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Schot R, Ferraro F, Geeven G, Diderich KEM, Barakat TS. Re-analysis of whole genome sequencing ends a diagnostic odyssey: Case report of an RNU4-2 related neurodevelopmental disorder. Clin Genet 2024. [PMID: 38859706 DOI: 10.1111/cge.14574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2024] [Revised: 05/31/2024] [Accepted: 06/03/2024] [Indexed: 06/12/2024]
Abstract
Despite increasing knowledge of disease-causing genes in human genetics, approximately half of the individuals affected by neurodevelopmental disorders remain genetically undiagnosed. Part of this missing heritability might be caused by genetic variants outside of protein-coding genes, which are not routinely diagnostically investigated. A recent preprint identified de novo variants in the non-coding spliceosomal snRNA gene RNU4-2 as a cause of a frequent novel syndromic neurodevelopmental disorder. Here we mined 164 whole genome sequencing (WGS) trios from individuals with neurodevelopmental or multiple congenital anomaly disorders that received diagnostic genomic investigations at our clinic. We identify a recurrent de novo RNU4-2 variant (NR_003137.2(RNU4-2):n.64_65insT) in a 5-year-old girl with severe global developmental delay, hypotonia, microcephaly, and seizures that likely explains her phenotype, given that extensive previous genetic investigations failed to identify an alternative cause. We present detailed phenotyping of the individual obtained during a 5-year follow-up. This includes photographs showing recognizable facial features for this novel disorder, which might allow prioritizing other currently unexplained affected individuals sharing similar facial features for targeted investigations of RNU4-2. This case illustrates the power of re-analysis to solve previously unexplained cases even when a diagnostic genome remains negative.
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Affiliation(s)
- Rachel Schot
- Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Discovery Unit, Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Federico Ferraro
- Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Geert Geeven
- Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Karin E M Diderich
- Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Tahsin Stefan Barakat
- Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Discovery Unit, Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
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2
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Ungar RA, Goddard PC, Jensen TD, Degalez F, Smith KS, Jin CA, Bonner DE, Bernstein JA, Wheeler MT, Montgomery SB. Impact of genome build on RNA-seq interpretation and diagnostics. Am J Hum Genet 2024:S0002-9297(24)00168-X. [PMID: 38834072 DOI: 10.1016/j.ajhg.2024.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 05/04/2024] [Accepted: 05/06/2024] [Indexed: 06/06/2024] Open
Abstract
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.
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Affiliation(s)
- Rachel A Ungar
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA; Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Pagé C Goddard
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA; Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Tanner D Jensen
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA; Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | | | - Kevin S Smith
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Christopher A Jin
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA
| | - Devon E Bonner
- Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA, USA; Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA
| | - Jonathan A Bernstein
- Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA
| | - Matthew T Wheeler
- Department of Cardiovascular Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Stephen B Montgomery
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA; Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
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3
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Cao X, Huber S, Ahari AJ, Traube FR, Seifert M, Oakes CC, Secheyko P, Vilov S, Scheller IF, Wagner N, Yépez VA, Blombery P, Haferlach T, Heinig M, Wachutka L, Hutter S, Gagneur J. Analysis of 3760 hematologic malignancies reveals rare transcriptomic aberrations of driver genes. Genome Med 2024; 16:70. [PMID: 38769532 PMCID: PMC11103968 DOI: 10.1186/s13073-024-01331-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 04/04/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND Rare oncogenic driver events, particularly affecting the expression or splicing of driver genes, are suspected to substantially contribute to the large heterogeneity of hematologic malignancies. However, their identification remains challenging. METHODS To address this issue, we generated the largest dataset to date of matched whole genome sequencing and total RNA sequencing of hematologic malignancies from 3760 patients spanning 24 disease entities. Taking advantage of our dataset size, we focused on discovering rare regulatory aberrations. Therefore, we called expression and splicing outliers using an extension of the workflow DROP (Detection of RNA Outliers Pipeline) and AbSplice, a variant effect predictor that identifies genetic variants causing aberrant splicing. We next trained a machine learning model integrating these results to prioritize new candidate disease-specific driver genes. RESULTS We found a median of seven expression outlier genes, two splicing outlier genes, and two rare splice-affecting variants per sample. Each category showed significant enrichment for already well-characterized driver genes, with odds ratios exceeding three among genes called in more than five samples. On held-out data, our integrative modeling significantly outperformed modeling based solely on genomic data and revealed promising novel candidate driver genes. Remarkably, we found a truncated form of the low density lipoprotein receptor LRP1B transcript to be aberrantly overexpressed in about half of hairy cell leukemia variant (HCL-V) samples and, to a lesser extent, in closely related B-cell neoplasms. This observation, which was confirmed in an independent cohort, suggests LRP1B as a novel marker for a HCL-V subclass and a yet unreported functional role of LRP1B within these rare entities. CONCLUSIONS Altogether, our census of expression and splicing outliers for 24 hematologic malignancy entities and the companion computational workflow constitute unique resources to deepen our understanding of rare oncogenic events in hematologic cancers.
