Exome copy number variant detection, analysis, and classification in a large cohort of families with undiagnosed rare genetic disease.

Copy number variants (CNVs) are significant contributors to the pathogenicity of rare genetic diseases and, with new innovative methods, can now reliably be identified from exome sequencing. Challenges still remain in accurate classification of CNV pathogenicity. CNV calling using GATK-gCNV was performed on exomes from a cohort of 6,633 families (15,759 individuals) with heterogeneous phenotypes and variable prior genetic testing collected at the Broad Institute Center for Mendelian Genomics of the Genomics Research to Elucidate the Genetics of Rare Diseases consortium and analyzed using the seqr platform. The addition of CNV detection to exome analysis identified causal CNVs for 171 families (2.6%). The estimated sizes of CNVs ranged from 293 bp to 80 Mb. The causal CNVs consisted of 140 deletions, 15 duplications, 3 suspected complex structural variants (SVs), 3 insertions, and 10 complex SVs, the latter two groups being identified by orthogonal confirmation methods. To classify CNV variant pathogenicity, we used the 2020 American College of Medical Genetics and Genomics/ClinGen CNV interpretation standards and developed additional criteria to evaluate allelic and functional data as well as variants on the X chromosome to further advance the framework. We interpreted 151 CNVs as likely pathogenic/pathogenic and 20 CNVs as high-interest variants of uncertain significance. Calling CNVs from existing exome data increases the diagnostic yield for individuals undiagnosed after standard testing approaches, providing a higher-resolution alternative to arrays at a fraction of the cost of genome sequencing. Our improvements to the classification approach advances the systematic framework to assess the pathogenicity of CNVs.

Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project.

BACKGROUND: A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting. METHODS: We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values. RESULTS: Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency. CONCLUSIONS: Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.

Genome and RNA sequencing boost neuromuscular diagnoses to 62% from 34% with exome sequencing alone.

OBJECTIVE: Most families with heritable neuromuscular disorders do not receive a molecular diagnosis. Here we evaluate diagnostic utility of exome, genome, RNA sequencing, and protein studies and provide evidence-based recommendations for their integration into practice. METHODS: In total, 247 families with suspected monogenic neuromuscular disorders who remained without a genetic diagnosis after standard diagnostic investigations underwent research-led massively parallel sequencing: neuromuscular disorder gene panel, exome, genome, and/or RNA sequencing to identify causal variants. Protein and RNA studies were also deployed when required. RESULTS: Integration of exome sequencing and auxiliary genome, RNA and/or protein studies identified causal or likely causal variants in 62% (152 out of 247) of families. Exome sequencing alone informed 55% (83 out of 152) of diagnoses, with remaining diagnoses (45%; 69 out of 152) requiring genome sequencing, RNA and/or protein studies to identify variants and/or support pathogenicity. Arrestingly, novel disease genes accounted for <4% (6 out of 152) of diagnoses while 36.2% of solved families (55 out of 152) harbored at least one splice-altering or structural variant in a known neuromuscular disorder gene. We posit that contemporary neuromuscular disorder gene-panel sequencing could likely provide 66% (100 out of 152) of our diagnoses today. INTERPRETATION: Our results emphasize thorough clinical phenotyping to enable deep scrutiny of all rare genetic variation in phenotypically consistent genes. Post-exome auxiliary investigations extended our diagnostic yield by 81% overall (34-62%). We present a diagnostic algorithm that details deployment of genomic and auxiliary investigations to obtain these diagnoses today most effectively. We hope this provides a practical guide for clinicians as they gain greater access to clinical genome and transcriptome sequencing.

Heterozygous MAP3K20 variants cause ectodermal dysplasia, craniosynostosis, sensorineural hearing loss, and limb anomalies.

Biallelic pathogenic variants in MAP3K20, which encodes a mitogen-activated protein kinase, are a rare cause of split-hand foot malformation (SHFM), hearing loss, and nail abnormalities or congenital myopathy. However, heterozygous variants in this gene have not been definitively associated with a phenotype. Here, we describe the phenotypic spectrum associated with heterozygous de novo variants in the linker region between the kinase domain and leucine zipper domain of MAP3K20. We report five individuals with diverse clinical features, including craniosynostosis, limb anomalies, sensorineural hearing loss, and ectodermal dysplasia-like phenotypes who have heterozygous de novo variants in this specific region of the gene. These individuals exhibit both shared and unique clinical manifestations, highlighting the complexity and variability of the disorder. We propose that the involvement of MAP3K20 in endothelial-mesenchymal transition provides a plausible etiology of these features. Together, these findings characterize a disorder that both expands the phenotypic spectrum associated with MAP3K20 and highlights the need for further studies on its role in early human development.

