1
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Phenotype-aware prioritisation of rare Mendelian disease variants. Trends Genet 2022; 38:1271-1283. [PMID: 35934592 PMCID: PMC9950798 DOI: 10.1016/j.tig.2022.07.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 06/06/2022] [Accepted: 07/05/2022] [Indexed: 01/24/2023]
Abstract
A molecular diagnosis from the analysis of sequencing data in rare Mendelian diseases has a huge impact on the management of patients and their families. Numerous patient phenotype-aware variant prioritisation (VP) tools have been developed to help automate this process, and shorten the diagnostic odyssey, but performance statistics on real patient data are limited. Here we identify, assess, and compare the performance of all up-to-date, freely available, and programmatically accessible tools using a whole-exome, retinal disease dataset from 134 individuals with a molecular diagnosis. All tools were able to identify around two-thirds of the genetic diagnoses as the top-ranked candidate, with LIRICAL performing best overall. Finally, we discuss the challenges to overcome most cases remaining undiagnosed after current, state-of-the-art practices.
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2
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Jacobsen JOB, Kelly C, Cipriani V, Research Consortium GE, Mungall CJ, Reese J, Danis D, Robinson PN, Smedley D. Phenotype-driven approaches to enhance variant prioritization and diagnosis of rare disease. Hum Mutat 2022; 43:1071-1081. [PMID: 35391505 PMCID: PMC9288531 DOI: 10.1002/humu.24380] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 01/25/2022] [Accepted: 04/03/2022] [Indexed: 11/20/2022]
Abstract
Rare disease diagnostics and disease gene discovery have been revolutionized by whole-exome and genome sequencing but identifying the causative variant(s) from the millions in each individual remains challenging. The use of deep phenotyping of patients and reference genotype-phenotype knowledge, alongside variant data such as allele frequency, segregation, and predicted pathogenicity, has proved an effective strategy to tackle this issue. Here we review the numerous tools that have been developed to automate this approach and demonstrate the power of such an approach on several thousand diagnosed cases from the 100,000 Genomes Project. Finally, we discuss the challenges that need to be overcome if we are going to improve detection rates and help the majority of patients that still remain without a molecular diagnosis after state-of-the-art genomic interpretation.
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Affiliation(s)
- Julius O. B. Jacobsen
- William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry QueenQueen Mary University of LondonLondonUK
| | - Catherine Kelly
- William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry QueenQueen Mary University of LondonLondonUK
| | - Valentina Cipriani
- William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry QueenQueen Mary University of LondonLondonUK
| | | | - Christopher J. Mungall
- Environmental Genomics and Systems Biology, Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Justin Reese
- Environmental Genomics and Systems Biology, Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Daniel Danis
- The Jackson Laboratory for Genomic MedicineFarmingtonConnecticutUSA
| | | | - Damian Smedley
- William Harvey Research Institute, Charterhouse Square, Barts and the London School of Medicine and Dentistry QueenQueen Mary University of LondonLondonUK
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3
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Cohen ASA, Farrow EG, Abdelmoity AT, Alaimo JT, Amudhavalli SM, Anderson JT, Bansal L, Bartik L, Baybayan P, Belden B, Berrios CD, Biswell RL, Buczkowicz P, Buske O, Chakraborty S, Cheung WA, Coffman KA, Cooper AM, Cross LA, Curran T, Dang TTT, Elfrink MM, Engleman KL, Fecske ED, Fieser C, Fitzgerald K, Fleming EA, Gadea RN, Gannon JL, Gelineau-Morel RN, Gibson M, Goldstein J, Grundberg E, Halpin K, Harvey BS, Heese BA, Hein W, Herd SM, Hughes SS, Ilyas M, Jacobson J, Jenkins JL, Jiang S, Johnston JJ, Keeler K, Korlach J, Kussmann J, Lambert C, Lawson C, Le Pichon JB, Leeder JS, Little VC, Louiselle DA, Lypka M, McDonald BD, Miller N, Modrcin A, Nair A, Neal SH, Oermann CM, Pacicca DM, Pawar K, Posey NL, Price N, Puckett LMB, Quezada JF, Raje N, Rowell WJ, Rush ET, Sampath V, Saunders CJ, Schwager C, Schwend RM, Shaffer E, Smail C, Soden S, Strenk ME, Sullivan BR, Sweeney BR, Tam-Williams JB, Walter AM, Welsh H, Wenger AM, Willig LK, Yan Y, Younger ST, Zhou D, Zion TN, Thiffault I, Pastinen T. Genomic answers for children: Dynamic analyses of >1000 pediatric rare disease genomes. Genet Med 2022; 24:1336-1348. [PMID: 35305867 DOI: 10.1016/j.gim.2022.02.007] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 02/05/2022] [Accepted: 02/07/2022] [Indexed: 12/17/2022] Open
Abstract
PURPOSE This study aimed to provide comprehensive diagnostic and candidate analyses in a pediatric rare disease cohort through the Genomic Answers for Kids program. METHODS Extensive analyses of 960 families with suspected genetic disorders included short-read exome sequencing and short-read genome sequencing (srGS); PacBio HiFi long-read genome sequencing (HiFi-GS); variant calling for single nucleotide variants (SNV), structural variant (SV), and repeat variants; and machine-learning variant prioritization. Structured phenotypes, prioritized variants, and pedigrees were stored in PhenoTips database, with data sharing through controlled access the database of Genotypes and Phenotypes. RESULTS Diagnostic rates ranged from 11% in patients with prior negative genetic testing to 34.5% in naive patients. Incorporating SVs from genome sequencing added up to 13% of new diagnoses in previously unsolved cases. HiFi-GS yielded increased discovery rate with >4-fold more rare coding SVs compared with srGS. Variants and genes of unknown significance remain the most common finding (58% of nondiagnostic cases). CONCLUSION Computational prioritization is efficient for diagnostic SNVs. Thorough identification of non-SNVs remains challenging and is partly mitigated using HiFi-GS sequencing. Importantly, community research is supported by sharing real-time data to accelerate gene validation and by providing HiFi variant (SNV/SV) resources from >1000 human alleles to facilitate implementation of new sequencing platforms for rare disease diagnoses.
