O'Donnell-Luria-Rodan syndrome: description of a second multinational cohort and refinement of the phenotypic spectrum

BACKGROUND

O'Donnell-Luria-Rodan syndrome (ODLURO) is an autosomal-dominant neurodevelopmental disorder caused by pathogenic, mostly truncating variants in KMT2E. It was first described by O'Donnell-Luria et al in 2019 in a cohort of 38 patients. Clinical features encompass macrocephaly, mild intellectual disability (ID), autism spectrum disorder (ASD) susceptibility and seizure susceptibility.

METHODS

Affected individuals were ascertained at paediatric and genetic centres in various countries by diagnostic chromosome microarray or exome/genome sequencing. Patients were collected into a case cohort and were systematically phenotyped where possible.

RESULTS

We report 18 additional patients from 17 families with genetically confirmed ODLURO. We identified 15 different heterozygous likely pathogenic or pathogenic sequence variants (14 novel) and two partial microdeletions of KMT2E. We confirm and refine the phenotypic spectrum of the KMT2E-related neurodevelopmental disorder, especially concerning cognitive development, with rather mild ID and macrocephaly with subtle facial features in most patients. We observe a high prevalence of ASD in our cohort (41%), while seizures are present in only two patients. We extend the phenotypic spectrum by sleep disturbances.

CONCLUSION

Our study, bringing the total of known patients with ODLURO to more than 60 within 2 years of the first publication, suggests an unexpectedly high relative frequency of this syndrome worldwide. It seems likely that ODLURO, although just recently described, is among the more common single-gene aetiologies of neurodevelopmental delay and ASD. We present the second systematic case series of patients with ODLURO, further refining the mutational and phenotypic spectrum of this not-so-rare syndrome.

Genes To Mental Health (G2MH): A Framework to Map the Combined Effects of Rare and Common Variants on Dimensions of Cognition and Psychopathology

Rare genomic disorders (RGDs) confer elevated risk for neurodevelopmental psychiatric disorders. In this era of intense genomics discoveries, the landscape of RGDs is rapidly evolving. However, there has not been comparable progress to date in scalable, harmonized phenotyping methods. As a result, beyond associations with categorical diagnoses, the effects on dimensional traits remain unclear for many RGDs. The nature and specificity of RGD effects on cognitive and behavioral traits is an area of intense investigation: RGDs are frequently associated with more than one psychiatric condition, and those studied to date affect, to varying degrees, a broad range of developmental and cognitive functions. Although many RGDs have large effects, phenotypic expression is typically influenced by additional genomic and environmental factors. There is emerging evidence that using polygenic risk scores in individuals with RGDs offers opportunities to refine prediction, thus allowing for the identification of those at greatest risk of psychiatric illness. However, translation into the clinic is hindered by roadblocks, which include limited genetic testing in clinical psychiatry, and the lack of guidelines for following individuals with RGDs, who are at high risk of developing psychiatric symptoms. The Genes to Mental Health Network (G2MH) is a newly funded National Institute of Mental Health initiative that will collect, share, and analyze large-scale data sets combining genomics and dimensional measures of psychopathology spanning diverse populations and geography. The authors present here the most recent understanding of the effects of RGDs on dimensional behavioral traits and risk for psychiatric conditions and discuss strategies that will be pursued within the G2MH network, as well as how expected results can be translated into clinical practice to improve patient outcomes.

Lessons learnt from multifaceted diagnostic approaches to the first 150 families in Victoria's Undiagnosed Diseases Program

BACKGROUND

Clinical exome sequencing typically achieves diagnostic yields of 30%-57.5% in individuals with monogenic rare diseases. Undiagnosed diseases programmes implement strategies to improve diagnostic outcomes for these individuals.

AIM

We share the lessons learnt from the first 3 years of the Undiagnosed Diseases Program-Victoria, an Australian programme embedded within a clinical genetics service in the state of Victoria with a focus on paediatric rare diseases.

METHODS

We enrolled families who remained without a diagnosis after clinical genomic (panel, exome or genome) sequencing between 2016 and 2018. We used family-based exome sequencing (family ES), family-based genome sequencing (family GS), RNA sequencing (RNA-seq) and high-resolution chromosomal microarray (CMA) with research-based analysis.

RESULTS

In 150 families, we achieved a diagnosis or strong candidate in 64 (42.7%) (37 in known genes with a consistent phenotype, 3 in known genes with a novel phenotype and 24 in novel disease genes). Fifty-four diagnoses or strong candidates were made by family ES, six by family GS with RNA-seq, two by high-resolution CMA and two by data reanalysis.

