Brain-expressed exons under purifying selection are enriched for de novo mutations in autism spectrum disorder

Application of multiple mosaic callers improves post-zygotic mutation detection from exome sequencing data

PURPOSE

The gold standard for identification of post-zygotic variants (PZVs) is droplet digital polymerase chain reaction or high-depth sequencing across multiple tissues types. These approaches are yet to be systematically implemented for monogenic disorders. We developed PZV detection pipelines for correct classification of de novo variants.

METHOD

Our pipelines detect PZV in parents (gonosomal mosaicism [pGoM]) and children (somatic mosaicism, "M3"). We applied them to research exome sequencing (ES) data from the Australian Cerebral Palsy Biobank (n = 145 trios) and Simons Simplex Collection (n = 405 families). Candidate mosaic variants were validated using deep amplicon sequencing or droplet digital polymerase chain reaction.

RESULTS

69.2% (M3trio), 63.9% (M3single), and 92.7% (pGoM) of detected variants were validated, with 48.6%, 56.7%, and 26.2% of variants, respectively, meeting strict criteria for mosaicism. In the Australian Cerebral Palsy Biobank, 16.6% of probands and 20.7% of parents had at least 1 true-positive somatic or pGoM variant, respectively. A large proportion of PZVs detected in Simons Simplex Collection parents (79.8%) and child (94.5%) were not previously reported. We reclassified 3.7% to 8.0% of germline de novo variants as mosaic.

CONCLUSION

Many PZVs were incorrectly classified as germline variants or missed by previous approaches. Systematic application of our pipelines could increase genetic diagnostic rate, improve estimates of recurrence risk in families, and benefit novel disease gene identification.

AIDA-1/ANKS1B Binds to the SynGAP Family RasGAPs with High Affinity and Specificity

AIDA-1, encoded by ANKS1B, is an abundant postsynaptic scaffold protein essential for brain development. Mutations of ANKS1B are closely associated with various psychiatric disorders. However, very little is known regarding the molecular mechanisms underlying AIDA-1's involvements under physiological and pathophysiological conditions. Here, we discovered an interaction between AIDA-1 and the SynGAP family Ras-GTPase activating protein (GAP) via affinity purification using AIDA-1d as the bait. Biochemical studies showed that the PTB domain of AIDA-1 binds to an extended NPx[F/Y]-motif of the SynGAP family proteins with high affinities. The high-resolution crystal structure of AIDA-1 PTB domain in complex with the SynGAP NPxF-motif revealed the molecular mechanism governing the specific interaction between AIDA-1 and SynGAP. Our study not only explains why patients with ANKS1B or SYNGAP1 mutations share overlapping clinical phenotypes, but also allows identification of new AIDA-1 binding targets such as Ras and Rab interactors.

Expanding deep phenotypic spectrum associated with atypical pathogenic structural variations overlapping 15q11-q13 imprinting region

BACKGROUND

The 15q11-q13 region is a genetic locus with genes subject to genomic imprinting, significantly influencing neurodevelopment. Genomic imprinting is an epigenetic phenomenon that causes differential gene expression based on the parent of origin. In most diploid organisms, gene expression typically involves an equal contribution from both maternal and paternal alleles, shaping the phenotype. Nevertheless, in mammals, including humans, mice, and marsupials, the functional equivalence of parental alleles is not universally maintained. Notably, during male and female gametogenesis, parental alleles may undergo differential marking or imprinting, thereby modifying gene expression without altering the underlying DNA sequence. Neurodevelopmental disorders, such as Prader-Willi syndrome (PWS) (resulting from the absence of paternally expressed genes in this region), Angelman syndrome (AS) (associated with the absence of the maternally expressed UBE3A gene), and 15q11-q13 duplication syndrome (resulting from the two common forms of duplications-either an extra isodicentric 15 chromosome or an interstitial 15 duplication), are the outcomes of genetic variations in this imprinting region.

METHODS

Conducted a genomic study to identify the frequency of pathogenic variants impacting the 15q11-q13 region in an ethnically homogenous population from Bangladesh. Screened all known disorders from the DECIPHER database and identified variant enrichment within this cohort. Using the Horizon analysis platform, performed enrichment analysis, requiring at least >60% overlap between a copy number variation and a disorder breakpoint. Deep clinical phenotyping was carried out through multiple examination sessions to evaluate a range of clinical symptoms.

RESULTS

This study included eight individuals with clinically suspected PWS/AS, all previously confirmed through chromosomal microarray analysis, which revealed chromosomal breakpoints within the 15q11-q13 region. Among this cohort, six cases (75%) exhibited variable lengths of deletions, whereas two cases (25%) showed duplications. These included one type 2 duplication, one larger atypical duplication, one shorter type 2 deletion, one larger type 1 deletion, and four cases with atypical deletions. Furthermore, thorough clinical assessments led to the diagnosis of four PWS patients, two AS patients, and two individuals with 15q11-q13 duplication syndrome.

CONCLUSION

Our deep phenotypic observations identified a spectrum of clinical features that overlap and are unique to PWS, AS, and Dup15q syndromes. Our findings establish genotype-phenotype correlation for patients impacted by variable structural variations within the 15q11-q13 region.

Genetic Alterations in a Large Population of Italian Patients Affected by Neurodevelopmental Disorders

Neurodevelopmental disorders are a group of complex multifactorial disorders characterized by cognitive impairment, communication deficits, abnormal behaviour, and/or motor skills resulting from abnormal neural development. Copy number variants (CNVs) are genetic alterations often associated with neurodevelopmental disorders. We evaluated the diagnostic efficacy of the array-comparative genomic hybridization (a-CGH) method and its relevance as a routine diagnostic test in patients with neurodevelopmental disorders for the identification of the molecular alterations underlying or contributing to the clinical manifestations. In the present study, we analysed 1800 subjects with neurodevelopmental disorders using a CGH microarray. We identified 208 (7%) pathogenetic CNVs, 2202 (78%) variants of uncertain significance (VOUS), and 504 (18%) benign CNVs in the 1800 patients analysed. Some alterations contain genes potentially related to neurodevelopmental disorders including CHRNA7, ANKS1B, ANKRD11, RBFOX1, ASTN2, GABRG3, SHANK2, KIF1A SETBP1, SNTG2, CTNNA2, TOP3B, CNTN4, CNTN5, and CNTN6. The identification of interesting significant genes related to neurological disorders with a-CGH is therefore an essential step in the diagnostic procedure, allowing a better understanding of both the pathophysiology of these disorders and the mechanisms underlying their clinical manifestations.

Reanalysis of Trio Whole-Genome Sequencing Data Doubles the Yield in Autism Spectrum Disorder: De Novo Variants Present in Half

Autism spectrum disorder (ASD) is a common condition with lifelong implications. The last decade has seen dramatic improvements in DNA sequencing and related bioinformatics and databases. We analyzed the raw DNA sequencing files on the Variantyx® bioinformatics platform for the last 50 ASD patients evaluated with trio whole-genome sequencing (trio-WGS). "Qualified" variants were defined as coding, rare, and evolutionarily conserved. Primary Diagnostic Variants (PDV), additionally, were present in genes directly linked to ASD and matched clinical correlation. A PDV was identified in 34/50 (68%) of cases, including 25 (50%) cases with heterozygous de novo and 10 (20%) with inherited variants. De novo variants in genes directly associated with ASD were far more likely to be Qualifying than non-Qualifying versus a control group of genes (p = 0.0002), validating that most are indeed disease related. Sequence reanalysis increased diagnostic yield from 28% to 68%, mostly through inclusion of de novo PDVs in genes not yet reported as ASD associated. Thirty-three subjects (66%) had treatment recommendation(s) based on DNA analyses. Our results demonstrate a high yield of trio-WGS for revealing molecular diagnoses in ASD, which is greatly enhanced by reanalyzing DNA sequencing files. In contrast to previous reports, de novo variants dominate the findings, mostly representing novel conditions. This has implications to the cause and rising prevalence of autism.

