Expanding the genetics and phenotypes of ocular congenital cranial dysinnervation disorders.

Purpose: To identify genetic etiologies and genotype/phenotype associations for unsolved ocular congenital cranial dysinnervation disorders (oCCDDs). Methods: We coupled phenotyping with exome or genome sequencing of 467 pedigrees with genetically unsolved oCCDDs, integrating analyses of pedigrees, human and animal model phenotypes, and de novo variants to identify rare candidate single nucleotide variants, insertion/deletions, and structural variants disrupting protein-coding regions. Prioritized variants were classified for pathogenicity and evaluated for genotype/phenotype correlations. Results: Analyses elucidated phenotypic subgroups, identified pathogenic/likely pathogenic variant(s) in 43/467 probands (9.2%), and prioritized variants of uncertain significance in 70/467 additional probands (15.0%). These included known and novel variants in established oCCDD genes, genes associated with syndromes that sometimes include oCCDDs (e.g., MYH10, KIF21B, TGFBR2, TUBB6), genes that fit the syndromic component of the phenotype but had no prior oCCDD association (e.g., CDK13, TGFB2), genes with no reported association with oCCDDs or the syndromic phenotypes (e.g., TUBA4A, KIF5C, CTNNA1, KLB, FGF21), and genes associated with oCCDD phenocopies that had resulted in misdiagnoses. Conclusion: This study suggests that unsolved oCCDDs are clinically and genetically heterogeneous disorders often overlapping other Mendelian conditions and nominates many candidates for future replication and functional studies.

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.

De novo variants in the non-coding spliceosomal snRNA gene RNU4-2 are a frequent cause of syndromic neurodevelopmental disorders.

Around 60% of individuals with neurodevelopmental disorders (NDD) remain undiagnosed after comprehensive genetic testing, primarily of protein-coding genes1. Increasingly, large genome-sequenced cohorts are improving our ability to discover new diagnoses in the non-coding genome. Here, we identify the non-coding RNA RNU4-2 as a novel syndromic NDD gene. RNU4-2 encodes the U4 small nuclear RNA (snRNA), which is a critical component of the U4/U6.U5 tri-snRNP complex of the major spliceosome2. We identify an 18 bp region of RNU4-2 mapping to two structural elements in the U4/U6 snRNA duplex (the T-loop and Stem III) that is severely depleted of variation in the general population, but in which we identify heterozygous variants in 119 individuals with NDD. The vast majority of individuals (77.3%) have the same highly recurrent single base-pair insertion (n.64_65insT). We estimate that variants in this region explain 0.41% of individuals with NDD. We demonstrate that RNU4-2 is highly expressed in the developing human brain, in contrast to its contiguous counterpart RNU4-1 and other U4 homologs, supporting RNU4-2's role as the primary U4 transcript in the brain. Overall, this work underscores the importance of non-coding genes in rare disorders. It will provide a diagnosis to thousands of individuals with NDD worldwide and pave the way for the development of effective treatments for these individuals.

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.

Spliceosome malfunction causes neurodevelopmental disorders with overlapping features.

Pre-mRNA splicing is a highly coordinated process. While its dysregulation has been linked to neurological deficits, our understanding of the underlying molecular and cellular mechanisms remains limited. We implicated pathogenic variants in U2AF2 and PRPF19, encoding spliceosome subunits in neurodevelopmental disorders (NDDs), by identifying 46 unrelated individuals with 23 de novo U2AF2 missense variants (including 7 recurrent variants in 30 individuals) and 6 individuals with de novo PRPF19 variants. Eight U2AF2 variants dysregulated splicing of a model substrate. Neuritogenesis was reduced in human neurons differentiated from human pluripotent stem cells carrying two U2AF2 hyper-recurrent variants. Neural loss of function (LoF) of the Drosophila orthologs U2af50 and Prp19 led to lethality, abnormal mushroom body (MB) patterning, and social deficits, which were differentially rescued by wild-type and mutant U2AF2 or PRPF19. Transcriptome profiling revealed splicing substrates or effectors (including Rbfox1, a third splicing factor), which rescued MB defects in U2af50-deficient flies. Upon reanalysis of negative clinical exomes followed by data sharing, we further identified 6 patients with NDD who carried RBFOX1 missense variants which, by in vitro testing, showed LoF. Our study implicates 3 splicing factors as NDD-causative genes and establishes a genetic network with hierarchy underlying human brain development and function.

Evaluation of the diagnostic accuracy of exome sequencing and its impact on diagnostic thinking for patients with rare disease in a publicly funded health care system: A prospective cohort study.

