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Schoch K, Ruegg MSG, Fellows BJ, Cao J, Uhrig S, Einsele-Scholz S, Biskup S, Hawarden SRA, Salpietro V, Capra V, Brown CM, Accogli A, Shashi V, Bicknell LS. A second hotspot for pathogenic exon-skipping variants in CDC45. Eur J Hum Genet 2024; 32:786-794. [PMID: 38467731 PMCID: PMC11219862 DOI: 10.1038/s41431-024-01583-1] [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: 12/16/2023] [Revised: 02/13/2024] [Accepted: 02/26/2024] [Indexed: 03/13/2024] Open
Abstract
Biallelic pathogenic variants in CDC45 are associated with Meier-Gorlin syndrome with craniosynostosis (MGORS type 7), which also includes short stature and absent/hypoplastic patellae. Identified variants act through a hypomorphic loss of function mechanism, to reduce CDC45 activity and impact DNA replication initiation. In addition to missense and premature termination variants, several pathogenic synonymous variants have been identified, most of which cause increased exon skipping of exon 4, which encodes an essential part of the RecJ-orthologue's DHH domain. Here we have identified a second cohort of families segregating CDC45 variants, where patients have craniosynostosis and a reduction in height, alongside common facial dysmorphisms, including thin eyebrows, consistent with MGORS7. Skipping of exon 15 is a consequence of two different variants, including a shared synonymous variant that is enriched in individuals of East Asian ancestry, while other variants in trans are predicted to alter key intramolecular interactions in α/β domain II, or cause retention of an intron within the 3'UTR. Our cohort and functional data confirm exon skipping is a relatively common pathogenic mechanism in CDC45, and highlights the need for alternative splicing events, such as exon skipping, to be especially considered for variants initially predicted to be less likely to cause the phenotype, particularly synonymous variants.
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Affiliation(s)
- Kelly Schoch
- Division of Medical Genetics, Department of Pediatrics, Duke University School of Medicine, Durham, NC, USA
| | - Mischa S G Ruegg
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Bridget J Fellows
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Joseph Cao
- Division of Pediatric Radiology, Department of Radiology Duke University School of Medicine, Durham, NC, USA
| | - Sabine Uhrig
- Institute of Clinical Genetics, Klinikum Stuttgart, Stuttgart, Germany
| | | | - Saskia Biskup
- Center for Human Genetics Tuebingen and CeGaT GmbH, Tuebingen, Germany
| | - Samuel R A Hawarden
- Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
| | - Vincenzo Salpietro
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Valeria Capra
- Genomics and Clinical Genetics, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Chris M Brown
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Andrea Accogli
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montreal, QC, Canada
- Department of Human Genetics, Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Vandana Shashi
- Division of Medical Genetics, Department of Pediatrics, Duke University School of Medicine, Durham, NC, USA
| | - Louise S Bicknell
- Department of Biochemistry, University of Otago, Dunedin, New Zealand.
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2
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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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3
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Ralli S, Vira T, Robles-Espinoza CD, Adams DJ, Brooks-Wilson AR. Variant ranking pipeline for complex familial disorders. Sci Rep 2024; 14:13599. [PMID: 38866901 PMCID: PMC11169219 DOI: 10.1038/s41598-024-64169-3] [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: 08/03/2023] [Accepted: 06/05/2024] [Indexed: 06/14/2024] Open
Abstract
Identifying genetic susceptibility factors for complex disorders remains a challenging task. To analyze collections of small and large pedigrees where genetic heterogeneity is likely, but biological commonalities are plausible, we have developed a weights-based pipeline to prioritize variants and genes. The Weights-based vAriant Ranking in Pedigrees (WARP) pipeline prioritizes variants using 5 weights: disease incidence rate, number of cases in a family, genome fraction shared amongst cases in a family, allele frequency and variant deleteriousness. Weights, except for the population allele frequency weight, are normalized between 0 and 1. Weights are combined multiplicatively to produce family-specific-variant weights that are then averaged across all families in which the variant is observed to generate a multifamily weight. Sorting multifamily weights in descending order creates a ranked list of variants and genes for further investigation. WARP was validated using familial melanoma sequence data from the European Genome-phenome Archive. The pipeline identified variation in known germline melanoma genes POT1, MITF and BAP1 in 4 out of 13 families (31%). Analysis of the other 9 families identified several interesting genes, some of which might have a role in melanoma. WARP provides an approach to identify disease predisposing genes in studies with small and large pedigrees.
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Affiliation(s)
- Sneha Ralli
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, V5Z 1L3, Canada
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
| | - Tariq Vira
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, V5Z 1L3, Canada
| | | | - David J Adams
- Experimental Cancer Genetics, Wellcome Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, UK
| | - Angela R Brooks-Wilson
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, V5Z 1L3, Canada.
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada.
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4
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Lynn N, Tuller T. Detecting and understanding meaningful cancerous mutations based on computational models of mRNA splicing. NPJ Syst Biol Appl 2024; 10:25. [PMID: 38453965 PMCID: PMC10920900 DOI: 10.1038/s41540-024-00351-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 02/22/2024] [Indexed: 03/09/2024] Open
Abstract
Cancer research has long relied on non-silent mutations. Yet, it has become overwhelmingly clear that silent mutations can affect gene expression and cancer cell fitness. One fundamental mechanism that apparently silent mutations can severely disrupt is alternative splicing. Here we introduce Oncosplice, a tool that scores mutations based on models of proteomes generated using aberrant splicing predictions. Oncosplice leverages a highly accurate neural network that predicts splice sites within arbitrary mRNA sequences, a greedy transcript constructor that considers alternate arrangements of splicing blueprints, and an algorithm that grades the functional divergence between proteins based on evolutionary conservation. By applying this tool to 12M somatic mutations we identify 8K deleterious variants that are significantly depleted within the healthy population; we demonstrate the tool's ability to identify clinically validated pathogenic variants with a positive predictive value of 94%; we show strong enrichment of predicted deleterious mutations across pan-cancer drivers. We also achieve improved patient survival estimation using a proposed set of novel cancer-involved genes. Ultimately, this pipeline enables accelerated insight-gathering of sequence-specific consequences for a class of understudied mutations and provides an efficient way of filtering through massive variant datasets - functionalities with immediate experimental and clinical applications.
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Affiliation(s)
- Nicolas Lynn
- Department of Biomedical Engineering, the Engineering Faculty, Tel Aviv University, Tel-Aviv, 69978, Israel
| | - Tamir Tuller
- Department of Biomedical Engineering, the Engineering Faculty, Tel Aviv University, Tel-Aviv, 69978, Israel.
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5
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Clay S, Evans A, Zambrano R, Otohinoyi D, Hicks C, Tsien F. Bioinformatics characterization of variants of uncertain significance in pediatric sensorineural hearing loss. Front Pediatr 2024; 12:1299341. [PMID: 38450295 PMCID: PMC10915201 DOI: 10.3389/fped.2024.1299341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 01/31/2024] [Indexed: 03/08/2024] Open
Abstract
Introduction Rapid advancements in Next Generation Sequencing (NGS) and bioinformatics tools have allowed physicians to obtain genetic testing results in a more rapid, cost-effective, and comprehensive manner than ever before. Around 50% of pediatric sensorineural hearing loss (SNHL) cases are due to a genetic etiology, thus physicians regularly utilize targeted sequencing panels that identify variants in genes related to SNHL. These panels allow for early detection of pathogenic variants which allows physicians to provide anticipatory guidance to families. Molecular testing does not always reveal a clear etiology due to the presence of multigenic variants with varying classifications, including the presence of Variants of Uncertain Significance (VUS). This study aims to perform a preliminary bioinformatics characterization of patients with variants associated with Type II Usher Syndrome in the presence of other multigenic variants. We also provide an interpretation algorithm for physicians reviewing molecular results with medical geneticists. Methods Review of records for multigenic and/or VUS results identified several potential subjects of interest. For the purposes of this study, two ADGRV1 compound heterozygotes met inclusion criteria. Sequencing, data processing, and variant calling (the process by which variants are identified from sequence data) was performed at Invitae (San Francisco CA). The preliminary analysis followed the recommendations outlined by the American College of Medical Genetics and Association for Molecular Pathology (ACMG-AMP) in 2015 and 2019. The present study utilizes computational analysis, predictive data, and population data as well as clinical information from chart review and publicly available information in the ClinVar database. Results Two subjects were identified as compound heterozygotes for variants in the gene ADGRV1. Subject 1's variants were predicted as deleterious, while Subject 2's variants were predicted as non-deleterious. These results were based on known information of the variants from ClinVar, multiple lines of computational data, population databases, as well as the clinical presentation. Discussion Early molecular diagnosis through NGS is ideal, as families are then able to access a wide range of resources that will ultimately support the child as their condition progresses. We recommend that physicians build strong relationships with medical geneticists and carefully review their interpretation before making recommendations to families, particularly when addressing the VUS. Reclassification efforts of VUS are supported by studies like ours that provide evidence of pathogenic or benign effects of variants.
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Affiliation(s)
- Sloane Clay
- Department of Genetics, Louisiana State University Health Sciences Center, New Orleans, LA, United States
| | - Adele Evans
- Department of Otolaryngology, Children's Hospital of New Orleans, New Orleans, LA, United States
| | - Regina Zambrano
- Department of Pediatrics, Division of Clinical Genetics, Louisiana State University Health Sciences Center and Children’s Hospital of New Orleans, New Orleans, LA, United States
| | - David Otohinoyi
- Department of Genetics, Bioinformatics and Genomics Program, Louisiana State University Health Sciences Center, New Orleans, LA, United States
| | - Chindo Hicks
- Department of Genetics, Bioinformatics and Genomics Program, Louisiana State University Health Sciences Center, New Orleans, LA, United States
| | - Fern Tsien
- Department of Genetics, Louisiana State University Health Sciences Center, New Orleans, LA, United States
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6
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Cheng N, Bi C, Shi Y, Liu M, Cao A, Ren M, Xia J, Liang Z. Effect Predictor of Driver Synonymous Mutations Based on Multi-Feature Fusion and Iterative Feature Representation Learning. IEEE J Biomed Health Inform 2024; 28:1144-1151. [PMID: 38096097 DOI: 10.1109/jbhi.2023.3343075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Accurate identification of driver mutations is crucial in genetic studies of human cancers. While numerous cancer driver missense mutations have been identified, research into potential cancer drivers for synonymous mutations has shown limited success to date. Here, we developed a novel machine learning framework, epSMic, for predicting cancer driver synonymous mutations. epSMic employs an iterative feature representation scheme that facilitates the learning of discriminative features from various sequential models in a supervised iterative mode. We constructed the benchmark datasets and encoded the embedding sequence, physicochemical property, and basic information such as conservation and splicing feature. The evaluation results on benchmark test datasets demonstrate that epSMic outperforms existing methods, making it a valuable tool for researchers in identifying functional synonymous mutations in cancer. We hope epSMic can enable researchers to concentrate on synonymous mutations that have a functional impact on cancer.
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7
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Boujemaa M, Nouira F, Jandoubi N, Mejri N, Bouaziz H, Charfeddine C, Ben Nasr S, Labidi S, El Benna H, Berrazega Y, Rachdi H, Daoud N, Benna F, Haddaoui A, Abdelhak S, Samir Boubaker M, Boussen H, Hamdi Y. Uncovering the clinical relevance of unclassified variants in DNA repair genes: a focus on BRCA negative Tunisian cancer families. Front Genet 2024; 15:1327894. [PMID: 38313678 PMCID: PMC10834681 DOI: 10.3389/fgene.2024.1327894] [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: 10/25/2023] [Accepted: 01/02/2024] [Indexed: 02/06/2024] Open
Abstract
Introduction: Recent advances in sequencing technologies have significantly increased our capability to acquire large amounts of genetic data. However, the clinical relevance of the generated data continues to be challenging particularly with the identification of Variants of Uncertain Significance (VUSs) whose pathogenicity remains unclear. In the current report, we aim to evaluate the clinical relevance and the pathogenicity of VUSs in DNA repair genes among Tunisian breast cancer families. Methods: A total of 67 unsolved breast cancer cases have been investigated. The pathogenicity of VUSs identified within 26 DNA repair genes was assessed using different in silico prediction tools including SIFT, PolyPhen2, Align-GVGD and VarSEAK. Effects on the 3D structure were evaluated using the stability predictor DynaMut and molecular dynamics simulation with NAMD. Family segregation analysis was also performed. Results: Among a total of 37 VUSs identified, 11 variants are likely deleterious affecting ATM, BLM, CHEK2, ERCC3, FANCC, FANCG, MSH2, PMS2 and RAD50 genes. The BLM variant, c.3254dupT, is novel and seems to be associated with increased risk of breast, endometrial and colon cancer. Moreover, c.6115G>A in ATM and c.592+3A>T in CHEK2 were of keen interest identified in families with multiple breast cancer cases and their familial cosegregation with disease has been also confirmed. In addition, functional in silico analyses revealed that the ATM variant may lead to protein immobilization and rigidification thus decreasing its activity. We have also shown that FANCC and FANCG variants may lead to protein destabilization and alteration of the structure compactness which may affect FANCC and FANCG protein activity. Conclusion: Our findings revealed that VUSs in DNA repair genes might be associated with increased cancer risk and highlight the need for variant reclassification for better disease management. This will help to improve the genetic diagnosis and therapeutic strategies of cancer patients not only in Tunisia but also in neighboring countries.
