Cloud-based genomics pipelines for ophthalmology: reviewed from research to clinical practice

Aim: To familiarize clinicians with clinical genomics, and to describe the potential of cloud computing for enabling the future routine use of genomics in eye hospital settings.Design: Review article exploring the potential for cloud-based genomic pipelines in eye hospitals.Methods: Narrative review of the literature relevant to clinical genomics and cloud computing, using PubMed and Google Scholar. A broad overview of these fields is provided, followed by key examples of their integration.Results: Cloud computing could benefit clinical genomics due to scalability of resources, potentially lower costs, and ease of data sharing between multiple institutions. Challenges include complex pricing of services, costs from mistakes or experimentation, data security, and privacy concerns.Conclusions and future perspectives: Clinical genomics is likely to become more routinely used in clinical practice. Currently this is delivered in highly specialist centers. In the future, cloud computing could enable delivery of clinical genomics services in non-specialist hospital settings, in a fast, cost-effective way, whilst enhancing collaboration between clinical and research teams.

[1]  M. Inouye,et al.  Genomic risk prediction of coronary artery disease in women with breast cancer: a prospective cohort study , 2021, Breast Cancer Research.

[2]  E. Trucco,et al.  Using machine learning approaches for multi-omics data analysis: A review. , 2021, Biotechnology advances.

[3]  Bennet J. McComish,et al.  A multi-ethnic genome-wide association study implicates collagen matrix integrity and cell differentiation pathways in keratoconus , 2021, Communications Biology.

[4]  R. Socher,et al.  Deep learning-enabled medical computer vision , 2021, npj Digital Medicine.

[5]  R. Dilley,et al.  Usher Syndrome: Genetics and Molecular Links of Hearing Loss and Directions for Therapy , 2020, Frontiers in Genetics.

[6]  Aaron Y. Lee,et al.  Big data requirements for artificial intelligence. , 2020, Current opinion in ophthalmology.

[7]  Keith W. Muir,et al.  Whole-genome sequencing of patients with rare diseases in a national health system , 2020, Nature.

[8]  Louis Ehwerhemuepha,et al.  HealtheDataLab – a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions , 2020, BMC Medical Informatics and Decision Making.

[9]  Tariq Ahmad,et al.  A structural variation reference for medical and population genetics , 2020, Nature.

[10]  Damian Smedley,et al.  An Improved Phenotype-Driven Tool for Rare Mendelian Variant Prioritization: Benchmarking Exomiser on Real Patient Whole-Exome Data , 2020, Genes.

[11]  G. Arno,et al.  Practical guide to genetic screening for inherited eye diseases , 2020, Therapeutic advances in ophthalmology.

[12]  Rachel L. Taylor,et al.  Clinical utility of genetic testing in 201 preschool children with inherited eye disorders , 2019, Genetics in Medicine.

[13]  Mohammad Sayad Haghighi,et al.  Legal framework for health cloud: A systematic review , 2019, Int. J. Medical Informatics.

[14]  Benjamin Neale,et al.  Genomic Risk Prediction of Coronary Artery Disease in 480,000 Adults Implications for Primary Prevention , 2019 .

[15]  A. Torkamani,et al.  Artificial intelligence in clinical and genomic diagnostics , 2019, Genome Medicine.

[16]  M. Michaelides,et al.  Macular dystrophies: clinical and imaging features, molecular genetics and therapeutic options , 2019, British Journal of Ophthalmology.

[17]  J. Wiggs,et al.  Clinical implications of recent advances in primary open-angle glaucoma genetics , 2019, Eye.

[18]  Ali Sunyaev,et al.  Context matters: A review of the determinant factors in the decision to adopt cloud computing in healthcare , 2019, Int. J. Inf. Manag..

[19]  Stephanie B. Johnson,et al.  Rethinking the ethical principles of genomic medicine services , 2019, European Journal of Human Genetics.

