The Role of De Novo Noncoding Regulatory Mutations in Neurodevelopmental Disorders

Diversity and Classification of Genetic Variations in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition with symptoms that affect the whole personality and all aspects of life. Although there is a high degree of heterogeneity in both its etiology and its characteristic behavioral patterns, the disorder is well-captured along the autistic triad. Currently, ASD status can be confirmed following an assessment of behavioral features, but there is a growing emphasis on conceptualizing autism as a spectrum, which allows for establishing a diagnosis based on the level of support need, free of discrete categories. Since ASD has a high genetic predominance, the number of genetic variations identified in the background of the condition is increasing exponentially as genetic testing methods are rapidly evolving. However, due to the huge amount of data to be analyzed, grouping the different DNA variations is still challenging. Therefore, in the present review, a multidimensional classification scheme was developed to accommodate most of the currently known genetic variants associated with autism. Genetic variations have been grouped according to six criteria (extent, time of onset, information content, frequency, number of genes involved, inheritance pattern), which are themselves not discrete categories, but form a coherent continuum in line with the autism spectrum approach.

Performance Comparison of Computational Methods for the Prediction of the Function and Pathogenicity of Non-coding Variants

Non-coding variants in the human genome significantly influence human traits and complex diseases via their regulation and modification effects. Hence, an increasing number of computational methods are developed to predict the effects of variants in human non-coding sequences. However, it is difficult for inexperienced users to select appropriate computational methods from dozens of available methods. To solve this issue, we assessed 12 performance metrics of 24 methods on four independent non-coding variant benchmark datasets: (1) rare germline variants from clinical relevant sequence variants (ClinVar), (2) rare somatic variants from Catalogue Of Somatic Mutations In Cancer (COSMIC), (3) common regulatory variants from curated expression quantitative trait locus (eQTL) data, and (4) disease-associated common variants from curated genome-wide association studies (GWAS). All 24 tested methods performed differently under various conditions, indicating varying strengths and weaknesses under different scenarios. Importantly, the performance of existing methods was acceptable for rare germline variants from ClinVar with the area under the receiver operating characteristic curve (AUROC) of 0.4481-0.8033 and poor for rare somatic variants from COSMIC (AUROC = 0.4984-0.7131), common regulatory variants from curated eQTL data (AUROC = 0.4837-0.6472), and disease-associated common variants from curated GWAS (AUROC = 0.4766-0.5188). We also compared the prediction performance of 24 methods for non-coding de novo mutations in autism spectrum disorder, and found that the combined annotation-dependent depletion (CADD) and context-dependent tolerance score (CDTS) methods showed better performance. Summarily, we assessed the performance of 24 computational methods under diverse scenarios, providing preliminary advice for proper tool selection and guiding the development of new techniques in interpreting non-coding 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.

Dissecting the 22q13 region to explore the genetic and phenotypic diversity of patients with Phelan-McDermid syndrome

SHANK3-related Phelan-McDermid syndrome (PMS) is caused by a loss of the distal part of chromosome 22, including SHANK3, or by a pathological SHANK3 variant. There is an important genetic and phenotypic diversity among patients who can present with developmental delay, language impairments, autism, epilepsy, and other symptoms. SHANK3, encoding a synaptic scaffolding protein, is deleted in the majority of patients with PMS and is considered a major gene involved in the neurological impairments of the patients. However, differences in deletion size can influence clinical features, and in some rare cases, deletions at the 22q13 locus in individuals with SHANK3-unrelated PMS do not encompass SHANK3. These individuals with SHANK3-unrelated PMS still display a PMS-like phenotype. This suggests the participation of other 22q13 genes in the pathogenesis of PMS. Here, we review the biological function and potential implication in PMS symptoms of 110 genes located in the 22q13 region, focusing on 35 genes with evidence for association with neurodevelopmental disorders, including 13 genes for epilepsy and 11 genes for microcephaly and/or macrocephaly. Our review is restricted to the 22q13 region, but future large-scale studies using whole genome sequencing and deep-phenotyping are warranted to develop predictive models of clinical trajectories and to target specific medical and educational care for each individual with PMS.

Hooked Up from a Distance: Charting Genome-Wide Long-Range Interaction Maps in Neural Cells Chromatin to Identify Novel Candidate Genes for Neurodevelopmental Disorders

DNA sequence variants (single nucleotide polymorphisms or variants, SNPs/SNVs; copy number variants, CNVs) associated to neurodevelopmental disorders (NDD) and traits often map on putative transcriptional regulatory elements, including, in particular, enhancers. However, the genes controlled by these enhancers remain poorly defined. Traditionally, the activity of a given enhancer, and the effect of its possible alteration associated to the sequence variants, has been thought to influence the nearest gene promoter. However, the obtainment of genome-wide long-range interaction maps in neural cells chromatin challenged this view, showing that a given enhancer is very frequently not connected to the nearest promoter, but to a more distant one, skipping genes in between. In this Perspective, we review some recent papers, who generated long-range interaction maps (by HiC, RNApolII ChIA-PET, Capture-HiC, or PLACseq), and overlapped the identified long-range interacting DNA segments with DNA sequence variants associated to NDD (such as schizophrenia, bipolar disorder and autism) and traits (intelligence). This strategy allowed to attribute the function of enhancers, hosting the NDD-related sequence variants, to a connected gene promoter lying far away on the linear chromosome map. Some of these enhancer-connected genes had indeed been already identified as contributive to the diseases, by the identification of mutations within the gene's protein-coding regions (exons), validating the approach. Significantly, however, the connected genes also include many genes that were not previously found mutated in their exons, pointing to novel candidate contributors to NDD and traits. Thus, long-range interaction maps, in combination with DNA variants detected in association with NDD, can be used as "pointers" to identify novel candidate disease-relevant genes. Functional manipulation of the long-range interaction network involving enhancers and promoters by CRISPR-Cas9-based approaches is beginning to probe for the functional significance of the identified interactions, and the enhancers and the genes involved, improving our understanding of neural development and its pathology.

