Cloud-based genomics pipelines for ophthalmology: reviewed from research to clinical practice
Vol. 3 No. 1 (2021), Review Articles
Vol. 3 No. 1 (2021)

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

Review Articles
https://doi.org/10.35119/maio.v3i1.115
Published 2021-09-17
David C.S. Wong
School of Clinical Medicine, University of Cambridge, Cambridge, UK
https://orcid.org/0000-0002-1712-9527
Maximiliano Olivera
Moorfields Eye Hospital NHS Foundation Trust, London, UK
https://orcid.org/0000-0001-8446-1469
Jing Yu
Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
https://orcid.org/0000-0001-8001-8494
Anita Szabo
Institute of Ophthalmology, University College London, London UK
https://orcid.org/0000-0002-8912-897X
Ismail Moghul
UCL Cancer Institute, University College London, London, UK
https://orcid.org/0000-0003-3653-2327
Konstantinos Balaskas
Moorfields Eye Hospital NHS Foundation Trust, London, UK; Institute of Ophthalmology, University College London, London UK
https://orcid.org/0000-0002-7690-6277
Robert Luben
Moorfields Eye Hospital NHS Foundation Trust, London, UK; Institute of Ophthalmology, University College London, London UK
https://orcid.org/0000-0002-5088-6343
Anthony P. Khawaja
Moorfields Eye Hospital NHS Foundation Trust, London, UK
https://orcid.org/0000-0001-6802-8585
Nikolas Pontikos
Moorfields Eye Hospital NHS Foundation Trust, London, UK; Institute of Ophthalmology, University College London, London UK
https://orcid.org/0000-0003-1782-4711
Pearse A. Keane
Moorfields Eye Hospital NHS Foundation Trust, London, UK; Institute of Ophthalmology, University College London, London UK
https://orcid.org/0000-0002-9239-745X
115 PDF

How to Cite

1.
Wong DC, Olivera M, Yu J, Szabo A, Moghul I, Balaskas K, Luben R, Khawaja AP, Pontikos N, Keane PA. Cloud-based genomics pipelines for ophthalmology: reviewed from research to clinical practice. MAIO [Internet]. 2021 Sep. 17 [cited 2024 Apr. 18];3(1):101-40. Available from: https://www.maio-journal.com/index.php/MAIO/article/view/115
Vancouver

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Copyright notice

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Copyright (c) 2021 David C.S. Wong, Maximiliano Olivera, Jing Yu, Anita Szabo, Ismail Moghul, Konstantinos Balaskas, Robert Luben, Anthony P. Khawaja, Nikolas Pontikos, Pearse A. Keane

Keywords

clinical genomics; cloud computing; hospital; ophthalmology; sequencing

Abstract

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.

https://doi.org/10.35119/maio.v3i1.115
115 PDF

References

Dulbecco R. A turning point in cancer research: sequencing the human genome. Science [Internet]. 1986 Mar 7;231(4742):1055–6. https://doi.org/10.1126/science.3945817

Collins FS. Shattuck lecture--medical and societal consequences of the Human Genome Project. N Engl J Med [Internet]. 1999 Jul 1;341(1):28–37. https://doi.org/10.1056/NEJM199907013410106

Guttmacher AE, Collins FS. Genomic medicine--a primer. N Engl J Med [Internet]. 2002 Nov 7;347(19):1512–20. https://doi.org/10.1056/NEJMra012240

Manolio TA, Chisholm RL, Ozenberger B, et al. Implementing genomic medicine in the clinic: the future is here. Genet Med [Internet]. 2013; Apr;15(4):258–67. https://doi.org/10.1038/gim.2012.157

Roberts MC, Kennedy AE, Chambers DA, Khoury MJ. The current state of implementation science in genomic medicine: opportunities for improvement. Genet Med [Internet]. 2017 Aug;19(8):858–63. https://doi.org/10.1038/gim.2016.210

Venter JC, Adams MD, Myers EW, et al. The sequence of the human genome. Science [Internet]. 2001 Feb 16;291(5507):1304–51. https://doi.org/10.1126/science.1058040

Lander ES, Linton LM, Birren B, et al. Initial sequencing and analysis of the human genome. Nature [Internet]. 2001 Feb 15;409(6822):860–921. https://doi.org/10.1038/35057062

Khoury MJ, Gwinn M, Yoon PW, Dowling N, Moore CA, Bradley L. 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? Genet Med [Internet]. 2007 Oct;9(10):665–74. https://doi.org/10.1097/GIM.0b013e31815699d0

Kalia SS, Adelman K, Bale SJ, 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. Genet Med [Internet]. 2017 Feb;19(2):249–55. https://doi.org/10.1038/gim.2016.190

