Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder

Regulatory genes and pathways disrupted in autism spectrum disorders

Autism spectrum disorder (ASD) is a highly prevalent and complex genetic disorder. The complex genetic make-up of ASD has been extensively studied and both common and rare genetic variants in up to 1000 genes have been linked to increased ASD risk. While these studies highlight the genetic complexity and begin to provide a window for delineating pathways at risk in ASD, the pathogenicity and specific contribution of many mutations to the disorder are poorly understood. Defining the convergent pathways disrupted by this large number of ASD-associated genetic variants will help to understand disease pathogenesis and direct future therapeutic efforts for the groups of patients with distinct etiologies. Here, we review some of the common regulatory pathways including chromatin remodeling, transcription, and alternative splicing that have emerged as common features from genetic and transcriptomic profiling of ASD. For each category, we focus on one gene (CHD8, FOXP1, and RBFOX1) that is significantly linked to ASD and functionally characterized in recent years. Finally, we discuss genetic and transcriptomic overlap between ASD and other neurodevelopmental disorders.

Neurodevelopmental heterogeneity and computational approaches for understanding autism

In recent years, the emerging field of computational psychiatry has impelled the use of machine learning models as a means to further understand the pathogenesis of multiple clinical disorders. In this paper, we discuss how autism spectrum disorder (ASD) was and continues to be diagnosed in the context of its complex neurodevelopmental heterogeneity. We review machine learning approaches to streamline ASD's diagnostic methods, to discern similarities and differences from comorbid diagnoses, and to follow developmentally variable outcomes. Both supervised machine learning models for classification outcome and unsupervised approaches to identify new dimensions and subgroups are discussed. We provide an illustrative example of how computational analytic methods and a longitudinal design can improve our inferential ability to detect early dysfunctional behaviors that may or may not reach threshold levels for formal diagnoses. Specifically, an unsupervised machine learning approach of anomaly detection is used to illustrate how community samples may be utilized to investigate early autism risk, multidimensional features, and outcome variables. Because ASD symptoms and challenges are not static within individuals across development, computational approaches present a promising method to elucidate subgroups of etiological contributions to phenotype, alternative developmental courses, interactions with biomedical comorbidities, and to predict potential responses to therapeutic interventions.

Subphenotype meta-analysis of testicular cancer genome-wide association study data suggests a role for RBFOX family genes in cryptorchidism susceptibility

STUDY QUESTION

Can subphenotype analysis of genome-wide association study (GWAS) data from subjects with testicular germ cell tumor (TGCT) provide insight into cryptorchidism (undescended testis, UDT) susceptibility?

SUMMARY ANSWER

Suggestive intragenic GWAS signals common to UDT, TGCT case-case and TGCT case-control analyses occur in genes encoding RBFOX RNA-binding proteins (RBPs) and their neurodevelopmental targets.

WHAT IS KNOWN ALREADY

UDT is a strong risk factor for TGCT, but while genetic risk factors for TGCT are well-known, genetic susceptibility to UDT is poorly understood and appears to be more complex.

STUDY DESIGN, SIZE, DURATION

We performed a secondary subphenotype analysis of existing GWAS data from the Testicular Cancer Consortium (TECAC) and compared these results with our previously published UDT GWAS data, and with data previously acquired from studies of the fetal rat gubernaculum.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Studies from the National Cancer Institute (NCI), United Kingdom (UK) and University of Pennsylvania (Penn) that enrolled white subjects were the source of the TGCT GWAS data. We completed UDT subphenotype case-case (TGCT/UDT vs TGCT/non-UDT) and case-control (TGCT/UDT vs control), collectively referred to as 'TECAC' analyses, followed by a meta-analysis comprising 129 TGCT/UDT cases, 1771 TGCT/non-UDT cases, and 3967 unaffected controls. We reanalyzed our UDT GWAS results comprising 844 cases and 2718 controls by mapping suggestive UDT and TECAC signals (defined as P < 0.001) to genes using Ingenuity Pathway Analysis (IPA®). We compared associated pathways and enriched gene categories common to all analyses after Benjamini-Hochberg multiple testing correction, and analyzed transcript levels and protein expression using qRT-PCR and rat fetal gubernaculum confocal imaging, respectively.

MAIN RESULTS AND THE ROLE OF CHANCE

We found suggestive signals within 19 genes common to all three analyses, including RBFOX1 and RBFOX3, neurodevelopmental paralogs that encode RBPs targeting (U)GCATG-containing transcripts. Ten of the 19 genes participate in neurodevelopment and/or contribute to risk of neurodevelopmental disorders. Experimentally predicted RBFOX gene targets were strongly overrepresented among suggestive intragenic signals for the UDT (117 of 628 (19%), P = 3.5 × 10-24), TECAC case-case (129 of 711 (18%), P = 2.5 × 10-27) and TECAC case-control (117 of 679 (17%), P = 2 × 10-21) analyses, and a majority of the genes common to all three analyses (12 of 19 (63%), P = 3 × 10-9) are predicted RBFOX targets. Rbfox1, Rbfox2 and their encoded proteins are expressed in the rat fetal gubernaculum. Predicted RBFOX targets are also enriched among transcripts differentially regulated in the fetal gubernaculum during normal development (P = 3 × 10-31), in response to in vitro hormonal stimulation (P = 5 × 10-45) and in the cryptorchid LE/orl rat (P = 2 × 10-42).

LARGE SCALE DATA

GWAS data included in this study are available in the database of Genotypes and Phenotypes (dbGaP accession numbers phs000986.v1.p1 and phs001349.v1p1).

LIMITATIONS, REASONS FOR CAUTION

These GWAS data did not reach genome-wide significance for any individual analysis. UDT appears to have a complex etiology that also includes environmental factors, and such complexity may require much larger sample sizes than are currently available. The current methodology may also introduce bias that favors false discovery of larger genes.

WIDER IMPLICATIONS OF THE FINDINGS

Common suggestive intragenic GWAS signals suggest that RBFOX paralogs and other neurodevelopmental genes are potential UDT risk candidates, and potential TGCT susceptibility modifiers. Enrichment of predicted RBFOX targets among differentially expressed transcripts in the fetal gubernaculum additionally suggests a role for this RBP family in regulation of testicular descent. As RBFOX proteins regulate alternative splicing of Calca to generate calcitonin gene-related peptide, a protein linked to development and function of the gubernaculum, additional studies that address the role of these proteins in UDT are warranted.

