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Refereed/Peer-reviewed Pregnancy loss and perinatal death are devastating events for families. We assessed ‘genomic autopsy’ as an adjunct to standard autopsy for 200 families who had experienced fetal or newborn death, providing a definitive or candidate genetic diagnosis in 105 families. Our cohort provides evidence of severe atypical in utero presentations of known genetic disorders and identifies novel phenotypes and disease genes. Inheritance of 42% of definitive diagnoses were either autosomal recessive (30.8%), X-linked recessive (3.8%) or autosomal dominant (excluding de novos, 7.7%), with risk of recurrence in future pregnancies. We report that at least ten families (5%) used their diagnosis for preimplantation (5) or prenatal diagnosis (5) of 12 pregnancies. We emphasize the clinical importance of genomic investigations of pregnancy loss and perinatal death, with short turnaround times for diagnostic reporting and followed by systematic research follow-up investigations. This approach has the potential to enable accurate counseling for future pregnancies.

Recommendations from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) for interpreting sequence variants specify the use of computational predictors as "supporting" level of evidence for pathogenicity or benignity using criteria PP3 and BP4, respectively. However, score intervals defined by tool developers, and ACMG/AMP recommendations that require the consensus of multiple predictors, lack quantitative support. Previously, we described a probabilistic framework that quantified the strengths of evidence (supporting, moderate, strong, very strong) within ACMG/AMP recommendations. We have extended this framework to computational predictors and introduce a new standard that converts a tool's scores to PP3 and BP4 evidence strengths. Our approach is based on estimating the local positive predictive value and can calibrate any computational tool or other continuous-scale evidence on any variant type. We estimate thresholds (score intervals) corresponding to each strength of evidence for pathogenicity and benignity for thirteen missense variant interpretation tools, using carefully assembled independent data sets. Most tools achieved supporting evidence level for both pathogenic and benign classification using newly established thresholds. Multiple tools reached score thresholds justifying moderate and several reached strong evidence levels. One tool reached very strong evidence level for benign classification on some variants. Based on these findings, we provide recommendations for evidence-based revisions of the PP3 and BP4 ACMG/AMP criteria using individual tools and future assessment of computational methods for clinical interpretation.

PurposeExome and genome sequencing have drastically accelerated novel disease gene discoveries. However, discovery is still hindered by myriad variants of uncertain significance found in genes of undetermined biological function. This necessitates intensive functional experiments on genes of equal predicted causality, leading to a major bottleneck.MethodsWe apply the loss-of-function observed/expected upper-bound fraction metric of intolerance to gene inactivation to curate a list of predicted haploinsufficient disease genes. Using data from the 100,000 Genomes Project, we adopt a gene-to-patient approach that matches de novo loss-of-function variants in constrained genes to patients with rare disease. Through large-scale aggregation of data, we reduce excess analytical noise currently hindering novel discoveries.ResultsResults from 13,949 trios revealed 643 rare, de novo predicted loss-of-function events filtered from 1044 loss-of-function observed/expected upper-bound fraction–constrained genes. A total of 168 variants occurred within 126 genes without a known disease-gene relationship. Of these, 27 genes had >1 kindred affected, and for 18 of these genes, multiple kindreds had overlapping phenotypes. Two years after initial analysis, 11 of 18 (61%) of these genes have been independently published as novel disease gene discoveries.ConclusionUsing large cohorts and adopting gene-based approaches can rapidly and objectively accelerate dominantly inherited novel gene discovery by targeting the most appropriate genes for functional validation.

PURPOSE: Mendelian disease genomic research has undergone a massive transformation over the past decade. With increasing availability of exome and genome sequencing, the role of Mendelian research has expanded beyond data collection, sequencing, and analysis to worldwide data sharing and collaboration. METHODS: Over the past 10 years, the National Institutes of Health–supported Centers for Mendelian Genomics (CMGs) have played a major role in this research and clinical evolution. RESULTS: We highlight the cumulative gene discoveries facilitated by the program, biomedical research leveraged by the approach, and the larger impact on the research community. Beyond generating a list of gene-phenotype relationships and participating in widespread data sharing, the CMGs have created resources, tools, and training for the larger community to foster understanding of genes and genome variation. The CMGs have participated in a wide range of data sharing activities, including deposition of all eligible CMG data into the Analysis, Visualization, and Informatics Lab-space (AnVIL), sharing candidate genes through the Matchmaker Exchange and the CMG website, and sharing variants in Genotypes to Mendelian Phenotypes (Geno2MP) and VariantMatcher. CONCLUSION: The work is far from complete; strengthening communication between research and clinical realms, continued development and sharing of knowledge and tools, and improving access to richly characterized data sets are all required to diagnose the remaining molecularly undiagnosed patients.