De novo variants cause complex symptoms in HSP-ATL1 (SPG3A) and uncover genotype-phenotype correlations

Pathogenic variants in ATL1 are a known cause of autosomal-dominantly inherited hereditary spastic paraplegia (HSP-ATL1, SPG3A) with a predominantly 'pure' HSP phenotype. Although a relatively large number of patients have been reported, no genotype-phenotype correlations have been established for specific ATL1 variants. Confronted with five children carrying de novo ATL1 variants showing early, complex and severe symptoms, we systematically investigated the molecular and phenotypic spectrum of HSP-ATL1. Through a cross-sectional analysis of 537 published and novel cases, we delineate a distinct phenotype observed in patients with de novo variants. Guided by this systematic phenotyping approach and structural modelling of disease-associated variants in atlastin-1, we demonstrate that this distinct phenotypic signature is also prevalent in a subgroup of patients with inherited ATL1 variants and is largely explained by variant localization within a three-dimensional mutational cluster. Establishing genotype-phenotype correlations, we find that symptoms that extend well beyond the typical pure HSP phenotype (i.e. neurodevelopmental abnormalities, upper limb spasticity, bulbar symptoms, peripheral neuropathy and brain imaging abnormalities) are prevalent in patients with variants located within this mutational cluster.

The performance of genome sequencing as a first-tier test for neurodevelopmental disorders

Genome sequencing (GS) can identify novel diagnoses for patients who remain undiagnosed after routine diagnostic procedures. We tested whether GS is a better first-tier genetic diagnostic test than current standard of care (SOC) by assessing the technical and clinical validity of GS for patients with neurodevelopmental disorders (NDD). We performed both GS and exome sequencing in 150 consecutive NDD patient-parent trios. The primary outcome was diagnostic yield, calculated from disease-causing variants affecting exonic sequence of known NDD genes. GS (30%, n = 45) and SOC (28.7%, n = 43) had similar diagnostic yield. All 43 conclusive diagnoses obtained with SOC testing were also identified by GS. SOC, however, required integration of multiple test results to obtain these diagnoses. GS yielded two more conclusive diagnoses, and four more possible diagnoses than ES-based SOC (35 vs. 31). Interestingly, these six variants detected only by GS were copy number variants (CNVs). Our data demonstrate the technical and clinical validity of GS to serve as routine first-tier genetic test for patients with NDD. Although the additional diagnostic yield from GS is limited, GS comprehensively identified all variants in a single experiment, suggesting that GS constitutes a more efficient genetic diagnostic workflow.

Evolution and dysfunction of human cognitive and social traits: A transcriptional regulation perspective

Evolutionary changes in brain and craniofacial development have endowed humans with unique cognitive and social skills, but also predisposed us to debilitating disorders in which these traits are disrupted. What are the developmental genetic underpinnings that connect the adaptive evolution of our cognition and sociality with the persistence of mental disorders with severe negative fitness effects? We argue that loss of function of genes involved in transcriptional regulation represents a crucial link between the evolution and dysfunction of human cognitive and social traits. The argument is based on the haploinsufficiency of many transcriptional regulator genes, which makes them particularly sensitive to loss-of-function mutations. We discuss how human brain and craniofacial traits evolved through partial loss of function (i.e. reduced expression) of these genes, a perspective compatible with the idea of human self-domestication. Moreover, we explain why selection against loss-of-function variants supports the view that mutation-selection-drift, rather than balancing selection, underlies the persistence of psychiatric disorders. Finally, we discuss testable predictions.

Focus on your locus with a massively parallel reporter assay

A growing number of variants associated with risk for neurodevelopmental disorders have been identified by genome-wide association and whole genome sequencing studies. As common risk variants often fall within large haplotype blocks covering long stretches of the noncoding genome, the causal variants within an associated locus are often unknown. Similarly, the effect of rare noncoding risk variants identified by whole genome sequencing on molecular traits is seldom known without functional assays. A massively parallel reporter assay (MPRA) is an assay that can functionally validate thousands of regulatory elements simultaneously using high-throughput sequencing and barcode technology. MPRA has been adapted to various experimental designs that measure gene regulatory effects of genetic variants within cis- and trans-regulatory elements as well as posttranscriptional processes. This review discusses different MPRA designs that have been or could be used in the future to experimentally validate genetic variants associated with neurodevelopmental disorders. Though MPRA has limitations such as it does not model genomic context, this assay can help narrow down the underlying genetic causes of neurodevelopmental disorders by screening thousands of sequences in one experiment. We conclude by describing future directions of this technique such as applications of MPRA for gene-by-environment interactions and pharmacogenetics.

