denovo-db: a compendium of human de novo variants

Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide

The connection and causality between cancer and neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, and mutations lead to pathologies with vastly different clinical presentations? And why do individuals with neurodevelopmental disorders, such as autism and schizophrenia, face higher chances of cancer emerging throughout their lifetime? Our broad review emphasizes the multi-scale aspect of this type of reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at the new understanding that can be gained. Within this framework, our review calls attention to computational strategies which can be powerful in discovering connections, causalities, predicting clinical outcomes, and are vital for drug discovery. Thus, rather than centering on the clinical features, we draw on the rapidly increasing data on the molecular level, including mutations, isoforms, three-dimensional structures, and expression levels of the respective disease-associated genes. Their integrated analysis, together with chromatin states, can delineate how, despite being connected, neurodevelopmental disorders and cancer differ, and how the same mutations can lead to different clinical symptoms. Here, we seek to uncover the emerging connection between cancer, including pediatric tumors, and neurodevelopmental disorders, and the tantalizing questions that this connection raises.

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

Background

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

Results

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

Conclusions

VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.

Availability

VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.

Exploring key genes and pathways associated with sex differences in autism spectrum disorder: integrated bioinformatic analysis

Autism spectrum disorder (ASD) is a heterogenous neurodevelopmental disorder marked by functional abnormalities in brain that causes social and linguistic difficulties. The incidence of ASD is more prevalent in males compared to females, but the underlying mechanism, as well as molecular indications for identifying sex-specific differences in ASD symptoms remain unknown. Thus, impacting the development of personalized strategy towards pharmacotherapy of ASD. The current study employs an integrated bioinformatic approach to investigate the genes and pathways uniquely associated with sex specific differences in autistic individuals. Based on microarray dataset (GSE6575) extracted from the gene expression omnibus, the dysregulated genes between the autistic and the neurotypical individuals for both sexes were identified. Gene set enrichment analysis was performed to ascertain biological activities linked to the dysregulated genes. Protein-protein interaction network analysis was carried out to identify hub genes. The identified hub genes were examined to determine their functions and involvement in the associated pathways using Enrichr. Additionally, hub genes were validated from autism-associated databases and the potential small molecules targeting the hub genes were identified. The present study utilized whole blood transcriptomic gene expression analysis data and identified 2211 and 958 differentially expressed unique genes in males and females respectively. The functional enrichment analysis revealed that male hub genes were functionally associated with RNA polymerase II mediated transcriptional regulation whereas female hub genes were involved in intracellular signal transduction and cell migration. The top male hub genes exhibited functional enrichment in tyrosine kinase signalling pathway. The pathway enrichment analysis of male hub genes indicates the enrichment of papillomavirus infection. Female hub genes were enriched in androgen receptor signalling pathway and functionally enriched in focal adhesion specific excision repair. Identified drug like candidates targeting these genes may serve as a potential sex specific therapeutics. Wortmannin for males, 5-Fluorouracil for females had the highest scores. Targeted and sex-specific pharmacotherapies may be created for the management of ASD. The current investigation identifies sex-specific molecular signatures derived from whole blood which may serve as a potential peripheral sex-specific biomarkers for ASD. The study also uncovers the possible pharmacological interventions against the selected genes/pathway, providing support in development of therapeutic strategies to mitigate ASD. However, experimental proofs on biological systems are warranted.

A recurrent missense variant in the E3 ubiquitin ligase substrate recognition subunit FEM1B causes a rare syndromic neurodevelopmental disorder

PURPOSE

Fem1 homolog B (FEM1B) acts as a substrate recognition subunit for ubiquitin ligase complexes belonging to the CULLIN 2-based E3 family. Several biological functions have been proposed for FEM1B, including a structurally resolved function as a sensor for redox cell status by controlling mitochondrial activity, but its implication in human disease remains elusive.

METHODS

To understand the involvement of FEM1B in human disease, we made use of Matchmaker exchange platforms to identify individuals with de novo variants in FEM1B and performed their clinical evaluation. We performed functional validation using primary neuronal cultures and in utero electroporation assays, as well as experiments on patient's cells.

RESULTS

Five individuals with a recurrent de novo missense variant in FEM1B were identified: NM_015322.5:c.377G>A NP_056137.1:p.(Arg126Gln) (FEM1BR126Q). Affected individuals shared a severe neurodevelopmental disorder with behavioral phenotypes and a variable set of malformations, including brain anomalies, clubfeet, skeletal abnormalities, and facial dysmorphism. Overexpression of the FEM1BR126Q variant but not FEM1B wild-type protein, during mouse brain development, resulted in delayed neuronal migration of the target cells. In addition, the individuals' cells exhibited signs of oxidative stress and induction of type I interferon signaling.

CONCLUSION

Overall, our data indicate that p.(Arg126Gln) induces aberrant FEM1B activation, resulting in a gain-of-function mechanism associated with a severe syndromic developmental disorder in humans.

Regulatory landscape enrichment analysis (RLEA): a computational toolkit for non-coding variant enrichment and cell type prioritization

BACKGROUND

As genomic studies continue to implicate non-coding sequences in disease, testing the roles of these variants requires insights into the cell type(s) in which they are likely to be mediating their effects. Prior methods for associating non-coding variants with cell types have involved approaches using linkage disequilibrium or ontological associations, incurring significant processing requirements. GaiaAssociation is a freely available, open-source software that enables thousands of genomic loci implicated in a phenotype to be tested for enrichment at regulatory loci of multiple cell types in minutes, permitting insights into the cell type(s) mediating the studied phenotype.

RESULTS

In this work, we present Regulatory Landscape Enrichment Analysis (RLEA) by GaiaAssociation and demonstrate its capability to test the enrichment of 12,133 variants across the cis-regulatory regions of 44 cell types. This analysis was completed in 134.0 ± 2.3 s, highlighting the efficient processing provided by GaiaAssociation. The intuitive interface requires only four inputs, offers a collection of customizable functions, and visualizes variant enrichment in cell-type regulatory regions through a heatmap matrix. GaiaAssociation is available on PyPi for download as a command line tool or Python package and the source code can also be installed from GitHub at https://github.com/GreallyLab/gaiaAssociation .

CONCLUSIONS

GaiaAssociation is a novel package that provides an intuitive and efficient resource to understand the enrichment of non-coding variants across the cis-regulatory regions of different cells, empowering studies seeking to identify disease-mediating cell types.

Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences

Alternative splicing plays a crucial role in protein diversity and gene expression regulation in higher eukaryotes and mutations causing dysregulated splicing underlie a range of genetic diseases. Computational prediction of alternative splicing from genomic sequences not only provides insight into gene-regulatory mechanisms but also helps identify disease-causing mutations and drug targets. However, the current methods for the quantitative prediction of splice site usage still have limited accuracy. Here, we present DeltaSplice, a deep neural network model optimized to learn the impact of mutations on quantitative changes in alternative splicing from the comparative analysis of homologous genes. The model architecture enables DeltaSplice to perform "reference-informed prediction" by incorporating the known splice site usage of a reference gene sequence to improve its prediction on splicing-altering mutations. We benchmarked DeltaSplice and several other state-of-the-art methods on various prediction tasks, including evolutionary sequence divergence on lineage-specific splicing and splicing-altering mutations in human populations and neurodevelopmental disorders, and demonstrated that DeltaSplice outperformed consistently. DeltaSplice predicted ~15% of splicing quantitative trait loci (sQTLs) in the human brain as causal splicing-altering variants. It also predicted splicing-altering de novo mutations outside the splice sites in a subset of patients affected by autism and other neurodevelopmental disorders, including 19 genes with recurrent splicing-altering mutations. Among the new candidate disease risk genes, MFN1 is involved in mitochondria fusion, which is frequently disrupted in autism patients. Our work expanded the capacity of in silico splicing models with potential applications in genetic diagnosis and the development of splicing-based precision medicine.