STUDY FUNDING/COMPETING INTEREST(S)

The Eunice Kennedy Shriver National Institute for Child Health and Human Development (R01HD060769); National Center for Research Resources (P20RR20173), National Institute of General Medical Sciences (P20GM103464), Nemours Biomedical Research, the Testicular Cancer Consortium (U01CA164947), the Intramural Research Program of the NCI, a support services contract HHSN26120130003C with IMS, Inc., the Abramson Cancer Center at Penn, National Cancer Institute (CA114478), the Institute of Cancer Research, UK and the Wellcome Trust Case-Control Consortium (WTCCC) 2. None of the authors reports a conflict of interest.

Single-cell transcriptomic analysis of mouse neocortical development

The development of the mammalian cerebral cortex depends on careful orchestration of proliferation, maturation, and migration events, ultimately giving rise to a wide variety of neuronal and non-neuronal cell types. To better understand cellular and molecular processes that unfold during late corticogenesis, we perform single-cell RNA-seq on the mouse cerebral cortex at a progenitor driven phase (embryonic day 14.5) and at birth-after neurons from all six cortical layers are born. We identify numerous classes of neurons, progenitors, and glia, their proliferative, migratory, and activation states, and their relatedness within and across age. Using the cell-type-specific expression patterns of genes mutated in neurological and psychiatric diseases, we identify putative disease subtypes that associate with clinical phenotypes. Our study reveals the cellular template of a complex neurodevelopmental process, and provides a window into the cellular origins of brain diseases.

Enhanced Molecular Appreciation of Psychiatric Disorders Through High-Dimensionality Data Acquisition and Analytics

The initial diagnosis, molecular investigation, treatment, and posttreatment care of major psychiatric disorders (schizophrenia and bipolar depression) are all still significantly hindered by the current inability to define these disorders in an explicit molecular signaling manner. High-dimensionality data analytics, using large datastreams from transcriptomic, proteomic, or metabolomic investigations, will likely advance both the appreciation of the molecular nature of major psychiatric disorders and simultaneously enhance our ability to more efficiently diagnose and treat these debilitating conditions. High-dimensionality data analysis in psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results. All of these issues combine to constrain the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges through the implementation of transcriptomic, proteomic, or metabolomics signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of intelligent high-dimensionality data-based differential diagnosis in mental disease diagnosis and treatment, future research will need large, well-defined cohorts in combination with state-of-the-art technologies.

The ASD Living Biology: from cell proliferation to clinical phenotype

Autism spectrum disorder (ASD) has captured the attention of scientists, clinicians and the lay public because of its uncertain origins and striking and unexplained clinical heterogeneity. Here we review genetic, genomic, cellular, postmortem, animal model, and cell model evidence that shows ASD begins in the womb. This evidence leads to a new theory that ASD is a multistage, progressive disorder of brain development, spanning nearly all of prenatal life. ASD can begin as early as the 1st and 2nd trimester with disruption of cell proliferation and differentiation. It continues with disruption of neural migration, laminar disorganization, altered neuron maturation and neurite outgrowth, disruption of synaptogenesis and reduced neural network functioning. Among the most commonly reported high-confidence ASD (hcASD) genes, 94% express during prenatal life and affect these fetal processes in neocortex, amygdala, hippocampus, striatum and cerebellum. A majority of hcASD genes are pleiotropic, and affect proliferation/differentiation and/or synapse development. Proliferation and subsequent fetal stages can also be disrupted by maternal immune activation in the 1st trimester. Commonly implicated pathways, PI3K/AKT and RAS/ERK, are also pleiotropic and affect multiple fetal processes from proliferation through synapse and neural functional development. In different ASD individuals, variation in how and when these pleiotropic pathways are dysregulated, will lead to different, even opposing effects, producing prenatal as well as later neural and clinical heterogeneity. Thus, the pathogenesis of ASD is not set at one point in time and does not reside in one process, but rather is a cascade of prenatal pathogenic processes in the vast majority of ASD toddlers. Despite this new knowledge and theory that ASD biology begins in the womb, current research methods have not provided individualized information: What are the fetal processes and early-age molecular and cellular differences that underlie ASD in each individual child? Without such individualized knowledge, rapid advances in biological-based diagnostic, prognostic, and precision medicine treatments cannot occur. Missing, therefore, is what we call ASD Living Biology. This is a conceptual and paradigm shift towards a focus on the abnormal prenatal processes underlying ASD within each living individual. The concept emphasizes the specific need for foundational knowledge of a living child's development from abnormal prenatal beginnings to early clinical stages. The ASD Living Biology paradigm seeks this knowledge by linking genetic and in vitro prenatal molecular, cellular and neural measurements with in vivo post-natal molecular, neural and clinical presentation and progression in each ASD child. We review the first such study, which confirms the multistage fetal nature of ASD and provides the first in vitro fetal-stage explanation for in vivo early brain overgrowth. Within-child ASD Living Biology is a novel research concept we coin here that advocates the integration of in vitro prenatal and in vivo early post-natal information to generate individualized and group-level explanations, clinically useful prognoses, and precision medicine approaches that are truly beneficial for the individual infant and toddler with ASD.

An interaction-based model for neuropsychiatric features of copy-number variants

Variably expressive copy-number variants (CNVs) are characterized by extensive phenotypic heterogeneity of neuropsychiatric phenotypes. Approaches to identify single causative genes for these phenotypes within each CNV have not been successful. Here, we posit using multiple lines of evidence, including pathogenicity metrics, functional assays of model organisms, and gene expression data, that multiple genes within each CNV region are likely responsible for the observed phenotypes. We propose that candidate genes within each region likely interact with each other through shared pathways to modulate the individual gene phenotypes, emphasizing the genetic complexity of CNV-associated neuropsychiatric features.

Modeling the quantitative nature of neurodevelopmental disorders using Collaborative Cross mice

Background

Animal models for neurodevelopmental disorders (NDD) generally rely on a single genetic mutation on a fixed genetic background. Recent human genetic studies however indicate that a clinical diagnosis with ASDAutism Spectrum Disorder (ASD) is almost always associated with multiple genetic fore- and background changes. The translational value of animal model studies would be greatly enhanced if genetic insults could be studied in a more quantitative framework across genetic backgrounds.

Methods

We used the Collaborative Cross (CC), a novel mouse genetic reference population, to investigate the quantitative genetic architecture of mouse behavioral phenotypes commonly used in animal models for NDD.

Results

Classical tests of social recognition and grooming phenotypes appeared insufficient for quantitative studies due to genetic dilution and limited heritability. In contrast, digging, locomotor activity, and stereotyped exploratory patterns were characterized by continuous distribution across our CC sample and also mapped to quantitative trait loci containing genes associated with corresponding phenotypes in human populations.