We examined 454,712 exomes for genes associated with a wide spectrum of complex traits and common diseases and observed that rare, penetrant mutations in genes implicated by genome-wide association studies confer ∼10-fold larger effects than common variants in the same genes. Consequently, an individual at the phenotypic extreme and at the greatest risk for severe, early-onset disease is better identified by a few rare penetrant variants than by the collective action of many common variants with weak effects. By combining rare variants across phenotype-associated genes into a unified genetic risk model, we demonstrate superior portability across diverse global populations compared to common variant polygenic risk scores, greatly improving the clinical utility of genetic-based risk prediction.One sentence summaryRare variant polygenic risk scores identify individuals with outlier phenotypes in common human diseases and complex traits.

IntroductionEpilepsy is a common central nervous system disorder characterized by abnormal brain electrical activity. We aimed to compare the metabolic profiles of plasma from patients with epilepsy across different etiologies, seizure frequency, seizure type, and patient age to try to identify common disrupted pathways.Material and methodsWe used data from three separate cohorts. The first cohort (PED-C) consisted of 31 pediatric patients with suspicion of a genetic disorder with unclear etiology; the second cohort (AD-C) consisted of 250 adults from the Estonian Biobank (EstBB), and the third cohort consisted of 583 adults ≥ 69 years of age from the EstBB (ELD-C). We compared untargeted metabolomics and lipidomics data between individuals with and without epilepsy in each cohort.ResultsIn the PED-C, significant alterations (p-value ConclusionOur results suggest that cell membrane fluidity may have a significant role in the mechanism of epilepsy, and changes in lipid balance may indicate epilepsy. However, further studies are needed to evaluate whether untargeted metabolomics analysis could prove helpful in diagnosing epilepsy earlier.

PurposeAdvances in genomic research have led to the diagnosis of rare, early-onset diseases for thousands of individuals. Unfortunately, the benefits of advanced genetic diagnostic technology are not distributed equitably among the population, as has been seen in many other healthcare contexts. Even quantifying and describing inequities in genetic diagnostic yield is challenging due to variation in referrals to clinical genetics practices and other barriers to clinical genetic testing.MethodsThe Rare Genomes Project (RGP) at the Broad Institute of MIT and Harvard offers research genome sequencing to individuals with rare disease who remain genetically undiagnosed through direct interaction with the individual or family. This presents an opportunity for diagnosis beyond the clinical context, thus eliminating many barriers to access.FindingsAn initial goal of RGP was to equalize access to genomic sequencing by decoupling testing access from proximity to a major medical center and physician referral. However, our study participants are overwhelmingly non-disadvantaged, as evidenced by their access to specialist care and genetic testing prior to RGP enrollment, and are also predominantly white.ImplicationsWe therefore describe our novel initiative to diversify RGP enrollment in order to advance equity in rare disease genetic diagnosis and research. In addition to the moral imperative of medical equity, this is also critical in order to fully understand the genomic underpinnings of rare disease. We utilize a mixed methods approach to understand the priorities and values of underrepresented communities, existing disparities, and the obstacles to addressing them: all of which is necessary to promote equity in future genomic medicine initiatives.

Predicted loss of function (pLoF) variants are highly deleterious and play an important role in disease biology, but many of these variants may not actually result in loss-of-function. Here we present a framework that advances interpretation of pLoF variants in research and clinical settings by considering three categories of LoF evasion: (1) predicted rescue by secondary sequence properties, (2) uncertain biological relevance, and (3) potential technical artifacts. We also provide recommendations on adjustments to ACMG/AMP guidelines’s PVS1 criterion. Applying this framework to all high-confidence pLoF variants in 22 autosomal recessive disease-genes from the Genome Aggregation Database (gnomAD, v2.1.1) revealed predicted LoF evasion or potential artifacts in 27.3% (304/1,113) of variants. The major reasons were location in the last exon, in a homopolymer repeat, in low per-base expression (pext) score regions, or the presence of cryptic splice rescues. Variants predicted to be potential artifacts or to evade LoF were enriched for ClinVar benign variants. PVS1 was downgraded in 99.4% (162/163) of LoF evading variants assessed, with 17.2% (28/163) downgraded as a result of our framework, adding to previous guidelines. Variant pathogenicity was affected (mostly from likely pathogenic to VUS) in 20 (71.4%) of these 28 variants. This framework guides assessment of pLoF variants beyond standard annotation pipelines, and substantially reduces false positive rates, which is key to ensure accurate LoF variant prediction in both a research and clinical setting.

