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Purpose: Identifying pathogenic noncoding variants is challenging. A single protein-altering variant is often identified in a recessive gene in individuals with developmental disorders (DD), but the prevalence of pathogenic noncoding "second hits" in trans with these is unknown., Methods: In 4073 genetically undiagnosed rare-disease trio probands from the 100,000 Genomes project, we identified rare heterozygous protein-altering variants in recessive DD-associated genes. We identified rare noncoding variants on the other haplotype in introns, untranslated regions, promoters, and candidate enhancer regions. We clinically evaluated the top candidates for phenotypic fit and performed functional testing where possible., Results: We identified 3761 rare heterozygous loss-of-function or ClinVar pathogenic variants in recessive DD-associated genes in 2430 probands. For 1366 (36.3%) of these, we identified at least 1 rare noncoding variant in trans. Bioinformatic filtering and clinical review, revealed 7 to be a good clinical fit. After detailed characterization, we identified likely diagnoses for 3 probands (in GAA, NPHP3, and PKHD1) and candidate diagnoses in a further 3 (PAH, LAMA2, and IGHMBP2)., Conclusion: We developed a systematic approach to uncover new diagnoses involving compound heterozygous coding/noncoding variants and conclude that this mechanism is likely to be a rare cause of DDs., Competing Interests: Conflict of Interest Elena Arciero is a current employee of Flagship Labs 86, Flagship Pioneering. Nicola Whiffin receives research funding from Novo Nordisk and has consulted for Argobio. All other authors declare no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: The 100,000 Genomes Project diagnosed a quarter of affected participants, but 26% of diagnoses were not on the applied gene panel(s); with many being de novo variants. Assessing biallelic variants without a gene panel is more challenging., Methods: We sought to identify missed biallelic diagnoses using GenePy, which incorporates allele frequency, zygosity, and a user-defined deleterious metric, generating an aggregate GenePy score per gene, per participant. We calculated GenePy scores for 2862 recessive disease genes in 78,216 100,000 Genomes Project participants. For each gene, we ranked participant GenePy scores and scrutinized affected participants without a diagnosis, whose scores ranked among the top 5 for each gene. In cases which participant phenotypes overlapped with the disease gene of interest, we extracted rare variants and applied phase, ClinVar, and ACMG classification., Results: 3184 affected individuals without a molecular diagnosis had a top-5-ranked GenePy score and 682 of 3184 (21%) had phenotypes overlapping with a top-ranking gene. In 122 of 669 (18%) phenotype-matched cases (excluding 13 withdrawn participants), we identified a putative missed diagnosis (2.2% of all undiagnosed participants). A further 334 of 669 (50%) cases have a possible missed diagnosis but require functional validation., Conclusion: Applying GenePy at scale has identified 456 potential diagnoses, demonstrating the value of novel diagnostic strategies., Competing Interests: Conflict of Interest The authors declare no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: Protein arginine methyltransferase 7 (PRMT7) is a member of a family of enzymes that catalyzes the methylation of arginine residues on several protein substrates. Biallelic pathogenic PRMT7 variants have previously been associated with a syndromic neurodevelopmental disorder characterized by short stature, brachydactyly, intellectual developmental disability, and seizures. To our knowledge, no comprehensive study describes the detailed clinical characteristics of this syndrome. Thus, we aim to delineate the phenotypic spectrum of PRMT7-related disorder., Methods: We assembled a cohort of 51 affected individuals from 39 different families, gathering clinical information from 36 newly described affected individuals and reviewing data of 15 individuals from the literature., Results: The main clinical characteristics of the PRMT7-related syndrome are short stature, mild to severe developmental delay/intellectual disability, hypotonia, brachydactyly, and distinct facial morphology, including bifrontal narrowing, prominent supraorbital ridges, sparse eyebrows, short nose with full/broad nasal tip, thin upper lip, full and everted lower lip, and a prominent or squared-off jaw. Additional variable findings include seizures, obesity, nonspecific magnetic resonance imaging abnormalities, eye abnormalities (i.e., strabismus or nystagmus), and hearing loss., Conclusion: This study further delineates and expands the molecular, phenotypic spectrum and natural history of PRMT7-related syndrome characterized by a neurodevelopmental disorder with skeletal, growth, and endocrine abnormalities., Competing Interests: Conflict of Interest Megan Li is an employee of Invitae. Erin Torti, Amber Begtrup, and Rhonda E Schnur are employees of GeneDx, Inc. All other authors declare no conflicts of interest., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: Exome and genome sequencing have drastically accelerated novel disease gene discoveries. However, discovery is still hindered by myriad variants of uncertain significance found in genes of undetermined biological function. This necessitates intensive functional experiments on genes of equal predicted causality, leading to a major bottleneck., Methods: We apply the loss-of-function observed/expected upper-bound fraction metric of intolerance to gene inactivation to curate a list of predicted