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Affiliation(s)
- Maroua Boujemaa
- Laboratory of Biomedical Genomics and Oncogenetics, LR20IPT05, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
| | - Fatma Nouira
- Laboratory of Bioactive Substances, Center of Biotechnology of Borj Cedria, University of Tunis El Manar, Hamam, Tunisia
| | - Nouha Jandoubi
- Laboratory of Biomedical Genomics and Oncogenetics, LR20IPT05, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
| | - Nesrine Mejri
- Laboratory of Biomedical Genomics and Oncogenetics, LR20IPT05, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
- Medical Oncology Department, Abderrahman Mami Hospital, Faculty of Medicine Tunis, University Tunis El Manar, Tunis, Tunisia
| | - Hanen Bouaziz
- Laboratory of Biomedical Genomics and Oncogenetics, LR20IPT05, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
- Surgical Oncology Department, Salah Azaiez Institute of Cancer, Tunis, Tunisia
| | - Cherine Charfeddine
- Laboratory of Biomedical Genomics and Oncogenetics, LR20IPT05, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
- High Institute of Biotechnology of Sidi Thabet, Biotechpole of Sidi Thabet, University of Manouba, Ariana, Tunisia
| | - Sonia Ben Nasr
- Laboratory of Biomedical Genomics and Oncogenetics, LR20IPT05, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
- Department of Medical Oncology, Military Hospital of Tunis, Tunis, Tunisia
| | - Soumaya Labidi
- Laboratory of Biomedical Genomics and Oncogenetics, LR20IPT05, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
- Medical Oncology Department, Abderrahman Mami Hospital, Faculty of Medicine Tunis, University Tunis El Manar, Tunis, Tunisia
| | - Houda El Benna
- Laboratory of Biomedical Genomics and Oncogenetics, LR20IPT05, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
- Medical Oncology Department, Abderrahman Mami Hospital, Faculty of Medicine Tunis, University Tunis El Manar, Tunis, Tunisia
| | - Yosra Berrazega
- Medical Oncology Department, Abderrahman Mami Hospital, Faculty of Medicine Tunis, University Tunis El Manar, Tunis, Tunisia
| | - Haifa Rachdi
- Medical Oncology Department, Abderrahman Mami Hospital, Faculty of Medicine Tunis, University Tunis El Manar, Tunis, Tunisia
| | - Nouha Daoud
- Medical Oncology Department, Abderrahman Mami Hospital, Faculty of Medicine Tunis, University Tunis El Manar, Tunis, Tunisia
| | - Farouk Benna
- Radiation Oncology Department, Salah Azaiez Institute, Tunis, Tunisia
| | | | - Sonia Abdelhak
- Laboratory of Biomedical Genomics and Oncogenetics, LR20IPT05, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
| | - Mohamed Samir Boubaker
- Laboratory of Biomedical Genomics and Oncogenetics, LR20IPT05, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
- Laboratory of Human and Experimental Pathology, Institut Pasteur de Tunis, Tunis, Tunisia
| | - Hamouda Boussen
- Laboratory of Biomedical Genomics and Oncogenetics, LR20IPT05, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
- Medical Oncology Department, Abderrahman Mami Hospital, Faculty of Medicine Tunis, University Tunis El Manar, Tunis, Tunisia
| | - Yosr Hamdi
- Laboratory of Biomedical Genomics and Oncogenetics, LR20IPT05, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
- Laboratory of Human and Experimental Pathology, Institut Pasteur de Tunis, Tunis, Tunisia
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Gorcenco S, Kafantari E, Wallenius J, Karremo C, Alinder E, Dobloug S, Landqvist Waldö M, Englund E, Ehrencrona H, Wictorin K, Karrman K, Puschmann A. Clinical and genetic analyses of a Swedish patient series diagnosed with ataxia. J Neurol 2024; 271:526-542. [PMID: 37787810 PMCID: PMC10770240 DOI: 10.1007/s00415-023-11990-x] [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: 05/16/2023] [Revised: 09/04/2023] [Accepted: 09/06/2023] [Indexed: 10/04/2023]
Abstract
Hereditary ataxia is a heterogeneous group of complex neurological disorders. Next-generation sequencing methods have become a great help in clinical diagnostics, but it may remain challenging to determine if a genetic variant is the cause of the patient's disease. We compiled a consecutive single-center series of 87 patients from 76 families with progressive ataxia of known or unknown etiology. We investigated them clinically and genetically using whole exome or whole genome sequencing. Test methods were selected depending on family history, clinical phenotype, and availability. Genetic results were interpreted based on the American College of Medical Genetics criteria. For high-suspicion variants of uncertain significance, renewed bioinformatical and clinical evaluation was performed to assess the level of pathogenicity. Thirty (39.5%) of the 76 families had received a genetic diagnosis at the end of our study. We present the predominant etiologies of hereditary ataxia in a Swedish patient series. In two families, we established a clinical diagnosis, although the genetic variant was classified as "of uncertain significance" only, and in an additional three families, results are pending. We found a pathogenic variant in one family, but we suspect that it does not explain the complete clinical picture. We conclude that correctly interpreting genetic variants in complex neurogenetic diseases requires genetics and clinical expertise. The neurologist's careful phenotyping remains essential to confirm or reject a diagnosis, also by reassessing clinical findings after a candidate genetic variant is suggested. Collaboration between neurology and clinical genetics and combining clinical and research approaches optimizes diagnostic yield.
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Affiliation(s)
- Sorina Gorcenco
- Neurology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden.
| | - Efthymia Kafantari
- Neurology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
| | - Joel Wallenius
- Neurology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
| | - Christin Karremo
- Neurology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
| | - Erik Alinder
- Neurology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
| | - Sigurd Dobloug
- Neurology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
- Division of Clinical Sciences Helsingborg, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Maria Landqvist Waldö
- Division of Clinical Sciences Helsingborg, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Elisabet Englund
- Pathology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
| | - Hans Ehrencrona
- Division of Clinical Genetics, Department of Laboratory Medicine, Lund University, Lund, Sweden
- Department of Clinical Genetics, Pathology and Molecular Diagnostics, Office for Medical Services, Region Skåne, Lund, Sweden
| | - Klas Wictorin
- Division of Clinical Sciences Helsingborg, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Kristina Karrman
- Division of Clinical Genetics, Department of Laboratory Medicine, Lund University, Lund, Sweden
- Department of Clinical Genetics, Pathology and Molecular Diagnostics, Office for Medical Services, Region Skåne, Lund, Sweden
| | - Andreas Puschmann
- Neurology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
- SciLifeLab National Research Infrastructure, Solna, Sweden
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9
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Smith C, Kitzman JO. Benchmarking splice variant prediction algorithms using massively parallel splicing assays. Genome Biol 2023; 24:294. [PMID: 38129864 PMCID: PMC10734170 DOI: 10.1186/s13059-023-03144-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Variants that disrupt mRNA splicing account for a sizable fraction of the pathogenic burden in many genetic disorders, but identifying splice-disruptive variants (SDVs) beyond the essential splice site dinucleotides remains difficult. Computational predictors are often discordant, compounding the challenge of variant interpretation. Because they are primarily validated using clinical variant sets heavily biased to known canonical splice site mutations, it remains unclear how well their performance generalizes. RESULTS We benchmark eight widely used splicing effect prediction algorithms, leveraging massively parallel splicing assays (MPSAs) as a source of experimentally determined ground-truth. MPSAs simultaneously assay many variants to nominate candidate SDVs. We compare experimentally measured splicing outcomes with bioinformatic predictions for 3,616 variants in five genes. Algorithms' concordance with MPSA measurements, and with each other, is lower for exonic than intronic variants, underscoring the difficulty of identifying missense or synonymous SDVs. Deep learning-based predictors trained on gene model annotations achieve the best overall performance at distinguishing disruptive and neutral variants, and controlling for overall call rate genome-wide, SpliceAI and Pangolin have superior sensitivity. Finally, our results highlight two practical considerations when scoring variants genome-wide: finding an optimal score cutoff, and the substantial variability introduced by differences in gene model annotation, and we suggest strategies for optimal splice effect prediction in the face of these issues. CONCLUSION SpliceAI and Pangolin show the best overall performance among predictors tested, however, improvements in splice effect prediction are still needed especially within exons.
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Affiliation(s)
- Cathy Smith
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Jacob O Kitzman
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
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Levy M, Bazak L, Lev-El N, Greenberg R, Kropach N, Basel-Salmon L, Maya I. Potential Founder Variants in COL4A4 Identified in Bukharian Jews Linked to Autosomal Dominant and Autosomal Recessive Alport Syndrome. Genes (Basel) 2023; 14:1854. [PMID: 37895203 PMCID: PMC10606019 DOI: 10.3390/genes14101854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 09/12/2023] [Accepted: 09/22/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND Alport syndrome is a hereditary disorder caused by pathogenic variants in the COL4A gene, which can be inherited in an autosomal recessive, dominant, or X-linked pattern. In the Bukharian Jewish population, no founder pathogenic variant has been reported in COL4A4. METHODS The cohort included 38 patients from 22 Bukharian Jewish families with suspected Alport syndrome who were referred the nephrogenetics clinic between 2012 and 2022. The study collected demographic, clinical, and genetic data from electronic medical records, which were used to evaluate the molecular basis of the disease using Sanger sequencing, and next-generation sequencing. RESULTS Molecular diagnosis was confirmed in 20/38 patients, with each patient having at least one of the three disease-causing COL4A4 variants detected: c.338G A (p.Gly1008Arg), and c.871-6T>C. In addition, two patients were obligate carriers. Overall, there were 17 heterozygotes, 2 compound heterozygotes, and 3 homozygotes. Each variant was detected in more than one unrelated family. All patients had hematuria with/without proteinuria at referral, and the youngest patient with proteinuria (age 5 years) was homozygous for the c.338G>A variant. End-stage renal disease was diagnosed in two patients at the age of 38 years, a compound heterozygote for c.338G>A and c.871-6T>C. Hearing deterioration was detected in three patients, the youngest aged 40 years, all of whom were heterozygous for c.338G>A. CONCLUSION This study unveils three novel disease-causing variants, c.3022G>A, c.871-6T>C, and c.338G>A, in the COL4A4 gene that are recurrent among Jews of Bukharian ancestry, and cause Alport syndrome in both dominant and recessive autosomal inheritance patterns.
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Affiliation(s)
- Michal Levy
- The Raphael Recanati Genetic Institute, Rabin Medical Center, Petah Tikva 49100, Israel (N.L.-E.); (L.B.-S.); (I.M.)
- School of Medicine, Tel Aviv University, Tel Aviv P.O.B 39040, Israel
| | - Lily Bazak
- The Raphael Recanati Genetic Institute, Rabin Medical Center, Petah Tikva 49100, Israel (N.L.-E.); (L.B.-S.); (I.M.)
| | - Noa Lev-El
- The Raphael Recanati Genetic Institute, Rabin Medical Center, Petah Tikva 49100, Israel (N.L.-E.); (L.B.-S.); (I.M.)
| | - Rotem Greenberg
- The Raphael Recanati Genetic Institute, Rabin Medical Center, Petah Tikva 49100, Israel (N.L.-E.); (L.B.-S.); (I.M.)
| | - Nesia Kropach
- School of Medicine, Tel Aviv University, Tel Aviv P.O.B 39040, Israel
- Pediatric Genetics Unit, Schneider Children’s Medical Center, Petah Tikva 4920235, Israel
| | - Lina Basel-Salmon
- The Raphael Recanati Genetic Institute, Rabin Medical Center, Petah Tikva 49100, Israel (N.L.-E.); (L.B.-S.); (I.M.)
- School of Medicine, Tel Aviv University, Tel Aviv P.O.B 39040, Israel
- Felsenstein Medical Research Center, Petach Tikva 4920235, Israel
| | - Idit Maya
- The Raphael Recanati Genetic Institute, Rabin Medical Center, Petah Tikva 49100, Israel (N.L.-E.); (L.B.-S.); (I.M.)
- School of Medicine, Tel Aviv University, Tel Aviv P.O.B 39040, Israel
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11
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Fan R, Ji X, Li J, Cui Q, Cui C. Defining the single base importance of human mRNAs and lncRNAs. Brief Bioinform 2023; 24:bbad321. [PMID: 37668090 DOI: 10.1093/bib/bbad321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/28/2023] [Accepted: 08/16/2023] [Indexed: 09/06/2023] Open
Abstract
As the fundamental unit of a gene and its transcripts, nucleotides have enormous impacts on the gene function and evolution, and thus on phenotypes and diseases. In order to identify the key nucleotides of one specific gene, it is quite crucial to quantitatively measure the importance of each base on the gene. However, there are still no sequence-based methods of doing that. Here, we proposed Base Importance Calculator (BIC), an algorithm to calculate the importance score of each single base based on sequence information of human mRNAs and long noncoding RNAs (lncRNAs). We then confirmed its power by applying BIC to three different tasks. Firstly, we revealed that BIC can effectively evaluate the pathogenicity of both genes and single bases through single nucleotide variations. Moreover, the BIC score in The Cancer Genome Atlas somatic mutations is able to predict the prognosis of some cancers. Finally, we show that BIC can also precisely predict the transmissibility of SARS-CoV-2. The above results indicate that BIC is a useful tool for evaluating the single base importance of human mRNAs and lncRNAs.
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Affiliation(s)
- Rui Fan
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, State Key Lab of Vascular Homeostasis and Remodeling, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, 100191, China
| | - Xiangwen Ji
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, State Key Lab of Vascular Homeostasis and Remodeling, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, 100191, China
| | - Jianwei Li
- Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300401, China
| | - Qinghua Cui
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, State Key Lab of Vascular Homeostasis and Remodeling, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, 100191, China
- School of Sports Medicine, Wuhan Institute of Physical Education, No.461 Luoyu Rd. Wuchang District, Wuhan 430079, Hubei Province, China
| | - Chunmei Cui
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, State Key Lab of Vascular Homeostasis and Remodeling, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, 100191, China
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12
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Shirvanizadeh N, Vihinen M. VariBench, new variation benchmark categories and data sets. FRONTIERS IN BIOINFORMATICS 2023; 3:1248732. [PMID: 37795169 PMCID: PMC10546188 DOI: 10.3389/fbinf.2023.1248732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/08/2023] [Indexed: 10/06/2023] Open
Affiliation(s)
| | - Mauno Vihinen
- Department of Experimental Medical Science, Lund University, Lund, Sweden
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13
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Xu S, Cai G, Zhu Y, Gu X, Wu J, Cheng X, Bao J, Yu H, Zhang L. A Combination of BRAF and EZH1/SPOP/ZNF148 Three-Gene Mutational Classifier Improves Benign Call Rate in Indeterminate Thyroid Nodules. Endocr Pathol 2023; 34:323-332. [PMID: 37572175 DOI: 10.1007/s12022-023-09782-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/05/2023] [Indexed: 08/14/2023]
Abstract
Reliable preoperative diagnosis of thyroid nodules remained challenging because of the inconclusiveness of fine-needle aspiration (FNA) cytology. In the present study, 583 formalin-fixed paraffin embedded (FFPE) thyroid nodule tissues and 161 FNA specimens were enrolled retrospectively. Then BRAF V600E, EZH1 Q571R, SPOP P94R, and ZNF148 mutations among these samples were identified using Sanger sequencing. Based on this four-gene genomic classifier, we proposed a two-step modality to diagnose thyroid nodules to differentiate benign and malignant thyroid nodules. In the FFPE group, thyroid cancers were effectively diagnosed in 37.7% (220/583) of neoplasms by the primary BRAF V600E testing, and 15.7% (57/363) of thyroid nodules could be further determined as benign by subsequent EZH1 Q571R, SPOP P94R, and ZNF148 (we called them "ESZ") mutation testing. In the FNA group, 161 BRAF wild-type specimens were classified according to The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC). A total of 7 mutated samples fell within Bethesda categories III-IV, and the mutation rate of "ESZ" in Bethesda III-IV categories was 8.4%. The two-step genomic classifier could further improve thyroid nodule diagnosis, which may inform more optimal patient management.
