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Your search keyword '"Anne O'Donnell-Luria"' showing total 6 results
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6 results on '"Anne O'Donnell-Luria"'

1. Leaving no patient behind! Expert recommendation in the use of innovative technologies for diagnosing rare diseases

Author

Clara D. M. van Karnebeek, Anne O’Donnell-Luria, Gareth Baynam, Anaïs Baudot, Tudor Groza, Judith J. M. Jans, Timo Lassmann, Mary Catherine V. Letinturier, Stephen B. Montgomery, Peter N. Robinson, Stefaan Sansen, Ruty Mehrian-Shai, Charles Steward, Kenjiro Kosaki, Patricia Durao, and Bekim Sadikovic

Subjects
Rare disease, Rare disease diagnosis, Innovative technologies, IRDiRC, Rare disease research, Genomics, Medicine
Abstract

Abstract Genetic diagnosis plays a crucial role in rare diseases, particularly with the increasing availability of emerging and accessible treatments. The International Rare Diseases Research Consortium (IRDiRC) has set its primary goal as: “Ensuring that all patients who present with a suspected rare disease receive a diagnosis within one year if their disorder is documented in the medical literature”. Despite significant advances in genomic sequencing technologies, more than half of the patients with suspected Mendelian disorders remain undiagnosed. In response, IRDiRC proposes the establishment of “a globally coordinated diagnostic and research pipeline”. To help facilitate this, IRDiRC formed the Task Force on Integrating New Technologies for Rare Disease Diagnosis. This multi-stakeholder Task Force aims to provide an overview of the current state of innovative diagnostic technologies for clinicians and researchers, focusing on the patient’s diagnostic journey. Herein, we provide an overview of a broad spectrum of emerging diagnostic technologies involving genomics, epigenomics and multi-omics, functional testing and model systems, data sharing, bioinformatics, and Artificial Intelligence (AI), highlighting their advantages, limitations, and the current state of clinical adaption. We provide expert recommendations outlining the stepwise application of these innovative technologies in the diagnostic pathways while considering global differences in accessibility. The importance of FAIR (Findability, Accessibility, Interoperability, and Reusability) and CARE (Collective benefit, Authority to control, Responsibility, and Ethics) data management is emphasized, along with the need for enhanced and continuing education in medical genomics. We provide a perspective on future technological developments in genome diagnostics and their integration into clinical practice. Lastly, we summarize the challenges related to genomic diversity and accessibility, highlighting the significance of innovative diagnostic technologies, global collaboration, and equitable access to diagnosis and treatment for people living with rare disease.

Published
2024
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2. Strategies to Uplift Novel Mendelian Gene Discovery for Improved Clinical Outcomes

Author

Eleanor G. Seaby, Heidi L. Rehm, and Anne O’Donnell-Luria

Subjects
Mendelian, novel gene discovery, disease–gene relationships, rare genetic disorders, genomics, rare disease, Genetics, QH426-470
Abstract

Rare genetic disorders, while individually rare, are collectively common. They represent some of the most severe disorders affecting patients worldwide with significant morbidity and mortality. Over the last decade, advances in genomic methods have significantly uplifted diagnostic rates for patients and facilitated novel and targeted therapies. However, many patients with rare genetic disorders still remain undiagnosed as the genetic etiology of only a proportion of Mendelian conditions has been discovered to date. This article explores existing strategies to identify novel Mendelian genes and how these discoveries impact clinical care and therapeutics. We discuss the importance of data sharing, phenotype-driven approaches, patient-led approaches, utilization of large-scale genomic sequencing projects, constraint-based methods, integration of multi-omics data, and gene-to-patient methods. We further consider the health economic advantages of novel gene discovery and speculate on potential future methods for improved clinical outcomes.

Published
2021
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3. Genomic autopsy to identify underlying causes of pregnancy loss and perinatal death

Author

Peer Arts, Anne O'Donnell-Luria, David Lawrence, Hamish Scott, Ali Moghimi, Chirag Patel, Alicia Byrne, Thi Thuong Ha, Thessa Kroes, Mathew Wallis, John Toubia, Byrne, Alicia B, Arts, Peer, Ha, Thuong T, Kassahn, Karin S, Babic, Milena, Frank, Mahalia SB, Feng, Jinghua, Wang, Paul, Lawrence, David M, Eshraghi, Leila, Arriola, Luis, Toubia, John, Gardner, Alison E, Morrow, Rebecca, Schreiber, Andreas W, King-Smith, Sarah L, Jackson, Matilda R, Scott, Hamish S, Broad Institute Center for Mendelian Genomics, and Genomic Autopsy Study Research Network