haploinsufficient disease genes. Using data from the 100,000 Genomes Project, we adopt a gene-to-patient approach that matches de novo loss-of-function variants in constrained genes to patients with rare disease. Through large-scale aggregation of data, we reduce excess analytical noise currently hindering novel discoveries., Results: Results from 13,949 trios revealed 643 rare, de novo predicted loss-of-function events filtered from 1044 loss-of-function observed/expected upper-bound fraction-constrained genes. A total of 168 variants occurred within 126 genes without a known disease-gene relationship. Of these, 27 genes had >1 kindred affected, and for 18 of these genes, multiple kindreds had overlapping phenotypes. Two years after initial analysis, 11 of 18 (61%) of these genes have been independently published as novel disease gene discoveries., Conclusion: Using large cohorts and adopting gene-based approaches can rapidly and objectively accelerate dominantly inherited novel gene discovery by targeting the most appropriate genes for functional validation., Competing Interests: Conflict of Interest All authors declare no conflicts of interest., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: Diagnosis of genetic disorders is hampered by large numbers of variants of uncertain significance (VUSs) identified through next-generation sequencing. Many such variants may disrupt normal RNA splicing. We examined effects on splicing of a large cohort of clinically identified variants and compared performance of bioinformatic splicing prediction tools commonly used in diagnostic laboratories., Methods: Two hundred fifty-seven variants (coding and noncoding) were referred for analysis across three laboratories. Blood RNA samples underwent targeted reverse transcription polymerase chain reaction (RT-PCR) analysis with Sanger sequencing of PCR products and agarose gel electrophoresis. Seventeen samples also underwent transcriptome-wide RNA sequencing with targeted splicing analysis based on Sashimi plot visualization. Bioinformatic splicing predictions were obtained using Alamut, HSF 3.1, and SpliceAI software., Results: Eighty-five variants (33%) were associated with abnormal splicing. The most frequent abnormality was upstream exon skipping (39/85 variants), which was most often associated with splice donor region variants. SpliceAI had greatest accuracy in predicting splicing abnormalities (0.91) and outperformed other tools in sensitivity and specificity., Conclusion: Splicing analysis of blood RNA identifies diagnostically important splicing abnormalities and clarifies functional effects of a significant proportion of VUSs. Bioinformatic predictions are improving but still make significant errors. RNA analysis should therefore be routinely considered in genetic disease diagnostics.

Primary cilia are essential signalling organelles found on the apical surface of epithelial cells, where they coordinate chemosensation, mechanosensation and light sensation. Motile cilia play a central role in establishing fluid flow in the respiratory tract, reproductive tract, brain ventricles and ear. Genetic defects affecting the structure or function of cilia can lead to a broad range of developmental and degenerative diseases known as ciliopathies. Splicing contributes to the pathogenesis, diagnosis and treatment of ciliopathies. Tissue-specific alternative splicing contributes to the tissue-specific manifestation of ciliopathy phenotypes, for example the retinal-specific effects of some genetic defects, due to specific transcript expression in the highly specialised ciliated cells of the retina, the photoreceptor cells. Ciliopathies can arise both as a result of genetic variants in spliceosomal proteins, or as a result of variants affecting splicing of specific cilia genes. Here we discuss the opportunities and challenges in diagnosing ciliopathies using RNA sequence analysis and the potential for treating ciliopathies in a relatively mutation-neutral way by targeting splicing. This article is part of a Special Issue entitled: RNA structure and splicing regulation edited by Francisco Baralle, Ravindra Singh and Stefan Stamm., (Copyright © 2019 Elsevier B.V. All rights reserved.)

High-throughput next-generation sequencing technologies have led to a rapid increase in the number of sequence variants identified in clinical practice via diagnostic genetic tests. Current bioinformatic analysis pipelines fail to take adequate account of the possible splicing effects of such variants, particularly where variants fall outwith canonical splice site sequences, and consequently the pathogenicity of such variants may often be missed. The regulation of splicing is highly complex and as a result, in silico prediction tools lack sufficient sensitivity and specificity for reliable use. Variants of all kinds can be linked to aberrant splicing in disease and the need for correct identification and diagnosis grows ever more crucial as novel splice-switching antisense oligonucleotide therapies start to enter clinical usage. RT-PCR provides a useful targeted assay of the splicing effects of identified variants, while minigene assays, massive parallel reporter assays and animal models can also be used for more detailed study of a particular splicing system, given enough time and resources. However, RNA-sequencing (RNA-seq) has the potential to be used as a rapid diagnostic tool in genomic medicine. By utilising data science approaches and machine learning, it may prove possible to finally understand and interpret the 'splicing code' and apply this knowledge in human disease diagnostics., (Copyright © 2018 Elsevier Ltd. All rights reserved.)