Statistical methods for assessing the effects of de novo variants on birth defects

With the development of next-generation sequencing technology, de novo variants (DNVs) with deleterious effects can be identified and investigated for their effects on birth defects such as congenital heart disease (CHD). However, statistical power is still limited for such studies because of the small sample size due to the high cost of recruiting and sequencing samples and the low occurrence of DNVs. DNV analysis is further complicated by genetic heterogeneity across diseased individuals. Therefore, it is critical to jointly analyze DNVs with other types of genomic/biological information to improve statistical power to identify genes associated with birth defects. In this review, we discuss the general workflow, recent developments in statistical methods, and future directions for DNV analysis.

The Genotype and Phenotypes in Families (GPF) platform manages the large and complex data at SFARI

The exploration of genotypic variants impacting phenotypes is a cornerstone in genetics research. The emergence of vast collections containing deeply genotyped and phenotyped families has made it possible to pursue the search for variants associated with complex diseases. However, managing these large-scale datasets requires specialized computational tools tailored to organize and analyze the extensive data. GPF (Genotypes and Phenotypes in Families) is an open-source platform ( https://github.com/iossifovlab/gpf ) that manages genotypes and phenotypes derived from collections of families. The GPF interface allows interactive exploration of genetic variants, enrichment analysis for de novo mutations, and phenotype/genotype association tools. In addition, GPF allows researchers to share their data securely with the broader scientific community. GPF is used to disseminate two large-scale family collection datasets (SSC, SPARK) for the study of autism funded by the SFARI foundation. However, GPF is versatile and can manage genotypic data from other small or large family collections. Our GPF-SFARI GPF instance ( https://gpf.sfari.org/ ) provides protected access to comprehensive genotypic and phenotypic data for the SSC and SPARK. In addition, GPF-SFARI provides public access to an extensive collection of de novo mutations identified in individuals with autism and related disorders and to gene-level statistics of the protected datasets characterizing the genes' roles in autism. Here, we highlight the primary features of GPF within the context of GPF-SFARI.

Next-generation sequencing and bioinformatics in rare movement disorders

The ability to sequence entire exomes and genomes has revolutionized molecular testing in rare movement disorders, and genomic sequencing is becoming an integral part of routine diagnostic workflows for these heterogeneous conditions. However, interpretation of the extensive genomic variant information that is being generated presents substantial challenges. In this Perspective, we outline multidimensional strategies for genetic diagnosis in patients with rare movement disorders. We examine bioinformatics tools and computational metrics that have been developed to facilitate accurate prioritization of disease-causing variants. Additionally, we highlight community-driven data-sharing and case-matchmaking platforms, which are designed to foster the discovery of new genotype-phenotype relationships. Finally, we consider how multiomic data integration might optimize diagnostic success by combining genomic, epigenetic, transcriptomic and/or proteomic profiling to enable a more holistic evaluation of variant effects. Together, the approaches that we discuss offer pathways to the improved understanding of the genetic basis of rare movement disorders.

Growth-suppressor microRNAs mediate synaptic overgrowth and behavioral deficits in Fragile X mental retardation protein deficiency

Abnormal neuronal and synapse growth is a core pathology resulting from deficiency of the Fragile X mental retardation protein (FMRP), but molecular links underlying the excessive synthesis of key synaptic proteins remain incompletely defined. We find that basal brain levels of the growth suppressor let-7 microRNA (miRNA) family are selectively lowered in FMRP-deficient mice and activity-dependent let-7 downregulation is abrogated. Primary let-7 miRNA transcripts are not altered in FMRP-deficiency and posttranscriptional misregulation occurs downstream of MAPK pathway induction and elevation of Lin28a, a let-7 biogenesis inhibitor. Neonatal restoration of brain let-7 miRNAs corrects hallmarks of FMRP-deficiency, including dendritic spine overgrowth and social and cognitive behavioral deficits, in adult mice. Blockade of MAPK hyperactivation normalizes let-7 miRNA levels in both brain and peripheral blood plasma from Fmr1 KO mice. These results implicate dysregulated let-7 miRNA biogenesis in the pathogenesis of FMRP-deficiency, and highlight let-7 miRNA-based strategies for future biomarker and therapeutic development.

Genomics 2 Proteins portal: A resource and discovery tool for linking genetic screening outputs to protein sequences and structures

Recent advances in AI-based methods have revolutionized the field of structural biology. Concomitantly, high-throughput sequencing and functional genomics technologies have enabled the detection and generation of variants at an unprecedented scale. However, efficient tools and resources are needed to link these two disparate data types - to "map" variants onto protein structures, to better understand how the variation causes disease and thereby design therapeutics. Here we present the Genomics 2 Proteins Portal (G2P; g2p.broadinstitute.org/): a human proteome-wide resource that maps 19,996,443 genetic variants onto 42,413 protein sequences and 77,923 structures, with a comprehensive set of structural and functional features. Additionally, the G2P portal generalizes the capability of linking genomics to proteins beyond databases by allowing users to interactively upload protein residue-wise annotations (variants, scores, etc.) as well as the protein structure to establish the connection. The portal serves as an easy-to-use discovery tool for researchers and scientists to hypothesize the structure-function relationship between natural or synthetic variations and their molecular phenotype.

Quantifying negative selection in human 3' UTRs uncovers constrained targets of RNA-binding proteins

Many non-coding variants associated with phenotypes occur in 3' untranslated regions (3' UTRs), and may affect interactions with RNA-binding proteins (RBPs) to regulate gene expression post-transcriptionally. However, identifying functional 3' UTR variants has proven difficult. We use allele frequencies from the Genome Aggregation Database (gnomAD) to identify classes of 3' UTR variants under strong negative selection in humans. We develop intergenic mutability-adjusted proportion singleton (iMAPS), a generalized measure related to MAPS, to quantify negative selection in non-coding regions. This approach, in conjunction with in vitro and in vivo binding data, identifies precise RBP binding sites, miRNA target sites, and polyadenylation signals (PASs) under strong selection. For each class of sites, we identify thousands of gnomAD variants under selection comparable to missense coding variants, and find that sites in core 3' UTR regions upstream of the most-used PAS are under strongest selection. Together, this work improves our understanding of selection on human genes and validates approaches for interpreting genetic variants in human 3' UTRs.

Single-cell long-read sequencing in human cerebral organoids uncovers cell-type-specific and autism-associated exons

Dysregulation of alternative splicing has been repeatedly associated with neurodevelopmental disorders, but the extent of cell-type-specific splicing in human neural development remains largely uncharted. Here, single-cell long-read sequencing in induced pluripotent stem cell (iPSC)-derived cerebral organoids identifies over 31,000 uncatalogued isoforms and 4,531 cell-type-specific splicing events. Long reads uncover coordinated splicing and cell-type-specific intron retention events, which are challenging to study with short reads. Retained neuronal introns are enriched in RNA splicing regulators, showing shorter lengths, higher GC contents, and weaker 5' splice sites. We use this dataset to explore the biological processes underlying neurological disorders, focusing on autism. In comparison with prior transcriptomic data, we find that the splicing program in autistic brains is closer to the progenitor state than differentiated neurons. Furthermore, cell-type-specific exons harbor significantly more de novo mutations in autism probands than in siblings. Overall, these results highlight the importance of cell-type-specific splicing in autism and neuronal gene regulation.

Neurodevelopmental disorders and cancer networks share pathways, but differ in mechanisms, signaling strength, and outcome

Epidemiological studies suggest that individuals with neurodevelopmental disorders (NDDs) are more prone to develop certain types of cancer. Notably, however, the case statistics can be impacted by late discovery of cancer in individuals afflicted with NDDs, such as intellectual disorders, autism, and schizophrenia, which may bias the numbers. As to NDD-associated mutations, in most cases, they are germline while cancer mutations are sporadic, emerging during life. However, somatic mosaicism can spur NDDs, and cancer-related mutations can be germline. NDDs and cancer share proteins, pathways, and mutations. Here we ask (i) exactly which features they share, and (ii) how, despite their commonalities, they differ in clinical outcomes. To tackle these questions, we employed a statistical framework followed by network analysis. Our thorough exploration of the mutations, reconstructed disease-specific networks, pathways, and transcriptome levels and profiles of autism spectrum disorder (ASD) and cancers, point to signaling strength as the key factor: strong signaling promotes cell proliferation in cancer, and weaker (moderate) signaling impacts differentiation in ASD. Thus, we suggest that signaling strength, not activating mutations, can decide clinical outcome.