Genome sequencing is now available as a clinical test on the National Health Service (NHS) through the Genome Medicine Service (GMS). The GMS have set out an analytical strategy that predominantly filters genome data on a pre-selected gene panel(s). Whilst this approach reduces the number of variants requiring assessment by reporting laboratories, pathogenic variants outside of the gene panel applied may be missed, and candidate variants in novel genes are largely ignored.This study sought to compare a research exome analysis to an independent clinical genome analysis performed through the NHS for the same group of patients. When analysing the exome data, we applied a panel agnostic approach filtering for variants withHighPathogenicPotential (HiPPo) using ClinVar, allele frequency, andin silicoprediction tools. We then compared this gene agnostic analysis to the panel-based approach as applied by the GMS to genome data. Later we restricted HiPPo variants to a panel of the Gene Curation Coalition (GenCC) morbid genes and compared the diagnostic yield with the variants filtered using the GMS strategy.24 patients from 8 families underwent parallel research exome sequencing and GMS genome sequencing. HiPPo analysis applied to research exome data identified a similar number of variants as the gene panel-based approach applied by the GMS. GMS clinical genome analysis identified and returned 2 pathogenic variants and 3 variants of uncertain significance. HiPPo research exome analysis identified the same variants plus an additional pathogenic variant and a further 3de novovariants of uncertain significance in novel genes, where case series and functional studies are underway. When HiPPo was restricted to GenCC disease genes (strong or definitive), the same pathogenic variants were identified yet statistically fewer variants required assessment to identify more diagnostic variants than reported by the GMS genome strategy. This gave a diagnostic rate per variant assessed of 20% for HiPPo restricted to GenCC versus 3% for the GMS panel-based approach. With plans to sequence 5 million more NHS patients, strategies are needed to optimise the full potential of genome data beyond gene panels whilst minimising the burden of variants that require clinical assessment.

Genome sequencing was first offered clinically in the UK through the 100,000 Genomes Project (100KGP). Analysis was restricted to predefined gene panels associated with the patient's phenotype. However, panels rely on clearly characterised phenotypes and risk missing diagnoses outside of the panel(s) applied. We propose a complementary method to rapidly identify pathogenic variants, including those missed by 100KGP methods.The Loss-of-function Observed/Expected Upper-bound Fraction (LOEUF) score quantifies gene constraint, with low scores correlated with haploinsufficiency. We applied DeNovoLOEUF, a filtering strategy to sequencing data from 13,949 rare disease trios in the 100KGP, by filtering for rare, de novo, loss-of-function variants in disease genes with a LOEUF score 0.2. We compared our findings with the corresponding patient's diagnostic reports.324/332 (98%) of the variants identified using DeNovoLOEUF were diagnostic or partially diagnostic (whereby the variant was responsible for some of the phenotype). We identified 39 diagnoses that were "missed" by 100KGP standard analyses, which are now being returned to patients.We have demonstrated a highly specific and rapid method with a 98% positive predictive value that has good concordance with standard analysis, low false-positive rate, and can identify additional diagnoses. Globally, as more patients are being offered genome sequencing, we anticipate that DeNovoLOEUF will rapidly identify new diagnoses and facilitate iterative analyses when new disease genes are discovered.

The precise regulation of gene expression is fundamental to neurodevelopment, plasticity and cognitive function. Although several studies have profiled transcription in the developing human brain, there is a gap in understanding of accompanying translational regulation. In this study, we performed ribosome profiling on 73 human prenatal and adult cortex samples. We characterized the translational regulation of annotated open reading frames (ORFs) and identified thousands of previously unknown translation events, including small ORFs that give rise to human-specific and/or brain-specific microproteins, many of which we independently verified using proteomics. Ribosome profiling in stem-cell-derived human neuronal cultures corroborated these findings and revealed that several neuronal activity-induced non-coding RNAs encode previously undescribed microproteins. Physicochemical analysis of brain microproteins identified a class of proteins that contain arginine-glycine-glycine (RGG) repeats and, thus, may be regulators of RNA metabolism. This resource expands the known translational landscape of the human brain and illuminates previously unknown brain-specific protein products.