Conclusions

These findings show that the CC can move animal model studies beyond comparative single gene-single background designs, and point out which type of behavioral phenotypes are most suitable to quantify the effect of developmental etiologies across multiple genetic backgrounds.

Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology

Identifying disease genes from a vast amount of genetic data is one of the most challenging tasks in the post-genomic era. Also, complex diseases present highly heterogeneous genotype, which difficult biological marker identification. Machine learning methods are widely used to identify these markers, but their performance is highly dependent upon the size and quality of available data. In this study, we demonstrated that machine learning classifiers trained on gene functional similarities, using Gene Ontology (GO), can improve the identification of genes involved in complex diseases. For this purpose, we developed a supervised machine learning methodology to predict complex disease genes. The proposed pipeline was assessed using Autism Spectrum Disorder (ASD) candidate genes. A quantitative measure of gene functional similarities was obtained by employing different semantic similarity measures. To infer the hidden functional similarities between ASD genes, various types of machine learning classifiers were built on quantitative semantic similarity matrices of ASD and non-ASD genes. The classifiers trained and tested on ASD and non-ASD gene functional similarities outperformed previously reported ASD classifiers. For example, a Random Forest (RF) classifier achieved an AUC of 0. 80 for predicting new ASD genes, which was higher than the reported classifier (0.73). Additionally, this classifier was able to predict 73 novel ASD candidate genes that were enriched for core ASD phenotypes, such as autism and obsessive-compulsive behavior. In addition, predicted genes were also enriched for ASD co-occurring conditions, including Attention Deficit Hyperactivity Disorder (ADHD). We also developed a KNIME workflow with the proposed methodology which allows users to configure and execute it without requiring machine learning and programming skills. Machine learning is an effective and reliable technique to decipher ASD mechanism by identifying novel disease genes, but this study further demonstrated that their performance can be improved by incorporating a quantitative measure of gene functional similarities. Source code and the workflow of the proposed methodology are available at https://github.com/Muh-Asif/ASD-genes-prediction.

Identification of transcription factors responsible for dysregulated networks in human osteoarthritis cartilage by global gene expression analysis

OBJECTIVE

Osteoarthritis (OA) is the most prevalent joint disease. As disease-modifying therapies are not available, novel therapeutic targets need to be discovered and prioritized for their importance in mediating the abnormal phenotype of cells in OA-affected joints. Here, we generated a genome-wide molecular profile of OA to elucidate regulatory mechanisms of OA pathogenesis and to identify possible therapeutic targets using integrative analysis of mRNA-sequencing data obtained from human knee cartilage.

DESIGN

RNA-sequencing (RNA-seq) was performed on 18 normal and 20 OA human knee cartilage tissues. RNA-seq datasets were analysed to identify genes, pathways and regulatory networks that were dysregulated in OA.

RESULTS

RNA-seq data analysis revealed 1332 differentially expressed (DE) genes between OA and non-OA samples, including known and novel transcription factors (TFs). Pathway analysis identified 15 significantly perturbed pathways in OA with ECM-related, PI3K-Akt, HIF-1, FoxO and circadian rhythm pathways being the most significantly dysregulated. We selected DE TFs that are enriched for regulating DE genes in OA and prioritized these TFs by creating a cartilage-specific interaction subnetwork. This analysis revealed eight TFs, including JUN, Early growth response (EGR)1, JUND, FOSL2, MYC, KLF4, RELA, and FOS that both target large numbers of dysregulated genes in OA and are themselves suppressed in OA.

CONCLUSIONS

We identified a novel subnetwork of dysregulated TFs that represent new mediators of abnormal gene expression and promising therapeutic targets in OA.

An integrative tissue-network approach to identify and test human disease genes

Effective discovery of causal disease genes must overcome the statistical challenges of quantitative genetics studies and the practical limitations of human biology experiments. Here we developed diseaseQUEST, an integrative approach that combines data from human genome-wide disease studies with in silico network models of tissue- and cell-type-specific function in model organisms to prioritize candidates within functionally conserved processes and pathways. We used diseaseQUEST to predict candidate genes for 25 different diseases and traits, including cancer, longevity, and neurodegenerative diseases. Focusing on Parkinson's disease (PD), a diseaseQUEST-directed Caenhorhabditis elegans behavioral screen identified several candidate genes, which we experimentally verified and found to be associated with age-dependent motility defects mirroring PD clinical symptoms. Furthermore, knockdown of the top candidate gene, bcat-1, encoding a branched chain amino acid transferase, caused spasm-like 'curling' and neurodegeneration in C. elegans, paralleling decreased BCAT1 expression in PD patient brains. diseaseQUEST is modular and generalizable to other model organisms and human diseases of interest.

Haploinsufficiency of autism spectrum disorder candidate gene NUAK1 impairs cortical development and behavior in mice

Recently, numerous rare de novo mutations have been identified in patients diagnosed with autism spectrum disorders (ASD). However, despite the predicted loss-of-function nature of some of these de novo mutations, the affected individuals are heterozygous carriers, which would suggest that most of these candidate genes are haploinsufficient and/or lead to expression of dominant-negative forms of the protein. Here, we tested this hypothesis with the candidate ASD gene Nuak1 that we previously identified for its role in the development of cortical connectivity. We report that Nuak1 is haploinsufficient in mice with regard to its function in cortical development. Furthermore Nuak1^+/− mice show a combination of abnormal behavioral traits ranging from defective spatial memory consolidation, defects in social novelty (but not social preference) and abnormal sensorimotor gating. Overall, our results demonstrate that Nuak1 haploinsufficiency leads to defects in the development of cortical connectivity and a complex array of behavorial deficits.

Nuak1 is an autism spectrum disorder candidate gene. Here the authors report behavioral and cortical development in mice heterozygous for Nuak1, suggesting loss of function mutations in one copy of Nuak1 may contribute to neurodevelopmental disorders.

Combinatorial Approach for Complex Disorder Prediction: Case Study of Neurodevelopmental Disorders

Early prediction of complex disorders (e.g., autism and other neurodevelopmental disorders) is one of the fundamental goals of precision medicine and personalized genomics. An early prediction of complex disorders can improve the prognosis, increase the effectiveness of interventions and treatments, and enhance the life quality of affected patients. Considering the genetic heritability of neurodevelopmental disorders, we are proposing a novel framework for utilizing rare coding variation for early prediction of these disorders in subset of affected samples. We provide a combinatorial framework for addressing this problem, denoted as Odin (Oracle for DIsorder predictioN), to make a prediction for a small, yet significant, subset of affected cases while having very low false positive rate (FPR) prediction for unaffected samples. Odin also takes advantage of the available functional information (e.g., pairwise coexpression of genes during brain development) to increase the prediction power beyond genes with recurrent variants. Application of our method accurately recovers an additional 8% of autism cases without any severe variant in known recurrent mutated genes with a <1% FPR. Furthermore, Odin predicted a set of 391 genes that severe variants in these genes can cause autism or other developmental delay disorders. Approaches such as the one presented in this paper are needed to translate the biomedical discoveries into actionable items by clinicians. Odin is publicly available at https://github.com/HormozdiariLab/Odin.