A cross-species proteomic map reveals neoteny of human synapse development

The molecular mechanisms and evolutionary changes accompanying synapse development are still poorly understood1,2. Here we generate a cross-species proteomic map of synapse development in the human, macaque and mouse neocortex. By tracking the changes of more than 1,000 postsynaptic density (PSD) proteins from midgestation to young adulthood, we find that PSD maturation in humans separates into three major phases that are dominated by distinct pathways. Cross-species comparisons reveal that human PSDs mature about two to three times slower than those of other species and contain higher levels of Rho guanine nucleotide exchange factors (RhoGEFs) in the perinatal period. Enhancement of RhoGEF signalling in human neurons delays morphological maturation of dendritic spines and functional maturation of synapses, potentially contributing to the neotenic traits of human brain development. In addition, PSD proteins can be divided into four modules that exert stage- and cell-type-specific functions, possibly explaining their differential associations with cognitive functions and diseases. Our proteomic map of synapse development provides a blueprint for studying the molecular basis and evolutionary changes of synapse maturation.

A ubiquitin-based effector-to-inhibitor switch coordinates early brain, craniofacial, and skin development

The molecular mechanisms that coordinate patterning of the embryonic ectoderm into spatially distinct lineages to form the nervous system, epidermis, and neural crest-derived craniofacial structures are unclear. Here, biochemical disease-variant profiling reveals a posttranslational pathway that drives early ectodermal differentiation in the vertebrate head. The anteriorly expressed ubiquitin ligase CRL3-KLHL4 restricts signaling of the ubiquitous cytoskeletal regulator CDC42. This regulation relies on the CDC42-activating complex GIT1-βPIX, which CRL3-KLHL4 exploits as a substrate-specific co-adaptor to recognize and monoubiquitylate PAK1. Surprisingly, we find that ubiquitylation converts the canonical CDC42 effector PAK1 into a CDC42 inhibitor. Loss of CRL3-KLHL4 or a disease-associated KLHL4 variant reduce PAK1 ubiquitylation causing overactivation of CDC42 signaling and defective ectodermal patterning and neurulation. Thus, tissue-specific restriction of CDC42 signaling by a ubiquitin-based effector-to-inhibitor is essential for early face, brain, and skin formation, revealing how cell-fate and morphometric changes are coordinated to ensure faithful organ development.

An updated catalog of CTCF variants associated with neurodevelopmental disorder phenotypes

Introduction

CTCF-related disorder (CRD) is a neurodevelopmental disorder (NDD) caused by monoallelic pathogenic variants in CTCF. The first CTCF variants in CRD cases were documented in 2013. To date, 76 CTCF variants have been further described in the literature. In recent years, due to the increased application of next-generation sequencing (NGS), growing numbers of CTCF variants are being identified, and multiple genotype-phenotype databases cataloging such variants are emerging.

Methods

In this study, we aimed to expand the genotypic spectrum of CRD, by cataloging NDD phenotypes associated with reported CTCF variants. Here, we systematically reviewed all known CTCF variants reported in case studies and large-scale exome sequencing cohorts. We also conducted a meta-analysis using public variant data from genotype-phenotype databases to identify additional CTCF variants, which we then curated and annotated.

Results

From this combined approach, we report an additional 86 CTCF variants associated with NDD phenotypes that have not yet been described in the literature. Furthermore, we describe and explain inconsistencies in the quality of reported variants, which impairs the reuse of data for research of NDDs and other pathologies.

Discussion

From this integrated analysis, we provide a comprehensive and annotated catalog of all currently known CTCF mutations associated with NDD phenotypes, to aid diagnostic applications, as well as translational and basic research.

The origins and functional effects of postzygotic mutations throughout the human life span

Postzygotic mutations (PZMs) begin to accrue in the human genome immediately after fertilization, but how and when PZMs affect development and lifetime health remain unclear. To study the origins and functional consequences of PZMs, we generated a multitissue atlas of PZMs spanning 54 tissue and cell types from 948 donors. Nearly half the variation in mutation burden among tissue samples can be explained by measured technical and biological effects, and 9% can be attributed to donor-specific effects. Through phylogenetic reconstruction of PZMs, we found that their type and predicted functional impact vary during prenatal development, across tissues, and through the germ cell life cycle. Thus, methods for interpreting effects across the body and the life span are needed to fully understand the consequences of genetic variants.

Autism spectrum disorder and brain volume link through a set of mTOR-related genes

BACKGROUND

Larger than average head and brain sizes are often observed in individuals with autism spectrum disorders (ASDs). ASD and brain volume are both highly heritable, with multiple genetic variants contributing. However, it is unclear whether ASD and brain volume share any genetic mechanisms. Genes from the mammalian target of rapamycin (mTOR) pathway influence brain volume, and variants are found in rare genetic syndromes that include ASD features. Here we investigated whether variants in mTOR-related genes are also associated with ASD and if they constitute a genetic link between large brains and ASD.

METHODS

We extended our analyses between large heads (macrocephaly) and rare de novo mTOR-related variants in an intellectual disability cohort (N = 2,258). Subsequently using Fisher's exact tests we investigated the co-occurrence of mTOR-related de novo variants and ASD in the de-novo-db database (N = 23,098). We next selected common genetic variants within a set of 96 mTOR-related genes in genome-wide genetic association data of ASD (N = 46,350) to test gene-set association using MAGMA. Lastly, we tested genetic correlation between genome-wide genetic association data of ASD (N = 46,350) and intracranial volume (N = 25,974) globally using linkage disequilibrium score regression as well as mTOR specific by restricting the genetic correlation to the mTOR-related genes using GNOVA.

RESULTS

Our results show that both macrocephaly and ASD occur above chance level in individuals carrying rare de novo variants in mTOR-related genes. We found a significant mTOR gene-set association with ASD (p = .0029) and an mTOR-stratified positive genetic correlation between ASD and intracranial volume (p = .027), despite the absence of a significant genome-wide correlation (p = .81).

CONCLUSIONS

This work indicates that both rare and common variants in mTOR-related genes are associated with brain volume and ASD and genetically correlate them in the expected direction. We demonstrate that genes involved in mTOR signalling are potential mediators of the relationship between having a large brain and having ASD.

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.

Rescue of neuropsychiatric phenotypes in a mouse model of 16p11.2 duplication syndrome by genetic correction of an epilepsy network hub

Neuropsychiatric disorders (NPDs) are frequently co-morbid with epilepsy, but the biological basis of shared risk remains poorly understood. The 16p11.2 duplication is a copy number variant that confers risk for diverse NPDs including autism spectrum disorder, schizophrenia, intellectual disability and epilepsy. We used a mouse model of the 16p11.2 duplication (16p11.2dup/+) to uncover molecular and circuit properties associated with this broad phenotypic spectrum, and examined genes within the locus capable of phenotype reversal. Quantitative proteomics revealed alterations to synaptic networks and products of NPD risk genes. We identified an epilepsy-associated subnetwork that was dysregulated in 16p11.2dup/+ mice and altered in brain tissue from individuals with NPDs. Cortical circuits from 16p11.2dup/+ mice exhibited hypersynchronous activity and enhanced network glutamate release, which increased susceptibility to seizures. Using gene co-expression and interactome analysis, we show that PRRT2 is a major hub in the epilepsy subnetwork. Remarkably, correcting Prrt2 copy number rescued aberrant circuit properties, seizure susceptibility and social deficits in 16p11.2dup/+ mice. We show that proteomics and network biology can identify important disease hubs in multigenic disorders, and reveal mechanisms relevant to the complex symptomatology of 16p11.2 duplication carriers.

