Genetic diagnosis of Mendelian disorders via RNA sequencing

Modelling phenotypes, variants and pathomechanisms of syndromic diseases in different systems

In this review we describe different model organisms and systems that are commonly used to study syndromic disorders. Different use cases in modeling diseases, underlying pathomechanisms and specific effects of certain variants are elucidated. We also highlight advantages and limitations of different systems. Models discussed include budding yeast, the nematode worm, the fruit fly, the frog, zebrafish, mice and human cell-based systems.

The expanding diagnostic toolbox for rare genetic diseases

Genomic technologies, such as targeted, exome and short-read genome sequencing approaches, have revolutionized the care of patients with rare genetic diseases. However, more than half of patients remain without a diagnosis. Emerging approaches from research-based settings such as long-read genome sequencing and optical genome mapping hold promise for improving the identification of disease-causal genetic variants. In addition, new omic technologies that measure the transcriptome, epigenome, proteome or metabolome are showing great potential for variant interpretation. As genetic testing options rapidly expand, the clinical community needs to be mindful of their individual strengths and limitations, as well as remaining challenges, to select the appropriate diagnostic test, correctly interpret results and drive innovation to address insufficiencies. If used effectively - through truly integrative multi-omics approaches and data sharing - the resulting large quantities of data from these established and emerging technologies will greatly improve the interpretative power of genetic and genomic diagnostics for rare diseases.

Combined exome and whole transcriptome sequencing identifies a de novo intronic SRCAP variant causing DEHMBA syndrome with severe sleep disorder

Rare heterozygous variants in exons 33-34 of the SRCAP gene are associated with Floating-Harbor syndrome and have a dominant-negative mechanism of action. At variance, heterozygous null alleles falling in other parts of the same gene cause developmental delay, hypotonia, musculoskeletal defects, and behavioral abnormalities (DEHMBA) syndrome. We report an 18-year-old man with DEHMBA syndrome and obstructive sleep apnea, who underwent exome sequencing (ES) and whole transcriptome sequencing (WTS) on peripheral blood. Trio analysis prioritized the de novo heterozygous c.5658+5 G > A variant. WTS promptly demostrated four different abnormal transcripts affecting >40% of the reads, three of which leading to a frameshift. This study demonstrated the efficacy of a combined ES-WTS approach in solving undiagnosed cases. We also speculated that sleep respiratory disorder may be an underdiagnosed complication of DEHMBA syndrome.

Impact of genome build on RNA-seq interpretation and diagnostics

Transcriptomics is a powerful tool for unraveling the molecular effects of genetic variants and disease diagnosis. Prior studies have demonstrated that choice of genome build impacts variant interpretation and diagnostic yield for genomic analyses. To identify the extent genome build also impacts transcriptomics analyses, we studied the effect of the hg19, hg38, and CHM13 genome builds on expression quantification and outlier detection in 386 rare disease and familial control samples from both the Undiagnosed Diseases Network and Genomics Research to Elucidate the Genetics of Rare Disease Consortium. Across six routinely collected biospecimens, 61% of quantified genes were not influenced by genome build. However, we identified 1,492 genes with build-dependent quantification, 3,377 genes with build-exclusive expression, and 9,077 genes with annotation-specific expression across six routinely collected biospecimens, including 566 clinically relevant and 512 known OMIM genes. Further, we demonstrate that between builds for a given gene, a larger difference in quantification is well correlated with a larger change in expression outlier calling. Combined, we provide a database of genes impacted by build choice and recommend that transcriptomics-guided analyses and diagnoses are cross referenced with these data for robustness.

Genetics of inherited peripheral neuropathies and the next frontier: looking backwards to progress forwards

Inherited peripheral neuropathies (IPNs) encompass a clinically and genetically heterogeneous group of disorders causing length-dependent degeneration of peripheral autonomic, motor and/or sensory nerves. Despite gold-standard diagnostic testing for pathogenic variants in over 100 known associated genes, many patients with IPN remain genetically unsolved. Providing patients with a diagnosis is critical for reducing their 'diagnostic odyssey', improving clinical care, and for informed genetic counselling. The last decade of massively parallel sequencing technologies has seen a rapid increase in the number of newly described IPN-associated gene variants contributing to IPN pathogenesis. However, the scarcity of additional families and functional data supporting variants in potential novel genes is prolonging patient diagnostic uncertainty and contributing to the missing heritability of IPNs. We review the last decade of IPN disease gene discovery to highlight novel genes, structural variation and short tandem repeat expansions contributing to IPN pathogenesis. From the lessons learnt, we provide our vision for IPN research as we anticipate the future, providing examples of emerging technologies, resources and tools that we propose that will expedite the genetic diagnosis of unsolved IPN families.

A multiomics approach reveals evidence for phenylbutyrate as a potential treatment for combined D,L-2- hydroxyglutaric aciduria

PURPOSE

To identify therapies for combined D, L-2-hydroxyglutaric aciduria (C-2HGA), a rare genetic disorder caused by recessive variants in the SLC25A1 gene.

METHODS

Patients C-2HGA were identified and diagnosed by whole exome sequencing and biochemical genetic testing. Patient derived fibroblasts were then treated with phenylbutyrate and the functional effects assessed by metabolomics and RNA-sequencing.

RESULTS

In this study, we demonstrated that C-2HGA patient derived fibroblasts exhibited impaired cellular bioenergetics. Moreover, Fibroblasts form one patient exhibited worsened cellular bioenergetics when supplemented with citrate. We hypothesized that treating patient cells with phenylbutyrate (PB), an FDA approved pharmaceutical drug that conjugates glutamine for renal excretion, would reduce mitochondrial 2-ketoglutarate, thereby leading to improved cellular bioenergetics. Metabolomic and RNA-seq analyses of PB-treated fibroblasts demonstrated a significant decrease in intracellular 2-ketoglutarate, 2-hydroxyglutarate, and in levels of mRNA coding for citrate synthase and isocitrate dehydrogenase. Consistent with the known action of PB, an increased level of phenylacetylglutamine in patient cells was consistent with the drug acting as 2-ketoglutarate sink.

CONCLUSION

Our pre-clinical studies suggest that citrate supplementation has the possibility exacerbating energy metabolism in this condition. However, improvement in cellular bioenergetics suggests phenylbutyrate might have interventional utility for this rare disease.

All exons are not created equal-exon vulnerability determines the effect of exonic mutations on splicing

It is now widely accepted that aberrant splicing of constitutive exons is often caused by mutations affecting cis-acting splicing regulatory elements (SREs), but there is a misconception that all exons have an equal dependency on SREs and thus a similar vulnerability to aberrant splicing. We demonstrate that some exons are more likely to be affected by exonic splicing mutations (ESMs) due to an inherent vulnerability, which is context dependent and influenced by the strength of exon definition. We have developed VulExMap, a tool which is based on empirical data that can designate whether a constitutive exon is vulnerable. Using VulExMap, we find that only 25% of all exons can be categorized as vulnerable, whereas two-thirds of 359 previously reported ESMs in 75 disease genes are located in vulnerable exons. Because VulExMap analysis is based on empirical data on splicing of exons in their endogenous context, it includes all features important in determining the vulnerability. We believe that VulExMap will be an important tool when assessing the effect of exonic mutations by pinpointing whether they are located in exons vulnerable to ESMs.