Green RC, Berg JS, Grody WW, Kalia SS, Korf BR, Martin CL, et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet Med [Internet]. 2013 Jul;15(7):565–74. https://doi.org/10.1038/gim.2013.73

Russell S, Bennett J, Wellman JA, Chung DC, Yu Z-F, Tillman A, 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. Lancet [Internet]. 2017 Aug 26;390(10097):849–60. https://doi.org/10.1016/S0140-6736(17)31868-8

Dorschner MO, Amendola LM, Turner EH, et al. Actionable, pathogenic incidental findings in 1,000 participants’ exomes. Am J Hum Genet [Internet]. 2013 Oct 3;93(4):631–40. https://doi.org/10.1016/j.ajhg.2013.08.006

Turro E, Astle WJ, Megy K, et al. Whole-genome sequencing of patients with rare diseases in a national health system. Nature [Internet]. 2020 Jul;583(7814):96–102. https://doi.org/10.1038/s41586-020-2434-2

Griebel L, Prokosch H-U, Köpcke F, et al. A scoping review of cloud computing in healthcare. BMC Med Inform Decis Mak [Internet]. 2015 Mar 19;15:17. https://doi.org/10.1186/s12911-015-0145-7

Ali O, Shrestha A, Soar J, Wamba SF. Cloud computing-enabled healthcare opportunities, issues, and applications: A systematic review. Int J Inf Manage [Internet]. 2018 Dec 1;43:146–58. Available from: http://www.sciencedirect.com/science/article/pii/S0268401218303736

Gao F, Sunyaev A. Context matters: A review of the determinant factors in the decision to adopt cloud computing in healthcare. Int J Inf Manage [Internet]. 2019 Oct 1;48:120–38. Available from: http://www.sciencedirect.com/science/article/pii/S0268401218307266

Zandesh Z, Ghazisaeedi M, Devarakonda MV, Haghighi MS. Legal framework for health cloud: A systematic review. Int J Med Inform [Internet]. 2019 Dec;132:103953. https://doi.org/10.1016/j.ijmedinf.2019.103953

Mell P, Grance T. The NIST definition of cloud computing [Internet]. National Institute of Standards and Technology; 2011. Report No.: 800-145. Available from: https://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-145.pdf

UK Government Cloud First policy [Internet]. Government Digital Service; 2017 Feb [cited 2021 Mar 3]. Available from: https://www.gov.uk/guidance/government-cloud-first-policy

Internet First Policy - NHS Digital [Internet]. NHS Digital; 2020 Jan [cited 2021 Mar 3]. Available from: https://digital.nhs.uk/services/internet-first/policy

Kurian T. How Google and Mayo Clinic will transform the future of healthcare [Internet]. 2019 [cited 2020 Dec 22]. Available from: https://cloud.google.com/blog/topics/customers/how-google-and-mayo-clinic-will-transform-the-future-of-healthcare

Sheffi J, Vaisipour S. Accelerating Mayo Clinic’s data platform with BigQuery and Variant Transforms [Internet]. 2020 [cited 2020 Dec 22]. Available from: https://cloud.google.com/blog/products/data-analytics/genome-data-analytics-with-google-cloud

Singh M, Tyagi SC. Genes and genetics in eye diseases: a genomic medicine approach for investigating hereditary and inflammatory ocular disorders. Int J Ophthalmol [Internet]. 2018 Jan 18;11(1):117–34. https://doi.org/10.18240/ijo.2018.01.20

Shendure J, Balasubramanian S, Church GM, Gilbert W, Rogers J, Schloss JA, et al. DNA sequencing at 40: past, present and future. Nature [Internet]. 2017 Oct 19;550(7676):345–53. https://doi.org/10.1038/nature24286

Goodwin S, McPherson JD, McCombie WR. Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet [Internet]. 2016 May 17;17(6):333–51. https://doi.org/10.1038/nrg.2016.49

Liew G, Michaelides M, Bunce C. A comparison of the causes of blindness certifications in England and Wales in working age adults (16-64 years), 1999-2000 with 2009-2010. BMJ Open [Internet]. 2014 Feb 12;4(2):e004015. https://doi.org/10.1136/bmjopen-2013-004015

Solebo AL, Rahi J. Epidemiology, aetiology and management of visual impairment in children. Arch Dis Child [Internet]. 2014 Apr;99(4):375–9. https://doi.org/10.1136/archdischild-2012-303002

Solebo AL, Teoh L, Rahi J. Epidemiology of blindness in children. Arch Dis Child [Internet]. 2017 Sep;102(9):853–7. https://doi.org/10.1136/archdischild-2016-310532