Delineation of functionally essential protein regions for 242 neurodevelopmental genes

Neurodevelopmental disorders (NDDs), including severe paediatric epilepsy, autism and intellectual disabilities are heterogeneous conditions in which clinical genetic testing can often identify a pathogenic variant. For many of them, genetic therapies will be tested in this or the coming years in clinical trials. In contrast to first-generation symptomatic treatments, the new disease-modifying precision medicines require a genetic test-informed diagnosis before a patient can be enrolled in a clinical trial. However, even in 2022, most identified genetic variants in NDD genes are 'variants of uncertain significance'. To safely enrol patients in precision medicine clinical trials, it is important to increase our knowledge about which regions in NDD-associated proteins can 'tolerate' missense variants and which ones are 'essential' and will cause a NDD when mutated. In addition, knowledge about functionally indispensable regions in the 3D structure context of proteins can also provide insights into the molecular mechanisms of disease variants. We developed a novel consensus approach that overlays evolutionary, and population based genomic scores to identify 3D essential sites (Essential3D) on protein structures. After extensive benchmarking of AlphaFold predicted and experimentally solved protein structures, we generated the currently largest expert curated protein structure set for 242 NDDs and identified 14 377 Essential3D sites across 189 gene disorders associated proteins. We demonstrate that the consensus annotation of Essential3D sites improves prioritization of disease mutations over single annotations. The identified Essential3D sites were enriched for functional features such as intermembrane regions or active sites and discovered key inter-molecule interactions in protein complexes that were otherwise not annotated. Using the currently largest autism, developmental disorders, and epilepsies exome sequencing studies including >360 000 NDD patients and population controls, we found that missense variants at Essential3D sites are 8-fold enriched in patients. In summary, we developed a comprehensive protein structure set for 242 NDDs and identified 14 377 Essential3D sites in these. All data are available at https://es-ndd.broadinstitute.org for interactive visual inspection to enhance variant interpretation and development of mechanistic hypotheses for 242 NDDs genes. The provided resources will enhance clinical variant interpretation and in silico drug target development for NDD-associated genes and encoded proteins.

Effect of exogenous IL-37 on immune cells from a patient carrying a potential IL37 loss-of-function variant: A case study

INTRODUCTION

Chronic inflammatory or autoimmune diseases are commonly treated with immunosuppressive medication such as NSAIDs, corticosteroids, or antibodies against specific cytokines (TNF, IL-1 IL-17, IL-23, etc.) or signalling cascades (e.g. JAK-STAT inhibitors). Using sequencing data to locate genetic mutations in relevant genes allows the identification of alternative targets in a patient-tailored therapy setting. Interleukin (IL)-37 is an anti-inflammatory cytokine with broad effects on innate and adaptive immune cell function. Dysfunctional IL-37 expression or signalling is linked to various autoinflammatory disorders. The administration of recombinant IL-37 to hyperinflammatory patients that are non-responsive to standard treatment bears the potential to alleviate symptoms.

METHODS

In this case study, the (hyper)responsiveness of immune cell subsets was investigated in a single patient with a seronegative autoimmune disorder who carries a heterozygous stop-gain variant in IL37 (IL37 Chr2(GRCh37):g.113670640G > A NM_014439.3:c.51G > A p.(Trp17*)). As the patient has been non-responsive to blockage of TNF or IL-1 by Etanercept or Anakinra, respectively, additional in-vitro experiments were set out to elucidate whether treatment with recombinant IL-37 could normalise observed immune cell functions.

FINDINGS

Characterisation of immune cell function showed no elevated overall production of acute-phase pro-inflammatory cytokines by patient PBMCs and neutrophils at baseline or upon stimulation. T-cell responses were elevated, as was the metabolic activity and IL-1Ra production of PBMCs at baseline. The identified stop-gain variant in IL37 does not result in the absence of the protein in circulation. In line with this, treatment with recombinant IL-37 did overall not dampen immune responses with the exception of the complete suppression of IL-17.

CONCLUSION

The heterozygous stop-gain variant in IL37 (IL37 NM_014439.3:c.51G > A p.(Trp17*)) is not of functional relevance as we observed no clear pro-inflammatory phenotype in immune cells of a patient carrying this variant.

De novo mutation hotspots in homologous protein domains identify function-altering mutations in neurodevelopmental disorders

Variant interpretation remains a major challenge in medical genetics. We developed Meta-Domain HotSpot (MDHS) to identify mutational hotspots across homologous protein domains. We applied MDHS to a dataset of 45,221 de novo mutations (DNMs) from 31,058 individuals with neurodevelopmental disorders (NDDs) and identified three significantly enriched missense DNM hotspots in the ion transport protein domain family (PF00520). The 37 unique missense DNMs that drive enrichment affect 25 genes, 19 of which were previously associated with NDDs. 3D protein structure modeling supports the hypothesis of function-altering effects of these mutations. Hotspot genes have a unique expression pattern in tissue, and we used this pattern alongside in silico predictors and population constraint information to identify candidate NDD-associated genes. We also propose a lenient version of our method, which identifies 32 hotspot positions across 16 different protein domains. These positions are enriched for likely pathogenic variation in clinical databases and DNMs in other genetic disorders.

Patient-facing digital tools for delivering genetic services: a systematic review

This study systematically reviewed the literature on the impact of digital genetics tools on patient care and system efficiencies. MEDLINE and Embase were searched for articles published between January 2010 and March 2021. Studies evaluating the use of patient-facing digital tools in the context of genetic service delivery were included. Two reviewers screened and extracted patient-reported and system-focused outcomes from each study. Data were synthesised using a descriptive approach. Of 3226 unique studies identified, 87 were included. A total of 70 unique digital tools were identified. As a result of using digital tools, 84% of studies reported a positive outcome in at least one of the following patient outcomes: knowledge, psychosocial well-being, behavioural/management changes, family communication, decision-making or level of engagement. Digital tools improved workflow and efficiency for providers and reduced the amount of time they needed to spend with patients. However, we identified a misalignment between study purpose and patient-reported outcomes measured and a lack of tools that encompass the entire genetic counselling and testing trajectory. Given increased demand for genetic services and the shift towards virtual care, this review provides evidence that digital tools can be used to efficiently deliver patient-centred care. Future research should prioritise development, evaluation and implementation of digital tools that can support the entire patient trajectory across a range of clinical settings. PROSPERO registration numberCRD42020202862.

A method to build extended sequence context models of point mutations and indels

The mutation rate of a specific position in the human genome depends on the sequence context surrounding it. Modeling the mutation rate by estimating a rate for each possible k-mer, however, only works for small values of k since the data becomes too sparse for larger values of k. Here we propose a new method that solves this problem by grouping similar k-mers. We refer to the method as k-mer pattern partition and have implemented it in a software package called kmerPaPa. We use a large set of human de novo mutations to show that this new method leads to improved prediction of mutation rates and makes it possible to create models using wider sequence contexts than previous studies. As the first method of its kind, it does not only predict rates for point mutations but also insertions and deletions. We have additionally created a software package called Genovo that, given a k-mer pattern partition model, predicts the expected number of synonymous, missense, and other functional mutation types for each gene. Using this software, we show that the created mutation rate models increase the statistical power to detect genes containing disease-causing variants and to identify genes under strong selective constraint.

Clinical validity assessment of genes frequently tested on intellectual disability/autism sequencing panels

PURPOSE

Neurodevelopmental disorders (NDDs), such as intellectual disability (ID) and autism spectrum disorder (ASD), exhibit genetic and phenotypic heterogeneity, making them difficult to differentiate without a molecular diagnosis. The Clinical Genome Resource Intellectual Disability/Autism Gene Curation Expert Panel (GCEP) uses systematic curation to distinguish ID/ASD genes that are appropriate for clinical testing (ie, with substantial evidence supporting their relationship to disease) from those that are not.

METHODS

Using the Clinical Genome Resource gene-disease validity curation framework, the ID/Autism GCEP classified genes frequently included on clinical ID/ASD testing panels as Definitive, Strong, Moderate, Limited, Disputed, Refuted, or No Known Disease Relationship.

RESULTS

As of September 2021, 156 gene-disease pairs have been evaluated. Although most (75%) were determined to have definitive roles in NDDs, 22 (14%) genes evaluated had either Limited or Disputed evidence. Such genes are currently not recommended for use in clinical testing owing to the limited ability to assess the effect of identified variants.