The clinical utility and diagnostic implementation of human subject cell transdifferentiation followed by RNA sequencing

RNA sequencing (RNA-seq) has recently been used in translational research settings to facilitate diagnoses of Mendelian disorders. A significant obstacle for clinical laboratories in adopting RNA-seq is the low or absent expression of a significant number of disease-associated genes/transcripts in clinically accessible samples. As this is especially problematic in neurological diseases, we developed a clinical diagnostic approach that enhanced the detection and evaluation of tissue-specific genes/transcripts through fibroblast-to-neuron cell transdifferentiation. The approach is designed specifically to suit clinical implementation, emphasizing simplicity, cost effectiveness, turnaround time, and reproducibility. For clinical validation, we generated induced neurons (iNeurons) from 71 individuals with primary neurological phenotypes recruited to the Undiagnosed Diseases Network. The overall diagnostic yield was 25.4%. Over a quarter of the diagnostic findings benefited from transdifferentiation and could not be achieved by fibroblast RNA-seq alone. This iNeuron transcriptomic approach can be effectively integrated into diagnostic whole-transcriptome evaluation of individuals with genetic disorders.

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.

Identification of skewed X chromosome inactivation using exome and transcriptome sequencing in patients with suspected rare genetic disease

BACKGROUND

X-chromosome inactivation (XCI) is an epigenetic process that occurs during early development in mammalian females by randomly silencing one of two copies of the X chromosome in each cell. The preferential inactivation of either the maternal or paternal copy of the X chromosome in a majority of cells results in a skewed or non-random pattern of X inactivation and is observed in over 25% of adult females. Identifying skewed X inactivation is of clinical significance in patients with suspected rare genetic diseases due to the possibility of biased expression of disease-causing genes present on the active X chromosome. The current clinical test for the detection of skewed XCI relies on the methylation status of the methylation-sensitive restriction enzyme (Hpall) binding site present in proximity of short tandem polymorphic repeats on the androgen receptor (AR) gene. This approach using one locus results in uninformative or inconclusive data for 10-20% of tests. Further, recent studies have shown inconsistency between methylation of the AR locus and the state of inactivation of the X chromosome. Herein, we develop a method for estimating X inactivation status, using exome and transcriptome sequencing data derived from blood in 227 female samples. We built a reference model for evaluation of XCI in 135 females from the GTEx consortium. We tested and validated the model on 11 female individuals with different types of undiagnosed rare genetic disorders who were clinically tested for X-skew using the AR gene assay and compared results to our outlier-based analysis technique.

RESULTS

In comparison to the AR clinical test for identification of X inactivation, our method was concordant with the AR method in 9 samples, discordant in 1, and provided a measure of X inactivation in 1 sample with uninformative clinical results. We applied this method on an additional 81 females presenting to the clinic with phenotypes consistent with different hereditary disorders without a known genetic diagnosis.

CONCLUSIONS

This study presents the use of transcriptome and exome sequencing data to provide an accurate and complete estimation of X-inactivation and skew status in a cohort of female patients with different types of suspected rare genetic disease.

Clinical, immunohistochemical, and genetic characterization of splice-altering biallelic DES variants: Therapeutic implications

Pathogenic variants in the DES gene clinically manifest as progressive skeletal muscle weakness, cardiomyopathy with associated severe arrhythmias, and respiratory insufficiency, and are collectively known as desminopathies. While most DES pathogenic variants act via a dominant mechanism, recessively acting variants have also been reported. Currently, there are no effective therapeutic interventions for desminopathies of any type. Here, we report an affected individual with rapidly progressive dilated cardiomyopathy, requiring heart transplantation at age 13 years, in the setting of childhood-onset skeletal muscle weakness. We identified biallelic DES variants (c.640-13 T>A and c.1288+1 G>A) and show aberrant DES gene splicing in the affected individual's muscle. Through the generation of an inducible lentiviral system, we transdifferentiated fibroblast cultures derived from the affected individual into myoblasts and validated this system using RNA sequencing. We tested rationally designed, custom antisense oligonucleotides to screen for splice correction in these transdifferentiated cells and a functional minigene splicing assay. However, rather than correctly redirecting splicing, we found them to induce undesired exon skipping. Our results indicate that, while an individual precision-based molecular therapeutic approach to splice-altering pathogenic variants is promising, careful preclinical testing is imperative for each novel variant to test the feasibility of this type of approach for translation.

Current genetic diagnostics in inborn errors of immunity

New technologies in genetic diagnostics have revolutionized the understanding and management of rare diseases. This review highlights the significant advances and latest developments in genetic diagnostics in inborn errors of immunity (IEI), which encompass a diverse group of disorders characterized by defects in the immune system, leading to increased susceptibility to infections, autoimmunity, autoinflammatory diseases, allergies, and malignancies. Various diagnostic approaches, including targeted gene sequencing panels, whole exome sequencing, whole genome sequencing, RNA sequencing, or proteomics, have enabled the identification of causative genetic variants of rare diseases. These technologies not only facilitated the accurate diagnosis of IEI but also provided valuable insights into the underlying molecular mechanisms. Emerging technologies, currently mainly used in research, such as optical genome mapping, single cell sequencing or the application of artificial intelligence will allow even more insights in the aetiology of hereditary immune defects in the near future. The integration of genetic diagnostics into clinical practice significantly impacts patient care. Genetic testing enables early diagnosis, facilitating timely interventions and personalized treatment strategies. Additionally, establishing a genetic diagnosis is necessary for genetic counselling and prognostic assessments. Identifying specific genetic variants associated with inborn errors of immunity also paved the way for the development of targeted therapies and novel therapeutic approaches. This review emphasizes the challenges related with genetic diagnosis of rare diseases and provides future directions, specifically focusing on IEI. Despite the tremendous progress achieved over the last years, several obstacles remain or have become even more important due to the increasing amount of genetic data produced for each patient. This includes, first and foremost, the interpretation of variants of unknown significance (VUS) in known IEI genes and of variants in genes of unknown significance (GUS). Although genetic diagnostics have significantly contributed to the understanding and management of IEI and other rare diseases, further research, exchange between experts from different clinical disciplines, data integration and the establishment of comprehensive guidelines are crucial to tackle the remaining challenges and maximize the potential of genetic diagnostics in the field of rare diseases, such as IEI.

Implication of transcription factor FOXD2 dysfunction in syndromic congenital anomalies of the kidney and urinary tract (CAKUT)

Congenital anomalies of the kidney and urinary tract (CAKUT) are the predominant cause for chronic kidney disease below age 30 years. Many monogenic forms have been discovered due to comprehensive genetic testing like exome sequencing. However, disease-causing variants in known disease-associated genes only explain a proportion of cases. Here, we aim to unravel underlying molecular mechanisms of syndromic CAKUT in three unrelated multiplex families with presumed autosomal recessive inheritance. Exome sequencing in the index individuals revealed three different rare homozygous variants in FOXD2, encoding a transcription factor not previously implicated in CAKUT in humans: a frameshift in the Arabic and a missense variant each in the Turkish and the Israeli family with segregation patterns consistent with autosomal recessive inheritance. CRISPR/Cas9-derived Foxd2 knockout mice presented with a bilateral dilated kidney pelvis accompanied by atrophy of the kidney papilla and mandibular, ophthalmologic, and behavioral anomalies, recapitulating the human phenotype. In a complementary approach to study pathomechanisms of FOXD2-dysfunction-mediated developmental kidney defects, we generated CRISPR/Cas9-mediated knockout of Foxd2 in ureteric bud-induced mouse metanephric mesenchyme cells. Transcriptomic analyses revealed enrichment of numerous differentially expressed genes important for kidney/urogenital development, including Pax2 and Wnt4 as well as gene expression changes indicating a shift toward a stromal cell identity. Histology of Foxd2 knockout mouse kidneys confirmed increased fibrosis. Further, genome-wide association studies suggest that FOXD2 could play a role for maintenance of podocyte integrity during adulthood. Thus, our studies help in genetic diagnostics of monogenic CAKUT and in understanding of monogenic and multifactorial kidney diseases.

Is there a dominant-negative effect in individuals with heterozygous disease-causing variants in COL4A3/COL4A4?