Cortical properties of adults with autism spectrum disorder and an IQ>100

Previous studies on cortical volume and thickness measures in autism spectrum disorders (ASD) show inconsistent results. We acquired structural magnetic resonance images of 30 individuals with ASD and individually matched controls and extracted surface-based and deformation-based morphometry measures. All participants had an IQ>100. Neither surface-based cortical thickness nor deformation based gyrification measures differed significantly across groups. Significant decreases but no increases of the gyrification index and sulcus depth could only be observed in the ASD group before correcting for multiple comparisons. This finding suggests that possible cortical anomalies in ASD are either weak or, given the heterogeneity of findings in earlier studies, might only apply to small ASD-subgroups.

De novo Mutations (DNMs) in Autism Spectrum Disorder (ASD): Pathway and Network Analysis

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder (NDD) defined by impairments in social communication and social interactions, accompanied by repetitive behavior and restricted interests. ASD is characterized by its clinical and etiological heterogeneity, which makes it difficult to elucidate the neurobiological mechanisms underlying its pathogenesis. Recently, de novo mutations (DNMs) have been recognized as strong source of genetic causality. Here, we review different aspects of the DNMs associated with ASD, including their functional annotation and classification. In addition, we also focus on the most recent advances in this area, such as the detection of PZMs (post-zygotic mutations), and we outline the main bioinformatics tools commonly employed to study these. Some of these approaches available allow DNMs to be analyzed in the context of gene networks and pathways, helping to shed light on the biological processes underlying ASD. To end this review, a brief insight into the future perspectives for genetic studies into ASD will be provided.

ASTN2 modulates synaptic strength by trafficking and degradation of surface proteins

Neurogenetic studies demonstrate that copy number variations (CNVs) in the ASTN2 gene occur in patients with neurodevelopmental disorders (NDDs), including autism spectrum. Here, we show that ASTN2 associates with recycling and degradative vesicles in cerebellar neurons, and binds to and promotes the endocytic trafficking and degradation of synaptic proteins. Overexpression of ASTN2 in neurons increases synaptic activity and reduces the levels of ASTN2 binding partners, an effect dependent on its FNIII domain, which is recurrently perturbed by CNVs in patients with NDDs. These findings suggest that ASTN2 is a key regulator of dynamic trafficking of synaptic proteins and lend support to the idea that aberrant regulation of protein homeostasis in neurons is a contributing cause of complex NDDs.

Surface protein dynamics dictate synaptic connectivity and function in neuronal circuits. ASTN2, a gene disrupted by copy number variations (CNVs) in neurodevelopmental disorders, including autism spectrum, was previously shown to regulate the surface expression of ASTN1 in glial-guided neuronal migration. Here, we demonstrate that ASTN2 binds to and regulates the surface expression of multiple synaptic proteins in postmigratory neurons by endocytosis, resulting in modulation of synaptic activity. In cerebellar Purkinje cells (PCs), by immunogold electron microscopy, ASTN2 localizes primarily to endocytic and autophagocytic vesicles in the cell soma and in subsets of dendritic spines. Overexpression of ASTN2 in PCs, but not of ASTN2 lacking the FNIII domain, recurrently disrupted by CNVs in patients, including in a family presented here, increases inhibitory and excitatory postsynaptic activity and reduces levels of ASTN2 binding partners. Our data suggest a fundamental role for ASTN2 in dynamic regulation of surface proteins by endocytic trafficking and protein degradation.

Network Analysis as a Grand Unifier in Biomedical Data Science

Biomedical data scientists study many types of networks, ranging from those formed by neurons to those created by molecular interactions. People often criticize these networks as uninterpretable diagrams termed hairballs; however, here we show that molecular biological networks can be interpreted in several straightforward ways. First, we can break down a network into smaller components, focusing on individual pathways and modules. Second, we can compute global statistics describing the network as a whole. Third, we can compare networks. These comparisons can be within the same context (e.g., between two gene regulatory networks) or cross-disciplinary (e.g., between regulatory networks and governmental hierarchies). The latter comparisons can transfer a formalism, such as that for Markov chains, from one context to another or relate our intuitions in a familiar setting (e.g., social networks) to the relatively unfamiliar molecular context. Finally, key aspects of molecular networks are dynamics and evolution, i.e., how they evolve over time and how genetic variants affect them. By studying the relationships between variants in networks, we can begin to interpret many common diseases, such as cancer and heart disease.

Prioritization and functional assessment of noncoding variants associated with complex diseases

Unraveling functional noncoding variants associated with complex diseases is still a great challenge. We present a novel algorithm, Prioritization And Functional Assessment (PAFA), that prioritizes and assesses the functionality of genetic variants by introducing population differentiation measures and recalibrating training variants. Comprehensive evaluations demonstrate that PAFA exhibits much higher sensitivity and specificity in prioritizing noncoding risk variants than existing methods. PAFA achieves improved performance in distinguishing both common and rare recurrent variants from non-recurrent variants by integrating multiple annotations and metrics. An integrated platform was developed, providing comprehensive functional annotations for noncoding variants by integrating functional genomic data, which can be accessed at http://159.226.67.237:8080/pafa .

Pervasive genetic interactions modulate neurodevelopmental defects of the autism-associated 16p11.2 deletion in Drosophila melanogaster

As opposed to syndromic CNVs caused by single genes, extensive phenotypic heterogeneity in variably-expressive CNVs complicates disease gene discovery and functional evaluation. Here, we propose a complex interaction model for pathogenicity of the autism-associated 16p11.2 deletion, where CNV genes interact with each other in conserved pathways to modulate expression of the phenotype. Using multiple quantitative methods in Drosophila RNAi lines, we identify a range of neurodevelopmental phenotypes for knockdown of individual 16p11.2 homologs in different tissues. We test 565 pairwise knockdowns in the developing eye, and identify 24 interactions between pairs of 16p11.2 homologs and 46 interactions between 16p11.2 homologs and neurodevelopmental genes that suppress or enhance cell proliferation phenotypes compared to one-hit knockdowns. These interactions within cell proliferation pathways are also enriched in a human brain-specific network, providing translational relevance in humans. Our study indicates a role for pervasive genetic interactions within CNVs towards cellular and developmental phenotypes.

