Systematic analysis of snRNA genes reveals frequent RNU2-2 variants in dominant and recessive developmental and epileptic encephalopathies.

Systematic analysis of snRNA genes reveals frequent RNU2-2 variants in dominant and recessive developmental and epileptic encephalopathies - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. 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Learn more: PMC Disclaimer | PMC Copyright Notice Nat Genet . 2026 Mar 30;58(4):782–797. doi: 10.1038/s41588-026-02547-5 Search in PMC Search in PubMed View in NLM Catalog Add to search Systematic analysis of snRNA genes reveals frequent RNU2-2 variants in dominant and recessive developmental and epileptic encephalopathies Elsa Leitão Elsa Leitão 1 Institute of Human Genetics, University Hospital Essen, University Duisburg-Essen, Essen, Germany Find articles by Elsa Leitão 1, # , Amandine Santini Amandine Santini 2 Université Rouen Normandie, Normandie Université, Inserm U1245 and CHU Rouen, Department of Genetics and Reference Center for Developmental Abnormalities, Rouen, France Find articles by Amandine Santini 2, # , Benjamin Cogne Benjamin Cogne 3 Nantes Université, CHU de Nantes, Service de Génétique Médicale, Nantes, France 4 Nantes Université, CHU de Nantes, CNRS, INSERM, l’Institut du Thorax, Nantes, France 5 Laboratoire SeqOIA, Paris, France Find articles by Benjamin Cogne 3, 4, 5, # , Miriam Essid Miriam Essid 6 Genetics Department, Hospices Civils de Lyon, Lyon, France 7 Pathophysiology and Genetics of Neuron and Muscle (PNMG), UCBL, CNRS UMR5261 - INSERM, Lyon, France 8 GCS AURAGEN, Lyon, France Find articles by Miriam Essid 6, 7, 8 , Maria Athanasiadou Maria Athanasiadou 9 CNRS, Inserm, Université de Strasbourg, IGBMC UMR 7104- UMR-S 1258, Illkirch, France 10 Department of Integrated Structural Biology, IGBMC, Illkirch, France Find articles by Maria Athanasiadou 9, 10 , Christy W LaFlamme Christy W LaFlamme 11 Center for Pediatric Neurological Disease Research, St. Jude Children’s Research Hospital, Memphis, TN USA 12 Graduate School of Biomedical Sciences, St. Jude Children’s Research Hospital, Memphis, Memphis, TN USA Find articles by Christy W LaFlamme 11, 12 , Pierre Marijon Pierre Marijon 5 Laboratoire SeqOIA, Paris, France Find articles by Pierre Marijon 5 , Virginie Bernard Virginie Bernard 8 GCS AURAGEN, Lyon, France Find articles by Virginie Bernard 8 , Kevin Jousselin Kevin Jousselin 5 Laboratoire SeqOIA, Paris, France Find articles by Kevin Jousselin 5 , Nicolas Chatron Nicolas Chatron 6 Genetics Department, Hospices Civils de Lyon, Lyon, France 7 Pathophysiology and Genetics of Neuron and Muscle (PNMG), UCBL, CNRS UMR5261 - INSERM, Lyon, France 8 GCS AURAGEN, Lyon, France Find articles by Nicolas Chatron 6, 7, 8 , Giulia Barcia Giulia Barcia 5 Laboratoire SeqOIA, Paris, France 13 Assistance Publique - Hôpitaux de Paris (APHP), Service de Médecine Génomique des Maladies Rares, Hôpital Necker-Enfants malades, Paris, France 14 Université Paris Cité, INSERM, IHU Imagine – Institut des maladies génétiques, Paris, France Find articles by Giulia Barcia 5, 13, 14 , Boris Keren Boris Keren 5 Laboratoire SeqOIA, Paris, France 15 Département de Génétique Médicale, Assistance Publique - Hôpitaux de Paris (APHP) Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France Find articles by Boris Keren 5, 15 , Cyril Mignot Cyril Mignot 15 Département de Génétique Médicale, Assistance Publique - Hôpitaux de Paris (APHP) Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France 16 Centre de Référence Déficiences Intellectuelles de Causes Rares, Paris, France Find articles by Cyril Mignot 15, 16 , Perrine Charles Perrine Charles 15 Département de Génétique Médicale, Assistance Publique - Hôpitaux de Paris (APHP) Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France Find articles by Perrine Charles 15 , Thomas Besnard Thomas Besnard 3 Nantes Université, CHU de Nantes, Service de Génétique Médicale, Nantes, France 4 Nantes Université, CHU de Nantes, CNRS, INSERM, l’Institut du Thorax, Nantes, France Find articles by Thomas Besnard 3, 4 , Robin Paluch Robin Paluch 1 Institute of Human Genetics, University Hospital Essen, University Duisburg-Essen, Essen, Germany Find articles by Robin Paluch 1 , Jean-Madeleine de Sainte Agathe Jean-Madeleine de Sainte Agathe 5 Laboratoire SeqOIA, Paris, France 15 Département de Génétique Médicale, Assistance Publique - Hôpitaux de Paris (APHP) Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France Find articles by Jean-Madeleine de Sainte Agathe 5, 15 , Edith P Almanza Fuerte Edith P Almanza Fuerte 11 Center for Pediatric Neurological Disease Research, St. Jude Children’s Research Hospital, Memphis, TN USA Find articles by Edith P Almanza Fuerte 11 , Soham Sengupta Soham Sengupta 11 Center for Pediatric Neurological Disease Research, St. Jude Children’s Research Hospital, Memphis, TN USA Find articles by Soham Sengupta 11 , Mathieu Milh Mathieu Milh 17 Service de Neurologie Pediatrique, AP-HM, Marseille, France 18 Aix Marseille Université, Inserm, INMED, Marseille, France Find articles by Mathieu Milh 17, 18 , Francis Ramond Francis Ramond 19 Département de Génétique, Centre Hospitalier Universitaire de Saint-Etienne, Saint-Etienne, France Find articles by Francis Ramond 19 , Talia Allan Talia Allan 20 Department of Medicine, Epilepsy Research Centre, University of Melbourne, Austin Health, Heidelberg, Victoria Australia Find articles by Talia Allan 20 , Isabelle An Isabelle An 21 Département de Neurologie, Assistance Publique - Hôpitaux de Paris (APHP) Sorbonne Université, Center of Reference for Rare Epilepsies, ERN EPICARE, Hôpital Pitié-Salpêtrière, Paris, France Find articles by Isabelle An 21 , Camila Araujo Camila Araujo 22 Department of Surgery and Anatomy, Ribeirão Preto Medical School, University of São Paulo, Ribeirao Preto, Brazil Find articles by Camila Araujo 22 , Stéphanie Arpin Stéphanie Arpin 23 Service de Génétique, CHU de Tours, Tours, France 24 Université de Tours, INSERM, Imaging Brain and Neuropsychiatry iBraiN U1253, Tours, France Find articles by Stéphanie Arpin 23, 24 , Christina Austin-Tse Christina Austin-Tse 25 Broad Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA USA Find articles by Christina Austin-Tse 25 , Stéphane Auvin Stéphane Auvin 26 Département de Neuropédiatrie, Assistance Publique - Hôpitaux de Paris (APHP), Hôpital Robert-Debré, Paris, France 27 Université Paris Cité, INSERM NeuroDiderot, Paris, France Find articles by Stéphane Auvin 26, 27 , Sarah Baer Sarah Baer 28 Service de Neuropédiatrie, Hôpitaux Universitaires de Strasbourg, Strasbourg, France Find articles by Sarah Baer 28 , Nadia Bahi-Buisson Nadia Bahi-Buisson 14 Université Paris Cité, INSERM, IHU Imagine – Institut des maladies génétiques, Paris, France Find articles by Nadia Bahi-Buisson 14 , Mads Bak Mads Bak 29 Department of Clinical Genetics, Copenhagen University Hospital, Rigshospitalet, Denmark Find articles by Mads Bak 29 , Magalie Barth Magalie Barth 30 Department of Medical Genetics, Angers University Hospital, Angers, France Find articles by Magalie Barth 30 , Stéphanie Baulac Stéphanie Baulac 31 Institut du Cerveau - Paris Brain Institute - ICM, Sorbonne Université, Inserm, CNRS, APHP, Hôpital de la Pitié 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Faculté de Pharmacie, Hôpitaux Universitaires Paris-Saclay, Centre de référence des Maladies Mitochondriales, Filière Filnemu, Paris, France Find articles by Pauline Gaignard 5, 63 , Benjamin Ganne Benjamin Ganne 8 GCS AURAGEN, Lyon, France 64 Laboratoire de Génétique Chromosomique, CHU de Montpellier, Montpellier, France Find articles by Benjamin Ganne 8, 64 , Aurore Garde Aurore Garde 65 Centre de Génétique, Université Bourgogne Europe, CHU Dijon Bourgogne, Centre de Référence maladies rares ‘Anomalies du Développement et syndromes malformatifs,’ FHU-TRANSLAD, Dijon, France Find articles by Aurore Garde 65 , Justine Géraud Justine Géraud 66 Neuropediatric Department, University Hospital Centre Toulouse, Toulouse, France Find articles by Justine Géraud 66 , Deepak Gill Deepak Gill 67 Kids Neuroscience Centre, Kids Research Institute, Sydney, New South Wales Australia 68 TY Nelson Department of Neurology and Neurosurgery, The Children’s Hospital at Westmead, Sydney Children’s Hospitals Network, Sydney, New South Wales Australia 69 Specialty of Child and Adolescent Health, University of Sydney, Sydney, New South Wales Australia Find articles by Deepak Gill 67, 68, 69 , Alice Goldenberg Alice Goldenberg 2 Université Rouen Normandie, Normandie Université, Inserm U1245 and CHU Rouen, Department of Genetics and Reference Center for Developmental Abnormalities, Rouen, France Find articles by Alice Goldenberg 2 , David Grabli David Grabli 21 Département de Neurologie, Assistance Publique - Hôpitaux de Paris (APHP) Sorbonne Université, Center of Reference for Rare Epilepsies, ERN EPICARE, Hôpital Pitié-Salpêtrière, Paris, France 31 Institut du Cerveau - Paris Brain Institute - ICM, Sorbonne Université, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France Find articles by David Grabli 21, 31 , Coraline Grisel Coraline Grisel 70 Service de Pédiatrie, Centre Hospitalier Intercommunal de Créteil, Créteil, France Find articles by Coraline Grisel 70 , Sophie Gueden Sophie Gueden 71 Department of Pediatric Neurology, Angers University Hospital, Angers, France Find articles by Sophie Gueden 71 , Paul Gueguen Paul Gueguen 5 Laboratoire SeqOIA, Paris, France 23 Service de Génétique, CHU de Tours, Tours, France 24 Université de Tours, INSERM, Imaging Brain and Neuropsychiatry iBraiN U1253, Tours, France Find articles by Paul Gueguen 5, 23, 24 , Anne-Marie Guerrot Anne-Marie Guerrot 2 Université Rouen Normandie, Normandie Université, Inserm U1245 and CHU Rouen, Department of Genetics and Reference Center for Developmental Abnormalities, Rouen, France Find articles by Anne-Marie Guerrot 2 , Agnès Guichet Agnès Guichet 5 Laboratoire SeqOIA, Paris, France 30 Department of Medical Genetics, Angers University Hospital, Angers, France 42 MitoLab, Unité MITOVASC, UMR CNRS 6015, INSERM U1083, SFR ICAT, University Hospital of Angers, Angers, France Find articles by Agnès Guichet 5, 30, 42 , Tobias B Haack Tobias B Haack 72 Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany Find articles by Tobias B Haack 72 , Nina Härting Nina Härting 1 Institute of Human Genetics, University Hospital Essen, University Duisburg-Essen, Essen, Germany Find articles by Nina Härting 1 , Martin Georg Häusler Martin Georg Häusler 73 Department of Pediatrics, University Hospital, Division of Neuropediatrics and Social Pediatrics, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany Find articles by Martin Georg Häusler 73 , Solveig Heide Solveig Heide 5 Laboratoire SeqOIA, Paris, France 15 Département de Génétique Médicale, Assistance Publique - Hôpitaux de Paris (APHP) Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France Find articles by Solveig Heide 5, 15 , Theresia Herget Theresia Herget 55 Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany Find articles by Theresia Herget 55 , Bénédicte Héron Bénédicte Héron 74 Département de Neurologie Pédiatrique, 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Institute of Human Genetics, University Hospital Essen, University Duisburg-Essen, Essen, Germany Find articles by Frank J Kaiser 1 , Sabine Kaya Sabine Kaya 1 Institute of Human Genetics, University Hospital Essen, University Duisburg-Essen, Essen, Germany Find articles by Sabine Kaya 1 , Chontelle King Chontelle King 77 Department of Paediatrics and Child Health, University of Otago, Wellington, New Zealand Find articles by Chontelle King 77 , Marina Konyukh Marina Konyukh 5 Laboratoire SeqOIA, Paris, France 78 Département de Génétique Médicale, Hôpital Henri Mondor, Assistance Publique des Hôpitaux de Paris, Créteil, France Find articles by Marina Konyukh 5, 78 , Florian Kraft Florian Kraft 35 Center for Human Genetics and Genomic Medicine, Medical Faculty, RWTH Aachen University Hospital, Aachen, Germany Find articles by Florian Kraft 35 , Jeremias Krause Jeremias Krause 35 Center for Human Genetics and Genomic Medicine, Medical Faculty, RWTH Aachen University Hospital, Aachen, Germany Find articles by Jeremias Krause 35 , Rémi Kirstetter Rémi Kirstetter 13 Assistance Publique - Hôpitaux de Paris (APHP), Service de Médecine Génomique des Maladies Rares, Hôpital Necker-Enfants malades, Paris, France 14 Université Paris Cité, INSERM, IHU Imagine – Institut des maladies génétiques, Paris, France Find articles by Rémi Kirstetter 13, 14 , Alma Kuechler Alma Kuechler 1 Institute of Human Genetics, University Hospital Essen, University Duisburg-Essen, Essen, Germany Find articles by Alma Kuechler 1 , Ingo Kurth Ingo Kurth 35 Center for Human Genetics and Genomic Medicine, Medical Faculty, RWTH Aachen University Hospital, Aachen, Germany Find articles by Ingo Kurth 35 , Kerstin Kutsche Kerstin Kutsche 55 Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany Find articles by Kerstin Kutsche 55 , Audrey Labalme Audrey Labalme 6 Genetics Department, Hospices Civils de Lyon, Lyon, France Find articles by Audrey Labalme 6 , Jean-Serene Laloy Jean-Serene Laloy 5 Laboratoire SeqOIA, Paris, France 13 Assistance Publique - Hôpitaux de Paris (APHP), Service de Médecine Génomique des Maladies Rares, Hôpital Necker-Enfants malades, Paris, France Find articles by Jean-Serene Laloy 5, 13 , Vincent Laugel Vincent Laugel 28 Service de Neuropédiatrie, Hôpitaux Universitaires de Strasbourg, Strasbourg, France Find articles by Vincent Laugel 28 , Floriane Le Bricquir Floriane Le Bricquir 79 Service de Pédiatrie, Nantes Université, CHU de Nantes, Service de Pédiatrie, Nantes, France Find articles by Floriane Le Bricquir 79 , Anne-Sophie Lèbre Anne-Sophie Lèbre 33 Université Reims Champagne Ardenne (URCA), UFR médecine, Reims, France 80 Laboratoire de Génétique, CHU de Reims, Reims, France 81 INSERM U1266 (Krebs team), Institute of Psychiatry and Neuroscience of Paris (IPNP),Université Paris Cité, Paris, France Find articles by Anne-Sophie Lèbre 33, 80, 81 , Marine Lebrun Marine Lebrun 8 GCS AURAGEN, Lyon, France 19 Département de Génétique, Centre Hospitalier Universitaire de Saint-Etienne, Saint-Etienne, France Find articles by Marine Lebrun 8, 19 , Eric Leguern Eric Leguern 15 Département de Génétique Médicale, Assistance Publique - Hôpitaux de Paris (APHP) Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France 31 Institut du Cerveau - Paris Brain Institute - ICM, Sorbonne Université, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France Find articles by Eric Leguern 15, 31 , Jonathan Levy Jonathan Levy 5 Laboratoire SeqOIA, Paris, France 82 Département de génétique, Assistance Publique - Hôpitaux de Paris (APHP), Hôpital Robert-Debré, Paris, France Find articles by Jonathan Levy 5, 82 , Nico Lieffering Nico Lieffering 77 Department of Paediatrics and Child Health, University of Otago, Wellington, New Zealand Find articles by Nico Lieffering 77 , Stanislas Lyonnet Stanislas Lyonnet 13 Assistance Publique - Hôpitaux de Paris (APHP), Service de Médecine Génomique des Maladies Rares, Hôpital Necker-Enfants malades, Paris, France 14 Université Paris Cité, INSERM, IHU Imagine – Institut des maladies génétiques, Paris, France Find articles by Stanislas Lyonnet 13, 14 , Kevin Lüthy Kevin Lüthy 1 Institute of Human Genetics, University Hospital Essen, University Duisburg-Essen, Essen, Germany Find articles by Kevin Lüthy 1 , Sian M W Macdonald Sian M W Macdonald 20 Department of Medicine, Epilepsy Research Centre, University of Melbourne, Austin Health, Heidelberg, Victoria Australia Find articles by Sian M W Macdonald 20 , Lamisse Mansour-Hendili Lamisse Mansour-Hendili 5 Laboratoire SeqOIA, Paris, France Find articles by Lamisse Mansour-Hendili 5 , Julien Maraval Julien Maraval 61 Centre de Référence Anomalies du Développement et Syndromes Malformatifs, Université Bourgogne Europe, CHU Dijon Bourgogne, Inserm, CTM UMR1231, équipe GAD, FHU TRANSLAD, Centre de génétique, Centre de référence Déficiences Intellectuelles de Causes Rares et Centre de référence GénoPsy, Dijon, France Find articles by Julien Maraval 61 , Iris Marquardt Iris Marquardt 83 MVZ Klinikum Oldenburg, Oldenburg, Germany Find articles by Iris Marquardt 83 , Carolin Mattausch Carolin Mattausch 1 Institute of Human Genetics, University Hospital Essen, University Duisburg-Essen, Essen, Germany Find articles by Carolin Mattausch 1 , Sandra Mercier Sandra Mercier 3 Nantes Université, CHU de Nantes, Service de Génétique Médicale, Nantes, France 4 Nantes Université, CHU de Nantes, CNRS, INSERM, l’Institut du Thorax, Nantes, France Find articles by Sandra Mercier 3, 4 , Olfa Messaoud Olfa Messaoud 25 Broad Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA USA 84 Harvard Medical School, Boston, MA USA 85 Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA USA 86 Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA Find articles by Olfa Messaoud 25, 84, 85, 86 , Godelieve Morel Godelieve Morel 87 Service de Génétique, CHU (Centre Hospitalier Universitaire) de La Réunion, Saint-Denis, France Find articles by Godelieve Morel 87 , Jérémie Mortreux Jérémie Mortreux 8 GCS AURAGEN, Lyon, France Find articles by Jérémie Mortreux 8 , Arnold Munnich