Alport syndrome (AS) shows a broad phenotypic spectrum ranging from isolated microscopic hematuria (MH) to end-stage kidney disease (ESKD). Monoallelic disease-causing variants in COL4A3/COL4A4 have been associated with autosomal dominant AS (ADAS) and biallelic variants with autosomal recessive AS (ARAS). The aim of this study was to analyze clinical and genetic data regarding a possible genotype-phenotype correlation in individuals with disease-causing variants in COL4A3/COL4A4. Eighty-nine individuals carrying at least one COL4A3/COL4A4 variant classified as (likely) pathogenic according to the American College of Medical Genetics guidelines and current amendments were recruited. Clinical data concerning the prevalence and age of first reported manifestation of MH, proteinuria, ESKD, and extrarenal manifestations were collected. Individuals with monoallelic non-truncating variants reported a significantly higher prevalence and earlier diagnosis of MH and proteinuria than individuals with monoallelic truncating variants. Individuals with biallelic variants were more severely affected than those with monoallelic variants. Those with biallelic truncating variants were more severely affected than those with compound heterozygous non-truncating/truncating variants or individuals with biallelic non-truncating variants. In this study an association of heterozygous non-truncating COL4A3/COL4A4 variants with a more severe phenotype in comparison to truncating variants could be shown indicating a potential dominant-negative effect as an explanation for this observation. The results for individuals with ARAS support the, still scarce, data in the literature.

Reprograming skin fibroblasts into Sertoli cells: a patient-specific tool to understand effects of genetic variants on gonadal development

BACKGROUND

Disorders/differences of sex development (DSD) are congenital conditions in which the development of chromosomal, gonadal, or anatomical sex is atypical. With overlapping phenotypes and multiple genes involved, poor diagnostic yields are achieved for many of these conditions. The current DSD diagnostic regimen can be augmented by investigating transcriptome/proteome in vivo, but it is hampered by the unavailability of affected gonadal tissue at the relevant developmental stage. We try to mitigate this limitation by reprogramming readily available skin tissue-derived dermal fibroblasts into Sertoli cells (SC), which could then be deployed for different diagnostic strategies. SCs form the target cell type of choice because they act like an organizing center of embryonic gonadal development and many DSD arise when these developmental processes go awry.

METHODS

We employed a computational predictive algorithm for cell conversions called Mogrify to predict the transcription factors (TFs) required for direct reprogramming of human dermal fibroblasts into SCs. We established trans-differentiation culture conditions where stable transgenic expression of these TFs was achieved in 46, XY adult dermal fibroblasts using lentiviral vectors. The resulting Sertoli like cells (SLCs) were validated for SC phenotype using several approaches.

RESULTS

SLCs exhibited Sertoli-like morphological and cellular properties as revealed by morphometry and xCelligence cell behavior assays. They also showed Sertoli-specific expression of molecular markers such as SOX9, PTGDS, BMP4, or DMRT1 as revealed by IF imaging, RNAseq and qPCR. The SLC transcriptome shared about two thirds of its differentially expressed genes with a human adult SC transcriptome and expressed markers typical of embryonic SCs. Notably, SLCs lacked expression of most markers of other gonadal cell types such as Leydig, germ, peritubular myoid or granulosa cells.

CONCLUSIONS

The trans-differentiation method was applied to a variety of commercially available 46, XY fibroblasts derived from patients with DSD and to a 46, XX cell line. The DSD SLCs displayed altered levels of trans-differentiation in comparison to normal 46, XY-derived SLCs, thus showcasing the robustness of this new trans-differentiation model. Future applications could include using the SLCs to improve definitive diagnosis of DSD in patients with variants of unknown significance.

Whole exome sequencing in Serbian patients with hereditary spastic paraplegia

Hereditary spastic paraplegia (HSP) is a group of neurodegenerative diseases with a high genetic and clinical heterogeneity. Numerous HSP patients remain genetically undiagnosed despite screening for known genetic causes of HSP. Therefore, identification of novel variants and genes is needed. Our previous study analyzed 74 adult Serbian HSP patients from 65 families using panel of the 13 most common HSP genes in combination with a copy number variation analysis. Conclusive genetic findings were established in 23 patients from 19 families (29%). In the present study, nine patients from nine families previously negative on the HSP gene panel were selected for the whole exome sequencing (WES). Further, 44 newly diagnosed adult HSP patients from 44 families were sent to WES directly, since many studies showed WES may be used as the first step in HSP diagnosis. WES analysis of cohort 1 revealed a likely genetic cause in five (56%) of nine HSP families, including variants in the ETHE1, ZFYVE26, RNF170, CAPN1, and WASHC5 genes. In cohort 2, possible causative variants were found in seven (16%) of 44 patients (later updated to 27% when other diagnosis were excluded), comprising six different genes: SPAST, SPG11, WASCH5, KIF1A, KIF5A, and ABCD1. These results expand the genetic spectrum of HSP patients in Serbia and the region with implications for molecular genetic diagnosis and future causative therapies. Wide HSP panel can be the first step in diagnosis, alongside with the copy number variation (CNV) analysis, while WES should be performed after.

Genomes in clinical care

In the era of precision medicine, genome sequencing (GS) has become more affordable and the importance of genomics and multi-omics in clinical care is increasingly being recognized. However, how to scale and effectively implement GS on an institutional level remains a challenge for many. Here, we present Genome First and Ge-Med, two clinical implementation studies focused on identifying the key pillars and processes that are required to make routine GS and predictive genomics a reality in the clinical setting. We describe our experience and lessons learned for a variety of topics including test logistics, patient care processes, data reporting, and infrastructure. Our model of providing clinical care and comprehensive genomic analysis from a single source may be used by other centers with a similar structure to facilitate the implementation of omics-based personalized health concepts in medicine.

GPAD: a natural language processing-based application to extract the gene-disease association discovery information from OMIM

BACKGROUND

Thousands of genes have been associated with different Mendelian conditions. One of the valuable sources to track these gene-disease associations (GDAs) is the Online Mendelian Inheritance in Man (OMIM) database. However, most of the information in OMIM is textual, and heterogeneous (e.g. summarized by different experts), which complicates automated reading and understanding of the data. Here, we used Natural Language Processing (NLP) to make a tool (Gene-Phenotype Association Discovery (GPAD)) that could syntactically process OMIM text and extract the data of interest.

RESULTS

GPAD applies a series of language-based techniques to the text obtained from OMIM API to extract GDA discovery-related information. GPAD can inform when a particular gene was associated with a specific phenotype, as well as the type of validation-whether through model organisms or cohort-based patient-matching approaches-for such an association. GPAD extracted data was validated with published reports and was compared with large language model. Utilizing GPAD's extracted data, we analysed trends in GDA discoveries, noting a significant increase in their rate after the introduction of exome sequencing, rising from an average of about 150-250 discoveries each year. Contrary to hopes of resolving most GDAs for Mendelian disorders by now, our data indicate a substantial decline in discovery rates over the past five years (2017-2022). This decline appears to be linked to the increasing necessity for larger cohorts to substantiate GDAs. The rising use of zebrafish and Drosophila as model organisms in providing evidential support for GDAs is also observed.

CONCLUSIONS

GPAD's real-time analyzing capacity offers an up-to-date view of GDA discovery and could help in planning and managing the research strategies. In future, this solution can be extended or modified to capture other information in OMIM and scientific literature.

Diagnostic work-up in malformations of cortical development

Malformations of cortical development (MCDs) represent a heterogeneous spectrum of disorders characterized by atypical development of the cerebral cortex. MCDs are most often diagnosed on the basis of imaging, although subtle lesions, such as focal cortical dysplasia, may only be revealed on neuropathology. Different subtypes have been defined, including lissencephaly, heterotopia, cobblestone malformation, polymicrogyria, and dysgyria. Many MCDs are of genetic origin, although acquired factors, such as congenital cytomegalovirus infections and twinning sequence, can lead to similar phenotypes. In this narrative review, we provide an overview of the diagnostic approach to MCDs, which is illustrated with clinical vignettes, on diagnostic pitfalls such as somatic mosaicism and consanguinity, and recognizable phenotypes on imaging, such as tubulinopathies, the lissencephaly spectrum, tuberous sclerosis complex, and FLNA-related periventricular nodular heterotopia.