Rare coding variation provides insight into the genetic architecture and phenotypic context of autism

Some individuals with autism spectrum disorder (ASD) carry functional mutations rarely observed in the general population. We explored the genes disrupted by these variants from joint analysis of protein-truncating variants (PTVs), missense variants and copy number variants (CNVs) in a cohort of 63,237 individuals. We discovered 72 genes associated with ASD at false discovery rate (FDR) ≤ 0.001 (185 at FDR ≤ 0.05). De novo PTVs, damaging missense variants and CNVs represented 57.5%, 21.1% and 8.44% of association evidence, while CNVs conferred greatest relative risk. Meta-analysis with cohorts ascertained for developmental delay (DD) (n = 91,605) yielded 373 genes associated with ASD/DD at FDR ≤ 0.001 (664 at FDR ≤ 0.05), some of which differed in relative frequency of mutation between ASD and DD cohorts. The DD-associated genes were enriched in transcriptomes of progenitor and immature neuronal cells, whereas genes showing stronger evidence in ASD were more enriched in maturing neurons and overlapped with schizophrenia-associated genes, emphasizing that these neuropsychiatric disorders may share common pathways to risk.

Bridging between Mouse and Human Enhancer-Promoter Long-Range Interactions in Neural Stem Cells, to Understand Enhancer Function in Neurodevelopmental Disease

Non-coding variation in complex human disease has been well established by genome-wide association studies, and is thought to involve regulatory elements, such as enhancers, whose variation affects the expression of the gene responsible for the disease. The regulatory elements often lie far from the gene they regulate, or within introns of genes differing from the regulated gene, making it difficult to identify the gene whose function is affected by a given enhancer variation. Enhancers are connected to their target gene promoters via long-range physical interactions (loops). In our study, we re-mapped, onto the human genome, more than 10,000 enhancers connected to promoters via long-range interactions, that we had previously identified in mouse brain-derived neural stem cells by RNApolII-ChIA-PET analysis, coupled to ChIP-seq mapping of DNA/chromatin regions carrying epigenetic enhancer marks. These interactions are thought to be functionally relevant. We discovered, in the human genome, thousands of DNA regions syntenic with the interacting mouse DNA regions (enhancers and connected promoters). We further annotated these human regions regarding their overlap with sequence variants (single nucleotide polymorphisms, SNPs; copy number variants, CNVs), that were previously associated with neurodevelopmental disease in humans. We document various cases in which the genetic variant, associated in humans to neurodevelopmental disease, affects an enhancer involved in long-range interactions: SNPs, previously identified by genome-wide association studies to be associated with schizophrenia, bipolar disorder, and intelligence, are located within our human syntenic enhancers, and alter transcription factor recognition sites. Similarly, CNVs associated to autism spectrum disease and other neurodevelopmental disorders overlap with our human syntenic enhancers. Some of these enhancers are connected (in mice) to homologs of genes already associated to the human disease, strengthening the hypothesis that the gene is indeed involved in the disease. Other enhancers are connected to genes not previously associated with the disease, pointing to their possible pathogenetic involvement. Our observations provide a resource for further exploration of neural disease, in parallel with the now widespread genome-wide identification of DNA variants in patients with neural disease.

The Role of Genetics, Epigenetics, and the Environment in ASD: A Mini Review

According to recent findings, variances in autism spectrum disorder (ASD) risk factors might be determined by several factors, including molecular genetic variants. Accumulated evidence has also revealed the important role of biological and chemical pathways in ASD aetiology. In this paper, we assess several reviews with regard to their quality of evidence and provide a brief outline of the presumed mechanisms of the genetic, epigenetic, and environmental risk factors of ASD. We also review some of the critical literature, which supports the basis of each factor in the underlying and specific risk patterns of ASD. Finally, we consider some of the implications of recent research regarding potential molecular targets for future investigations.

The noncoding genome and hearing loss

The age of sequencing has provided unprecedented insights into the human genome. The coding region of the genome comprises nearly 20,000 genes, of which approximately 4000 are associated with human disease. Beyond the protein-coding genome, which accounts for only 3% of the genome, lies a vast pool of regulatory elements in the form of promoters, enhancers, RNA species, and other intricate elements. These features undoubtably influence human health and disease, and as a result, a great deal of effort is currently being invested in deciphering their identity and mechanism. While a paucity of material has caused a lag in identifying these elements in the inner ear, the emergence of technologies for dealing with a minimal number of cells now has the field working overtime to catch up. Studies on microRNAs (miRNAs), long non-coding RNAs (lncRNAs), methylation, histone modifications, and more are ongoing. A number of microRNAs and other noncoding elements are known to be associated with hearing impairment and there is promise that regulatory elements will serve as future tools and targets of therapeutics and diagnostics. This review covers the current state of the field and considers future directions for the noncoding genome and implications for hearing loss.

RevUP, an online scoring system for regulatory variants implicated in rare diseases

Summary

To address the difficulty in assessing the implication of regulatory variants in diseases, a scoring scheme previously published allows the calculation of the Regulatory Variant Evidence score (RVE-score). The score represents the accumulated evidence for a causative role of a regulatory variant in a disease. Regulatory Evidence for Variants Underlying Phenotypes was built to calculate the RVE-score of regulatory variants, based on the 24 criteria, with a hybrid approach combining information retrieved from public databases and user input.

Availability and implementation

RevUP is freely available at http://www.revup-classifier.ca. The source code is available at https://github.com/wassermanlab/revup.

Supplementary information

Supplementary data are available at Bioinformatics online.