CONCLUSION

We share our lessons learnt from the programme. Flexible implementation of multiple strategies allowed for scalability and response to the availability of new technologies. Broad implementation of family ES with research-based analysis showed promising yields post a negative clinical singleton ES. RNA-seq offered multiple benefits in family ES-negative populations. International data sharing strategies were critical in facilitating collaborations to establish novel disease-gene associations. Finally, the integrated approach of a multiskilled, multidisciplinary team was fundamental to having diverse perspectives and strategic decision-making.

Apcdd1 is a dual BMP/Wnt inhibitor in the developing nervous system and skin

Animal development and homeostasis depend on precise temporal and spatial intercellular signaling. Components shared between signaling pathways, generally thought to decrease specificity, paradoxically can also provide a solution to pathway coordination. Here we show that the Bone Morphogenetic Protein (BMP) and Wnt signaling pathways share Apcdd1 as a common inhibitor and that Apcdd1 is a taxon-restricted gene with novel domains and signaling functions. Previously, we showed that Apcdd1 inhibits Wnt signaling (Shimomura et al., 2010), here we find that Apcdd1 potently inhibits BMP signaling in body axis formation and neural differentiation in chicken, frog, zebrafish. Furthermore, we find that Apcdd1 has an evolutionarily novel protein domain. Our results from experiments and modeling suggest that Apcdd1 may coordinate the outputs of two signaling pathways that are central to animal development and human disease.

Characterising the loss-of-function impact of 5' untranslated region variants in 15,708 individuals

Upstream open reading frames (uORFs) are tissue-specific cis-regulators of protein translation. Isolated reports have shown that variants that create or disrupt uORFs can cause disease. Here, in a systematic genome-wide study using 15,708 whole genome sequences, we show that variants that create new upstream start codons, and variants disrupting stop sites of existing uORFs, are under strong negative selection. This selection signal is significantly stronger for variants arising upstream of genes intolerant to loss-of-function variants. Furthermore, variants creating uORFs that overlap the coding sequence show signals of selection equivalent to coding missense variants. Finally, we identify specific genes where modification of uORFs likely represents an important disease mechanism, and report a novel uORF frameshift variant upstream of NF2 in neurofibromatosis. Our results highlight uORF-perturbing variants as an under-recognised functional class that contribute to penetrant human disease, and demonstrate the power of large-scale population sequencing data in studying non-coding variant classes.

Landscape of multi-nucleotide variants in 125,748 human exomes and 15,708 genomes

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.

The mutational constraint spectrum quantified from variation in 141,456 humans

Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes1. Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.

Transcript expression-aware annotation improves rare variant interpretation

The acceleration of DNA sequencing in samples from patients and population studies has resulted in extensive catalogues of human genetic variation, but the interpretation of rare genetic variants remains problematic. A notable example of this challenge is the existence of disruptive variants in dosage-sensitive disease genes, even in apparently healthy individuals. Here, by manual curation of putative loss-of-function (pLoF) variants in haploinsufficient disease genes in the Genome Aggregation Database (gnomAD)1, we show that one explanation for this paradox involves alternative splicing of mRNA, which allows exons of a gene to be expressed at varying levels across different cell types. Currently, no existing annotation tool systematically incorporates information about exon expression into the interpretation of variants. We develop a transcript-level annotation metric known as the 'proportion expressed across transcripts', which quantifies isoform expression for variants. We calculate this metric using 11,706 tissue samples from the Genotype Tissue Expression (GTEx) project2 and show that it can differentiate between weakly and highly evolutionarily conserved exons, a proxy for functional importance. We demonstrate that expression-based annotation selectively filters 22.8% of falsely annotated pLoF variants found in haploinsufficient disease genes in gnomAD, while removing less than 4% of high-confidence pathogenic variants in the same genes. Finally, we apply our expression filter to the analysis of de novo variants in patients with autism spectrum disorder and intellectual disability or developmental disorders to show that pLoF variants in weakly expressed regions have similar effect sizes to those of synonymous variants, whereas pLoF variants in highly expressed exons are most strongly enriched among cases. Our annotation is fast, flexible and generalizable, making it possible for any variant file to be annotated with any isoform expression dataset, and will be valuable for the genetic diagnosis of rare diseases, the analysis of rare variant burden in complex disorders, and the curation and prioritization of variants in recall-by-genotype studies.

