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Sun KY, Bai X, Chen S, Bao S, Zhang C, Kapoor M, Backman J, Joseph T, Maxwell E, Mitra G, Gorovits A, Mansfield A, Boutkov B, Gokhale S, Habegger L, Marcketta A, Locke AE, Ganel L, Hawes A, Kessler MD, Sharma D, Staples J, Bovijn J, Gelfman S, Di Gioia A, Rajagopal VM, Lopez A, Varela JR, Alegre-Díaz J, Berumen J, Tapia-Conyer R, Kuri-Morales P, Torres J, Emberson J, Collins R, Cantor M, Thornton T, Kang HM, Overton JD, Shuldiner AR, Cremona ML, Nafde M, Baras A, Abecasis G, Marchini J, Reid JG, Salerno W, Balasubramanian S. A deep catalogue of protein-coding variation in 983,578 individuals. Nature 2024; 631:583-592. [PMID: 38768635 PMCID: PMC11254753 DOI: 10.1038/s41586-024-07556-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 05/10/2024] [Indexed: 05/22/2024]
Abstract
Rare coding variants that substantially affect function provide insights into the biology of a gene1-3. However, ascertaining the frequency of such variants requires large sample sizes4-8. Here we present a catalogue of human protein-coding variation, derived from exome sequencing of 983,578 individuals across diverse populations. In total, 23% of the Regeneron Genetics Center Million Exome (RGC-ME) data come from individuals of African, East Asian, Indigenous American, Middle Eastern and South Asian ancestry. The catalogue includes more than 10.4 million missense and 1.1 million predicted loss-of-function (pLOF) variants. We identify individuals with rare biallelic pLOF variants in 4,848 genes, 1,751 of which have not been previously reported. From precise quantitative estimates of selection against heterozygous loss of function (LOF), we identify 3,988 LOF-intolerant genes, including 86 that were previously assessed as tolerant and 1,153 that lack established disease annotation. We also define regions of missense depletion at high resolution. Notably, 1,482 genes have regions that are depleted of missense variants despite being tolerant of pLOF variants. Finally, we estimate that 3% of individuals have a clinically actionable genetic variant, and that 11,773 variants reported in ClinVar with unknown significance are likely to be deleterious cryptic splice sites. To facilitate variant interpretation and genetics-informed precision medicine, we make this resource of coding variation from the RGC-ME dataset publicly accessible through a variant allele frequency browser.
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Affiliation(s)
| | | | - Siying Chen
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Suying Bao
- Regeneron Genetics Center, Tarrytown, NY, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Liron Ganel
- Regeneron Genetics Center, Tarrytown, NY, USA
| | | | | | | | | | | | | | | | | | | | | | - Jesús Alegre-Díaz
- Faculty of Medicine, National Autonomous University of Mexico (UNAM), Mexico City, Mexico
| | - Jaime Berumen
- Faculty of Medicine, National Autonomous University of Mexico (UNAM), Mexico City, Mexico
| | - Roberto Tapia-Conyer
- Faculty of Medicine, National Autonomous University of Mexico (UNAM), Mexico City, Mexico
| | - Pablo Kuri-Morales
- Faculty of Medicine, National Autonomous University of Mexico (UNAM), Mexico City, Mexico
- Instituto Tecnológico y de Estudios Superiores de Monterrey, Monterrey, Mexico
| | - Jason Torres
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jonathan Emberson
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Rory Collins
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | | | | | | | | | | | - Mona Nafde
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Aris Baras
- Regeneron Genetics Center, Tarrytown, NY, USA
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2
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Ashenden AJ, Chowdhury A, Anastasi LT, Lam K, Rozek T, Ranieri E, Siu CWK, King J, Mas E, Kassahn KS. The Multi-Omic Approach to Newborn Screening: Opportunities and Challenges. Int J Neonatal Screen 2024; 10:42. [PMID: 39051398 PMCID: PMC11270328 DOI: 10.3390/ijns10030042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 06/13/2024] [Accepted: 06/13/2024] [Indexed: 07/27/2024] Open
Abstract
Newborn screening programs have seen significant evolution since their initial implementation more than 60 years ago, with the primary goal of detecting treatable conditions within the earliest possible timeframe to ensure the optimal treatment and outcomes for the newborn. New technologies have driven the expansion of screening programs to cover additional conditions. In the current era, the breadth of screened conditions could be further expanded by integrating omic technologies such as untargeted metabolomics and genomics. Genomic screening could offer opportunities for lifelong care beyond the newborn period. For genomic newborn screening to be effective and ready for routine adoption, it must overcome barriers such as implementation cost, public acceptability, and scalability. Metabolomics approaches, on the other hand, can offer insight into disease phenotypes and could be used to identify known and novel biomarkers of disease. Given recent advances in metabolomic technologies, alongside advances in genomics including whole-genome sequencing, the combination of complementary multi-omic approaches may provide an exciting opportunity to leverage the best of both approaches and overcome their respective limitations. These techniques are described, along with the current outlook on multi-omic-based NBS research.
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Affiliation(s)
- Alex J. Ashenden
- Department of Biochemical Genetics, SA Pathology, Women’s and Children’s Hospital, Adelaide, SA 5006, Australia (T.R.)
| | - Ayesha Chowdhury
- Department of Molecular Pathology, SA Pathology, Adelaide, SA 5000, Australia; (A.C.); (L.T.A.)
| | - Lucy T. Anastasi
- Department of Molecular Pathology, SA Pathology, Adelaide, SA 5000, Australia; (A.C.); (L.T.A.)
| | - Khoa Lam
- Department of Biochemical Genetics, SA Pathology, Women’s and Children’s Hospital, Adelaide, SA 5006, Australia (T.R.)
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5000, Australia
| | - Tomas Rozek
- Department of Biochemical Genetics, SA Pathology, Women’s and Children’s Hospital, Adelaide, SA 5006, Australia (T.R.)
| | - Enzo Ranieri
- Department of Biochemical Genetics, SA Pathology, Women’s and Children’s Hospital, Adelaide, SA 5006, Australia (T.R.)
| | - Carol Wai-Kwan Siu
- Department of Biochemical Genetics, SA Pathology, Women’s and Children’s Hospital, Adelaide, SA 5006, Australia (T.R.)
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5000, Australia
| | - Jovanka King
- Immunology Directorate, SA Pathology, Adelaide, SA 5000, Australia
- Department of Allergy and Clinical Immunology, Women’s and Children’s Hospital, Adelaide, SA 5006, Australia
- Discipline of Paediatrics, Women’s and Children’s Hospital, The University of Adelaide, Adelaide, SA 5006, Australia
| | - Emilie Mas
- Department of Biochemical Genetics, SA Pathology, Women’s and Children’s Hospital, Adelaide, SA 5006, Australia (T.R.)
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5000, Australia
| | - Karin S. Kassahn
- Department of Molecular Pathology, SA Pathology, Adelaide, SA 5000, Australia; (A.C.); (L.T.A.)
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5000, Australia
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3
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Zhang L, Shen M, Shu X, Zhou J, Ding J, Lin H, Pan B, Zhang C, Wang B, Guo W. The recommendation of re-classification of variants of uncertain significance (VUS) in adult genetic disorders patients. J Hum Genet 2024:10.1038/s10038-024-01263-4. [PMID: 38839994 DOI: 10.1038/s10038-024-01263-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 05/07/2024] [Accepted: 05/28/2024] [Indexed: 06/07/2024]
Abstract
Since variants of uncertain significance (VUS) reported in genetic testing cannot be acted upon clinically, this classification may delay or prohibit precise diagnosis and genetic counseling in adult genetic disorders patients. Large-scale analyses about qualitatively distinct lines of evidence used for VUS can make them re-classification more accurately. We analyzed 458 Chinese adult patients WES data, within 15 pathogenic evidence PS1, PS2, PM1, PM6 and PP4 were not used for VUS pathogenic classification, meanwhile the PP3, BP4, PP2 were used much more frequently. The PM2_Supporting was used most widely for all reported variants. There were also 31 null variants (nonsense, frameshift, canonical ±1 or 2 splice sites) which were probably the disease-causing variants of the patients were classified as VUS. By analyzed the evidence used for all VUS we recommend that appropriate genetic counseling, reliable releasing of in-house data, allele frequency comparison between case and control, expanded verification in patient family, co-segregation analysis and functional assays were urgent need to gather more evidence to reclassify VUS. We also found adult patients with nervous system disease were reported the most phenotype-associated VUS and the lower the phenotypic specificity, the more reported VUS. This result emphasized the importance of pretest genetic counseling which would make less reporting of VUS. Our result revealed the characteristics of the pathogenic classification evidence used for VUS in adult genetic disorders patients for the first time, recommend a rules-based process to evaluate the pathogenicity of VUS which could provide a strong basis for accurately evaluating the pathogenicity and clinical grade information of VUS. Meanwhile, we further expanded the genetic spectrum and improve the diagnostic rate of adult genetic disorders.