[A perspective on the application of CRISPR/CAS9 genome editing system to study of molecular-genetic basis of mental disorders]

Schizophrenia, depression, bipolar disorder and autism spectrum disorders are common mental disorders that are among the leading causes of disability worldwide. The major complication to effective therapies for mental disorders is the poor understanding of their pathogenic mechanisms. Currently, an increasing number of research groups are focusing on uncovering the molecular mechanisms of mental disorders and developing novel therapies using the CRISPR/Cas9 (Clustered, Regularly Interspaced, Short Palindromic Repeats (CRISPR) - CRISPR-associated system 9 (Cas9)) system to determine the molecular mechanisms of developing mental disorders and novel therapy. The CRISPR/Cas9 system is the most promising among genome editing tools. Numerous advantages of the CRISPR/Cas9 system and its successful application in some studies provide wide opportunities for genome therapy and regeneration medicine. In this review we shortly describe structure and function of the CRISPR/Cas9 system and its application to study the molecular-genetic basis of mental disorders in human.

Identification of Neurotransmission and Synaptic Biological Processes Disrupted in Autism Spectrum Disorder Using Interaction Networks and Community Detection Analysis

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by communication deficits and repetitive behavioral patterns. Hundreds of candidate genes have been implicated in ASD, including neurotransmission and synaptic (NS) genes; however, the genetic architecture of this disease is far from clear. In this study, we seek to clarify the biological processes affected by NS gene variants identified in individuals with ASD and the global networks that link those processes together. For a curated list of 1216 NS candidate genes, identified in multiple databases and the literature, we searched for ultra-rare (UR) loss-of-function (LoF) variants in the whole-exome sequencing dataset from the Autism Sequencing Consortium (N = 3938 cases). Filtering for population frequency was carried out using gnomAD (N = 60,146 controls). NS genes with UR LoF variants were used to construct a network of protein-protein interactions, and the network's biological communities were identified by applying the Leiden algorithm. We further explored the expression enrichment of network genes in specific brain regions. We identified 356 variants in 208 genes, with a preponderance of UR LoF variants in the PDE11A and SYTL3 genes. Expression enrichment analysis highlighted several subcortical structures, particularly the basal ganglia. The interaction network defined seven network communities, clustering synaptic and neurotransmitter pathways with several ubiquitous processes that occur in multiple organs and systems. This approach also uncovered biological pathways that are not usually associated with ASD, such as brain cytochromes P450 and brain mitochondrial metabolism. Overall, the community analysis suggests that ASD involves the disruption of synaptic and neurotransmitter pathways but also ubiquitous, but less frequently implicated, biological processes.

Overlapping pathogenic de novo CNVs in neurodevelopmental disorders and congenital anomalies impacting constraint genes regulating early development

Neurodevelopmental disorders (NDDs) and congenital anomalies (CAs) are rare disorders with complex etiology. In this study, we investigated the less understood genomic overlap of copy number variants (CNVs) in two large cohorts of NDD and CA patients to identify de novo CNVs and candidate genes associated with both phenotypes. We analyzed clinical microarray CNV data from 10,620 NDD and 3176 CA cases annotated using Horizon platform of GenomeArc Analytics and applied rigorous downstream analysis to evaluate overlapping genes from NDD and CA CNVs. Out of 13,796 patients, only 195 cases contained 218 validated de novo CNVs. Eighteen percent (31/170) de novo CNVs in NDD cases and 40% (19/48) de novo CNVs in CA cases contained genomic overlaps impacting developmentally constraint genes. Seventy-nine constraint genes (10.1% non-OMIM entries) were found to have significantly enriched genomic overlap within rare de novo pathogenic deletions (P value = 0.01, OR = 1.58) and 45 constraint genes (13.3% non-OMIM entries) within rare de novo pathogenic duplications (P value = 0.01, OR = 1.97). Analysis of spatiotemporal transcriptome demonstrated both pathogenic deletion and duplication genes to be highly expressed during the prenatal stage in human developmental brain (P value = 4.95 X 10-6). From the list of overlapping genes, EHMT1, an interesting known NDD gene encompassed pathogenic deletion CNVs from both NDD and CA patients, whereas FAM189A1, and FSTL5 are new candidate genes from non-OMIM entries. In summary, we have identified constraint overlapping genes from CNVs (including de novo) in NDD and CA patients that have the potential to play a vital role in common disease etiology.

Phenotypic effects of genetic variants associated with autism

While over 100 genes have been associated with autism, little is known about the prevalence of variants affecting them in individuals without a diagnosis of autism. Nor do we fully appreciate the phenotypic diversity beyond the formal autism diagnosis. Based on data from more than 13,000 individuals with autism and 210,000 undiagnosed individuals, we estimated the odds ratios for autism associated to rare loss-of-function (LoF) variants in 185 genes associated with autism, alongside 2,492 genes displaying intolerance to LoF variants. In contrast to autism-centric approaches, we investigated the correlates of these variants in individuals without a diagnosis of autism. We show that these variants are associated with a small but significant decrease in fluid intelligence, qualification level and income and an increase in metrics related to material deprivation. These effects were larger for autism-associated genes than in other LoF-intolerant genes. Using brain imaging data from 21,040 individuals from the UK Biobank, we could not detect significant differences in the overall brain anatomy between LoF carriers and non-carriers. Our results highlight the importance of studying the effect of the genetic variants beyond categorical diagnosis and the need for more research to understand the association between these variants and sociodemographic factors, to best support individuals carrying these variants.

Prediction of Neurodevelopmental Disorders Based on De Novo Coding Variation

The early detection of neurodevelopmental disorders (NDDs) can significantly improve patient outcomes. The differential burden of non-synonymous de novo mutation among NDD cases and controls indicates that de novo coding variation can be used to identify a subset of samples that will likely display an NDD phenotype. Thus, we have developed an approach for the accurate prediction of NDDs with very low false positive rate (FPR) using de novo coding variation for a small subset of cases. We use a shallow neural network that integrates de novo likely gene-disruptive and missense variants, measures of gene constraint, and conservation information to predict a small subset of NDD cases at very low FPR and prioritizes NDD risk genes for future clinical study.

Construction of copy number variation landscape and characterization of associated genes in a Bangladeshi cohort of neurodevelopmental disorders

Introduction: Copy number variations (CNVs) play a critical role in the pathogenesis of neurodevelopmental disorders (NDD) among children. In this study, we aim to identify clinically relevant CNVs, genes and their phenotypic characteristics in an ethnically underrepresented homogenous population of Bangladesh. Methods: We have conducted chromosomal microarray analysis (CMA) for 212 NDD patients with male to female ratio of 2.2:1.0 to identify rare CNVs. To identify candidate genes within the rare CNVs, gene constraint metrics [i.e., "Critical-Exon Genes (CEGs)"] were applied to the population data. Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) was followed in a subset of 95 NDD patients to assess the severity of autism and all statistical tests were performed using the R package. Results: Of all the samples assayed, 12.26% (26/212) and 57.08% (121/212) patients carried pathogenic and variant of uncertain significance (VOUS) CNVs, respectively. While 2.83% (6/212) patients' pathogenic CNVs were found to be located in the subtelomeric regions. Further burden test identified females are significant carriers of pathogenic CNVs compared to males (OR = 4.2; p = 0.0007). We have observed an increased number of Loss of heterozygosity (LOH) within cases with 23.85% (26/109) consanguineous parents. Our analyses on imprinting genes show, 36 LOH variants disrupting 69 unique imprinted genes and classified these variants as VOUS. ADOS-2 subset shows severe social communication deficit (p = 0.014) and overall ASD symptoms severity (p = 0.026) among the patients carrying duplication CNV compared to the CNV negative group. Candidate gene analysis identified 153 unique CEGs in pathogenic CNVs and 31 in VOUS. Of the unique genes, 18 genes were found to be in smaller (<1 MB) focal CNVs in our NDD cohort and we identified PSMC3 gene as a strong candidate gene for Autism Spectrum Disorder (ASD). Moreover, we hypothesized that KMT2B gene duplication might be associated with intellectual disability. Conclusion: Our results show the utility of CMA for precise genetic diagnosis and its integration into the diagnosis, therapy and management of NDD patients.