PURPOSE: To evaluate the diagnostic utility of publicly funded clinical exome sequencing (ES) for patients with suspected rare genetic diseases. METHODS: We prospectively enrolled 297 probands who met eligibility criteria and received ES across 5 sites in Ontario, Canada, and extracted data from medical records and clinician surveys. Using the Fryback and Thornbury Efficacy Framework, we assessed diagnostic accuracy by examining laboratory interpretation of results and assessed diagnostic thinking by examining the clinical interpretation of results and whether clinical-molecular diagnoses would have been achieved via alternative hypothetical molecular tests. RESULTS: Laboratories reported 105 molecular diagnoses and 165 uncertain results in known and novel genes. Of these, clinicians interpreted 102 of 105 (97%) molecular diagnoses and 6 of 165 (4%) uncertain results as clinical-molecular diagnoses. The 108 clinical-molecular diagnoses were in 104 families (35% diagnostic yield). Each eligibility criteria resulted in diagnostic yields of 30% to 40%, and higher yields were achieved when >2 eligibility criteria were met (up to 45%). Hypothetical tests would have identified 61% of clinical-molecular diagnoses. CONCLUSION: We demonstrate robustness in eligibility criteria and high clinical validity of laboratory results from ES testing. The importance of ES was highlighted by the potential 40% of patients that would have gone undiagnosed without this test.

Specific heterozygous variants in MGP lead to endoplasmic reticulum stress and cause spondyloepiphyseal dysplasia.

Matrix Gla protein (MGP) is a vitamin K-dependent post-translationally modified protein, highly expressed in vascular and cartilaginous tissues. It is a potent inhibitor of extracellular matrix mineralization. Biallelic loss-of-function variants in the MGP gene cause Keutel syndrome, an autosomal recessive disorder characterized by widespread calcification of various cartilaginous tissues and skeletal and vascular anomalies. In this study, we report four individuals from two unrelated families with two heterozygous variants in MGP, both altering the cysteine 19 residue to phenylalanine or tyrosine. These individuals present with a spondyloepiphyseal skeletal dysplasia characterized by short stature with a short trunk, diffuse platyspondyly, midface retrusion, progressive epiphyseal anomalies and brachytelephalangism. We investigated the cellular and molecular effects of one of the heterozygous deleterious variants (C19F) using both cell and genetically modified mouse models. Heterozygous 'knock-in' mice expressing C19F MGP recapitulate most of the skeletal anomalies observed in the affected individuals. Our results suggest that the main underlying mechanism leading to the observed skeletal dysplasia is endoplasmic reticulum stress-induced apoptosis of the growth plate chondrocytes. Overall, our findings support that heterozygous variants in MGP altering the Cys19 residue cause autosomal dominant spondyloepiphyseal dysplasia, a condition distinct from Keutel syndrome both clinically and molecularly.

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.

Deep phenotyping of the neuroimaging and skeletal features in KBG syndrome: a study of 53 patients and review of the literature.

BACKGROUND: KBG syndrome is caused by haploinsufficiency of ANKRD11 and is characterised by macrodontia of upper central incisors, distinctive facial features, short stature, skeletal anomalies, developmental delay, brain malformations and seizures. The central nervous system (CNS) and skeletal features remain poorly defined. METHODS: CNS and/or skeletal imaging were collected from molecularly confirmed individuals with KBG syndrome through an international network. We evaluated the original imaging and compared our results with data in the literature. RESULTS: We identified 53 individuals, 44 with CNS and 40 with skeletal imaging. Common CNS findings included incomplete hippocampal inversion and posterior fossa malformations; these were significantly more common than previously reported (63.4% and 65.9% vs 1.1% and 24.7%, respectively). Additional features included patulous internal auditory canal, never described before in KBG syndrome, and the recurrence of ventriculomegaly, encephalic cysts, empty sella and low-lying conus medullaris. We found no correlation between these structural anomalies and epilepsy or intellectual disability. Prevalent skeletal findings comprised abnormalities of the spine including scoliosis, coccygeal anomalies and cervical ribs. Hand X-rays revealed frequent abnormalities of carpal bone morphology and maturation, including a greater delay in ossification compared with metacarpal/phalanx bones. CONCLUSION: This cohort enabled us to describe the prevalence of very heterogeneous neuroradiological and skeletal anomalies in KBG syndrome. Knowledge of the spectrum of such anomalies will aid diagnostic accuracy, improve patient care and provide a reference for future research on the effects of ANKRD11 variants in skeletal and brain development.

Systematic evaluation of genome sequencing for the diagnostic assessment of autism spectrum disorder and fetal structural anomalies.

Short-read genome sequencing (GS) holds the promise of becoming the primary diagnostic approach for the assessment of autism spectrum disorder (ASD) and fetal structural anomalies (FSAs). However, few studies have comprehensively evaluated its performance against current standard-of-care diagnostic tests: karyotype, chromosomal microarray (CMA), and exome sequencing (ES). To assess the clinical utility of GS, we compared its diagnostic yield against these three tests in 1,612 quartet families including an individual with ASD and in 295 prenatal families. Our GS analytic framework identified a diagnostic variant in 7.8% of ASD probands, almost 2-fold more than CMA (4.3%) and 3-fold more than ES (2.7%). However, when we systematically captured copy-number variants (CNVs) from the exome data, the diagnostic yield of ES (7.4%) was brought much closer to, but did not surpass, GS. Similarly, we estimated that GS could achieve an overall diagnostic yield of 46.1% in unselected FSAs, representing a 17.2% increased yield over karyotype, 14.1% over CMA, and 4.1% over ES with CNV calling or 36.1% increase without CNV discovery. Overall, GS provided an added diagnostic yield of 0.4% and 0.8% beyond the combination of all three standard-of-care tests in ASD and FSAs, respectively. This corresponded to nine GS unique diagnostic variants, including sequence variants in exons not captured by ES, structural variants (SVs) inaccessible to existing standard-of-care tests, and SVs where the resolution of GS changed variant classification. Overall, this large-scale evaluation demonstrated that GS significantly outperforms each individual standard-of-care test while also outperforming the combination of all three tests, thus warranting consideration as the first-tier diagnostic approach for the assessment of ASD and FSAs.