Developmental disorders caused by haploinsufficiency of transcriptional regulators: a perspective based on cell fate determination

Many human birth defects and neurodevelopmental disorders are caused by loss-of-function mutations in a single copy of transcription factor (TF) and chromatin regulator genes. Although this dosage sensitivity has long been known, how and why haploinsufficiency (HI) of transcriptional regulators leads to developmental disorders (DDs) is unclear. Here I propose the hypothesis that such DDs result from defects in cell fate determination that are based on disrupted bistability in the underlying gene regulatory network (GRN). Bistability, a crucial systems biology concept to model binary choices such as cell fate decisions, requires both positive feedback and ultrasensitivity, the latter often achieved through TF cooperativity. The hypothesis explains why dosage sensitivity of transcriptional regulators is an inherent property of fate decisions, and why disruption of either positive feedback or cooperativity in the underlying GRN is sufficient to cause disease. I present empirical and theoretical evidence in support of this hypothesis and discuss several issues for which it increases our understanding of disease, such as incomplete penetrance. The proposed framework provides a mechanistic, systems-level explanation of HI of transcriptional regulators, thus unifying existing theories, and offers new insights into outstanding issues of human disease. This article has an associated Future Leader to Watch interview with the author of the paper.

Genomic frontiers in congenital heart disease

The application of next-generation sequencing to study congenital heart disease (CHD) is increasingly providing new insights into the causes and mechanisms of this prevalent birth anomaly. Whole-exome sequencing analysis identifies damaging gene variants altering single or contiguous nucleotides that are assigned pathogenicity based on statistical analyses of families and cohorts with CHD, high expression in the developing heart and depletion of damaging protein-coding variants in the general population. Gene classes fulfilling these criteria are enriched in patients with CHD and extracardiac abnormalities, evidencing shared pathways in organogenesis. Developmental single-cell transcriptomic data demonstrate the expression of CHD-associated genes in particular cell lineages, and emerging insights indicate that genetic variants perturb multicellular interactions that are crucial for cardiogenesis. Whole-genome sequencing analyses extend these observations, identifying non-coding variants that influence the expression of genes associated with CHD and contribute to the estimated ~55% of unexplained cases of CHD. These approaches combined with the assessment of common and mosaic genetic variants have provided a more complete knowledge of the causes and mechanisms of CHD. Such advances provide knowledge to inform the clinical care of patients with CHD or other birth defects and deepen our understanding of the complexity of human development. In this Review, we highlight known and candidate CHD-associated human genes and discuss how the integration of advances in developmental biology research can provide new insights into the genetic contributions to CHD.

Structural variants are a major source of gene expression differences in humans and often affect multiple nearby genes

Structural variants (SVs) are an important source of human genome diversity, but their functional effects are poorly understood. We mapped 61,668 SVs in 613 individuals from the GTEx project and measured their effects on gene expression. We estimate that common SVs are causal at 2.66% of eQTLs, a 10.5-fold enrichment relative to their abundance in the genome. Duplications and deletions were the most impactful variant types, whereas the contribution of mobile element insertions was small (0.12% of eQTLs, 1.9-fold enriched). Multitissue analysis of eQTLs revealed that gene-altering SVs show more constitutive effects than other variant types, with 62.09% of coding SV-eQTLs active in all tissues with eQTL activity compared with 23.08% of coding SNV- and indel-eQTLs. Noncoding SVs, SNVs and indels show broadly similar patterns. We also identified 539 rare SVs associated with nearby gene expression outliers. Of these, 62.34% are noncoding SVs that affect gene expression but have modest enrichment at regulatory elements, showing that rare noncoding SVs are a major source of gene expression differences but remain difficult to predict from current annotations. Both common and rare SVs often affect the expression of multiple genes: SV-eQTLs affect an average of 1.82 nearby genes, whereas SNV- and indel-eQTLs affect an average of 1.09 genes, and 21.34% of rare expression-altering SVs show effects on two to nine different genes. We also observe significant effects on rare gene expression changes extending 1 Mb from the SV. This provides a mechanism by which individual SVs may have strong or pleiotropic effects on phenotypic variation.

Rare Does Not Mean Worthless: How Rare Diseases Have Shaped Neurodevelopment Research in the NGS Era

The advent of next-generation sequencing (NGS) is heavily changing both the diagnosis of human conditions and basic biological research. It is now possible to dig deep inside the genome of hundreds of thousands or even millions of people and find both common and rare genomic variants and to perform detailed phenotypic characterizations of both physiological organs and experimental models. Recent years have seen the introduction of multiple techniques using NGS to profile transcription, DNA and chromatin modifications, protein binding, etc., that are now allowing us to profile cells in bulk or even at a single-cell level. Although rare and ultra-rare diseases only affect a few people, each of these diseases represent scholarly cases from which a great deal can be learned about the pathological and physiological function of genes, pathways, and mechanisms. Therefore, for rare diseases, state-of-the-art investigations using NGS have double valence: their genomic cause (new variants) and the characterize the underlining the mechanisms associated with them (discovery of gene function) can be found. In a non-exhaustive manner, this review will outline the main usage of NGS-based techniques for the diagnosis and characterization of neurodevelopmental disorders (NDDs), under whose umbrella many rare and ultra-rare diseases fall.

The non-coding genome in genetic brain disorders: new targets for therapy?

The non-coding genome, consisting of more than 98% of all genetic information in humans and once judged as ‘Junk DNA’, is increasingly moving into the spotlight in the field of human genetics. Non-coding regulatory elements (NCREs) are crucial to ensure correct spatio-temporal gene expression. Technological advancements have allowed to identify NCREs on a large scale, and mechanistic studies have helped to understand the biological mechanisms underlying their function. It is increasingly becoming clear that genetic alterations of NCREs can cause genetic disorders, including brain diseases. In this review, we concisely discuss mechanisms of gene regulation and how to investigate them, and give examples of non-coding alterations of NCREs that give rise to human brain disorders. The cross-talk between basic and clinical studies enhances the understanding of normal and pathological function of NCREs, allowing better interpretation of already existing and novel data. Improved functional annotation of NCREs will not only benefit diagnostics for patients, but might also lead to novel areas of investigations for targeted therapies, applicable to a wide panel of genetic disorders. The intrinsic complexity and precision of the gene regulation process can be turned to the advantage of highly specific treatments. We further discuss this exciting new field of ‘enhancer therapy’ based on recent examples.