Genetic variants affecting NQO1 protein levels impact the efficacy of idebenone treatment in Leber hereditary optic neuropathy

Idebenone, the only approved treatment for Leber hereditary optic neuropathy (LHON), promotes recovery of visual function in up to 50% of patients, but we can neither predict nor understand the non-responders. Idebenone is reduced by the cytosolic NAD(P)H oxidoreductase I (NQO1) and directly shuttles electrons to respiratory complex III, bypassing complex I affected in LHON. We show here that two polymorphic variants drastically reduce NQO1 protein levels when homozygous or compound heterozygous. This hampers idebenone reduction. In its oxidized form, idebenone inhibits complex I, decreasing respiratory function in cells. By retrospectively analyzing a large cohort of idebenone-treated LHON patients, classified by their response to therapy, we show that patients with homozygous or compound heterozygous NQO1 variants have the poorest therapy response, particularly if carrying the m.3460G>A/MT-ND1 LHON mutation. These results suggest consideration of patient NQO1 genotype and mitochondrial DNA mutation in the context of idebenone therapy.

RNA sequencing and target long-read sequencing reveal an intronic transposon insertion causing aberrant splicing

More than half of cases with suspected genetic disorders remain unsolved by genetic analysis using short-read sequencing such as exome sequencing (ES) and genome sequencing (GS). RNA sequencing (RNA-seq) and long-read sequencing (LRS) are useful for interpretation of candidate variants and detection of structural variants containing repeat sequences, respectively. Recently, adaptive sampling on nanopore sequencers enables target LRS more easily. Here, we present a Japanese girl with premature chromatid separation (PCS)/mosaic variegated aneuploidy (MVA) syndrome. ES detected a known pathogenic maternal heterozygous variant (c.1402-5A>G) in intron 10 of BUB1B (NM_001211.6), a known responsive gene for PCS/MVA syndrome with autosomal recessive inheritance. Minigene splicing assay revealed that almost all transcripts from the c.1402-5G allele have mis-splicing with 4-bp insertion. GS could not detect another pathogenic variant, while RNA-seq revealed abnormal reads in intron 2. To extensively explore variants in intron 2, we performed adaptive sampling and identified a paternal 3.0 kb insertion. Consensus sequence of 16 reads spanning the insertion showed that the insertion consists of Alu and SVA elements. Realignment of RNA-seq reads to the new reference sequence containing the insertion revealed that 16 reads have 5' splice site within the insertion and 3' splice site at exon 3, demonstrating causal relationship between the insertion and aberrant splicing. In addition, immunoblotting showed severely diminished BUB1B protein level in patient derived cells. These data suggest that detection of transcriptomic abnormalities by RNA-seq can be a clue for identifying pathogenic variants, and determination of insert sequences is one of merits of LRS.

Next-generation sequencing and bioinformatics in rare movement disorders

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

Mitochondrial proteome research: the road ahead

Mitochondria are multifaceted organelles with key roles in anabolic and catabolic metabolism, bioenergetics, cellular signalling and nutrient sensing, and programmed cell death processes. Their diverse functions are enabled by a sophisticated set of protein components encoded by the nuclear and mitochondrial genomes. The extent and complexity of the mitochondrial proteome remained unclear for decades. This began to change 20 years ago when, driven by the emergence of mass spectrometry-based proteomics, the first draft mitochondrial proteomes were established. In the ensuing decades, further technological and computational advances helped to refine these 'maps', with current estimates of the core mammalian mitochondrial proteome ranging from 1,000 to 1,500 proteins. The creation of these compendia provided a systemic view of an organelle previously studied primarily in a reductionist fashion and has accelerated both basic scientific discovery and the diagnosis and treatment of human disease. Yet numerous challenges remain in understanding mitochondrial biology and translating this knowledge into the medical context. In this Roadmap, we propose a path forward for refining the mitochondrial protein map to enhance its discovery and therapeutic potential. We discuss how emerging technologies can assist the detection of new mitochondrial proteins, reveal their patterns of expression across diverse tissues and cell types, and provide key information on proteoforms. We highlight the power of an enhanced map for systematically defining the functions of its members. Finally, we examine the utility of an expanded, functionally annotated mitochondrial proteome in a translational setting for aiding both diagnosis of mitochondrial disease and targeting of mitochondria for treatment.

Functional genomics and small molecules in mitochondrial neurodevelopmental disorders

Mitochondria are critical for brain development and homeostasis. Therefore, pathogenic variation in the mitochondrial or nuclear genome which disrupts mitochondrial function frequently results in developmental disorders and neurodegeneration at the organismal level. Large-scale application of genome-wide technologies to individuals with mitochondrial diseases has dramatically accelerated identification of mitochondrial disease-gene associations in humans. Multi-omic and high-throughput studies involving transcriptomics, proteomics, metabolomics, and saturation genome editing are providing deeper insights into the functional consequence of mitochondrial genomic variation. Integration of deep phenotypic and genomic data through allelic series continues to uncover novel mitochondrial functions and permit mitochondrial gene function dissection on an unprecedented scale. Finally, mitochondrial disease-gene associations illuminate disease mechanisms and thereby direct therapeutic strategies involving small molecules and RNA-DNA therapeutics. This review summarizes progress in functional genomics and small molecule therapeutics in mitochondrial neurodevelopmental disorders.

Challenges and opportunities to bridge translational to clinical research for personalized mitochondrial medicine

Mitochondrial disorders are a group of rare and heterogeneous genetic diseases characterized by dysfunctional mitochondria leading to deficient adenosine triphosphate synthesis and chronic energy deficit in patients. The majority of these patients exhibit a wide range of phenotypic manifestations targeting several organ systems, making their clinical diagnosis and management challenging. Bridging translational to clinical research is crucial for improving the early diagnosis and prognosis of these intractable mitochondrial disorders and for discovering novel therapeutic drug candidates and modalities. This review provides the current state of clinical testing in mitochondrial disorders, discusses the challenges and opportunities for converting basic discoveries into clinical settings, explores the most suited patient-centric approaches to harness the extraordinary heterogeneity among patients affected by the same primary mitochondrial disorder, and describes the current outlook of clinical trials.

Integrating RNA-Seq into genome sequencing workflow enhances the analysis of structural variants causing neurodevelopmental disorders

BACKGROUND

Molecular diagnosis of neurodevelopmental disorders (NDDs) is mainly based on exome sequencing (ES), with a diagnostic yield of 31% for isolated and 53% for syndromic NDD. As sequencing costs decrease, genome sequencing (GS) is gradually replacing ES for genome-wide molecular testing. As many variants detected by GS only are in deep intronic or non-coding regions, the interpretation of their impact may be difficult. Here, we showed that integrating RNA-Seq into the GS workflow can enhance the analysis of the molecular causes of NDD, especially structural variants (SVs), by providing valuable complementary information such as aberrant splicing, aberrant expression and monoallelic expression.

METHODS

We performed trio-GS on a cohort of 33 individuals with NDD for whom ES was inconclusive. RNA-Seq on skin fibroblasts was then performed in nine individuals for whom GS was inconclusive and optical genome mapping (OGM) was performed in two individuals with an SV of unknown significance.

RESULTS

We identified pathogenic or likely pathogenic variants in 16 individuals (48%) and six variants of uncertain significance. RNA-Seq contributed to the interpretation in three individuals, and OGM helped to characterise two SVs.