Arnold Munnich 14 Université Paris Cité, INSERM, IHU Imagine – Institut des maladies génétiques, Paris, France Find articles by Arnold Munnich 14 , Rima Nabbout Rima Nabbout 14 Université Paris Cité, INSERM, IHU Imagine – Institut des maladies génétiques, Paris, France 46 Department of Pediatric Neurology, Reference Center for Rare Epilepsies, Necker Enfants Malades Hospital, Paris, France Find articles by Rima Nabbout 14, 46 , Sophie Nambot Sophie Nambot 65 Centre de Génétique, Université Bourgogne Europe, CHU Dijon Bourgogne, Centre de Référence maladies rares ‘Anomalies du Développement et syndromes malformatifs,’ FHU-TRANSLAD, Dijon, France Find articles by Sophie Nambot 65 , Vincent Navarro Vincent Navarro 21 Département de Neurologie, Assistance Publique - Hôpitaux de Paris (APHP) Sorbonne Université, Center of Reference for Rare Epilepsies, ERN EPICARE, Hôpital Pitié-Salpêtrière, Paris, France 31 Institut du Cerveau - Paris Brain Institute - ICM, Sorbonne Université, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France Find articles by Vincent Navarro 21, 31 , Ashana Neale Ashana Neale 25 Broad Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA USA Find articles by Ashana Neale 25 , Laetitia Nguyen Laetitia Nguyen 15 Département de Génétique Médicale, Assistance Publique - Hôpitaux de Paris (APHP) Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France Find articles by Laetitia Nguyen 15 , Mathilde Nizon Mathilde Nizon 3 Nantes Université, CHU de Nantes, Service de Génétique Médicale, Nantes, France Find articles by Mathilde Nizon 3 , Frédérique Nowak Frédérique Nowak 88 Health Technologies Institute, Inserm, Paris, France Find articles by Frédérique Nowak 88 , Melanie C O’Leary Melanie C O’Leary 25 Broad Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA USA Find articles by Melanie C O’Leary 25 , Sylvie Odent Sylvie Odent 58 IGDR (Institut de Génétique et Développement de Rennes)-UMR 6290, Université Rennes, CNRS, INSERM, Rennes, France 62 Service de Génétique Clinique, Centre de Référence ‘Anomalies du Développement et Syndromes Malformatifs’ de l’Inter-région Ouest, FHU GenOMedS, CHU Rennes Hôpital Sud, Rennes, France Find articles by Sylvie Odent 58, 62 , Naomi Meave Ojeda Naomi Meave Ojeda 89 Department of Neurosciences, University of California San Diego, La Jolla, CA USA 90 Rady Children’s Institute for Genomic Medicine, San Diego, CA USA Find articles by Naomi Meave Ojeda 89, 90 , Valérie Olin Valérie Olin 15 Département de Génétique Médicale, Assistance Publique - Hôpitaux de Paris (APHP) Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France Find articles by Valérie Olin 15 , Simone Olivieri Simone Olivieri 72 Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany Find articles by Simone Olivieri 72 , Katrin Õunap Katrin Õunap 91 Department of Genetics and Personalized Medicine, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia 92 Department of Clinical Genetics, Genetics and Personalized Medicine Clinic, Tartu University Hospital, Tartu, Estonia Find articles by Katrin Õunap 91, 92 , Lynn S Pais Lynn S Pais 25 Broad Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA USA Find articles by Lynn S Pais 25 , Eleni Panagiotakaki Eleni Panagiotakaki 93 Department of Clinical Epileptology, Sleep Disorders and Functional Neurology in Children, University Hospital of Lyon (HCL), Lyon, France 94 Lyon Neuroscience Research Center, Inserm U1028/CNRS UMR 5292, Lyon, France Find articles by Eleni Panagiotakaki 93, 94 , Olivier Patat Olivier Patat 47 Service de Génétique Médicale, CHU Purpan, Toulouse, France Find articles by Olivier Patat 47 , Laurence Perrin-Sabourin Laurence Perrin-Sabourin 82 Département de génétique, Assistance Publique - Hôpitaux de Paris (APHP), Hôpital Robert-Debré, Paris, France Find articles by Laurence Perrin-Sabourin 82 , Florence Petit Florence Petit 41 CHU Lille, Université Lille, ULR7364 – RADEME, Lille, France Find articles by Florence Petit 41 , Christophe Philippe Christophe Philippe 8 GCS AURAGEN, Lyon, France 95 Laboratoire de Génétique Médicale, CHR Metz-Thionville, Hôpital Mercy, Metz, France Find articles by Christophe Philippe 8, 95 , Amélie Piton Amélie Piton 8 GCS AURAGEN, Lyon, France 39 Laboratoire de Diagnostic Génétique, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, Strasbourg, France 96 Institute of Genetics and Cellular and Molecular Biology (IGBMC), INSERM-U964, CNRS-UMR7104, University of Strasbourg, Illkirch, France Find articles by Amélie Piton 8, 39, 96 , Marc Planes Marc Planes 97 Medical Genetics Department, Brest University Hospital, Brest, France Find articles by Marc Planes 97 , Céline Poirsier Céline Poirsier 56 UF de Génétique Clinique, CHU de Reims, Reims, France Find articles by Céline Poirsier 56 , Antoine Pouzet Antoine Pouzet 82 Département de génétique, Assistance Publique - Hôpitaux de Paris (APHP), Hôpital Robert-Debré, Paris, France Find articles by Antoine Pouzet 82 , Clément Prouteau Clément Prouteau 30 Department of Medical Genetics, Angers University Hospital, Angers, France 42 MitoLab, Unité MITOVASC, UMR CNRS 6015, INSERM U1083, SFR ICAT, University Hospital of Angers, Angers, France Find articles by Clément Prouteau 30, 42 , Sylvia Quéméner-Redon Sylvia Quéméner-Redon 5 Laboratoire SeqOIA, Paris, France 43 Center for Intellectual Disability Reference, Brest University Hospital, Brest, France 97 Medical Genetics Department, Brest University Hospital, Brest, France 98 University of Brest, Inserm, EFS, UMR 1078, GGB, Brest, France Find articles by Sylvia Quéméner-Redon 5, 43, 97, 98 , Mathilde Renaud Mathilde Renaud 99 Service de Génétique Clinique, CHRU Nancy, Vandoeuvre les Nancy, France Find articles by Mathilde Renaud 99 , Anne-Claire Richard Anne-Claire Richard 2 Université Rouen Normandie, Normandie Université, Inserm U1245 and CHU Rouen, Department of Genetics and Reference Center for Developmental Abnormalities, Rouen, France Find articles by Anne-Claire Richard 2 , Marlène Rio Marlène Rio 13 Assistance Publique - Hôpitaux de Paris (APHP), Service de Médecine Génomique des Maladies Rares, Hôpital Necker-Enfants malades, Paris, France 14 Université Paris Cité, INSERM, IHU Imagine – Institut des maladies génétiques, Paris, France Find articles by Marlène Rio 13, 14 , Clotilde Rivier Clotilde Rivier 100 Department of Pediatrics, Hôpital Nord-Ouest, Villefranche sur Saône, France Find articles by Clotilde Rivier 100 , Florence Robin-Renaldo Florence Robin-Renaldo 74 Département de Neurologie Pédiatrique, Assistance Publique - Hôpitaux de Paris (APHP) Sorbonne Université, Fédération Hospitalo-Universitaire I2-D2, Hôpital Armand Trousseau-La Roche Guyon, Paris, France Find articles by Florence Robin-Renaldo 74 , Paul Rollier Paul Rollier 58 IGDR (Institut de Génétique et Développement de Rennes)-UMR 6290, Université Rennes, CNRS, INSERM, Rennes, France 62 Service de Génétique Clinique, Centre de Référence ‘Anomalies du Développement et Syndromes Malformatifs’ de l’Inter-région Ouest, FHU GenOMedS, CHU Rennes Hôpital Sud, Rennes, France Find articles by Paul Rollier 58, 62 , Massimiliano Rossi Massimiliano Rossi 6 Genetics Department, Hospices Civils de Lyon, Lyon, France 94 Lyon Neuroscience Research Center, Inserm U1028/CNRS UMR 5292, Lyon, France Find articles by Massimiliano Rossi 6, 94 , Agathe Roubertie Agathe Roubertie 48 Service de Neuropédiatrie, CHU Montpellier, Montpellier, France 101 INM, INSERM U 1298, Montpellier, France Find articles by Agathe Roubertie 48, 101 , Valentin Ruault Valentin Ruault 102 Clinical Genetic Unit, Reference Center for Congenital Anomalies, CHU Montpellier, University of Montpellier, Montpellier, France Find articles by Valentin Ruault 102 , Maïlys Rupin-Mas Maïlys Rupin-Mas 71 Department of Pediatric Neurology, Angers University Hospital, Angers, France Find articles by Maïlys Rupin-Mas 71 , Pascale Saugier-Veber Pascale Saugier-Veber 2 Université Rouen Normandie, Normandie Université, Inserm U1245 and CHU Rouen, Department of Genetics and Reference Center for Developmental Abnormalities, Rouen, France 5 Laboratoire SeqOIA, Paris, France Find articles by Pascale Saugier-Veber 2, 5 , Aline Saunier Aline Saunier 95 Laboratoire de Génétique Médicale, CHR Metz-Thionville, Hôpital Mercy, Metz, France Find articles by Aline Saunier 95 , Russell Saneto Russell Saneto 103 Division of Pediatric Neurology, Neuroscience Institute, Norcliff Center for Integrative Brain Research, Seattle Children’s Hospital/University of Washington, Seattle, WA USA Find articles by Russell Saneto 103 , Elisabeth Sarrazin Elisabeth Sarrazin 104 Caribbean Reference Center for Neuromuscular Diseases, University Hospital Fort de France, Martinique, France Find articles by Elisabeth Sarrazin 104 , Catherine Sarret Catherine Sarret 105 Neurologie Pédiatrique et Génétique Médicale, CHU de Clermont-Ferrand, Clermont-Ferrand, France Find articles by Catherine Sarret 105 , Elise Schaefer Elise Schaefer 60 Service de Génétique Médicale, Institut de Génétique Médicale d’Alsace (IGMA), CHU Strasbourg, Strasbourg, France Find articles by Elise Schaefer 60 , Caroline Schluth-Bolard Caroline Schluth-Bolard 8 GCS AURAGEN, Lyon, France 39 Laboratoire de Diagnostic Génétique, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, Strasbourg, France 106 UMRS 1112, INSERM, Université de Strasbourg, Strasbourg, France Find articles by Caroline Schluth-Bolard 8, 39, 106 , Amy Schneider Amy Schneider 20 Department of Medicine, Epilepsy Research Centre, University of Melbourne, Austin Health, Heidelberg, Victoria Australia Find articles by Amy Schneider 20 , Isabell Schumann Isabell Schumann 107 Department of Medical Genetics, Centre of Medical Genetics, University and University Hospital Münster, Münster, Germany 108 Institute of Human Genetics, Leipzig University Medical Center, Leipzig, Germany Find articles by Isabell Schumann 107, 108 , Vladimir B Seplyarskiy Vladimir B Seplyarskiy 109 Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA 110 Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA Find articles by Vladimir B Seplyarskiy 109, 110 , Stephanie Spranger Stephanie Spranger 111 MVZ Humangenetik Bremen, Limbach Genetics, Bremen, Germany Find articles by Stephanie Spranger 111 , Thomas Smol Thomas Smol 5 Laboratoire SeqOIA, Paris, France 41 CHU Lille, Université Lille, ULR7364 – RADEME, Lille, France Find articles by Thomas Smol 5, 41 , Marc Sturm Marc Sturm 72 Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany Find articles by Marc Sturm 72 , Shamil R Sunyaev Shamil R Sunyaev 109 Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA 110 Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA Find articles by Shamil R Sunyaev 109, 110 , Brian Sperelakis-Beedham Brian Sperelakis-Beedham 5 Laboratoire SeqOIA, Paris, France 13 Assistance Publique - Hôpitaux de Paris (APHP), Service de Médecine Génomique des Maladies Rares, Hôpital Necker-Enfants malades, Paris, France 14 Université Paris Cité, INSERM, IHU Imagine – Institut des maladies génétiques, Paris, France Find articles by Brian Sperelakis-Beedham 5, 13, 14 , Sarah L Stenton Sarah L Stenton 25 Broad Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA USA 85 Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA USA Find articles by Sarah L Stenton 25, 85 , Friedrich Stock Friedrich Stock 1 Institute of Human Genetics, University Hospital Essen, University Duisburg-Essen, Essen, Germany Find articles by Friedrich Stock 1 , Mylène Tharreau Mylène Tharreau 112 Laboratory of Molecular Genetics of Rare Diseases, Montpellier University Hospital, Montpellier, France Find articles by Mylène Tharreau 112 , Deniz Torun Deniz Torun 113 Department of Medical Genetics, Gulhane Faculty of Medicine, University of Health Sciences, Ankara, Turkey Find articles by Deniz Torun 113 , Joseph Toulouse Joseph Toulouse 93 Department of Clinical Epileptology, Sleep Disorders and Functional Neurology in Children, University Hospital of Lyon (HCL), Lyon, France Find articles by Joseph Toulouse 93 , Harshini Thiyagarajah Harshini Thiyagarajah 20 Department of Medicine, Epilepsy Research Centre, University of Melbourne, Austin Health, Heidelberg, Victoria Australia Find articles by Harshini Thiyagarajah 20 , Stéphanie Valence Stéphanie Valence 74 Département de Neurologie Pédiatrique, Assistance Publique - Hôpitaux de Paris (APHP) Sorbonne Université, Fédération Hospitalo-Universitaire I2-D2, Hôpital Armand Trousseau-La Roche Guyon, Paris, France Find articles by Stéphanie Valence 74 , Sophie Valleix Sophie Valleix 5 Laboratoire SeqOIA, Paris, France 114 Department of Systemic and Organ Diseases, Assistance Publique - Hôpitaux de Paris (APHP), Paris City University, Genomic Medicine, Cochin Hospital, Paris, France Find articles by Sophie Valleix 5, 114 , Julien Van-Gils Julien Van-Gils 115 Service de Génétique Médicale, Centre Hospitalier Universitaire (CHU) de Bordeaux, Bordeaux, France 116 INSERM U1211, University of Bordeaux, Bordeaux, France Find articles by Julien Van-Gils 115, 116 , Laurent Villard Laurent Villard 117 Service de Génétique Médicale, AP-HM, Marseille, France 118 Inserm, MMG, U1251, Aix Marseille Université, Marseille, France Find articles by Laurent Villard 117, 118 , Dorothée Ville Dorothée Ville 119 Department of Pediatric Neurology and Reference Center for Rare Children Epilepsy and Tuberous Sclerosis, Hôpital Femme Mere Enfant, Centre Hospitalier Universitaire de Lyon, Lyon, France Find articles by Dorothée Ville 119 , Nathalie Villeneuve Nathalie Villeneuve 17 Service de Neurologie Pediatrique, AP-HM, Marseille, France Find articles by Nathalie Villeneuve 17 , Antonio Vitobello Antonio Vitobello 8 GCS AURAGEN, Lyon, France 44 Laboratoire de Génomique Médicale, Centre Neomics, FHU-TRANSLAD, Centre de Recherche Translationnelle en Médecine Moléculaire – Inserm UMR1231 équipe GAD, Université Bourgogne Europe, CHU Dijon Bourgogne, Dijon, France Find articles by Antonio Vitobello 8, 44 , Aurélie Waernessyckle Aurélie Waernessyckle 15 Département de Génétique Médicale, Assistance Publique - Hôpitaux de Paris (APHP) Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France Find articles by Aurélie Waernessyckle 15 , Jan Wagner Jan Wagner 120 Department of Neurology, Epilepsy Center Ulm, University Hospital Ulm, Ulm, Germany Find articles by Jan Wagner 120 , Yvonne Weber Yvonne Weber 121 Department Neurology, Section of Epileptology, Medical Faculty, University RWTH Aachen, Aachen, Germany Find articles by Yvonne Weber 121 , Dagmar Wieczorek Dagmar Wieczorek 122 Institute of Human Genetics, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany Find articles by Dagmar Wieczorek 122 , Tom Witkowski Tom Witkowski 20 Department of Medicine, Epilepsy Research Centre, University of Melbourne, Austin Health, Heidelberg, Victoria Australia Find articles by Tom Witkowski 20 , Manya Yadavilli Manya Yadavilli 89 Department of Neurosciences, University of California San Diego, La Jolla, CA USA 90 Rady Children’s Institute for Genomic Medicine, San Diego, CA USA Find articles by Manya Yadavilli 89, 90 , Tony Yammine Tony Yammine 80 Laboratoire de Génétique, CHU de Reims, Reims, France Find articles by Tony Yammine 80 , Khaoula Zaafrane-Khachnaoui Khaoula Zaafrane-Khachnaoui 123 Centre Hospitalier Universitaire de Nice, Inserm U1081, CNRS UMR7284, IRCAN, Université Côte d’Azur, Nice, France Find articles by Khaoula Zaafrane-Khachnaoui 123 , Maha S Zaki Maha S Zaki 124 Clinical Genetics Department, Human Genetics and Genome Research Institute, National Research Centre, Cairo, Egypt Find articles by Maha S Zaki 124 , Alban Ziegler Alban Ziegler 8 GCS AURAGEN, Lyon, France 47 Service de Génétique Médicale, CHU Purpan, Toulouse, France Find articles by Alban Ziegler 8, 47 , Nuria C Bramswig Nuria C Bramswig 107 Department of Medical Genetics, Centre of Medical Genetics, University and University Hospital Münster, Münster, Germany Find articles by Nuria C Bramswig 107 , Alban Lermine Alban Lermine 5 Laboratoire SeqOIA, Paris, France Find articles by Alban Lermine 5 , Gael Nicolas Gael Nicolas 2 Université Rouen Normandie, Normandie Université, Inserm U1245 and CHU Rouen, Department of Genetics and Reference Center for Developmental Abnormalities, Rouen, France 5 Laboratoire SeqOIA, Paris, France Find articles by Gael Nicolas 2, 5 , Joseph G Gleeson Joseph G Gleeson 89 Department of Neurosciences, University of California San Diego, La Jolla, CA USA 90 Rady Children’s Institute for Genomic Medicine, San Diego, CA USA Find articles by Joseph G Gleeson 89, 90 , Lynette G Sadleir Lynette G Sadleir 77 Department of Paediatrics and Child Health, University of Otago, Wellington, New Zealand Find articles by Lynette G Sadleir 77 , Michael S Hildebrand Michael S Hildebrand 20 Department of Medicine, Epilepsy Research Centre, University of Melbourne, Austin Health, Heidelberg, Victoria Australia 125 Neuroscience Group, Murdoch Children’s Research Institute, Parkville, Victoria Australia Find articles by Michael S Hildebrand 20, 125 , Ingrid E Scheffer Ingrid E Scheffer 20 Department of Medicine, Epilepsy Research Centre, University of Melbourne, Austin Health, Heidelberg, Victoria Australia 126 Department of Paediatrics, University of Melbourne, Royal Children’s Hospital, Melbourne, Victoria Australia 127 Florey Institute and