CONCLUSION

Our understanding of gene-disease relationships evolves over time; new relationships are discovered and previously-held conclusions may be questioned. Without periodic re-examination, inaccurate gene-disease claims may be perpetuated. The ID/Autism GCEP will continue to evaluate these claims to improve diagnosis and clinical care for NDDs.

Cross-Disorder Analysis of Genic and Regulatory Copy Number Variations in Bipolar Disorder, Schizophrenia, and Autism Spectrum Disorder

BACKGROUND

We aimed to determine the similarities and differences in the roles of genic and regulatory copy number variations (CNVs) in bipolar disorder (BD), schizophrenia (SCZ), and autism spectrum disorder (ASD).

METHODS

Based on high-resolution CNV data from 8708 Japanese samples, we performed to our knowledge the largest cross-disorder analysis of genic and regulatory CNVs in BD, SCZ, and ASD.

RESULTS

In genic CNVs, we found an increased burden of smaller (<100 kb) exonic deletions in BD, which contrasted with the highest burden of larger (>500 kb) exonic CNVs in SCZ/ASD. Pathogenic CNVs linked to neurodevelopmental disorders were significantly associated with the risk for each disorder, but BD and SCZ/ASD differed in terms of the effect size (smaller in BD) and subtype distribution of CNVs linked to neurodevelopmental disorders. We identified 3 synaptic genes (DLG2, PCDH15, and ASTN2) as risk factors for BD. Whereas gene set analysis showed that BD-associated pathways were restricted to chromatin biology, SCZ and ASD involved more extensive and similar pathways. Nevertheless, a correlation analysis of gene set results indicated weak but significant pathway similarities between BD and SCZ or ASD (r = 0.25-0.31). In SCZ and ASD, but not BD, CNVs were significantly enriched in enhancers and promoters in brain tissue.

CONCLUSIONS

BD and SCZ/ASD differ in terms of CNV burden, characteristics of CNVs linked to neurodevelopmental disorders, and regulatory CNVs. On the other hand, they have shared molecular mechanisms, including chromatin biology. The BD risk genes identified here could provide insight into the pathogenesis of BD.

KMT5B is required for early motor development

Disruptive variants in lysine methyl transferase 5B (KMT5B/SUV4-20H1) have been identified as likely-pathogenic among humans with neurodevelopmental phenotypes including motor deficits (i.e., hypotonia and motor delay). However, the role that this enzyme plays in early motor development is largely unknown. Using a Kmt5b gene trap mouse model, we assessed neuromuscular strength, skeletal muscle weight (i.e., muscle mass), neuromuscular junction (NMJ) structure, and myofiber type, size, and distribution. Tests were performed over developmental time (postnatal days 17 and 44) to represent postnatal versus adult structures in slow- and fast-twitch muscle types. Prior to the onset of puberty, slow-twitch muscle weight was significantly reduced in heterozygous compared to wild-type males but not females. At the young adult stage, we identified decreased neuromuscular strength, decreased skeletal muscle weights (both slow- and fast-twitch), increased NMJ fragmentation (in slow-twitch muscle), and smaller myofibers in both sexes. We conclude that Kmt5b haploinsufficiency results in a skeletal muscle developmental deficit causing reduced muscle mass and body weight.

Extreme purifying selection against point mutations in the human genome

Large-scale genome sequencing has enabled the measurement of strong purifying selection in protein-coding genes. Here we describe a new method, called ExtRaINSIGHT, for measuring such selection in noncoding as well as coding regions of the human genome. ExtRaINSIGHT estimates the prevalence of “ultraselection” by the fractional depletion of rare single-nucleotide variants, after controlling for variation in mutation rates. Applying ExtRaINSIGHT to 71,702 whole genome sequences from gnomAD v3, we find abundant ultraselection in evolutionarily ancient miRNAs and neuronal protein-coding genes, as well as at splice sites. By contrast, we find much less ultraselection in other noncoding RNAs and transcription factor binding sites, and only modest levels in ultraconserved elements. We estimate that ~0.4–0.7% of the human genome is ultraselected, implying ~ 0.26–0.51 strongly deleterious mutations per generation. Overall, our study sheds new light on the genome-wide distribution of fitness effects by combining deep sequencing data and classical theory from population genetics.

Previous work has investigated selection in the coding genome, but it is not as well characterized in the non-coding genome. By analyzing rare variants in 70k genome sequences from gnomAD, the authors detect very strong purifying selection ("ultraselection”) across the human genome, finding it in some microRNAs and coding sequences but generally rare in regulatory sequences.

Germline De Novo Mutations as a Cause of Childhood Cancer

Germline de novo mutations (DNMs) represent one of the important topics that need extensive attention from epidemiologists, geneticists, and other relevant stakeholders. Advances in next-generation sequencing technologies allowed examination of parent-offspring trios to ascertain the frequency of germline DNMs. Many epidemiological risk factors for childhood cancer are indicative of DNMs as a mechanism. The aim of this review was to give an overview of germline DNMs, their causes in general, and to discuss their relation to childhood cancer risk. In addition, we highlighted existing gaps in knowledge in many topics of germline DNMs in childhood cancer that need exploration and collaborative efforts.

Quantifying concordant genetic effects of de novo mutations on multiple disorders

Exome sequencing on tens of thousands of parent-proband trios has identified numerous deleterious de novo mutations (DNMs) and implicated risk genes for many disorders. Recent studies have suggested shared genes and pathways are enriched for DNMs across multiple disorders. However, existing analytic strategies only focus on genes that reach statistical significance for multiple disorders and require large trio samples in each study. As a result, these methods are not able to characterize the full landscape of genetic sharing due to polygenicity and incomplete penetrance. In this work, we introduce EncoreDNM, a novel statistical framework to quantify shared genetic effects between two disorders characterized by concordant enrichment of DNMs in the exome. EncoreDNM makes use of exome-wide, summary-level DNM data, including genes that do not reach statistical significance in single-disorder analysis, to evaluate the overall and annotation-partitioned genetic sharing between two disorders. Applying EncoreDNM to DNM data of nine disorders, we identified abundant pairwise enrichment correlations, especially in genes intolerant to pathogenic mutations and genes highly expressed in fetal tissues. These results suggest that EncoreDNM improves current analytic approaches and may have broad applications in DNM studies.

Protein interactome and cell-type expression analyses reveal that cytoplasmic FMR1-interacting protein 1 (CYFIP1), but not CYFIP2, associates with astrocytic focal adhesion

The two members of the cytoplasmic FMR1-interacting protein family, CYFIP1 and CYFIP2, are evolutionarily conserved multifunctional proteins whose defects are associated with distinct types of brain disorders. Even with high sequence homology between CYFIP1 and CYFIP2, several lines of evidence indicate their different functions in the brain; however, the underlying mechanisms remain largely unknown. Here, we performed reciprocal immunoprecipitation experiments using CYFIP1-2×Myc and CYFIP2-3×Flag knock-in mice and found that CYFIP1 and CYFIP2 are not significantly co-immunoprecipitated with each other in the knock-in brains compared to negative control wild-type brains. Moreover, CYFIP1 and CYFIP2 showed different size distributions by size-exclusion chromatography of wild-type mouse brains. Specifically, mass spectrometry-based analysis of CYFIP1-2×Myc knock-in brains identified 131 proteins in the CYFIP1 interactome. Comparison of the CYFIP1 interactome with the previously identified brain region- and age-matched CYFIP2 interactome, consisting of 140 proteins, revealed only eight common proteins. Investigations using single-cell RNA-sequencing databases suggested non-neuronal cell- and neuron-enriched expression of Cyfip1 and Cyfip2, respectively. At the protein level, CYFIP1 was detected in both neurons and astrocytes, while CYFIP2 was detected only in neurons, suggesting the predominant expression of CYFIP1 in astrocytes. Bioinformatic characterization of the CYFIP1 interactome, and co-expression analysis of Cyfip1 with astrocytic genes, commonly linked CYFIP1 with focal adhesion proteins. Immunocytochemical analysis and proximity ligation assay suggested partial co-localization of CYFIP1 and focal adhesion proteins in cultured astrocytes. Together, these results suggest a CYFIP1-specific association with astrocytic focal adhesion, which may contribute to the different brain functions and dysfunctions of CYFIP1 and CYFIP2.