PURPOSE LHX2 encodes the LIM homeobox 2 transcription factor (LHX2), which is highly expressed in brain and well conserved across species, but has not been clearly linked to neurodevelopmental disorders (NDD) to date. METHODS Through international collaboration, we identified 19 individuals from 18 families with variable neurodevelopmental phenotypes, carrying a small chromosomal deletion, likely gene-disrupting or missense variants in LHX2. Functional consequences of missense variants were investigated in cellular systems. RESULTS Affected individuals presented with developmental and/or behavioral abnormalities, autism-spectrum disorder, variable intellectual disability, and microcephaly. We observed nucleolar accumulation for two missense variants located within the DNA-binding HOX domain, impaired interaction with co-factor LDB1 for another variant located in the protein-protein interaction mediating LIM domain, and impaired transcriptional activation by luciferase assay for four missense variants. CONCLUSION We implicate LHX2 haploinsufficiency by deletion and likely gene-disrupting variants as causative for a variable NDD. Our findings suggest a loss-of-function mechanism also for likely pathogenic LHX2 missense variants. Together, our observations underscore the importance of LHX2 in nervous system and for variable neurodevelopmental phenotypes.

Many individuals with muscular dystrophies remain genetically undiagnosed despite clinical diagnostic testing, including exome sequencing. Some may harbor previously undetected structural variants (SVs) or cryptic splice sites. We enrolled 10 unrelated families: nine had muscular dystrophy but lacked complete genetic diagnoses and one had an asymptomatic DMD duplication. Nano-pore genomic long-read sequencing identified previously undetected pathogenic variants in four individuals: an SV in DMD, an SV in LAMA2, and two single nucleotide variants in DMD that alter splicing. The DMD duplication in the asymptomatic individual was in tandem. Nanopore sequencing may help streamline genetic diagnostic approaches for muscular dystrophy.

Multi-nucleotide variants (MNVs), defined as two or more nearby variants existing on the same haplotype in an individual, are a clinically and biologically important class of genetic variation. However, existing tools typically do not accurately classify MNVs, and understanding of their mutational origins remains limited. Here, we systematically survey MNVs in 125,748 whole exomes and 15,708 whole genomes from the Genome Aggregation Database (gnomAD). We identify 1,792,248 MNVs across the genome with constituent variants falling within 2 bp distance of one another, including 18,756 variants with a novel combined effect on protein sequence. Finally, we estimate the relative impact of known mutational mechanisms - CpG deamination, replication error by polymerase zeta, and polymerase slippage at repeat junctions - on the generation of MNVs. Our results demonstrate the value of haplotype-aware variant annotation, and refine our understanding of genome-wide mutational mechanisms of MNVs., Multi-nucleotide variants (MNV) are genetic variants in close proximity of each other on the same haplotype whose functional impact is difficult to predict if they reside in the same codon. Here, Wang et al. use the gnomAD dataset to assemble a catalogue of MNVs and estimate their global mutation rate.

Recommendations from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) for interpreting sequence variants specify the use of computational predictors as Supporting level of evidence for pathogenicity or benignity using criteria PP3 and BP4, respectively. However, score intervals defined by tool developers, and ACMG/AMP recommendations that require the consensus of multiple predictors, lack quantitative support. Previously, we described a probabilistic framework that quantified the strengths of evidence (Supporting, Moderate, Strong, Very Strong) within ACMG/AMP recommendations. We have extended this framework to computational predictors and introduce a new standard that converts a tool’s scores to PP3 and BP4 evidence strengths. Our approach is based on estimating the local positive predictive value and can calibrate any computational tool or other continuous-scale evidence on any variant type. We estimate thresholds (score intervals) corresponding to each strength of evidence for pathogenicity and benignity for thirteen missense variant interpretation tools, using carefully assembled independent data sets. Most tools achieved Supporting evidence level for both pathogenic and benign classification using newly established thresholds. Multiple tools reached score thresholds justifying Moderate and several reached Strong evidence levels. One tool reached Very Strong evidence level for benign classification on some variants. Based on these findings, we provide recommendations for evidence-based revisions of the PP3 and BP4 ACMG/AMP criteria using individual tools and future assessment of computational methods for clinical interpretation.