Murdoch Children’s Research Institute, Melbourne, Victoria Australia Find articles by Ingrid E Scheffer 20, 126, 127 , Nicola Whiffin Nicola Whiffin 25 Broad Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA USA 128 Big Data Institute, University of Oxford, Oxford, UK 129 Centre for Human Genetics, University of Oxford, Oxford, UK Find articles by Nicola Whiffin 25, 128, 129 , Anne O’Donnell-Luria Anne O’Donnell-Luria 25 Broad Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA USA 84 Harvard Medical School, Boston, MA USA 85 Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA USA 86 Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA Find articles by Anne O’Donnell-Luria 25, 84, 85, 86 , Heather C Mefford Heather C Mefford 11 Center for Pediatric Neurological Disease Research, St. Jude Children’s Research Hospital, Memphis, TN USA Find articles by Heather C Mefford 11 , Pierre Blanc Pierre Blanc 5 Laboratoire SeqOIA, Paris, France Find articles by Pierre Blanc 5 , Julien Thevenon Julien Thevenon 8 GCS AURAGEN, Lyon, France 45 Service de Génétique, Génomique et Procréation, CHU Grenoble Alpes, Grenoble, France 130 Institut for Advanced Biosciences, Université Grenoble Alpes, INSERM U 1209, CNRS UMR 5309, Grenoble, France Find articles by Julien Thevenon 8, 45, 130 , Camille Charbonnier Camille Charbonnier 131 Department of Biostatistics and Reference Center for Developmental Abnormalities, Université Rouen Normandie, Normandie Université, Inserm U1245 and CHU Rouen, Rouen, France Find articles by Camille Charbonnier 131 , Clément Charenton Clément Charenton 9 CNRS, Inserm, Université de Strasbourg, IGBMC UMR 7104- UMR-S 1258, Illkirch, France 10 Department of Integrated Structural Biology, IGBMC, Illkirch, France Find articles by Clément Charenton 9, 10 , Christel Depienne Christel Depienne 1 Institute of Human Genetics, University Hospital Essen, University Duisburg-Essen, Essen, Germany 5 Laboratoire SeqOIA, Paris, France Find articles by Christel Depienne 1, 5, ✉ , Gaetan Lesca Gaetan Lesca 6 Genetics Department, Hospices Civils de Lyon, Lyon, France 7 Pathophysiology and Genetics of Neuron and Muscle (PNMG), UCBL, CNRS UMR5261 - INSERM, Lyon, France 8 GCS AURAGEN, Lyon, France Find articles by Gaetan Lesca 6, 7, 8, ✉ , Caroline Nava Caroline Nava 5 Laboratoire SeqOIA, Paris, France 15 Département de Génétique Médicale, Assistance Publique - Hôpitaux de Paris (APHP) Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France 31 Institut du Cerveau - Paris Brain Institute - ICM, Sorbonne Université, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France Find articles by Caroline Nava 5, 15, 31, ✉ Author information Article notes Copyright and License information 1 Institute of Human Genetics, University Hospital Essen, University Duisburg-Essen, Essen, Germany 2 Université Rouen Normandie, Normandie Université, Inserm U1245 and CHU Rouen, Department of Genetics and Reference Center for Developmental Abnormalities, Rouen, France 3 Nantes Université, CHU de Nantes, Service de Génétique Médicale, Nantes, France 4 Nantes Université, CHU de Nantes, CNRS, INSERM, l’Institut du Thorax, Nantes, France 5 Laboratoire SeqOIA, Paris, France 6 Genetics Department, Hospices Civils de Lyon, Lyon, France 7 Pathophysiology and Genetics of Neuron and Muscle (PNMG), UCBL, CNRS UMR5261 - INSERM, Lyon, France 8 GCS AURAGEN, Lyon, France 9 CNRS, Inserm, Université de Strasbourg, IGBMC UMR 7104- UMR-S 1258, Illkirch, France 10 Department of Integrated Structural Biology, IGBMC, Illkirch, France 11 Center for Pediatric Neurological Disease Research, St. Jude Children’s Research Hospital, Memphis, TN USA 12 Graduate School of Biomedical Sciences, St. Jude Children’s Research Hospital, Memphis, Memphis, TN USA 13 Assistance Publique - Hôpitaux de Paris (APHP), Service de Médecine Génomique des Maladies Rares, Hôpital Necker-Enfants malades, Paris, France 14 Université Paris Cité, INSERM, IHU Imagine – Institut des maladies génétiques, Paris, France 15 Département de Génétique Médicale, Assistance Publique - Hôpitaux de Paris (APHP) Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France 16 Centre de Référence Déficiences Intellectuelles de Causes Rares, Paris, France 17 Service de Neurologie Pediatrique, AP-HM, Marseille, France 18 Aix Marseille Université, Inserm, INMED, Marseille, France 19 Département de Génétique, Centre Hospitalier Universitaire de Saint-Etienne, Saint-Etienne, France 20 Department of Medicine, Epilepsy Research Centre, University of Melbourne, Austin Health, Heidelberg, Victoria Australia 21 Département de Neurologie, Assistance Publique - Hôpitaux de Paris (APHP) Sorbonne Université, Center of Reference for Rare Epilepsies, ERN EPICARE, Hôpital Pitié-Salpêtrière, Paris, France 22 Department of Surgery and Anatomy, Ribeirão Preto Medical School, University of São Paulo, Ribeirao Preto, Brazil 23 Service de Génétique, CHU de Tours, Tours, France 24 Université de Tours, INSERM, Imaging Brain and Neuropsychiatry iBraiN U1253, Tours, France 25 Broad Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA USA 26 Département de Neuropédiatrie, Assistance Publique - Hôpitaux de Paris (APHP), Hôpital Robert-Debré, Paris, France 27 Université Paris Cité, INSERM NeuroDiderot, Paris, France 28 Service de Neuropédiatrie, Hôpitaux Universitaires de Strasbourg, Strasbourg, France 29 Department of Clinical Genetics, Copenhagen University Hospital, Rigshospitalet, Denmark 30 Department of Medical Genetics, Angers University Hospital, Angers, France 31 Institut du Cerveau - Paris Brain Institute - ICM, Sorbonne Université, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France 32 Service de Pédiatrie, CHU Reims, Reims, France 33 Université Reims Champagne Ardenne (URCA), UFR médecine, Reims, France 34 CReSTIC/EA 3804, URCA, Reims, France 35 Center for Human Genetics and Genomic Medicine, Medical Faculty, RWTH Aachen University Hospital, Aachen, Germany 36 Genetics and Gene Regulation Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria Australia 37 Department of Medical Biology, The University of Melbourne, Parkville, Victoria Australia 38 Service de Génétique Médicale, Centre Hospitalier Intercommunal de Créteil (CHIC), Créteil, France 39 Laboratoire de Diagnostic Génétique, Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg, Strasbourg, France 40 Danish Epilepsy center, Dianalund, Denmark 41 CHU Lille, Université Lille, ULR7364 – RADEME, Lille, France 42 MitoLab, Unité MITOVASC, UMR CNRS 6015, INSERM U1083, SFR ICAT, University Hospital of Angers, Angers, France 43 Center for Intellectual Disability Reference, Brest University Hospital, Brest, France 44 Laboratoire de Génomique Médicale, Centre Neomics, FHU-TRANSLAD, Centre de Recherche Translationnelle en Médecine Moléculaire – Inserm UMR1231 équipe GAD, Université Bourgogne Europe, CHU Dijon Bourgogne, Dijon, France 45 Service de Génétique, Génomique et Procréation, CHU Grenoble Alpes, Grenoble, France 46 Department of Pediatric Neurology, Reference Center for Rare Epilepsies, Necker Enfants Malades Hospital, Paris, France 47 Service de Génétique Médicale, CHU Purpan, Toulouse, France 48 Service de Neuropédiatrie, CHU Montpellier, Montpellier, France 49 Laboratoire de Génétique Moléculaire, CHU de Clermont-Ferrand, Clermont-Ferrand, France 50 Service de Pediatrie, CHR Metz-Thionville, Hôpital Mercy, Metz, France 51 Department of Pediatrics, Michigan Medicine, University of Michigan, Ann Arbor, MI USA 52 Service de Pédiatrie, Consultation de Neurologie Pédiatrique GHH Jacques Monod, Le Havre, France 53 Service de Génétique Clinique et Oncogénétique, CLAD Nord-ouest, CHU Amiens-Picardie, Amiens, France 54 CHIMERE - INSERM UA-21, Université Picardie Jules Verne, Amiens, France 55 Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany 56 UF de Génétique Clinique, CHU de Reims, Reims, France 57 Laboratoire de Génétique Moléculaire et Génomique, FHU GenOMedS, CHU Rennes, Rennes, France 58 IGDR (Institut de Génétique et Développement de Rennes)-UMR 6290, Université Rennes, CNRS, INSERM, Rennes, France 59 Département de Pathologie, Institut Universitaire du Cancer Toulouse - Oncopole, Toulouse, France 60 Service de Génétique Médicale, Institut de Génétique Médicale d’Alsace (IGMA), CHU Strasbourg, Strasbourg, France 61 Centre de Référence Anomalies du Développement et Syndromes Malformatifs, Université Bourgogne Europe, CHU Dijon Bourgogne, Inserm, CTM UMR1231, équipe GAD, FHU TRANSLAD, Centre de génétique, Centre de référence Déficiences Intellectuelles de Causes Rares et Centre de référence GénoPsy, Dijon, France 62 Service de Génétique Clinique, Centre de Référence ‘Anomalies du Développement et Syndromes Malformatifs’ de l’Inter-région Ouest, FHU GenOMedS, CHU Rennes Hôpital Sud, Rennes, France 63 Laboratoire de Biochimie Site Bicêtre, Faculté de Pharmacie, Hôpitaux Universitaires Paris-Saclay, Centre de référence des Maladies Mitochondriales, Filière Filnemu, Paris, France 64 Laboratoire de Génétique Chromosomique, CHU de Montpellier, Montpellier, France 65 Centre de Génétique, Université Bourgogne Europe, CHU Dijon Bourgogne, Centre de Référence maladies rares ‘Anomalies du Développement et syndromes malformatifs,’ FHU-TRANSLAD, Dijon, France 66 Neuropediatric Department, University Hospital Centre Toulouse, Toulouse, France 67 Kids Neuroscience Centre, Kids Research Institute, Sydney, New South Wales Australia 68 TY Nelson Department of Neurology and Neurosurgery, The Children’s Hospital at Westmead, Sydney Children’s Hospitals Network, Sydney, New South Wales Australia 69 Specialty of Child and Adolescent Health, University of Sydney, Sydney, New South Wales Australia 70 Service de Pédiatrie, Centre Hospitalier Intercommunal de Créteil, Créteil, France 71 Department of Pediatric Neurology, Angers University Hospital, Angers, France 72 Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany 73 Department of Pediatrics, University Hospital, Division of Neuropediatrics and Social Pediatrics, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany 74 Département de Neurologie Pédiatrique, Assistance Publique - Hôpitaux de Paris (APHP) Sorbonne Université, Fédération Hospitalo-Universitaire I2-D2, Hôpital Armand Trousseau-La Roche Guyon, Paris, France 75 Service de Neuropédiatrie, Hôpital Jean-Verdier, Bondy, France 76 Consultation de génétique, CCMR ANDDI rare, Centre Hospitalier d’Alençon, Alençon, France 77 Department of Paediatrics and Child Health, University of Otago, Wellington, New Zealand 78 Département de Génétique Médicale, Hôpital Henri Mondor, Assistance Publique des Hôpitaux de Paris, Créteil, France 79 Service de Pédiatrie, Nantes Université, CHU de Nantes, Service de Pédiatrie, Nantes, France 80 Laboratoire de Génétique, CHU de Reims, Reims, France 81 INSERM U1266 (Krebs team), Institute of Psychiatry and Neuroscience of Paris (IPNP),Université Paris Cité, Paris, France 82 Département de génétique, Assistance Publique - Hôpitaux de Paris (APHP), Hôpital Robert-Debré, Paris, France 83 MVZ Klinikum Oldenburg, Oldenburg, Germany 84 Harvard Medical School, Boston, MA USA 85 Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA USA 86 Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA 87 Service de Génétique, CHU (Centre Hospitalier Universitaire) de La Réunion, Saint-Denis, France 88 Health Technologies Institute, Inserm, Paris, France 89 Department of Neurosciences, University of California San Diego, La Jolla, CA USA 90 Rady Children’s Institute for Genomic Medicine, San Diego, CA USA 91 Department of Genetics and Personalized Medicine, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia 92 Department of Clinical Genetics, Genetics and Personalized Medicine Clinic, Tartu University Hospital, Tartu, Estonia 93 Department of Clinical Epileptology, Sleep Disorders and Functional Neurology in Children, University Hospital of Lyon (HCL), Lyon, France 94 Lyon Neuroscience Research Center, Inserm U1028/CNRS UMR 5292, Lyon, France 95 Laboratoire de Génétique Médicale, CHR Metz-Thionville, Hôpital Mercy, Metz, France 96 Institute of Genetics and Cellular and Molecular Biology (IGBMC), INSERM-U964, CNRS-UMR7104, University of Strasbourg, Illkirch, France 97 Medical Genetics Department, Brest University Hospital, Brest, France 98 University of Brest, Inserm, EFS, UMR 1078, GGB, Brest, France 99 Service de Génétique Clinique, CHRU Nancy, Vandoeuvre les Nancy, France 100 Department of Pediatrics, Hôpital Nord-Ouest, Villefranche sur Saône, France 101 INM, INSERM U 1298, Montpellier, France 102 Clinical Genetic Unit, Reference Center for Congenital Anomalies, CHU Montpellier, University of Montpellier, Montpellier, France 103 Division of Pediatric Neurology, Neuroscience Institute, Norcliff Center for Integrative Brain Research, Seattle Children’s Hospital/University of Washington, Seattle, WA USA 104 Caribbean Reference Center for Neuromuscular Diseases, University Hospital Fort de France, Martinique, France 105 Neurologie Pédiatrique et Génétique Médicale, CHU de Clermont-Ferrand, Clermont-Ferrand, France 106 UMRS 1112, INSERM, Université de Strasbourg, Strasbourg, France 107 Department of Medical Genetics, Centre of Medical Genetics, University and University Hospital Münster, Münster, Germany 108 Institute of Human Genetics, Leipzig University Medical Center, Leipzig, Germany 109 Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA 110 Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA 111 MVZ Humangenetik Bremen, Limbach Genetics, Bremen, Germany 112 Laboratory of Molecular Genetics of Rare Diseases, Montpellier University Hospital, Montpellier, France 113 Department of Medical Genetics, Gulhane Faculty of Medicine, University of Health Sciences, Ankara, Turkey 114 Department of Systemic and Organ Diseases, Assistance Publique - Hôpitaux de Paris (APHP), Paris City University, Genomic Medicine, Cochin Hospital, Paris, France 115 Service de Génétique Médicale, Centre Hospitalier Universitaire (CHU) de Bordeaux, Bordeaux, France 116 INSERM U1211, University of Bordeaux, Bordeaux, France 117 Service de Génétique Médicale, AP-HM, Marseille, France 118 Inserm, MMG, U1251, Aix Marseille Université, Marseille, France 119 Department of Pediatric Neurology and Reference Center for Rare Children Epilepsy and Tuberous Sclerosis, Hôpital Femme Mere Enfant, Centre Hospitalier Universitaire de Lyon, Lyon, France 120 Department of Neurology, Epilepsy Center Ulm, University Hospital Ulm, Ulm, Germany 121 Department Neurology, Section of Epileptology, Medical Faculty, University RWTH Aachen, Aachen, Germany 122 Institute of Human Genetics, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany 123 Centre Hospitalier Universitaire de Nice, Inserm U1081, CNRS UMR7284, IRCAN, Université Côte d’Azur, Nice, France 124 Clinical Genetics Department, Human Genetics and Genome Research Institute, National Research Centre, Cairo, Egypt 125 Neuroscience Group, Murdoch Children’s Research Institute, Parkville, Victoria Australia 126 Department of Paediatrics, University of Melbourne, Royal Children’s Hospital, Melbourne, Victoria Australia 127 Florey Institute and Murdoch Children’s Research Institute, Melbourne, Victoria Australia 128 Big Data Institute, University of Oxford, Oxford, UK 129 Centre for Human Genetics, University of Oxford, Oxford, UK 130 Institut for Advanced Biosciences, Université Grenoble Alpes, INSERM U 1209, CNRS UMR 5309, Grenoble, France 131 Department of Biostatistics and Reference Center for Developmental Abnormalities, Université Rouen Normandie, Normandie Université, Inserm U1245 and CHU Rouen, Rouen, France ✉ Corresponding author. # Contributed equally. Received 2025 Sep 2; Accepted 2026 Feb 12; Issue date 2026. © The Author(s) 2026, modified publication 2026 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . PMC Copyright notice PMCID: PMC13083260  PMID: 41912934 Previous version available: This article is based on a previously available preprint posted on medRxiv on September 4, 2025: " Systematic analysis of snRNA genes reveals frequent RNU2–2 variants in dominant and recessive developmental and epileptic encephalopathies ". Abstract Small nuclear RNAs (snRNAs) are essential components of the spliceosome. De novo variants in snRNA genes RNU4-2 (ReNU syndrome), RNU5B-1 and RNU2-2 have been linked to dominant neurodevelopmental disorders (NDDs), revealing a large unexpected contribution of noncoding RNA genes to genetic diseases. Here, through international collaborations, we analyze systematically 200 potentially functional snRNA genes in a French cohort of 34,329 people with rare disorders. We report RNU2-2 variants in 141 individuals, including 35 with recurrent dominant pathogenic variants and 91 affected members from 73 families with biallelic variants. Recessive RNU2-2 NDD is at least twice as frequent as the dominant form and often involves a de novo variant in trans with an inherited allele, consistent with the high mutability of snRNA genes. Dominant and recessive RNU2-2 NDDs share overlapping clinical features, with frequent epilepsy. Blood transcriptomics and DNA methylation analyses revealed subtle, variant-specific effects on splicing and episignatures. Our results support a gradient-of-impact model bridging dominant and recessive inheritance, and establish RNU2-2 