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Affiliation(s)
- Li Zhang
- Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Minna Shen
- Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xianhong Shu
- Department of Echocardiography, Zhongshan Hospital, Shanghai Institute of Cardiovascular Diseases, Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China
| | - Jingmin Zhou
- Department of Cardiology Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jing Ding
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Huandong Lin
- Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Baishen Pan
- Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Chunyan Zhang
- Department of Laboratory Medicine, Shanghai Geriatric Medical Center, Shanghai, China
| | - Beili Wang
- Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
| | - Wei Guo
- Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
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4
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Passi G, Lieberman S, Zahdeh F, Murik O, Renbaum P, Beeri R, Linial M, May D, Levy-Lahad E, Schneidman-Duhovny D. Discovering predisposing genes for hereditary breast cancer using deep learning. Brief Bioinform 2024; 25:bbae346. [PMID: 39038933 PMCID: PMC11262808 DOI: 10.1093/bib/bbae346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 04/18/2024] [Accepted: 07/04/2024] [Indexed: 07/24/2024] Open
Abstract
Breast cancer (BC) is the most common malignancy affecting Western women today. It is estimated that as many as 10% of BC cases can be attributed to germline variants. However, the genetic basis of the majority of familial BC cases has yet to be identified. Discovering predisposing genes contributing to familial BC is challenging due to their presumed rarity, low penetrance, and complex biological mechanisms. Here, we focused on an analysis of rare missense variants in a cohort of 12 families of Middle Eastern origins characterized by a high incidence of BC cases. We devised a novel, high-throughput, variant analysis pipeline adapted for family studies, which aims to analyze variants at the protein level by employing state-of-the-art machine learning models and three-dimensional protein structural analysis. Using our pipeline, we analyzed 1218 rare missense variants that are shared between affected family members and classified 80 genes as candidate pathogenic. Among these genes, we found significant functional enrichment in peroxisomal and mitochondrial biological pathways which segregated across seven families in the study and covered diverse ethnic groups. We present multiple evidence that peroxisomal and mitochondrial pathways play an important, yet underappreciated, role in both germline BC predisposition and BC survival.
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Affiliation(s)
- Gal Passi
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Sari Lieberman
- The Fuld Family Medical Genetics Institute, Shaare Zedek Medical Center 12 Bayit St., Jerusalem 9103101, Israel
- The Eisenberg R&D Authority, Shaare Zedek Medical Center, 12 Bayit St., Jerusalem 9103101, Israel
- Faculty of Medicine, The Hebrew University of Jerusalem, Ein Kerem PO Box 12271 Jerusalem 9112102, Israel
| | - Fouad Zahdeh
- The Fuld Family Medical Genetics Institute, Shaare Zedek Medical Center 12 Bayit St., Jerusalem 9103101, Israel
- The Eisenberg R&D Authority, Shaare Zedek Medical Center, 12 Bayit St., Jerusalem 9103101, Israel
| | - Omer Murik
- The Fuld Family Medical Genetics Institute, Shaare Zedek Medical Center 12 Bayit St., Jerusalem 9103101, Israel
- The Eisenberg R&D Authority, Shaare Zedek Medical Center, 12 Bayit St., Jerusalem 9103101, Israel
| | - Paul Renbaum
- The Fuld Family Medical Genetics Institute, Shaare Zedek Medical Center 12 Bayit St., Jerusalem 9103101, Israel
- The Eisenberg R&D Authority, Shaare Zedek Medical Center, 12 Bayit St., Jerusalem 9103101, Israel
| | - Rachel Beeri
- The Fuld Family Medical Genetics Institute, Shaare Zedek Medical Center 12 Bayit St., Jerusalem 9103101, Israel
- The Eisenberg R&D Authority, Shaare Zedek Medical Center, 12 Bayit St., Jerusalem 9103101, Israel
| | - Michal Linial
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem 91904, Israel
| | - Dalit May
- The Fuld Family Medical Genetics Institute, Shaare Zedek Medical Center 12 Bayit St., Jerusalem 9103101, Israel
- The Eisenberg R&D Authority, Shaare Zedek Medical Center, 12 Bayit St., Jerusalem 9103101, Israel
- Clalit Health Services, Jerusalem, Israel
| | - Ephrat Levy-Lahad
- The Fuld Family Medical Genetics Institute, Shaare Zedek Medical Center 12 Bayit St., Jerusalem 9103101, Israel
- The Eisenberg R&D Authority, Shaare Zedek Medical Center, 12 Bayit St., Jerusalem 9103101, Israel
- Faculty of Medicine, The Hebrew University of Jerusalem, Ein Kerem PO Box 12271 Jerusalem 9112102, Israel
| | - Dina Schneidman-Duhovny
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
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5
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Stenton SL, Pejaver V, Bergquist T, Biesecker LG, Byrne AB, Nadeau E, Greenblatt MS, Harrison S, Tavtigian S, Radivojac P, Brenner SE, O’Donnell-Luria A. Assessment of the evidence yield for the calibrated PP3/BP4 computational recommendations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.05.24303807. [PMID: 38496501 PMCID: PMC10942508 DOI: 10.1101/2024.03.05.24303807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Purpose To investigate the number of rare missense variants observed in human genome sequences by ACMG/AMP PP3/BP4 evidence strength, following the calibrated PP3/BP4 computational recommendations. Methods Missense variants from the genome sequences of 300 probands from the Rare Genomes Project with suspected rare disease were analyzed using computational prediction tools able to reach PP3_Strong and BP4_Moderate evidence strengths (BayesDel, MutPred2, REVEL, and VEST4). The numbers of variants at each evidence strength were analyzed across disease-associated genes and genome-wide. Results From a median of 75.5 rare (≤1% allele frequency) missense variants in disease-associated genes per proband, a median of one reached PP3_Strong, 3-5 PP3_Moderate, and 3-5 PP3_Supporting. Most were allocated BP4 evidence (median 41-49 per proband) or were indeterminate (median 17.5-19 per proband). Extending the analysis to all protein-coding genes genome-wide, the number of PP3_Strong variants increased approximately 2.6-fold compared to disease-associated genes, with a median per proband of 1-3 PP3_Strong, 8-16 PP3_Moderate, and 10-17 PP3_Supporting. Conclusion A small number of variants per proband reached PP3_Strong and PP3_Moderate in 3,424 disease-associated genes, and though not the intended use of the recommendations, also genome-wide. Use of PP3/BP4 evidence as recommended from calibrated computational prediction tools in the clinical diagnostic laboratory is unlikely to inappropriately contribute to the classification of an excessive number of variants as Pathogenic or Likely Pathogenic by ACMG/AMP rules.
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Affiliation(s)
- Sarah L. Stenton
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Vikas Pejaver
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Timothy Bergquist
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Leslie G. Biesecker
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Alicia B. Byrne
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Emily Nadeau
- Department of Medicine and University of Vermont Cancer Center, University of Vermont, Larner College of Medicine, Burlington, VT 05405, USA
| | - Marc S. Greenblatt
- Department of Medicine and University of Vermont Cancer Center, University of Vermont, Larner College of Medicine, Burlington, VT 05405, USA
| | - Steven Harrison
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Ambry Genetics, Aliso Viejo, CA, USA
| | - Sean Tavtigian
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
| | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA
| | - Steven E. Brenner
- Department of Plant and Microbial Biology and Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Anne O’Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
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6
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Saez-Matia A, Ibarluzea MG, M-Alicante S, Muguruza-Montero A, Nuñez E, Ramis R, Ballesteros OR, Lasa-Goicuria D, Fons C, Gallego M, Casis O, Leonardo A, Bergara A, Villarroel A. MLe-KCNQ2: An Artificial Intelligence Model for the Prognosis of Missense KCNQ2 Gene Variants. Int J Mol Sci 2024; 25:2910. [PMID: 38474157 DOI: 10.3390/ijms25052910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 02/27/2024] [Accepted: 02/29/2024] [Indexed: 03/14/2024] Open
Abstract
Despite the increasing availability of genomic data and enhanced data analysis procedures, predicting the severity of associated diseases remains elusive in the absence of clinical descriptors. To address this challenge, we have focused on the KV7.2 voltage-gated potassium channel gene (KCNQ2), known for its link to developmental delays and various epilepsies, including self-limited benign familial neonatal epilepsy and epileptic encephalopathy. Genome-wide tools often exhibit a tendency to overestimate deleterious mutations, frequently overlooking tolerated variants, and lack the capacity to discriminate variant severity. This study introduces a novel approach by evaluating multiple machine learning (ML) protocols and descriptors. The combination of genomic information with a novel Variant Frequency Index (VFI) builds a robust foundation for constructing reliable gene-specific ML models. The ensemble model, MLe-KCNQ2, formed through logistic regression, support vector machine, random forest and gradient boosting algorithms, achieves specificity and sensitivity values surpassing 0.95 (AUC-ROC > 0.98). The ensemble MLe-KCNQ2 model also categorizes pathogenic mutations as benign or severe, with an area under the receiver operating characteristic curve (AUC-ROC) above 0.67. This study not only presents a transferable methodology for accurately classifying KCNQ2 missense variants, but also provides valuable insights for clinical counseling and aids in the determination of variant severity. The research context emphasizes the necessity of precise variant classification, especially for genes like KCNQ2, contributing to the broader understanding of gene-specific challenges in the field of genomic research. The MLe-KCNQ2 model stands as a promising tool for enhancing clinical decision making and prognosis in the realm of KCNQ2-related pathologies.