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Affiliation(s)
- Ana S A Cohen
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; Department of Pathology and Laboratory Medicine, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO
| | - Emily G Farrow
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | | | - Joseph T Alaimo
- Department of Pathology and Laboratory Medicine, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO
| | - Shivarajan M Amudhavalli
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - John T Anderson
- Department of Orthopaedic Surgery, Children's Mercy Kansas City, Kansas City, MO
| | - Lalit Bansal
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Lauren Bartik
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | | | - Bradley Belden
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | | | - Rebecca L Biswell
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | | | | | | | - Warren A Cheung
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Keith A Coffman
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Ashley M Cooper
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Laura A Cross
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - Tom Curran
- Children's Mercy Research Institute, Kansas City, MO
| | - Thuy Tien T Dang
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Mary M Elfrink
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | | | - Erin D Fecske
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Cynthia Fieser
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Keely Fitzgerald
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Emily A Fleming
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - Randi N Gadea
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | | | - Rose N Gelineau-Morel
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Margaret Gibson
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Jeffrey Goldstein
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Elin Grundberg
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Kelsee Halpin
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Brian S Harvey
- Department of Orthopaedic Surgery, Children's Mercy Kansas City, Kansas City, MO
| | - Bryce A Heese
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - Wendy Hein
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Suzanne M Herd
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Susan S Hughes
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - Mohammed Ilyas
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Jill Jacobson
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Janda L Jenkins
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | | | | | - Kathryn Keeler
- Department of Orthopaedic Surgery, Children's Mercy Kansas City, Kansas City, MO
| | - Jonas Korlach
- Pacific Biosciences of California, Inc, Menlo Park, CA
| | | | | | - Caitlin Lawson
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | | | | | - Vicki C Little
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | | | | | | | - Neil Miller
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Division of Allergy Immunology Pulmonary and Sleep Medicine, Children's Mercy Kansas City, Kansas City, MO
| | - Ann Modrcin
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Annapoorna Nair
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Shelby H Neal
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | | | - Donna M Pacicca
- Department of Orthopaedic Surgery, Children's Mercy Kansas City, Kansas City, MO
| | - Kailash Pawar
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Nyshele L Posey
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Nigel Price
- Department of Orthopaedic Surgery, Children's Mercy Kansas City, Kansas City, MO
| | - Laura M B Puckett
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Julio F Quezada
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Nikita Raje
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Division of Neonatology, Children's Mercy Kansas City, Kansas City, MO
| | | | - Eric T Rush
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Division of Genetics, Children's Mercy Kansas City, Kansas City, MO; Department of Internal Medicine, University of Kansas School of Medicine, Kansas City, MO
| | - Venkatesh Sampath
- Division of Neonatology, Children's Mercy Hospital Kansas City, Kansas City, MO
| | - Carol J Saunders
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; Department of Pathology and Laboratory Medicine, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO
| | - Caitlin Schwager
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - Richard M Schwend
- Department of Orthopaedic Surgery, Children's Mercy Kansas City, Kansas City, MO
| | - Elizabeth Shaffer
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Craig Smail
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Sarah Soden
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Meghan E Strenk
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | | | - Brooke R Sweeney
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | | | - Adam M Walter
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Holly Welsh
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | | | - Laurel K Willig
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Yun Yan
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Scott T Younger
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Dihong Zhou
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - Tricia N Zion
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO; Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - Isabelle Thiffault
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; Department of Pathology and Laboratory Medicine, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO.
| | - Tomi Pastinen
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Children's Mercy Research Institute, Kansas City, MO.
