Integrated model of de novo and inherited genetic variants yields greater power to identify risk genes

Genetic evidence of gender difference in autism spectrum disorder supports the female-protective effect

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a male-to-female prevalence of 4:1. However, the genetic mechanisms underlying this gender difference remain unclear. Mutation burden analysis, a TADA model, and co-expression and functional network analyses were performed on de novo mutations (DNMs) and corresponding candidate genes. We found that the prevalence of putative functional DNMs (loss-of-function and predicted deleterious missense mutations) in females was significantly higher than that in males, suggesting that a higher genetic load was required in females to reach the threshold for a diagnosis. We then prioritized 174 candidate genes, including 60 shared genes, 91 male-specific genes, and 23 female-specific genes. All of the three subclasses of candidate genes were significantly more frequently co-expressed in female brains than male brains, suggesting that compensation effects of the deficiency of ASD candidate genes may be more likely in females. Nevertheless, the three subclasses of candidate genes were co-expressed with each other, suggesting a convergent functional network of male and female-specific genes. Our analysis of different aspects of genetic components provides suggestive evidence supporting the female-protective effect in ASD. Moreover, further study is needed to integrate neuronal and hormonal data to elucidate the underlying gender difference in ASD.

Gene4Denovo: an integrated database and analytic platform for de novo mutations in humans

De novo mutations (DNMs) significantly contribute to sporadic diseases, particularly in neuropsychiatric disorders. Whole-exome sequencing (WES) and whole-genome sequencing (WGS) provide effective methods for detecting DNMs and prioritizing candidate genes. However, it remains a challenge for scientists, clinicians, and biologists to conveniently access and analyse data regarding DNMs and candidate genes from scattered publications. To fill the unmet need, we integrated 580 799 DNMs, including 30 060 coding DNMs detected by WES/WGS from 23 951 individuals across 24 phenotypes and prioritized a list of candidate genes with different degrees of statistical evidence, including 346 genes with false discovery rates <0.05. We then developed a database called Gene4Denovo (http://www.genemed.tech/gene4denovo/), which allowed these genetic data to be conveniently catalogued, searched, browsed, and analysed. In addition, Gene4Denovo integrated data from >60 genomic sources to provide comprehensive variant-level and gene-level annotation and information regarding the DNMs and candidate genes. Furthermore, Gene4Denovo provides end-users with limited bioinformatics skills to analyse their own genetic data, perform comprehensive annotation, and prioritize candidate genes using custom parameters. In conclusion, Gene4Denovo conveniently allows for the accelerated interpretation of DNM pathogenicity and the clinical implication of DNMs in humans.

A Whole-Exome Sequencing Study of Tourette Disorder in a Chinese Population

To investigate the contribution of de novo variants to Tourette disorder (TD) probands in China. Whole-exome sequencing (WES) conducted on 15 child-parent trios (45 samples) detected 25 coding de novo variants, including 2 de novo Likely Gene Disrupting (LGD) variants and 6 Missense3 variants. The de novo LGD variants were consistently associated with TD risk (Fisher's exact test OR 2.69; p = 0.1952), although statistical significance was not achieved due to the small sample size. We then assessed the relationship between the genetic events and phenotypic data by comparing Yale Global Tic Severity Scale (YGTSS) scores. The TD probands with damaging variants (defined as LGD variants and Mis3 variants) had significantly higher YGTSS scores, suggesting more severe tic symptoms (p = 0.019). We also observed a hit for a damaging compound heterozygous (CH) mutation in CELSR3, a high-confidence TD risk gene, in one of the TD probands. To our knowledge, this is the first study to investigate de novo variants in TD in a Chinese population. Our results showed that de novo LGD variants contributed to TD risk in our cohort and that TD probands with de novo damaging variants have more severe symptoms. Furthermore, our observation of damaging CH mutations in CELSR3 in an individual affected with TD further strengthened the confidence in a role for this gene in TD etiology.

VariCarta: A Comprehensive Database of Harmonized Genomic Variants Found in Autism Spectrum Disorder Sequencing Studies

Recent years have seen a boom in the application of the next-generation sequencing technology to the study of human disorders, including Autism Spectrum Disorder (ASD), where the focus has been on identifying rare, possibly causative genomic variants in ASD individuals. Because of the high genetic heterogeneity of ASD, a large number of subjects is needed to establish evidence for a variant or gene ASD-association, thus aggregating data across cohorts and studies is necessary. However, methodological inconsistencies and subject overlap across studies complicate data aggregation. Here we present VariCarta, a web-based database developed to address these challenges by collecting, reconciling, and consistently cataloging literature-derived genomic variants found in ASD subjects using ongoing semi-manual curation. The careful manual curation combined with a robust data import pipeline rectifies errors, converts variants into a standardized format, identifies and harmonizes cohort overlaps, and documents data provenance. The harmonization aspect is especially important since it prevents the potential double counting of variants, which can lead to inflation of gene-based evidence for ASD-association. The database currently contains 170,416 variant events from 10,893 subjects, collected across 61 publications, and reconciles 16,202 variants that have been reported in literature multiple times. VariCarta is freely accessible at http://varicarta.msl.ubc.ca. Autism Res 2019, 12: 1728-1736. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: The search for genetic factors underlying Autism Spectrum Disorder (ASD) yielded numerous studies reporting potentially causative genomic variants found in ASD individuals. However, methodological differences and subject overlap across studies complicate the assembly of these data, diminishing its utility and accessibility. We developed VariCarta, a web-based database that aggregates carefully curated, annotated, and harmonized literature-derived variants identified in individuals with ASD using ongoing semi-manual curation.