[20]  Qi Yan,et al.  Deep-learning-based Prediction of Late Age-Related Macular Degeneration Progression , 2019, Nat. Mach. Intell..

[21]  R. Collin,et al.  Molecular Therapies for Inherited Retinal Diseases—Current Standing, Opportunities and Challenges , 2019, Genes.

[22]  Anit Kumar Sahu,et al.  Federated Learning: Challenges, Methods, and Future Directions , 2019, IEEE Signal Processing Magazine.

[23]  J. Sellors,et al.  The advantages of UK Biobank's open‐access strategy for health research , 2019, Journal of internal medicine.

[24]  Alexis B. Carter,et al.  Considerations for Genomic Data Privacy and Security when Working in the Cloud. , 2019, The Journal of molecular diagnostics : JMD.

[25]  K. Tsunoda,et al.  Prediction of Causative Genes in Inherited Retinal Disorders from Spectral-Domain Optical Coherence Tomography Utilizing Deep Learning Techniques , 2019, Journal of ophthalmology.

[26]  Yuan Tian,et al.  The Personal Genome Project-UK, an open access resource of human multi-omics data , 2019, bioRxiv.

[27]  Ryan L. Collins,et al.  The mutational constraint spectrum quantified from variation in 141,456 humans , 2020, Nature.

[28]  M. García-Closas,et al.  BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors , 2019, Genetics in Medicine.

[29]  Rachel Thompson,et al.  An ontological foundation for ocular phenotypes and rare eye diseases , 2019, Orphanet Journal of Rare Diseases.

[30]  Eric J Topol,et al.  High-performance medicine: the convergence of human and artificial intelligence , 2019, Nature Medicine.

[31]  Jeffrey Braithwaite,et al.  Integrating Genomics into Healthcare: A Global Responsibility. , 2019, American journal of human genetics.

[32]  Constantinos Patsakis,et al.  Backups and the right to be forgotten in the GDPR: An uneasy relationship , 2018, Comput. Law Secur. Rev..

[33]  Jeffrey Soar,et al.  Cloud computing-enabled healthcare opportunities, issues, and applications: A systematic review , 2018, Int. J. Inf. Manag..

[34]  Tudor Groza,et al.  Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources , 2018, Nucleic Acids Res..

[35]  Borut Peterlin,et al.  PEDIA: prioritization of exome data by image analysis , 2018, Genetics in Medicine.

[36]  R. Crutzen,et al.  Why and how we should care about the General Data Protection Regulation , 2018, Psychology & health.

[37]  Helen E. Parkinson,et al.  The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019 , 2018, Nucleic Acids Res..

[38]  Aaron Y. Lee,et al.  Artificial intelligence and deep learning in ophthalmology , 2018, British Journal of Ophthalmology.

[39]  A. D. den Hollander,et al.  Genetic screening for macular dystrophies in patients clinically diagnosed with dry age‐related macular degeneration , 2018, Clinical genetics.

[40]  Melissa Haendel,et al.  ClinGen advancing genomic data‐sharing standards as a GA4GH driver project , 2018, Human mutation.

[41]  Joel N Hirschhorn,et al.  Burden Testing of Rare Variants Identified through Exome Sequencing via Publicly Available Control Data. , 2018, American journal of human genetics.

[42]  P. Donnelly,et al.  The UK Biobank resource with deep phenotyping and genomic data , 2018, Nature.

[43]  David A Mackey,et al.  Current state and future prospects of artificial intelligence in ophthalmology: a review , 2018, Clinical & experimental ophthalmology.

[44]  Thomas Colthurst,et al.  A universal SNP and small-indel variant caller using deep neural networks , 2018, Nature Biotechnology.

[45]  K. Nakanishi,et al.  A review of clinical characteristics and genetic backgrounds in Alport syndrome , 2018, Clinical and Experimental Nephrology.