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Affiliation(s)
- Shichen Xu
- NHC Key Laboratory of Nuclear Medicine, Jiangsu Key Laboratory of Molecular Nuclear Medicine, Jiangsu Institute of Nuclear Medicine, 20 Qian Rong Road, Wuxi , Jiangsu, 214063, China
| | - Gangming Cai
- Clinical Molecular Biology Laboratory, Jiangyuan Hospital Affiliated to Jiangsu Institute of Nuclear Medicine, Wuxi , Jiangsu, 214063, China
| | - Yun Zhu
- Department of Pathology, Jiangyuan Hospital Affiliated to Jiangsu Institute of Nuclear Medicine, Wuxi , Jiangsu, 214063, China
| | - Xiaobo Gu
- Clinical Molecular Biology Laboratory, Jiangyuan Hospital Affiliated to Jiangsu Institute of Nuclear Medicine, Wuxi , Jiangsu, 214063, China
| | - Jing Wu
- NHC Key Laboratory of Nuclear Medicine, Jiangsu Key Laboratory of Molecular Nuclear Medicine, Jiangsu Institute of Nuclear Medicine, 20 Qian Rong Road, Wuxi , Jiangsu, 214063, China
| | - Xian Cheng
- NHC Key Laboratory of Nuclear Medicine, Jiangsu Key Laboratory of Molecular Nuclear Medicine, Jiangsu Institute of Nuclear Medicine, 20 Qian Rong Road, Wuxi , Jiangsu, 214063, China
| | - Jiandong Bao
- NHC Key Laboratory of Nuclear Medicine, Jiangsu Key Laboratory of Molecular Nuclear Medicine, Jiangsu Institute of Nuclear Medicine, 20 Qian Rong Road, Wuxi , Jiangsu, 214063, China
| | - Huixin Yu
- NHC Key Laboratory of Nuclear Medicine, Jiangsu Key Laboratory of Molecular Nuclear Medicine, Jiangsu Institute of Nuclear Medicine, 20 Qian Rong Road, Wuxi , Jiangsu, 214063, China
| | - Li Zhang
- NHC Key Laboratory of Nuclear Medicine, Jiangsu Key Laboratory of Molecular Nuclear Medicine, Jiangsu Institute of Nuclear Medicine, 20 Qian Rong Road, Wuxi , Jiangsu, 214063, China.
- Department of Radiopharmaceuticals, School of Pharmacy, Nanjing Medical University, Nanjing, 211166, China.
- School of Life Science and Technology, Southeast University, Nanjing, 210096, China.
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14
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Simonovsky E, Sharon M, Ziv M, Mauer O, Hekselman I, Jubran J, Vinogradov E, Argov CM, Basha O, Kerber L, Yogev Y, Segrè AV, Im HK, Birk O, Rokach L, Yeger‐Lotem E. Predicting molecular mechanisms of hereditary diseases by using their tissue-selective manifestation. Mol Syst Biol 2023; 19:e11407. [PMID: 37232043 PMCID: PMC10407743 DOI: 10.15252/msb.202211407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 04/30/2023] [Accepted: 05/10/2023] [Indexed: 05/27/2023] Open
Abstract
How do aberrations in widely expressed genes lead to tissue-selective hereditary diseases? Previous attempts to answer this question were limited to testing a few candidate mechanisms. To answer this question at a larger scale, we developed "Tissue Risk Assessment of Causality by Expression" (TRACE), a machine learning approach to predict genes that underlie tissue-selective diseases and selectivity-related features. TRACE utilized 4,744 biologically interpretable tissue-specific gene features that were inferred from heterogeneous omics datasets. Application of TRACE to 1,031 disease genes uncovered known and novel selectivity-related features, the most common of which was previously overlooked. Next, we created a catalog of tissue-associated risks for 18,927 protein-coding genes (https://netbio.bgu.ac.il/trace/). As proof-of-concept, we prioritized candidate disease genes identified in 48 rare-disease patients. TRACE ranked the verified disease gene among the patient's candidate genes significantly better than gene prioritization methods that rank by gene constraint or tissue expression. Thus, tissue selectivity combined with machine learning enhances genetic and clinical understanding of hereditary diseases.
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Affiliation(s)
- Eyal Simonovsky
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Moran Sharon
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Maya Ziv
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Omry Mauer
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Idan Hekselman
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Juman Jubran
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Ekaterina Vinogradov
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Chanan M Argov
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Omer Basha
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Lior Kerber
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Yuval Yogev
- Morris Kahn Laboratory of Human Genetics and the Genetics Institute at Soroka Medical Center, Faculty of Health SciencesBen Gurion University of the NegevBeer ShevaIsrael
| | - Ayellet V Segrè
- Ocular Genomics Institute, Massachusetts Eye and EarHarvard Medical SchoolBostonMAUSA
- The Broad Institute of MIT and HarvardCambridgeMAUSA
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of MedicineThe University of ChicagoChicagoILUSA
| | | | - Ohad Birk
- Morris Kahn Laboratory of Human Genetics and the Genetics Institute at Soroka Medical Center, Faculty of Health SciencesBen Gurion University of the NegevBeer ShevaIsrael
- The National Institute for Biotechnology in the NegevBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Lior Rokach
- Department of Software & Information Systems EngineeringBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Esti Yeger‐Lotem
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
- The National Institute for Biotechnology in the NegevBen‐Gurion University of the NegevBeer ShevaIsrael
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15
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Rossi A, Blok LS, Neuser S, Klöckner C, Platzer K, Faivre LO, Weigand H, Dentici ML, Tartaglia M, Niceta M, Alfieri P, Srivastava S, Coulter D, Smith L, Vinorum K, Cappuccio G, Brunetti-Pierri N, Torun D, Arslan M, Lauridsen MF, Murch O, Irving R, Lynch SA, Mehta SG, Carmichael J, Zonneveld-Huijssoon E, de Vries B, Kleefstra T, Johannesen KM, Westphall IT, Hughes SS, Smithson S, Evans J, Dudding-Byth T, Simon M, van Binsbergen E, Herkert JC, Beunders G, Oppermann H, Bakal M, Møller RS, Rubboli G, Bayat A. POU3F3-related disorder: Defining the phenotype and expanding the molecular spectrum. Clin Genet 2023; 104:186-197. [PMID: 37165752 PMCID: PMC10330344 DOI: 10.1111/cge.14353] [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: 01/23/2023] [Revised: 04/06/2023] [Accepted: 04/24/2023] [Indexed: 05/12/2023]
Abstract
POU3F3 variants cause developmental delay, behavioral problems, hypotonia and dysmorphic features. We investigated the phenotypic and genetic landscape, and genotype-phenotype correlations in individuals with POU3F3-related disorders. We recruited unpublished individuals with POU3F3 variants through international collaborations and obtained updated clinical data on previously published individuals. Trio exome sequencing or single exome sequencing followed by segregation analysis were performed in the novel cohort. Functional effects of missense variants were investigated with 3D protein modeling. We included 28 individuals (5 previously published) from 26 families carrying POU3F3 variants; 23 de novo and one inherited from an affected parent. Median age at study inclusion was 7.4 years. All had developmental delay mainly affecting speech, behavioral difficulties, psychiatric comorbidities and dysmorphisms. Additional features included gastrointestinal comorbidities, hearing loss, ophthalmological anomalies, epilepsy, sleep disturbances and joint hypermobility. Autism, hearing and eye comorbidities, dysmorphisms were more common in individuals with truncating variants, whereas epilepsy was only associated with missense variants. In silico structural modeling predicted that all (likely) pathogenic variants destabilize the DNA-binding region of POU3F3. Our study refined the phenotypic and genetic landscape of POU3F3-related disorders, it reports the functional properties of the identified pathogenic variants, and delineates some genotype-phenotype correlations.
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Affiliation(s)
- Alessandra Rossi
- Department of Epilepsy Genetics and Personalized Medicine, Danish Epilepsy Center, member of the ERN-EpiCARE, Dianalund, Denmark
- Pediatric Clinic, IRCCS San Matteo Hospital Foundation, University of Pavia, Pavia, Italy
| | - Lot Snijders Blok
- Human Genetics Department, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Sonja Neuser
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Chiara Klöckner
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Konrad Platzer
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Laurence Olivier Faivre
- Centre de Référence Anomalies du Développement et Syndromes Malformatifs, FHU TRANSLAD, Centre Hospitalier Universitaire Dijon, Dijon, France
- Genetics of Developmental Disorders Team, INSERM - Bourgogne Franche-Comté University, UMR 1231 GAD, Dijon, France
| | - Heike Weigand
- Department of Pediatric Neurology, Developmental Medicine and Social Pediatrics, Dr. von Hauner’s Children’s Hospital, University of Munich, Munich, Germany
| | - Maria L. Dentici
- Genetics and Rare Diseases Research Division, Ospedale Pediatrico Bambino Gesù, IRCCS, Rome, Italy
- Medical Genetics Unit, Academic Department of Pediatrics, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Marco Tartaglia
- Genetics and Rare Diseases Research Division, Ospedale Pediatrico Bambino Gesù, IRCCS, Rome, Italy
| | - Marcello Niceta
- Genetics and Rare Diseases Research Division, Ospedale Pediatrico Bambino Gesù, IRCCS, Rome, Italy
| | - Paolo Alfieri
- Child and Adolescent Neuropsychiatry Unit, Department of Neuroscience, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | | | - David Coulter
- Department of Neurology, Boston Children’s Hospital, Boston, Massachusetts, USA
| | - Lacey Smith
- Department of Neurology, Boston Children’s Hospital, Boston, Massachusetts, USA
| | | | - Gerarda Cappuccio
- Department of Translational Medicine, Federico II University, Naples, Italy
- Telethon Institute of Genetics and Medicine, Pozzuoli, Naples, Italy
| | - Nicola Brunetti-Pierri
- Department of Translational Medicine, Federico II University, Naples, Italy
- Telethon Institute of Genetics and Medicine, Pozzuoli, Naples, Italy
- Scuola Superiore Meridionale, School for Advanced Studies, Naples, Italy
| | - Deniz Torun
- Department of Medical Genetics, Gülhane Faculty of Medicine, University of Health Sciences, Ankara, Turkey
| | - Mutluay Arslan
- Department of Pediatric Neurology, Gülhane Faculty of Medicine, University of Health Sciences, Ankara, Turkey
| | | | - Oliver Murch
- All Wales Medical Genomics Service, University Hospital of Wales, Cardiff, UK
| | - Rachel Irving
- All Wales Medical Genomics Service, University Hospital of Wales, Cardiff, UK
| | - Sally A. Lynch
- Children’s Health Ireland at Crumlin, Dublin 12, Ireland
| | - Sarju G. Mehta
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Jenny Carmichael
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Evelien Zonneveld-Huijssoon
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Bert de Vries
- Human Genetics Department, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Tjitske Kleefstra
- Human Genetics Department, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Katrine M. Johannesen
- Department of Epilepsy Genetics and Personalized Medicine, Danish Epilepsy Center, member of the ERN-EpiCARE, Dianalund, Denmark
- Department of Genetics, University Hospital of Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Ian T. Westphall
- Department of Paediatrics, Copenhagen University Hospital, Hvidovre, Denmark
| | - Susan S. Hughes
- Division of Genetics, Children’s Mercy Kansas City, Kansas City, MO, USA
| | - Sarah Smithson
- Department of Clinical Genetics, University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
| | - Julie Evans
- Bristol Genetics Laboratory, North Bristol NHS Trust, Pathology Sciences Building, Southmead Hospital, Bristol, UK
| | - Tracy Dudding-Byth
- NSW Genetics of Learning Disability (GOLD) Service, University of Newcastle, NSW Australia
| | - Marleen Simon
- Department of Medical Genetics, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Ellen van Binsbergen
- Department of Medical Genetics, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Johanna C. Herkert
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Gea Beunders
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Henry Oppermann
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Mert Bakal
- Clinic of Radiology, University of Health Sciences Turkey, Haseki Training and Research Hospital, Istanbul, Turkey
| | - Rikke S. Møller
- Department of Epilepsy Genetics and Personalized Medicine, Danish Epilepsy Center, member of the ERN-EpiCARE, Dianalund, Denmark
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Guido Rubboli
- Department of Epilepsy Genetics and Personalized Medicine, Danish Epilepsy Center, member of the ERN-EpiCARE, Dianalund, Denmark
- Institute of Clinical Medicine, Copenhagen University, Copenhagen, Denmark
| | - Allan Bayat
- Department of Epilepsy Genetics and Personalized Medicine, Danish Epilepsy Center, member of the ERN-EpiCARE, Dianalund, Denmark
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
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16
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Zhang L, Shen M, Shu X, Zhou J, Ding J, Zhong C, Pan B, Wang B, Zhang C, Guo W. Intronic position +9 and -9 are potentially splicing sites boundary from intronic variants analysis of whole exome sequencing data. BMC Med Genomics 2023; 16:146. [PMID: 37365551 DOI: 10.1186/s12920-023-01542-7] [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: 10/25/2022] [Accepted: 05/12/2023] [Indexed: 06/28/2023] Open
Abstract
Whole exome sequencing (WES) can also detect some intronic variants, which may affect splicing and gene expression, but how to use these intronic variants, and the characteristics about them has not been reported. This study aims to reveal the characteristics of intronic variant in WES data, to further improve the clinical diagnostic value of WES. A total of 269 WES data was analyzed, 688,778 raw variants were called, among these 367,469 intronic variants were in intronic regions flanking exons which was upstream/downstream region of the exon (default is 200 bps). Contrary to expectation, the number of intronic variants with quality control (QC) passed was the lowest at the +2 and -2 positions but not at the +1 and -1 positions. The plausible explanation was that the former had the worst effect on trans-splicing, whereas the latter did not completely abolish splicing. And surprisingly, the number of intronic variants that passed QC was the highest at the +9 and -9 positions, indicating a potential splicing site boundary. The proportion of variants which could not pass QC filtering (false variants) in the intronic regions flanking exons generally accord with "S"-shaped curve. At +5 and -5 positions, the number of variants predicted damaging by software was most. This was also the position at which many pathogenic variants had been reported in recent years. Our study revealed the characteristics of intronic variant in WES data for the first time, we found the +9 and -9 positions might be a potentially splicing sites boundary and +5 and -5 positions were potentially important sites affecting splicing or gene expression, the +2 and -2 positions seem more important splicing site than +1 and -1 positions, and we found variants in intronic regions flanking exons over ± 50 bps may be unreliable. This result can help researchers find more useful variants and demonstrate that WES data is valuable for intronic variants analysis.
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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
| | - Chunjiu Zhong
- Department of Neurology, Zhongshan Hospital, State Key Laboratory, Fudan University, Shanghai, China
| | - Baishen Pan
- Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Beili Wang
- Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Chunyan Zhang
- Department of Laboratory Medicine, Xiamen Branch, Zhongshan Hospital, Fudan University, Xiamen, China.
| | - Wei Guo
- Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
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17
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Vihinen M. Nonsynonymous Synonymous Variants Demand for a Paradigm Shift in Genetics. Curr Genomics 2023; 24:18-23. [PMID: 37920730 PMCID: PMC10334700 DOI: 10.2174/1389202924666230417101020] [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: 12/09/2022] [Revised: 02/20/2023] [Accepted: 03/01/2023] [Indexed: 11/04/2023] Open
Abstract
Synonymous (also known as silent) variations are by definition not considered to change the coded protein. Still many variations in this category affect either protein abundance or properties. As this situation is confusing, we have recently introduced systematics for synonymous variations and those that may on the surface look like synonymous, but these may affect the coded protein in various ways. A new category, unsense variation, was introduced to describe variants that do not introduce a stop codon into the variation site, but which lead to different types of changes in the coded protein. Many of these variations lead to mRNA degradation and missing protein. Here, consequences of the systematics are discussed from the perspectives of variation annotation and interpretation, evolutionary calculations, nonsynonymous-to-synonymous substitution rates, phylogenetics and other evolutionary inferences that are based on the principle of (nearly) neutral synonymous variations. It may be necessary to reassess published results. Further, databases for synonymous variations and prediction methods for such variations should consider unsense variations. Thus, there is a need to evaluate and reflect principles of numerous aspects in genetics, ranging from variation naming and classification to evolutionary calculations.