Detecting missed diagnoses of spinal muscular atrophy in genome, exome, and panel sequencing datasets.

Spinal muscular atrophy (SMA) is a genetic disorder that causes progressive degeneration of lower motor neurons and the subsequent loss of muscle function throughout the body. It is the second most common recessive disorder in individuals of European descent and is present in all populations. Accurate tools exist for diagnosing SMA from short read and long read genome sequencing data. However, there are no publicly available tools for GRCh38-aligned data from panel or exome sequencing assays which continue to be used as first line tests for neuromuscular disorders. We therefore developed and extensively validated a new tool - SMA Finder - that can diagnose SMA not only in genome, but also exome and targeted sequencing samples aligned to GRCh37, GRCh38, or T2T-CHM13. It works by evaluating aligned reads that overlap the c.840 position of SMN1 and SMN2 in order to detect the most common molecular causes of SMA. We applied SMA Finder to 16,626 exomes and 3,911 genomes from heterogeneous rare disease cohorts sequenced at the Broad Institute Center for Mendelian Genomics as well as 1,157 exomes and 8,762 targeted sequencing samples from Tartu University Hospital. SMA Finder correctly identified all 16 known SMA cases and reported nine novel diagnoses which have since been confirmed by clinical testing, with another four novel diagnoses undergoing validation. Notably, out of the 29 total SMA positive cases, 21 had an initial clinical diagnosis of muscular dystrophy, congenital myasthenic syndrome, or congenital myopathy. This underscored the frequency with which SMA can be misdiagnosed as other neuromuscular disorders and confirmed the utility of using SMA Finder to reanalyze phenotypically diverse neuromuscular disease cohorts. Finally, we evaluated SMA Finder on 198,868 individuals that had both exome and genome sequencing data within the UK Biobank (UKBB) and found that SMA Finder's overall false positive rate was less than 1 / 200,000 exome samples, and its positive predictive value (PPV) was 96%. We also observed 100% concordance between UKBB exome and genome calls. This analysis showed that, even though it is located within a segmental duplication, the most common causal variant for SMA can be detected with comparable accuracy to monogenic disease variants in non-repetitive regions. Additionally, the high PPV demonstrated by SMA Finder, the existence of treatment options for SMA in which early diagnosis is imperative for therapeutic benefit, as well as widespread availability of clinical confirmatory testing for SMA, may warrant the addition of SMN1 to the ACMG list of genes with reportable secondary findings after genome and exome sequencing.

Exome copy number variant detection, analysis and classification in a large cohort of families with undiagnosed rare genetic disease.

Copy number variants (CNVs) are significant contributors to the pathogenicity of rare genetic diseases and with new innovative methods can now reliably be identified from exome sequencing. Challenges still remain in accurate classification of CNV pathogenicity. CNV calling using GATK-gCNV was performed on exomes from a cohort of 6,633 families (15,759 individuals) with heterogeneous phenotypes and variable prior genetic testing collected at the Broad Institute Center for Mendelian Genomics of the GREGoR consortium. Each family's CNV data was analyzed using the seqr platform and candidate CNVs classified using the 2020 ACMG/ClinGen CNV interpretation standards. We developed additional evidence criteria to address situations not covered by the current standards. The addition of CNV calling to exome analysis identified causal CNVs for 173 families (2.6%). The estimated sizes of CNVs ranged from 293 bp to 80 Mb with estimates that 44% would not have been detected by standard chromosomal microarrays. The causal CNVs consisted of 141 deletions, 15 duplications, 4 suspected complex structural variants (SVs), 3 insertions and 10 complex SVs, the latter two groups being identified by orthogonal validation methods. We interpreted 153 CNVs as likely pathogenic/pathogenic and 20 CNVs as high interest variants of uncertain significance. Calling CNVs from existing exome data increases the diagnostic yield for individuals undiagnosed after standard testing approaches, providing a higher resolution alternative to arrays at a fraction of the cost of genome sequencing. Our improvements to the classification approach advances the systematic framework to assess the pathogenicity of CNVs.