variants as a principal contributor to NDDs, nearly as prevalent as ReNU syndrome. Subject terms: Neurodevelopmental disorders, Genetics research, Transcriptomics, Epilepsy Analyses of snRNA genes in a French cohort of people with rare disorders, with validation through international collaboration, identify monoallelic and biallelic variants in RNU2-2 as frequent causes of neurodevelopmental disorders with epilepsy. Main Small nuclear RNAs (snRNAs) are noncoding RNAs essential for RNA processing and splicing of premessenger RNAs (mRNA). The spliceosome—a dynamic ribonucleoprotein (RNP) complex that catalyzes splicing—depends on five uridine-rich snRNAs for its assembly and function. In mammals, two spliceosome types operate according to intron class: the major spliceosome excises >99% of introns with GU–AG splice sites (U2-type) using snRNAs U1, U2, U4, U5 and U6, whereas the minor spliceosome removes rare introns with AU–AC or GU–AG splice sites (U12-type) with snRNAs U11, U12, U4atac and U6atac, sharing U5 with the major complex 1 – 3 . Although minor spliceosome snRNA genes are single-copy, major spliceosome genes have several functional copies 4 . Human genomes contain at least two U4, five U5 and five U6 functional genes. U1 is expressed from at least four identical copies ( RNU1-1 to RNU1-4 ) on chromosome (chr) 1p36.13, plus more than 30 variant U1 ( RNVU1 ) genes on 1q21.1–q23.3 5 – 7 . Most U2 genes (‘ RNU2-1’ ) are organized in large tandem arrays on chr17q21.31, with copy numbers ranging from 6 to >80 per chromosome 8 . Biallelic pathogenic variants were first described in RNU4ATAC and cause phenotypically variable developmental disorders: microcephalic osteodysplastic primordial dwarfism type I, Taybi–Linder, Lowry–Wood or Roifman syndromes 9 – 13 . Recessive variants in RNU12 may lead to craniosynostosis, anal anomalies and skin lesions and/or spinocerebellar ataxia 14 , 15 although definitive evidence is missing. In 2024, de novo variants in the major spliceosome gene RNU4-2 were shown to cause ReNU syndrome (OMIM 620851 )—one of the most common known NDDs 16 , 17 . These dominant variants are located within an 18-bp critical region spanning the T loop and part of stem III, facing the U6 ACAGAGA box that enables 5′ splice site (5′SS) recognition 16 , 18 . RNU4-2 pathogenic variants are associated with mild, but specific, widespread splicing and methylation abnormalities in blood cells of affected people, the degree of which correlates with disease severity 16 , 18 . De novo variants in RNU5B-1 (and possibly RNU5A-1 ) clustering in the U5 5′ loop I lead to NDD with variable malformations 18 , 19 . The recent discoveries of biallelic variants in other regions of RNU4-2 in NDD patients and heterozygous variants in RNU4-2 (and genes expressing U6) in families with retinitis pigmentosa 20 has expanded its mutational spectrum and added complexity to variant interpretation 21 , 22 . Identifying variants in snRNA genes is complicated by sequence redundancy and incomplete annotations, especially when distinguishing functional genes from pseudogenes. De novo pathogenic variants in RNU2-2P , initially annotated as a pseudogene, were linked recently to a dominant NDD with epilepsy 19 , 23 . Because RNU2-2P is expressed at levels similar to RNU2-1 , it was reclassified as RNU2-2 19 , 23 , 24 . However, unlike RNU4-2 , no splicing anomalies were detected in blood transcriptomes of patients with RNU2-2 variants 23 . Strikingly, active snRNA (and tRNA) genes are hypermutable, with up to tenfold more de novo variants than other genomic regions—a characteristic that may help distinguish functional genes from pseudogenes 25 . In this study, we build on our previous work encompassing 50 Human Genome Organization (HUGO) Gene Nomenclature Committee (HGNC)-approved snRNA genes 18 , extending it to investigate variants in possibly functional snRNA genes systematically in a large French cohort with rare diseases. This led us to report a highly prevalent recessive NDD with epilepsy caused by biallelic variants in RNU2-2 that was validated through international collaborations. Results Identification of potentially functional spliceosomal snRNA genes To distinguish functional snRNA genes from pseudogenes, we performed an in silico analysis of all annotated snRNA genes ( Methods ). The Ensembl database contains 2,094 snRNA genes in the hg38 reference genome: 1,741 are spliceosomal snRNA genes, and the remainder have other functions or are not on identifiable chromosomes. We prioritized genes overlapping proximal cis -regulatory elements (cCREs; from ENCODE 26 ), reported as hypermutable 25 , or HGNC-approved 18 . This analysis yielded 200 potentially functional snRNA genes, including 147 ‘pseudogenes’ and RNU2-2 (Fig. 1a and Supplementary Table 1 ). The breakdown of snRNA types aligns with previous observations: minor spliceosome snRNAs exist mainly as single copies (range 1–3), whereas major spliceosome snRNAs are present in many more copies (range 11–117). U6 has the highest number of copies and U5, shared by both complexes, is intermediate ( n = 9; Fig. 1b,c ). Fig. 1. Systematic in silico analysis of possible functional snRNAs. Open in a new tab a , Filtering strategy used to retain genes expressing possible functional snRNAs. b , c , Number and distribution of annotated spliceosomal snRNAs before ( b ) and after ( c ) filtering. d , Expression of snRNAs in the human brain from ENCODE small RNA-seq data. Top: all mapped reads (including multimapped). Bottom: uniquely mapped reads. Expression is shown as log 10 of the maximum normalized RNA-seq signal. Colors indicate the proportion of the snRNA length covered by mapped reads: 100% (purple), 75–99% (dark blue), 50–74% (green), <50% (yellow). Genes in red correspond to annotated pseudogenes. Asterisk, genes expressing U2-1 copies within the chr1 cluster lack HGNC approved symbols and were numbered in ascending order of their genomic coordinates. NA, not applicable. In parallel, we reanalyzed an ENCODE brain small RNA sequencing (RNA-seq) dataset 27 . We performed two analyses: one using all mapped reads, including multimapped, and another restricted to uniquely mapped reads. The first captures expression from all identical copies, whereas the second reflects only uniquely assignable snRNAs. In total, 87 putatively functional snRNAs, including 39 annotated as pseudogenes, were detectable in the human brain (Fig. 1d ), raising the possibility that more NDDs are driven by snRNA variants. Analysis of de novo and biallelic variants in putatively functional snRNA genes in the Plan France Médecine Génomique 2025 cohort We next analyzed variants in the 200 potentially functional snRNA genes in the Plan France Médecine Génomique 2025 (PFMG) cohort 28 , which comprised short-read genome data from 34,329 patients with rare disorders (22,775 with NDDs). We focused on de novo and biallelic variants, hypothesizing roles in dominant or recessive monogenic disorders. After accounting for artefacts from short-read mapping and excluding low-quality variants ( Supplementary Notes ), 843 high-confidence variants (330 de novo, 551 biallelic) in 66 genes were identified in 616 patients (Extended Data Figs. 1 – 3 and Supplementary Figs. 1 – 4 ). Extended Data Fig. 1. Details of the PFMG cohort and variant filtering strategy. Open in a new tab a , Number of patients and de novo or biallelic variants in snRNA genes the PFMG cohort. Supplementary Fig. 4 shows the same data in the SeqOIA and Auragen subcohorts separately. b , Number of patients with de novo or biallelic RNU2-2 variants in the PFMG and other cohorts. Details of other cohorts appear in Supplementary Table 7 . Note that the variant numbers include multiple occurrences of the same variants. The number of distinct (‘unique’) variants in RNU2-2 appear in brackets. Variants in cis with de novo or biallelic variants are not counted. Extended Data Fig. 3. Variant allele fraction (VAF) distributions of snRNA variants in the aggregated PFMG cohort. Open in a new tab a , VAF distribution of de novo and biallelic variants in snRNA genes with coverage ≥ 10. b , Detailed VAF distribution for snRNA genes harboring variants in the PFMG cohort. Supplementary Fig. 2 shows the same data in the SeqOIA and Auragen subcohorts separately. c , gnomAD allele count (AC) distribution of de novo and biallelic variants in snRNA genes (with coverage ≥ 10) in the PFMG cohort. Supplementary Fig. 3 shows the same data in the SeqOIA and Auragen subcohorts separately. To identify possible new disease gene associations, we divided the cohort into solved ( n = 9,180) and unsolved ( n = 20,735) cases. Pathogenic (P) and likely pathogenic (LP) variants in disease-associated snRNA genes ( RNU4ATAC , RNU4-2 , RNU5B-1 , RNU2-2 ) were curated manually and reassigned to the unsolved category, as they would have been before gene–disease association. We assessed both de novo and biallelic variants in the 66 genes focusing on rare variants (allele counts <100 in gnomAD v.3; Extended Data Fig. 3c ). De novo variants were enriched significantly in RNU4-2 and RNU2-2 when comparing solved and unsolved cases (or gene-solved cases). For biallelic variants, RNU2-2 showed significant enrichment across combined cohorts ( P = 6.9 × 10 −3 , two-sided Fisher’s test), whereas RNU4ATAC reached significance only in the SeqOIA subcohort (Fig. 2a,b , Extended Data Fig. 4 , Supplementary Fig. 5 and Supplementary Tables 2 and 3 ). We then compared NDD ( n = 22,775) and non-NDD ( n = 11,554) cases. In the entire PFMG cohort, de novo variants were enriched significantly in RNU4-2 and RNU2-2 , whereas biallelic variants were enriched only in RNU2-2 ( P = 8.9 × 10 −5 , two-sided Fisher’s test) (Fig. 2c,d , Extended Data Fig. 4 , Supplementary Fig. 6 and Supplementary Tables 4 and 5 ). These results indicate that RNU2-2 variants probably contribute to both dominant and recessive NDDs, similar to RNU4-2 21 , 22 . Fig. 2. Identification of potential new snRNA gene–disease associations in the PFMG cohort. Open in a new tab a , b , The cohort was divided into solved ( n = 9,180) and unsolved ( n = 20,735) cases for discovery analyses, with cases solved by variants in snRNAs with known disease association merged into the unsolved group. We compared the proportion of cases with rare variants (gnomAD allele count <100) between solved and unsolved groups for rare de novo variants ( a ) and rare biallelic variants ( b ). c , d , The cohort was divided into NDD ( n = 22,775) and non-NDD ( n = 11,554) cases for discovery analyses. We compared the proportion of cases with rare variants (gnomAD allele count <100) between NDD and non-NDD groups for rare de novo ( c ) and rare biallelic ( d ) variants. Two-sided Fisher’s tests were used to test statistical enrichment in unsolved versus solved cases for genes with at least ten patients carrying variants ( a , b ) and in NDD versus non-NDD cases for genes with at least nine such patients ( c , d ) (dashed gray line: minimum number of patients needed to reach statistical significance in the cohort). The number of patients per group and gene are shown in Supplementary Tables 2 and 4 . Correction for multiple comparisons using Bonferroni was performed independently for unsolved versus solved cases and NDD versus non-NDD cases. Adjusted P values are shown above the bars. * P = 0.01–0.05; ** P = 0.001–0.01; **** P < 0.0001. Extended Data Fig. 4. Identification of potential novel snRNA gene-disease associations in the PFMG cohort. Open in a new tab a-d , The cohort was divided into solved ( n = 9,180) and unsolved ( n = 20,735) cases for discovery analyses, with cases solved by variants in snRNAs with known disease association merged into the unsolved group. We compared the proportion of cases with rare variants (gnomAD allele count < 100) between solved and unsolved groups. a , b , Variant allele fraction (VAF) distribution for rare de novo variants ( a ) and rare biallelic variants ( b ). c , d , Odds ratio (OR; dot) and OR 95% confidence interval (error bars) of unsolved versus solved cases for genes in which at least 10 patients had de novo ( c ) or biallelic ( d ) variants (minimum number needed to reach statistical significance in the cohort). e - h , The cohort was divided into NDD ( n = 22,775) and non-NDD ( n = 11,554) cases for discovery analyses. We compared the proportion of cases with rare variants (gnomAD allele count < 100) between NDD and non-NDD groups. e , f , Variant allele fraction (VAF) distribution for rare de novo variants ( e ) and rare biallelic variants ( f ). g , h , Odds ratio (OR; dot) and OR 95% confidence interval (error bars) of NDD versus non-NDD cases for genes in which at least 9 patients had de novo ( g ) or biallelic ( h ) variants (minimum number needed to reach statistical significance in the cohort). Two-sided Fisher’s test P -values are shown in Fig. 2 . Supplementary Figs. 5 , 6 respectively show the same analyses performed separately in the SeqOIA and Auragen subcohorts. RNU2-2 variants as a cause of dominant and recessive disorders To refine the search for pathogenic variants, we reanalyzed the PFMG cohort restricting only the homozygote frequency (<3 in gnomAD v.3, <5 in internal databases), then applied filters on allele frequencies in All of Us (AoU) database (AC < 50 for de novo and <200 for biallelic variants) (Supplementary Fig. 7 ). Using these criteria, 42 unrelated patients had rare de novo RNU2-2 variants (Supplementary Table 6 ). Twenty-one unrelated patients and one monozygotic twin harbored previously reported pathogenic alleles: n.4G>A ( n = 11) and n.35A>G ( n = 11 including the twin). One patient with n.35A>G was mosaic, and deep-targeted sequencing (>2,000×) confirmed mutant allele fractions of 12% (678 of 5,218 reads) in blood, 20% (1,181 of 5,659 reads) in urine and 25% (574 of 2,267 reads) in buccal cells. In addition, n.4G>A was observed in a singleton. The remaining 21 patients had other de novo variants (Fig. 3 and Supplementary Notes ). In 7 of 21 individuals (with n.5C>A, n.6T>C, n.7_8insA, n.31G>A, n.37T>G ( n = 2) and n.40C>T), further examination revealed a second rare variant that was always in trans , suggesting recessive inheritance. In contrast, a single patient with n.4G>A had a second variant in trans (n.80A>G; Supplementary Table 6 ). In addition to these seven cases, 45 probands had rare biallelic variants in the PFMG cohort. Fig. 3. Overview of RNU2-2 variants identified in this study. Open in a new tab a , Two-dimensional predicted structure of U2-2 snRNA showing structural and functional domains. Arrowheads indicate point variants identified in this study. Variants are colored according to their inheritance. Dark blue: de novo, dominant (n.4G>A or n.35A>G); light blue: de novo other (VUS); orange: compound heterozygous, recessive; purple: homozygous, recessive. The numbers in black represent the count of patients with each variant, for variants identified in more than one family. Other variant types are shown with dotted (deletions), dashed (duplications) or dotted-dashed (indels) lines. Asterisk, deletion encompassing the 3′ box. The nucleotide differences between RNU2-2 and RNU2-1 are shown using IUPAC codes. Green numbers refer to the numbering of U2-2 nucleotides (nt). Ψ, pseudouridine; m, 2′-O-methyl residues; m6, N6-methyladenosine; 2,2,7 m3Gppp, 2,2,7-trimethylguanosine cap. Green-shaded regions: functional domains of U2 involved in spliceosomal activity: U2/U6 helix II (nt 1–13); BSL (nt 25–45) and U2/BS branch helix (nt 32–44). Gray-shaded region: Sm site. b , Locations of variants on the RNU2-2 gene, with different domains of the snRNA highlighted on the schematic, extending to the 3′ box (regulatory region). Note the clustering of variants in the U2/U6 helix, BSL regions and Sm site (gray-shaded), as well as preferential associations of compound heterozygous variants. Variants are ordered by inheritance mode and by the position of the first variant on the snRNA. Variant coloring as in a . To expand the RNU2-2 variant spectrum, we reanalyzed available genome data and/or performed targeted sequencing of RNU2-2 in 5,456 people with NDDs and identified additional cases with RNU2-2 variants through seqr 29 (Supplementary Table 7 ). This large international collaborative effort revealed 34 additional unrelated patients: 13 had monoallelic variants, including 9 patients with n.4G>A (8 de novo and 1 nonmaternal), 3 n.35A>G variants (2 de novo) and 1 with de novo n.38A>G; 21 families had biallelic variants (Extended Data Fig. 1b ). Combined with the PFMG cohort, we report a total of 141 patients from 122 unrelated families carrying 96 distinct point variants in RNU2-2 that we classified using American College of Medical Genetics and Genomics criteria (Supplementary Table 8 ) and two partial-/whole-gene deletions: 35 patients (including the twin) had the dominant n.4G>A and n.35A>G pathogenic variants; 15 patients had another single de novo heterozygous variant; 91 patients from 73 unrelated families exhibited biallelic variants. Of these, 54 carried compound heterozygous variants, 17 had homozygous variants and 2 harbored hemizygous variants associated in trans with a complete or partial gene deletion (Fig. 3 and Extended Data Fig. 5 ). In 16 