Experiment level curation of transcriptional regulatory interactions in neurodevelopment

To facilitate the development of large-scale transcriptional regulatory networks (TRNs) that may enable in-silico analyses of disease mechanisms, a reliable catalogue of experimentally verified direct transcriptional regulatory interactions (DTRIs) is needed for training and validation. There has been a long history of using low-throughput experiments to validate single DTRIs. Therefore, we reason that a reliable set of DTRIs could be produced by curating the published literature for such evidence. In our survey of previous curation efforts, we identified the lack of details about the quantity and the types of experimental evidence to be a major gap, despite the theoretical importance of such details for the identification of bona fide DTRIs. We developed a curation protocol to inspect the published literature for support of DTRIs at the experiment level, focusing on genes important to the development of the mammalian nervous system. We sought to record three types of low-throughput experiments: Transcription factor (TF) perturbation, TF-DNA binding, and TF-reporter assays. Using this protocol, we examined a total of 1,310 papers to assemble a collection of 1,499 unique DTRIs, involving 251 TFs and 825 target genes, many of which were not reported in any other DTRI resource. The majority of DTRIs (965; 64%) were supported by two or more types of experimental evidence and 27% were supported by all three. Of the DTRIs with all three types of evidence, 170 had been tested using primary tissues or cells and 44 had been tested directly in the central nervous system. We used our resource to document research biases among reports towards a small number of well-studied TFs. To demonstrate a use case for this resource, we compared our curation to a previously published high-throughput perturbation screen and found significant enrichment of the curated targets among genes differentially expressed in the developing brain in response to Pax6 deletion. This study demonstrates a proof-of-concept for the assembly of a high resolution DTRI resource to support the development of large-scale TRNs.

The capacity to computationally reconstruct gene regulatory networks using large-scale biological data is currently limited by the absence of a high confidence set of one-to-one regulatory interactions. Given the lengthy history of using small scale experimental assays to investigate individual interactions, we reason that a reliable collection of gene regulatory interactions could be compiled by systematically inspecting the published literature. To this end, we developed a curation protocol to examine and record evidence of regulatory interactions at the individual experiment level. Focusing on the area of brain development, we applied our pipeline to 1,310 publications. We identified 3,601 individual experiments, providing detailed information about 1,499 regulatory interactions. Many of these interactions have verified activity specifically in the embryonic brain. By capturing reports of regulatory interactions at this level of detail, we equip the users with more granular information than other similar resources, enabling more informed assessments of reliability.

Genome sequencing data analysis for rare disease gene discovery

Rare diseases occur in a smaller proportion of the general population, which is variedly defined as less than 200 000 individuals (US) or in less than 1 in 2000 individuals (Europe). Although rare, they collectively make up to approximately 7000 different disorders, with majority having a genetic origin, and affect roughly 300 million people globally. Most of the patients and their families undergo a long and frustrating diagnostic odyssey. However, advances in the field of genomics have started to facilitate the process of diagnosis, though it is hindered by the difficulty in genome data analysis and interpretation. A major impediment in diagnosis is in the understanding of the diverse approaches, tools and datasets available for variant prioritization, the most important step in the analysis of millions of variants to select a few potential variants. Here we present a review of the latest methodological developments and spectrum of tools available for rare disease genetic variant discovery and recommend appropriate data interpretation methods for variant prioritization. We have categorized the resources based on various steps of the variant interpretation workflow, starting from data processing, variant calling, annotation, filtration and finally prioritization, with a special emphasis on the last two steps. The methods discussed here pertain to elucidating the genetic basis of disease in individual patient cases via trio- or family-based analysis of the genome data. We advocate the use of a combination of tools and datasets and to follow multiple iterative approaches to elucidate the potential causative variant.

Variable Phenotypes of Epilepsy, Intellectual Disability, and Schizophrenia Caused by 12p13.33-p13.32 Terminal Microdeletion in a Korean Family: A Case Report and Literature Review

A simultaneous analysis of nucleotide changes and copy number variations (CNVs) based on exome sequencing data was demonstrated as a potential new first-tier diagnosis strategy for rare neuropsychiatric disorders. In this report, using depth-of-coverage analysis from exome sequencing data, we described variable phenotypes of epilepsy, intellectual disability (ID), and schizophrenia caused by 12p13.33–p13.32 terminal microdeletion in a Korean family. We hypothesized that CACNA1C and KDM5A genes of the six candidate genes located in this region were the best candidates for explaining epilepsy, ID, and schizophrenia and may be responsible for clinical features reported in cases with monosomy of the 12p13.33 subtelomeric region. On the background of microdeletion syndrome, which was described in clinical cases with mild, moderate, and severe neurodevelopmental manifestations as well as impairments, the clinician may determine whether the patient will end up with a more severe or milder end-phenotype, which in turn determines disease prognosis. In our case, the 12p13.33–p13.32 terminal microdeletion may explain the variable expressivity in the same family. However, further comprehensive studies with larger cohorts focusing on careful phenotyping across the lifespan are required to clearly elucidate the possible contribution of genetic modifiers and the environmental influence on the expressivity of 12p13.33 microdeletion and associated characteristics.

