Sleep and Activity Patterns in Autism Spectrum Disorder.

Background: Autism spectrum disorder (ASD) is a highly heritable and heterogeneous neurodevelopmental disorder characterized by impaired social interactions, repetitive behaviors, and a wide range of comorbidities. Between 44-83% of autistic individuals report sleep disturbances, which may share an underlying neurodevelopmental basis with ASD. Methods: We recruited 382 ASD individuals and 223 of their family members to obtain quantitative ASD-related traits and wearable device-based accelerometer data spanning three consecutive weeks. An unbiased approach identifying traits associated with ASD was achieved by applying the elastic net machine learning algorithm with five-fold cross-validation on 6,878 days of data. The relationship between sleep and physical activity traits was examined through linear mixed-effects regressions using each night of data. Results: This analysis yielded 59 out of 242 actimetry measures associated with ASD status in the training set, which were validated in a test set (AUC: 0.777). For several of these traits (e.g. total light physical activity), the day-to-day variability, in addition to the mean, was associated with ASD. Individuals with ASD were found to have a stronger correlation between physical activity and sleep, where less physical activity decreased their sleep more significantly than that of their non-ASD relatives. Conclusions: The average duration of sleep/physical activity and the variation in the average duration of sleep/physical activity strongly predict ASD status. Physical activity measures were correlated with sleep quality, traits, and regularity, with ASD individuals having stronger correlations. Interventional studies are warranted to investigate whether improvements in both sleep and increased physical activity may improve the core symptoms of ASD.

Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing.

Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

Therapeutic Potential of 5'-Methylschweinfurthin G in Merkel Cell Polyomavirus-Positive Merkel Cell Carcinoma.

Merkel cell carcinoma (MCC) is a rare but aggressive form of skin cancer predominantly caused by the human Merkel cell polyomavirus (MCPyV). Treatment for MCC includes excision and radiotherapy of local disease, and chemotherapy or immunotherapy for metastatic disease. The schweinfurthin family of natural compounds previously displayed potent and selective growth inhibitory activity against the NCI-60 panel of human-derived cancer cell lines. Here, we investigated the impact of schweinfurthin on human MCC cell lines. Treatment with the schweinfurthin analog, 5'-methylschweinfurth G (MeSG also known as TTI-3114), impaired metabolic activity through induction of an apoptotic pathway. MeSG also selectively inhibited PI3K/AKT and MAPK/ERK pathways in the MCPyV-positive MCC cell line, MS-1. Interestingly, expression of the MCPyV small T (sT) oncogene selectively sensitizes mouse embryonic fibroblasts to MeSG. These results suggest that the schweinfurthin family of compounds display promising potential as a novel therapeutic option for virus-induced MCCs.

Dysregulated Diurnal Cortisol Pattern and Heightened Night-Time Cortisol in Individuals with Bipolar Disorder.

INTRODUCTION: Hypothalamic-pituitary-adrenal (HPA) axis dysregulation may contribute to the symptom burden in bipolar disorder (BD). Further characterization of cortisol secretion is needed to improve understanding of the connection between mood, sleep, and the HPA axis. Here, we observe diurnal cortisol patterns in individuals with BD and healthy controls (HCs) to determine time points where differences may occur. METHODS: Salivary cortisol was measured at 6 time points (wake, 15, 30, and 45 min after wake, between 2:00 and 4:00 p.m. and 10:00 p.m.) for 3 consecutive days in individuals with symptomatic BD (N = 27) and HC participants (N = 31). A general linear model with correlated errors was utilized to determine if salivary cortisol changed differently throughout the day between the 2 study groups. RESULTS: A significant interaction (F = 2.74, df = 5, and p = 0.02) was observed between the time of day and the study group (BD vs. HC) when modeling salivary cortisol over time, indicating that salivary cortisol levels throughout the day significantly differed between the study groups. Specifically, salivary cortisol in BD was elevated compared to HCs at the 10:00 p.m. time point (p = 0.01). CONCLUSION: Significantly higher levels of cortisol in participants with BD in the night-time suggest that the attenuation of cortisol observed in healthy individuals may be impaired in those with BD. Reregulation of cortisol levels may be a target of further study and treatment intervention for individuals with BD.

Model-based assessment of replicability for genome-wide association meta-analysis.

Genome-wide association meta-analysis (GWAMA) is an effective approach to enlarge sample sizes and empower the discovery of novel associations between genotype and phenotype. Independent replication has been used as a gold-standard for validating genetic associations. However, as current GWAMA often seeks to aggregate all available datasets, it becomes impossible to find a large enough independent dataset to replicate new discoveries. Here we introduce a method, MAMBA (Meta-Analysis Model-based Assessment of replicability), for assessing the "posterior-probability-of-replicability" for identified associations by leveraging the strength and consistency of association signals between contributing studies. We demonstrate using simulations that MAMBA is more powerful and robust than existing methods, and produces more accurate genetic effects estimates. We apply MAMBA to a large-scale meta-analysis of addiction phenotypes with 1.2 million individuals. In addition to accurately identifying replicable common variant associations, MAMBA also pinpoints novel replicable rare variant associations from imputation-based GWAMA and hence greatly expands the set of analyzable variants.

Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits.

With the advent of Next Generation Sequencing (NGS) technologies, whole genome and whole exome DNA sequencing has become affordable for routine genetic studies. Coupled with improved genotyping arrays and genotype imputation methodologies, it is increasingly feasible to obtain rare genetic variant information in large datasets. Such datasets allow researchers to gain a more complete understanding of the genetic architecture of complex traits caused by rare variants. State-of-the-art statistical methods for the statistical genetics analysis of sequence-based association, including efficient algorithms for association analysis in biobank-scale datasets, gene-association tests, meta-analysis, fine mapping methods that integrate functional genomic dataset, and phenome-wide association studies (PheWAS), are reviewed here. These methods are expected to be highly useful for next generation statistical genetics analysis in the era of precision medicine. © 2019 by John Wiley & Sons, Inc.

Exome Chip Meta-analysis Fine Maps Causal Variants and Elucidates the Genetic Architecture of Rare Coding Variants in Smoking and Alcohol Use.

BACKGROUND: Smoking and alcohol use have been associated with common genetic variants in multiple loci. Rare variants within these loci hold promise in the identification of biological mechanisms in substance use. Exome arrays and genotype imputation can now efficiently genotype rare nonsynonymous and loss of function variants. Such variants are expected to have deleterious functional consequences and to contribute to disease risk. METHODS: We analyzed ∼250,000 rare variants from 16 independent studies genotyped with exome arrays and augmented this dataset with imputed data from the UK Biobank. Associations were tested for five phenotypes: cigarettes per day, pack-years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted stratified heritability analyses, single-variant tests, and gene-based burden tests of nonsynonymous/loss-of-function coding variants. We performed a novel fine-mapping analysis to winnow the number of putative causal variants within associated loci. RESULTS: Meta-analytic sample sizes ranged from 152,348 to 433,216, depending on the phenotype. Rare coding variation explained 1.1% to 2.2% of phenotypic variance, reflecting 11% to 18% of the total single nucleotide polymorphism heritability of these phenotypes. We identified 171 genome-wide associated loci across all phenotypes. Fine mapping identified putative causal variants with double base-pair resolution at 24 of these loci, and between three and 10 variants for 65 loci. Twenty loci contained rare coding variants in the 95% credible intervals. CONCLUSIONS: Rare coding variation significantly contributes to the heritability of smoking and alcohol use. Fine-mapping genome-wide association study loci identifies specific variants contributing to the biological etiology of substance use behavior.