Genetic Relationships between Attention-Deficit/Hyperactivity Disorder, Autism Spectrum Disorder, and Intelligence

INTRODUCTION

Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) commonly co-occur; both traits exert an influence on intelligence scores. Genetic relationships between these three traits are far from being clear.

METHODS

The summary results of genome-wide association studies of ADHD (20,183 cases and 35,191 controls), ASD (18,381 cases and 27,969 controls), and intelligence (269,867 participants) were used for the analyses. Local genetic correlation analysis and polygenic overlap analysis were used to explore the shared genetic components between ADHD, ASD, and intelligence. Mendelian randomization (MR) analysis was used to examine the causal associations between ADHD, ASD, and intelligence. A cross-trait meta-analysis was performed to identify pleiotropic genetic variants across the three traits.

RESULTS

Our results showed that intelligence has a positive and negative genetic correlation with ASD and ADHD, respectively, including three hub genomic regions showing correlated genetic effects across the three traits. Polygenic overlap analysis indicated that all the risk variants contributing to ADHD are overlapped with half of those for intelligence, and the majority of the shared variants have opposite effect directions between them. The majority of risk variants (80%) of ASD are overlapped with almost all the risk variants of intelligence (97%). Notably, some ASD/intelligence overlapping variants displayed opposing effects on these two conditions. MR analysis showed that the genetic liability to higher intelligence was associated with an increased risk for ASD (OR = 1.12) and a decreased risk for ADHD (OR = 0.78). Cross-trait meta-analyses identified 170 pleiotropic genomic loci across the three traits, including 12 novel loci. Functional analyses of the novel genes support their potential involvement in neurodevelopment.

CONCLUSION

Our results suggest that ADHD is associated with inheriting a reduced set of low-intelligence alleles, whereas ASD results from incongruous effects from a mixture of high-intelligence and low-intelligence contributing alleles summed up with additional, ASD-specific risk variants not associated with intelligence.

Functional Connectome-Based Predictive Modeling in Autism

Autism is a heterogeneous neurodevelopmental condition, and functional magnetic resonance imaging-based studies have helped advance our understanding of its effects on brain network activity. We review how predictive modeling, using measures of functional connectivity and symptoms, has helped reveal key insights into this condition. We discuss how different prediction frameworks can further our understanding of the brain-based features that underlie complex autism symptomatology and consider how predictive models may be used in clinical settings. Throughout, we highlight aspects of study interpretation, such as data decay and sampling biases, that require consideration within the context of this condition. We close by suggesting exciting future directions for predictive modeling in autism.

A rigorous in silico genomic interrogation at 1p13.3 reveals 16 autosomal dominant candidate genes in syndromic neurodevelopmental disorders

Genome-wide chromosomal microarray is extensively used to detect copy number variations (CNVs), which can diagnose microdeletion and microduplication syndromes. These small unbalanced chromosomal structural rearrangements ranging from 1 kb to 10 Mb comprise up to 15% of human mutations leading to monogenic or contiguous genomic disorders. Albeit rare, CNVs at 1p13.3 cause a variety of neurodevelopmental disorders (NDDs) including development delay (DD), intellectual disability (ID), autism, epilepsy, and craniofacial anomalies (CFA). Most of the 1p13.3 CNV cases reported in the pre-microarray era encompassed a large number of genes and lacked the demarcating genomic coordinates, hampering the discovery of positional candidate genes within the boundaries. In this study, we present four subjects with 1p13.3 microdeletions displaying DD, ID, autism, epilepsy, and CFA. In silico comparative genomic mapping with three previously reported subjects with CNVs and 22 unreported DECIPHER CNV cases has resulted in the identification of four different sub-genomic loci harboring five positional candidate genes for DD, ID, and CFA at 1p13.3. Most of these genes have pathogenic variants reported, and their interacting genes are involved in NDDs. RT-qPCR in various human tissues revealed a high expression pattern in the brain and fetal brain, supporting their functional roles in NDDs. Interrogation of variant databases and interacting protein partners led to the identification of another set of 11 potential candidate genes, which might have been dysregulated by the position effect of these CNVs at 1p13.3. Our studies define 1p13.3 as a genomic region harboring 16 NDD candidate genes and underscore the critical roles of small CNVs in in silico comparative genomic mapping for disease gene discovery. Our candidate genes will help accelerate the isolation of pathogenic heterozygous variants from exome/genome sequencing (ES/GS) databases.

Genomic imbalance in euploid pregnancy loss

PURPOSE

This study aims to investigate genomic imbalance in euploid products of conceptions (POCs) detected by chromosomal microarray analysis (CMA) and its association with clinical characteristics.

METHODS

In a retrospective cohort study where all women with singleton pregnancy losses underwent CMA detection of POCs, only patients with euploid POCs were included in the analysis. The clinical features were compared between those with and without a copy number variant (CNV). The pathogenic CNVs and the variant of uncertain significance (VOUS) were analyzed, and the common pathogenic CNVs and uniparental disomy (UPD) were investigated.

RESULTS

A total of 610 POCs were detected as chromosomal euploid, of which 176 were euploid with CNVs and 434 were euploid without CNVs. Regarding maternal age, gestational age, and history of pregnancy loss, no significant differences were found between the two groups. Furthermore, 104 pathogenic CNVs were identified in 93 POCs, and the deletion of 8p23.3 was found in 10 subjects. All CNVs greater than 3 Mb and 39.5% of CNVs ranging from 1 to 2 Mb were pathogenic, and only 3 CNVs < 1 Mb were pathogenic. UPD was detected in 12 POCs.

CONCLUSION

Besides aneuploidy, 15.24% pregnancy loss might have an association with pathogenic genomic imbalance, and the occurrence of genomic imbalance is not related to clinical characteristics. CNVs greater than 3 Mb in pregnancy losses have a high probability to be pathogenic, and approximately 40% of CNVs ranging from 1 to 2 Mb are pathogenic. The deletion of 8p23.3 is the most common pathogenic CVN in POCs of Chinese-Han women. The clinical significance of UPD in pregnancy loss needs further study.

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis

The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.

Genomics, convergent neuroscience and progress in understanding autism spectrum disorder

More than a hundred genes have been identified that, when disrupted, impart large risk for autism spectrum disorder (ASD). Current knowledge about the encoded proteins - although incomplete - points to a very wide range of developmentally dynamic and diverse biological processes. Moreover, the core symptoms of ASD involve distinctly human characteristics, presenting challenges to interpreting evolutionarily distant model systems. Indeed, despite a decade of striking progress in gene discovery, an actionable understanding of pathobiology remains elusive. Increasingly, convergent neuroscience approaches have been recognized as an important complement to traditional uses of genetics to illuminate the biology of human disorders. These methods seek to identify intersection among molecular-level, cellular-level and circuit-level functions across multiple risk genes and have highlighted developing excitatory neurons in the human mid-gestational prefrontal cortex as an important pathobiological nexus in ASD. In addition, neurogenesis, chromatin modification and synaptic function have emerged as key potential mediators of genetic vulnerability. The continued expansion of foundational 'omics' data sets, the application of higher-throughput model systems and incorporating developmental trajectories and sex differences into future analyses will refine and extend these results. Ultimately, a systems-level understanding of ASD genetic risk holds promise for clarifying pathobiology and advancing therapeutics.