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Affiliation(s)
| | - Markel G Ibarluzea
- Physics Department, Universidad del País Vasco, UPV/EHU, 48940 Leioa, Spain
- Donostia International Physics Center, 20018 Donostia, Spain
| | - Sara M-Alicante
- Instituto Biofisika, CSIC-UPV/EHU, 48940 Leioa, Spain
- Physics Department, Universidad del País Vasco, UPV/EHU, 48940 Leioa, Spain
| | | | - Eider Nuñez
- Instituto Biofisika, CSIC-UPV/EHU, 48940 Leioa, Spain
- Physics Department, Universidad del País Vasco, UPV/EHU, 48940 Leioa, Spain
| | - Rafael Ramis
- Physics Department, Universidad del País Vasco, UPV/EHU, 48940 Leioa, Spain
- Donostia International Physics Center, 20018 Donostia, Spain
| | - Oscar R Ballesteros
- Physics Department, Universidad del País Vasco, UPV/EHU, 48940 Leioa, Spain
- Centro de Física de Materiales CFM, CSIC-UPV/EHU, 20018 Donostia, Spain
| | | | - Carmen Fons
- Pediatric Neurology Department, Sant Joan de Déu Hospital, Institut de Recerca Sant Joan de Déu, Barcelona University, 08950 Barcelona, Spain
| | - Mónica Gallego
- Departamento de Fisiología, Universidad del País Vasco, UPV/EHU, 01006 Vitoria-Gasteiz, Spain
| | - Oscar Casis
- Departamento de Fisiología, Universidad del País Vasco, UPV/EHU, 01006 Vitoria-Gasteiz, Spain
| | - Aritz Leonardo
- Physics Department, Universidad del País Vasco, UPV/EHU, 48940 Leioa, Spain
- Donostia International Physics Center, 20018 Donostia, Spain
| | - Aitor Bergara
- Physics Department, Universidad del País Vasco, UPV/EHU, 48940 Leioa, Spain
- Donostia International Physics Center, 20018 Donostia, Spain
- Centro de Física de Materiales CFM, CSIC-UPV/EHU, 20018 Donostia, Spain
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7
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Zech M, Winkelmann J. Next-generation sequencing and bioinformatics in rare movement disorders. Nat Rev Neurol 2024; 20:114-126. [PMID: 38172289 DOI: 10.1038/s41582-023-00909-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/29/2023] [Indexed: 01/05/2024]
Abstract
The ability to sequence entire exomes and genomes has revolutionized molecular testing in rare movement disorders, and genomic sequencing is becoming an integral part of routine diagnostic workflows for these heterogeneous conditions. However, interpretation of the extensive genomic variant information that is being generated presents substantial challenges. In this Perspective, we outline multidimensional strategies for genetic diagnosis in patients with rare movement disorders. We examine bioinformatics tools and computational metrics that have been developed to facilitate accurate prioritization of disease-causing variants. Additionally, we highlight community-driven data-sharing and case-matchmaking platforms, which are designed to foster the discovery of new genotype-phenotype relationships. Finally, we consider how multiomic data integration might optimize diagnostic success by combining genomic, epigenetic, transcriptomic and/or proteomic profiling to enable a more holistic evaluation of variant effects. Together, the approaches that we discuss offer pathways to the improved understanding of the genetic basis of rare movement disorders.
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Affiliation(s)
- Michael Zech
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, Munich, Germany
- Institute for Advanced Study, Technical University of Munich, Garching, Germany
| | - Juliane Winkelmann
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany.
- Institute of Neurogenomics, Helmholtz Zentrum München, Munich, Germany.
- Munich Cluster for Systems Neurology, SyNergy, Munich, Germany.
- DZPG, Deutsches Zentrum für Psychische Gesundheit, Munich, Germany.
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8
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Schubach M, Maass T, Nazaretyan L, Röner S, Kircher M. CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions. Nucleic Acids Res 2024; 52:D1143-D1154. [PMID: 38183205 PMCID: PMC10767851 DOI: 10.1093/nar/gkad989] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/14/2023] [Accepted: 10/17/2023] [Indexed: 01/07/2024] Open
Abstract
Machine Learning-based scoring and classification of genetic variants aids the assessment of clinical findings and is employed to prioritize variants in diverse genetic studies and analyses. Combined Annotation-Dependent Depletion (CADD) is one of the first methods for the genome-wide prioritization of variants across different molecular functions and has been continuously developed and improved since its original publication. Here, we present our most recent release, CADD v1.7. We explored and integrated new annotation features, among them state-of-the-art protein language model scores (Meta ESM-1v), regulatory variant effect predictions (from sequence-based convolutional neural networks) and sequence conservation scores (Zoonomia). We evaluated the new version on data sets derived from ClinVar, ExAC/gnomAD and 1000 Genomes variants. For coding effects, we tested CADD on 31 Deep Mutational Scanning (DMS) data sets from ProteinGym and, for regulatory effect prediction, we used saturation mutagenesis reporter assay data of promoter and enhancer sequences. The inclusion of new features further improved the overall performance of CADD. As with previous releases, all data sets, genome-wide CADD v1.7 scores, scripts for on-site scoring and an easy-to-use webserver are readily provided via https://cadd.bihealth.org/ or https://cadd.gs.washington.edu/ to the community.
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Affiliation(s)
- Max Schubach
- Exploratory Diagnostic Sciences, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Thorben Maass
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck, Lübeck, Germany
| | - Lusiné Nazaretyan
- Exploratory Diagnostic Sciences, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Sebastian Röner
- Exploratory Diagnostic Sciences, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Martin Kircher
- Exploratory Diagnostic Sciences, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck, Lübeck, Germany
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9
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Christowitz C, Olivier DW, Schneider JW, Kotze MJ, Engelbrecht AM. Incorporating functional genomics into the pathology-supported genetic testing framework implemented in South Africa: A future view of precision medicine for breast carcinomas. MUTATION RESEARCH. REVIEWS IN MUTATION RESEARCH 2024; 793:108492. [PMID: 38631437 DOI: 10.1016/j.mrrev.2024.108492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 02/25/2024] [Accepted: 04/11/2024] [Indexed: 04/19/2024]
Abstract
A pathology-supported genetic testing (PSGT) framework was established in South Africa to improve access to precision medicine for patients with breast carcinomas. Nevertheless, the frequent identification of variants of uncertain significance (VUSs) with the use of genome-scale next-generation sequencing has created a bottleneck in the return of results to patients. This review highlights the importance of incorporating functional genomics into the PSGT framework as a proposed initiative. Here, we explore various model systems and experimental methods available for conducting functional studies in South Africa to enhance both variant classification and clinical interpretation. We emphasize the distinct advantages of using in vitro, in vivo, and translational ex vivo models to improve the effectiveness of precision oncology. Moreover, we highlight the relevance of methodologies such as protein modelling and structural bioinformatics, multi-omics, metabolic activity assays, flow cytometry, cell migration and invasion assays, tube-formation assays, multiplex assays of variant effect, and database mining and machine learning models. The selection of the appropriate experimental approach largely depends on the molecular mechanism of the gene under investigation and the predicted functional effect of the VUS. However, before making final decisions regarding the pathogenicity of VUSs, it is essential to assess the functional evidence and clinical outcomes under current variant interpretation guidelines. The inclusion of a functional genomics infrastructure within the PSGT framework will significantly advance the reclassification of VUSs and enhance the precision medicine pipeline for patients with breast carcinomas in South Africa.
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Affiliation(s)
- Claudia Christowitz
- Department of Physiological Sciences, Faculty of Science, Stellenbosch University, Stellenbosch 7600, South Africa.
| | - Daniel W Olivier
- Department of Physiological Sciences, Faculty of Science, Stellenbosch University, Stellenbosch 7600, South Africa; Division of Chemical Pathology, Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa
| | - Johann W Schneider
- Division of Anatomical Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa; National Health Laboratory Service, Tygerberg Hospital, Cape Town 7505, South Africa
| | - Maritha J Kotze
- Division of Chemical Pathology, Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa; National Health Laboratory Service, Tygerberg Hospital, Cape Town 7505, South Africa
| | - Anna-Mart Engelbrecht
- Department of Physiological Sciences, Faculty of Science, Stellenbosch University, Stellenbosch 7600, South Africa; Department of Global Health, African Cancer Institute, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa
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10
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Sun KY, Bai X, Chen S, Bao S, Kapoor M, Zhang C, Backman J, Joseph T, Maxwell E, Mitra G, Gorovits A, Mansfield A, Boutkov B, Gokhale S, Habegger L, Marcketta A, Locke A, Kessler MD, Sharma D, Staples J, Bovijn J, Gelfman S, Gioia AD, Rajagopal V, Lopez A, Varela JR, Alegre J, Berumen J, Tapia-Conyer R, Kuri-Morales P, Torres J, Emberson J, Collins R, Cantor M, Thornton T, Kang HM, Overton J, Shuldiner AR, Cremona ML, Nafde M, Baras A, Abecasis G, Marchini J, Reid JG, Salerno W, Balasubramanian S. A deep catalog of protein-coding variation in 985,830 individuals. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.09.539329. [PMID: 37214792 PMCID: PMC10197621 DOI: 10.1101/2023.05.09.539329] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Coding variants that have significant impact on function can provide insights into the biology of a gene but are typically rare in the population. Identifying and ascertaining the frequency of such rare variants requires very large sample sizes. Here, we present the largest catalog of human protein-coding variation to date, derived from exome sequencing of 985,830 individuals of diverse ancestry to serve as a rich resource for studying rare coding variants. Individuals of African, Admixed American, East Asian, Middle Eastern, and South Asian ancestry account for 20% of this Exome dataset. Our catalog of variants includes approximately 10.5 million missense (54% novel) and 1.1 million predicted loss-of-function (pLOF) variants (65% novel, 53% observed only once). We identified individuals with rare homozygous pLOF variants in 4,874 genes, and for 1,838 of these this work is the first to document at least one pLOF homozygote. Additional insights from the RGC-ME dataset include 1) improved estimates of selection against heterozygous loss-of-function and identification of 3,459 genes intolerant to loss-of-function, 83 of which were previously assessed as tolerant to loss-of-function and 1,241 that lack disease annotations; 2) identification of regions depleted of missense variation in 457 genes that are tolerant to loss-of-function; 3) functional interpretation for 10,708 variants of unknown or conflicting significance reported in ClinVar as cryptic splice sites using splicing score thresholds based on empirical variant deleteriousness scores derived from RGC-ME; and 4) an observation that approximately 3% of sequenced individuals carry a clinically actionable genetic variant in the ACMG SF 3.1 list of genes. We make this important resource of coding variation available to the public through a variant allele frequency browser. We anticipate that this report and the RGC-ME dataset will serve as a valuable reference for understanding rare coding variation and help advance precision medicine efforts.