A comparative study of the genetic components of three subcategories of autism spectrum disorder

The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) controversially combined previously distinct subcategories of autism spectrum disorder (ASD) into a single diagnostic category. However, genetic convergences and divergences between different ASD subcategories are unclear. By retrieving 1725 exonic de novo mutations (DNMs) from 1628 subjects with autistic disorder (AD), 1873 from 1564 subjects with pervasive developmental disorder not otherwise specified (PDD-NOS), 276 from 247 subjects with Asperger's syndrome (AS), and 2077 from 2299 controls, we found that rates of putative functional DNMs (loss-of-function, predicted deleterious missense, and frameshift) in all three subcategories were significantly higher than those in control. We then investigated the convergences and divergences of the three ASD subcategories based on four genetic aspects: whether any two ASD subcategories (1) shared significantly more genes with functional DNMs, (2) exhibited similar spatio-temporal expression patterns, (3) shared significantly more candidate genes, and (4) shared some ASD-associated functional pathways. It is revealed that AD and PDD-NOS were broadly convergent in terms of all four genetic aspects, suggesting these two ASD subcategories may be genetically combined. AS was divergent to AD and PDD-NOS for aspects of functional DNMs and expression patterns, whereas AS and AD/PDD-NOS were convergent for aspects of candidate genes and functional pathways. Our results indicated that the three ASD subcategories present more genetic convergences than divergences, favouring DSM-5's new classification. This study suggests that specifically defined genotypes and their corresponding phenotypes should be integrated analyzed for precise diagnosis of complex disorders, such as ASD.

A framework for the investigation of rare genetic disorders in neuropsychiatry

De novo and inherited rare genetic disorders (RGDs) are a major cause of human morbidity, frequently involving neuropsychiatric symptoms. Recent advances in genomic technologies and data sharing have revolutionized the identification and diagnosis of RGDs, presenting an opportunity to elucidate the mechanisms underlying neuropsychiatric disorders by investigating the pathophysiology of high-penetrance genetic risk factors. Here we seek out the best path forward for achieving these goals. We think future research will require consistent approaches across multiple RGDs and developmental stages, involving both the characterization of shared neuropsychiatric dimensions in humans and the identification of neurobiological commonalities in model systems. A coordinated and concerted effort across patients, families, researchers, clinicians and institutions, including rapid and broad sharing of data, is now needed to translate these discoveries into urgently needed therapies.

A perturbed gene network containing PI3K-AKT, RAS-ERK and WNT-β-catenin pathways in leukocytes is linked to ASD genetics and symptom severity

Hundreds of genes are implicated in autism spectrum disorder (ASD) but the mechanisms through which they contribute to ASD pathophysiology remain elusive. Here, we analyzed leukocyte transcriptomics from 1–4 year-old male toddlers with ASD or typical development from the general population. We discovered a perturbed gene network that includes genes that are highly expressed during fetal brain development and which is dysregulated in hiPSC-derived neuron models of ASD. High-confidence ASD risk genes emerge as upstream regulators of the network, and many risk genes may impact the network by modulating RAS/ERK, PI3K/AKT, and WNT/β-catenin signaling pathways. We found that the degree of dysregulation in this network correlated with the severity of ASD symptoms in the toddlers. These results demonstrate how the heterogeneous genetics of ASD may dysregulate a core network to influence brain development at prenatal and very early postnatal ages and, thereby, the severity of later ASD symptoms.

Getting to the Cores of Autism

The genetic architecture of autism spectrum disorder (ASD) is itself a diverse allelic spectrum that consists of rare de novo or inherited variants in hundreds of genes and common polygenic risk at thousands of loci. ASD susceptibility genes are interconnected at the level of transcriptional and protein networks, and many function as genetic regulators of neurodevelopment or synaptic proteins that regulate neural activity. So that the core underlying neuropathologies can be further elucidated, we emphasize the importance of first defining subtypes of ASD on the basis of the phenotypic signatures of genes in model systems and humans.

Exome sequencing of 457 autism families recruited online provides evidence for autism risk genes

Autism spectrum disorder (ASD) is a genetically heterogeneous condition, caused by a combination of rare de novo and inherited variants as well as common variants in at least several hundred genes. However, significantly larger sample sizes are needed to identify the complete set of genetic risk factors. We conducted a pilot study for SPARK (SPARKForAutism.org) of 457 families with ASD, all consented online. Whole exome sequencing (WES) and genotyping data were generated for each family using DNA from saliva. We identified variants in genes and loci that are clinically recognized causes or significant contributors to ASD in 10.4% of families without previous genetic findings. In addition, we identified variants that are possibly associated with ASD in an additional 3.4% of families. A meta-analysis using the TADA framework at a false discovery rate (FDR) of 0.1 provides statistical support for 26 ASD risk genes. While most of these genes are already known ASD risk genes, BRSK2 has the strongest statistical support and reaches genome-wide significance as a risk gene for ASD (p-value = 2.3e-06). Future studies leveraging the thousands of individuals with ASD who have enrolled in SPARK are likely to further clarify the genetic risk factors associated with ASD as well as allow accelerate ASD research that incorporates genetic etiology.

Inherited and De Novo Genetic Risk for Autism Impacts Shared Networks

We performed a comprehensive assessment of rare inherited variation in autism spectrum disorder (ASD) by analyzing whole-genome sequences of 2,308 individuals from families with multiple affected children. We implicate 69 genes in ASD risk, including 24 passing genome-wide Bonferroni correction and 16 new ASD risk genes, most supported by rare inherited variants, a substantial extension of previous findings. Biological pathways enriched for genes harboring inherited variants represent cytoskeletal organization and ion transport, which are distinct from pathways implicated in previous studies. Nevertheless, the de novo and inherited genes contribute to a common protein-protein interaction network. We also identified structural variants (SVs) affecting non-coding regions, implicating recurrent deletions in the promoters of DLG2 and NR3C2. Loss of nr3c2 function in zebrafish disrupts sleep and social function, overlapping with human ASD-related phenotypes. These data support the utility of studying multiplex families in ASD and are available through the Hartwell Autism Research and Technology portal.

Lost in Translation: Traversing the Complex Path from Genomics to Therapeutics in Autism Spectrum Disorder

Recent progress in the genomics of non-syndromic autism spectrum disorder (nsASD) highlights rare, large-effect, germline, heterozygous de novo coding mutations. This distinguishes nsASD from later-onset psychiatric disorders where gene discovery efforts have predominantly yielded common alleles of small effect. These differences point to distinctive opportunities for clarifying the neurobiology of nsASD and developing novel treatments. We argue that the path ahead also presents key challenges, including distinguishing human pathophysiology from the potentially pleiotropic neurobiology mediated by established risk genes. We present our view of some of the conceptual limitations of traditional studies of model organisms, suggest a strategy focused on investigating the convergence of multiple nsASD genes, and propose that the detailed characterization of the molecular and cellular landscapes of developing human brain is essential to illuminate disease mechanisms. Finally, we address how recent advances are leading to novel strategies for therapeutics that target various points along the path from genes to behavior.