CONCLUSION

Our study confirmed that GS significantly improves the diagnostic performance of NDDs. However, most variants detectable by GS alone are structural or located in non-coding regions, which can pose challenges for interpretation. Integration of RNA-Seq data overcame this limitation by confirming the impact of variants at the transcriptional or regulatory level. This result paves the way for new routinely applicable diagnostic protocols.

Evaluation of noninvasive biospecimens for transcriptome studies

Transcriptome studies disentangle functional mechanisms of gene expression regulation and may elucidate the underlying biology of disease processes. However, the types of tissues currently collected typically assay a single post-mortem timepoint or are limited to investigating cell types found in blood. Noninvasive tissues may improve disease-relevant discovery by enabling more complex longitudinal study designs, by capturing different and potentially more applicable cell types, and by increasing sample sizes due to reduced collection costs and possible higher enrollment from vulnerable populations. Here, we develop methods for sampling noninvasive biospecimens, investigate their performance across commercial and in-house library preparations, characterize their biology, and assess the feasibility of using noninvasive tissues in a multitude of transcriptomic applications. We collected buccal swabs, hair follicles, saliva, and urine cell pellets from 19 individuals over three to four timepoints, for a total of 300 unique biological samples, which we then prepared with replicates across three library preparations, for a final tally of 472 transcriptomes. Of the four tissues we studied, we found hair follicles and urine cell pellets to be most promising due to the consistency of sample quality, the cell types and expression profiles we observed, and their performance in disease-relevant applications. This is the first study to thoroughly delineate biological and technical features of noninvasive samples and demonstrate their use in a wide array of transcriptomic and clinical analyses. We anticipate future use of these biospecimens will facilitate discovery and development of clinical applications.

Improved detection of aberrant splicing with FRASER 2.0 and the intron Jaccard index

Detection of aberrantly spliced genes is an important step in RNA-seq-based rare-disease diagnostics. We recently developed FRASER, a denoising autoencoder-based method that outperformed alternative methods of detecting aberrant splicing. However, because FRASER's three splice metrics are partially redundant and tend to be sensitive to sequencing depth, we introduce here a more robust intron-excision metric, the intron Jaccard index, that combines the alternative donor, alternative acceptor, and intron-retention signal into a single value. Moreover, we optimized model parameters and filter cutoffs by using candidate rare-splice-disrupting variants as independent evidence. On 16,213 GTEx samples, our improved algorithm, FRASER 2.0, called typically 10 times fewer splicing outliers while increasing the proportion of candidate rare-splice-disrupting variants by 10-fold and substantially decreasing the effect of sequencing depth on the number of reported outliers. To lower the multiple-testing correction burden, we introduce an option to select the genes to be tested for each sample instead of a transcriptome-wide approach. This option can be particularly useful when prior information, such as candidate variants or genes, is available. Application on 303 rare-disease samples confirmed the relative reduction in the number of outlier calls for a slight loss of sensitivity; FRASER 2.0 recovered 22 out of 26 previously identified pathogenic splicing cases with default cutoffs and 24 when multiple-testing correction was limited to OMIM genes containing rare variants. Altogether, these methodological improvements contribute to more effective RNA-seq-based rare diagnostics by drastically reducing the amount of splicing outlier calls per sample at minimal loss of sensitivity.

Solving the unsolved genetic epilepsies: Current and future perspectives

Many patients with epilepsy undergo exome or genome sequencing as part of a diagnostic workup; however, many remain genetically unsolved. There are various factors that account for negative results in exome/genome sequencing for patients with epilepsy: (1) the underlying cause is not genetic; (2) there is a complex polygenic explanation; (3) the illness is monogenic but the causative gene remains to be linked to a human disorder; (4) family segregation with reduced penetrance; (5) somatic mosaicism or the complexity of, for example, a structural rearrangement; or (6) limited knowledge or diagnostic tools that hinder the proper classification of a variant, resulting in its designation as a variant of unknown significance. The objective of this review is to outline some of the diagnostic options that lie beyond the exome/genome, and that might become clinically relevant within the foreseeable future. These options include: (1) re-analysis of older exome/genome data as knowledge increases or symptoms change; (2) looking for somatic mosaicism or long-read sequencing to detect low-complexity repeat variants or specific structural variants missed by traditional exome/genome sequencing; (3) exploration of the non-coding genome including disruption of topologically associated domains, long range non-coding RNA, or other regulatory elements; and finally (4) transcriptomics, DNA methylation signatures, and metabolomics as complementary diagnostic methods that may be used in the assessment of variants of unknown significance. Some of these tools are currently not integrated into standard diagnostic workup. However, it is reasonable to expect that they will become increasingly available and improve current diagnostic capabilities, thereby enabling precision diagnosis in patients who are currently undiagnosed.

CRISPR/Cas9-based functional genomics strategy to decipher the pathogenicity of genetic variants in inherited metabolic disorders

The determination of the functional impact of variants of uncertain significance (VUS) is one of the major bottlenecks in the diagnostic workflow of inherited genetic diseases. To face this problem, we set up a CRISPR/Cas9-based strategy for knock-in cellular model generation, focusing on inherited metabolic disorders (IMDs). We selected variants in seven IMD-associated genes, including seven reported disease-causing variants and four benign/likely benign variants. Overall, 11 knock-in cell models were generated via homology-directed repair in HAP1 haploid cells using CRISPR/Cas9. The functional impact of the variants was determined by analyzing the characteristic biochemical alterations of each disorder. Functional studies performed in knock-in cell models showed that our approach accurately distinguished the functional effect of pathogenic from non-pathogenic variants in a reliable manner in a wide range of IMDs. Our study provides a generic approach to assess the functional impact of genetic variants to improve IMD diagnosis and this tool could emerge as a promising alternative to invasive tests, such as muscular or skin biopsies. Although the study has been performed only in IMDs, this strategy is generic and could be applied to other genetic disorders.

PDIVAS: Pathogenicity predictor for Deep-Intronic Variants causing Aberrant Splicing

BACKGROUND

Deep-intronic variants that alter RNA splicing were ineffectively evaluated in the search for the cause of genetic diseases. Determination of such pathogenic variants from a vast number of deep-intronic variants (approximately 1,500,000 variants per individual) represents a technical challenge to researchers. Thus, we developed a Pathogenicity predictor for Deep-Intronic Variants causing Aberrant Splicing (PDIVAS) to easily detect pathogenic deep-intronic variants.

RESULTS

PDIVAS was trained on an ensemble machine-learning algorithm to classify pathogenic and benign variants in a curated dataset. The dataset consists of manually curated pathogenic splice-altering variants (SAVs) and commonly observed benign variants within deep introns. Splicing features and a splicing constraint metric were used to maximize the predictive sensitivity and specificity, respectively. PDIVAS showed an average precision of 0.92 and a maximum MCC of 0.88 in classifying these variants, which were the best of the previous predictors. When PDIVAS was applied to genome sequencing analysis on a threshold with 95% sensitivity for reported pathogenic SAVs, an average of 27 pathogenic candidates were extracted per individual. Furthermore, the causative variants in simulated patient genomes were more efficiently prioritized than the previous predictors.

CONCLUSION

Incorporating PDIVAS into variant interpretation pipelines will enable efficient detection of disease-causing deep-intronic SAVs and contribute to improving the diagnostic yield. PDIVAS is publicly available at https://github.com/shiro-kur/PDIVAS .

Wide diagnostic and genotypic spectrum in patients with suspected mitochondrial disease

BACKGROUND

Mitochondrial Diseases (MDs) are a diverse group of neurometabolic disorders characterized by impaired mitochondrial oxidative phosphorylation and caused by pathogenic variants in more than 400 genes. The implementation of next-generation sequencing (NGS) technologies helps to increase the understanding of molecular basis and diagnostic yield of these conditions. The purpose of the study was to investigate diagnostic and genotypic spectrum in patients with suspected MD. The comprehensive analysis of mtDNA variants using Sanger sequencing was performed in the group of 83 unrelated individuals with clinically suspected mitochondrial disease. Additionally, targeted next generation sequencing or whole exome sequencing (WES) was performed for 30 patients of the study group.