Massively Parallel Reporter Assays: Defining Functional Psychiatric Genetic Variants Across Biological Contexts

Neuropsychiatric phenotypes have long been known to be influenced by heritable risk factors, directly confirmed by the past decade of genetic studies that have revealed specific genetic variants enriched in disease cohorts. However, the initial hope that a small set of genes would be responsible for a given disorder proved false. The more complex reality is that a given disorder may be influenced by myriad small-effect noncoding variants and/or by rare but severe coding variants, many de novo. Noncoding genomic sequences-for which molecular functions cannot usually be inferred-harbor a large portion of these variants, creating a substantial barrier to understanding higher-order molecular and biological systems of disease. Fortunately, novel genetic technologies-scalable oligonucleotide synthesis, RNA sequencing, and CRISPR (clustered regularly interspaced short palindromic repeats)-have opened novel avenues to experimentally identify biologically significant variants en masse. Massively parallel reporter assays (MPRAs) are an especially versatile technique resulting from such innovations. MPRAs are powerful molecular genetics tools that can be used to screen thousands of untranscribed or untranslated sequences and their variants for functional effects in a single experiment. This approach, though underutilized in psychiatric genetics, has several useful features for the field. We review methods for assaying putatively functional genetic variants and regions, emphasizing MPRAs and the opportunities they hold for dissection of psychiatric polygenicity. We discuss literature applying functional assays in neurogenetics, highlighting strengths, caveats, and design considerations-especially regarding disease-relevant variables (cell type, neurodevelopment, and sex), and we ultimately propose applications of MPRA to both computational and experimental neurogenetics of polygenic disease risk.

Diagnostic Utility of Genome-Wide DNA Methylation Analysis in Mendelian Neurodevelopmental Disorders

Mendelian neurodevelopmental disorders customarily present with complex and overlapping symptoms, complicating the clinical diagnosis. Individuals with a growing number of the so-called rare disorders exhibit unique, disorder-specific DNA methylation patterns, consequent to the underlying gene defects. Besides providing insights to the pathophysiology and molecular biology of these disorders, we can use these epigenetic patterns as functional biomarkers for the screening and diagnosis of these conditions. This review summarizes our current understanding of DNA methylation episignatures in rare disorders and describes the underlying technology and analytical approaches. We discuss the computational parameters, including statistical and machine learning methods, used for the screening and classification of genetic variants of uncertain clinical significance. Describing the rationale and principles applied to the specific computational models that are used to develop and adapt the DNA methylation episignatures for the diagnosis of rare disorders, we highlight the opportunities and challenges in this emerging branch of diagnostic medicine.

Multi-omics analysis reveals the functional transcription and potential translation of enhancers

Enhancer can transcribe RNAs, however, most of them were neglected in traditional RNA-seq analysis workflow. Here, we developed a Pipeline for Enhancer Transcription (PET, http://fun-science.club/PET) for quantifying enhancer RNAs (eRNAs) from RNA-seq. By applying this pipeline on lung cancer samples and cell lines, we showed that the transcribed enhancers are enriched with histone marks and transcription factor motifs (JUNB, Hand1-Tcf3 and GATA4). By training a machine learning model, we demonstrate that enhancers can predict prognosis better than their nearby genes. Integrating the Hi-C, ChIP-seq and RNA-seq data, we observe that transcribed enhancers associate with cancer hallmarks or oncogenes, among which LcsMYC-1 (Lung cancer-specific MYC eRNA-1) potentially supports MYC expression. Surprisingly, a significant proportion of transcribed enhancers contain small protein-coding open reading frames (sORFs) and can be translated into microproteins. Our study provides a computational method for eRNA quantification and deepens our understandings of the DNA, RNA and protein nature of enhancers.

Interpreting the impact of noncoding structural variation in neurodevelopmental disorders

The emergence of novel sequencing technologies has greatly improved the identification of structural variation, revealing that a human genome harbors tens of thousands of structural variants (SVs). Since these SVs primarily impact noncoding DNA sequences, the next challenge is one of interpretation, not least to improve our understanding of human disease etiology. However, this task is severely complicated by the intricacy of the gene regulatory landscapes embedded within these noncoding regions, their incomplete annotation, as well as their dependence on the three-dimensional (3D) conformation of the genome. Also in the context of neurodevelopmental disorders (NDDs), reports of putatively causal, noncoding SVs are accumulating and understanding their impact on transcriptional regulation is presenting itself as the next step toward improved genetic diagnosis.

Mechanisms for the Approach/Avoidance Decision Applied to Autism

As a neurodevelopmental disorder with serious lifelong consequences, autism has received considerable attention from neuroscientists and geneticists. We present a hypothesis of mechanisms plausibly affected during brain development in autism, based on neural pathways that are associated with social behavior and connect the prefrontal cortex (PFC) to the basal ganglia (BG). We consider failure of social approach in autism as a special case of imbalance in the fundamental dichotomy between behavioral approach and avoidance. Differential combinations of genes mutated, differences in the timing of their impact during development, and graded degrees of hormonal influences may help explain the heterogeneity in symptomatology in autism and predominance in boys.

Ocular genetics in the genomics age

Current genetic screening methods for inherited eye diseases are concentrated on the coding exons of known disease genes (gene panels, clinical exome). These tests have a variable and often limited diagnostic rate depending on the clinical presentation, size of the gene panel and our understanding of the inheritance of the disorder (with examples described in this issue). There are numerous possible explanations for the missing heritability of these cases including undetected variants within the relevant gene (intronic, up/down-stream and structural variants), variants harbored in genes outside the targeted panel, intergenic variants, variants undetectable by the applied technology, complex/non-Mendelian inheritance, and nongenetic phenocopies. In this article we further explore and review methods to investigate these sources of missing heritability.