Next-Generation Sequencing in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a common disorder that causes substantial distress. Heritability studies consistently show a strong genetic contribution, raising the hope that identifying ASD-associated genetic variants will offer insights into neurobiology and ultimately therapeutics. Next-generation sequencing (NGS) enabled the identification of disruptive variants throughout protein-coding regions of the genome. Alongside large cohorts and novel statistical methods, these NGS methods revolutionized ASD gene discovery. NGS methods have also contributed substantially to functional genetic data, such as gene expression, used to understand the neurobiological consequences of disrupting these ASD-associated genes. These functional data are also critical for annotating the noncoding genome as whole-genome sequencing (WGS) begins to provide initial insights outside of protein-coding regions. NGS methods still have a major role to play, as do similarly transformative advances in stem cell and gene-editing methods, in translating genetic discoveries into a first generation of ASD therapeutics.

Integrative Analyses of De Novo Mutations Provide Deeper Biological Insights into Autism Spectrum Disorder

Recent studies have established important roles of de novo mutations (DNMs) in autism spectrum disorders (ASDs). Here, we analyze DNMs in 262 ASD probands of Japanese origin and confirm the "de novo paradigm" of ASDs across ethnicities. Based on this consistency, we combine the lists of damaging DNMs in our and published ASD cohorts (total number of trios, 4,244) and perform integrative bioinformatics analyses. Besides replicating the findings of previous studies, our analyses highlight ATP-binding genes and fetal cerebellar/striatal circuits. Analysis of individual genes identified 61 genes enriched for damaging DNMs, including ten genes for which our dataset now contributes to statistical significance. Screening of compounds altering the expression of genes hit by damaging DNMs reveals a global downregulating effect of valproic acid, a known risk factor for ASDs, whereas cardiac glycosides upregulate these genes. Collectively, our integrative approach provides deeper biological and potential medical insights into ASDs.

Targeted resequencing of 358 candidate genes for autism spectrum disorder in a Chinese cohort reveals diagnostic potential and genotype-phenotype correlations

Autism spectrum disorder (ASD) is a childhood neuropsychiatric disorder with a complex genetic architecture. The diagnostic potential of a targeted panel of ASD genes has only been evaluated in small cohorts to date and is especially understudied in the Chinese population. Here, we designed a capture panel with 358 genes (111 syndromic and 247 nonsyndromic) for ASD and sequenced a Chinese cohort of 539 cases evaluated with the Autism Diagnostic Interview‐Revised (ADI‐R) and the Autism Diagnostic Observation Schedule (ADOS) as well as 512 controls. ASD cases were found to carry significantly more ultra‐rare functional variants than controls. A subset of 78 syndromic and 54 nonsyndromic genes was the most significantly associated and should be given high priority in the future screening of ASD patients. Pathogenic and likely pathogenic variants were detected in 9.5% of cases. Variants in SHANK3 and SHANK2 were the most frequent, especially in females, and occurred in 1.2% of cases. Duplications of 15q11–13 were detected in 0.8% of cases. Variants in CNTNAP2 and MEF2C were correlated with epilepsy/tics in cases. Our findings reveal the diagnostic potential of ASD genetic panel testing and new insights regarding the variant spectrum. Genotype–phenotype correlations may facilitate the diagnosis and management of ASD.

De novo Mutations From Whole Exome Sequencing in Neurodevelopmental and Psychiatric Disorders: From Discovery to Application

Neurodevelopmental and psychiatric disorders are a highly disabling and heterogeneous group of developmental and mental disorders, resulting from complex interactions of genetic and environmental risk factors. The nature of multifactorial traits and the presence of comorbidity and polygenicity in these disorders present challenges in both disease risk identification and clinical diagnoses. The genetic component has been firmly established, but the identification of all the causative variants remains elusive. The development of next-generation sequencing, especially whole exome sequencing (WES), has greatly enriched our knowledge of the precise genetic alterations of human diseases, including brain-related disorders. In particular, the extensive usage of WES in research studies has uncovered the important contribution of de novo mutations (DNMs) to these disorders. Trio and quad familial WES are a particularly useful approach to discover DNMs. Here, we review the major WES studies in neurodevelopmental and psychiatric disorders and summarize how genes hit by discovered DNMs are shared among different disorders. Next, we discuss different integrative approaches utilized to interrogate DNMs and to identify biological pathways that may disrupt brain development and shed light on our understanding of the genetic architecture underlying these disorders. Lastly, we discuss the current state of the transition from WES research to its routine clinical application. This review will assist researchers and clinicians in the interpretation of variants obtained from WES studies, and highlights the need to develop consensus analytical protocols and validated lists of genes appropriate for clinical laboratory analysis, in order to reach the growing demands.

Mega-analysis of Odds Ratio: A Convergent Method for a Deep Understanding of the Genetic Evidence in Schizophrenia

Numerous high-throughput omics studies have been conducted in schizophrenia, providing an accumulated catalog of susceptible variants and genes. The results from these studies, however, are highly heterogeneous. The variants and genes nominated by different omics studies often have limited overlap with each other. There is thus a pressing need for integrative analysis to unify the different types of data and provide a convergent view of schizophrenia candidate genes (SZgenes). In this study, we collected a comprehensive, multidimensional dataset, including 7819 brain-expressed genes. The data hosted genome-wide association evidence in genetics (eg, genotyping data, copy number variations, de novo mutations), epigenetics, transcriptomics, and literature mining. We developed a method named mega-analysis of odds ratio (MegaOR) to prioritize SZgenes. Application of MegaOR in the multidimensional data resulted in consensus sets of SZgenes (up to 530), each enriched with dense, multidimensional evidence. We proved that these SZgenes had highly tissue-specific expression in brain and nerve and had intensive interactions that were significantly stronger than chance expectation. Furthermore, we found these SZgenes were involved in human brain development by showing strong spatiotemporal expression patterns; these characteristics were replicated in independent brain expression datasets. Finally, we found the SZgenes were enriched in critical functional gene sets involved in neuronal activities, ligand gated ion signaling, and fragile X mental retardation protein targets. In summary, MegaOR analysis reported consensus sets of SZgenes with enriched association evidence to schizophrenia, providing insights into the pathophysiology underlying schizophrenia.