RESULTS

The overall diagnostic rate was 21.7% for the patients with suspected MD, increasing to 36.7% in the group of patients where NGS methods were applied. Mitochondrial disease was confirmed in 11 patients (13.3%), including few classical mitochondrial syndromes (MELAS, MERRF, Leigh and Kearns-Sayre syndrome) caused by pathogenic mtDNA variants (8.4%) and MDs caused by pathogenic variants in five nDNA genes. Other neuromuscular diseases caused by pathogenic variants in seven nDNA genes, were confirmed in seven patients (23.3%).

CONCLUSION

The wide spectrum of identified rare mitochondrial or neurodevelopmental diseases proves that MD suspected patients would mostly benefit from an extensive genetic profiling allowing rapid diagnostics and improving the care of these patients.

Downstream Assays for Variant Resolution: Epigenetics, RNA Sequnncing, and Metabolomics

As the availability of advanced molecular testing like whole exome and genome sequencing expands, it comes with the added complication of interpreting inconclusive results, including determining the relevance of variants of uncertain significance or failing to find a variant in an otherwise suspected specific genetic disorder. This complication necessitates the use of alternative testing methods to gather more information in support of, or against, a particular genetic diagnosis. Therefore, new genome-wide approaches, including DNA epigenetic testing, RNA sequencing, and metabolomics, are increasingly being used to increase the diagnostic yield when used in conjunction with more conventional genetic tests.

Two novel homozygous variants of ATP6V0A2 and ALDH18A1 lead to autosomal recessive cutis laxa type 2 and 3 in two Pakistani families

BACKGROUND

Autosomal recessive cutis laxa type 2A (ARCL2A; OMIM: 219200) is characterized by neurovegetative, developmental and progeroid elastic skin anomalies. It is caused by biallelic variation in ATPase, H+ transporting V0 subunit A2 (ATP6V0A2; OMIM: 611716) located on chromosome 12q24.31. Autosomal recessive cutis laxa type 3A (ARCL3A; OMIM: 219150) is another subclinical type characterized by short stature, ophthalmological abnormalities and a progeria-like appearance. The ARCL3A is caused by loss of function alterations in the aldehyde dehydrogenase 18 family member A1 (ALDH18A1; OMIM: 138250) gene located at chromosome 10q24.1.

METHODS

Whole-exome sequencing (WES), and Sanger sequencing were performed for molecular diagnosis. 3D protein modeling was performed to investigate the deleterious effect of the variant on protein structure.

RESULTS

In this study, clinical and molecular diagnosis were performed for two families, ED-01 and DWF-41, which displayed hallmark features of ARCL2A and ARCL3A, respectively. Three affected individuals in the ED-01 family (IV-4, IV-5 and V-3) displayed sagging loose skin, down-slanting palpebral fissures, excessive wrinkles on the abdomen, hands and feet, and prominent veins on the trunk. Meanwhile the affected individuals in the DWF-41 family (V-2 and V-3) had progeroid skin, short stature, dysmorphology, low muscle tone, epilepsy, lordosis, scoliosis, delayed puberty and internal genitalia. WES in the index patient (ED-01: IV-4) identified a novel homozygous deletion (NM_012463.3: c.1977_1980del; p.[Val660LeufsTer23]) in exon 16 of the ATP6V0A2 while in DWF-41 a novel homozygous missense variant (NM_001323413.1:c.1867G>A; p.[Asp623Asn]) in exon 15 of the ALDH18A1 was identified. Sanger validation in all available family members confirmed the autosomal recessive modes of inheritances in each family. Three dimensional in-silico protein modeling suggested deleterious impact of the identified variants. Furthermore, these variants were assigned class 1 or "pathogenic" as per guidelines of American College of Medical Genetics 2015. Screening of ethnically matched healthy controls (n = 200 chromosomes), excluded the presence of these variations in general population.

CONCLUSIONS

To the best of our knowledge, this is the first report of ATP6V0A2 and ALDH18A1 variations in the Pakhtun ethnicity of Pakistani population. The study confirms that WES can be used as a first-line diagnostic test in patients with cutis laxa, and provides basis for population screening and premarital testing to reduce the diseases burden in future generations.

Full-length Isoform Sequencing for Resolving the Molecular Basis of Charcot-Marie-Tooth 2A

Objectives

Transcript sequencing of patient-derived samples has been shown to improve the diagnostic yield for solving cases of suspected Mendelian conditions, yet the added benefit of full-length long-read transcript sequencing is largely unexplored.

Methods

We applied short-read and full-length transcript sequencing and mitochondrial functional studies to a patient-derived fibroblast cell line from an individual with neuropathy that previously lacked a molecular diagnosis.

Results

We identified an intronic homozygous MFN2 c.600-31T>G variant that disrupts the branch point critical for intron 6 splicing. Full-length long-read isoform complementary DNA (cDNA) sequencing after treatment with a nonsense-mediated mRNA decay (NMD) inhibitor revealed that this variant creates 5 distinct altered splicing transcripts. All 5 altered splicing transcripts have disrupted open reading frames and are subject to NMD. Furthermore, a patient-derived fibroblast line demonstrated abnormal lipid droplet formation, consistent with MFN2 dysfunction. Although correctly spliced full-length MFN2 transcripts are still produced, this branch point variant results in deficient MFN2 levels and autosomal recessive Charcot-Marie-Tooth disease, axonal, type 2A (CMT2A).

Discussion

This case highlights the utility of full-length isoform sequencing for characterizing the molecular mechanism of undiagnosed rare diseases and expands our understanding of the genetic basis for CMT2A.

Splicing defects in rare diseases: transcriptomics and machine learning strategies towards genetic diagnosis

Genomic variants affecting pre-messenger RNA splicing and its regulation are known to underlie many rare genetic diseases. However, common workflows for genetic diagnosis and clinical variant interpretation frequently overlook splice-altering variants. To better serve patient populations and advance biomedical knowledge, it has become increasingly important to develop and refine approaches for detecting and interpreting pathogenic splicing variants. In this review, we will summarize a few recent developments and challenges in using RNA sequencing technologies for rare disease investigation. Moreover, we will discuss how recent computational splicing prediction tools have emerged as complementary approaches for revealing disease-causing variants underlying splicing defects. We speculate that continuous improvements to sequencing technologies and predictive modeling will not only expand our understanding of splicing regulation but also bring us closer to filling the diagnostic gap for rare disease patients.

ParSE-seq: A Calibrated Multiplexed Assay to Facilitate the Clinical Classification of Putative Splice-altering Variants

Background

Interpreting the clinical significance of putative splice-altering variants outside 2-base pair canonical splice sites remains difficult without functional studies.

Methods

We developed Parallel Splice Effect Sequencing (ParSE-seq), a multiplexed minigene-based assay, to test variant effects on RNA splicing quantified by high-throughput sequencing. We studied variants in SCN5A, an arrhythmia-associated gene which encodes the major cardiac voltage-gated sodium channel. We used the computational tool SpliceAI to prioritize exonic and intronic candidate splice variants, and ClinVar to select benign and pathogenic control variants. We generated a pool of 284 barcoded minigene plasmids, transfected them into Human Embryonic Kidney (HEK293) cells and induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs), sequenced the resulting pools of splicing products, and calibrated the assay to the American College of Medical Genetics and Genomics scheme. Variants were interpreted using the calibrated functional data, and experimental data were compared to SpliceAI predictions. We further studied some splice-altering missense variants by cDNA-based automated patch clamping (APC) in HEK cells and assessed splicing and sodium channel function in CRISPR-edited iPSC-CMs.