Sohocki MM, Daiger SP, Bowne SJ, et al. Prevalence of mutations causing retinitis pigmentosa and other inherited retinopathies. Hum Mutat [Internet]. 2001;17(1):42–51. Available from: https://doi.org/10.1002/1098-1004(2001)17:1<42::AID-HUMU5>3.0.CO;2-K

Vázquez-Domínguez I, Garanto A, Collin RWJ. Molecular Therapies for Inherited Retinal Diseases-Current Standing, Opportunities and Challenges. Genes [Internet]. 2019 Aug 28;10(9). https://doi.org/10.3390/genes10090654

Neveling K, Collin RWJ, Gilissen C, et al. Next-generation genetic testing for retinitis pigmentosa. Hum Mutat [Internet]. 2012 Jun;33(6):963–72. https://doi.org/10.1002/humu.22045

Méjécase C, Malka S, Guan Z, Slater A, Arno G, Moosajee M. Practical guide to genetic screening for inherited eye diseases. Ther Adv Ophthalmol [Internet]. 2020 Jan;12:2515841420954592. https://doi.org/10.1177/2515841420954592

Bycroft C, Freeman C, Petkova D, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature [Internet]. 2018 Oct;562(7726):203–9. https://doi.org/10.1038/s41586-018-0579-z

Turnbull C, Scott RH, Thomas E, et al. The 100 000 Genomes Project: bringing whole genome sequencing to the NHS. BMJ [Internet]. 2018 Apr 24;361:k1687. https://doi.org/10.1136/bmj.k1687

Stark Z, Dolman L, Manolio TA, et al. Integrating Genomics into Healthcare: A Global Responsibility. Am J Hum Genet [Internet]. 2019 Jan 3;104(1):13–20. https://doi.org/10.1016/j.ajhg.2018.11.014

Turnbull C. Introducing whole-genome sequencing into routine cancer care: the Genomics England 100 000 Genomes Project. Ann Oncol [Internet]. 2018 Apr 1;29(4):784–7. https://doi.org/10.1093/annonc/mdy054

Pletcher BA, Toriello HV, Noblin SJ, et al. Indications for genetic referral: a guide for healthcare providers. Genet Med [Internet]. 2007 Jun;9(6):385–9. https://doi.org/10.1097/gim.0b013e318064e70c

Ali MU, Rahman MSU, Cao J, Yuan PX. Genetic characterization and disease mechanism of retinitis pigmentosa; current scenario. 3 Biotech [Internet]. 2017 Aug;7(4):251. https://doi.org/10.1007/s13205-017-0878-3

Parmeggiani F, Barbaro V, De Nadai K, et al. Identification of novel X-linked gain-of-function RPGR-ORF15 mutation in Italian family with retinitis pigmentosa and pathologic myopia. Sci Rep [Internet]. 2016 Dec 20;6:39179. https://doi.org/10.1038/srep39179

Kabir F, Ullah I, Ali S, et al. Loss of function mutations in RP1 are responsible for retinitis pigmentosa in consanguineous familial cases. Mol Vis [Internet]. 2016 Jun 10;22:610–25. Available from: https://www.ncbi.nlm.nih.gov/pubmed/27307693

Ullah I, Kabir F, Iqbal M, et al. Pathogenic mutations in TULP1 responsible for retinitis pigmentosa identified in consanguineous familial cases. Mol Vis [Internet]. 2016 Jul 16;22:797–815. Available from: https://www.ncbi.nlm.nih.gov/pubmed/27440997

Athanasiou D, Aguila M, Bellingham J, et al. The molecular and cellular basis of rhodopsin retinitis pigmentosa reveals potential strategies for therapy. Prog Retin Eye Res [Internet]. 2018 Jan 1;62:1–23. Available from: http://www.sciencedirect.com/science/article/pii/S1350946217300769

Sun Y, Li W, Li J-K, et al. Genetic and clinical findings of panel-based targeted exome sequencing in a northeast Chinese cohort with retinitis pigmentosa. Molecular genetics & genomic medicine [Internet]. 2020;8(4):e1184. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/mgg3.1184

Iannaccone A, Breuer DK, Wang XF, 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. J Med Genet. 2003 Nov;40(11):e118. https://doi.org/10.1136/jmg.40.11.e118.