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Affiliation(s)
- Xueqi Cao
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Graduate School of Quantitative Biosciences (QBM), Munich, Germany
| | - Sandra Huber
- Munich Leukemia Laboratory (MLL), Munich, Germany
| | - Ata Jadid Ahari
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Franziska R Traube
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany
| | - Marc Seifert
- Department of Haematology, Oncology and Clinical Immunology, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Christopher C Oakes
- Division of Hematology, Department of Internal Medicine, The Ohio State University, Columbus, OH, USA
| | - Polina Secheyko
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Faculty of Biology, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Sergey Vilov
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany
| | - Ines F Scheller
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany
| | - Nils Wagner
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Helmholtz Association - Munich School for Data Science (MUDS), Munich, Germany
| | - Vicente A Yépez
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Piers Blombery
- Peter MacCallum Cancer Centre, Melbourne, Australia
- University of Melbourne, Melbourne, Australia
- Torsten Haferlach Leukämiediagnostik Stiftung, Munich, Germany
| | | | - Matthias Heinig
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany
| | - Leonhard Wachutka
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
| | | | - Julien Gagneur
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Graduate School of Quantitative Biosciences (QBM), Munich, Germany.
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany.
- Institute of Human Genetics, School of Medicine and Health, Technical University of Munich, Munich, Germany.
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4
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Li S, Zhao S, Sinson JC, Bajic A, Rosenfeld JA, Neeley MB, Pena M, Worley KC, Burrage LC, Weisz-Hubshman M, Ketkar S, Craigen WJ, Clark GD, Lalani S, Bacino CA, Machol K, Chao HT, Potocki L, Emrick L, Sheppard J, Nguyen MTT, Khoramnia A, Hernandez PP, Nagamani SC, Liu Z, Eng CM, Lee B, Liu P. The clinical utility and diagnostic implementation of human subject cell transdifferentiation followed by RNA sequencing. Am J Hum Genet 2024; 111:841-862. [PMID: 38593811 PMCID: PMC11080285 DOI: 10.1016/j.ajhg.2024.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 03/08/2024] [Accepted: 03/11/2024] [Indexed: 04/11/2024] Open
Abstract
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.
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Affiliation(s)
- Shenglan Li
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Sen Zhao
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Jefferson C Sinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Aleksandar Bajic
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA; Advanced Technology Cores, Baylor College of Medicine, Houston, TX, USA
| | - Jill A Rosenfeld
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Matthew B Neeley
- Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA
| | - Mezthly Pena
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Kim C Worley
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Lindsay C Burrage
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Monika Weisz-Hubshman
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Shamika Ketkar
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - William J Craigen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Gary D Clark
- Department of Pediatrics, Section of Neurology, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Seema Lalani
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Carlos A Bacino
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Keren Machol
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Hsiao-Tuan Chao
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA; Department of Pediatrics, Section of Neurology, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA; Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA; Cain Pediatric Research Foundation Laboratories, Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA; McNair Medical Institute, The Robert and Janice McNair Foundation, Houston, TX, USA
| | - Lorraine Potocki
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Lisa Emrick
- Department of Pediatrics, Section of Neurology, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Jennifer Sheppard
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA; Department of Pediatrics, Section of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - My T T Nguyen
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA
| | - Anahita Khoramnia
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | | | - Sandesh Cs Nagamani
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Zhandong Liu
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, USA; Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA; Department of Pediatrics, Section of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Christine M Eng
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Baylor Genetics, Houston, TX, USA
| | - Brendan Lee
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Texas Children's Hospital, Houston, TX, USA
| | - Pengfei Liu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; Baylor Genetics, Houston, TX, USA.