The 16p11.2 deletion leads to a range of neurodevelopmental phenotypes, but to date, sequencing studies have not been able to pinpoint individual genes that are causative for the disease on their own. Here, using Drosophila homologs of 14 16p11.2 genes, the authors take a combinatorial approach to show that gene interactions contribute to a neurological phenotype.

A Statistical Framework for Mapping Risk Genes from De Novo Mutations in Whole-Genome-Sequencing Studies

Analysis of de novo mutations (DNMs) from sequencing data of nuclear families has identified risk genes for many complex diseases, including multiple neurodevelopmental and psychiatric disorders. Most of these efforts have focused on mutations in protein-coding sequences. Evidence from genome-wide association studies (GWASs) strongly suggests that variants important to human diseases often lie in non-coding regions. Extending DNM-based approaches to non-coding sequences is challenging, however, because the functional significance of non-coding mutations is difficult to predict. We propose a statistical framework for analyzing DNMs from whole-genome sequencing (WGS) data. This method, TADA-Annotations (TADA-A), is a major advance of the TADA method we developed earlier for DNM analysis in coding regions. TADA-A is able to incorporate many functional annotations such as conservation and enhancer marks, to learn from data which annotations are informative of pathogenic mutations, and to combine both coding and non-coding mutations at the gene level to detect risk genes. It also supports meta-analysis of multiple DNM studies, while adjusting for study-specific technical effects. We applied TADA-A to WGS data of ∼300 autism-affected family trios across five studies and discovered several autism risk genes. The software is freely available for all research uses.

Integrated multifactor analysis explores core dysfunctional modules in autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental disease in early childhood, and growing up to be a major cause of disability in children. However, the underlying molecular mechanism of ASD remains elusive. Hence, we represented integrated multifactor analysis exploring dysfunctional modules based on RNA-Seq data from corpus callosum in 6 patients with ASD and 6 normal individuals. According to protein-protein interactions (PPIs) and WGCNA, we performed co-expression modules analysis for ASD-associated genes, and identified 25 modules with differentially expressed genes (DEGs), observing that genes in these modules were significantly involved in various biological processes in nervous system, sensory system, phylogenetic system and variety of signaling pathways. Then, based on transcriptional and post-transcriptional regulations, integrating transcription factor (TF)-target and RNA-associated interactions, significant regulators of co-expression modules were identified as pivot regulators, including 67 pivot TFs, 13 pivot miRNAs and 6 pivot lncRNAs. GO and KEGG pathway enrichment analysis demonstrated that the pivot miRNAs significantly enriched in neural or mental-associated biological progresses. The pivot TFs were mainly involved in various regulation of transcription, immune system and organs development. Finally, our work deciphered a multifactor dysfunctional co-expression subnetwork involved in ASD, helps uncover core dysfunctional modules for this disease and improves our understanding of its underlying molecular mechanism.

A boy with developmental delay and mosaic supernumerary inv dup(5)(p15.33p15.1) leading to distal 5p tetrasomy - case report and review of the literature

Background

With only 11 patients reported, 5p tetrasomy belongs to rare postnatal findings. Most cases are due to small supernumerary marker chromosomes (sSMCs) or isochromosomes. The patients share common but unspecific symptoms such as developmental delay, seizures, ventriculomegaly, hypotonia, and fifth finger clinodactyly. Simple interstitial duplications leading to trisomies of parts of 5p are much more frequent and better described. Duplications encompassing 5p13.2 cause a defined syndrome with macrocephaly, distinct facial phenotype, heart defects, talipes equinovarus, feeding difficulties, respiratory distress and anomalies of the central nervous system, developmental delay and hypotonia.

Case presentation

We present a boy with dysmorphic features, developmental delay, intellectual disability and congenital anomalies, and a mosaic sSMC inv dup(5)(p15.33p15.1). He is the fourth and the oldest reported patient with distal 5p tetrasomy. His level of mosaicism was significantly different in lymphocytes (13.2%) and buccal cells (64.7%). The amplification in our patient is smaller than that in the three previously published patients but the only phenotype difference is the absence of seizures in our patient.

Conclusions

Our observations indicate that for the assessment of prognosis, especially with respect to intellectual functioning, the level of mosaicism could be more important than the extent of amplification and the number of extra copies. Evaluation of the phenotypical effect of rare chromosomal aberrations is challenging and each additional case is valuable for refinement of the genotype-phenotype correlation. Moreover, our patient demonstrates that if the phenotype is severe and if the level of sSMC mosaicism is low in lymphocytes, other tissues should be tested.

Electronic supplementary material

The online version of this article (10.1186/s13039-018-0377-1) contains supplementary material, which is available to authorized users.

The integrated landscape of causal genes and pathways in schizophrenia

Genome-wide association studies (GWAS) have identified more than 100 loci that show robust association with schizophrenia risk. However, due to the complexity of linkage disequilibrium and gene regulatory, it is challenging to pinpoint the causal genes at the risk loci and translate the genetic findings from GWAS into disease mechanism and clinical treatment. Here we systematically predicted the plausible candidate causal genes for schizophrenia at genome-wide level. We utilized different approaches and strategies to predict causal genes for schizophrenia, including Sherlock, SMR, DAPPLE, Prix Fixe, NetWAS, and DEPICT. By integrating the results from different prediction approaches, we identified six top candidates that represent promising causal genes for schizophrenia, including CNTN4, GATAD2A, GPM6A, MMP16, PSMA4, and TCF4. Besides, we also identified 35 additional high-confidence causal genes for schizophrenia. The identified causal genes showed distinct spatio-temporal expression patterns in developing and adult human brain. Cell-type-specific expression analysis indicated that the expression level of the predicted causal genes was significantly higher in neurons compared with oligodendrocytes and microglia (P < 0.05). We found that synaptic transmission-related genes were significantly enriched among the identified causal genes (P < 0.05), providing further support for the dysregulation of synaptic transmission in schizophrenia. Finally, we showed that the top six causal genes are dysregulated in schizophrenia cases compared with controls and knockdown of these genes impaired the proliferation of neuronal cells. Our study depicts the landscape of plausible schizophrenia causal genes for the first time. Further genetic and functional validation of these genes will provide mechanistic insights into schizophrenia pathogenesis and may facilitate to provide potential targets for future therapeutics and diagnostics.