Whatley M, Francis A, Ng ZY, et al. Usher Syndrome: Genetics and Molecular Links of Hearing Loss and Directions for Therapy. Front Genet [Internet]. 2020 Oct 22;11:565216. https://doi.org/10.3389/fgene.2020.565216

Carelli V, Sabatelli M, Carrozzo R, et al. “Behr syndrome” with OPA1 compound heterozygote mutations. Brain [Internet]. 2015 Jan;138(Pt 1):e321. https://doi.org/10.1093/brain/awu234

Katsanis N, Ansley SJ, Badano JL, et al. Triallelic inheritance in Bardet-Biedl syndrome, a Mendelian recessive disorder. Science [Internet]. 2001 Sep 21;293(5538):2256–9. https://doi.org/10.1126/science.1063525

Kersten E, Geerlings MJ, Pauper M, et al. Genetic screening for macular dystrophies in patients clinically diagnosed with dry age-related macular degeneration. Clin Genet [Internet]. 2018;94(6):569–74. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1111/cge.13447

Altschwager P, Ambrosio L, Swanson EA, Moskowitz A, Fulton AB. Juvenile Macular Degenerations. Semin Pediatr Neurol [Internet]. 2017 May;24(2):104–9. https://doi.org/10.1016/j.spen.2017.05.005

Rahman N, Georgiou M, Khan KN, Michaelides M. Macular dystrophies: clinical and imaging features, molecular genetics and therapeutic options. Br J Ophthalmol [Internet]. 2020 Apr;104(4):451–60. https://doi.org/10.1136/bjophthalmol-2019-315086

Chan B, Adam DN. A Review of Fabry Disease. Skin Therapy Lett [Internet]. 2018 Mar;23(2):4–6. Available from: https://www.ncbi.nlm.nih.gov/pubmed/29562089

Nozu K, Nakanishi K, Abe Y, et al. A review of clinical characteristics and genetic backgrounds in Alport syndrome. Clin Exp Nephrol [Internet]. 2019 Feb;23(2):158–68. https://doi.org/10.1007/s10157-018-1629-4

Savige J, Ariani F, Mari F, et al. Expert consensus guidelines for the genetic diagnosis of Alport syndrome. Pediatr Nephrol [Internet]. 2019 Jul;34(7):1175–89. https://doi.org/10.1007/s00467-018-3985-4

Retina International. The socioeconomic impact of inherited retinal dystrophies [Internet]. 2019 [cited 2021 Jul 13]. Available from: https://www2.deloitte.com/au/en/pages/economics/articles/socioeconomic-impact-inherited-retinal-dystrophies.html

Lenassi E, Clayton-Smith J, Douzgou S, et al. Clinical utility of genetic testing in 201 preschool children with inherited eye disorders. Genet Med [Internet]. 2020 Apr;22(4):745–51. https://doi.org/10.1038/s41436-019-0722-8

NHS National Genomic Test Directory [Internet]. NHS England. 2020 [cited 2021 Mar 2]. Available from: https://www.england.nhs.uk/publication/national-genomic-test-directories/

Berrios C, James CA, Raraigh Ket 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. J Genet Couns [Internet]. 2018 Feb;27(1):263–73. https://doi.org/10.1007/s10897-017-0143-2

Patch C, Middleton A. Genetic counselling in the era of genomic medicine. Br Med Bull [Internet]. 2018 Jun 1;126(1):27–36. https://doi.org/10.1093/bmb/ldy008

Report of the Joint Committee on Genomics in Medicine. Consent and confidentiality in genomic medicine: Guidance on the use of genetic and genomic information in the clinic [Internet]. Third Edition. RCP, RCPath and BSGM; 2019. Available from: https://www.rcplondon.ac.uk/projects/outputs/consent-and-confidentiality-genomic-medicine

Wolf SM, Annas GJ, Elias S. Point-counterpoint. Patient autonomy and incidental findings in clinical genomics. Science [Internet]. 2013 May 31;340(6136):1049–50. https://doi.org/10.1126/science.1239119

Fujinami-Yokokawa Y, Pontikos N, Yang L, et al. Prediction of Causative Genes in Inherited Retinal Disorders from Spectral-Domain Optical Coherence Tomography Utilizing Deep Learning Techniques. J Ophthalmol [Internet]. 2019 Apr 9;2019:1691064. https://doi.org/10.1155/2019/1691064

Cipriani V, Pontikos N, Arno G, et al. An Improved Phenotype-Driven Tool for Rare Mendelian Variant Prioritization: Benchmarking Exomiser on Real Patient Whole-Exome Data. Genes [Internet]. 2020 Apr 23;11(4). https://doi.org/10.3390/genes11040460

Pontikos N, Murphy C, Moghul I, et al. Phenogenon: Gene to phenotype associations for rare genetic diseases. PLoS One [Internet]. 2020 Apr 9;15(4):e0230587. https://doi.org/10.1371/journal.pone.0230587

Köhler S, Carmody L, Vasilevsky N, et al. Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources. Nucleic Acids Res [Internet]. 2019 Jan 8;47(D1):D1018–27. https://doi.org/10.1093/nar/gky1105