Large-scale discovery of novel neurodevelopmental disorder-related genes through a unified analysis of single-nucleotide and copy number variants

Background

Previous large-scale studies of de novo variants identified a number of genes associated with neurodevelopmental disorders (NDDs); however, it was also predicted that many NDD-associated genes await discovery. Such genes can be discovered by integrating copy number variants (CNVs), which have not been fully considered in previous studies, and increasing the sample size.

Methods

We first constructed a model estimating the rates of de novo CNVs per gene from several factors such as gene length and number of exons. Second, we compiled a comprehensive list of de novo single-nucleotide variants (SNVs) in 41,165 individuals and de novo CNVs in 3675 individuals with NDDs by aggregating our own and publicly available datasets, including denovo-db and the Deciphering Developmental Disorders study data. Third, summing up the de novo CNV rates that we estimated and SNV rates previously established, gene-based enrichment of de novo deleterious SNVs and CNVs were assessed in the 41,165 cases. Significantly enriched genes were further prioritized according to their similarity to known NDD genes using a deep learning model that considers functional characteristics (e.g., gene ontology and expression patterns).

Results

We identified a total of 380 genes achieving statistical significance (5% false discovery rate), including 31 genes affected by de novo CNVs. Of the 380 genes, 52 have not previously been reported as NDD genes, and the data of de novo CNVs contributed to the significance of three genes (GLTSCR1, MARK2, and UBR3). Among the 52 genes, we reasonably excluded 18 genes [a number almost identical to the theoretically expected false positives (i.e., 380 × 0.05 = 19)] given their constraints against deleterious variants and extracted 34 “plausible” candidate genes. Their validity as NDD genes was consistently supported by their similarity in function and gene expression patterns to known NDD genes. Quantifying the overall similarity using deep learning, we identified 11 high-confidence (> 90% true-positive probabilities) candidate genes: HDAC2, SUPT16H, HECTD4, CHD5, XPO1, GSK3B, NLGN2, ADGRB1, CTR9, BRD3, and MARK2.

Conclusions

We identified dozens of new candidates for NDD genes. Both the methods and the resources developed here will contribute to the further identification of novel NDD-associated genes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01042-w.

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.

POSTAR3: an updated platform for exploring post-transcriptional regulation coordinated by RNA-binding proteins

RNA-binding proteins (RBPs) play key roles in post-transcriptional regulation. Accurate identification of RBP binding sites in multiple cell lines and tissue types from diverse species is a fundamental endeavor towards understanding the regulatory mechanisms of RBPs under both physiological and pathological conditions. Our POSTAR annotation processes make use of publicly available large-scale CLIP-seq datasets and external functional genomic annotations to generate a comprehensive map of RBP binding sites and their association with other regulatory events as well as functional variants. Here, we present POSTAR3, an updated database with improvements in data collection, annotation infrastructure, and analysis that support the annotation of post-transcriptional regulation in multiple species including: we made a comprehensive update on the CLIP-seq and Ribo-seq datasets which cover more biological conditions, technologies, and species; we added RNA secondary structure profiling for RBP binding sites; we provided miRNA-mediated degradation events validated by degradome-seq; we included RBP binding sites at circRNA junction regions; we expanded the annotation of RBP binding sites, particularly using updated genomic variants and mutations associated with diseases. POSTAR3 is freely available at http://postar.ncrnalab.org.

De novo mutations in childhood cases of sudden unexplained death that disrupt intracellular Ca2+ regulation

Approximately 400 United States children 1 y of age and older die suddenly from unexplained causes annually. We studied whole-exome sequence data from 124 “trios” (decedent child and living parents) to identify genetic risk factors. Nonsynonymous mutations, mostly de novo (present in child but absent in both biological parents), were highly enriched in genes associated with cardiac and seizure disorders relative to controls, and contributed to 9% of deaths. We found significant overtransmission of loss-of-function or pathogenic missense variants in cardiac and seizure disorder genes. Most pathogenic variants were de novo in origin, highlighting the importance of trio studies. Many of these pathogenic de novo mutations altered a protein network regulating calcium-related excitability at submembrane junctions in cardiomyocytes and neurons.

Sudden unexplained death in childhood (SUDC) is an understudied problem. Whole-exome sequence data from 124 “trios” (decedent child, living parents) was used to test for excessive de novo mutations (DNMs) in genes involved in cardiac arrhythmias, epilepsy, and other disorders. Among decedents, nonsynonymous DNMs were enriched in genes associated with cardiac and seizure disorders relative to controls (odds ratio = 9.76, P = 2.15 × 10⁻⁴). We also found evidence for overtransmission of loss-of-function (LoF) or previously reported pathogenic variants in these same genes from heterozygous carrier parents (11 of 14 transmitted, P = 0.03). We identified a total of 11 SUDC proband genotypes (7 de novo, 1 transmitted parental mosaic, 2 transmitted parental heterozygous, and 1 compound heterozygous) as pathogenic and likely contributory to death, a genetic finding in 8.9% of our cohort. Two genes had recurrent missense DNMs, RYR2 and CACNA1C. Both RYR2 mutations are pathogenic (P = 1.7 × 10⁻⁷) and were previously studied in mouse models. Both CACNA1C mutations lie within a 104-nt exon (P = 1.0 × 10⁻⁷) and result in slowed L-type calcium channel inactivation and lower current density. In total, six pathogenic DNMs can alter calcium-related regulation of cardiomyocyte and neuronal excitability at a submembrane junction, suggesting a pathway conferring susceptibility to sudden death. There was a trend for excess LoF mutations in LoF intolerant genes, where ≥1 nonhealthy sample in denovo-db has a similar variant (odds ratio = 6.73, P = 0.02); additional uncharacterized genetic causes of sudden death in children might be discovered with larger cohorts.

M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits

Recent studies have demonstrated that multiple early-onset diseases have shared risk genes, based on findings from de novo mutations (DNMs). Therefore, we may leverage information from one trait to improve statistical power to identify genes for another trait. However, there are few methods that can jointly analyze DNMs from multiple traits. In this study, we develop a framework called M-DATA (Multi-trait framework for De novo mutation Association Test with Annotations) to increase the statistical power of association analysis by integrating data from multiple correlated traits and their functional annotations. Using the number of DNMs from multiple diseases, we develop a method based on an Expectation-Maximization algorithm to both infer the degree of association between two diseases as well as to estimate the gene association probability for each disease. We apply our method to a case study of jointly analyzing data from congenital heart disease (CHD) and autism. Our method was able to identify 23 genes for CHD from joint analysis, including 12 novel genes, which is substantially more than single-trait analysis, leading to novel insights into CHD disease etiology.

Improved pathogenicity prediction for rare human missense variants

The success of personalized genomic medicine depends on our ability to assess the pathogenicity of rare human variants, including the important class of missense variation. There are many challenges in training accurate computational systems, e.g., in finding the balance between quantity, quality, and bias in the variant sets used as training examples and avoiding predictive features that can accentuate the effects of bias. Here, we describe VARITY, which judiciously exploits a larger reservoir of training examples with uncertain accuracy and representativity. To limit circularity and bias, VARITY excludes features informed by variant annotation and protein identity. To provide a rationale for each prediction, we quantified the contribution of features and feature combinations to the pathogenicity inference of each variant. VARITY outperformed all previous computational methods evaluated, identifying at least 10% more pathogenic variants at thresholds achieving high (90% precision) stringency.