ObjectiveDuchenne muscular dystrophy (DMD) is caused by pathogenic variants in the dystrophin gene (DMD). Hypermethylated CGG expansions withinDIP2B5′ UTR are associated with an intellectual development disorder. Here, we demonstrate the diagnostic utility of genomic short-read sequencing (SRS) and transcriptome sequencing to identify a novelDMDstructural variant (SV) and aDIP2BCGG expansion in a patient with DMD for whom conventional diagnostic testing failed to yield a genetic diagnosis.MethodsWe performed genomic SRS, skeletal muscle transcriptome sequencing, and targeted programmable long-read sequencing (LRS).ResultsThe proband had a typical DMD clinical presentation, autism spectrum disorder (ASD), and dystrophinopathy on muscle biopsy. Transcriptome analysis identified 6 aberrantly expressed genes;DMDandDIP2Bwere the strongest underexpression and overexpression outliers, respectively. Genomic SRS identified a 216 kb paracentric inversion (NC_000023.11: g.33162217-33378800) overlapping 2DMDpromoters. ExpansionHunter indicated an expansion of 109 CGG repeats within the 5′ UTR ofDIP2B. Targeted genomic LRS confirmed the SV and genotyped theDIP2Brepeat expansion as 270 CGG repeats.DiscussionHere, transcriptome data heavily guided genomic analysis to resolve a complexDMDinversion and aDIP2Brepeat expansion. Longitudinal follow-up will be important for clarifying the clinical significance of theDIP2Bgenotype.

Exome and genome sequencing have become the tools of choice for rare disease diagnosis, leading to large amounts of data available for analyses. To identify causal variants in these datasets, powerful filtering and decision support tools that can be efficiently used by clinicians and researchers are required. To address this need, we developed seqr - an open source, web-based tool for family-based monogenic disease analysis that allows researchers to work collaboratively to search and annotate genomic callsets. To date, seqr is being used in several research pipelines and one clinical diagnostic lab. In our own experience through the Broad Institute Center for Mendelian Genomics, seqr has enabled analyses of over 10,000 families, supporting the diagnosis of more than 3,800 individuals with rare disease and discovery of over 300 novel disease genes. Here, we describe a framework for genomic analysis in rare disease that leverages seqr’s capabilities for variant filtration, annotation, and causal variant identification, as well as support for research collaboration and data sharing. The seqr platform is available as open-source software, allowing low-cost participation in rare disease research, and a community effort to support diagnosis and gene discovery in rare disease.

Background The majority of clinical genetic testing focuses almost exclusively on regions of the genome that directly encode proteins. The important role of variants in non-coding regions in penetrant disease is, however, increasingly being demonstrated, and the use of whole genome sequencing in clinical diagnostic settings is rising across a large range of genetic disorders. Despite this, there is no existing guidance on how current guidelines designed primarily for variants in protein-coding regions should be adapted for variants identified in other genomic contexts. Methods We convened a panel of nine clinical and research scientists with wide-ranging expertise in clinical variant interpretation, with specific experience in variants within non-coding regions. This panel discussed and refined an initial draft of the guidelines which were then extensively tested and reviewed by external groups. Results We discuss considerations specifically for variants in non-coding regions of the genome. We outline how to define candidate regulatory elements, highlight examples of mechanisms through which non-coding region variants can lead to penetrant monogenic disease, and outline how existing guidelines can be adapted for the interpretation of these variants. Conclusions These recommendations aim to increase the number and range of non-coding region variants that can be clinically interpreted, which, together with a compatible phenotype, can lead to new diagnoses and catalyse the discovery of novel disease mechanisms.

A proper interaction between muscle-derived collagen XXV and its motor neuron-derived receptors protein tyrosine phosphatases sigma and delta (PTP sigma/delta) is indispensable for intramuscular motor innervation. Despite this, thus far, pathogenic recessive variants in the COL25A1 gene had only been detected in a few patients with isolated ocular congenital cranial dysinnervation disorders. Here we describe five patients from three unrelated families with recessive missense and splice site COL25A1 variants presenting with a recognizable phenotype characterized by arthrogryposis multiplex congenita with or without an ocular congenital cranial dysinnervation disorder phenotype. The clinical features of the older patients remained stable over time, without central nervous system involvement. This study extends the phenotypic and genotypic spectrum of COL25A1 related conditions, and further adds to our knowledge of the complex process of intramuscular motor innervation. Our observations indicate a role for collagen XXV in regulating the appropriate innervation not only of extraocular muscles, but also of bulbar, axial, and limb muscles in the human.