NeuroSCORE is a genome-wide omics-based model that identifies candidate disease genes of the central nervous system

To identify candidate disease genes of central nervous system (CNS) phenotypes, we created the Neurogenetic Systematic Correlation of Omics-Related Evidence (NeuroSCORE). We identified five genome-wide metrics highly associated with CNS phenotypes to score 19,601 protein-coding genes. Genes scored one point per metric (range: 0-5), identifying 8298 scored genes (scores ≥ 1) and 1601 "high scoring" genes (scores ≥ 3). Using logistic regression, we determined the odds ratio that genes with a NeuroSCORE from 1 to 5 would be associated with known CNS-related phenotypes compared to genes that scored zero. We tested NeuroSCORE using microarray copy number variants (CNVs) in case-control cohorts and aggregate mouse model data. High scoring genes are associated with CNS phenotypes (OR = 5.5, p < 2E-16), enriched in case CNVs, and mouse ortholog genes that cause behavioral and nervous system abnormalities. We identified 1058 high scoring genes with no disease association in OMIM. Transforming the logistic regression results indicates high scoring genes have an 84-92% chance of being associated with a CNS phenotype. Top scoring genes include GRIA1, MAP4K4, SF1, TNPO2, and ZSWIM8. Finally, we interrogated CNVs in the Clinical Genome Resource, finding the majority of clinically significant CNVs contain high scoring genes. These findings can direct future research and improve molecular diagnostics.

Copy Number Variation Analysis of Euploid Pregnancy Loss

Objectives: Copy number variant (CNV) is believed to be the potential genetic cause of pregnancy loss. However, CNVs less than 3 Mb in euploid products of conceptions (POCs) remain largely unexplored. The aim of this study was to investigate the features of CNVs less than 3 Mb in POCs and their potential clinical significance in pregnancy loss/fetal death. Methods: CNV data were extracted from a cohort in our institution and 19 peer-reviewed publications, and only those CNVs less than 3 Mb detected in euploid pregnancy loss/fetal death were included. We conducted a CNV map to analyze the distribution of CNVs in chromosomes using R packages karyoploteR_1.10.5. Gene names and annotated gene types covered by those CNVs were mined from the human Release 19 reference genome file and GENECODE database. We assessed the expression patterns and the consequences of murine knock-out of those genes using TiGER and Mouse Genome Informatics (MGI) databases. Functional enrichment and pathway analysis for genes in CNVs were performed using clusterProfiler V3.12.0. Result: Breakpoints of 564 CNVs less than 3 Mb were obtained from 442 euploid POCs, with 349 gains and 185 losses. The CNV map showed that CNVs were distributed in all chromosomes, with the highest frequency detected in chromosome 22 and the lowest frequency in chromosome Y, and CNVs showed a higher density in the pericentromeric and sub-telomeric regions. A total of 5,414 genes mined from the CNV regions (CNVRs), Gene Ontology (GO), and pathway analysis showed that the genes were significantly enriched in multiple terms, especially in sensory perception, membrane region, and tight junction. A total of 995 protein-coding genes have been reported to present mammalian phenotypes in MGI, and 276 of them lead to embryonic lethality or abnormal embryo/placenta in knock-out mouse models. CNV located at 19p13.3 was the most common CNV of all POCs. Conclusion: CNVs less than 3 Mb in euploid POCs distribute unevenly in all chromosomes, and a higher density was seen in the pericentromeric and sub-telomeric regions. The genes in those CNVRs are significantly enriched in biological processes and pathways that are important to embryonic/fetal development. CNV in 19p13.3 and the variations of ARID3A and FSTL3 might contribute to pregnancy loss.

Single-cell transcriptome identifies molecular subtype of autism spectrum disorder impacted by de novo loss-of-function variants regulating glial cells

BACKGROUND

In recent years, several hundred autism spectrum disorder (ASD) implicated genes have been discovered impacting a wide range of molecular pathways. However, the molecular underpinning of ASD, particularly from the point of view of 'brain to behaviour' pathogenic mechanisms, remains largely unknown.

METHODS

We undertook a study to investigate patterns of spatiotemporal and cell type expression of ASD-implicated genes by integrating large-scale brain single-cell transcriptomes (> million cells) and de novo loss-of-function (LOF) ASD variants (impacting 852 genes from 40,122 cases).

RESULTS

We identified multiple single-cell clusters from three distinct developmental human brain regions (anterior cingulate cortex, middle temporal gyrus and primary visual cortex) that evidenced high evolutionary constraint through enrichment for brain critical exons and high pLI genes. These clusters also showed significant enrichment with ASD loss-of-function variant genes (p < 5.23 × 10^-11) that are transcriptionally highly active in prenatal brain regions (visual cortex and dorsolateral prefrontal cortex). Mapping ASD de novo LOF variant genes into large-scale human and mouse brain single-cell transcriptome analysis demonstrate enrichment of such genes into neuronal subtypes and are also enriched for subtype of non-neuronal glial cell types (astrocyte, p < 6.40 × 10^-11, oligodendrocyte, p < 1.31 × 10^-09).

CONCLUSION

Among the ASD genes enriched with pathogenic de novo LOF variants (i.e. KANK1, PLXNB1), a subgroup has restricted transcriptional regulation in non-neuronal cell types that are evolutionarily conserved. This association strongly suggests the involvement of subtype of non-neuronal glial cells in the pathogenesis of ASD and the need to explore other biological pathways for this disorder.

Genetic contributions to autism spectrum disorder

Autism spectrum disorder (autism) is a heterogeneous group of neurodevelopmental conditions characterized by early childhood-onset impairments in communication and social interaction alongside restricted and repetitive behaviors and interests. This review summarizes recent developments in human genetics research in autism, complemented by epigenetic and transcriptomic findings. The clinical heterogeneity of autism is mirrored by a complex genetic architecture involving several types of common and rare variants, ranging from point mutations to large copy number variants, and either inherited or spontaneous (de novo). More than 100 risk genes have been implicated by rare, often de novo, potentially damaging mutations in highly constrained genes. These account for substantial individual risk but a small proportion of the population risk. In contrast, most of the genetic risk is attributable to common inherited variants acting en masse, each individually with small effects. Studies have identified a handful of robustly associated common variants. Different risk genes converge on the same mechanisms, such as gene regulation and synaptic connectivity. These mechanisms are also implicated by genes that are epigenetically and transcriptionally dysregulated in autism. Major challenges to understanding the biological mechanisms include substantial phenotypic heterogeneity, large locus heterogeneity, variable penetrance, and widespread pleiotropy. Considerable increases in sample sizes are needed to better understand the hundreds or thousands of common and rare genetic variants involved. Future research should integrate common and rare variant research, multi-omics data including genomics, epigenomics, and transcriptomics, and refined phenotype assessment with multidimensional and longitudinal measures.

Full-length isoform transcriptome of the developing human brain provides further insights into autism

Alternative splicing plays an important role in brain development, but its global contribution to human neurodevelopmental diseases (NDDs) requires further investigation. Here we examine the relationships between splicing isoform expression in the brain and de novo loss-of-function mutations from individuals with NDDs. We analyze the full-length isoform transcriptome of the developing human brain and observe differentially expressed isoforms and isoform co-expression modules undetectable by gene-level analyses. These isoforms are enriched in loss-of-function mutations and microexons, are co-expressed with a unique set of partners, and have higher prenatal expression. We experimentally test the effect of splice-site mutations and demonstrate exon skipping in five NDD risk genes, including SCN2A, DYRK1A, and BTRC. Our results suggest that the splice site mutation in BTRC reduces translational efficiency, likely affecting Wnt signaling through impaired degradation of β-catenin. We propose that functional effects of mutations should be investigated at the isoform- rather than gene-level resolution.