The landscape of tolerated genetic variation in humans and primates.

Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole-genome sequencing data for 809 individuals from 233 primate species and identified 4.3 million common protein-altering variants with orthologs in humans. We show that these variants can be inferred to have nondeleterious effects in humans based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases.

The landscape of tolerated genetic variation in humans and primates.

Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole genome sequencing data for 809 individuals from 233 primate species, and identified 4.3 million common protein-altering variants with orthologs in human. We show that these variants can be inferred to have non-deleterious effects in human based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases. One Sentence Summary: Deep learning classifier trained on 4.3 million common primate missense variants predicts variant pathogenicity in humans.

LHX2 haploinsufficiency causes a variable neurodevelopmental disorder.

PURPOSE: LHX2 encodes the LIM homeobox 2 transcription factor (LHX2), which is highly expressed in brain and well conserved across species, but it has not been clearly linked to neurodevelopmental disorders (NDDs) 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 2 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 4 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 the nervous system and for variable neurodevelopmental phenotypes.

Bi-allelic TTI1 variants cause an autosomal-recessive neurodevelopmental disorder with microcephaly.

Telomere maintenance 2 (TELO2), Tel2 interacting protein 2 (TTI2), and Tel2 interacting protein 1 (TTI1) are the three components of the conserved Triple T (TTT) complex that modulates activity of phosphatidylinositol 3-kinase-related protein kinases (PIKKs), including mTOR, ATM, and ATR, by regulating the assembly of mTOR complex 1 (mTORC1). The TTT complex is essential for the expression, maturation, and stability of ATM and ATR in response to DNA damage. TELO2- and TTI2-related bi-allelic autosomal-recessive (AR) encephalopathies have been described in individuals with moderate to severe intellectual disability (ID), short stature, postnatal microcephaly, and a movement disorder (in the case of variants within TELO2). We present clinical, genomic, and functional data from 11 individuals in 9 unrelated families with bi-allelic variants in TTI1. All present with ID, and most with microcephaly, short stature, and a movement disorder. Functional studies performed in HEK293T cell lines and fibroblasts and lymphoblastoid cells derived from 4 unrelated individuals showed impairment of the TTT complex and of mTOR pathway activity which is improved by treatment with Rapamycin. Our data delineate a TTI1-related neurodevelopmental disorder and expand the group of disorders related to the TTT complex.

The implementation of an enhanced clinical model to improve the diagnostic yield of exome sequencing for patients with a rare genetic disease: A Canadian experience.

The introduction of clinical exome sequencing (ES) has provided a unique opportunity to decrease the diagnostic odyssey for patients living with a rare genetic disease (RGD). ES has been shown to provide a diagnosis in 29%-57% of patients with a suspected RGD, with as many as 70% remaining undiagnosed. There is a need to advance the clinical model of care by more formally integrating approaches that were previously considered research into an enhanced diagnostic workflow. We developed an Exome Clinic, which set out to evaluate a workflow for improving the diagnostic yield of ES for patients with an undiagnosed RGD. Here, we report the outcomes of 47 families who underwent clinical ES in the first year of the clinic. The diagnostic yield from clinical ES was 40% (19/47). Families who remained undiagnosed after ES had the opportunity for follow-up studies that included phenotyping and candidate variant segregation in relatives, genomic matchmaking, and ES reanalysis. This enhanced diagnostic workflow increased the diagnostic yield to 55% (26/47), predominantly through the resolution of variants and genes of uncertain significance. We advocate that this approach be integrated into mainstream clinical practice and highlight the importance of a coordinated translational approach for patients with RGD.

Bridging clinical care and research in Ontario, Canada: Maximizing diagnoses from reanalysis of clinical exome sequencing data.

We examined the utility of clinical and research processes in the reanalysis of publicly-funded clinical exome sequencing data in Ontario, Canada. In partnership with eight sites, we recruited 287 families with suspected rare genetic diseases tested between 2014 and 2020. Data from seven laboratories was reanalyzed with the referring clinicians. Reanalysis of clinically relevant genes identified diagnoses in 4% (13/287); four were missed by clinical testing. Translational research methods, including analysis of novel candidate genes, identified candidates in 21% (61/287). Of these, 24 families have additional evidence through data sharing to support likely diagnoses (8% of cohort). This study indicates few diagnoses are missed by clinical laboratories, the incremental gain from reanalysis of clinically-relevant genes is modest, and the highest yield comes from validation of novel disease-gene associations. Future implementation of translational research methods, including continued reporting of compelling genes of uncertain significance by clinical laboratories, should be considered to maximize diagnoses.