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Altered sensory processing is observed in many children with autism spectrum disorder (ASD), with growing evidence that these impairments extend to the integration of information across the different senses (that is, multisensory function). The serotonin system has an important role in sensory development and function, and alterations of serotonergic signaling have been suggested to have a role in ASD. A gain-of-function coding variant in the serotonin transporter (SERT) associates with sensory aversion in humans, and when expressed in mice produces traits associated with ASD, including disruptions in social and communicative function and repetitive behaviors. The current study set out to test whether these mice also exhibit changes in multisensory function when compared with wild-type (WT) animals on the same genetic background. Mice were trained to respond to auditory and visual stimuli independently before being tested under visual, auditory and paired audiovisual (multisensory) conditions. WT mice exhibited significant gains in response accuracy under audiovisual conditions. In contrast, although the SERT mutant animals learned the auditory and visual tasks comparably to WT littermates, they failed to show behavioral gains under multisensory conditions. We believe these results provide the first behavioral evidence of multisensory deficits in a genetic mouse model related to ASD and implicate the serotonin system in multisensory processing and in the multisensory changes seen in ASD.
MOTIVATION - Analyzing genome wide association data in the context of biological pathways helps us understand how genetic variation influences phenotype and increases power to find associations. However, the utility of pathway-based analysis tools is hampered by undercuration and reliance on a distribution of signal across all of the genes in a pathway. Methods that combine genome wide association results with genetic networks to infer the key phenotype-modulating subnetworks combat these issues, but have primarily been limited to network definitions with yes/no labels for gene-gene interactions. A recent method (EW_dmGWAS) incorporates a biological network with weighted edge probability by requiring a secondary phenotype-specific expression dataset. In this article, we combine an algorithm for weighted-edge module searching and a probabilistic interaction network in order to develop a method, STAMS, for recovering modules of genes with strong associations to the phenotype and probable biologic coherence. Our method builds on EW_dmGWAS but does not require a secondary expression dataset and performs better in six test cases.
RESULTS - We show that our algorithm improves over EW_dmGWAS and standard gene-based analysis by measuring precision and recall of each method on separately identified associations. In the Wellcome Trust Rheumatoid Arthritis study, STAMS-identified modules were more enriched for separately identified associations than EW_dmGWAS (STAMS P-value 3.0 × 10; EW_dmGWAS- P-value = 0.8). We demonstrate that the area under the Precision-Recall curve is 5.9 times higher with STAMS than EW_dmGWAS run on the Wellcome Trust Type 1 Diabetes data.
AVAILABILITY AND IMPLEMENTATION - STAMS is implemented as an R package and is freely available at https://simtk.org/projects/stams CONTACT: firstname.lastname@example.orgSupplementary information: Supplementary data are available at Bioinformatics online.
© The Author 2016. Published by Oxford University Press.
Increased brain volume is a consistent finding in young children with autism spectrum disorders (ASD); however, the regional specificity and developmental course of abnormal brain structure are less clear. Small sample sizes, particularly among voxel-based morphometry (VBM) investigations, likely contribute to this difficulty. Recently established large-scale neuroimaging data repositories have helped clarify the neuroanatomy of neuropsychiatric disorders such as schizophrenia and may prove useful in ASD. Structural brain images from the Autism Brain Imaging Database Exchange (ABIDE), which contains over 1100 participants, were analyzing using DARTEL VBM to investigate total brain and tissue volumes, and regional brain structure abnormalities in ASD. Two, overlapping cohorts were analyzed; an 'All Subjects' cohort (n = 833) that included all individuals with usable MRI data, and a 'Matched Samples' cohort (n = 600) comprised of ASD and TD individuals matched, within each site, on age and sex. Total brain and grey matter volumes were enlarged by approximately 1-2 % in ASD; however, the effect reached statistical significance in only the All Subjects cohort. Within the All Subjects cohort, VBM analysis revealed enlargement of the left anterior superior temporal gyrus in ASD. No significant regional changes were detected in the Matched Samples cohort. There was a non-significant reduction in the correlation between IQ and TBV in ASD compared to TD. Brain structure abnormalities in ASD individuals age 6 and older consists of a subtle increase in total brain volume due to enlargement of grey matter with little evidence of regionally specific effects.
