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Mendoza-Alvarez A, Tosco-Herrera E, Muñoz-Barrera A, Rubio-Rodríguez LA, Alonso-Gonzalez A, Corrales A, Iñigo-Campos A, Almeida-Quintana L, Martin-Fernandez E, Martinez-Beltran D, Perez-Rodriguez E, Callero A, Garcia-Robaina JC, González-Montelongo R, Marcelino-Rodriguez I, Lorenzo-Salazar JM, Flores C. A catalog of the genetic causes of hereditary angioedema in the Canary Islands (Spain). Front Immunol 2022; 13:997148. [PMID: 36203598 PMCID: PMC9531158 DOI: 10.3389/fimmu.2022.997148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 08/23/2022] [Indexed: 11/13/2022] Open
Abstract
Hereditary angioedema (HAE) is a rare disease where known causes involve C1 inhibitor dysfunction or dysregulation of the kinin cascade. The updated HAE management guidelines recommend performing genetic tests to reach a precise diagnosis. Unfortunately, genetic tests are still uncommon in the diagnosis routine. Here, we characterized for the first time the genetic causes of HAE in affected families from the Canary Islands (Spain). Whole-exome sequencing data was obtained from 41 affected patients and unaffected relatives from 29 unrelated families identified in the archipelago. The Hereditary Angioedema Database Annotation (HADA) tool was used for pathogenicity classification and causal variant prioritization among the genes known to cause HAE. Manual reclassification of prioritized variants was used in those families lacking known causal variants. We detected a total of eight different variants causing HAE in this patient series, affecting essentially SERPING1 and F12 genes, one of them being a novel SERPING1 variant (c.686-12A>G) with a predicted splicing effect which was reclassified as likely pathogenic in one family. Altogether, the diagnostic yield by assessing previously reported causal genes and considering variant reclassifications according to the American College of Medical Genetics guidelines reached 66.7% (95% Confidence Interval [CI]: 30.1-91.0) in families with more than one affected member and 10.0% (95% CI: 1.8-33.1) among cases without family information for the disease. Despite the genetic causes of many patients remain to be identified, our results reinforce the need of genetic tests as first-tier diagnostic tool in this disease, as recommended by the international WAO/EAACI guidelines for the management of HAE.
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Affiliation(s)
| | - Eva Tosco-Herrera
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
| | - Adrian Muñoz-Barrera
- Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
| | - Luis A. Rubio-Rodríguez
- Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
| | - Aitana Alonso-Gonzalez
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
- Universidad de Santiago de Compostela, Santiago de Compostela, Spain
| | - Almudena Corrales
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
| | - Antonio Iñigo-Campos
- Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
| | - Lourdes Almeida-Quintana
- Allergy Service, Hospital Universitario de Gran Canaria Dr. Negrín, Las Palmas de Gran Canaria, Spain
| | - Elena Martin-Fernandez
- Allergy Service, Hospital Universitario Dr. Molina Orosa, Las Palmas de Gran Canaria, Spain
| | - Dara Martinez-Beltran
- Allergy Service, Hospital Universitario Insular-Materno Infantil, Las Palmas de Gran Canaria, Spain
| | - Eva Perez-Rodriguez
- Allergy Service, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
| | - Ariel Callero
- Allergy Service, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
| | - Jose C. Garcia-Robaina
- Allergy Service, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
| | | | - Itahisa Marcelino-Rodriguez
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
- Public Health and Preventive Medicine Area, Universidad de La Laguna, Santa Cruz de Tenerife, Spain
| | - Jose M. Lorenzo-Salazar
- Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
| | - Carlos Flores
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
- Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias, Las Palmas de Gran Canaria, Spain
- *Correspondence: Carlos Flores,
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2
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ZHANG-RUTLEDGE K, OWEN M, SWEENEY NM, DIMMOCK D, KINGSMORE SF, LAURENT LC. Retrospective identification of prenatal fetal anomalies associated with diagnostic neonatal genomic sequencing results. Prenat Diagn 2022; 42:705-716. [PMID: 35141907 PMCID: PMC9886440 DOI: 10.1002/pd.6111] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 01/04/2022] [Accepted: 02/03/2022] [Indexed: 02/01/2023]
Abstract
OBJECTIVE To determine which types of fetal anomalies are associated with postnatal diagnoses of genetic diseases by genomic sequencing and to assess how prenatal genomic sequencing could affect clinical management. METHOD This was a secondary analysis of the second Newborn Sequencing in Genomic Medicine and Public Health study that compared fetal imaging results in critically ill infants who had actionable versus negative postnatal genomic sequencing results. RESULTS Of 213 infants who received genomic sequencing, 80 had available prenatal ultrasounds. Twenty-one (26%) of these were found to have genetic diseases by genomic sequencing. Fourteen (67%) of the 21 with genetic diseases had suspected anomalies prenatally, compared with 33 (56%) of 59 with negative results. Among fetuses with suspected anomalies, genetic diseases were 4.5 times more common in those with multiple anomalies and 6.7 times more common in those with anomalies of the extremities compared to those with negative results. Had the genetic diseases been diagnosed prenatally, clinical management would have been altered in 13 of 14. CONCLUSION Critically ill infants with diagnostic genomic sequencing were more likely to have multiple anomalies and anomalies of the extremities on fetal imaging. Among almost all infants with suspected fetal anomalies and diagnostic genomic sequencing results, prenatal diagnosis would have likely altered clinical management.