Sergouniotis PI, Maxime E, Leroux D, et al. An ontological foundation for ocular phenotypes and rare eye diseases. Orphanet J Rare Dis [Internet]. 2019 Jan 9;14(1):8. https://doi.org/10.1186/s13023-018-0980-6

Miller DT, Adam MP, Aradhya S, et al. Consensus statement: chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies. Am J Hum Genet [Internet]. 2010 May 14;86(5):749–64. https://doi.org/10.1016/j.ajhg.2010.04.006

South ST, Lee C, Lamb AN, Higgins AW, Kearney HM, Working Group for the American College of Medical Genetics and Genomics Laboratory Quality Assurance Committee. ACMG Standards and Guidelines for constitutional cytogenomic microarray analysis, including postnatal and prenatal applications: revision 2013. Genet Med [Internet]. 2013 Nov;15(11):901–9. https://doi.org/10.1038/gim.2013.129

Clark MM, Stark Z, Farnaes L, et al. Meta-analysis of the diagnostic and clinical utility of genome and exome sequencing and chromosomal microarray in children with suspected genetic diseases. NPJ Genom Med [Internet]. 2018 Jul 9;3:16. https://doi.org/10.1038/s41525-018-0053-8

Rabbani B, Tekin M, Mahdieh N. The promise of whole-exome sequencing in medical genetics. J Hum Genet [Internet]. 2014 Jan;59(1):5–15. https://doi.org/10.1038/jhg.2013.114

Ziccardi L, Cordeddu V, Gaddini L, et al. Gene Therapy in Retinal Dystrophies. Int J Mol Sci [Internet]. 2019 Nov 14;20(22). https://doi.org/10.3390/ijms20225722

US National Library of Medicine, National Institue of Health, Clinical Trials [Internet]. [cited 2021 Jan 22]. Available from: https://www.clinicaltrials.gov

Carter AB. Considerations for Genomic Data Privacy and Security when Working in the Cloud. J Mol Diagn [Internet]. 2019 Jul;21(4):542–52. https://doi.org/10.1016/j.jmoldx.2018.07.009

Van der Auwera G. DRAGEN-GATK Update: Let’s get more specific [Internet]. 2021 [cited 2021 Mar 2]. Available from: https://gatk.broadinstitute.org/hc/en-us/articles/360039984151-DRAGEN-GATK-Update-Let-s-get-more-specific

Van der Auwera GA, Carneiro MO, Hartl C, et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr Protoc Bioinformatics [Internet]. 2013;43:11.10.1–11.10.33. https://doi.org/10.1002/0471250953.bi1110s43

Poplin R, Chang P-C, Alexander D, et al. A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol [Internet]. 2018 Nov;36(10):983–7. https://doi.org/10.1038/nbt.4235

Hail Team. Hail Overview [Internet]. 2020 [cited 2020 Dec 22]. Available from: https://hail.is/docs/0.2/overview/index.html

McLaren W, Gil L, Hunt SE, et al. The Ensembl Variant Effect Predictor. Genome Biol [Internet]. 2016 Jun 6;17(1):122. https://doi.org/10.1186/s13059-016-0974-4

Rehm HL, Berg JS, Brooks LD, Bustamante CD, Evans JP, Landrum MJ, et al. ClinGen--the Clinical Genome Resource. N Engl J Med [Internet]. 2015 Jun 4;372(23):2235–42. https://doi.org/10.1056/NEJMsr1406261

Sherry ST, Ward MH, Kholodov M, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res [Internet]. 2001 Jan 1;29(1):308–11. https://doi.org/10.1093/nar/29.1.308

Karczewski KJ, Francioli LC, Tiao G, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature [Internet]. 2020 May;581(7809):434–43. https://doi.org/10.1038/s41586-020-2308-7

Pan C, McInnes G, Deflaux N, et al. Cloud-based interactive analytics for terabytes of genomic variants data. Bioinformatics [Internet]. 2017 Dec 1;33(23):3709–15. https://doi.org/10.1093/bioinformatics/btx468

Richards S, Aziz N, Bale S, 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. Genet Med [Internet]. 2015;17(5):405–23. Available from: https://www.nature.com/articles/gim201530?ux=07df2189-4e01-4c08-8ef3-5619cff0ca61&ux2=3739b439-66b5-4bf5-921e-0916eef236a7&ux3=&uxconf=Y

Sobreira NLM, Arachchi H, Buske OJ, et al. Matchmaker Exchange. Curr Protoc Hum Genet [Internet]. 2017 Oct 18;95:9.31.1–9.31.15. https://doi.org/10.1002/cphg.50

Buske OJ, Schiettecatte F, Hutton B, et al. The Matchmaker Exchange API: automating patient matching through the exchange of structured phenotypic and genotypic profiles. Hum Mutat [Internet]. 2015 Oct;36(10):922–7. https://doi.org/10.1002/humu.22850