[46]  Jonathan P. Beauchamp,et al.  Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals , 2018, Nature Genetics.

[47]  Daniel E. Runcie,et al.  Fast and flexible linear mixed models for genome-wide genetics , 2018, bioRxiv.

[48]  Nikolas Pontikos,et al.  Phenogenon: Gene to Phenotype Associations for Rare Genetic Diseases , 2018, bioRxiv.

[49]  Jie Ding,et al.  Expert consensus guidelines for the genetic diagnosis of Alport syndrome , 2018, Pediatric nephrology (Berlin, West).

[50]  S. Kingsmore,et al.  Meta-analysis of the diagnostic and clinical utility of genome and exome sequencing and chromosomal microarray in children with suspected genetic diseases , 2018, npj Genomic Medicine.

[51]  The 100 000 Genomes Project: bringing whole genome sequencing to the NHS , 2018, British Medical Journal.

[52]  C. Turnbull,et al.  Introducing whole-genome sequencing into routine cancer care: the Genomics England 100 000 Genomes Project. , 2018, Annals of oncology : official journal of the European Society for Medical Oncology.

[53]  Manuel Corpas,et al.  Personal Genome Project UK (PGP-UK): a research and citizen science hybrid project in support of personalized medicine , 2018, BMC Medical Genomics.

[54]  D. N. Adam,et al.  A Review of Fabry Disease. , 2018, Skin therapy letter.

[55]  Carolyn D Applegate,et al.  Enrolling Genomics Research Participants through a Clinical Setting: the Impact of Existing Clinical Relationships on Informed Consent and Expectations for Return of Research Results , 2018, Journal of Genetic Counseling.

[56]  Michael Snyder,et al.  Cloud-based interactive analytics for terabytes of genomic variants data , 2017, Bioinform..

[57]  J. Shendure,et al.  DNA sequencing at 40: past, present and future , 2017, Nature.

[58]  D. Athanasiou,et al.  The molecular and cellular basis of rhodopsin retinitis pigmentosa reveals potential strategies for therapy , 2017, Progress in Retinal and Eye Research.

[59]  Kathleen A. Marshall,et al.  Efficacy and safety of voretigene neparvovec (AAV2-hRPE65v2) in patients with RPE65-mediated inherited retinal dystrophy: a randomised, controlled, open-label, phase 3 trial , 2017, The Lancet.

[60]  Stephanie Halford,et al.  Phenopolis: an open platform for harmonization and analysis of genetic and phenotypic data , 2017, Bioinform..

[61]  A. L. Solebo,et al.  Epidemiology of blindness in children , 2017, Archives of Disease in Childhood.

[62]  Paolo Di Tommaso,et al.  Nextflow enables reproducible computational workflows , 2017, Nature Biotechnology.

[63]  David A. Chambers,et al.  The current state of implementation science in genomic medicine: opportunities for improvement , 2017, Genetics in Medicine.

[64]  E. Lavezzo,et al.  Identification of novel X-linked gain-of-function RPGR-ORF15 mutation in Italian family with retinitis pigmentosa and pathologic myopia , 2016, Scientific Reports.

[65]  W. Chung,et al.  Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SF v2.0): a policy statement of the American College of Medical Genetics and Genomics , 2016, Genetics in Medicine.

[66]  Shaheen N. Khan,et al.  Pathogenic mutations in TULP1 responsible for retinitis pigmentosa identified in consanguineous familial cases , 2016, Molecular vision.

[67]  Shaheen N. Khan,et al.  Loss of function mutations in RP1 are responsible for retinitis pigmentosa in consanguineous familial cases , 2016, Molecular vision.

[68]  J. McPherson,et al.  Coming of age: ten years of next-generation sequencing technologies , 2016, Nature Reviews Genetics.

[69]  Victor I. Chang,et al.  A model to compare cloud and non-cloud storage of Big Data , 2016, Future Gener. Comput. Syst..