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Affiliation(s)
- Mauno Vihinen
- Department of Experimental Medical Science, Lund University, Lund, BMC B13, Sweden
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18
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Lin BC, Katneni U, Jankowska KI, Meyer D, Kimchi-Sarfaty C. In silico methods for predicting functional synonymous variants. Genome Biol 2023; 24:126. [PMID: 37217943 PMCID: PMC10204308 DOI: 10.1186/s13059-023-02966-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 05/10/2023] [Indexed: 05/24/2023] Open
Abstract
Single nucleotide variants (SNVs) contribute to human genomic diversity. Synonymous SNVs are previously considered to be "silent," but mounting evidence has revealed that these variants can cause RNA and protein changes and are implicated in over 85 human diseases and cancers. Recent improvements in computational platforms have led to the development of numerous machine-learning tools, which can be used to advance synonymous SNV research. In this review, we discuss tools that should be used to investigate synonymous variants. We provide supportive examples from seminal studies that demonstrate how these tools have driven new discoveries of functional synonymous SNVs.
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Affiliation(s)
- Brian C Lin
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics CMC, Office of Therapeutic Products, Center for Biologics Evaluation and Research, US FDA, Silver Spring, MD, USA
| | - Upendra Katneni
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics CMC, Office of Therapeutic Products, Center for Biologics Evaluation and Research, US FDA, Silver Spring, MD, USA
| | - Katarzyna I Jankowska
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics CMC, Office of Therapeutic Products, Center for Biologics Evaluation and Research, US FDA, Silver Spring, MD, USA
| | - Douglas Meyer
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics CMC, Office of Therapeutic Products, Center for Biologics Evaluation and Research, US FDA, Silver Spring, MD, USA
| | - Chava Kimchi-Sarfaty
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics CMC, Office of Therapeutic Products, Center for Biologics Evaluation and Research, US FDA, Silver Spring, MD, USA.
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Freda PJ, Ghosh A, Zhang E, Luo T, Chitre AS, Polesskaya O, St Pierre CL, Gao J, Martin CD, Chen H, Garcia-Martinez AG, Wang T, Han W, Ishiwari K, Meyer P, Lamparelli A, King CP, Palmer AA, Li R, Moore JH. Automated quantitative trait locus analysis (AutoQTL). BioData Min 2023; 16:14. [PMID: 37038201 PMCID: PMC10088184 DOI: 10.1186/s13040-023-00331-3] [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: 11/07/2022] [Accepted: 03/31/2023] [Indexed: 04/12/2023] Open
Abstract
BACKGROUND Quantitative Trait Locus (QTL) analysis and Genome-Wide Association Studies (GWAS) have the power to identify variants that capture significant levels of phenotypic variance in complex traits. However, effort and time are required to select the best methods and optimize parameters and pre-processing steps. Although machine learning approaches have been shown to greatly assist in optimization and data processing, applying them to QTL analysis and GWAS is challenging due to the complexity of large, heterogenous datasets. Here, we describe proof-of-concept for an automated machine learning approach, AutoQTL, with the ability to automate many complicated decisions related to analysis of complex traits and generate solutions to describe relationships that exist in genetic data. RESULTS Using a publicly available dataset of 18 putative QTL from a large-scale GWAS of body mass index in the laboratory rat, Rattus norvegicus, AutoQTL captures the phenotypic variance explained under a standard additive model. AutoQTL also detects evidence of non-additive effects including deviations from additivity and 2-way epistatic interactions in simulated data via multiple optimal solutions. Additionally, feature importance metrics provide different insights into the inheritance models and predictive power of multiple GWAS-derived putative QTL. CONCLUSIONS This proof-of-concept illustrates that automated machine learning techniques can complement standard approaches and have the potential to detect both additive and non-additive effects via various optimal solutions and feature importance metrics. In the future, we aim to expand AutoQTL to accommodate omics-level datasets with intelligent feature selection and feature engineering strategies.
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Affiliation(s)
- Philip J Freda
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, USA
| | - Attri Ghosh
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, USA
| | - Elizabeth Zhang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, USA
| | - Tianhao Luo
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, USA
| | - Apurva S Chitre
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., Mail Code: 0667, La Jolla, CA, 92093-0667, USA
| | - Oksana Polesskaya
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., Mail Code: 0667, La Jolla, CA, 92093-0667, USA
| | - Celine L St Pierre
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., Mail Code: 0667, La Jolla, CA, 92093-0667, USA
| | - Jianjun Gao
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., Mail Code: 0667, La Jolla, CA, 92093-0667, USA
| | - Connor D Martin
- Department of Pharmacology & Toxicology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, 955 Main Street, Suite 3102, Buffalo, NY, 14203, USA
| | - Hao Chen
- Department of Pharmacology, Addiction Science, and Toxicology, University of Tennessee Health Science Center, Translational Research Building, 71 South Manassas, Memphis, TN, 38163, USA
| | - Angel G Garcia-Martinez
- Department of Pharmacology, Addiction Science, and Toxicology, University of Tennessee Health Science Center, Translational Research Building, 71 South Manassas, Memphis, TN, 38163, USA
| | - Tengfei Wang
- Department of Pharmacology, Addiction Science, and Toxicology, University of Tennessee Health Science Center, Translational Research Building, 71 South Manassas, Memphis, TN, 38163, USA
| | - Wenyan Han
- Department of Pharmacology, Addiction Science, and Toxicology, University of Tennessee Health Science Center, Translational Research Building, 71 South Manassas, Memphis, TN, 38163, USA
| | - Keita Ishiwari
- Department of Pharmacology & Toxicology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, 955 Main Street, Suite 3102, Buffalo, NY, 14203, USA
- Clinical and Research Institute on Addictions, University at Buffalo, 1021 Main Street, Buffalo, NY, 14203-1016, USA
| | - Paul Meyer
- Department of Psychology, University at Buffalo, 204 Park Hall, North Campus, Buffalo, NY, 14260-4110, USA
| | - Alexander Lamparelli
- Department of Psychology, University at Buffalo, 204 Park Hall, North Campus, Buffalo, NY, 14260-4110, USA
| | - Christopher P King
- Department of Psychology, University at Buffalo, 204 Park Hall, North Campus, Buffalo, NY, 14260-4110, USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., Mail Code: 0667, La Jolla, CA, 92093-0667, USA
- Institute for Genomic Medicine, University of California San Diego, 9500 Gilman Dr., Mail Code: 0667, La Jolla, CA, 92093-0667, USA
| | - Ruowang Li
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, USA
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G540, West Hollywood, CA, 90069, USA.
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20
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Wang L, Sun J, Ma S, Xia J, Li X. PredDSMC: A predictor for driver synonymous mutations in human cancers. Front Genet 2023; 14:1164593. [PMID: 37051593 PMCID: PMC10083435 DOI: 10.3389/fgene.2023.1164593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 03/09/2023] [Indexed: 03/29/2023] Open
Abstract
Introduction: Driver mutations play a critical role in the occurrence and development of human cancers. Most studies have focused on missense mutations that function as drivers in cancer. However, accumulating experimental evidence indicates that synonymous mutations can also act as driver mutations.Methods: Here, we proposed a computational method called PredDSMC to accurately predict driver synonymous mutations in human cancers. We first systematically explored four categories of multimodal features, including sequence features, splicing features, conservation scores, and functional scores. Further feature selection was carried out to remove redundant features and improve the model performance. Finally, we utilized the random forest classifier to build PredDSMC.Results: The results of two independent test sets indicated that PredDSMC outperformed the state-of-the-art methods in differentiating driver synonymous mutations from passenger mutations.Discussion: In conclusion, we expect that PredDSMC, as a driver synonymous mutation prediction method, will be a valuable method for gaining a deeper understanding of synonymous mutations in human cancers.
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Rufibach L, Berger K, Chakravorty S, Emmons S, Long L, Gibson G, Hegde M. Utilization of Targeted RNA-Seq for the Resolution of Variant Pathogenicity and Enhancement of Diagnostic Yield in Dysferlinopathy. J Pers Med 2023; 13:jpm13030520. [PMID: 36983702 PMCID: PMC10056012 DOI: 10.3390/jpm13030520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 02/28/2023] [Accepted: 03/08/2023] [Indexed: 03/15/2023] Open
Abstract
For inherited diseases, obtaining a definitive diagnosis is critical for proper disease management, family planning, and participation in clinical trials. This can be challenging for dysferlinopathy due to the significant clinical overlap between the 30+ subtypes of limb–girdle muscular dystrophy (LGMD) and the large number of variants of unknown significance (VUSs) that are identified in the dysferlin gene, DYSF. We performed targeted RNA-Seq using a custom gene-panel in 77 individuals with a clinical/genetic suspicion of dysferlinopathy and evaluated all 111 identified DYSF variants according to the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) guidelines. This evaluation identified 11 novel DYSF variants and allowed for the classification of 87 DYSF variants as pathogenic/likely pathogenic, 8 likely benign, while 16 variants remained VUSs. By the end of the study, 60 of the 77 cases had a definitive diagnosis of dysferlinopathy, which was a 47% increase in diagnostic yield over the rate at study onset. This data shows the ability of RNA-Seq to assist in variant pathogenicity classification and diagnosis of dysferlinopathy and is, therefore, a type of analysis that should be considered when DNA-based genetic analysis is not sufficient to provide a definitive diagnosis.
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Affiliation(s)
- Laura Rufibach
- Jain Foundation, Inc., Seattle, WA 98115, USA; (S.E.); (L.L.)
- Correspondence:
| | - Kiera Berger
- Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, GA 30332, USA; (K.B.); (G.G.)
| | - Samya Chakravorty
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA; (S.C.); (M.H.)
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Sarah Emmons
- Jain Foundation, Inc., Seattle, WA 98115, USA; (S.E.); (L.L.)
| | - Laurie Long
- Jain Foundation, Inc., Seattle, WA 98115, USA; (S.E.); (L.L.)
| | - Greg Gibson
- Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, GA 30332, USA; (K.B.); (G.G.)
| | - Madhuri Hegde
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA; (S.C.); (M.H.)
- PerkinElmer Genomics, Global Laboratory Services, Waltham, MA 02451, USA
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22
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Wang L, Zhang T, Yu L, Zheng CH, Yin W, Xia J, Zhang T. Deleterious synonymous mutation identification based on selective ensemble strategy. Brief Bioinform 2023; 24:6972297. [PMID: 36611253 DOI: 10.1093/bib/bbac598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/29/2022] [Accepted: 12/05/2022] [Indexed: 01/09/2023] Open
Abstract
Although previous studies have revealed that synonymous mutations contribute to various human diseases, distinguishing deleterious synonymous mutations from benign ones is still a challenge in medical genomics. Recently, computational tools have been introduced to predict the harmfulness of synonymous mutations. However, most of these computational tools rely on balanced training sets without considering abundant negative samples that could result in deficient performance. In this study, we propose a computational model that uses a selective ensemble to predict deleterious synonymous mutations (seDSM). We construct several candidate base classifiers for the ensemble using balanced training subsets randomly sampled from the imbalanced benchmark training sets. The diversity measures of the base classifiers are calculated by the pairwise diversity metrics, and the classifiers with the highest diversities are selected for integration using soft voting for synonymous mutation prediction. We also design two strategies for filling in missing values in the imbalanced dataset and constructing models using different pairwise diversity metrics. The experimental results show that a selective ensemble based on double fault with the ensemble strategy EKNNI for filling in missing values is the most effective scheme. Finally, using 40-dimensional biology features, we propose a novel model based on a selective ensemble for predicting deleterious synonymous mutations (seDSM). seDSM outperformed other state-of-the-art methods on the independent test sets according to multiple evaluation indicators, indicating that it has an outstanding predictive performance for deleterious synonymous mutations. We hope that seDSM will be useful for studying deleterious synonymous mutations and advancing our understanding of synonymous mutations. The source code of seDSM is freely accessible at https://github.com/xialab-ahu/seDSM.git.
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Affiliation(s)
- Lihua Wang
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, State Key Laboratory of Respiratory Disease, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong 511436, China.,Institutes of Physical Science and Information Technology and School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China
| | - Tao Zhang
- Institutes of Physical Science and Information Technology and School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China
| | - Lihong Yu
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Medical University, Guangzhou, Guangdong 511436, China
| | - Chun-Hou Zheng
- Institutes of Physical Science and Information Technology and School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China
| | - Wenguang Yin
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510180, China
| | - Junfeng Xia
- Institutes of Physical Science and Information Technology and School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China
| | - Tiejun Zhang
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, State Key Laboratory of Respiratory Disease, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong 511436, China
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23
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Eng ZH, Abdullah MI, Ng KL, Abdul Aziz A, Arba’ie NH, Mat Rashid N, Mat Junit S. Whole-exome sequencing and bioinformatic analyses revealed differences in gene mutation profiles in papillary thyroid cancer patients with and without benign thyroid goitre background. Front Endocrinol (Lausanne) 2023; 13:1039494. [PMID: 36686473 PMCID: PMC9846740 DOI: 10.3389/fendo.2022.1039494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 12/07/2022] [Indexed: 01/05/2023] Open
Abstract
Background Papillary thyroid cancer (PTC) is the most common thyroid malignancy. Concurrent presence of cytomorphological benign thyroid goitre (BTG) and PTC lesion is often detected. Aberrant protein profiles were previously reported in patients with and without BTG cytomorphological background. This study aimed to evaluate gene mutation profiles to further understand the molecular mechanism underlying BTG, PTC without BTG background and PTC with BTG background. Methods Patients were grouped according to the histopathological examination results: (i) BTG patients (n = 9), (ii) PTC patients without BTG background (PTCa, n = 8), and (iii) PTC patients with BTG background (PTCb, n = 5). Whole-exome sequencing (WES) was performed on genomic DNA extracted from thyroid tissue specimens. Nonsynonymous and splice-site variants with MAF of ≤ 1% in the 1000 Genomes Project were subjected to principal component analysis (PCA). PTC-specific SNVs were filtered against OncoKB and COSMIC while novel SNVs were screened through dbSNP and COSMIC databases. Functional impacts of the SNVs were predicted using PolyPhen-2 and SIFT. Protein-protein interaction (PPI) enrichment of the tumour-related genes was analysed using Metascape and MCODE algorithm. Results PCA plots showed distinctive SNV profiles among the three groups. OncoKB and COSMIC database screening identified 36 tumour-related genes including BRCA2 and FANCD2 in all groups. BRAF and 19 additional genes were found only in PTCa and PTCb. "Pathways in cancer", "DNA repair" and "Fanconi anaemia pathway" were among the top networks shared by all groups. However, signalling pathways related to tyrosine kinases were the most significantly enriched in PTCa while "Jak-STAT signalling pathway" and "Notch signalling pathway" were the only significantly enriched in PTCb. Ten SNVs were PTC-specific of which two were novel; DCTN1 c.2786C>G (p.Ala929Gly) and TRRAP c.8735G>C (p.Ser2912Thr). Four out of the ten SNVs were unique to PTCa. Conclusion Distinctive gene mutation patterns detected in this study corroborated the previous protein profile findings. We hypothesised that the PTCa and PTCb subtypes differed in the underlying molecular mechanisms involving tyrosine kinase, Jak-STAT and Notch signalling pathways. The potential applications of the SNVs in differentiating the benign from the PTC subtypes requires further validation in a larger sample size.