families, one or more siblings were also affected, with both variants cosegregating with the disease in affected siblings (Fig. 4a and Extended Data Fig. 6 ). Twenty-two recessive variants were found in at least two unrelated families, including n.20G>A, n.40C>G, n.40C>T, n.45C>T, n.100T>C, n.107_118del in at least five families and 16 additional variants in two or three families ( Supplementary Notes and Supplementary Table 8 ). Extended Data Fig. 5. Circos plot depicting preferential associations of biallelic variants. Open in a new tab This scheme suggests that, in affected individuals with compound heterozygote variants, variants preferentially co-occur with one affecting the 5′ domain and the other the 3′ domain, suggesting domain-specific combinatorial effects. Variant coloring is the same as in Fig. 3a . Fig. 4. Variant segregation and facial features associated with RNU2-2 variants. Open in a new tab a , Segregation of biallelic variants in the 16 families with at least two affected siblings. b , Facial photographs of people with monoallelic RNU2-2 variants: (i)–(iv) n.4G>A; (v)–(vii), n.35A>G; (viii) n.35A>G in mosaic. c , Facial photographs of individuals with biallelic RNU2-2 variants. (ix) maternal n.104T>C/paternal gene deletion; (x) maternal n.40C>G/paternal n.42G>A; (xi) homozygous n.61C>G; (xii) paternal n.48A>C/maternal n.61C>T; (xiii) homozygous n.152G>A; (xiv)–(xvi) homozygous n.107_118del in three affected sisters; (xvii) de novo n.6T>C/maternal n.100T>C; (xviii) maternal n.20G>A/paternal n.28C>G; (xix) homozygous n.100T>G; (xx) homozygous n.128C>T; (xxi) nonmaternal n.13C>T/maternal n.101T>A. Consent was obtained for publication of facial photographs. mo, months; yr, years. Extended Data Fig. 6. Examples of biallelic variants identified by genome sequencing in the PFMG cohort, with variant segregation consistent with recessive inheritance. Open in a new tab a , Integrative Genomics Viewer (IGV) screenshots of BAM file alignments showing segregation of the RNU2-2 n.127_118del variant in the homozygous state in three affected sisters, with both parents carrying the variant in heterozygous state. b , Left: IGV screenshot displaying the hemizygous RNU2-2 n.149A>T variant in two affected siblings and their heterozygous mother. Right: Copy-number analysis of genome sequencing data revealing a 642-kb deletion (hg38: chr11:62,781,800-63,424,493) encompassing RNU2-2 and 31 additional genes, present in the two affected siblings and their father. c , IGV screenshot showing the presence of the RNU2-2 n.142_153dup variant in homozygous state in the affected child and in heterozygous state in both parents. Genome data were available for 110 of the 122 index cases. A single person had a de novo nonsense variant in GATA3 , partially explaining his phenotype; all others had remained unsolved. Overall, 104 of the 110 patients presented with a NDD phenotype. Of the six remaining cases, three harbored de novo monoallelic variants (one person with cancer, one fetus with a cerebral malformation and one non-NDD patient) and three had biallelic variants (one terminated fetus and two non-NDD patients, including the person with the GATA3 variant). In the PFMG cohort, NDD was the predominant presentation, observed in 82 of 88 patients ( P = 1.89 × 10 −18 , one-tailed binomial test; 95% confidence interval, 0.87–1.00). Twenty-six de novo variants could be phased reliably: 16, including all phaseable occurrences of the pathogenic n.4G>A ( n = 8) and n.35A>G ( n = 2), along with n.5C>A, n.6T>C and n.7_8insA ( n = 2), n.31G>A and n.37T>G were phased to the maternal allele. The remaining ten (n.6T>C; n.21C>G; n.32T>G; n.37T>G; n.40C>T, n = 3; n.62T>G; n.129_139del; n.143_167del) arose de novo on the paternal allele (Supplementary Table 6 ). Dominant and recessive RNU2-2 NDDs share overlapping features We next aimed to delineate the clinical spectrum associated with both dominant and recessive RNU2-2 disorders. Detailed clinical data were collected for 112 patients (55 female, 57 male), including 20 with n.4G>A, 12 with n.35A>G, and 7 with another monoallelic de novo variant, as well as 73 patients from 55 unrelated families with biallelic inheritance (Table 1 and Supplementary Tables 9 – 11 ). The median age at inclusion in the study was 13 years (range: 0 (fetus) to 46 years). Table 1. Overview of clinical features in patients with monoallelic and biallelic RNU2-2 variants Group n.4G>A n.35A>G Monoallelic Biallelic Total Number of patients 20 12 32 73 105 Epilepsy 19 of 20 (95%) 9 of 12 (75%) 28 of 32 (88%) 60 of 72 (83%) 88 of 104 (85%) Generalized seizures 9 of 16 (56%) 6 of 8 (75%) 15 of 24 (62%) 39 of 44 (89%) 54 of 68 (79%) Tonic-clonic seizures 6 of 15 (40%) 4 of 8 (50%) 10 of 23 (43%) 28 of 37 (76%) 38 of 60 (63%) Myoclonic seizures 4 of 15 (27%) 3 of 9 (33%) 7 of 24 (29%) 30 of 40 (75%) 37 of 64 (58%) Tonic seizures 13 of 16 (81%) 6 of 8 (75%) 19 of 24 (79%) 13 of 32 (41%) 32 of 56 (57%) Focal seizures 7 of 15 (47%) 3 of 9 (33%) 10 of 24 (42%) 19 of 31 (61%) 29 of 55 (53%) Nonmotor seizures (absences) 9 of 16 (56%) 1 of 8 (12%) 10 of 24 (42%) 22 of 39 (56%) 32 of 63 (51%) Spasms 5 of 16 (31%) 1 of 8 (12%) 6 of 24 (25%) 15 of 35 (43%) 21 of 59 (36%) Atonic seizures 3 of 15 (20%) 3 of 8 (38%) 6 of 23 (26%) 13 of 33 (39%) 19 of 56 (34%) Clonic seizures 7 of 16 (44%) 1 of 8 (12%) 8 of 24 (33%) 9 of 27 (33%) 17 of 51 (33%) Generalized and focal seizures 7 of 16 (44%) 4 of 8 (50%) 11 of 24 (46%) 7 of 46 (15%) 18 of 70 (26%) Hemicorporal seizures 6 of 15 (40%) 1 of 8 (12%) 7 of 23 (30%) 4 of 27 (15%) 11 of 50 (22%) Febrile seizures 11 of 16 (69%) 0 of 10 (0%) 11 of 26 (42%) 6 of 52 (12%) 17 of 78 (22%) Status epilepticus 15 of 18 (83%) 5 of 7 (71%) 20 of 25 (80%) 40 of 57 (70%) 60 of 82 (73%) Pharmoresistance 8 of 17 (47%) 6 of 8 (75%) 14 of 25 (56%) 18 of 51 (35%) 32 of 76 (42%) Age of seizure onset: <3 years 15 of 19 (79%) 7 of 9 (78%) 22 of 28 (79%) 50 of 60 (83%) 72 of 88 (82%) Age of seizure onset: >3 years 4 of 19 (21%) 2 of 9 (22%) 6 of 28 (21%) 10 of 60 (17%) 16 of 88 (18%) Daily seizures 11 of 15 (73%) 1 of 8 (12%) 12 of 23 (52%) 29 of 51 (57%) 41 of 74 (55%) Severe intellectual disability 15 of 19 (79%) 10/10 (100%) 25 of 29 (86%) 50 of 68 (74%) 75 of 97 (77%) Severe developmental delay 13 of 20 (65%) 12 of 12 (100%) 25 of 32 (78%) 51 of 70 (73%) 76 of 102 (75%) No language 15 of 20 (75%) 8 of 11 (73%) 23 of 31 (74%) 41 of 62 (66%) 64 of 93 (69%) Autism spectrum disorder 14 of 20 (70%) 9 of 11 (82%) 23 of 31 (74%) 31 of 60 (52%) 54 of 91 (59%) Hospitalization 11 of 18 (61%) 5 of 12 (42%) 16 of 30 (53%) 37 of 61 (61%) 53 of 91 (58%) Stereotypies 14 of 19 (74%) 6 of 11 (55%) 20 of 30 (67%) 32 of 61 (52%) 52 of 91 (57%) Other behavioral anomalies 6 of 17 (35%) 8 of 11 (73%) 14 of 28 (50%) 31 of 58 (53%) 45 of 86 (52%) Feeding issues, gastro-intestinal reflux 8 of 18 (44%) 4 of 12 (33%) 12 of 30 (40%) 38 of 67 (57%) 50 of 97 (52%) Sleep disorders 11 of 19 (58%) 1 of 11 (9%) 12 of 30 (40%) 37 of 64 (58%) 49 of 94 (52%) MRI abnormalities 6 of 18 (33%) 3 of 10 (30%) 9 of 28 (32%) 38 of 63 (60%) 47 of 91 (52%) Constipation 9 of 18 (50%) 3 of 11 (27%) 12 of 29 (41%) 31 of 64 (48%) 43 of 93 (46%) Neonatal hypotonia 9 of 19 (47%) 5 of 12 (42%) 14 of 31 (45%) 29 of 71 (41%) 43 of 102 (42%) Dysmorphic features 9 of 19 (47%) 6 of 12 (50%) 15 of 31 (48%) 24 of 67 (36%) 39 of 98 (40%) Regression 5 of 19 (26%) 4 of 11 (36%) 9 of 30 (30%) 28 of 66 (42%) 37 of 96 (39%) Movement disorder 3 of 15 (20%) 0 of 8 (0%) 3 of 23 (13%) 26 of 53 (49%) 29 of 76 (38%) Hypersialorrhea/drooling 9 of 18 (50%) 4 of 12 (33%) 13 of 30 (43%) 21 of 64 (33%) 34 of 94 (36%) Short stature 8 of 20 (40%) 5 of 11 (45%) 13 of 31 (42%) 19 of 63 (30%) 32 of 94 (34%) Eyes/vision abnormalities 3 of 18 (17%) 3 of 12 (25%) 6 of 30 (20%) 25 of 64 (39%) 31 of 94 (33%) Joint hyperlaxity 5 of 18 (28%) 3 of 12 (25%) 8 of 30 (27%) 22 of 62 (35%) 30 of 92 (33%) Microcephaly 4/18 (22%) 3 of 11 (27%) 7 of 29 (24%) 22 of 64 (34%) 29 of 93 (31%) Ataxia 7 of 18 (39%) 5 of 9 (56%) 12 of 27 (44%) 11 of 51 (22%) 23 of 78 (29%) Failure to thrive/growth retardation 5 of 19 (26%) 3 of 12 (25%) 8 of 31 (26%) 19 of 62 (31%) 27 of 93 (29%) Bone/skeletal anomalies 4 of 18 (22%) 2 of 12 (17%) 6 of 30 (20%) 18 of 65 (28%) 24 of 95 (25%) Hyperventilation 3 of 18 (17%) 0 of 11 (0%) 3 of 29 (10%) 13 of 64 (20%) 16 of 93 (17%) Prenatal findings 1 of 18 (6%) 2 of 12 (17%) 3 of 30 (10%) 9 of 65 (14%) 12 of 95 (13%) Open in a new tab MRI, magnetic resonance imaging. Clinical features are listed by frequency across the entire patient cohort, with significant differences between groups shown in bold. Detailed clinical features and statistical comparisons for all three groups, including patients with de novo variants lacking a second pathogenic allele, are shown in Supplementary Table 10 (index cases only) and Supplementary Table 11 (index cases + siblings). Overall, considering only patients with dominant pathogenic ( n = 32, including the twin) and biallelic variants ( n = 73), all patients with available data had developmental delay, and all older than 3 years had intellectual disability (ID) except one with developmental delay without ID, showing fragile visuoconstructive reasoning (n.20G>A/n.145A>G). Severe/profound ID was most frequent (75 of 97, 77%), followed by moderate (17 of 97, 18%) and mild (4 of 97, 4%) ID. Epilepsy occurred in 85% patients (88 of 104), with identical rates in dominant (28 of 32, 88%) and recessive (60 of 72, 83%) cases. Age at seizure onset ranged from 8 weeks to 16 years (median: 1.5 years); 82% (72 of 88) had seizure onset between 8 weeks and 3 years (monoallelic: 22 of 28 biallelic: 50 of 60), while 18% (16 of 88) had seizures after 3 years (monoallelic: 6 of 28; biallelic: 10 of 60). Seizure types were variable. In biallelic families, 39 of 44 (89%) had generalized seizures, whereas in monoallelic cases, 19 of 24 (79%) had focal seizures, including hemicorporal seizures in 7 of 23 (30%). Generalized tonic-clonic seizures occurred in 38 of 60 patients (63%; biallelic 28 of 37, monoallelic 10 of 23), myoclonic seizures in 75% versus 29%, epileptic spasms in 43% versus 25%, and absence seizures in 32 of 63 patients (51%; monoallelic 10 of 24, biallelic 22 of 39). Patients were treated according to their seizure type, as is standard clinical practice, with 60 of 82 patients (73%; monoallelic 20 of 25, biallelic 40 of 57) exhibiting drug resistance. Patients with epilepsy met the criteria for developmental and epileptic encephalopathy. Among the 16 patients without epilepsy, 4 had dominant pathogenic variants (3 with n.35A>G, ages 29 months–4 years; 1 with n.4G>A, age 6 years). Twelve (ages 2.6–39 years) had biallelic variants, including three with n.40C>T. One terminated fetus (n.174A>C/n.176G>C) had a polymalformation syndrome. Clinical presentations varied within each group (n.4G>A, n.35A>G and biallelic variants) but, overall, the clinical spectrums were similar, with no clear genotype–phenotype correlations (Table 1 and Extended Data Fig. 7 ). Myoclonic seizures were more prevalent in biallelic than monoallelic cases (30 of 40, 75% versus 7 of 24, 29%; two-sided Fisher’s test P = 0.028). Febrile seizures occurred in biallelic (6 of 52, 12%) and monoallelic (11 of 26, 42%) cases but were preponderant in n.4G>A cases (11 of 16, 69%), and absent in n.35A>G carriers (0 of 10, P = 0.027; two-sided Fisher’s test). Movement disorders seemed more frequent in biallelic (26 of 53, 49%) than in dominant (3 of 23, 13%) cases (not significant after correction). The most severe phenotypes, including all eight reported deaths (ages 3–19 years; median 15 years) due to respiratory, infectious or epilepsy-related complications, were restricted to biallelic patients. Despite intragroup variability, ID severity, seizures and movement disorders were consistent within families, suggesting that variant combinations contribute to phenotypic variability of recessive NDDs. Minor dysmorphic features, such as broad forehead, midface hypoplasia, a large open mouth, a small chin and down-slanting palpebral fissures were common in patients with available photographs (Fig. 4b,c ). Extended Data Fig. 7. Patients with dominant and recessive RNU2-2 NDDs have similar clinical features without obvious genotype-phenotype correlations. Open in a new tab a , Hierarchical clustering of the clinical features ( n = 60, rows) of patients with de novo dominant or biallelic RNU2-2 variants ( n = 87, columns). Categorical data was converted to 0-1 scale, and values were Z -score scaled for each row. Blue-yellow-red scale depicts Z -scores. Lower values indicate a more favorable phenotype, while higher values represent a more severe phenotype. Missing values are shown in grey. Columns are colored based on the variant classification. Dark blue: de novo , dominant (n.4G>A or n.35A>G; n = 30); light blue: de novo other (VUS; n = 1); orange: compound heterozygous, recessive ( n = 45); purple: homozygous, recessive ( n = 11). b , Principal component analysis of clinical features in RNU2-2 variant carriers taking the variant status into account. Variant coloring is the same as in a . c , Principal component analysis of clinical features in RNU2-2 variant carriers, considering variant type, location, and combinations. PCA on the left include all patients while PCA on the right include only patients with biallelic variants. Missing values were imputed as 0. Heterozygous parents of patients with biallelic variants were generally unaffected, except in five cases: a mother with n.5C>T and a father with n.32T>C had childhood epilepsy; a mother with n.61C>T had epilepsy; both parents carrying n.17_35dup were affected, the father with ID and the mother with ID and epilepsy. Seven patients with monoallelic de novo variants other than n.4G>A and n.35A>G had highly variable phenotypes: four patients had mild ID without epilepsy ( n = 3) or moderate ID with epilepsy ( n = 1); one died at 6 days from status epilepticus; one fetus was terminated due to semi-lobar holoprosencephaly. The remaining patient had hearing loss and chronic bronchopulmonary disease. Predicted impact of RNU2-2 variants To assess the impact of RNU2-2 variants, we examined their distribution across U2 structural and functional domains and mapped them onto published U2 snRNP and spliceosome structures 30 , 31 . U2-2 can be divided into three functional domains, each remodeled to different extents throughout the splicing cycle and snRNP biogenesis. The 5′ domain forms four partially mutually exclusive structures: (1) the intramolecular branch-point-interacting stem–loop (BSL; n.25–45) 32 , (2) stem–loop I (SLI; n.7–26) 33 , (3) the intermolecular U2/U6 helix II (n.1–13) 1 and (4) the U2/U6 helix Ia and Ib (n.20–28) 34 . The branch interacting region oscillates between BSL conformation and stable intron binding through formation of the U2/BS branch helix (n.32–44) 1 , 35 . The 3′ end domain encompasses five structural elements: stem loops IIa (SLIIa; n.47–66) 36 and IIb (SLIIb; n.68–84) 32 , the Sm binding site (n.98–107) 37 and 3′ stem loops III and IV (SLIII; n.112–144/SLIV; n.147–184) 38 , 39 . RNU2-2 variants at the 5′ end probably destabilize U2/U6 helix II by disrupting key Watson–Crick base pairs 33 : n.4G>A breaks the G4–C94 pair, n.5C>A/T disrupts C5–G93, n.6T>C alters U6–A92 and n.8C>T the C8–G90 (Fig. 5 ). These changes may impair tri-snRNP recruitment to the prespliceosome and reduce splicing efficiency 40 – 42 . n.7_8insA, n.8C>T, n.9T>C, n.13C>T, n.14C>G, n.19G>A, n.20G>A, n.21C>G, n.23A>G, n.24A>G, n.25G>A and n.26A>G would affect SL1 stability and directly or indirectly alter U2/U6 helix II formation. Furthermore, n.20G>A, n.21C>G, n.26A>G and n.28C>G may destabilize helices Ia and Ib by replacing Watson–Crick base pairs with noncanonical pairs, perturbing active site formation. Fig. 5. Possible functional impact of RNU2-2 variants. Open in a new tab Overview of RNU2-2 variants identified in this study. a , Two-dimensional structure of the U2 snRNA (green). Orange boxes indicate variants from this study, with point mutations and single nucleotide insertions represented on the graph along with their respective changes. 