Critical assessment of variant prioritization methods for rare disease diagnosis within the Rare Genomes Project.

Background: A major obstacle faced by rare disease families is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years, and causal variants are identified in under 50%. The Rare Genomes Project (RGP) is a direct-to-participant research study on the utility of genome sequencing (GS) for diagnosis and gene discovery. Families are consented for sharing of sequence and phenotype data with researchers, allowing development of a Critical Assessment of Genome Interpretation (CAGI) community challenge, placing variant prioritization models head-to-head in a real-life clinical diagnostic setting. Methods: Predictors were provided a dataset of phenotype terms and variant calls from GS of 175 RGP individuals (65 families), including 35 solved training set families, with causal variants specified, and 30 test set families (14 solved, 16 unsolved). The challenge tasked teams with identifying the causal variants in as many test set families as possible. Ranked variant predictions were submitted with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on rank position of true positive causal variants and maximum F-measure, based on precision and recall of causal variants across EPCR thresholds. Results: Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performing teams recalled the causal variants in up to 13 of 14 solved families by prioritizing high quality variant calls that were rare, predicted deleterious, segregating correctly, and consistent with reported phenotype. In unsolved families, newly discovered diagnostic variants were returned to two families following confirmatory RNA sequencing, and two prioritized novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant, in an unsolved proband with phenotype overlap with asparagine synthetase deficiency. Conclusions: By objective assessment of variant predictions, we provide insights into current state-of-the-art algorithms and platforms for genome sequencing analysis for rare disease diagnosis and explore areas for future optimization. Identification of diagnostic variants in unsolved families promotes synergy between researchers with clinical and computational expertise as a means of advancing the field of clinical genome interpretation.

Macrocephaly and developmental delay caused by missense variants in RAB5C.

Rab GTPases are important regulators of intracellular vesicular trafficking. RAB5C is a member of the Rab GTPase family that plays an important role in the endocytic pathway, membrane protein recycling and signaling. Here we report on 12 individuals with nine different heterozygous de novo variants in RAB5C. All but one patient with missense variants (n = 9) exhibited macrocephaly, combined with mild-to-moderate developmental delay. Patients with loss of function variants (n = 2) had an apparently more severe clinical phenotype with refractory epilepsy and intellectual disability but a normal head circumference. Four missense variants were investigated experimentally. In vitro biochemical studies revealed that all four variants were damaging, resulting in increased nucleotide exchange rate, attenuated responsivity to guanine exchange factors and heterogeneous effects on interactions with effector proteins. Studies in C. elegans confirmed that all four variants were damaging in vivo and showed defects in endocytic pathway function. The variant heterozygotes displayed phenotypes that were not observed in null heterozygotes, with two shown to be through a dominant negative mechanism. Expression of the human RAB5C variants in zebrafish embryos resulted in defective development, further underscoring the damaging effects of the RAB5C variants. Our combined bioinformatic, in vitro and in vivo experimental studies and clinical data support the association of RAB5C missense variants with a neurodevelopmental disorder characterized by macrocephaly and mild-to-moderate developmental delay through disruption of the endocytic pathway.

Unique Capabilities of Genome Sequencing for Rare Disease Diagnosis.

Background: Causal variants underlying rare disorders may remain elusive even after expansive gene panels or exome sequencing (ES). Clinicians and researchers may then turn to genome sequencing (GS), though the added value of this technique and its optimal use remain poorly defined. We therefore investigated the advantages of GS within a phenotypically diverse cohort. Methods: GS was performed for 744 individuals with rare disease who were genetically undiagnosed. Analysis included review of single nucleotide, indel, structural, and mitochondrial variants. Results: We successfully solved 218/744 (29.3%) cases using GS, with most solves involving established disease genes (157/218, 72.0%). Of all solved cases, 148 (67.9%) had previously had non-diagnostic ES. We systematically evaluated the 218 causal variants for features requiring GS to identify and 61/218 (28.0%) met these criteria, representing 8.2% of the entire cohort. These included small structural variants (13), copy neutral inversions and complex rearrangements (8), tandem repeat expansions (6), deep intronic variants (15), and coding variants that may be more easily found using GS related to uniformity of coverage (19). Conclusion: We describe the diagnostic yield of GS in a large and diverse cohort, illustrating several types of pathogenic variation eluding ES or other techniques. Our results reveal a higher diagnostic yield of GS, supporting the utility of a genome-first approach, with consideration of GS as a secondary or tertiary test when higher-resolution structural variant analysis is needed or there is a strong clinical suspicion for a condition and prior targeted genetic testing has been negative.