[70]  F. Cunningham,et al.  The Ensembl Variant Effect Predictor , 2016, bioRxiv.

[71]  Alan Gray,et al.  A new tool called DISSECT for analysing large genomic data sets using a Big Data approach , 2015, Nature Communications.

[72]  Michael R. Crusoe,et al.  Common Workflow Language , 2015 .

[73]  Chris Mungall,et al.  The Matchmaker Exchange API: Automating Patient Matching Through the Exchange of Structured Phenotypic and Genotypic Profiles , 2015, Human mutation.

[74]  J. D. Watson,et al.  Human Genome Project: Twenty-five years of big biology , 2015, Nature.

[75]  Heidi L Rehm,et al.  ClinGen--the Clinical Genome Resource. , 2015, The New England journal of medicine.

[76]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[77]  Hans-Ulrich Prokosch,et al.  A scoping review of cloud computing in healthcare , 2015, BMC Medical Informatics and Decision Making.

[78]  Bale,et al.  Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology , 2015, Genetics in Medicine.

[79]  Kang Zhang,et al.  PHARMACOGENOMICS OF RESPONSE TO ANTI-VEGF THERAPY IN EXUDATIVE AGE-RELATED MACULAR DEGENERATION , 2015, Retina.

[80]  Q. Nguyen,et al.  Diabetic retinopathy: variations in patient therapeutic outcomes and pharmacogenomics , 2014, Pharmacogenomics and personalized medicine.

[81]  Gerald Liew,et al.  A comparison of the causes of blindness certifications in England and Wales in working age adults (16–64 years), 1999–2000 with 2009–2010 , 2014, BMJ Open.

[82]  Mustafa Tekin,et al.  The promise of whole-exome sequencing in medical genetics , 2013, Journal of Human Genetics.

[83]  A. L. Solebo,et al.  Epidemiology, aetiology and management of visual impairment in children , 2013, Archives of Disease in Childhood.

[84]  Emily H Turner,et al.  Actionable, pathogenic incidental findings in 1,000 participants' exomes. , 2013, American journal of human genetics.

[85]  Mauricio O. Carneiro,et al.  From FastQ Data to High‐Confidence Variant Calls: The Genome Analysis Toolkit Best Practices Pipeline , 2013, Current protocols in bioinformatics.

[86]  Hutton M. Kearney,et al.  ACMG Standards and Guidelines for constitutional cytogenomic microarray analysis, including postnatal and prenatal applications: revision 2013 , 2013, Genetics in Medicine.

[87]  Megan Allyse,et al.  Not-so-incidental findings: the ACMG recommendations on the reporting of incidental findings in clinical whole genome and whole exome sequencing. , 2013, Trends in biotechnology.

[88]  Paul G Nagy,et al.  Cloud computing in medical imaging. , 2013, Medical physics.

[89]  Marc S. Williams,et al.  ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing , 2013, Genetics in Medicine.

[90]  Susan M Wolf,et al.  Patient Autonomy and Incidental Findings in Clinical Genomics , 2013, Science.

[91]  Dan M. Roden,et al.  Implementing genomic medicine in the clinic: the future is here , 2013, Genetics in Medicine.

[92]  Bjarni J. Vilhjálmsson,et al.  A mixed-model approach for genome-wide association studies of correlated traits in structured populations , 2012, Nature Genetics.

[93]  Hong Zhao,et al.  Data Security and Privacy Protection Issues in Cloud Computing , 2012, 2012 International Conference on Computer Science and Electronics Engineering.

[94]  Christian Gilissen,et al.  Next-generation genetic testing for retinitis pigmentosa , 2012, Human mutation.

[95]  Robert C. Green,et al.  Managing incidental findings and research results in genomic research involving biobanks and archived data sets , 2012, Genetics in Medicine.

[96]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[97]  B. Shastry Pharmacogenomics in ophthalmology. , 2011, Discovery medicine.