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Affiliation(s)
- Zing Hong Eng
- Department of Molecular Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Mardiaty Iryani Abdullah
- Department of Molecular Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
- Department of Biomedical Science, Kulliyyah of Allied Health Sciences, International Islamic University Malaysia, Kuantan, Pahang, Malaysia
| | - Khoon Leong Ng
- Department of Surgery, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Azlina Abdul Aziz
- Department of Molecular Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Nurul Hannis Arba’ie
- Department of Surgery, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Nurullainy Mat Rashid
- Department of Molecular Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Sarni Mat Junit
- Department of Molecular Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
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Ciesielski TH, Bartlett J, Iyengar SK, Williams SM. Hemizygosity can reveal variant pathogenicity on the X-chromosome. Hum Genet 2023; 142:11-19. [PMID: 35994124 PMCID: PMC9840679 DOI: 10.1007/s00439-022-02478-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 08/10/2022] [Indexed: 01/24/2023]
Abstract
Pathogenic variants on the X-chromosome can have more severe consequences for hemizygous males, while heterozygote females can avoid severe consequences due to diploidy and the capacity for nonrandom expression. Thus, when an allele is more common in females this could indicate that it increases the probability of early death in the male hemizygous state, which can be considered a measure of pathogenicity. Importantly, large-scale genomic data now makes it possible to compare allele proportions between the sexes. To discover pathogenic variants on the X-chromosome, we analyzed exome data from 125,748 ancestrally diverse participants in the Genome Aggregation Database (gnomAD). After filtering out duplicates and extremely rare variants, 44,606 of the original 348,221 remained for analysis. We divided the proportion of variant alleles in females by the proportion in males for all variant sites, and then placed each variant into one of three a priori categories: (1) Reference (Primarily synonymous and intronic), (2) Unlikely-to-be-tolerated (Primarily missense), and (3) Least-likely-to-be-tolerated (Primarily frameshift). To assess the impact of ploidy, we compared the distribution of these ratios between pseudoautosomal and non-pseudoautosomal regions. In the non-pseudoautosomal regions, mean female-to-male ratios were lowest among Reference (2.40), greater for Unlikely-to-be-tolerated (2.77) and highest for Least-likely-to-be-tolerated (3.28) variants. Corresponding ratios were lower in the pseudoautosomal regions (1.52, 1.57, and 1.68, respectively), with the most extreme ratio being just below 11. Because pathogenic effects in the pseudoautosomal regions should not drive ratio increases, this maximum ratio provides an upper bound for baseline noise. In the non-pseudoautosomal regions, 319 variants had a ratio over 11. In sum, we identified a measure with a dataset specific threshold for identifying pathogenicity in non-pseudoautosomal X-chromosome variants: the female-to-male allele proportion ratio.
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Affiliation(s)
- Timothy H. Ciesielski
- The Department of Population and Quantitative Health Sciences at Case Western Reserve University School of Medicine, Cleveland, OH,Mary Ann Swetland Center for Environmental Health at Case Western Reserve University School of Medicine, Cleveland, OH,Ronin Institute, Montclair, NJ
| | - Jacquelaine Bartlett
- The Department of Population and Quantitative Health Sciences at Case Western Reserve University School of Medicine, Cleveland, OH
| | - Sudha K. Iyengar
- The Department of Population and Quantitative Health Sciences at Case Western Reserve University School of Medicine, Cleveland, OH,The Department of Genetics and Genome Sciences at Case Western Reserve University School of Medicine, Cleveland, OH,Cleveland Institute for Computational Biology, Cleveland, OH
| | - Scott M. Williams
- The Department of Population and Quantitative Health Sciences at Case Western Reserve University School of Medicine, Cleveland, OH,The Department of Genetics and Genome Sciences at Case Western Reserve University School of Medicine, Cleveland, OH,Cleveland Institute for Computational Biology, Cleveland, OH
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Wang H, Sun J, Liu M, Zheng CH, Xia J, Cheng N. frDSM: An Ensemble Predictor With Effective Feature Representation for Deleterious Synonymous Mutation in Human Genome. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:371-377. [PMID: 35420988 DOI: 10.1109/tcbb.2022.3167468] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
With the discovery of causality between synonymous mutations and diseases, it has become increasingly important to identify deleterious synonymous mutations for better understanding of their functional mechanisms. Although several machine learning methods have been proposed to solve the task, an effective feature representation method that can make use of the inner difference and relevance between deleterious and benign synonymous mutations is still challenging considering the vast number of synonymous mutations in human genome. In this work, we developed a robust and accurate predictor called frDSM for deleterious synonymous mutation prediction using logistic regression. More specifically, we introduced an effective feature representation learning method which exploits multiple feature descriptors from different perspectives including functional scores obtained from previously computational methods, evolutionary conservation, splicing and sequence feature descriptors, and these features descriptors were input into the 76 XGBoost classifiers to obtain the predictive probabilities values. These probabilities were concatenated to generate the 76-dimension new feature vector, and feature selection method was used to remove redundant and irrelevant features. Experimental results show that frDSM enables robust and accurate prediction than the competing prediction methods with 31 optimal features, which demonstrated the effectiveness of the feature representation learning method. frDSM is freely available at http://frdsm.xialab.info.
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Barbosa P, Savisaar R, Carmo-Fonseca M, Fonseca A. Computational prediction of human deep intronic variation. Gigascience 2022; 12:giad085. [PMID: 37878682 PMCID: PMC10599398 DOI: 10.1093/gigascience/giad085] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 06/07/2023] [Accepted: 09/20/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND The adoption of whole-genome sequencing in genetic screens has facilitated the detection of genetic variation in the intronic regions of genes, far from annotated splice sites. However, selecting an appropriate computational tool to discriminate functionally relevant genetic variants from those with no effect is challenging, particularly for deep intronic regions where independent benchmarks are scarce. RESULTS In this study, we have provided an overview of the computational methods available and the extent to which they can be used to analyze deep intronic variation. We leveraged diverse datasets to extensively evaluate tool performance across different intronic regions, distinguishing between variants that are expected to disrupt splicing through different molecular mechanisms. Notably, we compared the performance of SpliceAI, a widely used sequence-based deep learning model, with that of more recent methods that extend its original implementation. We observed considerable differences in tool performance depending on the region considered, with variants generating cryptic splice sites being better predicted than those that potentially affect splicing regulatory elements. Finally, we devised a novel quantitative assessment of tool interpretability and found that tools providing mechanistic explanations of their predictions are often correct with respect to the ground - information, but the use of these tools results in decreased predictive power when compared to black box methods. CONCLUSIONS Our findings translate into practical recommendations for tool usage and provide a reference framework for applying prediction tools in deep intronic regions, enabling more informed decision-making by practitioners.
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Affiliation(s)
- Pedro Barbosa
- LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016,, Lisboa, Portugal
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, 1649-028, Lisboa, Portugal
| | | | - Maria Carmo-Fonseca
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, 1649-028, Lisboa, Portugal
| | - Alcides Fonseca
- LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016,, Lisboa, Portugal
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Abdou M, Ramadan A, El-Agamy BE, EL-Farsy MS, Saleh EM. Mutational analysis of phospholipase C epsilon 1 gene in Egyptian children with steroid-resistant nephrotic syndrome. EGYPTIAN JOURNAL OF MEDICAL HUMAN GENETICS 2022. [DOI: 10.1186/s43042-022-00353-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Steroid-resistant nephrotic syndrome (SRNS) is characterized by unresponsiveness of nephrotic range proteinuria to standard steroid therapy, and is the main cause of childhood renal failure. The identification of more than 53 monogenic causes of SRNS has led researchers to focus on the genetic mutations related to the molecular mechanisms of the disease. Mutations in the PLCE1 gene, which encodes phospholipase C epsilon 1 (PLCε1), have been described in patients with early-onset SRNS characterized by progressive renal failure. In this study we screened for PLCE1 mutations in Egyptian children with SRNS. This is a descriptive case series study aiming to screen for PLCE1 gene mutations by direct sequencing of five exons—9, 12, 15, 19, 27—in 20 Egyptian children with SRNS who entered the Nephrology Unit, Faculty of Medicine, Ain-Shams University from November 2015 to December 2017. The variants detected were submitted to in silico analysis.
Results
We screened for mutations in five selected exons of PLCE1 gene. We identified seven variants in the five selected exons with homozygous and heterozygous inheritance pattern, two are intronic variants, two are silent variants, and three are missense variants. We identified four novel variants two are silent with no clinical significance and two are missense with uncertain clinical significance and pathogenic in-silico predictions; one p.Arg1230His in exon 12, the other is p.Glu1393Lys in exon 15.
Conclusions
We identified four novel mutations, findings which added to the registered SNP spectrum of the PLCE1 gene. These results widen the spectrum of PLCE1 gene mutations and support the importance of genetic testing in different populations of SRNS patients, therefore, to assess the vulnerability of Egyptian children to SRNS candidate genes, further studies needed on a larger number of cases which undoubtedly provide new insights into the pathogenic mechanisms of SRNS and might help in control of the patient. Additionally, the use of computational scoring and modeling tools may assist in the evaluation of the way in which the SNPs affect protein functionality.
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Postel MD, Culver JO, Ricker C, Craig DW. Transcriptome analysis provides critical answers to the "variants of uncertain significance" conundrum. Hum Mutat 2022; 43:1590-1608. [PMID: 35510381 PMCID: PMC9560997 DOI: 10.1002/humu.24394] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 03/16/2022] [Accepted: 04/26/2022] [Indexed: 12/30/2022]
Abstract
While whole-genome and exome sequencing have transformed our collective understanding of genetics' role in disease pathogenesis, there are certain conditions and populations for whom DNA-level data fails to identify the underlying genetic etiology. Specifically, patients of non-White race and non-European ancestry are disproportionately affected by "variants of unknown/uncertain significance" (VUS), limiting the scope of precision medicine for minority patients and perpetuating health disparities. VUS often include deep intronic and splicing variants which are difficult to interpret from DNA data alone. RNA analysis can illuminate the consequences of VUS, thereby allowing for their reclassification as pathogenic versus benign. Here we review the critical role transcriptome analysis plays in clarifying VUS in both neoplastic and non-neoplastic diseases.
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Affiliation(s)
- Mackenzie D. Postel
- Department of Translational GenomicsUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Keck School of Medicine of USCUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Julie O. Culver
- Keck School of Medicine of USCUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Charité Ricker
- Keck School of Medicine of USCUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - David W. Craig
- Department of Translational GenomicsUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Keck School of Medicine of USCUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
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Katsonis P, Wilhelm K, Williams A, Lichtarge O. Genome interpretation using in silico predictors of variant impact. Hum Genet 2022; 141:1549-1577. [PMID: 35488922 PMCID: PMC9055222 DOI: 10.1007/s00439-022-02457-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 04/17/2022] [Indexed: 02/06/2023]
Abstract
Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.
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Affiliation(s)
- Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
| | - Kevin Wilhelm
- Graduate School of Biomedical Sciences, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Amanda Williams
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
- Department of Biochemistry, Human Genetics and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
- Department of Pharmacology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
- Computational and Integrative Biomedical Research Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
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Mohammadi S, Mahmoudi J, Farajdokht F, Asadi M, Pirsarabi P, Kazeminiaei SF, Parvizpour S, Sadigh-Eteghad S. Polymorphisms of nicotinic acetylcholine receptors in Alzheimer’s disease: a systematic review and data analysis. EGYPTIAN JOURNAL OF MEDICAL HUMAN GENETICS 2022. [DOI: 10.1186/s43042-022-00357-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Objective
This study was conducted to accomplish a better insight into the impact of single nucleotide polymorphisms (SNPs) of nicotinic acetylcholine receptors (nAChR) at the risk of Alzheimer’s disease (AD) and their possible pathogenicity.
Methods
We carried out a systemic review of accessible studies. The case–control studies were assessed by an electronic search of international and local databases to identify relevant studies on SNPs relating to nAChR genes in AD. Two reviewers evaluated the inclusion/exclusion criteria, summarized, and analyzed the extracted data. We used odds ratios (ORs) with 95% confidence intervals (CIs) for reporting our data. Online databases were checked for possible pathogenicity of statistically significant SNPs. Also, online databases, including NCBI, NIH, ClinVar, RegulomeDB, and Ensemble, were used to analyze and identify structure and function, DNA features, and flank sequencing in SNPs.
Results
Among all collected SNPs, rs4779978 and rs1827294 on CHRNA7, rs1044394 on CHRNA4, and rs1127314 on CHRNB2 showed statistically significant between AD cases and controls.
Conclusions
Some SNPs from the reviewed reports show evidence supporting their possible involvement in AD pathology. However, more comprehensive studies are necessary to identify the exact correlation and their role on the pathogenicity of disease.
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When a Synonymous Variant Is Nonsynonymous. Genes (Basel) 2022; 13:genes13081485. [PMID: 36011397 PMCID: PMC9408308 DOI: 10.3390/genes13081485] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 08/17/2022] [Accepted: 08/17/2022] [Indexed: 12/27/2022] Open
Abstract
Term synonymous variation is widely used, but frequently in a wrong or misleading meaning and context. Twenty three point eight % of possible nucleotide substitution types in the universal genetic code are for synonymous amino acid changes, but when these variants have a phenotype and functional effect, they are very seldom synonymous. Such variants may manifest changes at DNA, RNA and/or protein levels. Large numbers of variations are erroneously annotated as synonymous, which causes problems e.g., in clinical genetics and diagnosis of diseases. To facilitate precise communication, novel systematics and nomenclature are introduced for variants that when looking only at the genetic code seem like synonymous, but which have phenotypes. A new term, unsense variant is defined as a substitution in the mRNA coding region that affects gene expression and protein production without introducing a stop codon in the variation site. Such variants are common and need to be correctly annotated. Proper naming and annotation are important also to increase awareness of these variants and their consequences.
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Liu H, Dai J, Li K, Sun Y, Wei H, Wang H, Zhao C, Wang DW. Performance evaluation of computational methods for splice-disrupting variants and improving the performance using the machine learning-based framework. Brief Bioinform 2022; 23:6670557. [PMID: 35976049 DOI: 10.1093/bib/bbac334] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 07/14/2022] [Accepted: 07/21/2022] [Indexed: 01/07/2023] Open
Abstract
A critical challenge in genetic diagnostics is the assessment of genetic variants associated with diseases, specifically variants that fall out with canonical splice sites, by altering alternative splicing. Several computational methods have been developed to prioritize variants effect on splicing; however, performance evaluation of these methods is hampered by the lack of large-scale benchmark datasets. In this study, we employed a splicing-region-specific strategy to evaluate the performance of prediction methods based on eight independent datasets. Under most conditions, we found that dbscSNV-ADA performed better in the exonic region, S-CAP performed better in the core donor and acceptor regions, S-CAP and SpliceAI performed better in the extended acceptor region and MMSplice performed better in identifying variants that caused exon skipping. However, it should be noted that the performances of prediction methods varied widely under different datasets and splicing regions, and none of these methods showed the best overall performance with all datasets. To address this, we developed a new method, machine learning-based classification of splice sites variants (MLCsplice), to predict variants effect on splicing based on individual methods. We demonstrated that MLCsplice achieved stable and superior prediction performance compared with any individual method. To facilitate the identification of the splicing effect of variants, we provided precomputed MLCsplice scores for all possible splice sites variants across human protein-coding genes (http://39.105.51.3:8090/MLCsplice/). We believe that the performance of different individual methods under eight benchmark datasets will provide tentative guidance for appropriate method selection to prioritize candidate splice-disrupting variants, thereby increasing the genetic diagnostic yield.