An Exploration of Physical and Phenotypic Characteristics of Bangladeshi Children with Autism Spectrum Disorder

This study explored the physical and clinical phenotype of Bangladeshi children with autism spectrum disorder (ASD). A totally of 283 children who were referred for screening and administered Module 1 of the Autism Diagnostic Observation Schedule (ADOS) were included. Overall, 209 met the ADOS algorithmic cutoff for ASD. A trend for greater weight and head circumference was observed in children with ASD versus non-ASD. Head circumference was significantly (p < 0.03) larger in ASD males compared with non-ASD males. A trend was also observed for symptom severity, higher in females than males (p = 0.068), with further analyses demonstrating that social reciprocity (p < 0.014) and functional play (p < 0.03) were significantly more impaired in ASD females than males. The findings help understand sex differences in ASD.

Variants in USP48 encoding ubiquitin hydrolase are associated with autosomal dominant non-syndromic hereditary hearing loss

Non-Syndromic Hereditary Hearing Loss (NSHHL) is a genetically heterogeneous sensory disorder with about 120 genes already associated. Through exome sequencing and data aggregation, we identified a family with six affected individuals and one unrelated NSHHL patient with predicted-to-be deleterious missense variants in USP48. We also uncovered an eighth patient presenting unilateral cochlear nerve aplasia and a de novo splice variant in the same gene. USP48 encodes a ubiquitin carboxyl-terminal hydrolase under evolutionary constraint. Pathogenicity of the variants is supported by in vitro assays that showed that the mutated proteins are unable to hydrolyze tetra-ubiquitin. Correspondingly, three-dimensional representation of the protein containing the familial missense variant affects a loop that controls binding to ubiquitin. Consistent with a contribution of USP48 to auditory function, immunohistology showed that the encoded protein is expressed in the developing human inner ear, specifically in the spiral ganglion neurons, outer sulcus, interdental cells of the spiral limbus, stria vascularis, Reissner's membrane, and in the transient Kolliker's organ that is essential for auditory development. Engineered zebrafish knocked-down for usp48, the USP48 ortholog, presented with a delayed development of primary motor neurons, less developed statoacoustic neurons innervating the ears, decreased swimming velocity and circling swimming behavior indicative of vestibular dysfunction and hearing impairment. Corroboratingly, acoustic startle response assays revealed a significant decrease of auditory response of zebrafish lacking usp48 at 600 Hz and 800 Hz wavelengths. In conclusion, we describe a novel autosomal dominant NSHHL gene through a multipronged approach combining exome sequencing, animal modeling, immunohistology and molecular assays.

Protein-protein interactions at the NMDA receptor complex: From synaptic retention to synaptonuclear protein messengers

N-methyl-d-aspartate receptors (NMDARs) are glutamate-gated ion channels that support essential functions throughout the brain. NMDARs are tetramers composed of the GluN1 subunit in complex with GluN2- and GluN3-type regulatory subunits, resulting in the formation of various receptor subtypes throughout the central nervous system (CNS), characterised by different kinetics, biophysical and pharmacological properties, and the abilities to interact with specific partners at dendritic spines. NMDARs are expressed at high levels, are widely distributed throughout the brain, and are involved in several physiological and pathological conditions. Here, we will focus on the GluN2A- and GluN2B-containing NMDARs found at excitatory synapses and their interactions with plasticity-relevant proteins, such as the postsynaptic density family of membrane-associated guanylate kinases (PSD-MAGUKs), Ca²⁺/calmodulin-dependent kinase II (CaMKII) and synaptonuclear protein messengers. The dynamic interactions between NMDAR subunits and various proteins regulating synaptic receptor retention and synaptonuclear signalling mediated by protein messengers suggest that the NMDAR serves as a key molecular player that coordinates synaptic activity and cell-wide events that require gene transcription. Importantly, protein-protein interactions at the NMDAR complex can also contribute to synaptic dysfunction in several brain disorders. Therefore, the modulation of the molecular composition of the NMDAR complex might represent a novel pharmacological approach for the treatment of certain disease states.

MTSplice predicts effects of genetic variants on tissue-specific splicing

We develop the free and open-source model Multi-tissue Splicing (MTSplice) to predict the effects of genetic variants on splicing of cassette exons in 56 human tissues. MTSplice combines MMSplice, which models constitutive regulatory sequences, with a new neural network that models tissue-specific regulatory sequences. MTSplice outperforms MMSplice on predicting tissue-specific variations associated with genetic variants in most tissues of the GTEx dataset, with largest improvements on brain tissues. Furthermore, MTSplice predicts that autism-associated de novo mutations are enriched for variants affecting splicing specifically in the brain. We foresee that MTSplice will aid interpreting variants associated with tissue-specific disorders.

CNS genomic profiling in the mouse chronic social stress model implicates a novel category of candidate genes integrating affective pathogenesis

Despite high prevalence, medical impact and societal burden, anxiety, depression and other affective disorders remain poorly understood and treated. Clinical complexity and polygenic nature complicate their analyses, often revealing genetic overlap and cross-disorder heritability. However, the interplay or overlaps between disordered phenotypes can also be based on shared molecular pathways and 'crosstalk' mechanisms, which themselves may be genetically determined. We have earlier predicted (Kalueff et al., 2014) a new class of 'interlinking' brain genes that do not affect the disordered phenotypes per se, but can instead specifically determine their interrelatedness. To test this hypothesis experimentally, here we applied a well-established rodent chronic social defeat stress model, known to progress in C57BL/6J mice from the Anxiety-like stage on Day 10 to Depression-like stage on Day 20. The present study analyzed mouse whole-genome expression in the prefrontal cortex and hippocampus during the Day 10, the Transitional (Day 15) and Day 20 stages in this model. Our main question here was whether a putative the Transitional stage (Day 15) would reveal distinct characteristic genomic responses from Days 10 and 20 of the model, thus reflecting unique molecular events underlining the transformation or switch from anxiety to depression pathogenesis. Overall, while in the Day 10 (Anxiety) group both brain regions showed major genomic alterations in various neurotransmitter signaling pathways, the Day 15 (Transitional) group revealed uniquely downregulated astrocyte-related genes, and the Day 20 (Depression) group demonstrated multiple downregulated genes of cell adhesion, inflammation and ion transport pathways. Together, these results reveal a complex temporal dynamics of mouse affective phenotypes as they develop. Our genomic profiling findings provide first experimental support to the idea that novel brain genes (activated here only during the Transitional stage) may uniquely integrate anxiety and depression pathogenesis and, hence, determine the progression from one pathological state to another. This concept can potentially be extended to other brain conditions as well. This preclinical study also further implicates cilial and astrocytal mechanisms in the pathogenesis of affective disorders.

Long-Read Sequencing Improves the Detection of Structural Variations Impacting Complex Non-Coding Elements of the Genome

The advent of long-read sequencing offers a new assessment method of detecting genomic structural variation (SV) in numerous rare genetic diseases. For autism spectrum disorders (ASD) cases where pathogenic variants fail to be found in the protein-coding genic regions along chromosomes, we proposed a scalable workflow to characterize the risk factor of SVs impacting non-coding elements of the genome. We applied whole-genome sequencing on an Emirati family having three children with ASD using long and short-read sequencing technology. A series of analytical pipelines were established to identify a set of SVs with high sensitivity and specificity. At 15-fold coverage, we observed that long-read sequencing technology (987 variants) detected a significantly higher number of SVs when compared to variants detected using short-read technology (509 variants) (p-value < 1.1020 × 10-57). Further comparison showed 97.9% of long-read sequencing variants were spanning within the 1-100 kb size range (p-value < 9.080 × 10-67) and impacting over 5000 genes. Moreover, long-read variants detected 604 non-coding RNAs (p-value < 9.02 × 10-9), comprising 58% microRNA, 31.9% lncRNA, and 9.1% snoRNA. Even at low coverage, long-read sequencing has shown to be a reliable technology in detecting SVs impacting complex elements of the genome.