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Affiliation(s)
- Kathy ZHANG-RUTLEDGE
- Department of Obstetrics, Gynecology, and Reproductive Sciences; University of California, San Diego, CA
| | - Mallory OWEN
- Rady Children’s Institute of Genomic Medicine, San Diego, CA
| | - Nathaly M. SWEENEY
- Rady Children’s Institute of Genomic Medicine, San Diego, CA, Department of Pediatrics; University of California, San Diego, CA
| | - David DIMMOCK
- Rady Children’s Institute of Genomic Medicine, San Diego, CA
| | | | - Louise C. LAURENT
- Department of Obstetrics, Gynecology, and Reproductive Sciences; University of California, San Diego, CA
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3
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Chen J, Althagafi A, Hoehndorf R. Predicting candidate genes from phenotypes, functions and anatomical site of expression. Bioinformatics 2021; 37:853-860. [PMID: 33051643 PMCID: PMC8248315 DOI: 10.1093/bioinformatics/btaa879] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 08/26/2020] [Accepted: 09/28/2020] [Indexed: 12/30/2022] Open
Abstract
Motivation Over the past years, many computational methods have been developed to
incorporate information about phenotypes for disease–gene
prioritization task. These methods generally compute the similarity between
a patient’s phenotypes and a database of gene-phenotype to find the
most phenotypically similar match. The main limitation in these methods is
their reliance on knowledge about phenotypes associated with particular
genes, which is not complete in humans as well as in many model organisms,
such as the mouse and fish. Information about functions of gene products and
anatomical site of gene expression is available for more genes and can also
be related to phenotypes through ontologies and machine-learning models. Results We developed a novel graph-based machine-learning method for biomedical
ontologies, which is able to exploit axioms in ontologies and other
graph-structured data. Using our machine-learning method, we embed genes
based on their associated phenotypes, functions of the gene products and
anatomical location of gene expression. We then develop a machine-learning
model to predict gene–disease associations based on the associations
between genes and multiple biomedical ontologies, and this model
significantly improves over state-of-the-art methods. Furthermore, we extend
phenotype-based gene prioritization methods significantly to all genes,
which are associated with phenotypes, functions or site of expression. Availability and implementation Software and data are available at https://github.com/bio-ontology-research-group/DL2Vec. Supplementary information Supplementary data
are available at Bioinformatics online.
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Affiliation(s)
- Jun Chen
- Computational Bioscience Research Center (CBRC), Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Azza Althagafi
- Computational Bioscience Research Center (CBRC), Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia.,Computer Science Department, College of Computers and Information Technology, Taif University, Taif 26571, Saudi Arabia
| | - Robert Hoehndorf
- Computational Bioscience Research Center (CBRC), Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
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4
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Yoon KH, Fox SC, Dicipulo R, Lehmann OJ, Waskiewicz AJ. Ocular coloboma: Genetic variants reveal a dynamic model of eye development. AMERICAN JOURNAL OF MEDICAL GENETICS PART C-SEMINARS IN MEDICAL GENETICS 2020; 184:590-610. [PMID: 32852110 DOI: 10.1002/ajmg.c.31831] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 07/27/2020] [Accepted: 07/28/2020] [Indexed: 12/21/2022]
Abstract
Ocular coloboma is a congenital disorder of the eye where a gap exists in the inferior retina, lens, iris, or optic nerve tissue. With a prevalence of 2-19 per 100,000 live births, coloboma, and microphthalmia, an associated ocular disorder, represent up to 10% of childhood blindness. It manifests due to the failure of choroid fissure closure during eye development, and it is a part of a spectrum of ocular disorders that include microphthalmia and anophthalmia. Use of genetic approaches from classical pedigree analyses to next generation sequencing has identified more than 40 loci that are associated with the causality of ocular coloboma. As we have expanded studies to include singleton cases, hereditability has been very challenging to prove. As such, researchers over the past 20 years, have unraveled the complex interrelationship amongst these 40 genes using vertebrate model organisms. Such research has greatly increased our understanding of eye development. These genes function to regulate initial specification of the eye field, migration of retinal precursors, patterning of the retina, neural crest cell biology, and activity of head mesoderm. This review will discuss the discovery of loci using patient data, their investigations in animal models, and the recent advances stemming from animal models that shed new light in patient diagnosis.