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Affiliation(s)
- Hao Liu
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology and Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan 430030, China
| | - Jiaqi Dai
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology and Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan 430030, China
| | - Ke Li
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology and Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan 430030, China
| | - Yang Sun
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology and Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan 430030, China
| | - Haoran Wei
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology and Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan 430030, China
| | - Hong Wang
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology and Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan 430030, China
| | - Chunxia Zhao
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology and Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan 430030, China
| | - Dao Wen Wang
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology and Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan 430030, China
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Yu Y, Alvarado R, Petty LE, Bohlender RJ, Shaw DM, Below JE, Bejar N, Ruiz OE, Tandon B, Eisenhoffer GT, Kiss DL, Huff CD, Letra A, Hecht JT. Polygenic risk impacts PDGFRA mutation penetrance in non-syndromic cleft lip and palate. Hum Mol Genet 2022; 31:2348-2357. [PMID: 35147171 PMCID: PMC9307317 DOI: 10.1093/hmg/ddac037] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 02/03/2022] [Accepted: 02/04/2022] [Indexed: 11/12/2022] Open
Abstract
Non-syndromic cleft lip with or without cleft palate (NSCL/P) is a common, severe craniofacial malformation that imposes significant medical, psychosocial and financial burdens. NSCL/P is a multifactorial disorder with genetic and environmental factors playing etiologic roles. Currently, only 25% of the genetic variation underlying NSCL/P has been identified by linkage, candidate gene and genome-wide association studies. In this study, whole-genome sequencing and genome-wide genotyping followed by polygenic risk score (PRS) and linkage analyses were used to identify the genetic etiology of NSCL/P in a large three-generation family. We identified a rare missense variant in PDGFRA (c.C2740T; p.R914W) as potentially etiologic in a gene-based association test using pVAAST (P = 1.78 × 10-4) and showed decreased penetrance. PRS analysis suggested that variant penetrance was likely modified by common NSCL/P risk variants, with lower scores found among unaffected carriers. Linkage analysis provided additional support for PRS-modified penetrance, with a 7.4-fold increase in likelihood after conditioning on PRS. Functional characterization experiments showed that the putatively causal variant was null for signaling activity in vitro; further, perturbation of pdgfra in zebrafish embryos resulted in unilateral orofacial clefting. Our findings show that a rare PDGFRA variant, modified by additional common NSCL/P risk variants, have a profound effect on NSCL/P risk. These data provide compelling evidence for multifactorial inheritance long postulated to underlie NSCL/P and may explain some unusual familial patterns.
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Affiliation(s)
- Yao Yu
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Rolando Alvarado
- Center for RNA Therapeutics, Department of Cardiovascular Sciences, Houston Methodist Research Institute, Houston, TX 77030, USA
- Department of Cardiovascular Sciences, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Lauren E Petty
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Ryan J Bohlender
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Douglas M Shaw
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Nada Bejar
- Center for RNA Therapeutics, Department of Cardiovascular Sciences, Houston Methodist Research Institute, Houston, TX 77030, USA
- Department of Cardiovascular Sciences, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Oscar E Ruiz
- Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Bhavna Tandon
- Department of Pediatrics and Pediatric Research Center, UTHealth McGovern Medical School, Houston, TX 77030, USA
| | - George T Eisenhoffer
- Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Daniel L Kiss
- Center for RNA Therapeutics, Department of Cardiovascular Sciences, Houston Methodist Research Institute, Houston, TX 77030, USA
- Department of Cardiovascular Sciences, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Chad D Huff
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ariadne Letra
- Department of Diagnostic and Biomedical Sciences, UTHealth School of Dentistry at Houston, Houston, TX 77054, USA
- Center for Craniofacial Research, UTHealth School of Dentistry at Houston, Houston 77054, TX, USA
| | - Jacqueline T Hecht
- Department of Pediatrics and Pediatric Research Center, UTHealth McGovern Medical School, Houston, TX 77030, USA
- Center for Craniofacial Research, UTHealth School of Dentistry at Houston, Houston 77054, TX, USA
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Huang QR, Li JW, Yan P, Jiang Q, Guo FZ, Zhao YN, Mo LG. Establishment and Validation of a Ferroptosis-Related lncRNA Signature for Prognosis Prediction in Lower-Grade Glioma. Front Neurol 2022; 13:861438. [PMID: 35832170 PMCID: PMC9271629 DOI: 10.3389/fneur.2022.861438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 05/26/2022] [Indexed: 11/25/2022] Open
Abstract
Background The prognosis of lower-grade glioma (LGG) is highly variable, and more accurate predictors are still needed. The aim of our study was to explore the prognostic value of ferroptosis-related long non-coding RNAs (lncRNAs) in LGG and to develop a novel risk signature for predicting survival with LGG. Methods We first integrated multiple datasets to screen for prognostic ferroptosis-related lncRNAs in LGG. A least absolute shrinkage and selection operator (LASSO) analysis was then utilized to develop a risk signature for prognostic prediction. Based on the results of multivariate Cox analysis, a prognostic nomogram model for LGG was constructed. Finally, functional enrichment analysis, single-sample gene set enrichment analysis (ssGSEA), immunity, and m6A correlation analyses were conducted to explore the possible mechanisms by which these ferroptosis-related lncRNAs affect survival with LGG. Results A total of 11 ferroptosis-related lncRNAs related to the prognosis of LGG were identified. Based on prognostic lncRNAs, a risk signature consisting of 8 lncRNAs was constructed and demonstrated good predictive performance in both the training and validation cohorts. Correlation analysis suggested that the risk signature was closely linked to clinical features. The nomogram model we constructed by combining the risk signature and clinical parameters proved to be more accurate in predicting the prognosis of LGG. In addition, there were differences in the levels of immune cell infiltration, immune-related functions, immune checkpoints, and m6A-related gene expression between the high- and low-risk groups. Conclusion In summary, our ferroptosis-related lncRNA signature exhibits good performance in predicting the prognosis of LGG. This study may provide useful insight into the treatment of LGG.
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Affiliation(s)
- Qian-Rong Huang
- Department of Neurosurgery, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Jian-Wen Li
- Department of Neurosurgery, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Ping Yan
- Department of Neurosurgery, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Qian Jiang
- Department of Neurosurgery, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Fang-Zhou Guo
- Department of Neurosurgery, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Yin-Nong Zhao
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
- Yin-Nong Zhao
| | - Li-Gen Mo
- Department of Neurosurgery, Guangxi Medical University Cancer Hospital, Nanning, China
- *Correspondence: Li-Gen Mo
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Targeted RNAseq Improves Clinical Diagnosis of Very Early-Onset Pediatric Immune Dysregulation. J Pers Med 2022; 12:jpm12060919. [PMID: 35743704 PMCID: PMC9224647 DOI: 10.3390/jpm12060919] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/26/2022] [Accepted: 05/27/2022] [Indexed: 02/05/2023] Open
Abstract
Despite increased use of whole exome sequencing (WES) for the clinical analysis of rare disease, overall diagnostic yield for most disorders hovers around 30%. Previous studies of mRNA have succeeded in increasing diagnoses for clearly defined disorders of monogenic inheritance. We asked if targeted RNA sequencing could provide similar benefits for primary immunodeficiencies (PIDs) and very early-onset inflammatory bowel disease (VEOIBD), both of which are difficult to diagnose due to high heterogeneity and variable severity. We performed targeted RNA sequencing of a panel of 260 immune-related genes for a cohort of 13 patients (seven suspected PID cases and six VEOIBD) and analyzed variants, splicing, and exon usage. Exonic variants were identified in seven cases, some of which had been previously prioritized by exome sequencing. For four cases, allele specific expression or lack thereof provided additional insights into possible disease mechanisms. In addition, we identified five instances of aberrant splicing associated with four variants. Three of these variants had been previously classified as benign in ClinVar based on population frequency. Digenic or oligogenic inheritance is suggested for at least two patients. In addition to validating the use of targeted RNA sequencing, our results show that rare disease research will benefit from incorporating contributing genetic factors into the diagnostic approach.
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Zheng Y, Peng Y, Zhang S, Zhao H, Chen W, Yang Y, Hu Z, Yin Q, Peng Y. Case Report: MYO5B Homozygous Variant c.2090+3A>T Causes Intron Retention Related to Chronic Cholestasis and Diarrhea. Front Genet 2022; 13:872836. [PMID: 35706451 PMCID: PMC9189387 DOI: 10.3389/fgene.2022.872836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 04/15/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Biallelically mutated MYO5B is associated with microvillus inclusion disease (MVID, MIM: 251850), cholestasis, or both. This study aims at validating the splicing alteration and clinical features of an intron variant for diagnosis.Case Presentation: A homozygous variant of MYO5B, NM_001080467.2:c.2090+3A > T (NP_001073936.1:p.?) in intron 17, was identified in a patient suffering from chronic cholestasis and diarrhea. Functional validation showed that this variant caused 185 bp of intron retention in its mRNA and was predicted to present a premature translation termination site for myoVb (p.Arg697fs*47) in the head motor domain. In addition, bowel biopsy revealed decreased microvilli and local lesions of microvillus inclusion in the duodena of the patient. The patient was presented with neonatal cholestasis leading to cirrhosis, intractable diarrhea, cholelithiasis, hepatic cyst, corneal opacity, and failure to thrive.Conclusion: Our study demonstrated an intronic homozygous variant of MYO5B that affected an intron, subsequently altering splicing and leading to combined cholestasis and MVID. Our results further supported the underlying genotype–phenotype correlations and extended clinical practices toward its diagnosis and management.
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Affiliation(s)
- Yu Zheng
- First Department of General Surgery & Pediatrics Research Institute of Hunan Province, Hunan Children’s Hospital, Changsha, China
| | - Yuming Peng
- First Department of General Surgery & Pediatrics Research Institute of Hunan Province, Hunan Children’s Hospital, Changsha, China
| | - Shuju Zhang
- First Department of General Surgery & Pediatrics Research Institute of Hunan Province, Hunan Children’s Hospital, Changsha, China
| | - Hongmei Zhao
- Department of Gastroenterology and Nutrition, Hunan Children’s Hospital, Changsha, China
| | - Weijian Chen
- Department of Pathology, Hunan Children’s Hospital, Changsha, China
| | - Yongjia Yang
- First Department of General Surgery & Pediatrics Research Institute of Hunan Province, Hunan Children’s Hospital, Changsha, China
| | - Zhengmao Hu
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Qiang Yin
- First Department of General Surgery & Pediatrics Research Institute of Hunan Province, Hunan Children’s Hospital, Changsha, China
- *Correspondence: Qiang Yin, ; Yu Peng,
| | - Yu Peng
- First Department of General Surgery & Pediatrics Research Institute of Hunan Province, Hunan Children’s Hospital, Changsha, China
- *Correspondence: Qiang Yin, ; Yu Peng,
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Manduchi E, Le TT, Fu W, Moore JH. Genetic Analysis of Coronary Artery Disease Using Tree-Based Automated Machine Learning Informed By Biology-Based Feature Selection. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1379-1386. [PMID: 34310318 PMCID: PMC9291719 DOI: 10.1109/tcbb.2021.3099068] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Machine Learning (ML) approaches are increasingly being used in biomedical applications. Important challenges of ML include choosing the right algorithm and tuning the parameters for optimal performance. Automated ML (AutoML) methods, such as Tree-based Pipeline Optimization Tool (TPOT), have been developed to take some of the guesswork out of ML thus making this technology available to users from more diverse backgrounds. The goals of this study were to assess applicability of TPOT to genomics and to identify combinations of single nucleotide polymorphisms (SNPs) associated with coronary artery disease (CAD), with a focus on genes with high likelihood of being good CAD drug targets. We leveraged public functional genomic resources to group SNPs into biologically meaningful sets to be selected by TPOT. We applied this strategy to data from the U.K. Biobank, detecting a strikingly recurrent signal stemming from a group of 28 SNPs. Importance analysis of these SNPs uncovered functional relevance of the top SNPs to genes whose association with CAD is supported in the literature and other resources. Furthermore, we employed game-theory based metrics to study SNP contributions to individual-level TPOT predictions and discover distinct clusters of well-predicted CAD cases. The latter indicates a promising approach towards precision medicine.
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Kaissarian NM, Meyer D, Kimchi-Sarfaty C. Synonymous Variants: Necessary Nuance in our Understanding of Cancer Drivers and Treatment Outcomes. J Natl Cancer Inst 2022; 114:1072-1094. [PMID: 35477782 PMCID: PMC9360466 DOI: 10.1093/jnci/djac090] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 03/24/2022] [Accepted: 04/18/2022] [Indexed: 11/13/2022] Open
Abstract
Once called "silent mutations" and assumed to have no effect on protein structure and function, synonymous variants are now recognized to be drivers for some cancers. There have been significant advances in our understanding of the numerous mechanisms by which synonymous single nucleotide variants (sSNVs) can affect protein structure and function by affecting pre-mRNA splicing, mRNA expression, stability, folding, miRNA binding, translation kinetics, and co-translational folding. This review highlights the need for considering sSNVs in cancer biology to gain a better understanding of the genetic determinants of human cancers and to improve their diagnosis and treatment. We surveyed the literature for reports of sSNVs in cancer and found numerous studies on the consequences of sSNVs on gene function with supporting in vitro evidence. We also found reports of sSNVs that have statistically significant associations with specific cancer types but for which in vitro studies are lacking to support the reported associations. Additionally, we found reports of germline and somatic sSNVs that were observed in numerous clinical studies and for which in silico analysis predicts possible effects on gene function. We provide a review of these investigations and discuss necessary future studies to elucidate the mechanisms by which sSNVs disrupt protein function and are play a role in tumorigeneses, cancer progression, and treatment efficacy. As splicing dysregulation is one of the most well recognized mechanisms by which sSNVs impact protein function, we also include our own in silico analysis for predicting which sSNVs may disrupt pre-mRNA splicing.
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Affiliation(s)
- Nayiri M Kaissarian
- Hemostasis Branch, Division of Plasma Protein Therapeutics, Office of Tissues and Advanced Therapies, Center for Biologics Evaluation & Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Douglas Meyer
- Hemostasis Branch, Division of Plasma Protein Therapeutics, Office of Tissues and Advanced Therapies, Center for Biologics Evaluation & Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Chava Kimchi-Sarfaty
- Hemostasis Branch, Division of Plasma Protein Therapeutics, Office of Tissues and Advanced Therapies, Center for Biologics Evaluation & Research, US Food and Drug Administration, Silver Spring, MD, USA
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Smetana J, Brož P. National Genome Initiatives in Europe and the United Kingdom in the Era of Whole-Genome Sequencing: A Comprehensive Review. Genes (Basel) 2022; 13:556. [PMID: 35328109 PMCID: PMC8953625 DOI: 10.3390/genes13030556] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 03/17/2022] [Accepted: 03/18/2022] [Indexed: 12/04/2022] Open
Abstract
Identification of genomic variability in population plays an important role in the clinical diagnostics of human genetic diseases. Thanks to rapid technological development in the field of massive parallel sequencing technologies, also known as next-generation sequencing (NGS), complex genomic analyses are now easier and cheaper than ever before, which consequently leads to more effective utilization of these techniques in clinical practice. However, interpretation of data from NGS is still challenging due to several issues caused by natural variability of DNA sequences in human populations. Therefore, development and realization of projects focused on description of genetic variability of local population (often called "national or digital genome") with a NGS technique is one of the best approaches to address this problem. The next step of the process is to share such data via publicly available databases. Such databases are important for the interpretation of variants with unknown significance or (likely) pathogenic variants in rare diseases or cancer or generally for identification of pathological variants in a patient's genome. In this paper, we have compiled an overview of published results of local genome sequencing projects from United Kingdom and Europe together with future plans and perspectives for newly announced ones.