Whole exome sequencing uncovered highly penetrant recessive mutations for a spectrum of rare genetic pediatric diseases in Bangladesh

Collectively, rare genetic diseases affect a significant number of individuals worldwide. In this study, we have conducted whole-exome sequencing (WES) and identified underlying pathogenic or likely pathogenic variants in five children with rare genetic diseases. We present evidence for disease-causing autosomal recessive variants in a range of disease-associated genes such as DHH-associated 46,XY gonadal dysgenesis (GD) or 46,XY sex reversal 7, GNPTAB-associated mucolipidosis II alpha/beta (ML II), BBS1-associated Bardet-Biedl Syndrome (BBS), SURF1-associated Leigh Syndrome (LS) and AP4B1-associated spastic paraplegia-47 (SPG47) in unrelated affected members from Bangladesh. Our analysis pipeline detected three homozygous mutations, including a novel c. 863 G > C (p.Pro288Arg) variant in DHH, and two compound heterozygous variants, including two novel variants: c.2972dupT (p.Met991Ilefs*) in GNPTAB and c.229 G > C (p.Gly77Arg) in SURF1. All mutations were validated by Sanger sequencing. Collectively, this study adds to the genetic heterogeneity of rare genetic diseases and is the first report elucidating the genetic profile of (consanguineous and nonconsanguineous) rare genetic diseases in the Bangladesh population.

What a finding of gene copy number variation can add to the diagnosis of developmental neuropsychiatric disorders

Among medical disciplines, diagnosis in psychiatry depends highly upon descriptive signs and symptoms, rather than biomarkers. Clear descriptions of specific genetic etiologies have been lacking; genomic technologies, however, are rapidly changing that landscape. Notably, chromosomal microarrays-which detect gene copy number variants (CNVs)-are a recommended standard of care for neurodevelopmental disorders. As a result, an increasing number of patients now receive a clinical diagnosis based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and an identified genetic etiological variant. However, psychiatric and genetic diagnoses are frequently communicated and managed as two disconnected diagnostic parameters. Here, we advocate for a transition model, allowing the integration of genetic etiological information-starting with diagnostically proven CNVs-within the DSM-5 classification framework.

Rare and de novo coding variants in chromodomain genes in Chiari I malformation

Chiari I malformation (CM1), the displacement of the cerebellum through the foramen magnum into the spinal canal, is one of the most common pediatric neurological conditions. Individuals with CM1 can present with neurological symptoms, including severe headaches and sensory or motor deficits, often as a consequence of brainstem compression or syringomyelia (SM). We conducted whole-exome sequencing (WES) on 668 CM1 probands and 232 family members and performed gene-burden and de novo enrichment analyses. A significant enrichment of rare and de novo non-synonymous variants in chromodomain (CHD) genes was observed among individuals with CM1 (combined p = 2.4 × 10-10), including 3 de novo loss-of-function variants in CHD8 (LOF enrichment p = 1.9 × 10-10) and a significant burden of rare transmitted variants in CHD3 (p = 1.8 × 10-6). Overall, individuals with CM1 were found to have significantly increased head circumference (p = 2.6 × 10-9), with many harboring CHD rare variants having macrocephaly. Finally, haploinsufficiency for chd8 in zebrafish led to macrocephaly and posterior hindbrain displacement reminiscent of CM1. These results implicate chromodomain genes and excessive brain growth in CM1 pathogenesis.

Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes

Artificial intelligence (AI) digital health systems have drawn much attention over the last decade. However, their implementation into medical practice occurs at a much slower pace than expected. This paper reviews some of the achievements of first-generation AI systems, and the barriers facing their implementation into medical practice. The development of second-generation AI systems is discussed with a focus on overcoming some of these obstacles. Second-generation systems are aimed at focusing on a single subject and on improving patients' clinical outcomes. A personalized closed-loop system designed to improve end-organ function and the patient's response to chronic therapies is presented. The system introduces a platform which implements a personalized therapeutic regimen and introduces quantifiable individualized-variability patterns into its algorithm. The platform is designed to achieve a clinically meaningful endpoint by ensuring that chronic therapies will have sustainable effect while overcoming compensatory mechanisms associated with disease progression and drug resistance. Second-generation systems are expected to assist patients and providers in adopting and implementing of these systems into everyday care.

Genomic profiling of 553 uncharacterized neurodevelopment patients reveals a high proportion of recessive pathogenic variant carriers in an outbred population

A substantial portion of Mendelian disease patients suffers from genetic variants that are inherited in a recessive manner. A precise understanding of pathogenic recessive variants in a population would assist in pre-screening births of such patients. However, a systematic understanding of the contribution of recessive variants to Mendelian diseases is still lacking. Therefore, genetic diagnosis and variant discovery of 553 undiagnosed Korean patients with complex neurodevelopmental problems (KND for Korean NeuroDevelopmental cohort) were performed using whole exome sequencing of patients and their parents. Disease-causing variants, including newly discovered variants, were identified in 57.5% of the probands of the KND cohort. Among the patients with the previous reported pathogenic variants, 35.1% inherited these variants in a recessive manner. Genes that cause recessive disorders in our cohort tend to be less constrained by loss-of-function variants and were enriched in lipid metabolism and mitochondrial functions. This observation was applied to an estimation that approximately 1 in 17 healthy Korean individuals carry at least one of these pathogenic variants that develop severe neurodevelopmental problems in a recessive manner. Furthermore, the feasibility of these genes for carrier screening was evaluated. Our results will serve as a foundation for recessive variant screening to reduce occurrences of rare Mendelian disease patients. Additionally, our results highlight the utility and necessity of whole exome sequencing-based diagnostics for improving patient care in a country with a centralized medical system.

Genetic Intersections of Language and Neuropsychiatric Conditions

PURPOSE OF REVIEW

To better understand the shared basis of language and mental health, this review examines the behavioral and neurobiological features of aberrant language in five major neuropsychiatric conditions. Special attention is paid to genes implicated in both language and neuropsychiatric disorders, as they reveal biological domains likely to underpin the processes controlling both.

RECENT FINDINGS

Abnormal language and communication are common manifestations of neuropsychiatric conditions, and children with impaired language are more likely to develop psychiatric disorders than their peers. Major themes in the genetics of both language and psychiatry include master transcriptional regulators, like FOXP2; key developmental regulators, like AUTS2; and mediators of neurotransmission, like GRIN2A and CACNA1C.

Critical exon indexing improves clinical interpretation of copy number variants in neurodevelopmental disorders

Objective

To evaluate a new tool to aid interpretation of copy number variants (CNVs) in individuals with neurodevelopmental disabilities.

Methods

Critical exon indexing (CEI) was used to identify genes with critical exons (CEGs) from clinically reported CNVs, which may contribute to neurodevelopmental disorders (NDDs). The 742 pathogenic CNVs and 1,363 variants of unknown significance (VUS) identified by chromosomal microarray analysis in 5,487 individuals with NDDs were subjected to CEI to identify CEGs. CEGs identified in a subsequent random series of VUS were evaluated for relevance to CNV interpretation.

Results

CEI identified a total of 2,492 unique CEGs in pathogenic CNVs and 953 in VUS compared with 259 CEGs in 6,965 CNVs from 873 controls. These differences are highly significant (p < 0.00001) whether compared as frequency, average, or normalized by CNV size. Twenty-one percent of VUS CEGs were not represented in Online Mendelian Inheritance in Man, highlighting limitations of existing resources for identifying potentially impactful genes within CNVs. CEGs were highly correlated with other indices and known pathways of relevance. Separately, 136 random VUS reports were reevaluated, and 76% of CEGs had not been commented on. In multiple cases, further investigation yielded additional relevant literature aiding interpretation. As one specific example, we discuss GTF2I as a CEG, which likely alters interpretation of several reported duplication VUS in the Williams-Beuren region.