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5
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Riess O, Sturm M, Menden B, Liebmann A, Demidov G, Witt D, Casadei N, Admard J, Schütz L, Ossowski S, Taylor S, Schaffer S, Schroeder C, Dufke A, Haack T. Genomes in clinical care. NPJ Genom Med 2024; 9:20. [PMID: 38485733 PMCID: PMC10940576 DOI: 10.1038/s41525-024-00402-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 02/07/2024] [Indexed: 03/18/2024] Open
Abstract
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.
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Affiliation(s)
- Olaf Riess
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany.
- NGS Competence Center Tübingen, University of Tübingen, Tübingen, Germany.
- Center for Rare Diseases Tübingen, University of Tübingen, Tübingen, Germany.
| | - Marc Sturm
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Benita Menden
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Alexandra Liebmann
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - German Demidov
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Dennis Witt
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Nicolas Casadei
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- NGS Competence Center Tübingen, University of Tübingen, Tübingen, Germany
| | - Jakob Admard
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Leon Schütz
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Stephan Ossowski
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- NGS Competence Center Tübingen, University of Tübingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany
| | | | | | - Christopher Schroeder
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Center for Rare Diseases Tübingen, University of Tübingen, Tübingen, Germany
| | - Andreas Dufke
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Center for Rare Diseases Tübingen, University of Tübingen, Tübingen, Germany
| | - Tobias Haack
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Center for Rare Diseases Tübingen, University of Tübingen, Tübingen, Germany
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6
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Weisschuh N, Mazzola P, Zuleger T, Schaeferhoff K, Kühlewein L, Kortüm F, Witt D, Liebmann A, Falb R, Pohl L, Reith M, Stühn LG, Bertrand M, Müller A, Casadei N, Kelemen O, Kelbsch C, Kernstock C, Richter P, Sadler F, Demidov G, Schütz L, Admard J, Sturm M, Grasshoff U, Tonagel F, Heinrich T, Nasser F, Wissinger B, Ossowski S, Kohl S, Riess O, Stingl K, Haack TB. Diagnostic genome sequencing improves diagnostic yield: a prospective single-centre study in 1000 patients with inherited eye diseases. J Med Genet 2024; 61:186-195. [PMID: 37734845 PMCID: PMC10850689 DOI: 10.1136/jmg-2023-109470] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 09/10/2023] [Indexed: 09/23/2023]
Abstract
PURPOSE Genome sequencing (GS) is expected to reduce the diagnostic gap in rare disease genetics. We aimed to evaluate a scalable framework for genome-based analyses 'beyond the exome' in regular care of patients with inherited retinal degeneration (IRD) or inherited optic neuropathy (ION). METHODS PCR-free short-read GS was performed on 1000 consecutive probands with IRD/ION in routine diagnostics. Complementary whole-blood RNA-sequencing (RNA-seq) was done in a subset of 74 patients. An open-source bioinformatics analysis pipeline was optimised for structural variant (SV) calling and combined RNA/DNA variation interpretation. RESULTS A definite genetic diagnosis was established in 57.4% of cases. For another 16.7%, variants of uncertain significance were identified in known IRD/ION genes, while the underlying genetic cause remained unresolved in 25.9%. SVs or alterations in non-coding genomic regions made up for 12.7% of the observed variants. The RNA-seq studies supported the classification of two unclear variants. CONCLUSION GS is feasible in clinical practice and reliably identifies causal variants in a substantial proportion of individuals. GS extends the diagnostic yield to rare non-coding variants and enables precise determination of SVs. The added diagnostic value of RNA-seq is limited by low expression levels of the major IRD disease genes in blood.