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4
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Molecular Diagnostic Outcomes from 700 Cases: What Can We Learn from a Retrospective Analysis of Clinical Exome Sequencing? J Mol Diagn 2022; 24:274-286. [PMID: 35065284 PMCID: PMC9904168 DOI: 10.1016/j.jmoldx.2021.12.002] [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: 01/18/2021] [Revised: 11/29/2021] [Accepted: 12/10/2021] [Indexed: 01/19/2023] Open
Abstract
Clinical exome sequencing (CES) aids in the diagnosis of rare genetic disorders. Herein, we report the molecular diagnostic yield and spectrum of genetic alterations contributing to disease in 700 pediatric cases analyzed at the Children's Hospital of Philadelphia. The overall diagnostic yield was 23%, with three cases having more than one molecular diagnosis and 2.6% having secondary/additional findings. A candidate gene finding was reported in another 8.4% of cases. The clinical indications with the highest diagnostic yield were neurodevelopmental disorders (including seizures), whereas immune- and oncology-related indications were negatively associated with molecular diagnosis. The rapid expansion of knowledge regarding the genome's role in human disease necessitates reanalysis of CES samples. To capture these new discoveries, a subset of cases (n = 240) underwent reanalysis, with an increase in diagnostic yield. We describe our experience reporting CES results in a pediatric setting, including reporting of secondary findings, reporting newly discovered genetic conditions, and revisiting negative test results. Finally, we highlight the challenges associated with implementing critical updates to the CES workflow. Although these updates are necessary, they demand an investment of time and resources from the laboratory. In summary, these data demonstrate the clinical utility of exome sequencing and reanalysis, while highlighting the critical considerations for continuous improvement of a CES test in a clinical laboratory.
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5
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Yuan X, Wang J, Dai B, Sun Y, Zhang K, Chen F, Peng Q, Huang Y, Zhang X, Chen J, Xu X, Chuan J, Mu W, Li H, Fang P, Gong Q, Zhang P. Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases. Brief Bioinform 2022; 23:6521702. [PMID: 35134823 PMCID: PMC8921623 DOI: 10.1093/bib/bbac019] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 01/10/2022] [Accepted: 01/13/2022] [Indexed: 12/31/2022] Open
Abstract
It’s challenging work to identify disease-causing genes from the next-generation sequencing (NGS) data of patients with Mendelian disorders. To improve this situation, researchers have developed many phenotype-driven gene prioritization methods using a patient’s genotype and phenotype information, or phenotype information only as input to rank the candidate’s pathogenic genes. Evaluations of these ranking methods provide practitioners with convenience for choosing an appropriate tool for their workflows, but retrospective benchmarks are underpowered to provide statistically significant results in their attempt to differentiate. In this research, the performance of ten recognized causal-gene prioritization methods was benchmarked using 305 cases from the Deciphering Developmental Disorders (DDD) project and 209 in-house cases via a relatively unbiased methodology. The evaluation results show that methods using Human Phenotype Ontology (HPO) terms and Variant Call Format (VCF) files as input achieved better overall performance than those using phenotypic data alone. Besides, LIRICAL and AMELIE, two of the best methods in our benchmark experiments, complement each other in cases with the causal genes ranked highly, suggesting a possible integrative approach to further enhance the diagnostic efficiency. Our benchmarking provides valuable reference information to the computer-assisted rapid diagnosis in Mendelian diseases and sheds some light on the potential direction of future improvement on disease-causing gene prioritization methods.