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Affiliation(s)
- Kevin H Yoon
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada.,Women & Children's Health Research Institute, University of Alberta, Edmonton, Canada
| | - Sabrina C Fox
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada.,Women & Children's Health Research Institute, University of Alberta, Edmonton, Canada
| | - Renée Dicipulo
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada.,Women & Children's Health Research Institute, University of Alberta, Edmonton, Canada
| | - Ordan J Lehmann
- Women & Children's Health Research Institute, University of Alberta, Edmonton, Canada.,Department of Medical Genetics, University of Alberta, Edmonton, Alberta, Canada.,Department of Ophthalmology, University of Alberta, Edmonton, Alberta, Canada
| | - Andrew J Waskiewicz
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada.,Women & Children's Health Research Institute, University of Alberta, Edmonton, Canada
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5
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Abstract
Dysmorphology is the practice of defining the morphologic phenotype of syndromic disorders. Genomic sequencing has advanced our understanding of human variation and molecular dysmorphology has evolved in response to the science of relating embryologic developmental implications of abnormal gene signaling pathways to the resultant phenotypic presentation. Machine learning has enabled the application of deep convoluted neural networks to recognize the comparative likeness of these phenotypes relative to the causal genotype or disrupted gene pathway.
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Affiliation(s)
- Donald Basel
- Department of Pediatrics, Division of Genetics, Medical College of Wisconsin, 9000 West Wisconsin Avenue, MS #716, Milwaukee, WI 53226, USA.
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6
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Dahary D, Golan Y, Mazor Y, Zelig O, Barshir R, Twik M, Iny Stein T, Rosner G, Kariv R, Chen F, Zhang Q, Shen Y, Safran M, Lancet D, Fishilevich S. Genome analysis and knowledge-driven variant interpretation with TGex. BMC Med Genomics 2019; 12:200. [PMID: 31888639 PMCID: PMC6937949 DOI: 10.1186/s12920-019-0647-8] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 12/15/2019] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND The clinical genetics revolution ushers in great opportunities, accompanied by significant challenges. The fundamental mission in clinical genetics is to analyze genomes, and to identify the most relevant genetic variations underlying a patient's phenotypes and symptoms. The adoption of Whole Genome Sequencing requires novel capacities for interpretation of non-coding variants. RESULTS We present TGex, the Translational Genomics expert, a novel genome variation analysis and interpretation platform, with remarkable exome analysis capacities and a pioneering approach of non-coding variants interpretation. TGex's main strength is combining state-of-the-art variant filtering with knowledge-driven analysis made possible by VarElect, our highly effective gene-phenotype interpretation tool. VarElect leverages the widely used GeneCards knowledgebase, which integrates information from > 150 automatically-mined data sources. Access to such a comprehensive data compendium also facilitates TGex's broad variant annotation, supporting evidence exploration, and decision making. TGex has an interactive, user-friendly, and easy adaptive interface, ACMG compliance, and an automated reporting system. Beyond comprehensive whole exome sequence capabilities, TGex encompasses innovative non-coding variants interpretation, towards the goal of maximal exploitation of whole genome sequence analyses in the clinical genetics practice. This is enabled by GeneCards' recently developed GeneHancer, a novel integrative and fully annotated database of human enhancers and promoters. Examining use-cases from a variety of TGex users world-wide, we demonstrate its high diagnostic yields (42% for single exome and 50% for trios in 1500 rare genetic disease cases) and critical actionable genetic findings. The platform's support for integration with EHR and LIMS through dedicated APIs facilitates automated retrieval of patient data for TGex's customizable reporting engine, establishing a rapid and cost-effective workflow for an entire range of clinical genetic testing, including rare disorders, cancer predisposition, tumor biopsies and health screening. CONCLUSIONS TGex is an innovative tool for the annotation, analysis and prioritization of coding and non-coding genomic variants. It provides access to an extensive knowledgebase of genomic annotations, with intuitive and flexible configuration options, allows quick adaptation, and addresses various workflow requirements. It thus simplifies and accelerates variant interpretation in clinical genetics workflows, with remarkable diagnostic yield, as exemplified in the described use cases. TGex is available at http://tgex.genecards.org/.