Brain-specific functional relationship networks inform autism spectrum disorder gene prediction

Autism spectrum disorder (ASD) is a neuropsychiatric disorder with strong evidence of genetic contribution, and increased research efforts have resulted in an ever-growing list of ASD candidate genes. However, only a fraction of the hundreds of nominated ASD-related genes have identified de novo or transmitted loss of function (LOF) mutations that can be directly attributed to the disorder. For this reason, a means of prioritizing candidate genes for ASD would help filter out false-positive results and allow researchers to focus on genes that are more likely to be causative. Here we constructed a machine learning model by leveraging a brain-specific functional relationship network (FRN) of genes to produce a genome-wide ranking of ASD risk genes. We rigorously validated our gene ranking using results from two independent sequencing experiments, together representing over 5000 simplex and multiplex ASD families. Finally, through functional enrichment analysis on our highly prioritized candidate gene network, we identified a small number of pathways that are key in early neural development, providing further support for their potential role in ASD.

Aluminium in brain tissue in autism

Autism spectrum disorder is a neurodevelopmental disorder of unknown aetiology. It is suggested to involve both genetic susceptibility and environmental factors including in the latter environmental toxins. Human exposure to the environmental toxin aluminium has been linked, if tentatively, to autism spectrum disorder. Herein we have used transversely heated graphite furnace atomic absorption spectrometry to measure, for the first time, the aluminium content of brain tissue from donors with a diagnosis of autism. We have also used an aluminium-selective fluor to identify aluminium in brain tissue using fluorescence microscopy. The aluminium content of brain tissue in autism was consistently high. The mean (standard deviation) aluminium content across all 5 individuals for each lobe were 3.82(5.42), 2.30(2.00), 2.79(4.05) and 3.82(5.17) μg/g dry wt. for the occipital, frontal, temporal and parietal lobes respectively. These are some of the highest values for aluminium in human brain tissue yet recorded and one has to question why, for example, the aluminium content of the occipital lobe of a 15year old boy would be 8.74 (11.59) μg/g dry wt.? Aluminium-selective fluorescence microscopy was used to identify aluminium in brain tissue in 10 donors. While aluminium was imaged associated with neurones it appeared to be present intracellularly in microglia-like cells and other inflammatory non-neuronal cells in the meninges, vasculature, grey and white matter. The pre-eminence of intracellular aluminium associated with non-neuronal cells was a standout observation in autism brain tissue and may offer clues as to both the origin of the brain aluminium as well as a putative role in autism spectrum disorder.

Genetic analysis of very obese children with autism spectrum disorder

Autism spectrum disorder (ASD) is defined by the triad of deficits in social interactions, deficits in communication, and repetitive behaviors. Common co-morbidities in syndromic forms of ASD include intellectual disability, seizures, and obesity. We asked whether very obese children with ASD had different behavioral, physical and genetic characteristics compared to children with ASD who were not obese. We found that very obese children with ASD had significantly poorer scores on standardized behavioral tests. Very obese boys with ASD had lower full scale IQ and increased impairments with respect to stereotypies, communication and social skills. Very obese girls with ASD had increased impairments with respect to irritability and oppositional defiant behavior. We identified genetic lesions in a subset of the children with ASD and obesity and attempted to identify enriched biological pathways. Our study demonstrates the value of identifying co-morbidities in children with ASD as we move forward towards understanding the biological processes that contribute to this complex disorder and prepare to design customized treatments that target the diverse genetic lesions present in individuals with ASD.

Revealing Alzheimer's disease genes spectrum in the whole-genome by machine learning

Background

Alzheimer’s disease (AD) is an important, progressive neurodegenerative disease, with a complex genetic architecture. A key goal of biomedical research is to seek out disease risk genes, and to elucidate the function of these risk genes in the development of disease. For this purpose, expanding the AD-associated gene set is necessary. In past research, the prediction methods for AD related genes has been limited in their exploration of the target genome regions. We here present a genome-wide method for AD candidate genes predictions.

Methods

We present a machine learning approach (SVM), based upon integrating gene expression data with human brain-specific gene network data, to discover the full spectrum of AD genes across the whole genome.

Results

We classified AD candidate genes with an accuracy and the area under the receiver operating characteristic (ROC) curve of 84.56% and 94%. Our approach provides a supplement for the spectrum of AD-associated genes extracted from more than 20,000 genes in a genome wide scale.

Conclusions

In this study, we have elucidated the whole-genome spectrum of AD, using a machine learning approach. Through this method, we expect for the candidate gene catalogue to provide a more comprehensive annotation of AD for researchers.

Electronic supplementary material

The online version of this article (10.1186/s12883-017-1010-3) contains supplementary material, which is available to authorized users.

Heritable genotype contrast mining reveals novel gene associations specific to autism subgroups

Though the genetic etiology of autism is complex, our understanding can be improved by identifying genes and gene-gene interactions that contribute to the development of specific autism subtypes. Identifying such gene groupings will allow individuals to be diagnosed and treated according to their precise characteristics. To this end, we developed a method to associate gene combinations with groups with shared autism traits, targeting genetic elements that distinguish patient populations with opposing phenotypes. Our computational method prioritizes genetic variants for genome-wide association, then utilizes Frequent Pattern Mining to highlight potential interactions between variants. We introduce a novel genotype assessment metric, the Unique Inherited Combination support, which accounts for inheritance patterns observed in the nuclear family while estimating the impact of genetic variation on phenotype manifestation at the individual level. High-contrast variant combinations are tested for significant subgroup associations. We apply this method by contrasting autism subgroups defined by severe or mild manifestations of a phenotype. Significant associations connected 286 genes to the subgroups, including 193 novel autism candidates. 71 pairs of genes have joint associations with subgroups, presenting opportunities to investigate interacting functions. This study analyzed 12 autism subgroups, but our informatics method can explore other meaningful divisions of autism patients, and can further be applied to reveal precise genetic associations within other phenotypically heterogeneous disorders, such as Alzheimer's disease.

Identification of de novo germline mutations and causal genes for sporadic diseases using trio-based whole-exome/genome sequencing

Whole-genome or whole-exome sequencing (WGS/WES) of the affected proband together with normal parents (trio) is commonly adopted to identify de novo germline mutations (DNMs) underlying sporadic cases of various genetic disorders. However, our current knowledge of the occurrence and functional effects of DNMs remains limited and accurately identifying the disease-causing DNM from a group of irrelevant DNMs is complicated. Herein, we provide a general-purpose discussion of important issues related to pathogenic gene identification based on trio-based WGS/WES data. Specifically, the relevance of DNMs to human sporadic diseases, current knowledge of DNM biogenesis mechanisms, and common strategies or software tools used for DNM detection are reviewed, followed by a discussion of pathogenic gene prioritization. In addition, several key factors that may affect DNM identification accuracy and causal gene prioritization are reviewed. Based on recent major advances, this review both sheds light on how trio-based WGS/WES technologies can play a significant role in the identification of DNMs and causal genes for sporadic diseases, and also discusses existing challenges.