Rett syndrome (RS) is a neurodevelopmental disorder that shares many symptomatic and pathological commonalities with idiopathic autism. Alterations in protein synthesis-dependent synaptic plasticity (PSDSP) are a hallmark of a number of syndromic forms of autism; in the present work, we explore the consequences of disruption and rescue of PSDSP in a mouse model of RS. We report that expression of a key regulator of synaptic protein synthesis, the metabotropic glutamate receptor 5 (mGlu) protein, is significantly reduced in both the brains of RS model mice and in the motor cortex of human RS autopsy samples. Furthermore, we demonstrate that reduced mGlu expression correlates with attenuated DHPG-induced long-term depression in the hippocampus of RS model mice, and that administration of a novel mGlu positive allosteric modulator (PAM), termed VU0462807, can rescue synaptic plasticity defects. Additionally, treatment of Mecp2-deficient mice with VU0462807 improves motor performance (open-field behavior and gait dynamics), corrects repetitive clasping behavior, as well as normalizes cued fear-conditioning defects. Importantly, due to the rationale drug discovery approach used in its development, our novel mGlu PAM improves RS phenotypes and synaptic plasticity defects without evoking the overt adverse effects commonly associated with potentiation of mGlu signaling (i.e. seizures), or affecting cardiorespiratory defects in RS model mice. These findings provide strong support for the continued development of mGlu PAMs as potential therapeutic agents for use in RS, and, more broadly, for utility in idiopathic autism.
© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: email@example.com.
MOTIVATION - A major focus of current sequencing studies for human genetics is to identify rare variants associated with complex diseases. Aside from reduced power of detecting associated rare variants, controlling for population stratification is particularly challenging for rare variants. Transmission/disequilibrium tests (TDT) based on family designs are robust to population stratification and admixture, and therefore provide an effective approach to rare variant association studies to eliminate spurious associations. To increase power of rare variant association analysis, gene-based collapsing methods become standard approaches for analyzing rare variants. Existing methods that extend this strategy to rare variants in families usually combine TDT statistics at individual variants and therefore lack the flexibility of incorporating other genetic models.
RESULTS - In this study, we describe a haplotype-based framework for group-wise TDT (gTDT) that is flexible to encompass a variety of genetic models such as additive, dominant and compound heterozygous (CH) (i.e. recessive) models as well as other complex interactions. Unlike existing methods, gTDT constructs haplotypes by transmission when possible and inherently takes into account the linkage disequilibrium among variants. Through extensive simulations we showed that type I error was correctly controlled for rare variants under all models investigated, and this remained true in the presence of population stratification. Under a variety of genetic models, gTDT showed increased power compared with the single marker TDT. Application of gTDT to an autism exome sequencing data of 118 trios identified potentially interesting candidate genes with CH rare variants.
AVAILABILITY AND IMPLEMENTATION - We implemented gTDT in C++ and the source code and the detailed usage are available on the authors' website (https://medschool.vanderbilt.edu/cgg).
CONTACT - firstname.lastname@example.org or email@example.com
SUPPLEMENTARY INFORMATION - Supplementary data are available at Bioinformatics online.
© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: firstname.lastname@example.org.
Emerging evidence associates dysfunction in the dopamine (DA) transporter (DAT) with the pathophysiology of autism spectrum disorder (ASD). The human DAT (hDAT; SLC6A3) rare variant with an Ala to Val substitution at amino acid 559 (hDAT A559V) was previously reported in individuals with bipolar disorder or attention-deficit hyperactivity disorder (ADHD). We have demonstrated that this variant is hyper-phosphorylated at the amino (N)-terminal serine (Ser) residues and promotes an anomalous DA efflux phenotype. Here, we report the novel identification of hDAT A559V in two unrelated ASD subjects and provide the first mechanistic description of its impaired trafficking phenotype. DAT surface expression is dynamically regulated by DAT substrates including the psychostimulant amphetamine (AMPH), which causes hDAT trafficking away from the plasma membrane. The integrity of DAT trafficking directly impacts DA transport capacity and therefore dopaminergic neurotransmission. Here, we show that hDAT A559V is resistant to AMPH-induced cell surface redistribution. This unique trafficking phenotype is conferred by altered protein kinase C β (PKCβ) activity. Cells expressing hDAT A559V exhibit constitutively elevated PKCβ activity, inhibition of which restores the AMPH-induced hDAT A559V membrane redistribution. Mechanistically, we link the inability of hDAT A559V to traffic in response to AMPH to the phosphorylation of the five most distal DAT N-terminal Ser. Mutation of these N-terminal Ser to Ala restores AMPH-induced trafficking. Furthermore, hDAT A559V has a diminished ability to transport AMPH, and therefore lacks AMPH-induced DA efflux. Pharmacological inhibition of PKCβ or Ser to Ala substitution in the hDAT A559V background restores AMPH-induced DA efflux while promoting intracellular AMPH accumulation. Although hDAT A559V is a rare variant, it has been found in multiple probands with neuropsychiatric disorders associated with imbalances in DA neurotransmission, including ADHD, bipolar disorder, and now ASD. These findings provide valuable insight into a new cellular phenotype (altered hDAT trafficking) supporting dysregulated DA function in these disorders. They also provide a novel potential target (PKCβ) for therapeutic interventions in individuals with ASD.