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Affiliation(s)
- Xiao Yuan
- Changsha KingMed Center for Clinical Laboratory, Changsha, China.,Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, China.,Genetalks Biotech. Co., Ltd., Changsha, China
| | - Jing Wang
- Changsha KingMed Center for Clinical Laboratory, Changsha, China
| | - Bing Dai
- Changsha KingMed Center for Clinical Laboratory, Changsha, China
| | - Yanfang Sun
- Changsha KingMed Center for Clinical Laboratory, Changsha, China
| | - Keke Zhang
- Changsha KingMed Center for Clinical Laboratory, Changsha, China
| | - Fangfang Chen
- Changsha KingMed Center for Clinical Laboratory, Changsha, China
| | - Qian Peng
- Changsha KingMed Center for Clinical Laboratory, Changsha, China
| | - Yixuan Huang
- Beijing Geneworks Technology Co., Ltd., Beijing, China
| | - Xinlei Zhang
- Reproductive & Genetics Hospital of Citic & Xiangya, Changsha, China
| | - Junru Chen
- Genetalks Biotech. Co., Ltd., Changsha, China
| | - Xilin Xu
- Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, China
| | - Jun Chuan
- Changsha KingMed Center for Clinical Laboratory, Changsha, China.,Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, China
| | - Wenbo Mu
- Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, China
| | - Huiyuan Li
- Changsha KingMed Center for Clinical Laboratory, Changsha, China.,Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, China
| | - Ping Fang
- Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, China
| | - Qiang Gong
- Changsha KingMed Center for Clinical Laboratory, Changsha, China.,Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, China
| | - Peng Zhang
- Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
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6
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O'Brien TD, Campbell NE, Potter AB, Letaw JH, Kulkarni A, Richards CS. Artificial intelligence (AI)-assisted exome reanalysis greatly aids in the identification of new positive cases and reduces analysis time in a clinical diagnostic laboratory. Genet Med 2021; 24:192-200. [PMID: 34906498 DOI: 10.1016/j.gim.2021.09.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/27/2021] [Accepted: 09/10/2021] [Indexed: 12/21/2022] Open
Abstract
PURPOSE Artificial intelligence (AI) and variant prioritization tools for genomic variant analysis are being rapidly developed for use in clinical diagnostic testing. However, their clinical utility and reliability are currently limited. Therefore, we performed a validation of a commercial AI tool (Moon) and a comprehensive reanalysis of previously collected clinical exome sequencing cases using an open-source variant prioritization tool (Exomiser) and the now-validated AI tool to test their feasibility in clinical diagnostics. METHODS A validation study of Moon was performed with 29 positive cases determined by previous manual analysis. After validation, reanalysis was performed on 80 previously manually analyzed nondiagnostic exome cases using Moon. Finally, a comparison between Moon and Exomiser was completed regarding their ability to identify previously completed positive cases and to identify new positive cases. RESULTS Moon correctly selected the causal variant(s) in 97% of manually analyzed positive cases and identified 7 new positive cases. Exomiser correctly identified the causal gene in 85% of positive cases and agreed with Moon by ranking the new gene in its top 10 list 43% of the time. CONCLUSION The use of AI in diagnostic laboratories greatly enhances exome sequencing analysis by reducing analysis time and increasing the diagnostic rate.
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Affiliation(s)
- Timothy D O'Brien
- Knight Diagnostic Laboratories, Oregon Health & Science University, Portland, OR.
| | - N Eleanor Campbell
- Knight Diagnostic Laboratories, Oregon Health & Science University, Portland, OR
| | - Amiee B Potter
- Knight Diagnostic Laboratories, Oregon Health & Science University, Portland, OR
| | - John H Letaw
- Knight Diagnostic Laboratories, Oregon Health & Science University, Portland, OR
| | - Arpita Kulkarni
- Knight Diagnostic Laboratories, Oregon Health & Science University, Portland, OR
| | - C Sue Richards
- Knight Diagnostic Laboratories, Oregon Health & Science University, Portland, OR; Department of Molecular and Medical Genetics, School of Medicine, Oregon Health & Science University, Portland, OR
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7
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Kim SY, Lee S, Seo GH, Kim BJ, Oh DY, Han JH, Park MK, Lee SM, Kim B, Yi N, Kim NJ, Koh DH, Hwang S, Keum C, Choi BY. Powerful use of automated prioritization of candidate variants in genetic hearing loss with extreme etiologic heterogeneity. Sci Rep 2021; 11:19476. [PMID: 34593925 PMCID: PMC8484668 DOI: 10.1038/s41598-021-99007-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 08/17/2021] [Indexed: 01/02/2023] Open
Abstract
Variant prioritization of exome sequencing (ES) data for molecular diagnosis of sensorineural hearing loss (SNHL) with extreme etiologic heterogeneity poses a significant challenge. This study used an automated variant prioritization system (“EVIDENCE”) to analyze SNHL patient data and assess its diagnostic accuracy. We performed ES of 263 probands manifesting mild to moderate or higher degrees of SNHL. Candidate variants were classified according to the 2015 American College of Medical Genetics guidelines, and we compared the accuracy, call rates, and efficiency of variant prioritizations performed manually by humans or using EVIDENCE. In our in silico panel, 21 synthetic cases were successfully analyzed by EVIDENCE. In our cohort, the ES diagnostic yield for SNHL by manual analysis was 50.19% (132/263) and 50.95% (134/263) by EVIDENCE. EVIDENCE processed ES data 24-fold faster than humans, and the concordant call rate between humans and EVIDENCE was 97.72% (257/263). Additionally, EVIDENCE outperformed human accuracy, especially at discovering causative variants of rare syndromic deafness, whereas flexible interpretations that required predefined specific genotype–phenotype correlations were possible only by manual prioritization. The automated variant prioritization system remarkably facilitated the molecular diagnosis of hearing loss with high accuracy and efficiency, fostering the popularization of molecular genetic diagnosis of SNHL.