Results

ParSE-seq revealed the splicing effect of 224 SCN5A variants in iPSC-CMs and 244 variants in HEK293 cells. The scores between the cell types were highly correlated (R2=0.84). In iPSCs, the assay had concordant scores for 21/22 benign/likely benign and 24/25 pathogenic/likely pathogenic control variants from ClinVar. 43/112 exonic variants and 35/70 intronic variants with determinate scores disrupted splicing. 11 of 42 variants of uncertain significance were reclassified, and 29 of 34 variants with conflicting interpretations were reclassified using the functional data. SpliceAI computational predictions correlated well with experimental data (AUC = 0.96). We identified 20 unique SCN5A missense variants that disrupted splicing, and 2 clinically observed splice-altering missense variants of uncertain significance had normal function when tested with the cDNA-based APC assay. A splice-altering intronic variant detected by ParSE-seq, c.1891-5C>G, also disrupted splicing and sodium current when introduced into iPSC-CMs at the endogenous locus by CRISPR editing.

Conclusions

ParSE-seq is a calibrated, multiplexed, high-throughput assay to facilitate the classification of candidate splice-altering variants.

Novel ITPA variants identified by whole genome sequencing and RNA sequencing

Approximately 80% of rare diseases have a genetic cause, and an accurate genetic diagnosis is necessary for disease management, prognosis prediction, and genetic counseling. Whole-exome sequencing (WES) is a cost-effective approach for exploring the genetic cause, but several cases often remain undiagnosed. We combined whole genome sequencing (WGS) and RNA sequencing (RNA-seq) to identify the pathogenic variants in an unsolved case using WES. RNA-seq revealed aberrant exon 4 and exon 6 splicing of ITPA. WGS showed a previously unreported splicing donor variant, c.263+1G>A, and a novel heterozygous deletion, including exon 6. Detailed examination of the breakpoint indicated the deletion caused by recombination between Alu elements in different introns. The proband was found to have developmental and epileptic encephalopathies caused by variants in the ITPA gene. The combination of WGS and RNA-seq may be effective in diagnosing conditions in proband who could not be diagnosed using WES.

Integrative omics approaches to advance rare disease diagnostics

Over the past decade high-throughput DNA sequencing approaches, namely whole exome and whole genome sequencing became a standard procedure in Mendelian disease diagnostics. Implementation of these technologies greatly facilitated diagnostics and shifted the analysis paradigm from variant identification to prioritisation and evaluation. The diagnostic rates vary widely depending on the cohort size, heterogeneity and disease and range from around 30% to 50% leaving the majority of patients undiagnosed. Advances in omics technologies and computational analysis provide an opportunity to increase these unfavourable rates by providing evidence for disease-causing variant validation and prioritisation. This review aims to provide an overview of the current application of several omics technologies including RNA-sequencing, proteomics, metabolomics and DNA-methylation profiling for diagnostics of rare genetic diseases in general and inborn errors of metabolism in particular.

Identification of Splice Variants and Isoforms in Transcriptomics and Proteomics

Alternative splicing is pivotal to the regulation of gene expression and protein diversity in eukaryotic cells. The detection of alternative splicing events requires specific omics technologies. Although short-read RNA sequencing has successfully supported a plethora of investigations on alternative splicing, the emerging technologies of long-read RNA sequencing and top-down mass spectrometry open new opportunities to identify alternative splicing and protein isoforms with less ambiguity. Here, we summarize improvements in short-read RNA sequencing for alternative splicing analysis, including percent splicing index estimation and differential analysis. We also review the computational methods used in top-down proteomics analysis regarding proteoform identification, including the construction of databases of protein isoforms and statistical analyses of search results. While many improvements in sequencing and computational methods will result from emerging technologies, there should be future endeavors to increase the effectiveness, integration, and proteome coverage of alternative splicing events.

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.

Beyond the exome: What's next in diagnostic testing for Mendelian conditions

Despite advances in clinical genetic testing, including the introduction of exome sequencing (ES), more than 50% of individuals with a suspected Mendelian condition lack a precise molecular diagnosis. Clinical evaluation is increasingly undertaken by specialists outside of clinical genetics, often occurring in a tiered fashion and typically ending after ES. The current diagnostic rate reflects multiple factors, including technical limitations, incomplete understanding of variant pathogenicity, missing genotype-phenotype associations, complex gene-environment interactions, and reporting differences between clinical labs. Maintaining a clear understanding of the rapidly evolving landscape of diagnostic tests beyond ES, and their limitations, presents a challenge for non-genetics professionals. Newer tests, such as short-read genome or RNA sequencing, can be challenging to order, and emerging technologies, such as optical genome mapping and long-read DNA sequencing, are not available clinically. Furthermore, there is no clear guidance on the next best steps after inconclusive evaluation. Here, we review why a clinical genetic evaluation may be negative, discuss questions to be asked in this setting, and provide a framework for further investigation, including the advantages and disadvantages of new approaches that are nascent in the clinical sphere. We present a guide for the next best steps after inconclusive molecular testing based upon phenotype and prior evaluation, including when to consider referral to research consortia focused on elucidating the underlying cause of rare unsolved genetic disorders.

Single-cell genomics meets human genetics

Single-cell genomic technologies are revealing the cellular composition, identities and states in tissues at unprecedented resolution. They have now scaled to the point that it is possible to query samples at the population level, across thousands of individuals. Combining single-cell information with genotype data at this scale provides opportunities to link genetic variation to the cellular processes underpinning key aspects of human biology and disease. This strategy has potential implications for disease diagnosis, risk prediction and development of therapeutic solutions. But, effectively integrating large-scale single-cell genomic data, genetic variation and additional phenotypic data will require advances in data generation and analysis methods. As single-cell genetics begins to emerge as a field in its own right, we review its current state and the challenges and opportunities ahead.

Multi-omics identifies large mitoribosomal subunit instability caused by pathogenic MRPL39 variants as a cause of pediatric onset mitochondrial disease

MRPL39 encodes one of 52 proteins comprising the large subunit of the mitochondrial ribosome (mitoribosome). In conjunction with 30 proteins in the small subunit, the mitoribosome synthesizes the 13 subunits of the mitochondrial oxidative phosphorylation (OXPHOS) system encoded by mitochondrial Deoxyribonucleic acid (DNA). We used multi-omics and gene matching to identify three unrelated individuals with biallelic variants in MRPL39 presenting with multisystem diseases with severity ranging from lethal, infantile-onset (Leigh syndrome spectrum) to milder with survival into adulthood. Clinical exome sequencing of known disease genes failed to diagnose these patients; however quantitative proteomics identified a specific decrease in the abundance of large but not small mitoribosomal subunits in fibroblasts from the two patients with severe phenotype. Re-analysis of exome sequencing led to the identification of candidate single heterozygous variants in mitoribosomal genes MRPL39 (both patients) and MRPL15. Genome sequencing identified a shared deep intronic MRPL39 variant predicted to generate a cryptic exon, with transcriptomics and targeted studies providing further functional evidence for causation. The patient with the milder disease was homozygous for a missense variant identified through trio exome sequencing. Our study highlights the utility of quantitative proteomics in detecting protein signatures and in characterizing gene-disease associations in exome-unsolved patients. We describe Relative Complex Abundance analysis of proteomics data, a sensitive method that can identify defects in OXPHOS disorders to a similar or greater sensitivity to the traditional enzymology. Relative Complex Abundance has potential utility for functional validation or prioritization in many hundreds of inherited rare diseases where protein complex assembly is disrupted.