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Affiliation(s)
- Nicole Weisschuh
- Institute for Ophthalmic Research, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Pascale Mazzola
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Theresia Zuleger
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Karin Schaeferhoff
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Laura Kühlewein
- University Eye Hospital, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Friederike Kortüm
- University Eye Hospital, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Dennis Witt
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Alexandra Liebmann
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Ruth Falb
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Lisa Pohl
- University Eye Hospital, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Milda Reith
- University Eye Hospital, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Lara G Stühn
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Miriam Bertrand
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Amelie Müller
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Nicolas Casadei
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Olga Kelemen
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Carina Kelbsch
- University Eye Hospital, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Christoph Kernstock
- University Eye Hospital, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Paul Richter
- University Eye Hospital, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Francoise Sadler
- University Eye Hospital, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - German Demidov
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Leon Schütz
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Jakob Admard
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Marc Sturm
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Ute Grasshoff
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Felix Tonagel
- University Eye Hospital, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Tilman Heinrich
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- MVZ für Humangenetik und Molekularpathologie, Rostock, Germany
| | - Fadi Nasser
- University Eye Hospital, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Bernd Wissinger
- Institute for Ophthalmic Research, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Stephan Ossowski
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany
| | - Susanne Kohl
- Institute for Ophthalmic Research, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Olaf Riess
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Center for Rare Disease, University of Tübingen, Tübingen, Germany
| | - Katarina Stingl
- University Eye Hospital, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Tobias B Haack
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Center for Rare Disease, University of Tübingen, Tübingen, Germany
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7
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Ungar RA, Goddard PC, Jensen TD, Degalez F, Smith KS, Jin CA, Bonner DE, Bernstein JA, Wheeler MT, Montgomery SB. Impact of genome build on RNA-seq interpretation and diagnostics. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.11.24301165. [PMID: 38260490 PMCID: PMC10802764 DOI: 10.1101/2024.01.11.24301165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
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 (UDN) and Genomics Research to Elucidate the Genetics of Rare Disease (GREGoR) Consortium. We identified 2,800 genes with build-dependent quantification across six routinely-collected biospecimens, including 1,391 protein-coding genes and 341 known rare disease genes. We further observed multiple genes that only have detectable expression in a subset of genome builds. Finally, we characterized how genome build impacts the detection of outlier transcriptomic events. 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.
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Affiliation(s)
- Rachel A. Ungar
- Department of Genetics, School of Medicine, Stanford University
- Department of Pathology, School of Medicine, Stanford University
| | - Pagé C. Goddard
- Department of Genetics, School of Medicine, Stanford University
- Department of Pathology, School of Medicine, Stanford University
| | - Tanner D. Jensen
- Department of Genetics, School of Medicine, Stanford University
- Department of Pathology, School of Medicine, Stanford University
| | | | - Kevin S. Smith
- Department of Pathology, School of Medicine, Stanford University
| | | | | | - Devon E. Bonner
- Department of Pediatrics, School of Medicine, Stanford University
- Stanford Center for Undiagnosed Diseases, Stanford University
| | | | - Matthew T. Wheeler
- Department of Cardiovascular Medicine, School of Medicine, Stanford University
| | - Stephen B. Montgomery
- Department of Genetics, School of Medicine, Stanford University
- Department of Pathology, School of Medicine, Stanford University
- Department of Biomedical Data Science, Stanford University
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Riquin K, Isidor B, Mercier S, Nizon M, Colin E, Bonneau D, Pasquier L, Odent S, Le Guillou Horn XM, Le Guyader G, Toutain A, Meyer V, Deleuze JF, Pichon O, Doco-Fenzy M, Bézieau S, Cogné B. Integrating RNA-Seq into genome sequencing workflow enhances the analysis of structural variants causing neurodevelopmental disorders. J Med Genet 2023; 61:47-56. [PMID: 37495270 DOI: 10.1136/jmg-2023-109263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 07/09/2023] [Indexed: 07/28/2023]
Abstract
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.