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Affiliation(s)
- Jan Smetana
- Institute of Food Science and Biotechnology, Faculty of Chemistry, Brno University of Technology, 61200 Brno, Czech Republic
| | - Petr Brož
- Department of Genetics and Molecular Biology, Institute of Experimental Biology, Faculty of Science, Masaryk University, 61137 Brno, Czech Republic;
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Alkelai A, Greenbaum L, Docherty AR, Shabalin AA, Povysil G, Malakar A, Hughes D, Delaney SL, Peabody EP, McNamara J, Gelfman S, Baugh EH, Zoghbi AW, Harms MB, Hwang HS, Grossman-Jonish A, Aggarwal V, Heinzen EL, Jobanputra V, Pulver AE, Lerer B, Goldstein DB. The benefit of diagnostic whole genome sequencing in schizophrenia and other psychotic disorders. Mol Psychiatry 2022; 27:1435-1447. [PMID: 34799694 DOI: 10.1038/s41380-021-01383-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 10/25/2021] [Accepted: 10/27/2021] [Indexed: 01/04/2023]
Abstract
Schizophrenia has a multifactorial etiology, involving a polygenic architecture. The potential benefit of whole genome sequencing (WGS) in schizophrenia and other psychotic disorders is not well studied. We investigated the yield of clinical WGS analysis in 251 families with a proband diagnosed with schizophrenia (N = 190), schizoaffective disorder (N = 49), or other conditions involving psychosis (N = 48). Participants were recruited in Israel and USA, mainly of Jewish, Arab, and other European ancestries. Trio (parents and proband) WGS was performed for 228 families (90.8%); in the other families, WGS included parents and at least two affected siblings. In the secondary analyses, we evaluated the contribution of rare variant enrichment in particular gene sets, and calculated polygenic risk score (PRS) for schizophrenia. For the primary outcome, diagnostic rate was 6.4%; we found clinically significant, single nucleotide variants (SNVs) or small insertions or deletions (indels) in 14 probands (5.6%), and copy number variants (CNVs) in 2 (0.8%). Significant enrichment of rare loss-of-function variants was observed in a gene set of top schizophrenia candidate genes in affected individuals, compared with population controls (N = 6,840). The PRS for schizophrenia was significantly increased in the affected individuals group, compared to their unaffected relatives. Last, we were also able to provide pharmacogenomics information based on CYP2D6 genotype data for most participants, and determine their antipsychotic metabolizer status. In conclusion, our findings suggest that WGS may have a role in the setting of both research and genetic counseling for individuals with schizophrenia and other psychotic disorders and their families.
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Affiliation(s)
- Anna Alkelai
- Institute for Genomic Medicine, Columbia University Medical Center, New York, NY, USA.
| | - Lior Greenbaum
- The Danek Gertner Institute of Human Genetics, Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel
- The Joseph Sagol Neuroscience Center, Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Anna R Docherty
- Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Andrey A Shabalin
- Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Gundula Povysil
- Institute for Genomic Medicine, Columbia University Medical Center, New York, NY, USA
| | - Ayan Malakar
- Institute for Genomic Medicine, Columbia University Medical Center, New York, NY, USA
| | - Daniel Hughes
- Institute for Genomic Medicine, Columbia University Medical Center, New York, NY, USA
| | - Shannon L Delaney
- New York State Psychiatric Institute, Columbia University, New York City, NY, USA
| | - Emma P Peabody
- Psychology Research Laboratory, McLean Hospital, Harvard Medical School, Belmont, MA, USA
| | - James McNamara
- Psychology Research Laboratory, McLean Hospital, Harvard Medical School, Belmont, MA, USA
| | - Sahar Gelfman
- Institute for Genomic Medicine, Columbia University Medical Center, New York, NY, USA
| | - Evan H Baugh
- Institute for Genomic Medicine, Columbia University Medical Center, New York, NY, USA
| | - Anthony W Zoghbi
- Institute for Genomic Medicine, Columbia University Medical Center, New York, NY, USA
- New York State Psychiatric Institute, Columbia University, New York City, NY, USA
- New York State Psychiatric Institute, Office of Mental Health, New York, NY, USA
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Matthew B Harms
- Institute for Genomic Medicine, Columbia University Medical Center, New York, NY, USA
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- Center for Motor Neuron Biology and Disease, Columbia University Irving Medical Center, New York, NY, USA
| | - Hann-Shyan Hwang
- Department of Medicine, National Taiwan University School of Medicine, Taipei, Taiwan
| | - Anat Grossman-Jonish
- The Danek Gertner Institute of Human Genetics, Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel
| | - Vimla Aggarwal
- Department of Pathology and Cell Biology, Columbia University, New York, NY, USA
| | - Erin L Heinzen
- Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Vaidehi Jobanputra
- Center for Motor Neuron Biology and Disease, Columbia University Irving Medical Center, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | - Ann E Pulver
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Bernard Lerer
- Biological Psychiatry Laboratory, Department of Psychiatry, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | - David B Goldstein
- Institute for Genomic Medicine, Columbia University Medical Center, New York, NY, USA
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Bayat A, de Valles-Ibáñez G, Pendziwiat M, Knaus A, Alt K, Biamino E, Bley A, Calvert S, Carney P, Caro-Llopis A, Ceulemans B, Cousin J, Davis S, des Portes V, Edery P, England E, Ferreira C, Freeman J, Gener B, Gorce M, Heron D, Hildebrand MS, Jezela-Stanek A, Jouk PS, Keren B, Kloth K, Kluger G, Kuhn M, Lemke JR, Li H, Martinez F, Maxton C, Mefford HC, Merla G, Mierzewska H, Muir A, Monfort S, Nicolai J, Norman J, O'Grady G, Oleksy B, Orellana C, Orec LE, Peinhardt C, Pronicka E, Rosello M, Santos-Simarro F, Schwaibold EMC, Stegmann APA, Stumpel CT, Szczepanik E, Terczyńska I, Thevenon J, Tzschach A, Van Bogaert P, Vittorini R, Walsh S, Weckhuysen S, Weissman B, Wolfe L, Reymond A, De Nittis P, Poduri A, Olson H, Striano P, Lesca G, Scheffer IE, Møller RS, Sadleir LG. PIGN encephalopathy: Characterizing the epileptology. Epilepsia 2022; 63:974-991. [PMID: 35179230 DOI: 10.1111/epi.17173] [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: 10/05/2021] [Revised: 01/12/2022] [Accepted: 01/12/2022] [Indexed: 12/21/2022]
Abstract
OBJECTIVE Epilepsy is common in patients with PIGN diseases due to biallelic variants; however, limited epilepsy phenotyping data have been reported. We describe the epileptology of PIGN encephalopathy. METHODS We recruited patients with epilepsy due to biallelic PIGN variants and obtained clinical data regarding age at seizure onset/offset and semiology, development, medical history, examination, electroencephalogram, neuroimaging, and treatment. Seizure and epilepsy types were classified. RESULTS Twenty six patients (13 female) from 26 families were identified, with mean age 7 years (range = 1 month to 21 years; three deceased). Abnormal development at seizure onset was present in 25 of 26. Developmental outcome was most frequently profound (14/26) or severe (11/26). Patients presented with focal motor (12/26), unknown onset motor (5/26), focal impaired awareness (1/26), absence (2/26), myoclonic (2/26), myoclonic-atonic (1/26), and generalized tonic-clonic (2/26) seizures. Twenty of 26 were classified as developmental and epileptic encephalopathy (DEE): 55% (11/20) focal DEE, 30% (6/20) generalized DEE, and 15% (3/20) combined DEE. Six had intellectual disability and epilepsy (ID+E): two generalized and four focal epilepsy. Mean age at seizure onset was 13 months (birth to 10 years), with a lower mean onset in DEE (7 months) compared with ID+E (33 months). Patients with DEE had drug-resistant epilepsy, compared to 4/6 ID+E patients, who were seizure-free. Hyperkinetic movement disorder occurred in 13 of 26 patients. Twenty-seven of 34 variants were novel. Variants were truncating (n = 7), intronic and predicted to affect splicing (n = 7), and missense or inframe indels (n = 20, of which 11 were predicted to affect splicing). Seven variants were recurrent, including p.Leu311Trp in 10 unrelated patients, nine with generalized seizures, accounting for nine of the 11 patients in this cohort with generalized seizures. SIGNIFICANCE PIGN encephalopathy is a complex autosomal recessive disorder associated with a wide spectrum of epilepsy phenotypes, typically with substantial profound to severe developmental impairment.
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Affiliation(s)
- Allan Bayat
- Institute for Regional Health Services, University of Southern Denmark, Odense, Denmark.,Department of Epilepsy Genetics and Personalized Medicine, Danish Epilepsy Center, Dianalund, Denmark
| | | | - Manuela Pendziwiat
- Department of Neuropediatrics, University Medical Center Schleswig-Holstein, Christian Albrecht University, Kiel, Germany.,Institute of Clinical Molecular Biology, Christian Albrecht University of Kiel, Kiel, Germany
| | - Alexej Knaus
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Rhenish Friedrich Wilhelm University of Bonn, Bonn, Germany
| | | | - Elisa Biamino
- Department of Pediatrics, Regina Margherita Children's Hospital, Turin, Italy
| | - Annette Bley
- University Children's Hospital, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Center for Rare Diseases, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Sophie Calvert
- Department of Neurosciences, Queensland Children's Hospital, South Brisbane, Queensland, Australia
| | - Patrick Carney
- Department of Medicine, University of Melbourne, Parkville, Victoria, Australia
| | | | - Berten Ceulemans
- Department of Pediatric Neurology, Antwerp University Hospital, Edegem, Belgium
| | - Janice Cousin
- Section of Human Biochemical Genetics, National Human Genome Research Institute, Bethesda, Maryland, USA
| | - Suzanne Davis
- Department of Paediatrics and Child Health, University of Otago, Wellington, New Zealand
| | | | - Patrick Edery
- Department of Medical Genetics, University Hospital of Lyon, Lyon, France
| | - Eleina England
- Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA
| | - Carlos Ferreira
- National Human Genome Research Institute, Bethesda, Maryland, USA
| | - Jeremy Freeman
- Royal Children's Hospital, Parkville, Victoria, Australia.,Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - Blanca Gener
- Department of Genetics, Cruces University Hospital, Biocruces Bizkaia Health Research Institute, Barakaldo, Spain
| | | | - Delphine Heron
- Department of Genetics, Intellectual Disability and Autism Clinical Research Group, Pierre and Marie Curie University, Pitié-Salpêtrière Hospital, Public Hospital Network of Paris, Paris, France
| | - Michael S Hildebrand
- Royal Children's Hospital, Florey institute and Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Epilepsy Research Centre, Department of Medicine (Austin Health), University of Melbourne, Heidelberg, Victoria, Australia
| | - Aleksandra Jezela-Stanek
- Department of Genetics and Clinical Immunology, National Institute of Tuberculosis and Lung Diseases, Warsaw, Poland
| | - Pierre-Simon Jouk
- Inserm U1209, Grenoble Alpes University Hospital Center, University of Grenoble Alpes, Grenoble, France
| | - Boris Keren
- Department of Genetics, Intellectual Disability and Autism Clinical Research Group, Pierre and Marie Curie University, Pitié-Salpêtrière Hospital, Public Hospital Network of Paris, Paris, France
| | - Katja Kloth
- Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | | | | | - Johannes R Lemke
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany.,Center for Rare Diseases, University of Leipzig Medical Center, Leipzig, Germany
| | - Hong Li
- Emory University School of Medicine, Atlanta, Georgia, USA
| | - Francisco Martinez
- Genomics Unit, University and Polytechnic Hospital La Fe, Valencia, Spain
| | | | - Heather C Mefford
- Center for Pediatric Neurological Disease Research, Department of Cell and Molecular Biology, St, Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Giuseppe Merla
- Department of Pediatrics, Regina Margherita Children's Hospital, Turin, Italy
| | - Hanna Mierzewska
- Department of Mother and Child Neurology, Institute of Mother and Child, Warsaw, Poland
| | - Alison Muir
- Center for Pediatric Neurological Disease Research, Department of Cell and Molecular Biology, St, Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Sandra Monfort
- Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Joost Nicolai
- Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
| | | | - Gina O'Grady
- Starship Children's Hospital, Auckland, New Zealand
| | - Barbara Oleksy
- Department of Child and Adolescent Neurology, Institute of Mother and Child, Warsaw, Poland
| | - Carmen Orellana
- Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Laura Elena Orec
- Center for Child and Adolescent Medicine, Pediatric Neurology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Ewa Pronicka
- Department of Medical Genetics, Children's Memorial Health Institute, Warsaw, Poland
| | - Monica Rosello
- Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
| | | | | | - Alexander P A Stegmann
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Constance T Stumpel
- Department of Clinical Genetics and School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Elzbieta Szczepanik
- Department of Child and Adolescent Neurology, Institute of Mother and Child, Warsaw, Poland
| | - Iwona Terczyńska
- Department of Medical Genetics, Warsaw Medical University, Warsaw, Poland
| | - Julien Thevenon
- Department of Genetics, University of Bourgogne-Franche Comté, Dijon, France
| | - Andreas Tzschach
- Institute of Clinical Genetics, Dresden University of Technology, Dresden, Germany
| | | | - Roberta Vittorini
- Department of Pediatrics, Regina Margherita Children's Hospital, Turin, Italy
| | - Sonja Walsh
- Institute of Clinical Genetics, Dresden University of Technology, Dresden, Germany
| | - Sarah Weckhuysen
- Neurology Department, University Hospital Antwerp, Antwerp, Belgium.,Applied and Translational Genomics Group, Center for Molecular Neurology, University of Antwerp, Antwerp, Belgium
| | - Barbara Weissman
- Center for Child and Adolescent Medicine, Pediatric Neurology, Heidelberg University Hospital, Heidelberg, Germany
| | - Lynne Wolfe
- National Human Genome Research Institute, Bethesda, Maryland, USA
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
| | | | - Annapurna Poduri
- Epilepsy Genetics Program, Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Heather Olson
- Epilepsy Genetics Program, Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, USA
| | | | - Gaetan Lesca
- Department of Medical Genetics, University Hospital of Lyon, Lyon, France
| | - Ingrid E Scheffer
- Royal Children's Hospital, Florey institute and Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Departments of Medicine and Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
| | - Rikke S Møller
- Institute for Regional Health Services, University of Southern Denmark, Odense, Denmark.,Department of Epilepsy Genetics and Personalized Medicine, Danish Epilepsy Center, Dianalund, Denmark
| | - Lynette G Sadleir
- Department of Paediatrics and Child Health, University of Otago, Wellington, New Zealand
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42
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Whole exome sequencing in Alopecia Areata identifies rare variants in KRT82. Nat Commun 2022; 13:800. [PMID: 35145093 PMCID: PMC8831607 DOI: 10.1038/s41467-022-28343-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 11/22/2021] [Indexed: 01/31/2023] Open
Abstract
Alopecia areata is a complex genetic disease that results in hair loss due to the autoimmune-mediated attack of the hair follicle. We previously defined a role for both rare and common variants in our earlier GWAS and linkage studies. Here, we identify rare variants contributing to Alopecia Areata using a whole exome sequencing and gene-level burden analyses approach on 849 Alopecia Areata patients compared to 15,640 controls. KRT82 is identified as an Alopecia Areata risk gene with rare damaging variants in 51 heterozygous Alopecia Areata individuals (6.01%), achieving genome-wide significance (p = 2.18E−07). KRT82 encodes a hair-specific type II keratin that is exclusively expressed in the hair shaft cuticle during anagen phase, and its expression is decreased in Alopecia Areata patient skin and hair follicles. Finally, we find that cases with an identified damaging KRT82 variant and reduced KRT82 expression have elevated perifollicular CD8 infiltrates. In this work, we utilize whole exome sequencing to successfully identify a significant Alopecia Areata disease-relevant gene, KRT82, and reveal a proposed mechanism for rare variant predisposition leading to disrupted hair shaft integrity. Common variants have been discovered to be associated with Alopecia Areata; however, rare variants have been less well studied. Here, the authors use whole-exome sequencing to identify associated rare variants in the hair keratin gene KRT82. Further, they find that individuals with Alopecia Areata have reduced expression of KRT82 in the skin and hair follicle.