Variants in ATP5F1B are associated with dominantly inherited dystonia

ATP5F1B is a subunit of the mitochondrial ATP synthase or complex V of the mitochondrial respiratory chain. Pathogenic variants in nuclear genes encoding assembly factors or structural subunits are associated with complex V deficiency, typically characterized by autosomal recessive inheritance and multisystem phenotypes. Movement disorders have been described in a subset of cases carrying autosomal dominant variants in structural subunits genes ATP5F1A and ATP5MC3. Here, we report the identification of two different ATP5F1B missense variants (c.1000A>C; p.Thr334Pro and c.1445T>C; p.Val482Ala) segregating with early-onset isolated dystonia in two families, both with autosomal dominant mode of inheritance and incomplete penetrance. Functional studies in mutant fibroblasts revealed no decrease of ATP5F1B protein amount but severe reduction of complex V activity and impaired mitochondrial membrane potential, suggesting a dominant-negative effect. In conclusion, our study describes a new candidate gene associated with isolated dystonia and confirms that heterozygous variants in genes encoding subunits of the mitochondrial ATP synthase may cause autosomal dominant isolated dystonia with incomplete penetrance, likely through a dominant-negative mechanism.

RNA sequencing reveals a complete picture of a homozygous missense variant in a patient with VPS13D movement disorder: a case report and review of the literature

RNA sequencing (RNA-seq) is a complementary diagnostic tool to exome sequencing (ES), only recently clinically available to undiagnosed patients post-ES, that provides functional information on variants of unknown significance (VUS) by evaluating its effect on RNA transcription. ES became clinically available in the early 2010s and promised an agnostic platform for patients with a neurological disease, especially for those who believed to have a genetic etiology. However, the massive data generated by ES pose challenges in variant interpretation, especially for rare missense, synonymous, and deep intronic variants that may have a splicing effect. Without functional study and/or family segregation analysis, these rare variants would be likely interpreted as VUS which is difficult for clinicians to use in clinical care. Clinicians are able to assess the VUS for phenotypic overlap, but this additional information alone is usually not enough to re-classify a variant. Here, we report a case of a 14-month-old male who presented to clinic with a history of seizures, nystagmus, cerebral palsy, oral aversion, global developmental delay, and poor weight gain requiring gastric tube placement. ES revealed a previously unreported homozygous missense VUS, c.7406A > G p.(Asn2469Ser), in VPS13D. This variant has not been previously reported in genome aggregation database (gnomAD), ClinVar, or in any peer-reviewed published literature. By RNA-seq, we demonstrated that this variant mainly impacts splicing and results in a frameshift and early termination. It is expected to generate either a truncated protein, p.(Val2468fs*19), or no protein from this transcript due to nonsense-mediated mRNA decay leading to VPS13D deficiency. To our knowledge, this is the first case utilizing RNA-seq to further functionally characterize a homozygous novel missense VUS in VPS13D and confirm its impact on splicing. This confirmed pathogenicity gave the diagnosis of VPS13D movement disorder to this patient. Therefore, clinicians should consider utilizing RNA-seq to clarify VUS by evaluating its effect on RNA transcription.

Clinical spectrum and genetic causes of mitochondrial hepatopathy phenotype in children

BACKGROUND

Alterations in both mitochondrial DNA (mtDNA) and nuclear DNA genes affect mitochondria function, causing a range of liver-based conditions termed mitochondrial hepatopathies (MH), which are subcategorized as mtDNA depletion, RNA translation, mtDNA deletion, and enzymatic disorders. We aim to enhance the understanding of pathogenesis and natural history of MH.

METHODS

We analyzed data from patients with MH phenotypes to identify genetic causes, characterize the spectrum of clinical presentation, and determine outcomes.

RESULTS

Three enrollment phenotypes, that is, acute liver failure (ALF, n = 37), chronic liver disease (Chronic, n = 40), and post-liver transplant (n = 9), were analyzed. Patients with ALF were younger [median 0.8 y (range, 0.0, 9.4) vs 3.4 y (0.2, 18.6), p < 0.001] with fewer neurodevelopmental delays (40.0% vs 81.3%, p < 0.001) versus Chronic. Comprehensive testing was performed more often in Chronic than ALF (90.0% vs 43.2%); however, etiology was identified more often in ALF (81.3% vs 61.1%) with mtDNA depletion being most common (ALF: 77% vs Chronic: 41%). Of the sequenced cohort (n = 60), 63% had an identified mitochondrial disorder. Cluster analysis identified a subset without an underlying genetic etiology, despite comprehensive testing. Liver transplant-free survival was 40% at 2 years (ALF vs Chronic, 16% vs 65%, p < 0.001). Eighteen (21%) underwent transplantation. With 33 patient-years of follow-up after the transplant, 3 deaths were reported.

CONCLUSIONS

Differences between ALF and Chronic MH phenotypes included age at diagnosis, systemic involvement, transplant-free survival, and genetic etiology, underscoring the need for ultra-rapid sequencing in the appropriate clinical setting. Cluster analysis revealed a group meeting enrollment criteria but without an identified genetic or enzymatic diagnosis, highlighting the need to identify other etiologies.

Exome sequencing in individuals with congenital anomalies of the kidney and urinary tract (CAKUT): a single-center experience

Individuals with congenital anomalies of the kidney and urinary tract (CAKUT) show a broad spectrum of malformations. CAKUT can occur in an isolated fashion or as part of a syndromic disorder and can lead to end-stage kidney failure. A monogenic cause can be identified in ~12% of affected individuals. This study investigated a single-center CAKUT cohort analyzed by exome sequencing (ES). Emphasis was placed on the question whether diagnostic yield differs between certain CAKUT phenotypes (e.g., bilateral kidney affection, unilateral kidney affection or only urinary tract affection). 86 unrelated individuals with CAKUT were categorized according to their phenotype and analyzed by ES to identify a monogenic cause. Prioritized variants were rated according to the recommendations of the American College of Medical Genetics and Genomics and the Association for Clinical Genomic Science. Diagnostic yields of different phenotypic categories were compared. Clinical data were collected using a standardized questionnaire. In the study cohort, 7/86 individuals had a (likely) pathogenic variant in the genes PAX2, PBX1, EYA1, or SALL1. Additionally, in one individual, a 17q12 deletion syndrome (including HNF1B) was detected. 64 individuals had a kidney affection, which was bilateral in 36. All solved cases (8/86, 9%) had bilateral kidney affection (diagnostic yield in subcohort: 8/36, 22%). Although the diagnostic yield in CAKUT cohorts is low, our single-center experience argues, that, in individuals with bilateral kidney affection, monogenic burden is higher than in those with unilateral kidney or only urinary tract affection.

Ontologizing health systems data at scale: making translational discovery a reality

Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.

Dystonia genes and their biological pathways

High-throughput sequencing has been instrumental in uncovering the spectrum of pathogenic genetic alterations that contribute to the etiology of dystonia. Despite the immense heterogeneity in monogenic causes, studies performed during the past few years have highlighted that many rare deleterious variants associated with dystonic presentations affect genes that have roles in certain conserved pathways in neural physiology. These various gene mutations that appear to converge towards the disruption of interconnected cellular networks were shown to produce a wide range of different dystonic disease phenotypes, including isolated and combined dystonias as well as numerous clinically complex, often neurodevelopmental disorder-related conditions that can manifest with dystonic features in the context of multisystem disturbances. In this chapter, we summarize the manifold dystonia-gene relationships based on their association with a discrete number of unifying pathophysiological mechanisms and molecular cascade abnormalities. The themes on which we focus comprise dopamine signaling, heavy metal accumulation and calcifications in the brain, nuclear envelope function and stress response, gene transcription control, energy homeostasis, lysosomal trafficking, calcium and ion channel-mediated signaling, synaptic transmission beyond dopamine pathways, extra- and intracellular structural organization, and protein synthesis and degradation. Enhancing knowledge about the concept of shared etiological pathways in the pathogenesis of dystonia will motivate clinicians and researchers to find more efficacious treatments that allow to reverse pathologies in patient-specific core molecular networks and connected multipathway loops.