Neurodevelopmental disease genes implicated by de novo mutation and copy number variation morbidity

We combined de novo mutation (DNM) data from 10,927 individuals with developmental delay and autism to identify 253 candidate neurodevelopmental disease genes with an excess of missense and/or likely gene-disruptive (LGD) mutations. Of these genes, 124 reach exome-wide significance (P < 5 × 10^-7) for DNM. Intersecting these results with copy number variation (CNV) morbidity data shows an enrichment for genomic disorder regions (30/253, likelihood ratio (LR) +1.85, P = 0.0017). We identify genes with an excess of missense DNMs overlapping deletion syndromes (for example, KIF1A and the 2q37 deletion) as well as duplication syndromes, such as recurrent MAPK3 missense mutations within the chromosome 16p11.2 duplication, recurrent CHD4 missense DNMs in the 12p13 duplication region, and recurrent WDFY4 missense DNMs in the 10q11.23 duplication region. Network analyses of genes showing an excess of DNMs highlights functional networks, including cell-specific enrichments in the D1⁺ and D2⁺ spiny neurons of the striatum.

Genomic landscapes of Chinese sporadic autism spectrum disorders revealed by whole-genome sequencing

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with considerable clinical and genetic heterogeneity. In this study, we identified all classes of genomic variants from whole-genome sequencing (WGS) dataset of 32 Chinese trios with ASD, including de novo mutations, inherited variants, copy number variants (CNVs) and genomic structural variants. A higher mutation rate (Poisson test, P < 2.2 × 10^-16) in exonic (1.37 × 10^-8) and 3'-UTR regions (1.42 × 10^-8) was revealed in comparison with that of whole genome (1.05 × 10^-8). Using an integrated model, we identified 87 potentially risk genes (P < 0.01) from 4832 genes harboring various rare deleterious variants, including CHD8 and NRXN2, implying that the disorders may be in favor to multiple-hit. In particular, frequent rare inherited mutations of several microcephaly-associated genes (ASPM, WDR62, and ZNF335) were found in ASD. In chromosomal structure analyses, we found four de novo CNVs and one de novo chromosomal rearrangement event, including a de novo duplication of UBE3A-containing region at 15q11.2-q13.1, which causes Angelman syndrome and microcephaly, and a disrupted TNR due to de novo chromosomal translocation t(1; 5)(q25.1; q33.2). Taken together, our results suggest that abnormalities of centrosomal function and chromatin remodeling of the microcephaly-associated genes may be implicated in pathogenesis of ASD. Adoption of WGS as a new yet efficient technique to illustrate the full genetic spectrum in complex disorders, such as ASD, could provide novel insights into pathogenesis, diagnosis and treatment.

De Novo Sequence and Copy Number Variants Are Strongly Associated with Tourette Disorder and Implicate Cell Polarity in Pathogenesis

We previously established the contribution of de novo damaging sequence variants to Tourette disorder (TD) through whole-exome sequencing of 511 trios. Here, we sequence an additional 291 TD trios and analyze the combined set of 802 trios. We observe an overrepresentation of de novo damaging variants in simplex, but not multiplex, families; we identify a high-confidence TD risk gene, CELSR3 (cadherin EGF LAG seven-pass G-type receptor 3); we find that the genes mutated in TD patients are enriched for those related to cell polarity, suggesting a common pathway underlying pathobiology; and we confirm a statistically significant excess of de novo copy number variants in TD. Finally, we identify significant overlap of de novo sequence variants between TD and obsessive-compulsive disorder and de novo copy number variants between TD and autism spectrum disorder, consistent with shared genetic risk.

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.

Prioritized High-Confidence Risk Genes for Intellectual Disability Reveal Molecular Convergence During Brain Development

Dissecting the genetic susceptibility to intellectual disability (ID) based on de novo mutations (DNMs) will aid our understanding of the neurobiological and genetic basis of ID. In this study, we identify 63 high-confidence ID genes with q-values < 0.1 based on four background DNM rates and coding DNM data sets from multiple sequencing cohorts. Bioinformatic annotations revealed a higher burden of these 63 ID genes in FMRP targets and CHD8 targets, and these genes show evolutionary constraint against functional genetic variation. Moreover, these ID risk genes were preferentially expressed in the cortical regions from the early fetal to late mid-fetal stages. In particular, a genome-wide weighted co-expression network analysis suggested that ID genes tightly converge onto two biological modules (M1 and M2) during human brain development. Functional annotations showed specific enrichment of chromatin modification and transcriptional regulation for M1 and synaptic function for M2, implying the divergent etiology of the two modules. In addition, we curated 12 additional strong ID risk genes whose molecular interconnectivity with known ID genes (q-values < 0.3) was greater than random. These findings further highlight the biological convergence of ID risk genes and help improve our understanding of the genetic architecture of ID.

The Psychiatric Cell Map Initiative: A Convergent Systems Biological Approach to Illuminating Key Molecular Pathways in Neuropsychiatric Disorders

Although gene discovery in neuropsychiatric disorders, including autism spectrum disorder, intellectual disability, epilepsy, schizophrenia, and Tourette disorder, has accelerated, resulting in a large number of molecular clues, it has proven difficult to generate specific hypotheses without the corresponding datasets at the protein complex and functional pathway level. Here, we describe one path forward-an initiative aimed at mapping the physical and genetic interaction networks of these conditions and then using these maps to connect the genomic data to neurobiology and, ultimately, the clinic. These efforts will include a team of geneticists, structural biologists, neurobiologists, systems biologists, and clinicians, leveraging a wide array of experimental approaches and creating a collaborative infrastructure necessary for long-term investigation. This initiative will ultimately intersect with parallel studies that focus on other diseases, as there is a significant overlap with genes implicated in cancer, infectious disease, and congenital heart defects.

Calculating the statistical significance of rare variants causal for Mendelian and complex disorders

BACKGROUND

With the expanding use of next-gen sequencing (NGS) to diagnose the thousands of rare Mendelian genetic diseases, it is critical to be able to interpret individual DNA variation. To calculate the significance of finding a rare protein-altering variant in a given gene, one must know the frequency of seeing a variant in the general population that is at least as damaging as the variant in question.

METHODS

We developed a general method to better interpret the likelihood that a rare variant is disease causing if observed in a given gene or genic region mapping to a described protein domain, using genome-wide information from a large control sample. Based on data from 2504 individuals in the 1000 Genomes Project dataset, we calculated the number of individuals who have a rare variant in a given gene for numerous filtering threshold scenarios, which may be used for calculating the significance of an observed rare variant being causal for disease. Additionally, we calculated mutational burden data on the number of individuals with rare variants in genic regions mapping to protein domains.