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Affiliation(s)
- So Young Kim
- Department of Otorhinolaryngology-Head and Neck Surgery, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea
| | - Seungmin Lee
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.,R&D Center, ENCell Co. Ltd, Seoul, Republic of Korea
| | - Go Hun Seo
- 3billion, Inc., Seoul, Republic of Korea
| | - Bong Jik Kim
- Department of Otolaryngology-Head and Neck Surgery, Chungnam National University Sejong Hospital, College of Medicine, Chungnam National University, Daejeon, Republic of Korea
| | - Doo Yi Oh
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Jin Hee Han
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Moo Kyun Park
- Department of Otorhinolaryngology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - So Min Lee
- Department of Otorhinolaryngology-Head and Neck Surgery, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea
| | - Bonggi Kim
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Nayoung Yi
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Namju Justin Kim
- Department of Biological Sciences, Vanderbilt University, Nashville, USA
| | - Doo Hyun Koh
- Department of Biomedical Science, The Graduate School, CHA University, Seongnam, Republic of Korea
| | - Sohyun Hwang
- Department of Biomedical Science, The Graduate School, CHA University, Seongnam, Republic of Korea.,Department of Pathology, CHA University, CHA Bundang Medical Center, Seongnam, Republic of Korea
| | | | - Byung Yoon Choi
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
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8
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Molina-Ramírez LP, Kyle C, Ellingford JM, Wright R, Taylor A, Bhaskar SS, Campbell C, Jackson H, Fairclough A, Rousseau A, Burghel GJ, Dutton L, Banka S, Briggs TA, Clayton-Smith J, Douzgou S, Jones EA, Kingston HM, Kerr B, Ealing J, Somarathi S, Chandler KE, Stuart HM, Burkitt-Wright EM, Newman WG, Bruce IA, Black GC, Gokhale D. Personalised virtual gene panels reduce interpretation workload and maintain diagnostic rates of proband-only clinical exome sequencing for rare disorders. J Med Genet 2021; 59:393-398. [PMID: 33879512 PMCID: PMC8961756 DOI: 10.1136/jmedgenet-2020-107303] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 01/17/2021] [Accepted: 02/14/2021] [Indexed: 01/20/2023]
Abstract
Purpose The increased adoption of genomic strategies in the clinic makes it imperative for diagnostic laboratories to improve the efficiency of variant interpretation. Clinical exome sequencing (CES) is becoming a valuable diagnostic tool, capable of meeting the diagnostic demand imposed by the vast array of different rare monogenic disorders. We have assessed a clinician-led and phenotype-based approach for virtual gene panel generation for analysis of targeted CES in patients with rare disease in a single institution. Methods Retrospective survey of 400 consecutive cases presumed by clinicians to have rare monogenic disorders, referred on singleton basis for targeted CES. We evaluated diagnostic yield and variant workload to characterise the usefulness of a clinician-led approach for generation of virtual gene panels that can incorporate up to three different phenotype-driven gene selection methods. Results Abnormalities of the nervous system (54.5%), including intellectual disability, head and neck (19%), skeletal system (16%), ear (15%) and eye (15%) were the most common clinical features reported in referrals. Combined phenotype-driven strategies for virtual gene panel generation were used in 57% of cases. On average, 7.3 variants (median=5) per case were retained for clinical interpretation. The overall diagnostic rate of proband-only CES using personalised phenotype-driven virtual gene panels was 24%. Conclusions Our results show that personalised virtual gene panels are a cost-effective approach for variant analysis of CES, maintaining diagnostic yield and optimising the use of resources for clinical genomic sequencing in the clinic.