Coexpression network architecture reveals the brain-wide and multiregional basis of disease susceptibility

Gene networks have yielded numerous neurobiological insights, yet an integrated view across brain regions is lacking. We leverage RNA sequencing in 864 samples representing 12 brain regions to robustly identify 12 brain-wide, 50 cross-regional and 114 region-specific coexpression modules. Nearly 40% of genes fall into brain-wide modules, while 25% comprise region-specific modules reflecting regional biology, such as oxytocin signaling in the hypothalamus, or addiction pathways in the nucleus accumbens. Schizophrenia and autism genetic risk are enriched in brain-wide and multiregional modules, indicative of broad impact; these modules implicate neuronal proliferation and activity-dependent processes, including endocytosis and splicing, in disease pathophysiology. We find that cell-type-specific long noncoding RNA and gene isoforms contribute substantially to regional synaptic diversity and that constrained, mutation-intolerant genes are primarily enriched in neurons. We leverage these data using an omnigenic-inspired network framework to characterize how coexpression and gene regulatory networks reflect neuropsychiatric disease risk, supporting polygenic models.

Rates of contributory de novo mutation in high and low-risk autism families

Autism arises in high and low-risk families. De novo mutation contributes to autism incidence in low-risk families as there is a higher incidence in the affected of the simplex families than in their unaffected siblings. But the extent of contribution in low-risk families cannot be determined solely from simplex families as they are a mixture of low and high-risk. The rate of de novo mutation in nearly pure populations of high-risk families, the multiplex families, has not previously been rigorously determined. Moreover, rates of de novo mutation have been underestimated from studies based on low resolution microarrays and whole exome sequencing. Here we report on findings from whole genome sequence (WGS) of both simplex families from the Simons Simplex Collection (SSC) and multiplex families from the Autism Genetic Resource Exchange (AGRE). After removing the multiplex samples with excessive cell-line genetic drift, we find that the contribution of de novo mutation in multiplex is significantly smaller than the contribution in simplex. We use WGS to provide high resolution CNV profiles and to analyze more than coding regions, and revise upward the rate in simplex autism due to an excess of de novo events targeting introns. Based on this study, we now estimate that de novo events contribute to 52-67% of cases of autism arising from low risk families, and 30-39% of cases of all autism.

De novo variation in bipolar disorder

Bipolar disorder (BD) is a common, highly heritable disorder that affects 1-2% of the world's population. To date, most genetic studies of BD have focused on common gene variation, and while robustly associated loci have been identified, a substantial proportion of the heritability remains missing and could be partially attributable to rare variation. In this study, we apply a de novo paradigm in BD to identify newly arisen variants that have yet to undergo natural selection and may represent highly pathogenic variants. We performed whole genome sequencing of 97 trios of Ashkenazi Jewish descent, selecting "simplex" families with no family history of BD and an early age of onset. We found a total of 6882 de novo variants (an average of 70.9 ± 12.9 S.D. variants per trio), including 107 variants within protein-coding genes. We combined our exonic variations with the results of 79 previously published BD trios, identifying 20 loss-of-function (LoF) and 77 missense damaging de novo variants in BD. These variants showed significant enrichment for constrained genes and for genes located to the postsynaptic density (PSD) (all Bonferroni corrected p < 0.05). Pathway analyses showed enrichment in several pathways, including "Phosphoinositides (PI) and their downstream targets" (Bonferroni p = 4.2 × 10-6), a pathway prominently featured in lithium's hypothesized mechanism of action. In addition, while we found overall evidence for transmission of common variant polygenic risk of BD in our full sample (pTDT p = 2.21 × 10-4), specific trios with LoF variants showed no evidence of polygenic transmission. In sum, our findings support the de novo paradigm as a contributor to the genetic architecture of BD and provide evidence that constrained genes, as well as genes within the PSD and PI pathway harbor rare variation associated with BD.

CNVxplorer: a web tool to assist clinical interpretation of CNVs in rare disease patients

Copy Number Variants (CNVs) are an important cause of rare diseases. Array-based Comparative Genomic Hybridization tests yield a ∼12% diagnostic rate, with ∼8% of patients presenting CNVs of unknown significance. CNVs interpretation is particularly challenging on genomic regions outside of those overlapping with previously reported structural variants or disease-associated genes. Recent studies showed that a more comprehensive evaluation of CNV features, leveraging both coding and non-coding impacts, can significantly improve diagnostic rates. However, currently available CNV interpretation tools are mostly gene-centric or provide only non-interactive annotations difficult to assess in the clinical practice. Here, we present CNVxplorer, a web server suited for the functional assessment of CNVs in a clinical diagnostic setting. CNVxplorer mines a comprehensive set of clinical, genomic, and epigenomic features associated with CNVs. It provides sequence constraint metrics, impact on regulatory elements and topologically associating domains, as well as expression patterns. Analyses offered cover (a) agreement with patient phenotypes; (b) visualizations of associations among genes, regulatory elements and transcription factors; (c) enrichment on functional and pathway annotations and (d) co-occurrence of terms across PubMed publications related to the query CNVs. A flexible evaluation workflow allows dynamic re-interrogation in clinical sessions. CNVxplorer is publicly available at http://cnvxplorer.com.

Combined glyoxalase 1 dysfunction and vitamin B6 deficiency in a schizophrenia model system causes mitochondrial dysfunction in the prefrontal cortex

Methylglyoxal (MG) is a reactive and cytotoxic α-dicarbonyl byproduct of glycolysis. Our bodies have several bio-defense systems to detoxify MG, including an enzymatic system by glyoxalase (GLO) 1 and GLO2. We identified a subtype of schizophrenia patients with novel mutations in the GLO1 gene that results in reductions of enzymatic activity. Moreover, we found that vitamin B6 (VB6) levels in peripheral blood of the schizophrenia patients with GLO1 dysfunction are significantly lower than that of healthy controls. However, the effects of GLO1 dysfunction and VB6 deficiency on the pathophysiology of schizophrenia remains poorly understood. Here, we generated a novel mouse model for this subgroup of schizophrenia patients by feeding Glo1 knockout mice VB6-deficent diets (KO/VB6(−)) and evaluated the combined effects of GLO1 dysfunction and VB6 deficiency on brain function. KO/VB6(−) mice accumulated homocysteine in plasma and MG in the prefrontal cortex (PFC), hippocampus, and striatum, and displayed behavioral deficits, such as impairments of social interaction and cognitive memory and a sensorimotor deficit in the prepulse inhibition test. Furthermore, we found aberrant gene expression related to mitochondria function in the PFC of the KO/VB6(−) mice by RNA-sequencing and weighted gene co-expression network analysis (WGCNA). Finally, we demonstrated abnormal mitochondrial respiratory function and subsequently enhanced oxidative stress in the PFC of KO/VB6(−) mice in the PFC. These findings suggest that the combination of GLO1 dysfunction and VB6 deficiency may cause the observed behavioral deficits via mitochondrial dysfunction and oxidative stress in the PFC.

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A combination of Glo1 KO and VB6 deficiency induces MG accumulation in the brain.

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Glo1 KO/VB6(−) mice exhibit schizophrenia-like behavioral deficits.

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Gene expression related to mitochondria is impaired in the PFC of the Glo1 KO/VB6(−).

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Mitochondria in the PFC of the Glo1 KO/VB6(−) mice show respiratory dysfunction.

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Oxidative stress is enhanced in the PFC of the Glo1 KO/VB6(−).

Reflections on the genetics-first approach to advancements in molecular genetic and neurobiological research on neurodevelopmental disorders

BACKGROUND

Neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD) and intellectual disability (ID), are common diagnoses with highly heterogeneous phenotypes and etiology. The genetics-first approach to research on NDDs has led to the identification of hundreds of genes conferring risk for ASD, ID, and related symptoms.

MAIN BODY

Although relatively few individuals with NDDs share likely gene-disruptive (LGD) mutations in the same gene, characterization of overlapping functions, protein networks, and temporospatial expression patterns among these genes has led to increased understanding of the neurobiological etiology of NDDs. This shift in focus away from single genes and toward broader gene-brain-behavior pathways has been accelerated by the development of publicly available transcriptomic databases, cell type-specific research methods, and sequencing of non-coding genomic regions.