RESULTS

We describe methods to use the mutational burden data for calculating the significance of observing rare variants in a given proportion of sequenced individuals. We present SORVA, an implementation of these methods as a web tool, and we demonstrate application to 20 relevant but diverse next-gen sequencing studies. Specifically, we calculate the statistical significance of findings involving multi-family studies with rare Mendelian disease and a large-scale study of a complex disorder, autism spectrum disorder. If we use the frequency counts to rank genes based on intolerance for variation, the ranking correlates well with pLI scores derived from the Exome Aggregation Consortium (ExAC) dataset (ρ = 0.515), with the benefit that the scores are directly interpretable.

CONCLUSIONS

We have presented a strategy that is useful for vetting candidate genes from NGS studies and allows researchers to calculate the significance of seeing a variant in a given gene or protein domain. This approach is an important step towards developing a quantitative, statistics-based approach for presenting clinical findings.

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.

Distinct epigenomic patterns are associated with haploinsufficiency and predict risk genes of developmental disorders

Haploinsufficiency is a major mechanism of genetic risk in developmental disorders. Accurate prediction of haploinsufficient genes is essential for prioritizing and interpreting deleterious variants in genetic studies. Current methods based on mutation intolerance in population data suffer from inadequate power for genes with short transcripts. Here we show haploinsufficiency is strongly associated with epigenomic patterns, and develop a computational method (Episcore) to predict haploinsufficiency leveraging epigenomic data from a broad range of tissue and cell types by machine learning methods. Based on data from recent exome sequencing studies on developmental disorders, Episcore achieves better performance in prioritizing likely-gene-disrupting (LGD) de novo variants than current methods. We further show that Episcore is less-biased by gene size, and complementary to mutation intolerance metrics for prioritizing LGD variants. Our approach enables new applications of epigenomic data and facilitates discovery and interpretation of novel risk variants implicated in developmental disorders.

Predicting haploinsufficient genes helps to understand the genetic risk underlying developmental disorders. Here, the authors develop a Random Forest-based method that uses epigenomic data to predict haploinsufficiency, Episcore, which is complementary to methods based on mutation intolerance scores.

Convergent roles of de novo mutations and common variants in schizophrenia in tissue-specific and spatiotemporal co-expression network

Genetic components susceptible to complex disease such as schizophrenia include a wide spectrum of variants, including common variants (CVs) and de novo mutations (DNMs). Although CVs and DNMs differ by origin, it remains elusive whether and how they interact at the gene, pathway, and network levels that leads to the disease. In this work, we characterized the genes harboring schizophrenia-associated CVs (CVgenes) and the genes harboring DNMs (DNMgenes) using measures from network, tissue-specific expression profile, and spatiotemporal brain expression profile. We developed an algorithm to link the DNMgenes and CVgenes in spatiotemporal brain co-expression networks. DNMgenes tended to have central roles in the human protein-protein interaction (PPI) network, evidenced in their high degree and high betweenness values. DNMgenes and CVgenes connected with each other significantly more often than with other genes in the networks. However, only CVgenes remained significantly connected after adjusting for their degree. In our gene co-expression PPI network, we found DNMgenes and CVgenes connected in a tissue-specific fashion, and such a pattern was similar to that in GTEx brain but not in other GTEx tissues. Importantly, DNMgene-CVgene subnetworks were enriched with pathways of chromatin remodeling, MHC protein complex binding, and neurotransmitter activities. In summary, our results unveiled that both DNMgenes and CVgenes contributed to a core set of biologically important pathways and networks, and their interactions may attribute to the risk for schizophrenia. Our results also suggested a stronger biological effect of DNMgenes than CVgenes in schizophrenia.

De novo FBXO11 mutations are associated with intellectual disability and behavioural anomalies

Intellectual disability (ID) has an estimated prevalence of 1.5-2%. In most affected individuals, its genetic basis remains unclear. Whole exome sequencing (WES) studies have identified a multitude of novel causative gene defects and have shown that a large proportion of sporadic ID cases results from de novo mutations. Here, we present two unrelated individuals with similar clinical features and deleterious de novo variants in FBXO11 detected by WES. Individual 1, a 14-year-old boy, has mild ID as well as mild microcephaly, corrected cleft lip and alveolus, hyperkinetic disorder, mild brain atrophy and minor facial dysmorphism. WES detected a heterozygous de novo 1 bp insertion in the splice donor site of exon 3. Individual 2, a 3-year-old boy, showed ID and pre- and postnatal growth retardation, postnatal mild microcephaly, hyperkinetic and restless behaviour, as well as mild dysmorphism. WES detected a heterozygous de novo frameshift mutation. While ten individuals with ID and de novo variants in FBXO11 have been reported as part of larger studies, only one of the reports has some additional clinical data. Interestingly, the latter individual carries the identical mutation as our individual 2 and also displays ID, intrauterine growth retardation, microcephaly, behavioural anomalies, and dysmorphisms. Thus, we confirm deleterious de novo mutations in FBXO11 as a cause of ID and start the delineation of the associated clinical picture which may also comprise postnatal microcephaly or borderline small head size and behavioural anomalies.

AutismKB 2.0: a knowledgebase for the genetic evidence of autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with strong genetic contributions. To provide a comprehensive resource for the genetic evidence of ASD, we have updated the Autism KnowledgeBase (AutismKB) to version 2.0. AutismKB 2.0 integrates multiscale genetic data on 1379 genes, 5420 copy number variations and structural variations, 11 669 single-nucleotide variations or small insertions/deletions (SNVs/indels) and 172 linkage regions. In particular, AutismKB 2.0 highlights 5669 de novo SNVs/indels due to their significant contribution to ASD genetics and includes 789 mosaic variants due to their recently discovered contributions to ASD pathogenesis. The genes and variants are annotated extensively with genetic evidence and clinical evidence. To help users fully understand the functional consequences of SNVs and small indels, we provided comprehensive predictions of pathogenicity with iFish, SIFT, Polyphen etc. To improve user experiences, the new version incorporates multiple query methods, including simple query, advanced query and batch query. It also functionally integrates two analytical tools to help users perform downstream analyses, including a gene ranking tool and an enrichment analysis tool, KOBAS. AutismKB 2.0 is freely available and can be a valuable resource for researchers.