Unique bioinformatic approach and comprehensive reanalysis improve diagnostic yield of clinical exomes

Clinical exome sequencing (CES) is increasingly being utilized; however, a large proportion of patients remain undiagnosed, creating a need for a systematic approach to increase the diagnostic yield. We have reanalyzed CES data for a clinically heterogeneous cohort of 102 probands with likely Mendelian conditions, including 74 negative cases and 28 cases with candidate variants, but reanalysis requested by clinicians. Reanalysis was performed by an interdisciplinary team using a validated custom-built pipeline, "Variant Explorer Pipeline" (VExP). This reanalysis approach and results were compared with existing literature. Reanalysis of candidate variants from CES in 28 cases revealed 1 interpretation that needed to be reclassified. A confirmed or potential genetic diagnosis was identified in 24 of 75 CES-negative/reclassified cases (32.0%), including variants in known disease-causing genes (n = 6) or candidate genes (n = 18). This yield was higher compared with similar studies demonstrating the utility of this approach. In summary, reanalysis of negative CES in a research setting enhances diagnostic yield by about a third. This study suggests the need for comprehensive, continued reanalysis of exome data when molecular diagnosis is elusive.

Heterozygous Variants in KMT2E Cause a Spectrum of Neurodevelopmental Disorders and Epilepsy

We delineate a KMT2E-related neurodevelopmental disorder on the basis of 38 individuals in 36 families. This study includes 31 distinct heterozygous variants in KMT2E (28 ascertained from Matchmaker Exchange and three previously reported), and four individuals with chromosome 7q22.2-22.23 microdeletions encompassing KMT2E (one previously reported). Almost all variants occurred de novo, and most were truncating. Most affected individuals with protein-truncating variants presented with mild intellectual disability. One-quarter of individuals met criteria for autism. Additional common features include macrocephaly, hypotonia, functional gastrointestinal abnormalities, and a subtle facial gestalt. Epilepsy was present in about one-fifth of individuals with truncating variants and was responsive to treatment with anti-epileptic medications in almost all. More than 70% of the individuals were male, and expressivity was variable by sex; epilepsy was more common in females and autism more common in males. The four individuals with microdeletions encompassing KMT2E generally presented similarly to those with truncating variants, but the degree of developmental delay was greater. The group of four individuals with missense variants in KMT2E presented with the most severe developmental delays. Epilepsy was present in all individuals with missense variants, often manifesting as treatment-resistant infantile epileptic encephalopathy. Microcephaly was also common in this group. Haploinsufficiency versus gain-of-function or dominant-negative effects specific to these missense variants in KMT2E might explain this divergence in phenotype, but requires independent validation. Disruptive variants in KMT2E are an under-recognized cause of neurodevelopmental abnormalities.

Insights into genetics, human biology and disease gleaned from family based genomic studies

Identifying genes and variants contributing to rare disease phenotypes and Mendelian conditions informs biology and medicine, yet potential phenotypic consequences for variation of >75% of the ~20,000 annotated genes in the human genome are lacking. Technical advances to assess rare variation genome-wide, particularly exome sequencing (ES), enabled establishment in the United States of the National Institutes of Health (NIH)-supported Centers for Mendelian Genomics (CMGs) and have facilitated collaborative studies resulting in novel "disease gene" discoveries. Pedigree-based genomic studies and rare variant analyses in families with suspected Mendelian conditions have led to the elucidation of hundreds of novel disease genes and highlighted the impact of de novo mutational events, somatic variation underlying nononcologic traits, incompletely penetrant alleles, phenotypes with high locus heterogeneity, and multilocus pathogenic variation. Herein, we highlight CMG collaborative discoveries that have contributed to understanding both rare and common diseases and discuss opportunities for future discovery in single-locus Mendelian disorder genomics. Phenotypic annotation of all human genes; development of bioinformatic tools and analytic methods; exploration of non-Mendelian modes of inheritance including reduced penetrance, multilocus variation, and oligogenic inheritance; construction of allelic series at a locus; enhanced data sharing worldwide; and integration with clinical genomics are explored. Realizing the full contribution of rare disease research to functional annotation of the human genome, and further illuminating human biology and health, will lay the foundation for the Precision Medicine Initiative.