Subjects
autopsy, female, prenatal diagnosis, spontaneous abortion, etiology, perinatal death, genomics, genetics, human, pregnancy, General Medicine, General Biochemistry, Genetics and Molecular Biology
Abstract

Refereed/Peer-reviewed Pregnancy loss and perinatal death are devastating events for families. We assessed ‘genomic autopsy’ as an adjunct to standard autopsy for 200 families who had experienced fetal or newborn death, providing a definitive or candidate genetic diagnosis in 105 families. Our cohort provides evidence of severe atypical in utero presentations of known genetic disorders and identifies novel phenotypes and disease genes. Inheritance of 42% of definitive diagnoses were either autosomal recessive (30.8%), X-linked recessive (3.8%) or autosomal dominant (excluding de novos, 7.7%), with risk of recurrence in future pregnancies. We report that at least ten families (5%) used their diagnosis for preimplantation (5) or prenatal diagnosis (5) of 12 pregnancies. We emphasize the clinical importance of genomic investigations of pregnancy loss and perinatal death, with short turnaround times for diagnostic reporting and followed by systematic research follow-up investigations. This approach has the potential to enable accurate counseling for future pregnancies.

Published
2023
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4. Centers for Mendelian Genomics: A decade of facilitating gene discovery

Author

Samantha M. Baxter, Jennifer E. Posey, Nicole J. Lake, Nara Sobreira, Jessica X. Chong, Steven Buyske, Elizabeth E. Blue, Lisa H. Chadwick, Zeynep H. Coban-Akdemir, Kimberly F. Doheny, Colleen P. Davis, Monkol Lek, Christopher Wellington, Shalini N. Jhangiani, Mark Gerstein, Richard A. Gibbs, Richard P. Lifton, Daniel G. MacArthur, Tara C. Matise, James R. Lupski, David Valle, Michael J. Bamshad, Ada Hamosh, Shrikant Mane, Deborah A. Nickerson, Heidi L. Rehm, Anne O’Donnell-Luria, Marcia Adams, François Aguet, Gulsen Akay, Peter Anderson, Corina Antonescu, Harindra M. Arachchi, Mehmed M. Atik, Christina A. Austin-Tse, Larry Babb, Tamara J. Bacus, Vahid Bahrambeigi, Suganthi Balasubramanian, Yavuz Bayram, Arthur L. Beaudet, Christine R. Beck, John W. Belmont, Jennifer E. Below, Kaya Bilguvar, Corinne D. Boehm, Eric Boerwinkle, Philip M. Boone, Sara J. Bowne, Harrison Brand, Kati J. Buckingham, Alicia B. Byrne, Daniel Calame, Ian M. Campbell, Xiaolong Cao, Claudia Carvalho, Varuna Chander, Jaime Chang, Katherine R. Chao, Ivan K. Chinn, Declan Clarke, Ryan L. Collins, Beryl Cummings, Zain Dardas, Moez Dawood, Kayla Delano, Stephanie P. DiTroia, Harshavardhan Doddapaneni, Haowei Du, Renqian Du, Ruizhi Duan, Mohammad Eldomery, Christine M. Eng, Eleina England, Emily Evangelista, Selin Everett, Jawid Fatih, Adam Felsenfeld, Laurent C. Francioli, Christian D. Frazar, Jack Fu, Emmanuel Gamarra, Tomasz Gambin, Weiniu Gan, Mira Gandhi, Vijay S. Ganesh, Kiran V. Garimella, Laura D. Gauthier, Danielle Giroux, Claudia Gonzaga-Jauregui, Julia K. Goodrich, William W. Gordon, Sean Griffith, Christopher M. Grochowski, Shen Gu, Sanna Gudmundsson, Stacey J. Hall, Adam Hansen, Tamar Harel, Arif O. Harmanci, Isabella Herman, Kurt Hetrick, Hadia Hijazi, Martha Horike-Pyne, Elvin Hsu, Jianhong Hu, Yongqing Huang, Jameson R. Hurless, Steve Jahl, Gail P. Jarvik, Yunyun Jiang, Eric Johanson, Angad Jolly, Ender Karaca, Michael Khayat, James Knight, J. Thomas Kolar, Sushant Kumar, Seema Lalani, Kristen M. Laricchia, Kathryn E. Larkin, Suzanne M. Leal, Gabrielle Lemire, Richard A. Lewis, He Li, Hua Ling, Rachel B. Lipson, Pengfei Liu, Alysia Kern Lovgren, Francesc López-Giráldez, Melissa P. MacMillan, Brian E. Mangilog, Stacy Mano, Dana Marafi, Beth Marosy, Jamie L. Marshall, Renan Martin, Colby T. Marvin, Michelle Mawhinney, Sean McGee, Daniel J. McGoldrick, Michelle Mehaffey, Betselote Mekonnen, Xiaolu Meng, Tadahiro Mitani, Christina Y. Miyake, David Mohr, Shaine Morris, Thomas E. Mullen, David R. Murdock, Mullai Murugan, Donna M. Muzny, Ben Myers, Juanita Neira, Kevin K. Nguyen, Patrick M. Nielsen, Natalie Nudelman, Emily O’Heir, Melanie C. O’Leary, Chrissie Ongaco, Jordan Orange, Ikeoluwa A. Osei-Owusu, Ingrid S. Paine, Lynn S. Pais, Justin Paschall, Karynne Patterson, Davut Pehlivan, Benjamin Pelle, Samantha Penney, Jorge Perez de Acha Chavez, Emma Pierce-Hoffman, Cecilia M. Poli, Jaya Punetha, Aparna Radhakrishnan, Matthew A. Richardson, Eliete Rodrigues, Gwendolin T. Roote, Jill A. Rosenfeld, Erica L. Ryke, Aniko Sabo, Alice Sanchez, Isabelle Schrauwen, Daryl A. Scott, Fritz Sedlazeck, Jillian Serrano, Chad A. Shaw, Tameka Shelford, Kathryn M. Shively, Moriel Singer-Berk, Joshua D. Smith, Hana Snow, Grace Snyder, Matthew Solomonson, Rachel G. Son, Xiaofei Song, Pawel Stankiewicz, Taylorlyn Stephan, V. Reid Sutton, Abigail Sveden, Diana Cornejo Sánchez, Monica Tackett, Michael Talkowski, Machiko S. Threlkeld, Grace Tiao, Miriam S. Udler, Laura Vail, Zaheer Valivullah, Elise Valkanas, Grace E. VanNoy, Qingbo S. Wang, Gao Wang, Lu Wang, Michael F. Wangler, Nicholas A. Watts, Ben Weisburd, Jeffrey M. Weiss, Marsha M. Wheeler, Janson J. White, Clara E. Williamson, Michael W. Wilson, Wojciech Wiszniewski, Marjorie A. Withers, Dane Witmer, Lauren Witzgall, Elizabeth Wohler, Monica H. Wojcik, Isaac Wong, Jordan C. Wood, Nan Wu, Jinchuan Xing, Yaping Yang, Qian Yi, Bo Yuan, Jordan E. Zeiger, Chaofan Zhang, Peng Zhang, Yan Zhang, Xiaohong Zhang, Yeting Zhang, Shifa Zhang, Huda Zoghbi, and Igna van den Veyver