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Affiliation(s)
- Dvir Dahary
- Clinical Genetics, LifeMap Sciences Inc., Marshfield, MA, 02050, USA.
| | - Yaron Golan
- Clinical Genetics, LifeMap Sciences Inc., Marshfield, MA, 02050, USA
| | - Yaron Mazor
- Clinical Genetics, LifeMap Sciences Inc., Marshfield, MA, 02050, USA
| | - Ofer Zelig
- Clinical Genetics, LifeMap Sciences Inc., Marshfield, MA, 02050, USA
| | - Ruth Barshir
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | - Michal Twik
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | - Tsippi Iny Stein
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | - Guy Rosner
- Department of Gastroenterology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel.,Faculty of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - Revital Kariv
- Department of Gastroenterology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel.,Faculty of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - Fei Chen
- Genetic and Metabolic Central Laboratory, Birth Defect Prevention Research Institute, Maternal and Child Health Hospital, Children's Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530002, China
| | - Qiang Zhang
- Genetic and Metabolic Central Laboratory, Birth Defect Prevention Research Institute, Maternal and Child Health Hospital, Children's Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530002, China
| | - Yiping Shen
- Genetic and Metabolic Central Laboratory, Birth Defect Prevention Research Institute, Maternal and Child Health Hospital, Children's Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530002, China.,Department of Medical Genetics and Molecular Diagnostic Laboratory, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.,Department of Neurology, Harvard Medical School, Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Marilyn Safran
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | - Doron Lancet
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel.
| | - Simon Fishilevich
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel.
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7
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Aggarwal S, Vineeth VS, Das Bhowmik A, Tandon A, Kulkarni A, Narayanan DL, Bhattacherjee A, Dalal A. Exome sequencing for perinatal phenotypes: The significance of deep phenotyping. Prenat Diagn 2019; 40:260-273. [PMID: 31742715 DOI: 10.1002/pd.5616] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 09/27/2019] [Accepted: 10/09/2019] [Indexed: 12/13/2022]
Abstract
OBJECTIVE To ascertain the performance of exome sequencing (ES) technology for determining the etiological basis of abnormal perinatal phenotypes and to study the impact of comprehensive phenotyping on variant prioritization. METHODS A carefully selected cohort of 32/204 fetuses with abnormal perinatal phenotypes following postmortem/postnatal deep phenotyping underwent ES to identify a causative variant for the fetal phenotype. A retrospective comparative analysis of the prenatal versus postmortem/postnatal phenotype-based variant prioritization was performed with aid of Phenolyzer software. A review of selected literature reports was done to examine the completeness of phenotypic information for cases in those reports and how it impacted the performance of fetal ES. RESULTS In 18/32 (56%) fetuses, a pathogenic/likely pathogenic variant was identified. This included novel genotype-phenotype associations, expanded prenatal phenotypes of known Mendelian disorders and dual Mendelian diagnoses. The retrospective analysis revealed that the putative diagnostic variant could not be identified on basis of prenatal findings alone in 15/22 (68%) cases, indicating the importance of comprehensive postmortem/postnatal phenotype information. Literature review was supportive of these findings but could not be conclusive due to marked heterogeneity of involved studies. CONCLUSION Comprehensive phenotyping is essential for improving diagnostic performance and facilitating identification of novel genotype-phenotype associations in perinatal cohorts undergoing ES.