MOTIVATION - The development of cost-effective next-generation sequencing methods has spurred the development of high-throughput bioinformatics tools for detection of sequence variation. With many disparate variant-calling algorithms available, investigators must ask, 'Which method is best for my data?' Machine learning research has shown that so-called ensemble methods that combine the output of multiple models can dramatically improve classifier performance. Here we describe a novel variant-calling approach based on an ensemble of variant-calling algorithms, which we term the Consensus Genotyper for Exome Sequencing (CGES). CGES uses a two-stage voting scheme among four algorithm implementations. While our ensemble method can accept variants generated by any variant-calling algorithm, we used GATK2.8, SAMtools, FreeBayes and Atlas-SNP2 in building CGES because of their performance, widespread adoption and diverse but complementary algorithms.
RESULTS - We apply CGES to 132 samples sequenced at the Hudson Alpha Institute for Biotechnology (HAIB, Huntsville, AL) using the Nimblegen Exome Capture and Illumina sequencing technology. Our sample set consisted of 40 complete trios, two families of four, one parent-child duo and two unrelated individuals. CGES yielded the fewest total variant calls (N(CGES) = 139° 897), the highest Ts/Tv ratio (3.02), the lowest Mendelian error rate across all genotypes (0.028%), the highest rediscovery rate from the Exome Variant Server (EVS; 89.3%) and 1000 Genomes (1KG; 84.1%) and the highest positive predictive value (PPV; 96.1%) for a random sample of previously validated de novo variants. We describe these and other quality control (QC) metrics from consensus data and explain how the CGES pipeline can be used to generate call sets of varying quality stringency, including consensus calls present across all four algorithms, calls that are consistent across any three out of four algorithms, calls that are consistent across any two out of four algorithms or a more liberal set of all calls made by any algorithm.
AVAILABILITY AND IMPLEMENTATION - To enable accessible, efficient and reproducible analysis, we implement CGES both as a stand-alone command line tool available for download in GitHub and as a set of Galaxy tools and workflows configured to execute on parallel computers.
SUPPLEMENTARY INFORMATION - Supplementary data are available at Bioinformatics online.
© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: email@example.com.
Autism is a heritable disorder, with over 250 associated genes identified to date, yet no single gene accounts for >1-2% of cases. The clinical presentation, behavioural symptoms, imaging and histopathology findings are strikingly heterogeneous. A more complete understanding of autism can be obtained by examining multiple genetic or behavioural mouse models of autism using magnetic resonance imaging (MRI)-based neuroanatomical phenotyping. Twenty-six different mouse models were examined and the consistently found abnormal brain regions across models were parieto-temporal lobe, cerebellar cortex, frontal lobe, hypothalamus and striatum. These models separated into three distinct clusters, two of which can be linked to the under and over-connectivity found in autism. These clusters also identified previously unknown connections between Nrxn1α, En2 and Fmr1; Nlgn3, BTBR and Slc6A4; and also between X monosomy and Mecp2. With no single treatment for autism found, clustering autism using neuroanatomy and identifying these strong connections may prove to be a crucial step in predicting treatment response.
BACKGROUND - Brain development follows a different trajectory in children with autism spectrum disorders (ASD) than in typically developing children. A proxy for neurodevelopment could be head circumference (HC), but studies assessing HC and its clinical correlates in ASD have been inconsistent. This study investigates HC and clinical correlates in the Simons Simplex Collection cohort.
METHODS - We used a mixed linear model to estimate effects of covariates and the deviation from the expected HC given parental HC (genetic deviation). After excluding individuals with incomplete data, 7225 individuals in 1891 families remained for analysis. We examined the relationship between HC/genetic deviation of HC and clinical parameters.
RESULTS - Gender, age, height, weight, genetic ancestry, and ASD status were significant predictors of HC (estimate of the ASD effect = .2 cm). HC was approximately normally distributed in probands and unaffected relatives, with only a few outliers. Genetic deviation of HC was also normally distributed, consistent with a random sampling of parental genes. Whereas larger HC than expected was associated with ASD symptom severity and regression, IQ decreased with the absolute value of the genetic deviation of HC.
CONCLUSIONS - Measured against expected values derived from covariates of ASD subjects, statistical outliers for HC were uncommon. HC is a strongly heritable trait, and population norms for HC would be far more accurate if covariates including genetic ancestry, height, and age were taken into account. The association of diminishing IQ with absolute deviation from predicted HC values suggests HC could reflect subtle underlying brain development and warrants further investigation.
© 2013 Society of Biological Psychiatry.