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Maroilley T, Wright NAM, Diao C, MacLaren L, Pfeffer G, Sarna JR, Billie Au PY, Tarailo-Graovac M. Case Report: Biallelic Loss of Function ATM due to Pathogenic Synonymous and Novel Deep Intronic Variant c.1803-270T > G Identified by Genome Sequencing in a Child With Ataxia–Telangiectasia. Front Genet 2022; 13:815210. [PMID: 35145552 PMCID: PMC8822238 DOI: 10.3389/fgene.2022.815210] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 01/03/2022] [Indexed: 11/26/2022] Open
Abstract
Ataxia–telangiectasia (AT) is a complex neurodegenerative disease with an increased risk for bone marrow failure and malignancy. AT is caused by biallelic loss of function variants in ATM, which encodes a phosphatidylinositol 3-kinase that responds to DNA damage. Herein, we report a child with progressive ataxia, chorea, and genome instability, highly suggestive of AT. The clinical ataxia gene panel identified a maternal heterozygous synonymous variant (NM_000051.3: c.2250G > A), previously described to result in exon 14 skipping. Subsequently, trio genome sequencing led to the identification of a novel deep intronic variant [NG_009830.1(NM_000051.3): c.1803-270T > G] inherited from the father. Transcript analyses revealed that c.1803-270T > G results in aberrant inclusion of 56 base pairs of intron 11. In silico tests predicted a premature stop codon as a consequence, suggesting non-functional ATM; and DNA repair analyses confirmed functional loss of ATM. Our findings highlight the power of genome sequencing, considering deep intronic variants in undiagnosed rare disease patients.
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Affiliation(s)
- Tatiana Maroilley
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Nicola A. M. Wright
- Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada
- Section of Pediatric Hematology-Immunology, Department of Pediatrics, Alberta Children’s Hospital, University of Calgary, Calgary, AB, Canada
| | - Catherine Diao
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Linda MacLaren
- Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Gerald Pfeffer
- Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Justyna R. Sarna
- Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Ping Yee Billie Au
- Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada
- *Correspondence: Ping Yee Billie Au, ; Maja Tarailo-Graovac,
| | - Maja Tarailo-Graovac
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada
- *Correspondence: Ping Yee Billie Au, ; Maja Tarailo-Graovac,
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KDM6A missense variants hamper H3 histone demethylation in lung squamous cell carcinoma. Comput Struct Biotechnol J 2022; 20:3151-3160. [PMID: 35782738 PMCID: PMC9232545 DOI: 10.1016/j.csbj.2022.06.041] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/15/2022] [Accepted: 06/15/2022] [Indexed: 11/22/2022] Open
Abstract
KDM6A is the disease causative gene of type 2 Kabuki Syndrome, a rare multisystem disease; it is also a known cancer driver gene, with multiple somatic mutations found in a few cancer types. In this study, we looked at eleven missense variants in lung squamous cell carcinoma, one of the most common lung cancer subtypes, to see how they affect the KDM6A catalytic mechanisms. We found that they influence the interaction with histone H3 and the exposure of the trimethylated Lys27, which is critical for wild-type physiological function to varying degrees, by altering the conformational transition.
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45
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Zeng Z, Aptekmann AA, Bromberg Y. Decoding the effects of synonymous variants. Nucleic Acids Res 2021; 49:12673-12691. [PMID: 34850938 PMCID: PMC8682775 DOI: 10.1093/nar/gkab1159] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 11/02/2021] [Accepted: 11/08/2021] [Indexed: 12/12/2022] Open
Abstract
Synonymous single nucleotide variants (sSNVs) are common in the human genome but are often overlooked. However, sSNVs can have significant biological impact and may lead to disease. Existing computational methods for evaluating the effect of sSNVs suffer from the lack of gold-standard training/evaluation data and exhibit over-reliance on sequence conservation signals. We developed synVep (synonymous Variant effect predictor), a machine learning-based method that overcomes both of these limitations. Our training data was a combination of variants reported by gnomAD (observed) and those unreported, but possible in the human genome (generated). We used positive-unlabeled learning to purify the generated variant set of any likely unobservable variants. We then trained two sequential extreme gradient boosting models to identify subsets of the remaining variants putatively enriched and depleted in effect. Our method attained 90% precision/recall on a previously unseen set of variants. Furthermore, although synVep does not explicitly use conservation, its scores correlated with evolutionary distances between orthologs in cross-species variation analysis. synVep was also able to differentiate pathogenic vs. benign variants, as well as splice-site disrupting variants (SDV) vs. non-SDVs. Thus, synVep provides an important improvement in annotation of sSNVs, allowing users to focus on variants that most likely harbor effects.
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Affiliation(s)
- Zishuo Zeng
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ 08873, USA
| | - Ariel A Aptekmann
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ 08873, USA
| | - Yana Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ 08873, USA
- Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA
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46
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Lin X. Genomic Variation Prediction: A Summary From Different Views. Front Cell Dev Biol 2021; 9:795883. [PMID: 34901036 PMCID: PMC8656232 DOI: 10.3389/fcell.2021.795883] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 11/11/2021] [Indexed: 12/02/2022] Open
Abstract
Structural variations in the genome are closely related to human health and the occurrence and development of various diseases. To understand the mechanisms of diseases, find pathogenic targets, and carry out personalized precision medicine, it is critical to detect such variations. The rapid development of high-throughput sequencing technologies has accelerated the accumulation of large amounts of genomic mutation data, including synonymous mutations. Identifying pathogenic synonymous mutations that play important roles in the occurrence and development of diseases from all the available mutation data is of great importance. In this paper, machine learning theories and methods are reviewed, efficient and accurate pathogenic synonymous mutation prediction methods are developed, and a standardized three-level variant analysis framework is constructed. In addition, multiple variation tolerance prediction models are studied and integrated, and new ideas for structural variation detection based on deep information mining are explored.
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Affiliation(s)
- Xiuchun Lin
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
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47
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Valori M, Jansson L, Tienari PJ. CD8+ cell somatic mutations in multiple sclerosis patients and controls-Enrichment of mutations in STAT3 and other genes implicated in hematological malignancies. PLoS One 2021; 16:e0261002. [PMID: 34874980 PMCID: PMC8651110 DOI: 10.1371/journal.pone.0261002] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 11/23/2021] [Indexed: 01/14/2023] Open
Abstract
Somatic mutations have a central role in cancer but their role in other diseases such as common autoimmune disorders is not clear. Previously we and others have demonstrated that especially CD8+ T cells in blood can harbor persistent somatic mutations in some patients with multiple sclerosis (MS) and rheumatoid arthritis. Here we concentrated on CD8+ cells in more detail and tested (i) how commonly somatic mutations are detectable, (ii) does the overall mutation load differ between MS patients and controls, and (iii) do the somatic mutations accumulate non-randomly in certain genes? We separated peripheral blood CD8+ cells from newly diagnosed relapsing MS patients (n = 21) as well as matched controls (n = 21) and performed next-generation sequencing of the CD8+ cells' DNA, limiting our search to a custom panel of 2524 immunity and cancer related genes, which enabled us to obtain a median sequencing depth of over 2000x. We discovered nonsynonymous somatic mutations in all MS patients' and controls' CD8+ cell DNA samples, with no significant difference in number between the groups (p = 0.60), at a median allelic fraction of 0.5% (range 0.2-8.6%). The mutations showed statistically significant clustering especially to the STAT3 gene, and also enrichment to the SMARCA2, DNMT3A, SOCS1 and PPP3CA genes. Known activating STAT3 mutations were found both in MS patients and controls and overall 1/5 of the mutations were previously described cancer mutations. The detected clustering suggests a selection advantage of the mutated CD8+ clones and calls for further research on possible phenotypic effects.
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Affiliation(s)
- Miko Valori
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
- * E-mail:
| | - Lilja Jansson
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
- Department of Neurology, Neurocenter, Helsinki University Hospital, Helsinki, Finland
| | - Pentti J. Tienari
- Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
- Department of Neurology, Neurocenter, Helsinki University Hospital, Helsinki, Finland
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Di Costanzo A, Minicocci I, D'Erasmo L, Commodari D, Covino S, Bini S, Ghadiri A, Ceci F, Maranghi M, Catapano AL, Gazzotti M, Casula M, Montali A, Arca M. Refinement of pathogenicity classification of variants associated with familial hypercholesterolemia: Implications for clinical diagnosis. J Clin Lipidol 2021; 15:822-831. [PMID: 34756585 DOI: 10.1016/j.jacl.2021.10.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 10/04/2021] [Accepted: 10/05/2021] [Indexed: 01/22/2023]
Abstract
BACKGROUND The lack of functional evidence for most variants detected during the molecular screening of patients with clinical familial hypercholesterolemia (FH) makes the definitive diagnosis difficult. METHODS A total of 552 variants in LDLR, APOB, PCSK9 and LDLRAP1 genes found in 449 mutation-positive FH (FH/M+) patients were considered. Pathogenicity update was performed following the American College of Medical Genetics and Genomics (ACMG) guidelines with additional specifications on copy number variants, functional studies, in silico prediction and co-segregation criteria for LDLR, APOB and PCSK9 genes. Pathogenicity of LDLRAP1 variants was updated by using ACMG criteria with no change to original scoring. RESULTS After reclassification, the proportion of FH/M+ carriers of pathogenic (P) or likely pathogenic (LP) variants, and FH/M+ carriers of likely benign (LB) or benign (B) variants, was higher than that defined by standard criteria (81.5% vs. 79.7% and 7.1% vs. 2.7%). The refinement of pathogenicity classification also reduced the percentage of FH with variants of uncertain significance (VUS) (17.7% vs. 11.4%). After adjustment, the FH diagnosis by refined criteria best predicted LDL-C levels (Padj <0.001). Notably, FH with VUS variants had higher LDL-C than those with LB (all Padj ≤ 0.033), but similar to those with LP variants. CONCLUSION Accurate variant interpretation best predicts the increase of LDL-C levels and shows its clinical utility in the molecular diagnosis of FH.
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Affiliation(s)
- Alessia Di Costanzo
- Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy.
| | - Ilenia Minicocci
- Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy
| | - Laura D'Erasmo
- Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy
| | - Daniela Commodari
- Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy
| | - Stella Covino
- Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy
| | - Simone Bini
- Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy
| | - Ameneh Ghadiri
- Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy
| | - Fabrizio Ceci
- Department of Experimental Medicine, Sapienza University of Rome, 00161 Rome, Italy
| | - Marianna Maranghi
- Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy
| | - Alberico L Catapano
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Milan, Italy; I.R.C.C.S. Multimedica, Sesto S. Giovanni, Milan, Italy
| | - Marta Gazzotti
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Milan, Italy
| | - Manuela Casula
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Milan, Italy; I.R.C.C.S. Multimedica, Sesto S. Giovanni, Milan, Italy
| | - Anna Montali
- Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy
| | - Marcello Arca
- Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy
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Accurate interpretation of genetic variants in sudden unexpected death in infancy by trio-targeted gene-sequencing panel analysis. Sci Rep 2021; 11:21532. [PMID: 34728707 PMCID: PMC8563990 DOI: 10.1038/s41598-021-00962-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 10/20/2021] [Indexed: 12/18/2022] Open
Abstract
In sudden unexpected death in infancy cases, postmortem genetic analysis with next-generation sequencing potentially can extract candidate genes associated with sudden death. However, it is difficult to accurately interpret the clinically significant genetic variants. The study aim was to conduct trio analysis of cases of sudden unexpected death in infancy and their parents to more accurately interpret the clinically significant disease-associated gene variants associated with cause of death. From the TruSight One panel targeting 4813 genes we extracted candidate genetic variants of 66 arrhythmia-, 63 inherited metabolic disease-, 81 mitochondrial disease-, and 6 salt-losing tubulopathy-related genes in 7 cases and determined if they were de novo or parental-derived variants. Thirty-four parental-derived variants and no de novo variants were found, but none appeared to be related to the cause of death. Using trio analysis and an in silico algorithm to analyze all 4813 genes, we identified OBSCN of compound heterozygous and HCCS of hemizygous variants as new candidate genetic variants related to cause of death. Genetic analysis of these deceased infants and their living parents can provide more accurate interpretation of the clinically significant genetic variants than previously possible and help confirm the cause of death.
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Manduchi E, Romano JD, Moore JH. The promise of automated machine learning for the genetic analysis of complex traits. Hum Genet 2021; 141:1529-1544. [PMID: 34713318 PMCID: PMC9360157 DOI: 10.1007/s00439-021-02393-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 10/22/2021] [Indexed: 12/24/2022]
Abstract
The genetic analysis of complex traits has been dominated by parametric statistical methods due to their theoretical properties, ease of use, computational efficiency, and intuitive interpretation. However, there are likely to be patterns arising from complex genetic architectures which are more easily detected and modeled using machine learning methods. Unfortunately, selecting the right machine learning algorithm and tuning its hyperparameters can be daunting for experts and non-experts alike. The goal of automated machine learning (AutoML) is to let a computer algorithm identify the right algorithms and hyperparameters thus taking the guesswork out of the optimization process. We review the promises and challenges of AutoML for the genetic analysis of complex traits and give an overview of several approaches and some example applications to omics data. It is our hope that this review will motivate studies to develop and evaluate novel AutoML methods and software in the genetics and genomics space. The promise of AutoML is to enable anyone, regardless of training or expertise, to apply machine learning as part of their genetic analysis strategy.
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Affiliation(s)
- Elisabetta Manduchi
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA.,Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Joseph D Romano
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jason H Moore
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA. .,Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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