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Affiliation(s)
- Leslie Patricia Molina-Ramírez
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.,North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - Claire Kyle
- North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - Jamie M Ellingford
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.,North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - Ronnie Wright
- North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - Algy Taylor
- North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - Sanjeev S Bhaskar
- North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - Christopher Campbell
- North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - Harriet Jackson
- North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - Adele Fairclough
- North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - Abigail Rousseau
- North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - George J Burghel
- North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - Laura Dutton
- North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - Siddharth Banka
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.,North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - Tracy A Briggs
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.,North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - Jill Clayton-Smith
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.,North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - Sofia Douzgou
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.,North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - Elizabeth A Jones
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.,Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, UK
| | - Helen M Kingston
- North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - Bronwyn Kerr
- North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - John Ealing
- Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, UK.,Department of Neurology, Salford Royal NHS Foundation Trust, Salford, Salford, UK
| | - Suresh Somarathi
- North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - Kate E Chandler
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.,North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - Helen M Stuart
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.,North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - Emma Mm Burkitt-Wright
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.,North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - William G Newman
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.,North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - Iain A Bruce
- Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.,Paediatric ENT Department, Royal Manchester Children's Hospital, Manchester University NHS Foundation Trust, Manchester, UK
| | - Graeme C Black
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK .,North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
| | - David Gokhale
- North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
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9
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JARID2 haploinsufficiency is associated with a clinically distinct neurodevelopmental syndrome. Genet Med 2020; 23:374-383. [PMID: 33077894 DOI: 10.1038/s41436-020-00992-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 09/15/2020] [Accepted: 09/23/2020] [Indexed: 11/08/2022] Open
Abstract
PURPOSE JARID2, located on chromosome 6p22.3, is a regulator of histone methyltransferase complexes that is expressed in human neurons. So far, 13 individuals sharing clinical features including intellectual disability (ID) were reported with de novo heterozygous deletions in 6p22-p24 encompassing the full length JARID2 gene (OMIM 601594). However, all published individuals to date have a deletion of at least one other adjoining gene, making it difficult to determine if JARID2 is the critical gene responsible for the shared features. We aim to confirm JARID2 as a human disease gene and further elucidate the associated clinical phenotype. METHODS Chromosome microarray analysis, exome sequencing, and an online matching platform (GeneMatcher) were used to identify individuals with single-nucleotide variants or deletions involving JARID2. RESULTS We report 16 individuals in 15 families with a deletion or single-nucleotide variant in JARID2. Several of these variants are likely to result in haploinsufficiency due to nonsense-mediated messenger RNA (mRNA) decay. All individuals have developmental delay and/or ID and share some overlapping clinical characteristics such as facial features with those who have larger deletions involving JARID2. CONCLUSION We report that JARID2 haploinsufficiency leads to a clinically distinct neurodevelopmental syndrome, thus establishing gene-disease validity for the purpose of diagnostic reporting.
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10
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Wu C, Zhao X, Welsh M, Costello K, Cao K, Abou Tayoun A, Li M, Sarmady M. Using Machine Learning to Identify True Somatic Variants from Next-Generation Sequencing. Clin Chem 2020; 66:239-246. [PMID: 31672855 DOI: 10.1373/clinchem.2019.308213] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 08/19/2019] [Indexed: 12/25/2022]
Abstract
BACKGROUND Molecular profiling has become essential for tumor risk stratification and treatment selection. However, cancer genome complexity and technical artifacts make identification of real variants a challenge. Currently, clinical laboratories rely on manual screening, which is costly, subjective, and not scalable. We present a machine learning-based method to distinguish artifacts from bona fide single-nucleotide variants (SNVs) detected by next-generation sequencing from nonformalin-fixed paraffin-embedded tumor specimens. METHODS A cohort of 11278 SNVs identified through clinical sequencing of tumor specimens was collected and divided into training, validation, and test sets. Each SNV was manually inspected and labeled as either real or artifact as part of clinical laboratory workflow. A 3-class (real, artifact, and uncertain) model was developed on the training set, fine-tuned with the validation set, and then evaluated on the test set. Prediction intervals reflecting the certainty of the classifications were derived during the process to label "uncertain" variants. RESULTS The optimized classifier demonstrated 100% specificity and 97% sensitivity over 5587 SNVs of the test set. Overall, 1252 of 1341 true-positive variants were identified as real, 4143 of 4246 false-positive calls were deemed artifacts, whereas only 192 (3.4%) SNVs were labeled as "uncertain," with zero misclassification between the true positives and artifacts in the test set. CONCLUSIONS We presented a computational classifier to identify variant artifacts detected from tumor sequencing. Overall, 96.6% of the SNVs received definitive labels and thus were exempt from manual review. This framework could improve quality and efficiency of the variant review process in clinical laboratories.