Lee JJ, Wedow R, Okbay A, et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat Genet [Internet]. 2018 Jul 23;50(8):1112–21. https://doi.org/10.1038/s41588-018-0147-3

Guo MH, Plummer L, Chan Y-M, Hirschhorn JN, Lippincott MF. Burden Testing of Rare Variants Identified through Exome Sequencing via Publicly Available Control Data. Am J Hum Genet [Internet]. 2018 Oct 4;103(4):522–34. https://doi.org/10.1016/j.ajhg.2018.08.016

Wang L, Jia P, Wolfinger RD, et al. An efficient hierarchical generalized linear mixed model for pathway analysis of genome-wide association studies. Bioinformatics [Internet]. 2011 Mar 1;27(5):686–92. https://doi.org/10.1093/bioinformatics/btq728

Korte A, Vilhjálmsson BJ, Segura V, Platt A, Long Q, Nordborg M. A mixed-model approach for genome-wide association studies of correlated traits in structured populations. Nat Genet [Internet]. 2012 Sep;44(9):1066–71. https://doi.org/10.1038/ng.2376

Runcie DE, Crawford L. Fast and flexible linear mixed models for genome-wide genetics. PLoS Genet [Internet]. 2019 Feb;15(2):e1007978. https://doi.org/10.1371/journal.pgen.1007978

Kang HM, Sul JH, Service SK, et al. Variance component model to account for sample structure in genome-wide association studies. Nat Genet [Internet]. 2010 Apr;42(4):348–54. https://doi.org/10.1038/ng.548

Hardcastle AJ, Liskova P, Bykhovskaya Y, et al. A multi-ethnic genome-wide association study implicates collagen matrix integrity and cell differentiation pathways in keratoconus. Communications Biology [Internet]. 2021 Mar 1;4(1):266. https://doi.org/10.1038/s42003-021-01784-0

Buniello A, MacArthur JAL, Cerezo M, et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res [Internet]. 2019 Jan 8;47(D1):D1005–12. https://doi.org/10.1093/nar/gky1120

Dias R, Torkamani A. Artificial intelligence in clinical and genomic diagnostics. Genome Med [Internet]. 2019 Nov 19;11(1):70. https://doi.org/10.1186/s13073-019-0689-8

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature [Internet]. 2015 May 28;521(7553):436–44. Available from: https://doi.org/10.1038/nature14539

Hsieh T-C, Mensah MA, Pantel JT, et al. PEDIA: prioritization of exome data by image analysis. Genet Med [Internet]. 2019 Dec;21(12):2807–14. https://doi.org/10.1038/s41436-019-0566-2

Lee A, Mavaddat N, Wilcox AN, et al. BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors [Internet]. Vol. 21, Genetics in Medicine. 2019. p. 1708–18. https://doi.org/10.1038/s41436-018-0406-9

Inouye M, Abraham G, Nelson CP, et al. Genomic Risk Prediction of Coronary Artery Disease in 480,000 Adults: Implications for Primary Prevention. J Am Coll Cardiol [Internet]. 2018 Oct 16;72(16):1883–93. https://doi.org/10.1016/j.jacc.2018.07.079

Reel PS, Reel S, Pearson E, Trucco E, Jefferson E. Using machine learning approaches for multi-omics data analysis: A review. Biotechnol Adv [Internet]. 2021 Jul;49:107739. https://doi.org/10.1016/j.biotechadv.2021.107739

LeCun Y. 1.1 Deep Learning Hardware: Past, Present, and Future. In: 2019 IEEE International Solid- State Circuits Conference - (ISSCC) [Internet]. 2019. p. 12–9. https://doi.org/10.1109/ISSCC.2019.8662396

Broad Institute [Internet]. [cited 2021 Mar 2]. Available from: https://www.broadinstitute.org/

Sheffi J. In our genes: How Google Cloud helps the Broad Institute slash the cost of research [Internet]. Google. 2018 [cited 2020 Dec 22]. Available from: https://www.blog.google/products/google-cloud/our-genes-how-google-cloud-helps-broad-institute-slash-cost-research/

gnomAD Production Team. gnomAD v3.1 [Internet]. 2020 [cited 2020 Dec 22]. Available from: https://gnomad.broadinstitute.org/blog/2020-10-gnomad-v3-1/

Ehwerhemuepha L, Gasperino G, Bischoff N, Taraman S, Chang A, Feaster W. HealtheDataLab - a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions. BMC Med Inform Decis Mak [Internet]. 2020 Jun 19;20(1):115. https://doi.org/10.1186/s12911-020-01153-7