Perinatal death, of a fetus or newborn, is a devastating event for families. Following nationwide multicentre recruitment, we assessed ‘genomic autopsy’ as an adjunct to standard autopsy for 200 families who experienced perinatal death, and provided a definite or candidate genetic diagnosis in 105 families. From this understudied cohort, half of the (candidate) diagnoses were phenotype expansions or novel disease genes, revealing previously unknown in-utero presentations of existing developmental disorders, and genomic disorders that are likely incompatible with life. Among the definite diagnoses, 43% were recessively or dominantly inherited, posing a 25% or 50% recurrence risk for future pregnancies. Ten families used their diagnosis for preimplantation or prenatal diagnosis of 12 pregnancies, facilitating the delivery of ten healthy newborns and management of two affected pregnancies. We emphasize the clinical importance of genomic investigations of perinatal death, with short turn-around times, enabling accurate counselling and options for families to prevent recurrence.

PurposeThe majority of clinical genetic testing focuses almost exclusively on regions of the genome that directly encode proteins. The important role of variants in non-coding regions in penetrant disease is, however, increasingly being demonstrated, and the use of whole genome sequencing in clinical diagnostic settings is rising across a large range of genetic disorders. Despite this, there is no existing guidance on how current guidelines designed primarily for variants in protein-coding regions should be adapted for variants identified in other genomic contexts.MethodsWe convened a panel of clinical and research scientists with wide-ranging expertise in clinical variant interpretation, with specific experience in variants within non-coding regions. This panel discussed and refined an initial draft of the guidelines which were then extensively tested and reviewed by external groups.ResultsWe discuss considerations specifically for variants in non-coding regions of the genome. We outline how to define candidate regulatory elements, highlight examples of mechanisms through which non-coding region variants can lead to penetrant monogenic disease, and outline how existing guidelines can be adapted for these variants.ConclusionThese recommendations aim to increase the number and range of non-coding region variants that can be clinically interpreted, which, together with a compatible phenotype, can lead to new diagnoses and catalyse the discovery of novel disease mechanisms.

Cerebral palsy (CP) is the most common childhood motor disability, yet its link to single-gene disorders is under-characterized. To explore the genetic landscape of CP, we conducted whole exome sequencing (WES) in a cohort of patients with CP.We performed comprehensive phenotyping and WES on a prospective cohort of individuals with cryptogenic CP (who meet criteria for CP; have no risk factors), non-cryptogenic CP (who meet criteria for CP; have at least one risk factor), and CP masqueraders (who could be diagnosed with CP, but have regression/progressive symptoms). We characterized motor phenotypes, ascertained medical comorbidities, and classified brain MRIs. We analyzed WES data using an institutional pipeline.We included 50 probands in this analysis (20 females, 30 males). Twenty-four had cryptogenic CP, 20 had non-cryptogenic CP, five had CP masquerader classification, and one had unknown classification. Hypotonic-ataxic subtype showed a difference in prevalence across the classification groups (p = 0.01). Twenty-six percent of participants (13/50) had a pathogenic/likely pathogenic variant in 13 unique genes (ECHS1, SATB2, ZMYM2, ADAT3, COL4A1, THOC2, SLC16A2, SPAST, POLR2A, GNAO1, PDHX, ACADM, ATL1), including one patient with two genetic disorders (ACADM, PDHX) and two patients with a SPAST-related disorder. The CP masquerader category had the highest diagnostic yield (n = 3/5, 60%), followed by the cryptogenic CP category (n = 7/24, 29%). Fifteen percent of patients with non-cryptogenic CP (n = 3/20) had a Mendelian disorder on WES.WES demonstrated a significant prevalence of Mendelian disorders in individuals clinically diagnosed with CP, including in individuals with known CP risk factors.