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.

seqr: A web-based analysis and collaboration tool for rare disease genomics.

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.

Characterization of an unbalanced translocation causing 3q28qter duplication and 10q26.2qter deletion in a patient with global developmental delay and self-injury.

Chromosomal structural variation can cause severe neurodevelopmental and neuropsychiatric phenotypes. Here we present a nonverbal female adolescent with severe stereotypic movement disorder with severe problem behavior (e.g., self-injurious behavior, aggression, and disruptive and destructive behaviors), autism spectrum disorder, severe intellectual disability, attention deficit hyperactivity disorder, and global developmental delay. Previous cytogenetic analysis revealed balanced translocations present in the patient's apparently normal mother. We hypothesized the presence of unbalanced translocations in the patient due to maternal history of spontaneous abortions. Whole-genome sequencing and whole-genome optical mapping, complementary next-generation genomic technologies capable of the accurate and robust detection of structural variants, identified t(3;10), t(10;14), and t(3;14) three-way balanced translocations in the mother and der(10)t(3;14;10) and der(14)t(3;14;10) translocations in the patient. Instead of a t(3;10), she inherited a normal maternal copy of Chromosome 3, resulting in an unbalanced state of a 3q28qter duplication and 10q26.2qter deletion. Copy-imbalanced genes in one or both of these regions, such as DLG1, DOCK1, and EBF3, may contribute to the patient's phenotype that spans neurodevelopmental, musculoskeletal, and psychiatric domains, with the possible contribution of a maternally inherited 15q13.2q13.3 deletion.

Mosaicism in Human Health and Disease.

Mosaicism refers to the occurrence of two or more genomes in an individual derived from a single zygote. Germline mosaicism is a mutation that is limited to the gonads and can be transmitted to offspring. Somatic mosaicism is a postzygotic mutation that occurs in the soma, and it may occur at any developmental stage or in adult tissues. Mosaic variation may be classified in six ways: (a) germline or somatic origin, (b) class of DNA mutation (ranging in scale from single base pairs to multiple chromosomes), (c) developmental context, (d) body location(s), (e) functional consequence (including deleterious, neutral, or advantageous), and (f) additional sources of mosaicism, including mitochondrial heteroplasmy, exogenous DNA sources such as vectors, and epigenetic changes such as imprinting and X-chromosome inactivation. Technological advances, including single-cell and other next-generation sequencing, have facilitated improved sensitivity and specificity to detect mosaicism in a variety of biological contexts.

Disruptive variants of CSDE1 associate with autism and interfere with neuronal development and synaptic transmission.

RNA binding proteins are key players in posttranscriptional regulation and have been implicated in neurodevelopmental and neuropsychiatric disorders. Here, we report a significant burden of heterozygous, likely gene-disrupting variants in CSDE1 (encoding a highly constrained RNA binding protein) among patients with autism and related neurodevelopmental disabilities. Analysis of 17 patients identifies common phenotypes including autism, intellectual disability, language and motor delay, seizures, macrocephaly, and variable ocular abnormalities. HITS-CLIP revealed that Csde1-binding targets are enriched in autism-associated gene sets, especially FMRP targets, and in neuronal development and synaptic plasticity-related pathways. Csde1 knockdown in primary mouse cortical neurons leads to an overgrowth of the neurites and abnormal dendritic spine morphology/synapse formation and impaired synaptic transmission, whereas mutant and knockdown experiments in Drosophila result in defects in synapse growth and synaptic transmission. Our study defines a new autism-related syndrome and highlights the functional role of CSDE1 in synapse development and synaptic transmission.