Subjects
Phenotype, Exome Sequencing, Humans, Exome, Genomics, Article, Genetic Association Studies, Genetics (clinical)
Abstract

PURPOSE: Mendelian disease genomic research has undergone a massive transformation over the past decade. With increasing availability of exome and genome sequencing, the role of Mendelian research has expanded beyond data collection, sequencing, and analysis to worldwide data sharing and collaboration. METHODS: Over the past 10 years, the National Institutes of Health–supported Centers for Mendelian Genomics (CMGs) have played a major role in this research and clinical evolution. RESULTS: We highlight the cumulative gene discoveries facilitated by the program, biomedical research leveraged by the approach, and the larger impact on the research community. Beyond generating a list of gene-phenotype relationships and participating in widespread data sharing, the CMGs have created resources, tools, and training for the larger community to foster understanding of genes and genome variation. The CMGs have participated in a wide range of data sharing activities, including deposition of all eligible CMG data into the Analysis, Visualization, and Informatics Lab-space (AnVIL), sharing candidate genes through the Matchmaker Exchange and the CMG website, and sharing variants in Genotypes to Mendelian Phenotypes (Geno2MP) and VariantMatcher. CONCLUSION: The work is far from complete; strengthening communication between research and clinical realms, continued development and sharing of knowledge and tools, and improving access to richly characterized data sets are all required to diagnose the remaining molecularly undiagnosed patients.