Ephrin-A2 regulates excitatory neuron differentiation and interneuron migration in the developing neocortex

The development of the neocortex requires co-ordination between proliferation and differentiation, as well as the precise orchestration of neuronal migration. Eph/ephrin signaling is crucial in guiding neurons and their projections during embryonic development. In adult ephrin-A2 knockout mice we consistently observed focal patches of disorganized neocortical laminar architecture, ranging in severity from reduced neuronal density to a complete lack of neurons. Loss of ephrin-A2 in the pre-optic area of the diencephalon reduced the migration of neocortex-bound interneurons from this region. Furthermore, ephrin-A2 participates in the creation of excitatory neurons by inhibiting apical progenitor proliferation in the ventricular zone, with the disruption of ephrin-A2 signaling in these cells recapitulating the abnormal neocortex observed in the knockout. The disturbance to the architecture of the neocortex observed following deletion of ephrin-A2 signaling shares many similarities with defects found in the neocortex of children diagnosed with autism spectrum disorder.

Exonic Mosaic Mutations Contribute Risk for Autism Spectrum Disorder

Genetic risk factors for autism spectrum disorder (ASD) have yet to be fully elucidated. Postzygotic mosaic mutations (PMMs) have been implicated in several neurodevelopmental disorders and overgrowth syndromes. By leveraging whole-exome sequencing data on a large family-based ASD cohort, the Simons Simplex Collection, we systematically evaluated the potential role of PMMs in autism risk. Initial re-evaluation of published single-nucleotide variant (SNV) de novo mutations showed evidence consistent with putative PMMs for 11% of mutations. We developed a robust and sensitive SNV PMM calling approach integrating complementary callers, logistic regression modeling, and additional heuristics. In our high-confidence call set, we identified 470 PMMs in children, increasing the proportion of mosaic SNVs to 22%. Probands have a significant burden of synonymous PMMs and these mutations are enriched for computationally predicted impacts on splicing. Evidence of increased missense PMM burden was not seen in the full cohort. However, missense burden signal increased in subcohorts of families where probands lacked nonsynonymous germline mutations, especially in genes intolerant to mutations. Parental mosaic mutations that were transmitted account for 6.8% of the presumed de novo mutations in the children. PMMs were identified in previously implicated high-confidence neurodevelopmental disorder risk genes, such as CHD2, CTNNB1, SCN2A, and SYNGAP1, as well as candidate risk genes with predicted functions in chromatin remodeling or neurodevelopment, including ACTL6B, BAZ2B, COL5A3, SSRP1, and UNC79. We estimate that PMMs potentially contribute risk to 3%-4% of simplex ASD case subjects and future studies of PMMs in ASD and related disorders are warranted.

Targeted sequencing and functional analysis reveal brain-size-related genes and their networks in autism spectrum disorders

Autism spectrum disorder (ASD) represents a set of complex neurodevelopmental disorders with large degrees of heritability and heterogeneity. We sequenced 136 microcephaly or macrocephaly (Mic-Mac)-related genes and 158 possible ASD-risk genes in 536 Chinese ASD probands and detected 22 damaging de novo mutations (DNMs) in 20 genes, including CHD8 and SCN2A, with recurrent events. Nine of the 20 genes were previously reported to harbor DNMs in ASD patients from other populations, while 11 of them were first identified in present study. We combined genetic variations of the 294 sequenced genes from publicly available whole-exome or whole-genome sequencing studies (4167 probands plus 1786 controls) with our Chinese population (536 cases plus 1457 controls) to optimize the power of candidate-gene prioritization. As a result, we prioritized 67 ASD-candidate genes that exhibited significantly higher probabilities of haploinsufficiency and genic intolerance, and significantly interacted and co-expressed with each another, as well as other known ASD-risk genes. Probands with DNMs or rare inherited mutations in the 67 candidate genes exhibited significantly lower intelligence quotients, supporting their strong functional impact. In addition, we prioritized 39 ASD-related Mic-Mac-risk genes, and showed their interaction and co-expression in a functional network that converged on chromatin remodeling, synapse transmission and cell cycle progression. Genes within the three functional subnetworks exhibited distinct and recognizable spatiotemporal-expression patterns in human brains and laminar-expression profiles in the developing neocortex, highlighting their important roles in brain development. Our results indicate some of Mic-Mac-risk genes are involved in ASD.

Transcriptomic Perspectives on Neocortical Structure, Development, Evolution, and Disease

The cerebral cortex is the source of our most complex cognitive capabilities and a vulnerable target of many neurological and neuropsychiatric disorders. Transcriptomics offers a new approach to understanding the cortex at the level of its underlying genetic code, and rapid technological advances have propelled this field to the high-throughput study of the complete set of transcribed genes at increasingly fine resolution to the level of individual cells. These tools have revealed features of the genetic architecture of adult cortical areas, layers, and cell types, as well as spatiotemporal patterning during development. This has allowed a fresh look at comparative anatomy as well, illustrating surprisingly large differences between mammals while at the same time revealing conservation of some features from avians to mammals. Finally, transcriptomics is fueling progress in understanding the causes of neurodevelopmental diseases such as autism, linking genetic association studies to specific molecular pathways and affected brain regions.

Exploring the heterogeneity of neural social indices for genetically distinct etiologies of autism

Background

Autism spectrum disorder (ASD) is a genetically and phenotypically heterogeneous disorder. Promising initiatives utilizing interdisciplinary characterization of ASD suggest phenotypic subtypes related to specific likely gene-disrupting mutations (LGDMs). However, the role of functionally associated LGDMs in the neural social phenotype is unknown.

Methods

In this study of 26 children with ASD (n = 13 with an LGDM) and 13 control children, we characterized patterns of mu attenuation and habituation as children watched videos containing social and nonsocial motions during electroencephalography acquisition.

Results

Diagnostic comparisons were consistent with prior work suggesting aberrant mu attenuation in ASD within the upper mu band (10–12 Hz), but typical patterns within the lower mu band (8–10 Hz). Preliminary exploration indicated distinct social sensitization patterns (i.e., increasing mu attenuation for social motion) for children with an LGDM that is primarily expressed during embryonic development. In contrast, children with an LGDM primarily expressed post-embryonic development exhibited stable typical patterns of lower mu attenuation. Neural social indices were associated with social responsiveness, but not cognition.