PAX5 is a transcription factor associated with abnormal posterior midbrain and cerebellum development in mice. PAX5 is highly loss-of-function intolerant and missense constrained, and has been identified as a candidate gene for autism spectrum disorder (ASD). We describe 16 individuals from 12 families who carry deletions involving PAX5 and surrounding genes, de novo frameshift variants that are likely to trigger nonsense-mediated mRNA decay, a rare stop-gain variant, or missense variants that affect conserved amino acid residues. Four of these individuals were published previously but without detailed clinical descriptions. All these individuals have been diagnosed with one or more neurodevelopmental phenotypes including delayed developmental milestones (DD), intellectual disability (ID), and/or ASD. Seizures were documented in four individuals. No recurrent patterns of brain magnetic resonance imaging (MRI) findings, structural birth defects, or dysmorphic features were observed. Our findings suggest that PAX5 haploinsufficiency causes a neurodevelopmental disorder whose cardinal features include DD, variable ID, and/or ASD.

Hundreds of thousands of genetic variants have been reported to cause severe monogenic diseases, but the probability that a variant carrier develops the disease (termed penetrance) is unknown for virtually all of them. Additionally, the clinical utility of common polygenetic variation remains uncertain. Using exome sequencing from 77,184 adult individuals (38,618 multi-ancestral individuals from a type 2 diabetes case-control study and 38,566 participants from the UK Biobank, for whom genotype array data were also available), we apply clinical standard-of-care gene variant curation for eight monogenic metabolic conditions. Rare variants causing monogenic diabetes and dyslipidemias display effect sizes significantly larger than the top 1% of the corresponding polygenic scores. Nevertheless, penetrance estimates for monogenic variant carriers average 60% or lower for most conditions. We assess epidemiologic and genetic factors contributing to risk prediction in monogenic variant carriers, demonstrating that inclusion of polygenic variation significantly improves biomarker estimation for two monogenic dyslipidemias., Penetrance of variants in monogenic disease and clinical utility of common polygenic variation has not been well explored on a large-scale. Here, the authors use exome sequencing data from 77,184 individuals to generate penetrance estimates and assess the utility of polygenic variation in risk prediction of monogenic variants.

The traditional model of genomic data analysis - downloading data from centralized warehouses for analysis with local computing resources - is increasingly unsustainable. Not only are transfers slow and cost prohibitive, but this approach also leads to redundant and siloed compute infrastructure that makes it difficult to ensure security and compliance of protected data. The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) inverts this model, providing a unified cloud computing environment for data storage, management, and analysis. AnVIL eliminates the need for data movement, allows for active threat detection and monitoring, and provides scalable, shared computing resources that can be acquired by researchers as needed. This presents many new opportunities for collaboration and data sharing that will ultimately lead to scientific discoveries at scales not previously possible.

Rare genetic disorders, while individually rare, are collectively common. They represent some of the most severe disorders affecting patients worldwide with significant morbidity and mortality. Over the last decade, advances in genomic methods have significantly uplifted diagnostic rates for patients and facilitated novel and targeted therapies. However, many patients with rare genetic disorders still remain undiagnosed as the genetic etiology of only a proportion of Mendelian conditions has been discovered to date. This article explores existing strategies to identify novel Mendelian genes and how these discoveries impact clinical care and therapeutics. We discuss the importance of data sharing, phenotype-driven approaches, patient-led approaches, utilization of large-scale genomic sequencing projects, constraint-based methods, integration of multi-omics data, and gene-to-patient methods. We further consider the health economic advantages of novel gene discovery and speculate on potential future methods for improved clinical outcomes.

The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) was developed to address a widespread community need for a unified computing environment for genomics data storage, management, and analysis. In this perspective, we present AnVIL, describe its ecosystem and interoperability with other platforms, and highlight how this platform and associated initiatives contribute to improved genomic data sharing efforts. The AnVIL is a federated cloud platform designed to manage and store genomics and related data, enable population-scale analysis, and facilitate collaboration through the sharing of data, code, and analysis results. By inverting the traditional model of data sharing, the AnVIL eliminates the need for data movement while also adding security measures for active threat detection and monitoring and provides scalable, shared computing resources for any researcher. We describe the core data management and analysis components of the AnVIL, which currently consists of Terra, Gen3, Galaxy, RStudio/Bioconductor, Dockstore, and Jupyter, and describe several flagship genomics datasets available within the AnVIL. We continue to extend and innovate the AnVIL ecosystem by implementing new capabilities, including mechanisms for interoperability and responsible data sharing, while streamlining access management. The AnVIL opens many new opportunities for analysis, collaboration, and data sharing that are needed to drive research and to make discoveries through the joint analysis of hundreds of thousands to millions of genomes along with associated clinical and molecular data types.