The Genetic Landscape of Diamond-Blackfan Anemia

Diamond-Blackfan anemia (DBA) is a rare bone marrow failure disorder that affects 7 out of 1,000,000 live births and has been associated with mutations in components of the ribosome. In order to characterize the genetic landscape of this heterogeneous disorder, we recruited a cohort of 472 individuals with a clinical diagnosis of DBA and performed whole-exome sequencing (WES). We identified relevant rare and predicted damaging mutations for 78% of individuals. The majority of mutations were singletons, absent from population databases, predicted to cause loss of function, and located in 1 of 19 previously reported ribosomal protein (RP)-encoding genes. Using exon coverage estimates, we identified and validated 31 deletions in RP genes. We also observed an enrichment for extended splice site mutations and validated their diverse effects using RNA sequencing in cell lines obtained from individuals with DBA. Leveraging the size of our cohort, we observed robust genotype-phenotype associations with congenital abnormalities and treatment outcomes. We further identified rare mutations in seven previously unreported RP genes that may cause DBA, as well as several distinct disorders that appear to phenocopy DBA, including nine individuals with biallelic CECR1 mutations that result in deficiency of ADA2. However, no new genes were identified at exome-wide significance, suggesting that there are no unidentified genes containing mutations readily identified by WES that explain >5% of DBA-affected case subjects. Overall, this report should inform not only clinical practice for DBA-affected individuals, but also the design and analysis of rare variant studies for heterogeneous Mendelian disorders.

Megaloblastic Anemia Progressing to Severe Thrombotic Microangiopathy in Patients with Disordered Vitamin B12 Metabolism: Case Reports and Literature Review

We describe 2 children with cobalamin G disease, a disorder of vitamin B₁₂ metabolism with normal serum B₁₂ levels. They presented with megaloblastic anemia progressing rapidly to severe thrombotic microangiopathy. In infants presenting with acute thrombotic microangiopathy, cobalamin disorders should be considered early as diagnosis and targeted treatment can be lifesaving.

matchbox: An open-source tool for patient matching via the Matchmaker Exchange

Rare disease investigators constantly face challenges in identifying additional cases to build evidence for gene-disease causality. The Matchmaker Exchange (MME) addresses this limitation by providing a mechanism for matching patients across genomic centers via a federated network. The MME has revolutionized searching for additional cases by making it possible to query across institutional boundaries, so that what was once a laborious and manual process of contacting researchers is now automated and computable. However, while the MME network is beginning to scale, the growth of additional nodes is limited by the lack of easy-to-use solutions that can be implemented by any rare disease database owner, even one without significant software engineering resources. Here, we describe matchbox, which is an open-source, platform-independent, portable bridge between any given rare disease genomic center and the MME network, which has already led to novel gene discoveries. We also describe how matchbox greatly reduces the barrier to participation by overcoming challenges for new databases to join the MME.

Improving genetic diagnosis in Mendelian disease with transcriptome sequencing

Exome and whole-genome sequencing are becoming increasingly routine approaches in Mendelian disease diagnosis. Despite their success, the current diagnostic rate for genomic analyses across a variety of rare diseases is approximately 25 to 50%. We explore the utility of transcriptome sequencing [RNA sequencing (RNA-seq)] as a complementary diagnostic tool in a cohort of 50 patients with genetically undiagnosed rare muscle disorders. We describe an integrated approach to analyze patient muscle RNA-seq, leveraging an analysis framework focused on the detection of transcript-level changes that are unique to the patient compared to more than 180 control skeletal muscle samples. We demonstrate the power of RNA-seq to validate candidate splice-disrupting mutations and to identify splice-altering variants in both exonic and deep intronic regions, yielding an overall diagnosis rate of 35%. We also report the discovery of a highly recurrent de novo intronic mutation in COL6A1 that results in a dominantly acting splice-gain event, disrupting the critical glycine repeat motif of the triple helical domain. We identify this pathogenic variant in a total of 27 genetically unsolved patients in an external collagen VI-like dystrophy cohort, thus explaining approximately 25% of patients clinically suggestive of having collagen VI dystrophy in whom prior genetic analysis is negative. Overall, this study represents a large systematic application of transcriptome sequencing to rare disease diagnosis and highlights its utility for the detection and interpretation of variants missed by current standard diagnostic approaches.

ClinVar data parsing

This software repository provides a pipeline for converting raw ClinVar data files into analysis-friendly tab-delimited tables, and also provides these tables for the most recent ClinVar release. Separate tables are generated for genome builds GRCh37 and GRCh38 as well as for mono-allelic variants and complex multi-allelic variants. Additionally, the tables are augmented with allele frequencies from the ExAC and gnomAD datasets as these are often consulted when analyzing ClinVar variants. Overall, this work provides ClinVar data in a format that is easier to work with and can be directly loaded into a variety of popular analysis tools such as R, python pandas, and SQL databases.