Brain Organoids: Expanding Our Understanding of Human Development and Disease

Stem cell-derived brain organoids replicate important stages of the prenatal human brain development and combined with the induced pluripotent stem cell (iPSC) technology offer an unprecedented model for investigating human neurological diseases including autism and microcephaly. We describe the history and birth of organoids and their application, focusing on cerebral organoids derived from embryonic stem cells and iPSCs. We discuss new insights into organoid-based model of schizophrenia and shed light on challenges and future applications of organoid-based disease model system. This review also suggests hitherto unrevealed potential applications of organoids in combining with new technologies such as nanophotonics/optogenomics for controlling brain development and atomic force microscopy for studying mechanical forces that shape the developing brain.

Integrated Bayesian analysis of rare exonic variants to identify risk genes for schizophrenia and neurodevelopmental disorders

BACKGROUND

Integrating rare variation from trio family and case-control studies has successfully implicated specific genes contributing to risk of neurodevelopmental disorders (NDDs) including autism spectrum disorders (ASD), intellectual disability (ID), developmental disorders (DDs), and epilepsy (EPI). For schizophrenia (SCZ), however, while sets of genes have been implicated through the study of rare variation, only two risk genes have been identified.

METHODS

We used hierarchical Bayesian modeling of rare-variant genetic architecture to estimate mean effect sizes and risk-gene proportions, analyzing the largest available collection of whole exome sequence data for SCZ (1,077 trios, 6,699 cases, and 13,028 controls), and data for four NDDs (ASD, ID, DD, and EPI; total 10,792 trios, and 4,058 cases and controls).

RESULTS

For SCZ, we estimate there are 1,551 risk genes. There are more risk genes and they have weaker effects than for NDDs. We provide power analyses to predict the number of risk-gene discoveries as more data become available. We confirm and augment prior risk gene and gene set enrichment results for SCZ and NDDs. In particular, we detected 98 new DD risk genes at FDR < 0.05. Correlations of risk-gene posterior probabilities are high across four NDDs (ρ>0.55), but low between SCZ and the NDDs (ρ<0.3). An in-depth analysis of 288 NDD genes shows there is highly significant protein-protein interaction (PPI) network connectivity, and functionally distinct PPI subnetworks based on pathway enrichment, single-cell RNA-seq cell types, and multi-region developmental brain RNA-seq.

CONCLUSIONS

We have extended a pipeline used in ASD studies and applied it to infer rare genetic parameters for SCZ and four NDDs ( https://github.com/hoangtn/extTADA ). We find many new DD risk genes, supported by gene set enrichment and PPI network connectivity analyses. We find greater similarity among NDDs than between NDDs and SCZ. NDD gene subnetworks are implicated in postnatally expressed presynaptic and postsynaptic genes, and for transcriptional and post-transcriptional gene regulation in prenatal neural progenitor and stem cells.

Genomic resources for the study of neuropsychiatric disorders

The National Institute of Mental Health (NIMH) has made sustained investments in the development of genomic resources over the last two decades. These investments have led to the development of the largest biorepository for psychiatric genetics as a centralized national resource. In the realm of genomic resources, NIMH has been supporting large team science (TS) consortia focused on gene discovery, fine mapping of loci, and functional genomics using state-of-the-art technologies. The scientific output from these efforts has not only begun to transform our understanding of the genetic architecture of neuropsychiatric disorders, but it has also led to a broader cultural change among the investigator community towards deeper collaborations and broad pre-publication sharing of data and resources. The NIMH supported efforts have led to a vast increase in the amount of genetic and genomic resources available to the mental health research community. Here we provide an account of the existing resources and estimates of the scale and scope of what will be available in the near future. All biosamples and data described are intended for broad sharing with researchers worldwide, as allowed by the subject consent and applicable laws.

De novo variants in EBF3 are associated with hypotonia, developmental delay, intellectual disability, and autism

Using whole-exome sequencing, we identified seven unrelated individuals with global developmental delay, hypotonia, dysmorphic facial features, and an increased frequency of short stature, ataxia, and autism with de novo heterozygous frameshift, nonsense, splice, and missense variants in the Early B-cell Transcription Factor Family Member 3 (EBF3) gene. EBF3 is a member of the collier/olfactory-1/early B-cell factor (COE) family of proteins, which are required for central nervous system (CNS) development. COE proteins are highly evolutionarily conserved and regulate neuronal specification, migration, axon guidance, and dendritogenesis during development and are essential for maintaining neuronal identity in adult neurons. Haploinsufficiency of EBF3 may affect brain development and function, resulting in developmental delay, intellectual disability, and behavioral differences observed in individuals with a deleterious variant in EBF3.

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.

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.

Bio-collections in autism research

Autism spectrum disorder (ASD) is a group of complex neurodevelopmental disorders with diverse clinical manifestations and symptoms. In the last 10 years, there have been significant advances in understanding the genetic basis for ASD, critically supported through the establishment of ASD bio-collections and application in research. Here, we summarise a selection of major ASD bio-collections and their associated findings. Collectively, these include mapping ASD candidate genes, assessing the nature and frequency of gene mutations and their association with ASD clinical subgroups, insights into related molecular pathways such as the synapses, chromatin remodelling, transcription and ASD-related brain regions. We also briefly review emerging studies on the use of induced pluripotent stem cells (iPSCs) to potentially model ASD in culture. These provide deeper insight into ASD progression during development and could generate human cell models for drug screening. Finally, we provide perspectives concerning the utilities of ASD bio-collections and limitations, and highlight considerations in setting up a new bio-collection for ASD research.

Genetic Approaches to Understanding Psychiatric Disease

Human genetic studies have been the driving force in bringing to light the underlying biology of psychiatric conditions. As these studies fill in the gaps in our knowledge of the mechanisms at play, we will be better equipped to design therapies in rational and targeted ways, or repurpose existing therapies in previously unanticipated ways. This review is intended for those unfamiliar with psychiatric genetics as a field and provides a primer on different modes of genetic variation, the technologies currently used to probe them, and concepts that provide context for interpreting the gene-phenotype relationship. Like other subfields in human genetics, psychiatric genetics is moving from microarray technology to sequencing-based approaches as barriers of cost and expertise are removed, and the ramifications of this transition are discussed here. A summary is then given of recent genetic discoveries in a number of neuropsychiatric conditions, with particular emphasis on neurodevelopmental conditions. The general impact of genetics on drug development has been to underscore the extensive etiological heterogeneity in seemingly cohesive diagnostic categories. Consequently, the path forward is not in therapies hoping to reach large swaths of patients sharing a clinically defined diagnosis, but rather in targeting patients belonging to specific "biotypes" defined through a combination of objective, quantifiable data, including genotype.