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Affiliation(s)
- Kevin Riquin
- l'institut du thorax, Nantes Université, CHU de Nantes, CNRS, INSERM, Nantes, France
| | - Bertrand Isidor
- l'institut du thorax, Nantes Université, CHU de Nantes, CNRS, INSERM, Nantes, France
- Service de Génétique médicale, Nantes Université, CHU de Nantes, Nantes, France
| | - Sandra Mercier
- l'institut du thorax, Nantes Université, CHU de Nantes, CNRS, INSERM, Nantes, France
- Service de Génétique médicale, Nantes Université, CHU de Nantes, Nantes, France
| | - Mathilde Nizon
- l'institut du thorax, Nantes Université, CHU de Nantes, CNRS, INSERM, Nantes, France
- Service de Génétique médicale, Nantes Université, CHU de Nantes, Nantes, France
| | - Estelle Colin
- CHU Angers, Service de Génétique médicale, Angers, France
- UMR CNRS 6214-INSERM 1083, Université d'Angers, Angers, France
| | - Dominique Bonneau
- CHU Angers, Service de Génétique médicale, Angers, France
- UMR CNRS 6214-INSERM 1083, Université d'Angers, Angers, France
| | | | - Sylvie Odent
- Service de Génétique Clinique, ERN ITHACA, Rennes, France
- Institut de Génétique et Développement de Rennes, IGDR UMR 6290 CNRS, INSERM, IGDR Univ Rennes, Rennes, France
| | - Xavier Maximin Le Guillou Horn
- Service de génétique médicale, CHU de Poitiers, Poitiers, France
- LabCom I3M-Dactim mis/LMA CNRS 7348, Université de Poitiers, Poitiers, France
| | | | - Annick Toutain
- UF de Génétique Médicale, Centre Hospitalier Universitaire, Tours, France
- UMR 1253, iBrain, Université de Tours, INSERM, Tours, France
| | - Vincent Meyer
- Centre National de Recherche en Génomique Humaine (CNRGH), Université Paris-Saclay, CEA, Evry, France
| | - Jean-François Deleuze
- Centre National de Recherche en Génomique Humaine (CNRGH), Université Paris-Saclay, CEA, Evry, France
| | - Olivier Pichon
- Service de Génétique médicale, Nantes Université, CHU de Nantes, Nantes, France
| | - Martine Doco-Fenzy
- l'institut du thorax, Nantes Université, CHU de Nantes, CNRS, INSERM, Nantes, France
- Service de Génétique médicale, Nantes Université, CHU de Nantes, Nantes, France
| | - Stéphane Bézieau
- l'institut du thorax, Nantes Université, CHU de Nantes, CNRS, INSERM, Nantes, France
- Service de Génétique médicale, Nantes Université, CHU de Nantes, Nantes, France
| | - Benjamin Cogné
- l'institut du thorax, Nantes Université, CHU de Nantes, CNRS, INSERM, Nantes, France
- Service de Génétique médicale, Nantes Université, CHU de Nantes, Nantes, France
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Johannesen KM, Tümer Z, Weckhuysen S, Barakat TS, Bayat A. Solving the unsolved genetic epilepsies: Current and future perspectives. Epilepsia 2023; 64:3143-3154. [PMID: 37750451 DOI: 10.1111/epi.17780] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/21/2023] [Accepted: 09/22/2023] [Indexed: 09/27/2023]
Abstract
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.
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Affiliation(s)
- Katrine M Johannesen
- Department of Genetics, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Epilepsy Genetics and Personalized Medicine, The Danish Epilepsy Center, Dianalund, Denmark
| | - Zeynep Tümer
- Department of Genetics, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sarah Weckhuysen
- Applied and Translational Neurogenomics Group, VIB Centre for Molecular Neurology, Antwerp, Belgium
- Translational Neurosciences, Faculty of Medicine and Health Science, University of Antwerp, Antwerp, Belgium
- Department of Neurology, University Hospital Antwerp, Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Tahsin Stefan Barakat
- Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Discovery Unit, Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- ENCORE Expertise Center for Neurodevelopmental Disorders, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Allan Bayat
- Department of Epilepsy Genetics and Personalized Medicine, The Danish Epilepsy Center, Dianalund, Denmark
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
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10
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Smirnov D, Konstantinovskiy N, Prokisch H. Integrative omics approaches to advance rare disease diagnostics. J Inherit Metab Dis 2023; 46:824-838. [PMID: 37553850 DOI: 10.1002/jimd.12663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 07/26/2023] [Accepted: 07/27/2023] [Indexed: 08/10/2023]
Abstract
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.
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Affiliation(s)
- Dmitrii Smirnov
- School of Medicine, Institute of Human Genetics, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Computational Health Center, Helmholtz Munich, Neuherberg, Germany
| | - Nikita Konstantinovskiy
- School of Medicine, Institute of Human Genetics, Technical University of Munich, Munich, Germany
| | - Holger Prokisch
- School of Medicine, Institute of Human Genetics, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Computational Health Center, Helmholtz Munich, Neuherberg, Germany
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