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Affiliation(s)
- Shagun Aggarwal
- Department of Medical Genetics, Nizam's Institute of Medical Sciences, Hyderabad, India.,Diagnostics Division, Centre for DNA Fingerprinting and Diagnostics, Hyderabad, India
| | | | - Aneek Das Bhowmik
- Diagnostics Division, Centre for DNA Fingerprinting and Diagnostics, Hyderabad, India
| | - Ashwani Tandon
- Department of Pathology, All India Institute of Medical Sciences, Bhopal, India
| | | | - Dhanya Lakshmi Narayanan
- Department of Medical Genetics, Nizam's Institute of Medical Sciences, Hyderabad, India.,Diagnostics Division, Centre for DNA Fingerprinting and Diagnostics, Hyderabad, India
| | - Amrita Bhattacherjee
- Diagnostics Division, Centre for DNA Fingerprinting and Diagnostics, Hyderabad, India
| | - Ashwin Dalal
- Department of Medical Genetics, Nizam's Institute of Medical Sciences, Hyderabad, India.,Diagnostics Division, Centre for DNA Fingerprinting and Diagnostics, Hyderabad, India
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8
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Kasak L, Hunter JM, Udani R, Bakolitsa C, Hu Z, Adhikari AN, Babbi G, Casadio R, Gough J, Guerrero RF, Jiang Y, Joseph T, Katsonis P, Kotte S, Kundu K, Lichtarge O, Martelli PL, Mooney SD, Moult J, Pal LR, Poitras J, Radivojac P, Rao A, Sivadasan N, Sunderam U, VG S, Yin Y, Zaucha J, Brenner SE, Meyn MS. CAGI SickKids challenges: Assessment of phenotype and variant predictions derived from clinical and genomic data of children with undiagnosed diseases. Hum Mutat 2019; 40:1373-1391. [PMID: 31322791 PMCID: PMC7318886 DOI: 10.1002/humu.23874] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2019] [Revised: 07/15/2019] [Accepted: 07/15/2019] [Indexed: 01/02/2023]
Abstract
Whole-genome sequencing (WGS) holds great potential as a diagnostic test. However, the majority of patients currently undergoing WGS lack a molecular diagnosis, largely due to the vast number of undiscovered disease genes and our inability to assess the pathogenicity of most genomic variants. The CAGI SickKids challenges attempted to address this knowledge gap by assessing state-of-the-art methods for clinical phenotype prediction from genomes. CAGI4 and CAGI5 participants were provided with WGS data and clinical descriptions of 25 and 24 undiagnosed patients from the SickKids Genome Clinic Project, respectively. Predictors were asked to identify primary and secondary causal variants. In addition, for CAGI5, groups had to match each genome to one of three disorder categories (neurologic, ophthalmologic, and connective), and separately to each patient. The performance of matching genomes to categories was no better than random but two groups performed significantly better than chance in matching genomes to patients. Two of the ten variants proposed by two groups in CAGI4 were deemed to be diagnostic, and several proposed pathogenic variants in CAGI5 are good candidates for phenotype expansion. We discuss implications for improving in silico assessment of genomic variants and identifying new disease genes.
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Affiliation(s)
- Laura Kasak
- Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA
- Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Jesse M. Hunter
- Department of Pediatrics and Wisconsin State Lab of Hygiene, University of Wisconsin Madison, WI, USA
| | - Rupa Udani
- Department of Pediatrics and Wisconsin State Lab of Hygiene, University of Wisconsin Madison, WI, USA
| | - Constantina Bakolitsa
- Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA
| | - Zhiqiang Hu
- Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA
| | - Aashish N. Adhikari
- Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA
| | - Giulia Babbi
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Rita Casadio
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Julian Gough
- Department of Computer Science, University of Bristol, Bristol, UK
| | | | - Yuxiang Jiang
- Department of Computer Science, Indiana University, IN, USA
| | | | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | | | - Kunal Kundu
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD, USA
- Computational Biology, Bioinformatics and Genomics, Biological Sciences Graduate Program, University of Maryland, College Park, MD, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Department of Biochemistry & Molecular Biology, Department of Pharmacology, Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, TX, USA
| | - Pier Luigi Martelli
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Sean D. Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, WA, USA
| | - John Moult
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, MD, USA
| | - Lipika R. Pal
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD, USA
| | | | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, MA, USA
| | | | | | | | | | - Yizhou Yin
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD, USA
- Computational Biology, Bioinformatics and Genomics, Biological Sciences Graduate Program, University of Maryland, College Park, MD, USA
| | - Jan Zaucha
- Department of Computer Science, University of Bristol, Bristol, UK
| | - Steven E. Brenner
- Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA
| | - M. Stephen Meyn
- Center for Human Genomics and Precision Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Department of Paediatrics, The Hospital for Sick Children, Toronto, Canada
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