CONCLUSIONS

The genetics-first approach to research on NDDs has advanced the identification of critical protein function pathways and temporospatial expression patterns, expanding the impact of this research beyond individuals with single-gene mutations to the broader population of patients with NDDs.

Mutational Landscape of the Proglucagon-Derived Peptides

Strong efforts have been placed on understanding the physiological roles and therapeutic potential of the proglucagon peptide hormones including glucagon, GLP-1 and GLP-2. However, little is known about the extent and magnitude of variability in the amino acid composition of the proglucagon precursor and its mature peptides. Here, we identified 184 unique missense variants in the human proglucagon gene GCG obtained from exome and whole-genome sequencing of more than 450,000 individuals across diverse sub-populations. This provides an unprecedented source of population-wide genetic variation data on missense mutations and insights into the evolutionary constraint spectrum of proglucagon-derived peptides. We show that the stereotypical peptides glucagon, GLP-1 and GLP-2 display fewer evolutionary alterations and are more likely to be functionally affected by genetic variation compared to the rest of the gene products. Elucidating the spectrum of genetic variations and estimating the impact of how a peptide variant may influence human physiology and pathophysiology through changes in ligand binding and/or receptor signalling, are vital and serve as the first important step in understanding variability in glucose homeostasis, amino acid metabolism, intestinal epithelial growth, bone strength, appetite regulation, and other key physiological parameters controlled by these hormones.

Essential genes from genome-wide screenings as a resource for neuropsychiatric disorders gene discovery

Genome-wide screenings of "essential genes", i.e., genes required for an organism or cell survival, have been traditionally conducted in vitro in cancer cell lines, limiting the translation of results to other tissues and non-cancerous cells. Recently, an in vivo screening was conducted in adult mouse striatum tissue, providing the first genome-wide dataset of essential genes in neuronal cells. Here, we aim to investigate the role of essential genes in brain development and disease risk with a comprehensive set of bioinformatics tools, including integration with transcriptomic data from developing human brain, publicly available data from genome-wide association studies, de novo mutation datasets for different neuropsychiatric disorders, and case-control transcriptomic data from postmortem brain tissues. For the first time, we found that the expression of neuronal essential genes (NEGs) increases before birth during the early development of human brain and maintains a relatively high expression after birth. On the contrary, common essential genes from cancer cell line screenings (ACEGs) tend to be expressed at high levels during development but quickly drop after birth. Both gene sets were enriched in neurodevelopmental disorders, but only NEGs were robustly associated with neuropsychiatric disorders risk genes. Finally, NEGs were more likely to show differential expression in the brains of neuropsychiatric disorders patients than ACEGs. Overall, genome-wide central nervous system screening of essential genes can provide new insights into neuropsychiatric diseases.

Rare deleterious mutations of HNRNP genes result in shared neurodevelopmental disorders

BACKGROUND

With the increasing number of genomic sequencing studies, hundreds of genes have been implicated in neurodevelopmental disorders (NDDs). The rate of gene discovery far outpaces our understanding of genotype-phenotype correlations, with clinical characterization remaining a bottleneck for understanding NDDs. Most disease-associated Mendelian genes are members of gene families, and we hypothesize that those with related molecular function share clinical presentations.

METHODS

We tested our hypothesis by considering gene families that have multiple members with an enrichment of de novo variants among NDDs, as determined by previous meta-analyses. One of these gene families is the heterogeneous nuclear ribonucleoproteins (hnRNPs), which has 33 members, five of which have been recently identified as NDD genes (HNRNPK, HNRNPU, HNRNPH1, HNRNPH2, and HNRNPR) and two of which have significant enrichment in our previous meta-analysis of probands with NDDs (HNRNPU and SYNCRIP). Utilizing protein homology, mutation analyses, gene expression analyses, and phenotypic characterization, we provide evidence for variation in 12 HNRNP genes as candidates for NDDs. Seven are potentially novel while the remaining genes in the family likely do not significantly contribute to NDD risk.

RESULTS

We report 119 new NDD cases (64 de novo variants) through sequencing and international collaborations and combined with published clinical case reports. We consider 235 cases with gene-disruptive single-nucleotide variants or indels and 15 cases with small copy number variants. Three hnRNP-encoding genes reach nominal or exome-wide significance for de novo variant enrichment, while nine are candidates for pathogenic mutations. Comparison of HNRNP gene expression shows a pattern consistent with a role in cerebral cortical development with enriched expression among radial glial progenitors. Clinical assessment of probands (n = 188-221) expands the phenotypes associated with HNRNP rare variants, and phenotypes associated with variation in the HNRNP genes distinguishes them as a subgroup of NDDs.

CONCLUSIONS

Overall, our novel approach of exploiting gene families in NDDs identifies new HNRNP-related disorders, expands the phenotypes of known HNRNP-related disorders, strongly implicates disruption of the hnRNPs as a whole in NDDs, and supports that NDD subtypes likely have shared molecular pathogenesis. To date, this is the first study to identify novel genetic disorders based on the presence of disorders in related genes. We also perform the first phenotypic analyses focusing on related genes. Finally, we show that radial glial expression of these genes is likely critical during neurodevelopment. This is important for diagnostics, as well as developing strategies to best study these genes for the development of therapeutics.

ATP6V0A1 encoding the a1-subunit of the V0 domain of vacuolar H+-ATPases is essential for brain development in humans and mice

Vacuolar H⁺-ATPases (V-ATPases) transport protons across cellular membranes to acidify various organelles. ATP6V0A1 encodes the a1-subunit of the V0 domain of V-ATPases, which is strongly expressed in neurons. However, its role in brain development is unknown. Here we report four individuals with developmental and epileptic encephalopathy with ATP6V0A1 variants: two individuals with a de novo missense variant (R741Q) and the other two individuals with biallelic variants comprising one almost complete loss-of-function variant and one missense variant (A512P and N534D). Lysosomal acidification is significantly impaired in cell lines expressing three missense ATP6V0A1 mutants. Homozygous mutant mice harboring human R741Q (Atp6v0a1^R741Q) and A512P (Atp6v0a1^A512P) variants show embryonic lethality and early postnatal mortality, respectively, suggesting that R741Q affects V-ATPase function more severely. Lysosomal dysfunction resulting in cell death, accumulated autophagosomes and lysosomes, reduced mTORC1 signaling and synaptic connectivity, and lowered neurotransmitter contents of synaptic vesicles are observed in the brains of Atp6v0a1^A512P/A512P mice. These findings demonstrate the essential roles of ATP6V0A1/Atp6v0a1 in neuronal development in terms of integrity and connectivity of neurons in both humans and mice.

A member of the vacuolar H⁺-ATPase family, ATP6V0A1 is involved in lysosomal activity. Here, the authors report that ATP6V0A1 variants identified in individuals with developmental and epileptic encephalopathy are associated with impairment of lysosomal acidification, autophagy and mTORC1 signaling, suggesting an essential role of ATP6V0A1 in brain development.

Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning

Elucidating functionality in non-coding regions is a key challenge in human genomics. It has been shown that intolerance to variation of coding and proximal non-coding sequence is a strong predictor of human disease relevance. Here, we integrate intolerance to variation, functional genomic annotations and primary genomic sequence to build JARVIS: a comprehensive deep learning model to prioritize non-coding regions, outperforming other human lineage-specific scores. Despite being agnostic to evolutionary conservation, JARVIS performs comparably or outperforms conservation-based scores in classifying pathogenic single-nucleotide and structural variants. In constructing JARVIS, we introduce the genome-wide residual variation intolerance score (gwRVIS), applying a sliding-window approach to whole genome sequencing data from 62,784 individuals. gwRVIS distinguishes Mendelian disease genes from more tolerant CCDS regions and highlights ultra-conserved non-coding elements as the most intolerant regions in the human genome. Both JARVIS and gwRVIS capture previously inaccessible human-lineage constraint information and will enhance our understanding of the non-coding genome.

Intolerance to variation is a strong indicator of disease relevance for coding regions of the human genome. Here, the authors present JARVIS, a deep learning method integrating intolerance to variation in non-coding regions and sequence-specific annotations to infer non-coding variant pathogenicity.