Published
2022
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5. Landscape of multi-nucleotide variants in 125,748 human exomes and 15,708 genomes

Author

Ruth Loos, Qingbo Wang, Anne O'Donnell-Luria, Benjamin Glaser, James Ware, John D. Rioux, ROBERTO ELOSUA, Kristian Cibulskis, Laurent Francioli, E Shyong Tai, Terho Lehtimäki, Daniel MacArthur, Irina Armean, Matthew Bown, Tiinamaija Tuomi, Jeanette Erdmann, Matthew Solomonson, Harry Sokol, Aarno Palotie, Martti Färkkilä, Ronald Ma, Olle Melander, Emilia Solinas, Tampere University, Clinical Medicine, Department of Clinical Chemistry, Centre of Excellence in Complex Disease Genetics, HUS Abdominal Center, Institute for Molecular Medicine Finland, Helsinki Institute of Life Science HiLIFE, Department of Medicine, Clinicum, Gastroenterologian yksikkö, Helsinki University Hospital Area, Research Programs Unit, Aarno Palotie / Principal Investigator, Genomics of Neurological and Neuropsychiatric Disorders, Department of Public Health, Samuli Olli Ripatti / Principal Investigator, Complex Disease Genetics, Biostatistics Helsinki, Biosciences, and Rosetrees Trust

Subjects
0301 basic medicine, Mutation rate, Science, Nonsense mutation, General Physics and Astronomy, Computational biology, Genome, Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, Protein sequencing, 0302 clinical medicine, DNA Mutational Analysis, Genetic variation, ELEMENTS, lcsh:Science, Exome, SIGNATURES, Exome sequencing, Polymerase, 030304 developmental biology, 0303 health sciences, Multidisciplinary, biology, Haplotype, 1184 Genetics, developmental biology, physiology, Genome Aggregation Database Production Team, Genomics, General Chemistry, FRAMEWORK, EVOLUTION, SLIPPAGE, 030104 developmental biology, Haplotypes, CpG site, DE-NOVO MUTATIONS, Genome Aggregation Database Consortium, 030220 oncology & carcinogenesis, Mutation (genetic algorithm), WHOLE-GENOME, PATTERNS, biology.protein, DNA-POLYMERASE-ZETA, REPEATS, lcsh:Q, 3111 Biomedicine, 030217 neurology & neurosurgery
Abstract

Multi-nucleotide variants (MNVs), defined as two or more nearby variants existing on the same haplotype in an individual, are a clinically and biologically important class of genetic variation. However, existing tools typically do not accurately classify MNVs, and understanding of their mutational origins remains limited. Here, we systematically survey MNVs in 125,748 whole exomes and 15,708 whole genomes from the Genome Aggregation Database (gnomAD). We identify 1,792,248 MNVs across the genome with constituent variants falling within 2 bp distance of one another, including 18,756 variants with a novel combined effect on protein sequence. Finally, we estimate the relative impact of known mutational mechanisms - CpG deamination, replication error by polymerase zeta, and polymerase slippage at repeat junctions - on the generation of MNVs. Our results demonstrate the value of haplotype-aware variant annotation, and refine our understanding of genome-wide mutational mechanisms of MNVs., Multi-nucleotide variants (MNV) are genetic variants in close proximity of each other on the same haplotype whose functional impact is difficult to predict if they reside in the same codon. Here, Wang et al. use the gnomAD dataset to assemble a catalogue of MNVs and estimate their global mutation rate.

Published
2020
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6. seqr: A web-based analysis and collaboration tool for rare disease genomics

Author

Lynn S. Pais, Hana Snow, Ben Weisburd, Shifa Zhang, Samantha M. Baxter, Stephanie DiTroia, Emily O'Heir, Eleina England, Katherine R. Chao, Gabrielle Lemire, Ikeoluwa Osei‐Owusu, Grace E. VanNoy, Michael Wilson, Kevin Nguyen, Harindra Arachchi, William Phu, Matthew Solomonson, Stacy Mano, Melanie O'Leary, Alysia Lovgren, Lawrence Babb, Christina A. Austin‐Tse, Heidi L. Rehm, Daniel G. MacArthur, and Anne O'Donnell‐Luria

Subjects
Internet, Rare Diseases, Genetics, Humans, Exome, Genomics, Genetics (clinical), Software, Article
Abstract

Exome and genome sequencing have become the tools of choice for rare disease diagnosis, leading to large amounts of data available for analyses. To identify causal variants in these datasets, powerful filtering and decision support tools that can be efficiently used by clinicians and researchers are required. To address this need, we developed seqr - an open source, web-based tool for family-based monogenic disease analysis that allows researchers to work collaboratively to search and annotate genomic callsets. To date, seqr is being used in several research pipelines and one clinical diagnostic lab. In our own experience through the Broad Institute Center for Mendelian Genomics, seqr has enabled analyses of over 10,000 families, supporting the diagnosis of more than 3,800 individuals with rare disease and discovery of over 300 novel disease genes. Here, we describe a framework for genomic analysis in rare disease that leverages seqr’s capabilities for variant filtration, annotation, and causal variant identification, as well as support for research collaboration and data sharing. The seqr platform is available as open-source software, allowing low-cost participation in rare disease research, and a community effort to support diagnosis and gene discovery in rare disease.

Published
2022

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