Genomic Diagnosis for Pediatric Disorders: Revolution and Evolution

Powerful, recent advances in technologies to analyze the genome have had a profound impact on the practice of medical genetics, both in the laboratory and in the clinic. Increasing utilization of genome-wide testing such as chromosomal microarray analysis and exome sequencing have lead a shift toward a "genotype-first" approach. Numerous techniques are now available to diagnose a particular syndrome or phenotype, and while traditional techniques remain efficient tools in certain situations, higher-throughput technologies have become the de facto laboratory tool for diagnosis of most conditions. However, selecting the right assay or technology is challenging, and the wrong choice may lead to prolonged time to diagnosis, or even a missed diagnosis. In this review, we will discuss current core technologies for the diagnosis of classic genetic disorders to shed light on the benefits and disadvantages of these strategies, including diagnostic efficiency, variant interpretation, and secondary findings. Finally, we review upcoming technologies posed to impart further changes in the field of genetic diagnostics as we move toward "genome-first" practice.

Recent Advances in Understanding the Genetic Architecture of Autism

Recent advances in understanding the genetic architecture of autism spectrum disorder have allowed for unprecedented insight into its biological underpinnings. New studies have elucidated the contributions of a variety of forms of genetic variation to autism susceptibility. While the roles of de novo copy number variants and single-nucleotide variants-causing loss-of-function or missense changes-have been increasingly recognized and refined, mosaic single-nucleotide variants have been implicated more recently in some cases. Moreover, inherited variants (including common variants) and, more recently, rare recessive inherited variants have come into greater focus. Finally, noncoding variants-both inherited and de novo-have been implicated in the last few years. This work has revealed a convergence of diverse genetic drivers on common biological pathways and has highlighted the ongoing importance of increasing sample size and experimental innovation. Continuing to synthesize these genetic findings with functional and phenotypic evidence and translating these discoveries to clinical care remain considerable challenges for the field.

Modeling neuronal consequences of autism-associated gene regulatory variants with human induced pluripotent stem cells

Genetic factors contribute to the development of autism spectrum disorder (ASD), and although non-protein-coding regions of the genome are being increasingly implicated in ASD, the functional consequences of these variants remain largely uncharacterized. Induced pluripotent stem cells (iPSCs) enable the production of personalized neurons that are genetically matched to people with ASD and can therefore be used to directly test the effects of genomic variation on neuronal gene expression, synapse function, and connectivity. The combined use of human pluripotent stem cells with genome editing to introduce or correct specific variants has proved to be a powerful approach for exploring the functional consequences of ASD-associated variants in protein-coding genes and, more recently, long non-coding RNAs (lncRNAs). Here, we review recent studies that implicate lncRNAs, other non-coding mutations, and regulatory variants in ASD susceptibility. We also discuss experimental design considerations for using iPSCs and genome editing to study the role of the non-protein-coding genome in ASD.

Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing Human Prefrontal Cortex

Gene expression levels vary across developmental stage, cell type, and region in the brain. Genomic variants also contribute to the variation in expression, and some neuropsychiatric disorder loci may exert their effects through this mechanism. To investigate these relationships, we present BrainVar, a unique resource of paired whole-genome and bulk tissue RNA sequencing from the dorsolateral prefrontal cortex of 176 individuals across prenatal and postnatal development. Here we identify common variants that alter gene expression (expression quantitative trait loci [eQTLs]) constantly across development or predominantly during prenatal or postnatal stages. Both "constant" and "temporal-predominant" eQTLs are enriched for loci associated with neuropsychiatric traits and disorders and colocalize with specific variants. Expression levels of more than 12,000 genes rise or fall in a concerted late-fetal transition, with the transitional genes enriched for cell-type-specific genes and neuropsychiatric risk loci, underscoring the importance of cataloging developmental trajectories in understanding cortical physiology and pathology.

Cellular and molecular characterization of multiplex autism in human induced pluripotent stem cell-derived neurons

Background

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with pronounced heritability in the general population. This is largely attributable to the effects of polygenic susceptibility, with inherited liability exhibiting distinct sex differences in phenotypic expression. Attempts to model ASD in human cellular systems have principally involved rare de novo mutations associated with ASD phenocopies. However, by definition, these models are not representative of polygenic liability, which accounts for the vast share of population-attributable risk.

Methods

Here, we performed what is, to our knowledge, the first attempt to model multiplex autism using patient-derived induced pluripotent stem cells (iPSCs) in a family manifesting incremental degrees of phenotypic expression of inherited liability (absent, intermediate, severe). The family members share an inherited variant of uncertain significance (VUS) in GPD2, a gene that was previously associated with developmental disability but here is insufficient by itself to cause ASD. iPSCs from three first-degree relatives and an unrelated control were differentiated into both cortical excitatory (cExN) and cortical inhibitory (cIN) neurons, and cellular phenotyping and transcriptomic analysis were conducted.

Results

cExN neurospheres from the two affected individuals were reduced in size, compared to those derived from unaffected related and unrelated individuals. This reduction was, at least in part, due to increased apoptosis of cells from affected individuals upon initiation of cExN neural induction. Likewise, cIN neural progenitor cells from affected individuals exhibited increased apoptosis, compared to both unaffected individuals. Transcriptomic analysis of both cExN and cIN neural progenitor cells revealed distinct molecular signatures associated with affectation, including the misregulation of suites of genes associated with neural development, neuronal function, and behavior, as well as altered expression of ASD risk-associated genes.