2,2,7 m3Gppp, 2,2,7-trimethylguanosine cap; Ψ, pseudouridine; m, 2′-O-methyl residues. b , Zoom-in box representing the two-dimensional predicted structure of the U2 (teal) and U6 (red) snRNAs U2/U6 helix II during formation of precatalytic spliceosome. Interactions stabilizing this structure as well as mutations potentially affecting its stability are represented on coordindates (PDB 5XJC ). c , Zoom-in box representing the two-dimensional predicted structure of U2/U6 helices Ia and Ib. Nucleotides involved in the structure and associated with pathological variants are represented (PDB 5XJC ). d , Close-up of U2/BS branch helix where the two-dimensional predicted structure of the interaction between branch-point recognition region of U2 snRNA and the intronic BS (light brown) is represented. The branch-point adenosine is highlighted in purple. ‘YNYURAY,’ consensus BS in metazoans: Y, pyrimidine; N, any nucleotide; R, purine. The mutations in this region are depicted on the structure (PDB 5XJC ). e , Zoom-in box representing the two-dimensional predicted structure of U2 snRNA within the 17S snRNP with the BSL highlighted. In black are nucleotides involved directly on the BS recognition. The structure of the BSLwithin the 17S U2 snRNP is represented (PDB 7Q3L ). f , Close-up of the U2 snRNA Sm ring (teal), with nucleotides with mutations close to, or within, it marked in orange (PDB 5XJC ). Some variants within the BSL probably affect its stability. For instance, n.40C>T or n.30A>G may create a Watson–Crick base pair between positions 30 and 40 that hyperstabilizes the BSL, which was linked previously to reduced splicing fidelity in yeast 32 . Likewise, n.40C>G may stabilize the BSL by creating a G–A base pair replacing the C–A mismatch 43 . Conversely, n.25G>A, n.26A>G, n.28C>G, n.32T>C/G, n.38A>G, n.42G>A, n.44T>C and n.45C>T may disrupt BSL integrity 44 . Notably, n.35A>G alters the invariant A35 of U2 that pairs with the conserved U upstream of the branch site (BS), which may increase pairing flexibility, promoting cryptic BS usage 45 (Fig. 5 ). Besides, n.28C>G, which may affect both BSL formation and the active site, is a recurrent somatic variant detected in cancers 24 . In contrast, variants in SLIIa, the Sm site and the 3′ stem loops SLIII/SLIV probably impair U2 snRNP biogenesis and nuclear import, destabilizing U2 and preventing proper spliceosome assembly. Likewise, n.190_191+30del would perturb proper 3′ end processing. These changes may behave effectively as null alleles such as gene deletions, as the affected U2 molecules may fail to reach functional spliceosomes. Impact of RNU2-2 variants on lymphocyte transcriptome We showed previously that pathogenic RNU4-2 variants lead to splicing defects affecting primarily alternative 5′SS, detectable in cultured lymphocytes 18 . Here we investigated splicing alterations in 19 people with either dominant (n.4G>A, n = 5; n.35A>G, n = 5) or recessive ( n = 9) RNU2-2 variants and compared them with data of 49 patients with other disorders (controls). Using the same approach as for RNU4-2 , we found no consistent splicing signature among RNU2-2 variant carriers. To increase the specificity of our analysis, we applied a linear regression on percent-spliced-in (PSI) values (ΔPSI > 0.05), integrating age, sex and cell composition as covariates. Significant alternative splicing events ( P < 0.01) were detected for 5′SS, alternative 3′ splice sites (3′SS), skipped exons (SEs) and retained introns (RIs). SEs represented the main effect in both dominant (n.4G>A and n.35A>G) and recessive RNU2-2 (Fig. 6a ) cases but showed minimal overlap between all three conditions (Fig. 6b ). Clustering of PSI residuals for SE events distinguished RNU2-2 variant carriers successfully from controls (Fig. 6c and Supplementary Tables 12 – 14 ). Analysis of ΔPSI distributions revealed a global decrease in exon inclusion (that is, increased exon skipping) in dominant cases, whereas recessive cases exhibited both increased and decreased exon skipping to a similar extent (Fig. 6d ). All SE events were also present among controls (Fig. 6e ), indicating that RNU2-2 variants induce subtle quantitative perturbations in alternative splicing rather than generating new splice junctions in lymphocytes. Fig. 6. Variant-specific alternative splicing perturbations in RNU2-2 variant carriers. Open in a new tab a , Number of significant alternative splicing events (ΔPSI > 0.05) detected using rMATS-turbo and linear regression ( P < 0.01), comparing RNU2-2 patients (dominant: n.4G>A, n = 5; n.35A>G, n = 5; biallelic: n = 9) to 49 controls. Splicing categories are color-coded: SE (blue), alternative 5′SS (A5′SS, orange), alternative 3′SS (A3′SS, green) and RI (red). b , Venn diagram showing minimal overlap of exon skipping events shared among dominant (n.4G>A, n.35A>G) and recessive (biallelic) variant groups. c , Clustermaps of PSI value residuals after linear regression for SE events of patients carrying n.4G>A (left, n = 5), n.35A>G (middle, n = 5) and biallelic (right, n = 9) variants. Blue: controls; red: RNU2-2 cases. d , Distribution of ΔPSI across the three conditions. Raincloud plots show the density of ΔPSI values (half-violins), individual splicing events (points) and mean ΔPSI (horizontal bars). Percentages above each group indicate the proportion of events with increased (ΔPSI > 0) or decreased (ΔPSI < 0) exon inclusion. e , Sashimi plots illustrating an SE isoform shift in ACLY observed in RNU2-2 n.35A>G variant carriers compared to controls. RPKM, reads per kilobase of transcript per million mapped reads. Impact of RNU2-2 variants on blood methylome We next analyzed DNA methylation profiles in 24 patients with RNU2-2 variants (8 with n.4G>A, 6 with n.35A>G and 10 with biallelic variants) and compared them with 68 controls to identify potential episignatures, using a similar methodology to RNU4-2 18 . Cases and controls were matched for age at sampling, and analyses also corrected for age, sex and cell composition. Consistent with transcriptomic findings, patient groups seemed heterogeneous, limiting statistical power for variant-specific analyses. We then implemented a combined model simultaneously evaluating all three variant types while allowing for both shared and variant-specific effects. Overall, methylation signatures associated with RNU2-2 variants were subtle, heterogeneous and weaker than reported previously for RNU4-2 (Fig. 7a ). Principal component (PC) analysis (PCA) across all 201 differentially methylated positions showed that n.4G>A carriers exhibited the strongest signal, although only ~25% of variance was captured by PC1 (Extended Data Fig. 8a and Supplementary Table 15 ). Separation of n.35A>G and biallelic carriers from controls became apparent only at higher PCs, reflecting the minor effect size. Excluding n.4G>A carriers improved visualization, with n.35A>G and biallelic variants separating partially from controls along PC2 (Extended Data Fig. 8b,c ). Methylation profiles showed that n.4G>A carriers were largely hypomethylated, whereas n.35A>G carriers displayed modest hypermethylation (~2.5% Δ β ) and biallelic carriers had heterogeneous patterns without consistent hypo- or hypermethylation (Fig. 7b ). Cross-validation with a single four-class support vector machine (SVM) classifier confirmed the strongest signature for n.4G>A (sensitivity 100%), with lower performance for n.35A>G (83%) and biallelic (80%) variants and an overall specificity of 87% (Extended Data Fig. 8d ). Overall, methylation differences between variant carriers and controls were less than 5% Δ β , near the technical detection limit, indicating subtle and heterogeneous effects across variants. Fig. 7. Methylation profiles across differentially methylated probes in RNU2-2 variant carriers and normal controls. Open in a new tab In all panels, controls ( n = 68) are shown in light blue, dominant n.4G>A carriers ( n = 8) in red, dominant n.35A>G carriers ( n = 6) in orange and recessive biallelic cases ( n = 10) in dark blue. a , Heatmap of adjusted methylation levels at differentially methylated CpGs. Selected probes are represented in rows whereas patients and normal controls are represented in columns after hierarchical clustering of their methylation profiles. Probes are grouped according to their association pattern: common to all variants, specific to n.35A>G, specific to biallelic recessive variants and specific to n.4G>A. Samples are annotated by type (control, dominant n.4G>A, dominant n.35A>G, recessive biallelic) but sample clustering was unsupervised and blind to this classification. b , Raincloud plots showing the average methylation level per sample within each probe association pattern. Normal controls and variant carriers display significantly different distributions, with P values from two-sided Wilcoxon rank-sum tests indicated on the plots. The number of individuals per group is the same as in a . Boxplot elements are as follows: centerline, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range. Extended Data Fig. 8. Principal component analysis and classification performance of the RNU2-2 episignature. Open in a new tab In all panels, controls are shown in light blue, dominant n.4G>A carriers in red, dominant n.35A>G carriers in orange, and recessive biallelic cases in dark blue. a , PCA of adjusted methylation levels at differentially methylated positions ( n = 201) up to component 6, after correction for expected baseline methylation level based on age at sampling, sex and estimated blood cell counts ( n = 92 individuals in total) showing separation of dominant n.4G>A carriers from normal controls on axis 1, and the weaker separation between dominant n.35A>G carriers or recessive biallelic cases from normal controls along axis 6. The percentage of variance explained is provided for each component within the axis title. b , PCA of adjusted methylation levels at differentially methylated positions ( n = 201), after correction for expected baseline methylation level based on age at sampling, sex and estimated blood cell counts on the restriction to normal controls and carriers of the dominant n.35A>G variant along the first two principal components ( n = 74 individuals in total). The percentage of variance explained is provided for each axis. c , PCA of adjusted methylation levels at differentially methylated positions ( n = 201), after correction for expected baseline methylation level based on age at sampling, sex and estimated blood cell counts on the restriction to normal controls and carriers of recessive biallelic variants along the first two principal components ( n = 78 individuals in total). The percentage of variance explained is provided for each axis. d , Predicted probabilities from a three-block cross-validation using a four-class SVM classifier (control, dominant n.4G>A, dominant n.35A>G, recessive biallelic). Discussion Although historically overlooked due to their high conservation and redundancy, variants in snRNAs are now recognized as principal contributors to genetic disorders. In this study, we analyzed all annotated snRNA genes, hypothesizing that some ‘pseudogenes’ may be functional disease genes. Our main finding was the identification of biallelic variants in RNU2-2 as the cause of a frequent recessive NDD. Clinically, this recessive NDD closely resembles the recently described dominant RNU2-2 –associated disorder caused by n.4G>A and n.35A>G 19 , 23 , but seems to be at least twice as common. The clinical phenotype associated with dominant or biallelic RNU2-2 variants is characterized by neurodevelopmental delay, severe ID and epilepsy in 85% of cases, usually starting before 3 years of age. Excluding monoallelic de novo variants other than n.4G>A and n.35A>G, we estimate that dominant and recessive RNU2-2 variants altogether account for at least ~0.35% of NDDs (Supplementary Table 7 ), similar to the prevalence of ReNU syndrome 16 , 18 . Interpreting RNU2-2 variants is complicated by the relatively high and overlapping allele frequencies of pathogenic and benign variants. It is thus possible that a few biallelic variants reported in our study are nonpathogenic, whereas others not considered could contribute to disease or modify penetrance (like for example, n.99A>G). Compared to RNU4-2 , which is associated mainly with dominant ReNU syndrome, RNU2-2 shows lower constraint and greater tolerance to variation, consistent with its association with predominant recessive disorders. snRNA genes are hypermutable and saturated with variants 16 , 25 , probably explaining the discrepancy between their small size and the high frequency of the associated phenotypes. The identification of RNU2-2 biallelic variants as a common cause of recessive disorders was replicated independently by two groups 46 – 48 . Both other studies relied mainly on Genomics England data and report largely the same pathogenic variants, whereas our work is based on the PFMG and other independent cohorts, with no (or negligible) patient overlap. Despite this, the results are remarkably consistent, differing mainly in their interpretation. Unlike previous studies from the same authors, which reported de novo variants separately 19 , 23 , our data show that de novo variants in snRNA genes can underlie both recessive and dominant inheritance, reflecting their high mutability 25 . Therefore, a second pathogenic variant in trans should always be sought for when evaluating de novo variants in snRNA genes. Jackson et al. 46 suggest distinct mechanisms and clinical features for dominant versus recessive RNU2-2 variants, whereas our detailed clinical analysis of >100 patients, combined with structural predictions of variant impacts, rather indicates a continuum between dominant and recessive disorders. RNU2-2 exhibits a broad and complex spectrum of variant effects that blurs the lines between dominant and recessive inheritance. De novo variants near n.4G>A and n.35A>G are particularly hard to interpret in the absence of a second pathogenic allele. These variants are extremely rare or absent from population databases (that is, under stronger negative selection than other biallelic variants) and recur de novo across unsolved patients with NDD. Together, these findings suggest that certain heterozygous variants could contribute to dominant disease with variable penetrance, yet produce a classic recessive phenotype when paired with a second pathogenic allele in trans . These observations must be interpreted in light of predicted variant effects on U2-2 snRNA and the presence of additional U2 copies, including canonical U2-1 ( RNU2-1 ) paralogs. U2 contributes largely to defining splicing outcomes by selecting the intronic BS (consensus ‘YNYURAY’ in metazoans) and thereby the acceptor splice sites 49 . Before its integration within the spliceosome, U2 snRNAs undergo a maturation process that includes Sm-core assembly, nuclear reimport and protein recruitment to form the U2 snRNP 38 , 39 , 50 – 53 . The 3′ domain mediates structural interactions necessary for U2 snRNP biogenesis and spliceosome assembly 38 , 53 , 54 , such as SLIIa binding SF3B1 50 , 55 . In contrast, the 5′ domain contributes to spliceosome assembly, active site formation and branch-point recognition through highly conserved RNA–RNA interactions. During early spliceosome assembly, U2 initially probes the BS and forms a stable U2/BS branch helix 32 . This process requires the PRP5 ATPase, which remodels the BSL and enforces fidelity of BS recognition 40 . Formation of the branch helix may trigger SLI formation 55 , which is disrupted subsequently to allow tri-snRNP recruitment through creation of the U2/U6 helix II, to form the precatalytic spliceosome. Upon spliceosome activation, U2 forms two additional contiguous helices with U6 (Ia, Ib) that, along with helix II, flank and stabilize the RNA-based active site 56 , 57 . Variants in the Sm site or 3′ stem loops probably prevent U2 snRNP maturation and spliceosome incorporation. Mutant U2-2 RNAs that fail to mature properly behave like null alleles, similar to larger indels or whole-gene deletions. These null alleles are tolerated when heterozygous but pathogenic in homozygous or compound heterozygous states, highlighting the essential role of RNU2-2 in human brain development. In contrast, variants in the U2-2 5′ region, spanning the U2/U6 helix II and BSL, are predicted to be incorporated into spliceosomes, although some may also alter U2-2 snRNA levels 58 . Recurrent variants n.4G>A and n.35A>G seem sufficient to reach the pathogenic threshold in the heterozygous state, whereas nearby variants are typically insufficient alone but can cause disease when paired with another variant in trans . This corresponds to a gradient-of-impact framework, where RNU2-2 variants (1) can act dominantly on their own (n.4G>A; n.35A>G); (2) require a second nonfunctional allele to cause disease (one non-null allele, one null allele); (3) are both incorporated in spliceosomes, having additive effects (two non-null alleles) or (4) fail to enter the spliceosome entirely (two null alleles). Jackson et al. suggested that the U2-2:U2-1 ratio could serve as a biomarker for RNU2-2 recessive disorders 46 . However, it is possible that this biomarker is valid only when at least one variant impairs U2-2 maturation. Contrary to RNU4-2 , RNU2-2 variants exert a subtler effect on splicing in blood, causing primarily exon skipping for dominant variants and broader splicing alterations for recessive variants. These findings align with the lower expression of RNU2-2 than RNU2-1 in blood, which contrasts with its higher relative expression in the brain 19 , 23 , and is consistent with the primarily neurodevelopmental features of RNU2-2 –associated disorders. Similarly, DNA methylation analyses in RNU2-2 variant carriers revealed only modest, variant-specific changes. These results should be interpreted cautiously and validated in larger cohorts, as some differences may reflect stochastic variability rather than consistent, biologically meaningful episignatures, and may also mirror the variants’ diverse functional effects. Despite the size of the PFMG cohort (>34,000 participants), we did not identify additional significant snRNA-disease associations. This probably reflects limited statistical power to detect variants in snRNA genes causing rarer disorders, as