Chang V, Wills G. A model to compare cloud and non-cloud storage of Big Data. Future Gener Comput Syst [Internet]. 2016 Apr 1;57:56–76. Available from: https://www.sciencedirect.com/science/article/pii/S0167739X15003167

Canela-Xandri O, Law A, Gray A, Woolliams JA, Tenesa A. A new tool called DISSECT for analysing large genomic data sets using a Big Data approach. Nat Commun [Internet]. 2015 Dec 11;6:10162. https://doi.org/10.1038/ncomms10162

Köster J, Rahmann S. Snakemake--a scalable bioinformatics workflow engine. Bioinformatics [Internet]. 2012 Oct 1;28(19):2520–2. https://doi.org/10.1093/bioinformatics/bts480

Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol [Internet]. 2017 Apr 11;35(4):316–9. https://doi.org/10.1038/nbt.3820

Amstutz P, Crusoe MR, Tijanić N, Chapman B, Chilton J, Heuer M, et al. Common Workflow Language [Internet]. Common Workflow Language working group; 2016. Available from: https://w3id.org/cwl/v1.0/

Google Cloud and the Broad Institute are providing free access to the Genome Aggregation Database [Internet]. [cited 2021 Feb 26]. Available from: https://cloud.google.com/blog/topics/healthcare-life-sciences/google-cloud-providing-free-access-to-genome-aggregation-database

Collins RL, Brand H, Karczewski KJ, et al. A structural variation reference for medical and population genetics. Nature [Internet]. 2020 May;581(7809):444–51. https://doi.org/10.1038/s41586-020-2287-8

Pontikos N, Yu J, Moghul I, et al. Phenopolis: an open platform for harmonization and analysis of genetic and phenotypic data. Bioinformatics [Internet]. 2017 Aug 1;33(15):2421–3. https://doi.org/10.1093/bioinformatics/btx147

Dolman L, Page A, Babb L, et al. ClinGen advancing genomic data-sharing standards as a GA4GH driver project. Hum Mutat [Internet]. 2018 Nov;39(11):1686–9. https://doi.org/10.1002/humu.23625

Ting DSW, Pasquale LR, Peng L, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol [Internet]. 2019 Feb;103(2):167–75. https://doi.org/10.1136/bjophthalmol-2018-313173

Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med [Internet]. 2019 Jan;25(1):44–56. https://doi.org/10.1038/s41591-018-0300-7

Hogarty DT, Mackey DA, Hewitt AW. Current state and future prospects of artificial intelligence in ophthalmology: a review. Clin Experiment Ophthalmol [Internet]. 2019 Jan;47(1):128–39. https://doi.org/10.1111/ceo.13381

Esteva A, Chou K, Yeung S, et al. Deep learning-enabled medical computer vision. NPJ Digit Med [Internet]. 2021 Jan 8;4(1):5. https://doi.org/10.1038/s41746-020-00376-2

Wang SY, Pershing S, Lee AY, AAO Taskforce on AI and AAO Medical Information Technology Committee. Big data requirements for artificial intelligence. Curr Opin Ophthalmol [Internet]. 2020 Sep;31(5):318–23. https://doi.org/10.1097/ICU.0000000000000676

Kagadis GC, Kloukinas C, Moore K, et al. Cloud computing in medical imaging. Med Phys [Internet]. 2013 Jul;40(7):070901. https://doi.org/10.1118/1.4811272

Cloud Healthcare API documentation [Internet]. Google Cloud Platform. [cited 2021 Mar 2]. Available from: https://cloud.google.com/healthcare/docs

Yan Q, Weeks DE, Xin H, et al. Deep-learning-based Prediction of Late Age-Related Macular Degeneration Progression. Nat Mach Intell [Internet]. 2020 Feb;2(2):141–50. https://doi.org/10.1038/s42256-020-0154-9

Chen D, Zhao H. Data Security and Privacy Protection Issues in Cloud Computing. In: 2012 International Conference on Computer Science and Electronics Engineering [Internet]. 2012. p. 647–51. https://doi.org/10.1109/ICCSEE.2012.193

Google. Handling genomic data in the cloud [Internet]. Google Cloud Platform; 2019. Available from: https://cloud.google.com/files/genomics-data-wp.pdf

Google. Google Cloud security foundations guide [Internet]. Google Cloud Platform; 2020. Available from: https://services.google.com/fh/files/misc/google-cloud-security-foundations-guide.pdf

Cloud Security - Amazon Web Services [Internet]. Amazon Web Services. [cited 05 Mar, 2021]. Available from: https://aws.amazon.com/security/

Crutzen R, Ygram Peters G-J, Mondschein C. Why and how we should care about the General Data Protection Regulation. Psychol Health [Internet]. 2019 Nov;34(11):1347–57. https://doi.org/10.1080/08870446.2019.1606222