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Affiliation(s)
| | | | - Siying Chen
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Suying Bao
- Regeneron Genetics Center, Tarrytown, NY, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | - Adam Locke
- Regeneron Genetics Center, Tarrytown, NY, USA
| | | | | | | | | | | | | | | | | | | | - Jesus Alegre
- Experimental Research Unit from the Faculty of Medicine (UIME), National Autonomous University of Mexico (UNAM)
| | - Jaime Berumen
- Experimental Research Unit from the Faculty of Medicine (UIME), National Autonomous University of Mexico (UNAM)
| | - Roberto Tapia-Conyer
- Experimental Research Unit from the Faculty of Medicine (UIME), National Autonomous University of Mexico (UNAM)
| | - Pablo Kuri-Morales
- Experimental Research Unit from the Faculty of Medicine (UIME), National Autonomous University of Mexico (UNAM)
| | - Jason Torres
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jonathan Emberson
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Rory Collins
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | | | | | | | | | | | | | | | - Mona Nafde
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Aris Baras
- Regeneron Genetics Center, Tarrytown, NY, USA
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11
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Prasad M, Brar B, Bala K, Singh N. Emerging Microbial Technologies. Indian J Microbiol 2023; 63:231-234. [PMID: 37781007 PMCID: PMC10533750 DOI: 10.1007/s12088-023-01103-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/03/2023] Open
Affiliation(s)
| | - Basanti Brar
- Om Sterling Global University Hisar, Hisar, India
| | - Kiran Bala
- Om Sterling Global University Hisar, Hisar, India
| | - Namita Singh
- Microbial Biotechnology Laboratory, Department of Bio and Nano Technology, Guru Jambheshwar University of Science and Technology, Hisar, 125001 India
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12
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Hellwig LD, Villar J, Turner C. Computational in silico genetic variant prediction tools in cardiovascular disease. Ann Noninvasive Electrocardiol 2023; 28:e13079. [PMID: 37607111 PMCID: PMC10475887 DOI: 10.1111/anec.13079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 07/30/2023] [Indexed: 08/24/2023] Open
Affiliation(s)
- Lydia D. Hellwig
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc.BethesdaMarylandUSA
- Center for Military Precision HealthUniformed Services University of the Health SciencesBethesdaMarylandUSA
- Department of PediatricsUniformed Services University of the Health SciencesBethesdaMarylandUSA
| | - Joaquin Villar
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc.BethesdaMarylandUSA
- Center for Military Precision HealthUniformed Services University of the Health SciencesBethesdaMarylandUSA
- Department of PediatricsUniformed Services University of the Health SciencesBethesdaMarylandUSA
| | - Clesson Turner
- Center for Precision Health Research, National Human Genome Research InstituteNational Institutes of HealthBethesdaMarylandUSA
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13
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Salma M, Alaterre E, Moreaux J, Soler E. Var∣Decrypt: a novel and user-friendly tool to explore and prioritize variants in whole-exome sequencing data. Epigenetics Chromatin 2023; 16:23. [PMID: 37312221 DOI: 10.1186/s13072-023-00497-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 05/23/2023] [Indexed: 06/15/2023] Open
Abstract
BACKGROUND High-throughput sequencing (HTS) offers unprecedented opportunities for the discovery of causative gene variants in multiple human disorders including cancers, and has revolutionized clinical diagnostics. However, despite more than a decade of use of HTS-based assays, extracting relevant functional information from whole-exome sequencing (WES) data remains challenging, especially for non-specialists lacking in-depth bioinformatic skills. RESULTS To address this limitation, we developed Var∣Decrypt, a web-based tool designed to greatly facilitate WES data browsing and analysis. Var∣Decrypt offers a wide range of gene and variant filtering possibilities, clustering and enrichment tools, providing an efficient way to derive patient-specific functional information and to prioritize gene variants for functional analyses. We applied Var∣Decrypt on WES datasets of 10 acute erythroid leukemia patients, a rare and aggressive form of leukemia, and recovered known disease oncogenes in addition to novel putative drivers. We additionally validated the performance of Var∣Decrypt using an independent dataset of ~ 90 multiple myeloma WES, recapitulating the identified deregulated genes and pathways, showing the general applicability and versatility of Var∣Decrypt for WES analysis. CONCLUSION Despite years of use of WES in human health for diagnosis and discovery of disease drivers, WES data analysis still remains a complex task requiring advanced bioinformatic skills. In that context, there is a need for user-friendly all-in-one dedicated tools for data analysis, to allow biologists and clinicians to extract relevant biological information from patient datasets. Here, we provide Var∣Decrypt (trial version accessible here: https://vardecrypt.com/app/vardecrypt ), a simple and intuitive Rshiny application created to fill this gap. Source code and detailed user tutorial are available at https://gitlab.com/mohammadsalma/vardecrypt .
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Affiliation(s)
- Mohammad Salma
- Institut de Génétique Moléculaire de Montpellier, Univ Montpellier, CNRS, Montpellier, France.
- Laboratory of Excellence GR-Ex, Université de Paris, Paris, France.
| | - Elina Alaterre
- Institute of Human Genetics, UMR 9002 CNRS-UM, Montpellier, France
| | - Jérôme Moreaux
- Department of Biological Hematology, CHU Montpellier, Montpellier, France
- Institute of Human Genetics, UMR 9002 CNRS-UM, Montpellier, France
- Institut Universitaire de France (IUF), Paris, France
| | - Eric Soler
- Institut de Génétique Moléculaire de Montpellier, Univ Montpellier, CNRS, Montpellier, France.
- Laboratory of Excellence GR-Ex, Université de Paris, Paris, France.
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14
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Wilcox JE, Beussink-Nelson L, Cao J, Kumar R, Jordan E, Ni H, Shah SJ, Hershberger RE, Kinnamon DD. Differences in Cardiac Mechanics among Genetically At-Risk First-Degree Relatives: The DCM Precision Medicine Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.30.23290123. [PMID: 37398079 PMCID: PMC10312893 DOI: 10.1101/2023.05.30.23290123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Aims Among genetically at-risk first-degree relatives (FDRs) of probands with dilated cardiomyopathy (DCM), the ability to detect changes in left ventricular (LV) mechanics with normal LV size and ejection fraction (LVEF) remains incompletely explored. We sought to define a pre-DCM phenotype among at-risk FDRs, including those with variants of uncertain significance (VUSs), using echocardiographic measures of cardiac mechanics. Methods and Results LV structure and function, including speckle-tracking analysis for LV global longitudinal strain (GLS), were evaluated in 124 FDRs (65% female; median age 44.9 [IQR: 30.6-60.3] years) of 66 DCM probands of European ancestry sequenced for rare variants in 35 DCM genes. FDRs had normal LV size and LVEF. Negative FDRs of probands with pathogenic or likely pathogenic (P/LP) variants (n=28) were a reference group to which negative FDRs of probands without P/LP variants (n=30), FDRs with only VUSs (n=27), and FDRs with P/LP variants (n=39) were compared. In an analysis accounting for age-dependent penetrance, FDRs below the median age showed minimal differences in LV GLS across groups while those above it with P/LP variants or VUSs had lower absolute values than the reference group (-3.9 [95% CI: -5.7, -2.1] or -3.1 [-4.8, -1.4] %-units) and negative FDRs of probands without P/LP variants (-2.6 [-4.0, -1.2] or -1.8 [-3.1, -0.6]). Conclusions Older FDRs with normal LV size and LVEF who harbored P/LP variants or VUSs had lower absolute LV GLS values, indicating that some DCM-related VUSs are clinically relevant. LV GLS may have utility for defining a pre-DCM phenotype. Clinical Trial Registration clinicaltrials.gov, NCT03037632.
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Affiliation(s)
- Jane E. Wilcox
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Lauren Beussink-Nelson
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Jinwen Cao
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University, Columbus, OH
- The Davis Heart and Lung Research Institute, The Ohio State University, Columbus, OH
| | - Ritika Kumar
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Elizabeth Jordan
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University, Columbus, OH
- The Davis Heart and Lung Research Institute, The Ohio State University, Columbus, OH
| | - Hanyu Ni
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University, Columbus, OH
- The Davis Heart and Lung Research Institute, The Ohio State University, Columbus, OH
| | - Sanjiv J. Shah
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Ray E. Hershberger
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University, Columbus, OH
- The Davis Heart and Lung Research Institute, The Ohio State University, Columbus, OH
- Division of Cardiovascular Medicine, Department of Internal Medicine, The Ohio State University, Columbus, OH
| | - Daniel D. Kinnamon
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University, Columbus, OH
- The Davis Heart and Lung Research Institute, The Ohio State University, Columbus, OH
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15
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Pan A, Scodellaro S, Khan T, Ushcatz I, Wu W, Curtis M, Cohen E, Cohn RD, Hayeems RZ, Meyn MS, Orkin J, Otal J, Reuter MS, Walker S, Scherer SW, Marshall CR, Cohn I, Costain G. Pharmacogenetic profiling via genome sequencing in children with medical complexity. Pediatr Res 2023; 93:905-910. [PMID: 36167815 PMCID: PMC10033400 DOI: 10.1038/s41390-022-02313-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 07/25/2022] [Accepted: 09/03/2022] [Indexed: 11/09/2022]
Abstract
BACKGROUND Children with medical complexity (CMC) are a priority pediatric population, with high resource use and associated costs. Genome-wide sequencing is increasingly organized for CMC early in life as a diagnostic test. Polypharmacy becomes common as CMC age. Clinically relevant pharmacogenetic (PGx) information can be extracted from existing genome sequencing (GS) data via GS-PGx profiling. The role of GS-PGx profiling in the CMC population is unclear. METHODS Prescribed medications were extracted from care plans of 802 eligible CMC enrolled in a structured Complex Care Program over a 10-year period. Drug-gene associations were annotated using curated Clinical Pharmacogenetics Implementation Consortium data. GS-PGx profiling was then performed for a subset of 50 CMC. RESULTS Overall, 546 CMC (68%) were prescribed at least one medication with an established PGx association. In the GS-PGx subgroup, 24 (48%) carried variants in pharmacogenes with drug-gene guidelines for one or more of their current medications. All had findings of potential relevance to some medications, including 32 (64%) with variants in CYP2C19 that could affect their metabolism of proton-pump inhibitors. CONCLUSION GS-PGx profiling at the time of diagnostics-focused genetic testing could be an efficient way to incorporate precision prescribing practices into the lifelong care of CMC. IMPACT Polypharmacy and genetic test utilization are both common in children with medical complexity. The role of repurposing genome sequencing data for pharmacogenetic profiling in children with medical complexity was previously unclear. We identified a high rate of medication use with clinically relevant drug-gene associations in this priority pediatric population and demonstrated that relevant pharmacogenetic information can be extracted from their existing genome sequencing data. Pharmacogenetic profiling at the time of diagnostics-focused genetic testing could be an efficient way to incorporate precision prescribing practices into the lifelong care of children with medical complexity.