Postzygotic single-nucleotide mosaicisms contribute to the etiology of autism spectrum disorder and autistic traits and the origin of mutations

The roles and characteristics of postzygotic single‐nucleotide mosaicisms (pSNMs) in autism spectrum disorders (ASDs) remain unclear. In this study of the whole exomes of 2,361 families in the Simons Simplex Collection, we identified 1,248 putative pSNMs in children and 285 de novo SNPs in children with detectable parental mosaicism. Ultra‐deep amplicon resequencing suggested a validation rate of 51%. Analyses of validated pSNMs revealed that missense/loss‐of‐function (LoF) pSNMs with a high mutant allele fraction (MAF≥ 0.2) contributed to ASD diagnoses (P = 0.022, odds ratio [OR] = 5.25), whereas missense/LoF pSNMs with a low MAF (MAF<0.2) contributed to autistic traits in male non‐ASD siblings (P = 0.033). LoF pSNMs in parents were less likely to be transmitted to offspring than neutral pSNMs (P = 0.037), and missense/LoF pSNMs in parents with a low MAF were transmitted more to probands than to siblings (P = 0.016, OR = 1.45). We estimated that pSNMs in probands or de novo mutations inherited from parental pSNMs increased the risk of ASD by approximately 6%. Adding pSNMs into the transmission and de novo association test model revealed 13 new ASD risk genes. These results expand the existing repertoire of genes involved in ASD and shed new light on the contribution of genomic mosaicisms to ASD diagnoses and autistic traits.

De Novo Coding Variants Are Strongly Associated with Tourette Disorder

Whole-exome sequencing (WES) and de novo variant detection have proven a powerful approach to gene discovery in complex neurodevelopmental disorders. We have completed WES of 325 Tourette disorder trios from the Tourette International Collaborative Genetics cohort and a replication sample of 186 trios from the Tourette Syndrome Association International Consortium on Genetics (511 total). We observe strong and consistent evidence for the contribution of de novo likely gene-disrupting (LGD) variants (rate ratio [RR] 2.32, p = 0.002). Additionally, de novo damaging variants (LGD and probably damaging missense) are overrepresented in probands (RR 1.37, p = 0.003). We identify four likely risk genes with multiple de novo damaging variants in unrelated probands: WWC1 (WW and C2 domain containing 1), CELSR3 (Cadherin EGF LAG seven-pass G-type receptor 3), NIPBL (Nipped-B-like), and FN1 (fibronectin 1). Overall, we estimate that de novo damaging variants in approximately 400 genes contribute risk in 12% of clinical cases. VIDEO ABSTRACT.

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.

De novo mutational profile in RB1 clarified using a mutation rate modeling algorithm

BACKGROUND

Studies of de novo mutations offer great promise to improve our understanding of human disease. After a causal gene has been identified, it is natural to hypothesize that disease relevant mutations accumulate within a sub-sequence of the gene - for example, an exon, a protein domain, or at CpG sites. These assessments are typically qualitative, because we lack methodology to assess the statistical significance of sub-gene mutational burden ultimately to infer disease-relevant biology.

METHODS

To address this issue, we present a generalized algorithm to grade the significance of de novo mutational burden within a gene ascertained from affected probands, based on our model for mutation rate informed by local sequence context.

RESULTS

We applied our approach to 268 newly identified de novo germline mutations by re-sequencing the coding exons and flanking intronic regions of RB1 in 642 sporadic, bilateral probands affected with retinoblastoma (RB). We confirm enrichment of loss-of-function mutations, but demonstrate that previously noted 'hotspots' of nonsense mutations in RB1 are compatible with the elevated mutation rates expected at CpG sites, refuting a RB specific pathogenic mechanism. Our approach demonstrates an enrichment of splice-site donor mutations of exon 6 and 12 but depletion at exon 5, indicative of previously unappreciated heterogeneity in penetrance within this class of substitution. We demonstrate the enrichment of missense mutations to the pocket domain of RB1, which contains the known Arg661Trp low-penetrance mutation.

CONCLUSION

Our approach is generalizable to any phenotype, and affirms the importance of statistical interpretation of de novo mutations found in human genomes.

The Rare-Variant Generalized Disequilibrium Test for Association Analysis of Nuclear and Extended Pedigrees with Application to Alzheimer Disease WGS Data

Whole-genome and exome sequence data can be cost-effectively generated for the detection of rare-variant (RV) associations in families. Causal variants that aggregate in families usually have larger effect sizes than those found in sporadic cases, so family-based designs can be a more powerful approach than population-based designs. Moreover, some family-based designs are robust to confounding due to population admixture or substructure. We developed a RV extension of the generalized disequilibrium test (GDT) to analyze sequence data obtained from nuclear and extended families. The GDT utilizes genotype differences of all discordant relative pairs to assess associations within a family, and the RV extension combines the single-variant GDT statistic over a genomic region of interest. The RV-GDT has increased power by efficiently incorporating information beyond first-degree relatives and allows for the inclusion of covariates. Using simulated genetic data, we demonstrated that the RV-GDT method has well-controlled type I error rates, even when applied to admixed populations and populations with substructure. It is more powerful than existing family-based RV association methods, particularly for the analysis of extended pedigrees and pedigrees with missing data. We analyzed whole-genome sequence data from families affected by Alzheimer disease to illustrate the application of the RV-GDT. Given the capability of the RV-GDT to adequately control for population admixture or substructure and analyze pedigrees with missing genotype data and its superior power over other family-based methods, it is an effective tool for elucidating the involvement of RVs in the etiology of complex traits.