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Affiliation(s)
- Chao Wu
- Division of Genomic Diagnostics, The Children's Hospital of Philadelphia, Philadelphia, PA
| | - Xiaonan Zhao
- Division of Genomic Diagnostics, The Children's Hospital of Philadelphia, Philadelphia, PA
| | - Mark Welsh
- Division of Genomic Diagnostics, The Children's Hospital of Philadelphia, Philadelphia, PA
| | | | - Kajia Cao
- Division of Genomic Diagnostics, The Children's Hospital of Philadelphia, Philadelphia, PA
| | - Ahmad Abou Tayoun
- Department of Genetics, Al Jalila Children's Specialty Hospital, Dubai, UAE
| | - Marilyn Li
- Division of Genomic Diagnostics, The Children's Hospital of Philadelphia, Philadelphia, PA.,Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA
| | - Mahdi Sarmady
- Division of Genomic Diagnostics, The Children's Hospital of Philadelphia, Philadelphia, PA.,Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA.,Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA
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11
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Zhao M, Havrilla JM, Fang L, Chen Y, Peng J, Liu C, Wu C, Sarmady M, Botas P, Isla J, Lyon GJ, Weng C, Wang K. Phen2Gene: rapid phenotype-driven gene prioritization for rare diseases. NAR Genom Bioinform 2020; 2:lqaa032. [PMID: 32500119 PMCID: PMC7252576 DOI: 10.1093/nargab/lqaa032] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 04/10/2020] [Accepted: 04/28/2020] [Indexed: 02/07/2023] Open
Abstract
Human Phenotype Ontology (HPO) terms are increasingly used in diagnostic settings to aid in the characterization of patient phenotypes. The HPO annotation database is updated frequently and can provide detailed phenotype knowledge on various human diseases, and many HPO terms are now mapped to candidate causal genes with binary relationships. To further improve the genetic diagnosis of rare diseases, we incorporated these HPO annotations, gene-disease databases and gene-gene databases in a probabilistic model to build a novel HPO-driven gene prioritization tool, Phen2Gene. Phen2Gene accesses a database built upon this information called the HPO2Gene Knowledgebase (H2GKB), which provides weighted and ranked gene lists for every HPO term. Phen2Gene is then able to access the H2GKB for patient-specific lists of HPO terms or PhenoPacket descriptions supported by GA4GH (http://phenopackets.org/), calculate a prioritized gene list based on a probabilistic model and output gene-disease relationships with great accuracy. Phen2Gene outperforms existing gene prioritization tools in speed and acts as a real-time phenotype-driven gene prioritization tool to aid the clinical diagnosis of rare undiagnosed diseases. In addition to a command line tool released under the MIT license (https://github.com/WGLab/Phen2Gene), we also developed a web server and web service (https://phen2gene.wglab.org/) for running the tool via web interface or RESTful API queries. Finally, we have curated a large amount of benchmarking data for phenotype-to-gene tools involving 197 patients across 76 scientific articles and 85 patients' de-identified HPO term data from the Children's Hospital of Philadelphia.