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Affiliation(s)
- Amy Pan
- Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada
| | - Sierra Scodellaro
- Program in Translational Medicine, The Hospital for Sick Children, Toronto, ON, Canada
- Division of Clinical Pharmacology and Toxicology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Tayyaba Khan
- Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada
| | - Inna Ushcatz
- Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada
| | - Wendy Wu
- Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada
| | - Meredith Curtis
- Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada
| | - Eyal Cohen
- Department of Paediatrics, University of Toronto, Toronto, ON, Canada
- Division of Paediatric Medicine, The Hospital for Sick Children, Toronto, ON, Canada
- Child Health Evaluative Sciences, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, ON, Canada
- Edwin S.H. Leong Centre for Healthy Children, University of Toronto, Toronto, ON, Canada
| | - Ronald D Cohn
- Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Paediatrics, University of Toronto, Toronto, ON, Canada
- Division of Paediatric Medicine, The Hospital for Sick Children, Toronto, ON, Canada
- Division of Clinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Robin Z Hayeems
- Child Health Evaluative Sciences, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, ON, Canada
- Centre for Genetic Medicine, The Hospital for Sick Children, Toronto, ON, Canada
| | - M Stephen Meyn
- Department of Paediatrics, University of Toronto, Toronto, ON, Canada
- Division of Clinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Centre for Genetic Medicine, The Hospital for Sick Children, Toronto, ON, Canada
- Center for Human Genomics and Precision Medicine, University of Wisconsin, Madison, WI, USA
| | - Julia Orkin
- Department of Paediatrics, University of Toronto, Toronto, ON, Canada
- Division of Paediatric Medicine, The Hospital for Sick Children, Toronto, ON, Canada
- Child Health Evaluative Sciences, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada
| | - Jaskiran Otal
- Division of Paediatric Medicine, The Hospital for Sick Children, Toronto, ON, Canada
| | - Miriam S Reuter
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada
| | - Susan Walker
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada
| | - Stephen W Scherer
- Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Centre for Genetic Medicine, The Hospital for Sick Children, Toronto, ON, Canada
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada
| | - Christian R Marshall
- Centre for Genetic Medicine, The Hospital for Sick Children, Toronto, ON, Canada
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada
- Genome Diagnostics, Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, ON, Canada
- Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Iris Cohn
- Program in Translational Medicine, The Hospital for Sick Children, Toronto, ON, Canada
- Division of Clinical Pharmacology and Toxicology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Gregory Costain
- Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada.
- Department of Paediatrics, University of Toronto, Toronto, ON, Canada.
- Division of Clinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, ON, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
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16
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Wright JT, Abbott BM, Salois MN, Gugger JA, Parraga SP, Swanson AK, Fete M, Koster MI. Rare diseases of ectoderm: Translating discovery to therapy. Am J Med Genet A 2023; 191:902-909. [PMID: 36534506 DOI: 10.1002/ajmg.a.63090] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 12/08/2022] [Indexed: 12/23/2022]
Abstract
Heritable conditions known as ectodermal dysplasias are rare and can be associated with marked morbidity, mortality, and a reduced quality of life. The diagnosis and care of individuals affected by one of the many ectodermal dysplasias presents myriad challenges due to their rarity and the diverse phenotypes. These conditions are caused by abnormalities in multiple genes and signaling pathways that are essential for the development and function of ectodermal derivatives. During a 2021 international conference focused on translating discovery to therapy, researchers and clinicians gathered with the goal of advancing the diagnosis and treatment of conditions affecting ectodermal tissues with an emphasis on skin, hair, tooth, and eye phenotypes. Conference participants presented a variety of promising treatment strategies including gene or protein replacement, gene editing, cell therapy, and the identification of druggable targets. Further, barriers that negatively influence the current development of novel therapeutics were identified. These barriers include a lack of accurate prevalence data for rare conditions, absence of an inclusive patient registry with deep phenotyping data, and insufficient animal models and cell lines. Overcoming these barriers will need to be prioritized in order to facilitate the development of novel treatments for genetic disorders of the ectoderm.
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Affiliation(s)
- John Timothy Wright
- Division of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Becky M Abbott
- National Foundation for Ectodermal Dysplasias, Fairview Heights, Illinois, USA
| | - Maddison N Salois
- Department of Biochemistry and Molecular Biology, Brody School of Medicine, East Carolina University, Greenville, North Carolina, USA
| | - Jessica A Gugger
- Department of Biochemistry and Molecular Biology, Brody School of Medicine, East Carolina University, Greenville, North Carolina, USA
| | - Shirley P Parraga
- Department of Biochemistry and Molecular Biology, Brody School of Medicine, East Carolina University, Greenville, North Carolina, USA
| | - Amanda K Swanson
- Division of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Mary Fete
- National Foundation for Ectodermal Dysplasias, Fairview Heights, Illinois, USA
| | - Maranke I Koster
- Department of Biochemistry and Molecular Biology, Brody School of Medicine, East Carolina University, Greenville, North Carolina, USA
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17
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Phenotypic Modulation of Cancer-Associated Antioxidant NQO1 Activity by Post-Translational Modifications and the Natural Diversity of the Human Genome. Antioxidants (Basel) 2023; 12:antiox12020379. [PMID: 36829939 PMCID: PMC9952366 DOI: 10.3390/antiox12020379] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/31/2023] [Accepted: 02/02/2023] [Indexed: 02/08/2023] Open
Abstract
Human NAD(P)H:quinone oxidoreductase 1 (hNQO1) is a multifunctional and antioxidant stress protein whose expression is controlled by the Nrf2 signaling pathway. hNQO1 dysregulation is associated with cancer and neurological disorders. Recent works have shown that its activity is also modulated by different post-translational modifications (PTMs), such as phosphorylation, acetylation and ubiquitination, and these may synergize with naturally-occurring and inactivating polymorphisms and mutations. Herein, I describe recent advances in the study of the effect of PTMs and genetic variations on the structure and function of hNQO1 and their relationship with disease development in different genetic backgrounds, as well as the physiological roles of these modifications. I pay particular attention to the long-range allosteric effects exerted by PTMs and natural variation on the multiple functions of hNQO1.
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18
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SAV-Pred: A Freely Available Web Application for the Prediction of Pathogenic Amino Acid Substitutions for Monogenic Hereditary Diseases Studied in Newborn Screening. Int J Mol Sci 2023; 24:ijms24032463. [PMID: 36768784 PMCID: PMC9917004 DOI: 10.3390/ijms24032463] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/22/2023] [Accepted: 01/23/2023] [Indexed: 01/28/2023] Open
Abstract
Next Generation Sequencing (NGS) technologies are rapidly entering clinical practice. A promising area for their use lies in the field of newborn screening. The mass screening of newborns using NGS technology leads to the discovery of a large number of new missense variants that need to be assessed for association with the development of hereditary diseases. Currently, the primary analysis and identification of pathogenic variations is carried out using bioinformatic tools. Although extensive efforts have been made in the computational approach to variant interpretation, there is currently no generally accepted pathogenicity predictor. In this study, we used the sequence-structure-property relationships (SSPR) approach, based on the representation of protein fragments by molecular structural formula. The approach predicts the pathogenic effect of single amino acid substitutions in proteins related with twenty-five monogenic heritable diseases from the Uniform Screening Panel for Major Conditions recommended by the Advisory Committee on Hereditary Disorders in Newborns and Children. In order to create SSPR models of classification, we modified a piece of cheminformatics software, MultiPASS, that was originally developed for the prediction of activity spectra for drug-like substances. The created SSPR models were compared with traditional bioinformatic tools (SIFT 4G, Polyphen-2 HDIV, MutationAssessor, PROVEAN and FATHMM). The average AUC of our approach was 0.804 ± 0.040. Better quality scores were achieved for 15 from 25 proteins with a significantly higher accuracy for some proteins (IVD, HADHB, HBB). The best SSPR models of classification are freely available in the online resource SAV-Pred (Single Amino acid Variants Predictor).