known disease associations with de novo variants in RNU5B-1 18 , 19 or recessive variants in RNU4-2 21 , 22 , RNU6ATAC 59 and RNU12 14 , 15 did not reach significance. Increasing statistical power will require pooling international cohorts for robust genotype–phenotype association studies. Another important limitation of our study is the reliance on short-read genome sequencing, which cannot accurately resolve highly similar snRNA loci, including most U1 and U2 canonical genes, due to the presence of (nearly) identical copies contained in regions spanning >5–10 kb in sequence homology. The analysis of these regions thus requires long-read sequencing technologies. In conclusion, our results support a continuum of dominant and recessive NDDs caused by RNU2-2 variants, which altogether are nearly as prevalent as ReNU syndrome. They also underscore the challenges of interpreting variants, especially those occurring de novo, in these highly mutable loci, and suggest that additional snRNA-associated disorders remain to be discovered. Methods Inclusion and ethics statement This study was conducted in accordance with the ethical standards and regulations of all participating countries. Written informed consent was obtained for all patients from their parents or legal guardians, with an additional consent form for families agreeing to the publication of photographs. For genetic analyses, patient samples were pseudonymized at each participating center. Information on the patients’ sex (but not gender) was extracted from clinical records. The promoters of this research study are Assistance Publique–Hôpitaux de Paris (AP–HP) for hospitals associated with the SeqOIA laboratory (project ID APHP241333) and Grenoble-Alpes University Hospital (CHU Grenoble-Alpes, research ID 19814188) for hospitals affiliated with the Auragen laboratory. Ethical approval was obtained from the University Hospital Essen (24-12010-BO) and the Comité Éthique et Scientifique pour les Recherches, les Études et les Évaluations dans le domaine de la Santé (CESREES; reference 21082803 Bis / 2038764). AP–HP has received an authorization from the Commission Nationale de l’Informatique et des Libertés (CNIL; reference HGTHGT/MFIMFI/AR2426865; request no. 924924336666) for data processing. Additional approvals were obtained from the ethics committee of CHU de Nantes (CCTIRS number 14.556) and from CPP Ouest V (File 06/15, Ref MESR DC 2017 2987; approval date 4 August 2015). For methylation analyses, DNA from patients and controls had been previously collected in a medical context for genetic testing, with written consent including authorization for research use of leftover material. Control samples consisted of participants without neurodevelopmental disorders, either unaffected relatives or persons tested presymptomatically for other conditions who were found not to carry pathogenic variants. DNA samples used for methylation profiling were stored within the genetics biobank of the CRBi, Rouen, France (collection DC 2008-711, authorization MCRBi/2024/02). The use of these samples was approved by the CERDE ethics committee of Rouen University Hospital (notification E2023-13). Researchers and clinicians from all contributing centers participated throughout the study, from design and implementation to data collection, analysis and manuscript preparation, and are listed as co-authors of this article. snRNA genes Genes and pseudogenes corresponding to snRNAs were retrieved from Ensembl 113 BioMart 60 , 61 by filtering on gene type ‘snRNA.’ Of the 2,094 genes, we excluded the ones placed in contigs, scaffolds or patches, keeping 1,901 genomic regions located in identifiable chromosomes. Of these, 1,741 genes are annotated as spliceosomal snRNAs. We downloaded through the UCSC Table Browser 62 : (1) the coordinates of the ENCODE Registry of candidate cCREs in the human genome 26 and (2) the peaks of histone H3 acetylation of lysine 27 (H3K27ac) obtained for the H1 human embryonic stem cell line (H1-hESC) 27 . We annotated the 1,901 snRNAs by analyzing their genomic coordinates for overlaps with ENCODE cCREs and H3K27ac peaks. The RNU genes were further annotated for their hypermutability 25 . We kept for further analyses 200 putative functional snRNA genes that either overlap with ENCODE cCREs, are considered hypermutable or are approved by the HUGO Gene Nomenclature Committee (HGNC). Small RNA-seq data from embryonic brain tissues We analyzed the expression level of the 1,901 snRNAs using small RNA-seq data generated from six human embryonic brain regions by the ENCODE Consortium 63 : diencephalon ( GSE78292 ), temporal lobe ( GSE78303 ), occipital lobe ( GSE78298 ), frontal cortex ( GSE78293 ), parietal lobe ( GSE78299 ) and cerebellum ( GSE78291 ). We downloaded 24 bigwig files from the ENCODE portal 26 containing signals for ‘all reads’ and ‘unique reads,’ for plus and minus strands, from the default anisogenic replicate. For each genomic coordinate, we calculated the maximum normalized RNA-seq signal obtained from the bigwig files for the corresponding gene strand, as well as the number of covered bases. For plotting, we considered only snRNA genes for which at least 50% of the expected number of bases for the specific snRNA type were covered in at least one tissue when considering ‘all reads.’ The expected number of bases for each snRNA type were 164 (U1), 191 (U2), 141 (U4), 116 (U5), 107 (U6), 63 (U7), 127 (U4ATAC), 126 (U6ATAC), 134 (U11), 150 (U12) and 40 (other snRNAs). Patient cohorts and variant filtering We first investigated monoallelic de novo and biallelic variants in snRNA genes in patients who underwent genome sequencing as part of the diagnostic process in France through Plan France Médecine Génomique 2025 (PFMG2025) 28 on one of the two national clinical sequencing laboratories, SeqOIA ( https://laboratoire-seqoia.fr/ ) and Auragen ( https://www.auragen.fr/ ). Sequencing was performed in two subsets (Auragen and SeqOIA), with final inclusion dates in May and November 2025, respectively. gLEAVES, the system used for genome analysis on the SeqOIA platform, which is restricted to registered and accredited users, was used to investigate inheritance of variants and visualize bam files on Integrative Genomics Viewer (IGV). Because bioinformatics pipelines were run independently in each laboratory and differed slightly, each dataset was analyzed both separately and combined. The combined cohort included 34,329 patients with rare disorders (20,690 from SeqOIA and 13,639 from Auragen). Characteristics of the combined PFMG cohort and separated subcohorts and details of the filters appear in Extended Data Fig. 1 and Supplementary Fig. 4 . Variants from 200 snRNA genes were extracted initially using the criteria: de novo or biallelic inheritance, and present in fewer than three homozygotes in gnomAD v.3 and fewer than five homozygotes in internal databases. Biallelic variants were defined as: (1) homozygous or hemizygous variants; (2) two heterozygous variants inherited from different parents for trios; (3) one inherited and one variant absent from the sequenced parent for duos or (4) one inherited and one de novo variant, with a trans configuration verified manually in IGV. Only variants with coverage at least ten in genes with median VAF ≥ 0.3 with less than 50% overlap with problematic regions in ‘Genome in a Bottle’ were considered for further analysis. Additional filtering for gene enrichment analyses included variants with gnomAD AC < 100 (‘rare variants’) and no gnomAD flags (for example, variants flagged as ‘LCR’). Two-sided Fisher’s test was used to test statistical enrichment in unsolved versus solved and in NDD versus non-NDD cases. Genes were kept for these analyses when the minimum number of patients carrying variants was sufficient to reach statistical significance ( P < 0.05) in the cohort before multiple testing (unsolved versus solved: ten [PFMG, SeqOIA, Auragen]; NDD versus non-NDD: nine [PFMG, SeqOIA] or ten [Auragen]). After the initial filtering, the criteria to select RNU2-2 variants in the PFMG cohort were relaxed to broaden the inclusion of potentially pathogenic variants, restricting only on the homozygote frequency (<3 in gnomAD_v3 and <5 in internal databases) while removing restrictions on heterozygote frequency. We examined all cases with biallelic variants manually, and transitioned to the AoU database, which encompasses a larger cohort of genomes from healthy people, thereby providing greater power to assess population-level variation. Refined criteria were: AC < 50 for de novo variants and AC < 200 for biallelic variants and no homozygotes in AoU. Cases with de novo or P/LP variants were screened for a second variant in trans , revealing potentially pathogenic or modifier alleles with slightly higher population frequencies in a few families. Variant nomenclature was checked systematically with Variant Validator ( https://variantvalidator.org/service/validate/ ). To identify additional cases with RNU2- 2 variants, we performed sequencing of RNU2-2 and/or reanalyzed genome data in a cohort of 5,456 patients with NDDs (Extended Data Fig. 1 and Supplementary Table 7 ). Furthermore, cases with biallelic variants present in 24,958 genomes were identified using seqr 29 . This large international collaborative effort identified 34 additional cases: 13 patients with monoallelic variants (n.4G>A, n = 9; n.35A>G, n = 3; n.38A>G, n = 1) and 21 families with biallelic variants, originating from Germany ( n = 9), France ( n = 8), the US ( n = 6), New Zealand ( n = 3), Egypt ( n = 3), Australia ( n = 2), Denmark ( n = 1), Turkey ( n = 1) and Brazil ( n = 1). None of these patients had been published previously, and we carefully verified the absence of duplicates among people carrying the same variant(s) by cross-checking year of birth and initials. Variant classification We classified variants according to the American College of Medical Genetics and Genomics/Association for Molecular Pathology criteria using the recommendations of Ellingford et al. 64 . For de novo variants, PS2 was applied but downgraded in light of the hypermutable nature of snRNA genes: PS2_Supporting was applied for one de novo, PS2_Moderate when the same de novo variant was found in two to ten patients, and PS2 when the same de novo variant was found in more than ten patients (for n.4G>A and n.35A>G). PM2_Supporting was applied to variants absent or very rare in gnomAD v.3 (allele count <10) and AoU (allele count <50). PM1 was applied to variants located in functional domains U2/U6 helix II, SLI and BSL (n.1–n.45) and Sm binding site (n.98–n.107) and PM1_Supporting to variants in n.47-n.66 (SLIIa), n.68-n.84 (SLIIb), n.112–n.144 (SLIII) and n.147–n.184 (SLIV). PS4_Supporting was applied for variants present in two unrelated patients, PS4_Moderate when observed in three to four unrelated patients, and PS4 when present in five or more unrelated patients when the variant is rare (that is, PM2_sup is met). PS4 was downgraded for variants with allele count comprised between 11 and 50 in gnomAD v.3 and 51 to 250 in AoU (PS4_supp when observed in three to four unrelated patients, and PS4_Moderate when present in five or more unrelated patients). This criterion was not applied for more frequent variants. PP1 was applied if a variant cosegregated with disease in two or three affected family members, PP1_Moderate was applied when cosegregation was observed in five family members (affected or unaffected) or across several unrelated families. PM3 was assigned to variants detected in trans with a likely pathogenic or pathogenic variant. We also annotated variants with MobiDeep available on Mobidetails website ( https://mobidetails.chu-montpellier.fr ) 65 . MobiDeep is a metascore for noncoding variants based on a multilayer perceptron using five features: REMM v.0.4, CADD 1.7, GPN-MSA and two conservation scores (Cactus 241-way vertebrates and PhyloP primates) to capture different evolutionary depths. Variants were stratified according to MobiDeep thresholds: <0.6 (neutral), >0.6 (probably deleterious) and >0.9684 (high-confidence deleterious). Sanger sequencing Sanger targeted sequencing was performed to screen for variants in RNU2-2 and/or to perform segregation analysis. PCR amplification of RNU2-2 was performed using the HotStarTaq Master Mix Kit (Qiagen, cat. no. 203445) with the following primers: F: 5′- CAAACACGCGTCATTCAACACAC-3′; R: 5′- ATAACTGGTTGGAAGATGGGAAGG-3′ (designed using primer3), according to the manufacturer’s instructions. Forward and reverse sequencing reactions were performed using the BrilliantDye Terminator v.1.1 Cycle Sequencing Kit (Nimagen; cat. no. BRD1-1000) or the BigDye Terminator v.3.1 Sequencing Kit (Life Technologies, cat. no. 4337457). ExoSAP-Purified sequencing products (ExoSAP-IT, Applied Biosystem, cat. no. 78205) were run on Pop-7 polymer (Life Technologies, cat. no. 4335615) using an ABI 3730 or 3730XL automated sequencer (Applied Biosystems). Sequences were analyzed using Geneious Prime 2019 (Biomatters Ltd.) or Seqscape v.2.6 software (Applied Biosystems). Clinical analyses Clinical data were collected retrospectively from the referring physician using an anonymized Excel sheet, as published previously 18 . We collected categorical data on 66 clinical features from 112 patients. For clustering and statistical comparisons, we used 60 features from 87 index patients, except in the analysis restricted to patients carrying biallelic variants, where 59 features from 56 patients were included since the remaining features showed no variation among patients. Data were converted to a 0–1 scale, with 0 representing a more favorable phenotype presentation and 1 a more severe phenotype. Hierarchical clustering was performed using pheatmap R package, performing Z score scaling for each row (across different patients), and ward.D2 clustering method keeping missing values. PCA was generated after replacing missing data by 0 and performing variable scaling. Microcephaly was defined as head circumference measurements less than the third percentile. We used charts established by Fenton et al. 66 to calculate head circumference percentile at birth and define congenital microcephaly. Fisher’s tests (two-sided; 2 × 2, 2 × 3, 2 × 4 or 2 × 5 contingency tables) adjusted for multiple comparisons using Bonferroni correction were used to compare clinical features from patients with different variant types (n.4G>A versus n.35A>G: 39 (index only) or 37 (including siblings) clinical features; de novo versus biallelic variants: 48 (index only) or 50 (including siblings) clinical features). Clinical features were kept for the statistical analysis when the minimum number of patients with gathered information was sufficient to reach statistical significance ( P < 0.05) before multiple testing. Prediction of variant impact Structural analysis of variants and corresponding figures were performed using the PyMol v.3.0.0 visualization software 67 on published coordinates of the human U2 snRNP structures: PDB 7Q3L ref. 30 for the BSL structure and 5XJC ref. 31 for the representation of the rest. RNA sequencing RNA-seq experiments were conducted following a similar protocol and workflow previously described for RNU4-2 18 . Briefly, PBMCs were isolated from 2–4 ml of EDTA blood and cultured in lymphocyte-stimulating medium for 48–72 h. RNA was extracted and stranded RNA-seq libraries prepared from 100 ng total RNA using the SureSelect XT-HS2 kit (Human All Exon V8 capture probes; cat. no. G9774C), followed by sequencing on an Illumina NextSeq 550 to obtain ~25–30 million paired-end reads per sample. Reads were aligned to GRCh38 with STAR v.2.7.11a. Quality control was performed with FastQC v.0.11.3 and Fastp v.0.23.4. Cell type composition was estimated using CIBERSORTx v.1.0 with the LM22 signature matrix. For splicing analysis, we focused on 19 people carrying either dominant ( n .4G>A, n = 5; n .35A>G, n = 5) or biallelic ( n = 9) RNU2-2 variants that we compared to 49 controls (probands without RNU2-2 variants). rMATS-turbo v.4.3.0 was used to detect alternative splicing events with the following parameters: -t paired –anchorLength 1 –libType fr-firststrand –novelSS –variable-read-length –allow-clipping. rMATS outputs were filtered for mean coverage >10 and ΔPSI > 0.05. For each splicing event, we fitted a linear regression model adjusting for age, sex and estimated cell composition using ordinary least squares with statsmodels (v.0.14.5) in Python (v.3.12.2). P values were obtained for association with affected status; significant splicing events were defined as those with P value < 0.01, retaining only the most significant event per gene. To visualize results, the linear regression model was trained on the 49 control samples and applied to both controls and affected patients; residual PSI values were then used for PCA using sklearn (v.1.7.2) and clustermaps using seaborn (v.0.13.2) for the splicing categories: SE, 5′SS, 3′SS and RI. Sashimi plot was performed with rmats2sashimi. DNA methylation study DNA methylation analysis was performed as described previously 18 . Genomic DNA was extracted from whole blood and bisulfite converted. DNA methylation profiles were obtained using Infinium MethylationEPIC v.2.0 BeadChips (Illumina, cat. no. 20087708) following the manufacturer’s protocol. Patients and controls were balanced across 50 arrays and array rows to reduce technical bias and matched for age at sampling. Arrays were processed at the ASGARD-Rouen genomic platform (Rouen University Hospital, University of Rouen) on an Illumina NextSeq 550 scanner. Raw IDAT files were normalized using Meffil R package, including all 50 arrays to estimate variability within and across arrays. Functional normalization was performed with random effect adjustment for array and sentrix row and fixed effect adjustment for the first two PCs, generating β -values. An epigenome-wide association study was conducted using normalized β values from controls and patients with P or LP dominant or biallelic RNU2-2 variants. Multiple linear regression was applied per CpG to assess methylation differences, adjusting for age, sex and predicted blood cell composition. To account for potential heterogeneity, two variant-specific coefficients (dominant n.35A>G and biallelic recessive) were added alongside the main case–control effect, with n.4G>A as the reference. Regression coefficients and P values were extracted for all three variables. CpGs were considered significant if P < 1 × 10 −5 and absolute effect size >0.05 for any coefficient. Significant CpGs were classified based on association patterns. Probes with only a main differential effect were labeled ‘common to all variants.’ Probes significant only for n.35A>G or biallelic coefficients were labeled ‘specific to n.35A>G’ or ‘specific to biallelic recessive carriers,’ respectively. Probes significant for both main and variant-specific coefficients corresponded to n.4G>A-specific probes after visual confirmation of effect directions. Adjusted methylation levels were visualized using PCA and heatmaps (pheatmap, Euclidean distance, Ward aggregation method). Baseline methylation models were fitted on controls, correcting normalized β value for age, sex and blood composition. CpGs were grouped by association patterns (common, n.35A>G-specific, biallelic-specific, n.4G>A-specific) and mean adjusted methylation per sample was calculated. Raincloud plots were generated (ggplot2, ggdist) combining half-violin distributions, boxplots and jittered points colored by group. Pairwise comparisons were performed using two-sided Wilcoxon rank-sum tests (ggpubr), with displaying significant P values only. Predictive performance of the CpG signature was assessed using a multiclass linear kernel SVM (e1071) with four outcomes: Control, Dominant n.4G>A, Dominant n.35A>G, Recessive biallelic. Three-block cross-validation was applied within each variant group, training on two blocks and testing on the third, ensuring each sample was predicted once. For each cross-validation iteration, a new differential methylation analysis was performed and the SVM classifier was trained on the resulting CpG signature. Predicted labels and class probabilities were obtained, and sensitivity and specificity per class were calculated with 95% confidence intervals (Wilson method). Probabilities were averaged and visualized to illustrate class separation and model confidence. Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. Online content Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41588-026-02547-5. Supplementary information Supplementary Information (3.7MB, pdf) Supplementary Notes and Figs. 1–7. Reporting Summary (68KB, pdf) Peer Review File (370.1KB, pdf) Supplementary Table 1 (577.2KB, xlsx) Supplementary Tables 1–15. Acknowledgements We are deeply grateful to the patients and their families for their participation in this study. Patients included in this study were diagnosed through several studies. We thank S. Mazoyer for critical reading of the preprint, which led to text edits in the final version of this article. Patients from the PFMG received funding from Plan France Médecine Génomique 2025 (PFMG2025). Some of the results were supported by AP–HP and the RNU-SPLICE project, financed by Health philantropic program of Mutuelles AXA dedicated to supporting innovative research projects in France (to C.N.). The structural interpretation was conducted by C. Charenton and M.A. as part of the ERC Starting Grant project ‘SPLIFEM.’ A. Santini received a grant from Region Normandie and GIRCI Nord Ouest (FHU-A2M2P). Sequencing and/or analysis of cases identified in Germany were supported by grants from the Deutsche Forschungsgemeinschaft (DFG), project number 458099954 (DE 2979/6-1) to C. Depienne and KU 1240/17-1 to K.K. Sequencing and analysis of cases identified by the Broad Institute of MIT and Harvard Center for Mendelian Genomics (A.O’D.-L., O.M., A.N., M.C.O’L., L.S.P., C.A.-T. and S.L.S.) were funded by the National Human Genome Research Institute (NHGRI) grants UM1HG008900 (with additional support from the National Eye Institute, and the National Heart, Lung and Blood Institute), U01HG011755, R21HG012397 and R01HG009141, and in part by the Chan Zuckerberg Initiative Donor-Advised Fund at the Silicon Valley Community Foundation (funder 10.13039/100014989) grants 2019-199278, 2020-224274, 2022-316726 and 2022-309464 (10.37921/236582yuakxy). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies. Genetic analyses (cases from Gleeson laboratory) were supported by the Gabriella Miller Kids First Pediatric Research Program, funded by the Common Fund of the NIH Office of the Director, with sequencing performed at the Broad Institute Sequencing Center (U24HD090743). Genetic analyses of Australian cases were supported by the Genetic Basis of Epilepsy Research program (NHMRC grants GNT1172897, GNT2033247, GNT2006841) and by Victorian State Government Operational Infrastructure Support and the NHMRC IRIISS. M.F.B., M.S.H. and I.E.S. were supported by an Australian Medical Research Future Fund Genomics Health Futures Mission grant (2007707). This study uses resources generated by the ENCODE Consortium: (1) the ENCODE Data Analysis Center (ZLab at UMass Medical Center), H. Pratt, J. Moore, M. Purcaro and Z. Weng for providing ENCODE cCREs data; (2) Bernstein laboratory at the Broad Institute for the H3K27ac peaks in H1-hESC; (3) T. Gingeras laboratory at Cold Spring Harbor Laboratory for generating the small RNA-seq data from six human embryonic brain regions. N.W. is supported by a Wellcome Career Development Award (grant no. 305292/Z/23/Z) and a research prize from the Lister Institute. H.C.M. is funded by ALSAC of St. Jude Children’s Research Hospital. O.M. was supported by the Hazem Ben-Gacem Tunisia Medical Fellowship Fund. K.Õ. was supported by Estonian Research Council grants PRG471 and PRG2040. Health Research Council of New Zealand and Curekids New Zealand supported the recruitment, phenotyping and sequencing of the New Zealand participants. C. Charbonnier is supported by the 2022 MESSIDORE program (project Inserm-MESSIDORE 19). M. Essid, G.B., S. Auvin, S. Baer, N. Chemaly, A.d.S.M., M.M., R.N., E.P., J. Toulouse, N.V. and G. Lesca are members of the EPICARE European Reference Network (ERN-EPICARE). A.K., F.J.K., D.W., L.F., K.Õ., N.C.B., I.S. and P.S.-W. are members of the European Reference Network for Developmental Anomalies and Intellectual Disability (ERN-ITHACA). Support for title page creation and format was provided by AuthorArranger—a tool developed at the National Cancer Institute. Extended data Extended Data Fig. 2. Distribution of variants across 200 putatively functional snRNA genes in the PFMG cohort. Open in a new tab Supplementary Figure 1 shows the same distribution in the SeqOIA and Auragen subcohorts separately. Variants were found in 121 of the 200 genes. Author contributions E. Leitão performed the in silico analyses of snRNA genes, burden tests, genotype–phenotype correlations and statistical analyses. G.L., C.N., V.B., P.M. and J. Thevenon contributed snRNA variant data from the PFMG cohort. B. Cogne and T.B. performed RNA sequencing and data analysis. A. Santini and C. Charbonnier carried out DNA methylation experiments and analyzed episignatures. M.A. and C. Charenton conducted the structural analyses. G.L., C.N., M. Essid and C.W.L. collected clinical information. C.N., G.L., C. Charenton, A. Santini and B. Cogne contributed to sections of the manuscript and C. Depienne drafted the article. C. Depienne, C.N. and G.L. supervised the study. P.M., V.B., K.J., N. Chatron, G.B., B.K., C. Mignot, P. Charles, T.B., R.P., J.-M.d.S.A., E.P.A.F., S. Sengupta, M.M., F.R., T.A., I.A., C.A., S. Arpin, C.A.-T., S. Auvin, S. Baer, N.B.-B., M. Bak, M. Barth, S. Baulac, N.B.-W., M.B., M.F.B., U.B., S. Bézieau, R.B., M. Biehler, T. B. Hammer, J. Bogoin, E.B., S. Boussion, C.B., A.B.-B., A.-L.B., A.B.-S., J. Buratti, T. Celse, P. Chambon, N. Chemaly, B. Chesneau, E.C., M.C., C. Colson, S.C., T. Courtin, I.C., A.-C.C., L.T.D., A.d.S.M., C.d.V.d.B.L., B. Demeer, A.-S.D.-P., P.D., S. DiTroia, M.D.-F., C. Dubourg, C. Dubucs, S. Ducreux, L.D., R.D., B. Durand, S.E.C., M. Elbracht, L.F., M. Faoucher, A.F., S.F., M. Fradin, P. Gaignard, B.G., A. Garde, J.G., D. Gill, A. Goldenberg, D. Grabli, C.G., S.G., P. Gueguen, A.-M.G., A. Guichet, T. B. Haack, N.H., M.G.H., S.H., T. Herget, B.H., D.H., J.H., M.H., T. Holling, C.H., B.I., A.J., L.J., N.J.-M., F.J.K., S.K., C.K., M.K., F.K., J.K., R.K., A.K., I.K., K.K., A. Labalme, J.-S.L., V.L., F.L.B., A.-S.L., M.L., E. Leguern, J.L., N.L., S.L., K.L., S.M.W.M., L.M.-H., J. Maraval, I.M., C. Mattausch, S. Mercier, O.M., G.M., J. Mortreux, A.M., R.N., S.N., V.N., A.N., L.N., M.N., F.N., M.C.O’L., S. Odent, N.M.O., V.O., S. Olivieri, K.Õ., L.S.P., E.P., O.P., L.P.-S., F.P., C. Philippe, A. Piton, M.P., C. Poirsier, A. Pouzet, C. Prouteau, S.Q.-R., M. Renaud, A.-C.R., M. Rio, C.R., F.R.-R., P.R., M. Rossi, A.R., V.R., M.R.-M., P.S.-V., A. Saunier, R.S., E. Sarrazin, C.S., E. Schaefer, C.S.-B., A. Schneider, I.S., V.B.S., S. Spranger, T.S., M.S., S.R.S., B.S.-B., S.L.S., F.S., M.T., D.T., J. Toulouse, H.T., S. Valence, S. Valleix, J.V.-G., L.V., D.V., N.V., A.V., A.W., J.W., Y.W., D.W., T.W., M.Y., T.Y., K.Z.-K., M.S.Z., A.Z., N.C.B., A. Lermine, G.N., J.G.G., L.G.S., M.S.H., I.E.S., N.W., A.O’D.-L., H.C.M. and P.B. contributed molecular and/or clinical data and provided intellectual input. All authors approved the final manuscript. Peer review Peer review information Nature Genetics thanks Stylianos Antonarakis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Funding Open access funding provided by Universität Duisburg-Essen. Data availability Variant details have been submitted to ClinVar ( SUB15855213 ). RNA-seq and methylation data have been deposited in the European Genome-phenome Archive (EGA, http://www.ebi.ac.uk/ega ), hosted by the EBI. RNA-seq data are available under the Study Accession Number EGAS50000001410 . Methylation data are accessible under the study accession EGAS00001008070 . Both are subject to a Data Processing Agreement, and access requests will be reviewed by a Data Access Committee to ensure compliance with ethical and legal standards. Due to ethical considerations, individual genome data cannot be made publicly available. Controlled access is required to safeguard participant privacy and to comply with data protection regulations, including the GDPR in Europe. Access to genome data from the PFMG2025 cohort is governed by French data protection laws and is only possible through the Collecteur Analyseur de Données (CAD). More details can be found on the PFMG2025 website at https://pfmg2025.fr/le-plan/collecteur-analyseur-de-donnees-cad/ . The coordinates of the ENCODE Registry of cCREs in the human genome 26 and bigwig files concerning the peaks of histone H3 acetylation of lysine 27 (H3K27ac) obtained for the H1 human embryonic stem cell line (H1-hESC) 27 were downloaded through the UCSC Table Browser 62 . Bigwig files concerning small RNA-seq data from six human embryonic brain regions were downloaded from the ENCODE portal 68 ( https://www.encodeproject.org/ ) with the following identifiers: ENCFF013RLG , ENCFF029RIV , ENCFF034QAV , ENCFF197SSE , ENCFF221KEN , ENCFF343ZBS , ENCFF405QIN , ENCFF532SOY , ENCFF654ONK , ENCFF738LDD , ENCFF870FMA , ENCFF887TOS , ENCFF106ESQ , ENCFF222WBQ , ENCFF250WEA , ENCFF254UEQ , ENCFF319GRF , ENCFF425WUZ , ENCFF443ONL , ENCFF820JTT , ENCFF897IWP , ENCFF915WAC , ENCFF946YVE and ENCFF965GHD . Code availability No custom code was developed for this study. All analyses were performed using existing, publicly available pipelines and software tools listed in the Methods and Reporting Summary . Competing interests N.W. receives research funding from Novo Nordisk and Biomarin Pharmaceutical. L.T.D. receives research funding from Stoke Therapeutics. L.G.S. receives funding from the Health Research Council of New Zealand and Cure Kids New Zealand. She has served as a paid consultant of the Epilepsy Study Consortium for consulting work for Epygenix Therapeutics, Ovid Therapeutics, Stoke Therapeutics, Takeda Pharmaceuticals, UCB and Zogenix. L.G.S. has received research grants and consultancy fees from Zynerba Pharmaceuticals and has served on Takeda and Eisai Pharmaceuticals scientific advisory panels. I.E.S. has served on scientific advisory boards for CAMP4 Therapeutics, Longboard Pharmaceuticals, Mosaica Therapeutics, Takeda Pharmaceuticals and UCB; has received speaker honoraria from Akumentis, Biocodex, Chiesi, Stoke Therapeutics, UCB and Zuellig Pharma; has received funding for travel from Stoke Therapeutics and UCB; has served as an investigator for Anavex Life Sciences, Biohaven Ltd., Bright Minds Biosciences, Cerebral Therapeutics, Cerecin Inc., Cereval Therapeutics, Encoded Therapeutics, EpiMinder Inc., ES-Therapeutics, Longboard Pharmaceuticals, Marinus, Neuren Pharmaceuticals, Neurocrine BioSciences, Praxis Precision Medicines, Shanghai Zhimeng Biopharma, SK Life Science, Supernus Pharmaceuticals, Takeda Pharmaceuticals, UCB, Ultragenyx, Xenon Pharmaceuticals and Zogenix; has consulted for Biohaven Pharmaceuticals, Eisai, Epilepsy Consortium, Longboard Pharmaceuticals, Praxis, Stoke Therapeutics and UCB and is a executive Director of Bellberry Ltd. and a Director of the Australian Academy of Health and Medical Sciences. She may accrue future revenue on pending patent WO61/010176 (filed: 2008): Therapeutic Compound; has a patent for SCN1A testing held by Bionomics Inc and licensed to various diagnostic companies; has a patent molecular diagnostic/theranostic target for benign familial infantile epilepsy (BFIE) [PRRT2] 2011904493 and 2012900190 and PCT/AU2012/001321 (TECH ID:2012-009). The other authors declare no competing interests. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. These authors contributed equally: Elsa Leitão, Amandine Santini, Benjamin Cogne. These authors jointly supervised this work: Christel Depienne, Gaetan Lesca, Caroline Nava. Change history 4/2/2026 In the version of the article initially published, the text “C. 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Supplementary Materials Supplementary Information (3.7MB, pdf) Supplementary Notes and Figs. 1–7. Reporting Summary (68KB, pdf) Peer Review File (370.1KB, pdf) Supplementary Table 1 (577.2KB, xlsx) Supplementary Tables 1–15. Data Availability Statement Variant details have been submitted to ClinVar ( SUB15855213 ). RNA-seq and methylation data have been deposited in the European Genome-phenome Archive (EGA, http://www.ebi.ac.uk/ega ), hosted by the EBI. RNA-seq data are available under the Study Accession Number EGAS50000001410 . Methylation data are accessible under the study accession EGAS00001008070 . Both are subject to a Data Processing Agreement, and access requests will be reviewed by a Data Access Committee to ensure compliance with ethical and legal standards. Due to ethical considerations, individual genome data cannot be made publicly available. Controlled access is required to safeguard participant privacy and to comply with data protection regulations, including the GDPR in Europe. Access to genome data from the PFMG2025 cohort is governed by French data protection laws and is only possible through the Collecteur Analyseur de Données (CAD). More details can be found on the PFMG2025 website at https://pfmg2025.fr/le-plan/collecteur-analyseur-de-donnees-cad/ . The coordinates of the ENCODE Registry of cCREs in the human genome 26 and bigwig files concerning the peaks of histone H3 acetylation of lysine 27 (H3K27ac) obtained for the H1 human embryonic stem cell line (H1-hESC) 27 were downloaded through the UCSC Table Browser 62 . Bigwig files concerning small RNA-seq data from six human embryonic brain regions were downloaded from the ENCODE portal 68 ( https://www.encodeproject.org/ ) with the following identifiers: ENCFF013RLG , ENCFF029RIV , ENCFF034QAV , ENCFF197SSE , ENCFF221KEN , ENCFF343ZBS , ENCFF405QIN , ENCFF532SOY , ENCFF654ONK , ENCFF738LDD , ENCFF870FMA , ENCFF887TOS , ENCFF106ESQ , ENCFF222WBQ , ENCFF250WEA , ENCFF254UEQ , ENCFF319GRF , ENCFF425WUZ , ENCFF443ONL , ENCFF820JTT , ENCFF897IWP , ENCFF915WAC , ENCFF946YVE and ENCFF965GHD . No custom code was developed for this study. All analyses were performed using existing, publicly available pipelines and software tools listed in the Methods and Reporting Summary . 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