Conclusions

Application of CEI to CNVs in individuals with NDDs can identify genes of potential clinical relevance, aid laboratories in effectively searching the clinical literature, and support the clinical reporting of poorly annotated VUS.

Artificial intelligence for precision medicine in neurodevelopmental disorders

The ambition of precision medicine is to design and optimize the pathway for diagnosis, therapeutic intervention, and prognosis by using large multidimensional biological datasets that capture individual variability in genes, function and environment. This offers clinicians the opportunity to more carefully tailor early interventions- whether treatment or preventative in nature-to each individual patient. Taking advantage of high performance computer capabilities, artificial intelligence (AI) algorithms can now achieve reasonable success in predicting risk in certain cancers and cardiovascular disease from available multidimensional clinical and biological data. In contrast, less progress has been made with the neurodevelopmental disorders, which include intellectual disability (ID), autism spectrum disorder (ASD), epilepsy and broader neurodevelopmental disorders. Much hope is pinned on the opportunity to quantify risk from patterns of genomic variation, including the functional characterization of genes and variants, but this ambition is confounded by phenotypic and etiologic heterogeneity, along with the rare and variable penetrant nature of the underlying risk variants identified so far. Structural and functional brain imaging and neuropsychological and neurophysiological markers may provide further dimensionality, but often require more development to achieve sensitivity for diagnosis. Herein, therefore, lies a precision medicine conundrum: can artificial intelligence offer a breakthrough in predicting risks and prognosis for neurodevelopmental disorders? In this review we will examine these complexities, and consider some of the strategies whereby artificial intelligence may overcome them.

Gonadal mosaicism of large terminal de novo duplication and deletion in siblings with variable intellectual disability phenotypes

Background

Intellectual disability (ID) is a complex condition that can impact multiple domains of development. The genetic contribution to ID’s etiology is significant, with more than 100 implicated genes and loci currently identified. The majority of such variants are rare and de novo genetic mutations.

Methods

We have applied whole‐genome microarray to identify large, rare, clinically relevant copy number variants (CNVs). We have applied well‐established algorithms for variants call. Quantitative polymerase chain reaction (qPCR) was applied to validate the variants using three technical replicates for each family member. To assess whether the copy number variation was due to balanced translocation or mosaicism, we further conducted droplet digital PCR (ddPCR) on the whole family. We have, as well, applied “critical‐exon” mapping, human developmental brain transcriptome, and a database of known associated neurodevelopmental disorder variants to identify candidate genes.

Results

Here we present two siblings who are both impacted by a large terminal duplication and a deletion. Whole‐genome microarray revealed an 18.82 megabase (MB) duplication at terminal locus (7q34‐q36.3) of chromosome 7 and a 3.90 MB deletion impacting the terminal locus (15q26.3) of chromosome 15. qPCR and ddPCR experiments confirmed the de novo origin of the variants and the co‐occurrence of these two de novo events among the siblings, but their absence in both parents, implicates an unbalanced translocation that could have mal‐segregated among the siblings or a possible germline mosaicism. These terminal events impact IGF1R, CNTNAP2, and DPP6, shown to be strongly associated with neurodevelopmental disorders. Detailed clinical examination of the siblings revealed the presence of both shared and distinct phenotypic features.

Conclusions

This study identified two large rare terminal de novo events impacting two siblings. Further phenotypic investigation highlights that even in the presence of identical large high penetrant variants, spectrum of clinical features can be different between the siblings.

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.

Copy number variant discrepancy resolution using the ClinGen dosage sensitivity map results in updated clinical interpretations in ClinVar

Conflict resolution in genomic variant interpretation is a critical step toward improving patient care. Evaluating interpretation discrepancies in copy number variants (CNVs) typically involves assessing overlapping genomic content with focus on genes/regions that may be subject to dosage sensitivity (haploinsufficiency (HI) and/or triplosensitivity (TS)). CNVs containing dosage sensitive genes/regions are generally interpreted as "likely pathogenic" (LP) or "pathogenic" (P), and CNVs involving the same known dosage sensitive gene(s) should receive the same clinical interpretation. We compared the Clinical Genome Resource (ClinGen) Dosage Map, a publicly available resource documenting known HI and TS genes/regions, against germline, clinical CNV interpretations within the ClinVar database. We identified 251 CNVs overlapping known dosage sensitive genes/regions but not classified as LP or P; these were sent back to their original submitting laboratories for re-evaluation. Of 246 CNVs re-evaluated, an updated clinical classification was warranted in 157 cases (63.8%); no change was made to the current classification in 79 cases (32.1%); and 10 cases (4.1%) resulted in other types of updates to ClinVar records. This effort will add curated interpretation data into the public domain and allow laboratories to focus attention on more complex discrepancies.

CRISPR/Cas9-mediated Knockout of the Neuropsychiatric Risk Gene KCTD13 Causes Developmental Deficits in Human Cortical Neurons Derived from Induced Pluripotent Stem Cells

The human KCTD13 gene is located within the 16p11.2 locus and copy number variants of this locus are associated with a high risk for neuropsychiatric diseases including autism spectrum disorder and schizophrenia. Studies in zebrafish point to a role of KCTD13 in proliferation of neural precursor cells which may contribute to macrocephaly in 16p11.2 deletion carriers. KCTD13 is highly expressed in the fetal human brain and in mouse cortical neurons, but its contribution to the development and function of mammalian neurons is not completely understood. In the present study, we deleted the KCTD13 gene in human-induced pluripotent stem cells (iPSCs) using CRISPR/Cas9 nickase. Following neural differentiation of KCTD13 deficient and isogenic control iPSC lines, we detected a moderate but significant inhibition of DNA synthesis and proliferation in KCTD13 deficient human neural precursor cells. KCTD13 deficient cortical neurons derived from iPSCs showed decreased neurite formation and reduced spontaneous network activity. RNA-sequencing and pathway analysis pointed to a role for ERBB signaling in these phenotypic changes. Consistently, activating and inhibiting ERBB kinases rescued and aggravated, respectively, impaired neurite formation. In contrast to findings in non-neuronal human HeLa cells, we did not detect an accumulation of the putative KCTD13/Cullin-3 substrate RhoA, and treatment with inhibitors of RhoA signaling did not rescue decreased neurite formation in human KCTD13 knockout neurons. Taken together, our data provide insight into the role of KCTD13 in neurodevelopmental disorders, and point to ERBB signaling as a potential target for neuropsychiatric disorders associated with KCTD13 deficiency.

Haploinsufficiency in the ANKS1B gene encoding AIDA-1 leads to a neurodevelopmental syndrome

Neurodevelopmental disorders, including autism spectrum disorder, have complex polygenic etiologies. Single-gene mutations in patients can help define genetic factors and molecular mechanisms underlying neurodevelopmental disorders. Here we describe individuals with monogenic heterozygous microdeletions in ANKS1B, a predicted risk gene for autism and neuropsychiatric diseases. Affected individuals present with a spectrum of neurodevelopmental phenotypes, including autism, attention-deficit hyperactivity disorder, and speech and motor deficits. Neurons generated from patient-derived induced pluripotent stem cells demonstrate loss of the ANKS1B-encoded protein AIDA-1, a brain-specific protein highly enriched at neuronal synapses. A transgenic mouse model of Anks1b haploinsufficiency recapitulates a range of patient phenotypes, including social deficits, hyperactivity, and sensorimotor dysfunction. Identification of the AIDA-1 interactome using quantitative proteomics reveals protein networks involved in synaptic function and the etiology of neurodevelopmental disorders. Our findings formalize a link between the synaptic protein AIDA-1 and a rare, previously undefined genetic disease we term ANKS1B haploinsufficiency syndrome.

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.