Conclusions

We have provided evidence of morphological, physiological, and transcriptomic signatures of polygenic liability to ASD from an analysis of cellular models derived from a multiplex autism family. ASD is commonly inherited on the basis of additive genetic liability. Therefore, identifying convergent cellular and molecular phenotypes resulting from polygenic and monogenic susceptibility may provide a critical bridge for determining which of the disparate effects of rare highly deleterious mutations might also apply to common autistic syndromes.

Ranking of non-coding pathogenic variants and putative essential regions of the human genome

A gene is considered essential if loss of function results in loss of viability, fitness or in disease. This concept is well established for coding genes; however, non-coding regions are thought less likely to be determinants of critical functions. Here we train a machine learning model using functional, mutational and structural features, including new genome essentiality metrics, 3D genome organization and enhancer reporter data to identify deleterious variants in non-coding regions. We assess the model for functional correlates by using data from tiling-deletion-based and CRISPR interference screens of activity of cis-regulatory elements in over 3 Mb of genome sequence. Finally, we explore two user cases that involve indels and the disruption of enhancers associated with a developmental disease. We rank variants in the non-coding genome according to their predicted deleteriousness. The model prioritizes non-coding regions associated with regulation of important genes and with cell viability, an in vitro surrogate of essentiality.

Whole genome sequencing (WGS) holds promise to solve a subset of Mendelian disease cases for which exome sequencing did not provide a genetic diagnosis. Here, Wells et al. report a supervised machine learning model trained on functional, mutational and structural features for rank-scoring and interpreting variants in non-coding regions from WGS.

Mendelian Gene Discovery: Fast and Furious with No End in Sight

Gene discovery for Mendelian conditions (MCs) offers a direct path to understanding genome function. Approaches based on next-generation sequencing applied at scale have dramatically accelerated gene discovery and transformed genetic medicine. Finding the genetic basis of ∼6,000-13,000 MCs yet to be delineated will require both technical and computational innovation, but will rely to a larger extent on meaningful data sharing.

Sox2-Dependent 3D Chromatin Interactomes in Transcription, Neural Stem Cell Proliferation and Neurodevelopmental Diseases

In our article, we asked whether Sox2, a transcription factor important in brain development and disease, is involved in gene regulation through its action on long-range interactions between promoters and distant enhancers. Our findings highlight that Sox2 shapes a genome-wide network of promoter-enhancer interactions, acting by direct binding to these elements. Sox2 loss affects the three-dimensional (3D) genome and decreases the activity of a subset of genes involved in Sox2-bound interactions. At least one of such downregulated genes, Socs3, is critical for long-term neural stem cell maintenance. These results point to the possibility of identifying a transcriptional network downstream to Sox2, and involved in neural stem cell maintenance. In addition, interacting Sox2-bound enhancers are often connected to genes which are relevant, in man, to neurodevelopmental disease; this may facilitate the detection of functionally relevant mutations in regulatory elements in man, contributing to neural disease.

Brain organoids and insights on human evolution

Human brain organoids, generated from pluripotent stem cells, have emerged as a promising technique for modeling early stages of human neurodevelopment in controlled laboratory conditions. Although the applications for disease modeling in a dish have become routine, the use of these brain organoids as evolutionary tools is only now getting momentum. Here, we will review the current state of the art on the use of brain organoids from different species and the molecular and cellular insights generated from these studies. Besides, we will discuss how this model might be beneficial for human health and the limitations and future perspectives of this technology.

Serum microRNAs in ASD: Association With Monocyte Cytokine Profiles and Mitochondrial Respiration

Our previous research has shown that purified peripheral blood monocytes (PRMo) from individuals who are diagnosed with autism spectrum disorders (ASDs) and have innate immune abnormalities reveal altered interleukin-1ß (IL-1ß)/IL-10 ratios. We also found, in separate studies, that microRNA (miRNA) expression in PBMo and mitochondrial respiration in peripheral blood mononuclear cells (PBMCs) differed in the IL-1ß/IL-10-based ASD subgroups. This study explored whether serum miRNAs are associated with both altered innate immune responses and changes in mitochondrial respiration as a link of regulatory mechanisms for these two common abnormalities in ASD subjects. Serum miRNA levels were examined by high-throughput deep sequencing in ASD and non-ASD control sera with concurrent measurement of PBMo cytokine production and mitochondrial respiration by PBMCs. ASD samples were examined as a whole group and with respect to the previously defined IL-1ß/IL-10-based ASD subgroups (high, normal, and low groups). Serum miRNA levels differed between the overall ASD sera (N = 116) and non-ASD control sera (N = 35) and also differed across the IL-1ß/IL-10-based ASD subgroups. Specifically, miRNA levels were increased and decreased in eight and nine miRNAs, respectively, in the high-ratio ASD subgroup (N = 48). In contrast, the low- (N = 25) and normal- (N = 43) ratio ASD subgroups only showed decreased miRNAs levels (18 and 10 miRNAs, respectively). Gene targets of the altered miRNAs in the high and/or low IL-1β/IL-10 ratio ASD subgroups were enriched in pathways critical for monocyte functions and metabolic regulation. Gene targets of the altered miRNAs in all the ASD subgroups were enriched in pathways of neuronal development and synaptic plasticity, along with cell proliferation/differentiation. ASD subgroup-specific associations were observed between serum miRNA expression and IL-1ß/IL-10 ratios, mitochondrial respiration, and monocyte cytokine profiles (IL-10, CCL2, and TNF-α). In summary, our results indicate that serum levels of select miRNAs may serve as promising biomarkers for screening and monitoring changes in innate immunity and mitochondrial respiration in ASD.