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19
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Fritzsche MC, Akyüz K, Cano Abadía M, McLennan S, Marttinen P, Mayrhofer MT, Buyx AM. Ethical layering in AI-driven polygenic risk scores-New complexities, new challenges. Front Genet 2023; 14:1098439. [PMID: 36816027 PMCID: PMC9933509 DOI: 10.3389/fgene.2023.1098439] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 01/04/2023] [Indexed: 01/27/2023] Open
Abstract
Researchers aim to develop polygenic risk scores as a tool to prevent and more effectively treat serious diseases, disorders and conditions such as breast cancer, type 2 diabetes mellitus and coronary heart disease. Recently, machine learning techniques, in particular deep neural networks, have been increasingly developed to create polygenic risk scores using electronic health records as well as genomic and other health data. While the use of artificial intelligence for polygenic risk scores may enable greater accuracy, performance and prediction, it also presents a range of increasingly complex ethical challenges. The ethical and social issues of many polygenic risk score applications in medicine have been widely discussed. However, in the literature and in practice, the ethical implications of their confluence with the use of artificial intelligence have not yet been sufficiently considered. Based on a comprehensive review of the existing literature, we argue that this stands in need of urgent consideration for research and subsequent translation into the clinical setting. Considering the many ethical layers involved, we will first give a brief overview of the development of artificial intelligence-driven polygenic risk scores, associated ethical and social implications, challenges in artificial intelligence ethics, and finally, explore potential complexities of polygenic risk scores driven by artificial intelligence. We point out emerging complexity regarding fairness, challenges in building trust, explaining and understanding artificial intelligence and polygenic risk scores as well as regulatory uncertainties and further challenges. We strongly advocate taking a proactive approach to embedding ethics in research and implementation processes for polygenic risk scores driven by artificial intelligence.
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Affiliation(s)
- Marie-Christine Fritzsche
- Institute of History and Ethics in Medicine, TUM School of Medicine, Technical University of Munich, Munich, Germany,Department of Science, Technology and Society (STS), School of Social Sciences and Technology, Technical University of Munich, Munich, Germany,*Correspondence: Marie-Christine Fritzsche,
| | - Kaya Akyüz
- Biobanking and Biomolecular Resources Research Infrastructure Consortium - European Research Infrastructure Consortium (BBMRI-ERIC), Graz, Austria,Department of Science and Technology Studies, University of Vienna, Vienna, Austria
| | - Mónica Cano Abadía
- Biobanking and Biomolecular Resources Research Infrastructure Consortium - European Research Infrastructure Consortium (BBMRI-ERIC), Graz, Austria
| | - Stuart McLennan
- Institute of History and Ethics in Medicine, TUM School of Medicine, Technical University of Munich, Munich, Germany,Department of Science, Technology and Society (STS), School of Social Sciences and Technology, Technical University of Munich, Munich, Germany
| | - Pekka Marttinen
- Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland
| | - Michaela Th. Mayrhofer
- Biobanking and Biomolecular Resources Research Infrastructure Consortium - European Research Infrastructure Consortium (BBMRI-ERIC), Graz, Austria
| | - Alena M. Buyx
- Institute of History and Ethics in Medicine, TUM School of Medicine, Technical University of Munich, Munich, Germany,Department of Science, Technology and Society (STS), School of Social Sciences and Technology, Technical University of Munich, Munich, Germany
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20
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Molecular Mechanisms, Genotype-Phenotype Correlations and Patient-Specific Treatments in Inherited Metabolic Diseases. J Pers Med 2023; 13:jpm13010117. [PMID: 36675778 PMCID: PMC9864038 DOI: 10.3390/jpm13010117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
Advances in DNA sequencing technologies are revealing a vast genetic heterogeneity in human population, which may predispose to metabolic alterations if the activity of metabolic enzymes is affected [...].
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21
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Zhou XJ, Zhong XH, Duan LX. Integration of artificial intelligence and multi-omics in kidney diseases. FUNDAMENTAL RESEARCH 2023; 3:126-148. [PMID: 38933564 PMCID: PMC11197676 DOI: 10.1016/j.fmre.2022.01.037] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/14/2021] [Accepted: 01/24/2022] [Indexed: 10/18/2022] Open
Abstract
Kidney disease is a leading cause of death worldwide. Currently, the diagnosis of kidney diseases and the grading of their severity are mainly based on clinical features, which do not reveal the underlying molecular pathways. More recent surge of ∼omics studies has greatly catalyzed disease research. The advent of artificial intelligence (AI) has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically actionable knowledge. This review discusses how AI and multi-omics can be applied and integrated, to offer opportunities to develop novel diagnostic and therapeutic means in kidney diseases. The combination of new technology and novel analysis pipelines can lead to breakthroughs in expanding our understanding of disease pathogenesis, shedding new light on biomarkers and disease classification, as well as providing possibilities of precise treatment.
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Affiliation(s)
- Xu-Jie Zhou
- Renal Division, Peking University First Hospital, Beijing 100034, China
- Kidney Genetics Center, Peking University Institute of Nephrology, Beijing 100034, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing 100034, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China
| | - Xu-Hui Zhong
- Department of Pediatrics, Peking University First Hospital, Beijing, China
| | - Li-Xin Duan
- The Big Data Research Center, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
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22
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Zhou Y, Lauschke VM. Challenges Related to the Use of Next-Generation Sequencing for the Optimization of Drug Therapy. Handb Exp Pharmacol 2023; 280:237-260. [PMID: 35792943 DOI: 10.1007/164_2022_596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Over the last decade, next-generation sequencing (NGS) methods have become increasingly used in various areas of human genomics. In routine clinical care, their use is already implemented in oncology to profile the mutational landscape of a tumor, as well as in rare disease diagnostics. However, its utilization in pharmacogenomics is largely lacking behind. Recent population-scale genome data has revealed that human pharmacogenes carry a plethora of rare genetic variations that are not interrogated by conventional array-based profiling methods and it is estimated that these variants could explain around 30% of the genetically encoded functional pharmacogenetic variability.To interpret the impact of such variants on drug response a multitude of computational tools have been developed, but, while there have been major advancements, it remains to be shown whether their accuracy is sufficient to improve personalized pharmacogenetic recommendations in robust trials. In addition, conventional short-read sequencing methods face difficulties in the interrogation of complex pharmacogenes and high NGS test costs require stringent evaluations of cost-effectiveness to decide about reimbursement by national healthcare programs. Here, we illustrate current challenges and discuss future directions toward the clinical implementation of NGS to inform genotype-guided decision-making.
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Affiliation(s)
- Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden.
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.
- University of Tuebingen, Tuebingen, Germany.
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23
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Pejaver V, Byrne AB, Feng BJ, Pagel KA, Mooney SD, Karchin R, O'Donnell-Luria A, Harrison SM, Tavtigian SV, Greenblatt MS, Biesecker LG, Radivojac P, Brenner SE. Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria. Am J Hum Genet 2022; 109:2163-2177. [PMID: 36413997 PMCID: PMC9748256 DOI: 10.1016/j.ajhg.2022.10.013] [Citation(s) in RCA: 134] [Impact Index Per Article: 67.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 10/21/2022] [Indexed: 11/23/2022] Open
Abstract
Recommendations from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) for interpreting sequence variants specify the use of computational predictors as "supporting" level of evidence for pathogenicity or benignity using criteria PP3 and BP4, respectively. However, score intervals defined by tool developers, and ACMG/AMP recommendations that require the consensus of multiple predictors, lack quantitative support. Previously, we described a probabilistic framework that quantified the strengths of evidence (supporting, moderate, strong, very strong) within ACMG/AMP recommendations. We have extended this framework to computational predictors and introduce a new standard that converts a tool's scores to PP3 and BP4 evidence strengths. Our approach is based on estimating the local positive predictive value and can calibrate any computational tool or other continuous-scale evidence on any variant type. We estimate thresholds (score intervals) corresponding to each strength of evidence for pathogenicity and benignity for thirteen missense variant interpretation tools, using carefully assembled independent data sets. Most tools achieved supporting evidence level for both pathogenic and benign classification using newly established thresholds. Multiple tools reached score thresholds justifying moderate and several reached strong evidence levels. One tool reached very strong evidence level for benign classification on some variants. Based on these findings, we provide recommendations for evidence-based revisions of the PP3 and BP4 ACMG/AMP criteria using individual tools and future assessment of computational methods for clinical interpretation.
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Affiliation(s)
- Vikas Pejaver
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA
| | - Alicia B Byrne
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Bing-Jian Feng
- Department of Dermatology, University of Utah, Salt Lake City, UT 84132, USA; Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Kymberleigh A Pagel
- The Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Sean D Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA
| | - Rachel Karchin
- The Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA; Departments of Biomedical Engineering, Oncology, and Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Anne O'Donnell-Luria
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA
| | - Steven M Harrison
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Ambry Genetics, Aliso Viejo, CA 92656, USA
| | - Sean V Tavtigian
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
| | - Marc S Greenblatt
- Department of Medicine and University of Vermont Cancer Center, University of Vermont, Larner College of Medicine, Burlington, VT 05405, USA
| | - Leslie G Biesecker
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA.
| | - Steven E Brenner
- Department of Plant and Microbial Biology and Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA.
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24
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Pacheco-Garcia JL, Cagiada M, Tienne-Matos K, Salido E, Lindorff-Larsen K, L. Pey A. Effect of naturally-occurring mutations on the stability and function of cancer-associated NQO1: Comparison of experiments and computation. Front Mol Biosci 2022; 9:1063620. [PMID: 36504709 PMCID: PMC9730889 DOI: 10.3389/fmolb.2022.1063620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 11/03/2022] [Indexed: 11/25/2022] Open
Abstract
Recent advances in DNA sequencing technologies are revealing a large individual variability of the human genome. Our capacity to establish genotype-phenotype correlations in such large-scale is, however, limited. This task is particularly challenging due to the multifunctional nature of many proteins. Here we describe an extensive analysis of the stability and function of naturally-occurring variants (found in the COSMIC and gnomAD databases) of the cancer-associated human NAD(P)H:quinone oxidoreductase 1 (NQO1). First, we performed in silico saturation mutagenesis studies (>5,000 substitutions) aimed to identify regions in NQO1 important for stability and function. We then experimentally characterized twenty-two naturally-occurring variants in terms of protein levels during bacterial expression, solubility, thermal stability, and coenzyme binding. These studies showed a good overall correlation between experimental analysis and computational predictions; also the magnitude of the effects of the substitutions are similarly distributed in variants from the COSMIC and gnomAD databases. Outliers in these experimental-computational genotype-phenotype correlations remain, and we discuss these on the grounds and limitations of our approaches. Our work represents a further step to characterize the mutational landscape of NQO1 in the human genome and may help to improve high-throughput in silico tools for genotype-phenotype correlations in this multifunctional protein associated with disease.