Parent-of-Origin Effects in 15q11.2 BP1-BP2 Microdeletion (Burnside-Butler) Syndrome

To identify whether parent-of-origin effects (POE) of the 15q11.2 BP1-BP2 microdeletion are associated with differences in clinical features in individuals inheriting the deletion, we collected 71 individuals reported with phenotypic data and known inheritance from a clinical cohort, a research cohort, the DECIPHER database, and the primary literature. Chi-squared and Mann-Whitney U tests were used to test for differences in specific and grouped clinical symptoms based on parental inheritance and proband gender. Analyses controlled for sibling sets and individuals with additional variants of uncertain significance (VOUS). Among all probands, maternal deletions were associated with macrocephaly (p = 0.016) and autism spectrum disorder (ASD; p = 0.02), while paternal deletions were associated with congenital heart disease (CHD; p = 0.004). Excluding sibling sets, maternal deletions were associated with epilepsy as well as macrocephaly (p < 0.05), while paternal deletions were associated with CHD and abnormal muscular phenotypes (p < 0.05). Excluding sibling sets and probands with an additional VOUS, maternal deletions were associated with epilepsy (p = 0.019) and paternal deletions associated with muscular phenotypes (p = 0.008). Significant gender-based differences were also observed. Our results supported POEs of this deletion and included macrocephaly, epilepsy and ASD in maternal deletions with CHD and abnormal muscular phenotypes seen in paternal deletions.

CNTN5-/+or EHMT2-/+human iPSC-derived neurons from individuals with autism develop hyperactive neuronal networks

Induced pluripotent stem cell (iPSC)-derived neurons are increasingly used to model Autism Spectrum Disorder (ASD), which is clinically and genetically heterogeneous. To study the complex relationship of penetrant and weaker polygenic risk variants to ASD, 'isogenic' iPSC-derived neurons are critical. We developed a set of procedures to control for heterogeneity in reprogramming and differentiation, and generated 53 different iPSC-derived glutamatergic neuronal lines from 25 participants from 12 unrelated families with ASD. Heterozygous de novo and rare-inherited presumed-damaging variants were characterized in ASD risk genes/loci. Combinations of putative etiologic variants (GLI3/KIF21A or EHMT2/UBE2I) in separate families were modeled. We used a multi-electrode array, with patch-clamp recordings, to determine a reproducible synaptic phenotype in 25% of the individuals with ASD (other relevant data on the remaining lines was collected). Our most compelling new results revealed a consistent spontaneous network hyperactivity in neurons deficient for CNTN5 or EHMT2. The biobank of iPSC-derived neurons and accompanying genomic data are available to accelerate ASD research.

Editorial note

This article has been through an editorial process in which authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

Rare copy number variations affecting the synaptic gene DMXL2 in neurodevelopmental disorders

Background

Ultra-rare genetic variants, including non-recurrent copy number variations (CNVs) affecting important dosage-sensitive genes, are important contributors to the etiology of neurodevelopmental disorders (NDDs). Pairing family-based whole-genome sequencing (WGS) with detailed phenotype data can enable novel gene associations in NDDs.

Methods

We performed WGS of six members from a three-generation family, where three individuals each had a spectrum of features suggestive of a NDD. CNVs and sequence-level variants were identified and further investigated in disease and control databases.

Results

We identified a novel 252-kb deletion at 15q21 that overlaps the synaptic gene DMXL2 and the gene GLDN. The microdeletion segregated in NDD-affected individuals. Additional rare inherited and de novo sequence-level variants were found that may also be involved, including a missense change in GRIK5. Multiple CNVs and loss-of-function sequence variants affecting DMXL2 were discovered in additional unrelated individuals with a range of NDDs.

Conclusions

Disruption of DMXL2 may predispose to NDDs including autism spectrum disorder. The robust interpretation of private variants requires a multifaceted approach that incorporates multigenerational pedigrees and genome-wide and population-scale data.

iMEGES: integrated mental-disorder GEnome score by deep neural network for prioritizing the susceptibility genes for mental disorders in personal genomes

BACKGROUND

A range of rare and common genetic variants have been discovered to be potentially associated with mental diseases, but many more have not been uncovered. Powerful integrative methods are needed to systematically prioritize both variants and genes that confer susceptibility to mental diseases in personal genomes of individual patients and to facilitate the development of personalized treatment or therapeutic approaches.

METHODS

Leveraging deep neural network on the TensorFlow framework, we developed a computational tool, integrated Mental-disorder GEnome Score (iMEGES), for analyzing whole genome/exome sequencing data on personal genomes. iMEGES takes as input genetic mutations and phenotypic information from a patient with mental disorders, and outputs the rank of whole genome susceptibility variants and the prioritized disease-specific genes for mental disorders by integrating contributions from coding and non-coding variants, structural variants (SVs), known brain expression quantitative trait loci (eQTLs), and epigenetic information from PsychENCODE.

RESULTS

iMEGES was evaluated on multiple datasets of mental disorders, and it achieved improved performance than competing approaches when large training dataset is available.

CONCLUSION

iMEGES can be used in population studies to help the prioritization of novel genes or variants that might be associated with the susceptibility to mental disorders, and also on individual patients to help the identification of genes or variants related to mental diseases.

The transcription factor POU3F2 regulates a gene coexpression network in brain tissue from patients with psychiatric disorders

Schizophrenia and bipolar disorder are complex psychiatric diseases with risks contributed by multiple genes. Dysregulation of gene expression has been implicated in these disorders, but little is known about such dysregulation in the human brain. We analyzed three transcriptome datasets from 394 postmortem brain tissue samples from patients with schizophrenia or bipolar disorder or from healthy control individuals without a known history of psychiatric disease. We built genome-wide coexpression networks that included microRNAs (miRNAs). We identified a coexpression network module that was differentially expressed in the brain tissue from patients compared to healthy control individuals. This module contained genes that were principally involved in glial and neural cell genesis and glial cell differentiation, and included schizophrenia risk genes carrying rare variants. This module included five miRNAs and 545 mRNAs, with six transcription factors serving as hub genes in this module. We found that the most connected transcription factor gene POU3F2, also identified on a genome-wide association study for bipolar disorder, could regulate the miRNA hsa-miR-320e and other putative target mRNAs. These regulatory relationships were replicated using PsychENCODE/BrainGVEX datasets and validated by knockdown and overexpression experiments in SH-SY5Y cells and human neural progenitor cells in vitro. Thus, we identified a brain gene expression module that was enriched for rare coding variants in genes associated with schizophrenia and that contained the putative bipolar disorder risk gene POU3F2 The transcription factor POU3F2 may be a key regulator of gene expression in this disease-associated gene coexpression module.

Structural Analysis of Hippocampal Kinase Signal Transduction

Kinases are a major clinical target for human diseases. Identifying the proteins that interact with kinases in vivo will provide information on unreported substrates and will potentially lead to more specific methods for therapeutic kinase regulation. Here, endogenous immunoprecipitations of evolutionally distinct kinases (i.e., Akt, ERK2, and CAMK2) from rodent hippocampi were analyzed by mass spectrometry to generate three highly confident kinase protein-protein interaction networks. Proteins of similar function were identified in the networks, suggesting a universal model for kinase signaling complexes. Protein interactions were observed between kinases with reported symbiotic relationships. The kinase networks were significantly enriched in genes associated with specific neurodevelopmental disorders providing novel structural connections between these disease-associated genes. To demonstrate a functional relationship between the kinases and the network, pharmacological manipulation of Akt in hippocampal slices was shown to regulate the activity of potassium/sodium hyperpolarization-activated cyclic nucleotide-gated channel(HCN1), which was identified in the Akt network. Overall, the kinase protein-protein interaction networks provide molecular insight of the spatial complexity of in vivo kinase signal transduction which is required to achieve the therapeutic potential of kinase manipulation in the brain.