopmental progress at age 18 months and ASD traits at age 6 years among children from the general population. DESIGN, SETTING, AND PARTICIPANTS In this cohort study, 876 children in the Hamamatsu Birth Cohort for Mothers and Children in Hamamatsu, Japan, underwent testing for the association of an ASD PRS with neurodevelopmental progress and ASD traits. Data collection began in December 2007 and is ongoing. Data analysis was conducted from April to December 2019. MAIN OUTCOMES AND MEASURES Summary data from the largest genome-wide association study were used to generate ASD PRSs, and significance of thresholds was calculated for each outcome. The Autism Diagnostic Observation Schedule 2 was used to measure ASD traits at age 6 years, and the Mullen Scales of Early Learning was used to measure neurodevelopmental progress at age 18 months. RESULTS Of 876 participants (mean [SD] gestational age at birth, 38.9 [1.6] weeks; 438 [50.0%] boys; 868 [99.1%] Japanese), 734 were analyzed. The ASD PRS was associated with ASD traits (R2 = 0.024; β, 0.71; SE, 0.24; P = .03). The association of ASD PRS with infant neurodevelopment was most pronounced in gross motor (R2 = 0.015; β, −1.25; SE, 0.39; P = .01) and receptive language (R2 = 0.014; β, −1.19; SE, 0.39; P = .02) scores on the Mullen Scales of Early Learning. Gene set enrichment analyses found that several pathways, such as cell maturation (R2 = 0.057; β, −5.28; SE, 1.40; P \u3c .001) and adenylyl cyclase activity and cyclic adenosine monophosphate concentration (R2 = 0.064; β, −5.30; SE 1.30; P \u3c .001), were associated with ASD traits. Gene sets associated with inflammation were commonly enriched with ASD traits and gross motor skills (eg, chemokine motif ligand 2 production: R2 = 0.051; β, −6.04; SE, 1.75; P = .001; regulation of monocyte differentiation: R2 = 0.052; β, −6.63; SE, 1.90; P = .001; and B-cell differentiation: R2 = 0.051; β, 7.37; SE, 2.15; P = .001); glutamatergic signaling–associated gene sets were commonly enriched with ASD traits and receptive language skills (eg, regulation of glutamate secretion: R2 = 0.052; β, −5.82; SE, 1.68; P = .001; ionotropic glutamate receptor signaling pathway: R2 = 0.047; β, 3.54; SE, 1.09; P = .001; and negative regulation of glutamate secretion: R2 = 0.045; β, −5.38; SE, 1.74; P = .002). CONCLUSIONS AND RELEVANCE In this study, the ASD PRS was associated with ASD traits among children from the general population. Genetic risks for ASD might be associated with delays in some neurodevelopmental domains, such as gross motor and receptive language skills

Polygenic Risk Score Analysis Revealed Shared Genetic Background in Attention Deficit Hyperactivity Disorder and Narcolepsy

Attention deficit hyperactive disorder (ADHD) is a highly heritable neurodevelopmental disorder, and excessive daytime sleepiness is frequently observed in ADHD patients. Excessive daytime sleepiness is also a core symptom of narcolepsy and essential hypersomnia (EHS), which are also heritable conditions. Psychostimulants are effective for the symptomatic control of ADHD (primary recommended intervention) and the two sleep disorders (frequent off-label use). However, the common biological mechanism for these disorders has not been well understood. Using a previously collected genome-wide association study of narcolepsy and EHS, we calculated polygenic risk scores (PRS) for each individual. We investigated a possible genetic association between ADHD and narcolepsy traits in the Hamamatsu Birth Cohort for mothers and children (HBC study) (n=876). Gene-set enrichment analyses were used to identify common pathways underlying these disorders. Narcolepsy PRS were significantly associated with ADHD traits both in the hyperactivity domain (e.g.,P-value threshold \u3c 0.05,β[SE], 5.815 [1.774];P=0.002) and inattention domain(e.g.,P-value threshold \u3c 0.05,β[SE], 5.734 [1.761];P=0.004). However, EHS PRS was not significantly associated with either domain of ADHD traits. Gene-set enrichment analyses revealed that pathways related to dopaminergic signaling, immune systems, iron metabolism, and glial cell function involved in both ADHD and narcolepsy. Findings indicate that ADHD and narcolepsy are genetically related, and there are possible common underlying biological mechanisms for this relationship. Future studies replicating these findings would be warranted to elucidate the genetic vulnerability for daytime sleepiness in individuals with ADHD

Neural mechanisms underlying rule selection based on response evaluation: a near-infrared spectroscopy study

Abstract The ability of humans to use rules for organizing action demands a high level of executive control. Situational complexity mediates rule selection, from the adoption of a given rule to the selection of complex rules to achieve an appropriate response. Several rules have been proposed to be superordinate to human behavior in a cognitive hierarchy and mediated by different brain regions. In the present study, using a novel rule-selection task based on pre-response evaluations that require several cognitive operations, we examined whether the task is mediated by a specific region of the prefrontal cortex using near-infrared spectroscopy. We showed that the selection of rules, including prior evaluation of a stimulus, activates broader areas of the prefrontal and premotor regions than response selection based on a given rule. The results are discussed in terms of hierarchical cognitive models, the functional specialization of multiple-cognitive operations in the prefrontal cortex, and their contribution to a novel cognitive task