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Affiliation(s)
- Mengge Zhao
- Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - James M Havrilla
- Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Li Fang
- Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Ying Chen
- Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Jacqueline Peng
- Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.,Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Cong Liu
- Department of Biomedical Informatics, Columbia University Medical Center, New York, NY 10032, USA
| | - Chao Wu
- Division of Genomic Diagnostics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Mahdi Sarmady
- Division of Genomic Diagnostics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.,Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Pablo Botas
- Foundation 29, Pozuelo de Alarcon, 28223 Madrid, Spain
| | - Julián Isla
- Foundation 29, Pozuelo de Alarcon, 28223 Madrid, Spain.,Dravet Syndrome European Federation, 29200 Brest, France
| | - Gholson J Lyon
- Institute for Basic Research in Developmental Disabilities (IBR), Staten Island, NY 10314, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University Medical Center, New York, NY 10032, USA
| | - Kai Wang
- Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.,Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
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12
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Cipriani V, Pontikos N, Arno G, Sergouniotis PI, Lenassi E, Thawong P, Danis D, Michaelides M, Webster AR, Moore AT, Robinson PN, Jacobsen JO, Smedley D. An Improved Phenotype-Driven Tool for Rare Mendelian Variant Prioritization: Benchmarking Exomiser on Real Patient Whole-Exome Data. Genes (Basel) 2020; 11:E460. [PMID: 32340307 PMCID: PMC7230372 DOI: 10.3390/genes11040460] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 04/08/2020] [Accepted: 04/16/2020] [Indexed: 02/06/2023] Open
Abstract
Next-generation sequencing has revolutionized rare disease diagnostics, but many patients remain without a molecular diagnosis, particularly because many candidate variants usually survive despite strict filtering. Exomiser was launched in 2014 as a Java tool that performs an integrative analysis of patients' sequencing data and their phenotypes encoded with Human Phenotype Ontology (HPO) terms. It prioritizes variants by leveraging information on variant frequency, predicted pathogenicity, and gene-phenotype associations derived from human diseases, model organisms, and protein-protein interactions. Early published releases of Exomiser were able to prioritize disease-causative variants as top candidates in up to 97% of simulated whole-exomes. The size of the tested real patient datasets published so far are very limited. Here, we present the latest Exomiser version 12.0.1 with many new features. We assessed the performance using a set of 134 whole-exomes from patients with a range of rare retinal diseases and known molecular diagnosis. Using default settings, Exomiser ranked the correct diagnosed variants as the top candidate in 74% of the dataset and top 5 in 94%; not using the patients' HPO profiles (i.e., variant-only analysis) decreased the performance to 3% and 27%, respectively. In conclusion, Exomiser is an effective support tool for rare Mendelian phenotype-driven variant prioritization.
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Affiliation(s)
- Valentina Cipriani
- William Harvey Research Institute, Queen Mary University of London, London EC1M 6BQ, UK; (J.O.B.J.); (D.S.)
- UCL Institute of Ophthalmology, University College London, London EC1V 9EL, UK; (N.P.); (G.A.); (M.M.); (A.R.W.); (A.T.M.)
- Moorfields Eye Hospital NHS Foundation Trust, London EC1V 2PD, UK
- UCL Genetics Institute, University College London, London WC1E 6AA, UK
| | - Nikolas Pontikos
- UCL Institute of Ophthalmology, University College London, London EC1V 9EL, UK; (N.P.); (G.A.); (M.M.); (A.R.W.); (A.T.M.)
- Moorfields Eye Hospital NHS Foundation Trust, London EC1V 2PD, UK
| | - Gavin Arno
- UCL Institute of Ophthalmology, University College London, London EC1V 9EL, UK; (N.P.); (G.A.); (M.M.); (A.R.W.); (A.T.M.)
- Moorfields Eye Hospital NHS Foundation Trust, London EC1V 2PD, UK
- North East Thames Regional Genetics Laboratory, Great Ormond Street Hospital NHS Trust, London WC1N 3BH, UK
| | | | - Eva Lenassi
- Manchester Royal Eye Hospital & University of Manchester, Manchester M13 9WL, UK; (P.I.S.); (E.L.)
| | - Penpitcha Thawong
- Department of Medical Sciences, Medical Genetics Section, National Institute of Health, Ministry of Public Health, Nonthaburi 11000, Thailand;
| | - Daniel Danis
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; (D.D.); (P.N.R.)
| | - Michel Michaelides
- UCL Institute of Ophthalmology, University College London, London EC1V 9EL, UK; (N.P.); (G.A.); (M.M.); (A.R.W.); (A.T.M.)
- Moorfields Eye Hospital NHS Foundation Trust, London EC1V 2PD, UK
| | - Andrew R. Webster
- UCL Institute of Ophthalmology, University College London, London EC1V 9EL, UK; (N.P.); (G.A.); (M.M.); (A.R.W.); (A.T.M.)
- Moorfields Eye Hospital NHS Foundation Trust, London EC1V 2PD, UK
| | - Anthony T. Moore
- UCL Institute of Ophthalmology, University College London, London EC1V 9EL, UK; (N.P.); (G.A.); (M.M.); (A.R.W.); (A.T.M.)
- Moorfields Eye Hospital NHS Foundation Trust, London EC1V 2PD, UK
- Ophthalmology Department, UCSF School of Medicine, San Francisco, CA 94143-0644, USA
| | - Peter N. Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; (D.D.); (P.N.R.)
| | - Julius O.B. Jacobsen
- William Harvey Research Institute, Queen Mary University of London, London EC1M 6BQ, UK; (J.O.B.J.); (D.S.)
| | - Damian Smedley
- William Harvey Research Institute, Queen Mary University of London, London EC1M 6BQ, UK; (J.O.B.J.); (D.S.)
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