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Affiliation(s)
| | - Matteo Cagiada
- Department of Biology, Linderstrøm-Lang Centre for Protein Science, University of Copenhagen, Copenhagen, Denmark
| | | | - Eduardo Salido
- Center for Rare Diseases (CIBERER), Hospital Universitario de Canarias, Universidad de la Laguna, La Laguna, TenerifeTenerife, Spain
| | - Kresten Lindorff-Larsen
- Department of Biology, Linderstrøm-Lang Centre for Protein Science, University of Copenhagen, Copenhagen, Denmark
| | - Angel L. Pey
- Departamento de Química Física, Unidad de Excelencia en Química Aplicada a Biomedicina y Medioambiente e Instituto de Biotecnología, Universidad de Granada, Granada, Spain,*Correspondence: Angel L. Pey,
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25
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Koleske ML, McInnes G, Brown JEH, Thomas N, Hutchinson K, Chin MY, Koehl A, Arkin MR, Schlessinger A, Gallagher RC, Song YS, Altman RB, Giacomini KM. Functional genomics of OCTN2 variants informs protein-specific variant effect predictor for Carnitine Transporter Deficiency. Proc Natl Acad Sci U S A 2022; 119:e2210247119. [PMID: 36343260 PMCID: PMC9674959 DOI: 10.1073/pnas.2210247119] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 09/16/2022] [Indexed: 11/09/2022] Open
Abstract
Genetic variants in SLC22A5, encoding the membrane carnitine transporter OCTN2, cause the rare metabolic disorder Carnitine Transporter Deficiency (CTD). CTD is potentially lethal but actionable if detected early, with confirmatory diagnosis involving sequencing of SLC22A5. Interpretation of missense variants of uncertain significance (VUSs) is a major challenge. In this study, we sought to characterize the largest set to date (n = 150) of OCTN2 variants identified in diverse ancestral populations, with the goals of furthering our understanding of the mechanisms leading to OCTN2 loss-of-function (LOF) and creating a protein-specific variant effect prediction model for OCTN2 function. Uptake assays with 14C-carnitine revealed that 105 variants (70%) significantly reduced transport of carnitine compared to wild-type OCTN2, and 37 variants (25%) severely reduced function to less than 20%. All ancestral populations harbored LOF variants; 62% of green fluorescent protein (GFP)-tagged variants impaired OCTN2 localization to the plasma membrane of human embryonic kidney (HEK293T) cells, and subcellular localization significantly associated with function, revealing a major LOF mechanism of interest for CTD. With these data, we trained a model to classify variants as functional (>20% function) or LOF (<20% function). Our model outperformed existing state-of-the-art methods as evaluated by multiple performance metrics, with mean area under the receiver operating characteristic curve (AUROC) of 0.895 ± 0.025. In summary, in this study we generated a rich dataset of OCTN2 variant function and localization, revealed important disease-causing mechanisms, and improved upon machine learning-based prediction of OCTN2 variant function to aid in variant interpretation in the diagnosis and treatment of CTD.
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Affiliation(s)
- Megan L. Koleske
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94143
| | - Gregory McInnes
- Biomedical Informatics Training Program, Stanford University, Stanford, CA 94305
- Empirico Inc., San Diego, CA 92122
| | - Julia E. H. Brown
- Program in Bioethics, University of California, San Francisco, CA 94143
- Institute for Health & Aging, University of California, San Francisco, CA 94143
| | - Neil Thomas
- Computer Science Division, University of California, Berkeley, CA 94720
| | - Keino Hutchinson
- Department of Pharmacological Sciences, Icahn School of Medicine at Mt. Sinai, New York, NY 10029
| | - Marcus Y. Chin
- Small Molecule Discovery Center, Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94143
| | - Antoine Koehl
- Department of Statistics, University of California, Berkeley, CA 94720
| | - Michelle R. Arkin
- Small Molecule Discovery Center, Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94143
| | - Avner Schlessinger
- Department of Pharmacological Sciences, Icahn School of Medicine at Mt. Sinai, New York, NY 10029
| | - Renata C. Gallagher
- Institute for Human Genetics, University of California, San Francisco, CA 94143
- Department of Pediatrics, University of California, San Francisco, CA 94143
| | - Yun S. Song
- Computer Science Division, University of California, Berkeley, CA 94720
- Department of Statistics, University of California, Berkeley, CA 94720
| | - Russ B. Altman
- Department of Bioengineering, Stanford University, Stanford, CA 94305
- Department of Genetics, Stanford University, Stanford, CA 94305
| | - Kathleen M. Giacomini
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94143
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26
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Computational interpretation of human genetic variation. Hum Genet 2022; 141:1545-1548. [PMID: 36149496 DOI: 10.1007/s00439-022-02483-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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27
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Babbi G, Savojardo C, Baldazzi D, Martelli PL, Casadio R. Pathogenic variation types in human genes relate to diseases through Pfam and InterPro mapping. Front Mol Biosci 2022; 9:966927. [PMID: 36188216 PMCID: PMC9523224 DOI: 10.3389/fmolb.2022.966927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 08/31/2022] [Indexed: 11/13/2022] Open
Abstract
Grouping residue variations in a protein according to their physicochemical properties allows a dimensionality reduction of all the possible substitutions in a variant with respect to the wild type. Here, by using a large dataset of proteins with disease-related and benign variations, as derived by merging Humsavar and ClinVar data, we investigate to which extent our physicochemical grouping procedure can help in determining whether patterns of variation types are related to specific groups of diseases and whether they occur in Pfam and/or InterPro gene domains. Here, we download 75,145 germline disease-related and benign variations of 3,605 genes, group them according to physicochemical categories and map them into Pfam and InterPro gene domains. Statistically validated analysis indicates that each cluster of genes associated to Mondo anatomical system categorizations is characterized by a specific variation pattern. Patterns identify specific Pfam and InterPro domain–Mondo category associations. Our data suggest that the association of variation patterns to Mondo categories is unique and may help in associating gene variants to genetic diseases. This work corroborates in a much larger data set previous observations from our group.
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Affiliation(s)
- Giulia Babbi
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Castrense Savojardo
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | | | - Pier Luigi Martelli
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
- *Correspondence: Pier Luigi Martelli, ; Rita Casadio,
| | - Rita Casadio
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies (IBIOM), Italian National Research Council (CNR), Bari, Italy
- *Correspondence: Pier Luigi Martelli, ; Rita Casadio,
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28
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Abstract
Genetic diseases disrupt the functionality of an infant's genome during fetal-neonatal adaptation and represent a leading cause of neonatal and infant mortality in the United States. Due to disease acuity, gene locus and allelic heterogeneity, and overlapping and diverse clinical phenotypes, diagnostic genome sequencing in neonatal intensive care units has required the development of methods to shorten turnaround times and improve genomic interpretation. From 2012 to 2021, 31 clinical studies documented the diagnostic and clinical utility of first-tier rapid or ultrarapid whole-genome sequencing through cost-effective identification of pathogenic genomic variants that change medical management, suggest new therapeutic strategies, and refine prognoses. Genomic diagnosis also permits prediction of reproductive recurrence risk for parents and surviving probands. Using implementation science and quality improvement, deployment of a genomic learning healthcare system will contribute to a reduction of neonatal and infant mortality through the integration of genome sequencing into best-practice neonatal intensive care.
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Affiliation(s)
- Stephen F. Kingsmore
- Rady Children’s Hospital Institute for Genomic Medicine, Rady Children’s Hospital-San Diego
| | - F. Sessions Cole
- Division of Newborn Medicine, Edward Mallinckrodt Department of Pediatrics, Washington University School of Medicine in St. Louis
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29
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Blee AM, Li B, Pecen T, Meiler J, Nagel ZD, Capra JA, Chazin WJ. An Active Learning Framework Improves Tumor Variant Interpretation. Cancer Res 2022; 82:2704-2715. [PMID: 35687855 PMCID: PMC9357215 DOI: 10.1158/0008-5472.can-21-3798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 04/26/2022] [Accepted: 06/07/2022] [Indexed: 02/05/2023]
Abstract
SIGNIFICANCE A novel machine learning approach predicts the impact of tumor mutations on cellular phenotypes, overcomes limited training data, minimizes costly functional validation, and advances efforts to implement cancer precision medicine.