Conclusions

These findings suggest unique neurophysiological profiles for certain genetic etiologies of ASD, further clarifying possible genetic functional subtypes of ASD and providing insight into mechanisms for targeted treatment approaches.

Electronic supplementary material

The online version of this article (doi:10.1186/s11689-017-9199-4) contains supplementary material, which is available to authorized users.

The connectomes of males and females with autism spectrum disorder have significantly different white matter connectivity densities

Autism spectrum disorder (ASD) encompasses a set of neurodevelopmental conditions whose striking sex-related disparity (with an estimated male-to-female ratio of 4:1) remains unknown. Here we use magnetic resonance imaging (MRI) and diffusion weighted imaging (DWI) to identify the brain structure correlates of the sex-by-ASD diagnosis interaction in a carefully selected cohort of 110 ASD patients (55 females) and 83 typically-developing (TD) subjects (40 females). The interaction was found to be predicated primarily upon white matter connectivity density innervating, bilaterally, the lateral aspect of the temporal lobe, the temporo-parieto-occipital junction and the medial parietal lobe. By contrast, regional gray matter (GM) thickness and volume are not found to modulate this interaction significantly. When interpreted in the context of previous studies, our findings add considerable weight to three long-standing hypotheses according to which the sex disparity of ASD incidence is (A) due to WM connectivity rather than to GM differences, (B) modulated to a large extent by temporoparietal connectivity, and (C) accompanied by brain function differences driven by these effects. Our results contribute substantially to the task of unraveling the biological mechanisms giving rise to the sex disparity in ASD incidence, whose clinical implications are significant.

Identification of De Novo DNMT3A Mutations That Cause West Syndrome by Using Whole-Exome Sequencing

Epileptic encephalopathies (EEs) are a group of severe neurodevelopmental disorders with extreme genetic heterogeneity. Recent trio-based whole-exome sequencing (WES) studies have demonstrated that de novo mutations (DNMs) play prominent roles in severe EE. In this study, we searched for potential causal DNMs by using high-coverage WES of four unrelated Chinese parent-offspring trios affected by West syndrome. Through extensive bioinformatic analysis, we identified three novel DNMs in DNMT3A, CDKL5, and MAMDC2 in three trios and two compound heterozygous mutations in KMT2A in one trio. The DNMs in CDKL5 and DNMT3A were considered to be deleterious on the basis of the consensus of several genetic damage prediction tools. In addition, spatiotemporal expression patterns revealed a high level of DNMT3A expression during the early embryonic stage in nearly all brain regions. We also observed that certain high-confidence genes for epilepsy were shared among the co-expression and genetic interaction networks of DNMT3A, CDKL5, and KMT2A. Furthermore, all the candidate epilepsy genes in the co-expression network of DNMT3A were significantly enriched in the early developmental stages of the brain according to a rank-based enrichment test. In particular, we found that the DNMs of DNMT3A were shared among EE, autism spectrum disorder (ASD), and intellectual disability (ID) and mainly occurred in the functional domain of DNMT3A. Together, our findings support an association between DNMT3A mutations and EE susceptibility and suggest a shared molecular pathophysiology among EE and other neuropsychiatric disorders.

An Overview of Autism Spectrum Disorder, Heterogeneity and Treatment Options

Since the documented observations of Kanner in 1943, there has been great debate about the diagnoses, the sub-types, and the diagnostic threshold that relates to what is now known as autism spectrum disorder (ASD). Reflecting this complicated history, there has been continual refinement from DSM-III with 'Infantile Autism' to the current DSM-V diagnosis. The disorder is now widely accepted as a complex, pervasive, heterogeneous condition with multiple etiologies, sub-types, and developmental trajectories. Diagnosis remains based on observation of atypical behaviors, with criteria of persistent deficits in social communication and restricted and repetitive patterns of behavior. This review provides a broad overview of the history, prevalence, etiology, clinical presentation, and heterogeneity of ASD. Factors contributing to heterogeneity, including genetic variability, comorbidity, and gender are reviewed. We then explore current evidence-based pharmacological and behavioral treatments for ASD and highlight the complexities of conducting clinical trials that evaluate therapeutic efficacy in ASD populations. Finally, we discuss the potential of a new wave of research examining objective biomarkers to facilitate the evaluation of sub-typing, diagnosis, and treatment response in ASD.

A Cell Type-Specific Expression Signature Predicts Haploinsufficient Autism-Susceptibility Genes

Recent studies have identified many genes with rare de novo mutations in autism, but a limited number of these have been conclusively established as disease-susceptibility genes due to the lack of recurrence and confounding background mutations. Such extreme genetic heterogeneity severely limits recurrence-based statistical power even in studies with a large sample size. Here, we use cell-type specific expression profiles to differentiate mutations in autism patients from those in unaffected siblings. We report a gene expression signature in different neuronal cell types shared by genes with likely gene-disrupting (LGD) mutations in autism cases. This signature reflects haploinsufficiency of risk genes enriched in transcriptional and post-transcriptional regulators, with the strongest positive associations with specific types of neurons in different brain regions, including cortical neurons, cerebellar granule cells, and striatal medium spiny neurons. When used to prioritize genes with a single LGD mutation in cases, a D-score derived from the signature achieved a precision of 40% as compared with the 15% baseline with a minimal loss in sensitivity. An ensemble model combining D-score with mutation intolerance metrics from Exome Aggregation Consortium further improved the precision to 60%, resulting in 117 high-priority candidates. These prioritized lists can facilitate identification of additional autism-susceptibility genes.

Advanced paternal age effects in neurodevelopmental disorders-review of potential underlying mechanisms

Multiple epidemiological studies suggest a relationship between advanced paternal age (APA) at conception and adverse neurodevelopmental outcomes in offspring, particularly with regard to increased risk for autism and schizophrenia. Conclusive evidence about how age-related changes in paternal gametes, or age-independent behavioral traits affect neural development is still lacking. Recent evidence suggests that the origins of APA effects are likely to be multidimensional, involving both inherited predisposition and de novo events. Here we provide a review of the epidemiological and molecular findings to date. Focusing on the latter, we present the evidence for genetic and epigenetic mechanisms underpinning the association between late fatherhood and disorder in offspring. We also discuss the limitations of the APA literature. We propose that different hypotheses relating to the origins of the APA effects are not mutually exclusive. Instead, multiple mechanisms likely contribute, reflecting the etiological complexity of neurodevelopmental disorders.