Using high-resolution variant frequencies to empower clinical genome interpretation

Purpose

Whole-exome and whole-genome sequencing have transformed the discovery of genetic variants that cause human Mendelian disease, but discriminating pathogenic from benign variants remains a daunting challenge. Rarity is recognized as a necessary, although not sufficient, criterion for pathogenicity, but frequency cutoffs used in Mendelian analysis are often arbitrary and overly lenient. Recent very large reference datasets, such as the Exome Aggregation Consortium (ExAC), provide an unprecedented opportunity to obtain robust frequency estimates even for very rare variants.

Methods

We present a statistical framework for the frequency-based filtering of candidate disease-causing variants, accounting for disease prevalence, genetic and allelic heterogeneity, inheritance mode, penetrance, and sampling variance in reference datasets.

Results

Using the example of cardiomyopathy, we show that our approach reduces by two-thirds the number of candidate variants under consideration in the average exome, without removing true pathogenic variants (false-positive rate<0.001).

Conclusion

We outline a statistically robust framework for assessing whether a variant is “too common” to be causative for a Mendelian disorder of interest. We present precomputed allele frequency cutoffs for all variants in the ExAC dataset.

Brain MRS glutamine as a biomarker to guide therapy of hyperammonemic coma

Acute idiopathic hyperammonemia in an adult patient is a life-threatening condition often resulting in a rapid progression to irreversible cerebral edema and death. While ammonia-scavenging therapies lower blood ammonia levels, in comparison, clearance of waste nitrogen from the brain may be delayed. Therefore, we used magnetic resonance spectroscopy (MRS) to monitor cerebral glutamine levels, the major reservoir of ammonia, in a gastric bypass patient with hyperammonemic coma undergoing therapy with N-carbamoyl glutamate and the ammonia-scavenging agents, sodium phenylacetate and sodium benzoate. Improvement in mental status mirrored brain glutamine levels, as coma persisted for 48h after plasma ammonia normalized. We hypothesize that the slower clearance for brain glutamine levels accounts for the delay in improvement following initiation of treatment in cases of chronic hyperammonemia. We propose MRS to monitor brain glutamine as a noninvasive approach to be utilized for diagnostic and therapeutic monitoring purposes in adult patients presenting with idiopathic hyperammonemia.

Pathogenic ASXL1 somatic variants in reference databases complicate germline variant interpretation for Bohring-Opitz Syndrome

The clinical interpretation of genetic variants has come to rely heavily on reference population databases such as the Exome Aggregation Consortium (ExAC) database. Pathogenic variants in genes associated with severe, pediatric-onset, highly penetrant, autosomal dominant conditions are assumed to be absent or rare in these databases. Exome sequencing of a 6-year-old female patient with seizures, developmental delay, dysmorphic features, and failure to thrive identified an ASXL1 variant previously reported as causative of Bohring-Opitz syndrome (BOS). Surprisingly, the variant was observed seven times in the ExAC database, presumably in individuals without BOS. Although the BOS phenotype fit, the presence of the variant in reference population databases introduced ambiguity in result interpretation. Review of the literature revealed that acquired somatic mosaicism of ASXL1 variants (including pathogenic variants) during hematopoietic clonal expansion can occur with aging in healthy individuals. We examined all ASXL1 truncating variants in the ExAC database and determined most are likely somatic. Failure to consider somatic mosaicism may lead to the inaccurate assumption that conditions like BOS have reduced penetrance, or the misclassification of potentially pathogenic variants.

Quantifying prion disease penetrance using large population control cohorts

More than 100,000 genetic variants are reported to cause Mendelian disease in humans, but the penetrance-the probability that a carrier of the purported disease-causing genotype will indeed develop the disease-is generally unknown. We assess the impact of variants in the prion protein gene (PRNP) on the risk of prion disease by analyzing 16,025 prion disease cases, 60,706 population control exomes, and 531,575 individuals genotyped by 23andMe Inc. We show that missense variants in PRNP previously reported to be pathogenic are at least 30 times more common in the population than expected on the basis of genetic prion disease prevalence. Although some of this excess can be attributed to benign variants falsely assigned as pathogenic, other variants have genuine effects on disease susceptibility but confer lifetime risks ranging from <0.1 to ~100%. We also show that truncating variants in PRNP have position-dependent effects, with true loss-of-function alleles found in healthy older individuals, a finding that supports the safety of therapeutic suppression of prion protein expression.

Analysis of protein-coding genetic variation in 60,706 humans

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human 'knockout' variants in protein-coding genes.