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Affiliation(s)
- Alexandra M. Blee
- Department of Biochemistry and Center for Structural Biology, Vanderbilt University, Nashville, TN 37240, USA
| | - Bian Li
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Turner Pecen
- John B. Little Center of Radiation Sciences, Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Jens Meiler
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, TN 37240, USA
- Institute for Drug Discovery, Leipzig University Medical School, Leipzig, SAC 04103, Germany
| | - Zachary D. Nagel
- John B. Little Center of Radiation Sciences, Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - John A. Capra
- Bakar Computational Health Sciences Institute and Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94107, USA
| | - Walter J. Chazin
- Department of Biochemistry and Center for Structural Biology, Vanderbilt University, Nashville, TN 37240, USA
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, TN 37240, USA
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30
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Li B, Roden DM, Capra JA. The 3D mutational constraint on amino acid sites in the human proteome. Nat Commun 2022; 13:3273. [PMID: 35672414 PMCID: PMC9174330 DOI: 10.1038/s41467-022-30936-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 05/19/2022] [Indexed: 12/16/2022] Open
Abstract
Quantification of the tolerance of protein sites to genetic variation has become a cornerstone of variant interpretation. We hypothesize that the constraint on missense variation at individual amino acid sites is largely shaped by direct interactions with 3D neighboring sites. To quantify this constraint, we introduce a framework called COntact Set MISsense tolerance (or COSMIS) and comprehensively map the landscape of 3D mutational constraint on 6.1 million amino acid sites covering 16,533 human proteins. We show that 3D mutational constraint is pervasive and that the level of constraint is strongly associated with disease relevance both at the site and the protein level. We demonstrate that COSMIS performs significantly better at variant interpretation tasks than other population-based constraint metrics while also providing structural insight into the functional roles of constrained sites. We anticipate that COSMIS will facilitate the interpretation of protein-coding variation in evolution and prioritization of sites for mechanistic investigation.
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Affiliation(s)
- Bian Li
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, 37203, USA.
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
| | - Dan M Roden
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Departments of Pharmacology and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - John A Capra
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, 37203, USA.
- Bakar Computational Health Sciences Institute and Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, 94143, USA.
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31
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Affiliation(s)
- Michael R. Waarts
- Gerstner Sloan Kettering Graduate Program in Biomedical Sciences
- Human Oncology and Pathogenesis Program
- Center for Hematologic Malignancies
- Center for Epigenetics Research, and
| | - Aaron J. Stonestrom
- Human Oncology and Pathogenesis Program
- Center for Hematologic Malignancies
- Center for Epigenetics Research, and
- Leukemia Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Young C. Park
- Human Oncology and Pathogenesis Program
- Center for Hematologic Malignancies
- Center for Epigenetics Research, and
| | - Ross L. Levine
- Human Oncology and Pathogenesis Program
- Center for Hematologic Malignancies
- Center for Epigenetics Research, and
- Leukemia Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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32
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Dikow N, Ditzen B, Kölker S, Hoffmann GF, Schaaf CP. From newborn screening to genomic medicine: challenges and suggestions on how to incorporate genomic newborn screening in public health programs. MED GENET-BERLIN 2022; 34:13-20. [PMID: 38836020 PMCID: PMC11006367 DOI: 10.1515/medgen-2022-2113] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 02/17/2022] [Indexed: 06/06/2024]
Abstract
Newborn screening (NBS) programs are considered among the most effective and efficient measures of secondary prevention in medicine. In individuals with medical conditions, genomic sequencing has become available in routine healthcare, and results from exome or genome sequencing may help to guide treatment decisions. Genomic sequencing in healthy or asymptomatic newborns (gNBS) is feasible and reveals clinically relevant disorders that are not detectable by biochemical analyses alone. However, the implementation of genomic sequencing in population-based screening programs comes with technological, clinical, ethical, and psychological issues, as well as economic and legal topics. Here, we address and discuss the most important questions to be considered when implementing gNBS, such as "which categories of results should be reported" or "which is the best time to return results". We also offer ideas on how to balance expected benefits against possible harms to children and their families.
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Affiliation(s)
- Nicola Dikow
- Institute of Human Genetics, Heidelberg University, Heidelberg, Germany
| | - Beate Ditzen
- Institute of Medical Psychology, Center for Psychosocial Medicine, University Hospital Heidelberg, Heidelberg, Germany
| | - Stefan Kölker
- University Hospital Heidelberg, Center for Pediatric and Adolescent Medicine, Clinic I, Heidelberg, Germany
| | - Georg F Hoffmann
- University Hospital Heidelberg, Center for Pediatric and Adolescent Medicine, Clinic I, Heidelberg, Germany
| | - Christian P Schaaf
- Institute of Human Genetics, Heidelberg University, Heidelberg, Germany
- Baylor College of Medicine, Houston, Texas, USA
- Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, Texas, USA
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33
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Capriotti E, Fariselli P. Evaluating the relevance of sequence conservation in the prediction of pathogenic missense variants. Hum Genet 2022; 141:1649-1658. [DOI: 10.1007/s00439-021-02419-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 12/12/2021] [Indexed: 12/28/2022]
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Khani M, Gibbons E, Bras J, Guerreiro R. Challenge accepted: uncovering the role of rare genetic variants in Alzheimer's disease. Mol Neurodegener 2022; 17:3. [PMID: 35000612 PMCID: PMC8744312 DOI: 10.1186/s13024-021-00505-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 12/06/2021] [Indexed: 12/11/2022] Open
Abstract
The search for rare variants in Alzheimer's disease (AD) is usually deemed a high-risk - high-reward situation. The challenges associated with this endeavor are real. Still, the application of genome-wide technologies to large numbers of cases and controls or to small, well-characterized families has started to be fruitful.Rare variants associated with AD have been shown to increase risk or cause disease, but also to protect against the development of AD. All of these can potentially be targeted for the development of new drugs.Multiple independent studies have now shown associations of rare variants in NOTCH3, TREM2, SORL1, ABCA7, BIN1, CLU, NCK2, AKAP9, UNC5C, PLCG2, and ABI3 with AD and suggested that they may influence disease via multiple mechanisms. These genes have reported functions in the immune system, lipid metabolism, synaptic plasticity, and apoptosis. However, the main pathway emerging from the collective of genes harboring rare variants associated with AD is the Aβ pathway. Associations of rare variants in dozens of other genes have also been proposed, but have not yet been replicated in independent studies. Replication of this type of findings is one of the challenges associated with studying rare variants in complex diseases, such as AD. In this review, we discuss some of these primary challenges as well as possible solutions.Integrative approaches, the availability of large datasets and databases, and the development of new analytical methodologies will continue to produce new genes harboring rare variability impacting AD. In the future, more extensive and more diverse genetic studies, as well as studies of deeply characterized families, will enhance our understanding of disease pathogenesis and put us on the correct path for the development of successful drugs.
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Affiliation(s)
- Marzieh Khani
- School of Biology, College of Science, University of Tehran, Tehran, Iran
| | - Elizabeth Gibbons
- Department of Neurodegenerative Science, Van Andel Institute, 333 Bostwick Ave. N.E., Grand Rapids, Michigan 49503-2518 USA
| | - Jose Bras
- Department of Neurodegenerative Science, Van Andel Institute, 333 Bostwick Ave. N.E., Grand Rapids, Michigan 49503-2518 USA
- Division of Psychiatry and Behavioral Medicine, Michigan State University College of Human Medicine, Grand Rapids, MI USA
| | - Rita Guerreiro
- Department of Neurodegenerative Science, Van Andel Institute, 333 Bostwick Ave. N.E., Grand Rapids, Michigan 49503-2518 USA
- Division of Psychiatry and Behavioral Medicine, Michigan State University College of Human Medicine, Grand Rapids, MI USA
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35
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Dunn CD. The population frequency of human mitochondrial DNA variants is highly dependent upon mutational bias. Biol Open 2021; 10:272468. [PMID: 34643212 PMCID: PMC8565468 DOI: 10.1242/bio.059072] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 10/07/2021] [Indexed: 12/23/2022] Open
Abstract
Next-generation sequencing can quickly reveal genetic variation potentially linked to heritable disease. As databases encompassing human variation continue to expand, rare variants have been of high interest, since the frequency of a variant is expected to be low if the genetic change leads to a loss of fitness or fecundity. However, the use of variant frequency when seeking genomic changes linked to disease remains very challenging. Here, I explored the role of selection in controlling human variant frequency using the HelixMT database, which encompasses hundreds of thousands of mitochondrial DNA (mtDNA) samples. I found that a substantial number of synonymous substitutions, which have no effect on protein sequence, were never encountered in this large study, while many other synonymous changes are found at very low frequencies. Further analyses of human and mammalian mtDNA datasets indicate that the population frequency of synonymous variants is predominantly determined by mutational biases rather than by strong selection acting upon nucleotide choice. My work has important implications that extend to the interpretation of variant frequency for non-synonymous substitutions.
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Affiliation(s)
- Cory D Dunn
- Institute of Biotechnology, University of Helsinki, Helsinki 00014, Finland
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36
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McInnes G, Yee SW, Pershad Y, Altman RB. Genomewide Association Studies in Pharmacogenomics. Clin Pharmacol Ther 2021; 110:637-648. [PMID: 34185318 PMCID: PMC8376796 DOI: 10.1002/cpt.2349] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/15/2021] [Indexed: 12/24/2022]
Abstract
The increasing availability of genotype data linked with information about drug-response phenotypes has enabled genomewide association studies (GWAS) that uncover genetic determinants of drug response. GWAS have discovered associations between genetic variants and both drug efficacy and adverse drug reactions. Despite these successes, the design of GWAS in pharmacogenomics (PGx) faces unique challenges. In this review, we analyze the last decade of GWAS in PGx. We review trends in publications over time, including the drugs and drug classes studied and the clinical phenotypes used. Several data sharing consortia have contributed substantially to the PGx GWAS literature. We anticipate increased focus on biobanks and highlight phenotypes that would best enable future PGx discoveries.
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Affiliation(s)
- Gregory McInnes
- Biomedical Informatics Training Program, Stanford University, Stanford, California, USA
| | - Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, California, USA
| | - Yash Pershad
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Russ B Altman
- Department of Bioengineering, Stanford University, Stanford, California, USA.,Departments of Genetics, Medicine, Biomedical Data Science, Stanford, California, USA
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