[98]  Richard M Weinshilboum,et al.  Genomics and drug response. , 2011, The New England journal of medicine.

[99]  Xi Chen,et al.  An efficient hierarchical generalized linear mixed model for pathway analysis of genome-wide association studies , 2011, Bioinform..

[100]  Leslie G Biesecker,et al.  Consensus statement: chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies. , 2010, American journal of human genetics.

[101]  H. Kang,et al.  Variance component model to account for sample structure in genome-wide association studies , 2010, Nature Genetics.

[102]  A. McGuire,et al.  Research ethics and the challenge of whole-genome sequencing , 2008, Nature Reviews Genetics.

[103]  M. Khoury,et al.  The continuum of translation research in genomic medicine: how can we accelerate the appropriate integration of human genome discoveries into health care and disease prevention? , 2007, Genetics in Medicine.

[104]  D. Driscoll,et al.  Indications for genetic referral: a guide for healthcare providers , 2007, Genetics in Medicine.

[105]  R. Weinshilboum,et al.  Pharmacogenetics and pharmacogenomics: development, science, and translation. , 2006, Annual review of genomics and human genetics.

[106]  R. Altman,et al.  The incidentalome: a threat to genomic medicine. , 2006, JAMA.

[107]  A. Swaroop,et al.  Clinical and immunohistochemical evidence for an X linked retinitis pigmentosa syndrome with recurrent infections and hearing loss in association with an RPGR mutation , 2003, Journal of medical genetics.

[108]  Francis S. Collins,et al.  Genomic medicine--a primer. , 2002, The New England journal of medicine.

[109]  J. Weil Genetic counselling in the era of genomic medicine , 2002, EMBO reports.

[110]  Bethan E. Hoskins,et al.  Triallelic Inheritance in Bardet-Biedl Syndrome, a Mendelian Recessive Disorder , 2001, Science.

[111]  F. Collins,et al.  Shattuck lecture--medical and societal consequences of the Human Genome Project. , 1999, The New England journal of medicine.

[112]  M. Crawford The Human Genome Project. , 1990, Human biology.

[113]  D. Mccormick Sequence the Human Genome , 1986, Bio/Technology.

[114]  R Dulbecco,et al.  A turning point in cancer research: sequencing the human genome. , 1986, Science.

[115]  J. François [Juvenile macular degenerations]. , 1974, Archives d'ophtalmologie et revue generale d'ophtalmologie.

[116]  Emily Jefferson,et al.  The challenges of assembling, maintaining and making available large data sets of clinical data for research , 2019, Computational Retinal Image Analysis.

[117]  Mahavir Singh,et al.  Genes and genetics in eye diseases: a genomic medicine approach for investigating hereditary and inflammatory ocular disorders. , 2018, International journal of ophthalmology.

[118]  Shai Halevi,et al.  Homomorphic Encryption , 2017, Tutorials on the Foundations of Cryptography.

[119]  張正儀,et al.  基於Google Cloud Platform設計高效能日誌分析平台之研究 , 2017 .

[120]  E. Bertini,et al.  'Behr syndrome' with OPA1 compound heterozygote mutations. , 2015, Brain : a journal of neurology.

[121]  Sven Rahmann,et al.  Genome analysis , 2022 .

[122]  P. Charbel Issa,et al.  [Gene therapy for retinal dystrophies]. , 2012, Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft.

[123]  Carl Eklund,et al.  National Institute for Standards and Technology , 2009, Encyclopedia of Biometrics.

[124]  International Human Genome Sequencing Consortium Initial sequencing and analysis of the human genome , 2001, Nature.

[125]  S. Daiger,et al.  Prevalence of mutations causing retinitis pigmentosa and other inherited retinopathies , 2001, Human mutation.

[126]  Elizabeth M. Smigielski,et al.  dbSNP: the NCBI database of genetic variation , 2001, Nucleic Acids Res..