Anatomy and Cell Biology of Autism Spectrum Disorder: Lessons from Human Genetics

Until recently autism spectrum disorder (ASD) was regarded as a neurodevelopmental condition with unknown causes and pathogenesis. In the footsteps of the revolution of genome technologies and genetics, and with its high degree of heritability, ASD became the first neuropsychiatric disorder for which clues towards molecular and cellular pathogenesis were uncovered by genetic identification of susceptibility genes. Currently several hundreds of risk genes have been assigned, with a recurrence below 1% in the ASD population. The multitude and diversity of known ASD genes has extended the clinical notion that ASD comprises very heterogeneous conditions ranging from severe intellectual disabilities to mild high-functioning forms. The results of genetics have allowed to pinpoint a limited number of cellular and molecular processes likely involved in ASD including protein synthesis, signal transduction, transcription/chromatin remodelling and synaptic function all playing an essential role in the regulation of synaptic homeostasis during brain development. In this context, we highlight the role of protein synthesis as a key process in ASD pathogenesis as it might be central in synaptic deregulation and a potential target for intervention. These current insights should lead to a rational design of interventions in molecular and cellular pathways of ASD pathogenesis that may be applied to affected individuals in the future.

Increased burden of deleterious variants in essential genes in autism spectrum disorder

Autism spectrum disorder (ASD) is a heterogeneous, highly heritable neurodevelopmental syndrome characterized by impaired social interaction, communication, and repetitive behavior. It is estimated that hundreds of genes contribute to ASD. We asked if genes with a strong effect on survival and fitness contribute to ASD risk. Human orthologs of genes with an essential role in pre- and postnatal development in the mouse [essential genes (EGs)] are enriched for disease genes and under strong purifying selection relative to human orthologs of mouse genes with a known nonlethal phenotype [nonessential genes (NEGs)]. This intolerance to deleterious mutations, commonly observed haploinsufficiency, and the importance of EGs in development suggest a possible cumulative effect of deleterious variants in EGs on complex neurodevelopmental disorders. With a comprehensive catalog of 3,915 mammalian EGs, we provide compelling evidence for a stronger contribution of EGs to ASD risk compared with NEGs. By examining the exonic de novo and inherited variants from 1,781 ASD quartet families, we show a significantly higher burden of damaging mutations in EGs in ASD probands compared with their non-ASD siblings. The analysis of EGs in the developing brain identified clusters of coexpressed EGs implicated in ASD. Finally, we suggest a high-priority list of 29 EGs with potential ASD risk as targets for future functional and behavioral studies. Overall, we show that large-scale studies of gene function in model organisms provide a powerful approach for prioritization of genes and pathogenic variants identified by sequencing studies of human disease.

mirDNMR: a gene-centered database of background de novo mutation rates in human

De novo germline mutations (DNMs) are the rarest genetic variants proven to cause a considerable number of sporadic genetic diseases, such as autism spectrum disorders, epileptic encephalopathy, schizophrenia, congenital heart disease, type 1 diabetes, and hearing loss. However, it is difficult to accurately assess the cause of DNMs and identify disease-causing genes from the considerable number of DNMs in probands. A common method to this problem is to identify genes that harbor significantly more DNMs than expected by chance, with accurate background DNM rate (DNMR) required. Therefore, in this study, we developed a novel database named mirDNMR for the collection of gene-centered background DNMRs obtained from different methods and population variation data. The database has the following functions: (i) browse and search the background DNMRs of each gene predicted by four different methods, including GC content (DNMR-GC), sequence context (DNMR-SC), multiple factors (DNMR-MF) and local DNA methylation level (DNMR-DM); (ii) search variant frequencies in publicly available databases, including ExAC, ESP6500, UK10K, 1000G and dbSNP and (iii) investigate the DNM burden to prioritize candidate genes based on the four background DNMRs using three statistical methods (TADA, Binomial and Poisson test). As a case study, we successfully employed our database in candidate gene prioritization for a sporadic complex disease: intellectual disability. In conclusion, mirDNMR (https://www.wzgenomics.cn/mirdnmr/) can be widely used to identify the genetic basis of sporadic genetic diseases.

A systematic variant annotation approach for ranking genes associated with autism spectrum disorders

Background

The search for genetic factors underlying autism spectrum disorders (ASD) has led to the identification of hundreds of genes containing thousands of variants that differ in mode of inheritance, effect size, frequency, and function. A major challenge involves assessing the collective evidence in an unbiased, systematic manner for their functional relevance.

Methods

Here, we describe a scoring algorithm for prioritization of candidate genes based on the cumulative strength of evidence for each ASD-associated variant cataloged in AutDB (also known as SFARI Gene). We retrieved data from 889 publications to generate a dataset of 2187 rare and 711 common variants distributed across 461 genes implicated in ASD. Each individual variant was manually annotated with multiple attributes extracted from the original report, followed by score assignment using a set of standardized parameters yielding a single score for each gene.

Results

There was a wide variation in scores; SHANK3, CHD8, and ADNP had distinctly higher scores than all other genes in the dataset. Our gene scores were significantly correlated with other recently published rankings of ASD genes (R_Spearman = 0.40–0.63; p< 0.0001), providing support for our scoring algorithm.

Conclusions

This new resource, which is freely available, for the first time aggregates on one-platform variants identified from various study types (simplex, multiplex, multigenerational, and consanguineous families), from both common and rare variants, and also incorporates their putative functional consequences to arrive at a genetically and biologically driven ranking scheme. This work represents a major step in moving from simply cataloging autism variants to using data-driven approaches to gain insight into their significance.

Electronic supplementary material

The online version of this article (doi:10.1186/s13229-016-0103-y) contains supplementary material, which is available to authorized users.

De novo missense variants in PPP1CB are associated with intellectual disability and congenital heart disease

Intellectual disabilities are genetically heterogeneous and can be associated with congenital anomalies. Using whole-exome sequencing (WES), we identified five different de novo missense variants in the protein phosphatase-1 catalytic subunit beta (PPP1CB) gene in eight unrelated individuals who share an overlapping phenotype of dysmorphic features, macrocephaly, developmental delay or intellectual disability (ID), congenital heart disease, short stature, and skeletal and connective tissue abnormalities. Protein phosphatase-1 (PP1) is a serine/threonine-specific protein phosphatase involved in the dephosphorylation of a variety of proteins. The PPP1CB gene encodes a PP1 subunit that regulates the level of protein phosphorylation. All five altered amino acids we observed are highly conserved among the PP1 subunit family, and all are predicted to disrupt PP1 subunit binding and impair dephosphorylation. Our data suggest that our heterozygous de novo PPP1CB pathogenic variants are associated with syndromic intellectual disability.