Determinants and Projections of Minimum Acceptable Diet among Children Aged 6–23 Months: A National and Subnational Inequality Assessment in Bangladesh

Subnational evidence on the level of inequality in receiving complementary feeding practice among Bangladeshi children is lacking. This study estimated inequality in the minimum acceptable diet (MAD) among Bangladeshi children aged 6–23 months, and identified risk factors for and developed projections of the MAD up to 2030. Data from the Bangladesh Demographic and Health Survey 2017–2018 were used in this cross-sectional study. Regression-based slope (SII) and relative index of inequality (RII) were used to quantify the level of absolute and relative inequality, respectively. A Bayesian logistic regression model was used to identify the potential determinants of a MAD and project prevalence up to 2030. About 38% of children aged 6–23 months received a MAD. The national prevalence of a MAD was 26.0 percentage points higher among children from the richest compared to the poorest households, and 32.1 percentage points higher among children of higher-educated over illiterate mothers. Socioeconomic inequality was found to be the highest in the Chattogram division (SII: 43.9), while education-based inequality was highest in the Sylhet division (SII: 47.7). Maternal employment and the number of ANC visits were also identified as significant determinants of a MAD, and the prevalence of a MAD was projected to increase from 42.5% in 2020 to 67.9% in 2030. Approximately two out of five children received a MAD in Bangladesh and significant socioeconomic and education-based inequalities in the MAD were observed. Subnational variation in socioeconomic and education-based inequalities in the MAD requires further public health attention, and poverty reduction programs need to be strengthened

HIV LTR-driven antisense RNA by itself has regulatory function and may curtail virus reactivation from latency

Latently infected T lymphocytes are an important barrier toward eliminating a persistent HIV infection. Here we describe an HIV-based recombinant fluorescent-lentivirus referred to as “rfl-HIV” that enables to analyze sense and antisense transcription by means of fluorescence reporter genes. This model virus exhibited similar transcriptional and functional properties of the antisense transcript as observed with a wild type HIV, and largely facilitated the generation of latently-infected T cells clones. We show that latently-infected cells can be divided into two types, those with and those without antisense transcription. Upon addition of latency reversal agents, only the cells that lack antisense transcripts are readily reactivated to transcribe HIV. Thus, antisense transcripts may exhibit a dominant suppressor activity and can lock an integrated provirus into a non-reactivatable state. These findings could have important implications for the development of strategies to eradicate HIV from infected individuals.This work was supported by grants from Japan Society for the Promotion of Science (JSPS KAKENHI #15H06877 for MK-I, #JP17K08800 for KT), ViiV Healthcare Japan Research Grant 2015 (MK-I), Grants-in-Aid from the Ministry of Health, Labour and Welfare (H24-AIDS-008 to YT-Y) and Japan Agency for Medical Research and Development (AMED #JP17fk0410305h0103 to YT-Y and #JP18fk0410003 to KT). MK-I received Fellowships from Japan Foundation for AIDS Prevention and JSPS Oversea Research Fellow Program. AM and JM were supported by a grant from the Spanish Ministry of Economy, Industry and Competitiveness and FEDER grant no. SAF2016-75505-R (AEI/MINEICO/FEDER, UE) and through the “María de Maeztu” Program for Units of Excellence in R&D (MDM-2014-0370)

Amyloid-β prediction machine learning model using source-based morphometry across neurocognitive disorders

Abstract Previous studies have developed and explored magnetic resonance imaging (MRI)-based machine learning models for predicting Alzheimer’s disease (AD). However, limited research has focused on models incorporating diverse patient populations. This study aimed to build a clinically useful prediction model for amyloid-beta (Aβ) deposition using source-based morphometry, using a data-driven algorithm based on independent component analyses. Additionally, we assessed how the predictive accuracies varied with the feature combinations. Data from 118 participants clinically diagnosed with various conditions such as AD, mild cognitive impairment, frontotemporal lobar degeneration, corticobasal syndrome, progressive supranuclear palsy, and psychiatric disorders, as well as healthy controls were used for the development of the model. We used structural MR images, cognitive test results, and apolipoprotein E status for feature selection. Three-dimensional T1-weighted images were preprocessed into voxel-based gray matter images and then subjected to source-based morphometry. We used a support vector machine as a classifier. We applied SHapley Additive exPlanations, a game-theoretical approach, to ensure model accountability. The final model that was based on MR-images, cognitive test results, and apolipoprotein E status yielded 89.8% accuracy and a receiver operating characteristic curve of 0.888. The model based on MR-images alone showed 84.7% accuracy. Aβ-positivity was correctly detected in non-AD patients. One of the seven independent components derived from source-based morphometry was considered to represent an AD-related gray matter volume pattern and showed the strongest impact on the model output. Aβ-positivity across neurological and psychiatric disorders was predicted with moderate-to-high accuracy and was associated with a probable AD-related gray matter volume pattern. An MRI-based data-driven machine learning approach can be beneficial as a diagnostic aid