Politou E, Michota A, Alepis E, Pocs M, Patsakis C. Backups and the right to be forgotten in the GDPR: An uneasy relationship. Computer Law & Security Review [Internet]. 2018 Dec 1;34(6):1247–57. Available from: https://www.sciencedirect.com/science/article/pii/S0267364918301389

Li T, Sahu AK, Talwalkar A, Smith V. Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Process Mag [Internet]. 2020 May;37(3):50–60. https://doi.org/10.1109/MSP.2020.2975749

Yi X, Paulet R, Bertino E. Homomorphic Encryption. In: Yi X, Paulet R, Bertino E, editors. Homomorphic Encryption and Applications [Internet]. Cham: Springer International Publishing; 2014. p. 27–46. https://doi.org/10.1007/978-3-319-12229-8_2

Guevara M. How we’re helping developers with differential privacy [Internet]. Google Developers. 2021 [cited 2021 Mar 2]. Available from: https://developers.googleblog.com/2021/01/how-were-helping-developers-with-differential-privacy.html

Porter N, Golan G, Lugani S. Introducing Google Cloud Confidential Computing with Confidential VMs [Internet]. Google Cloud Blog. 2020 [cited 2021 Mar 2]. Available from: https://cloud.google.com/blog/products/identity-security/introducing-google-cloud-confidential-computing-with-confidential-vms

NHS and social care data: off-shoring and the use of public cloud services [Internet]. NHS Digital; 2018 Apr [cited 2021 Mar 5]. Available from: https://digital.nhs.uk/data-and-information/looking-after-information/data-security-and-information-governance/nhs-and-social-care-data-off-shoring-and-the-use-of-public-cloud-services

Hot Cloud Storage [Internet]. Wasabi. [cited 2021 Mar 7]. Available from: https://wasabi.com/hot-cloud-storage/

Wang L, McLeod HL, Weinshilboum RM. Genomics and drug response. N Engl J Med [Internet]. 2011 Mar 24;364(12):1144–53. https://doi.org/10.1056/NEJMra1010600

Weinshilboum RM, Wang L. Pharmacogenetics and pharmacogenomics: development, science, and translation. Annu Rev Genomics Hum Genet [Internet]. 2006;7:223–45. https://doi.org/10.1146/annurev.genom.6.080604.162315

Shastry BS. Pharmacogenomics in ophthalmology. Discov Med [Internet]. 2011 Aug;12(63):159–67. Available from: https://www.ncbi.nlm.nih.gov/pubmed/21878193

Dedania VS, Grob S, Zhang K, Bakri SJ. Pharmacogenomics of response to anti-VEGF therapy in exudative age-related macular degeneration. Retina [Internet]. 2015 Mar;35(3):381–91. https://doi.org/10.1097/IAE.0000000000000466

Agarwal A, Soliman MK, Sepah YJ, Do DV, Nguyen QD. Diabetic retinopathy: variations in patient therapeutic outcomes and pharmacogenomics. Pharmgenomics Pers Med [Internet]. 2014 Dec 12;7:399–409. https://doi.org/10.2147/PGPM.S52821

Jefferson ER, Trucco E. Chapter 20 - The challenges of assembling, maintaining and making available large data sets of clinical data for research. In: Trucco E, MacGillivray T, Xu Y, editors. Computational Retinal Image Analysis [Internet]. Academic Press; 2019. p. 429–44. Available from: https://www.sciencedirect.com/science/article/pii/B9780081028162000216

Conroy M, Sellors J, Effingham M, et al. The advantages of UK Biobank’s open-access strategy for health research. J Intern Med [Internet]. 2019 Oct;286(4):389–97. https://doi.org/10.1111/joim.12955

Wolf SM, Crock BN, Van Ness B, et al. Managing incidental findings and research results in genomic research involving biobanks and archived data sets. Genet Med [Internet]. 2012 Apr;14(4):361–84. https://doi.org/10.1038/gim.2012.23

McGuire AL, Caulfield T, Cho MK. Research ethics and the challenge of whole-genome sequencing. Nat Rev Genet [Internet]. 2008 Feb;9(2):152–6. https://doi.org/10.1038/nrg2302

Kohane IS, Masys DR, Altman RB. The incidentalome: a threat to genomic medicine. JAMA [Internet]. 2006 Jul 12;296(2):212–5. https://doi.org/10.1001/jama.296.2.212

McGuire AL, Joffe S, Koenig BA, et al. Point-counterpoint. Ethics and genomic incidental findings. Science [Internet]. 2013 May 31;340(6136):1047–8. https://doi.org/10.1126/science.1240156

Allyse M, Michie M. Not-so-incidental findings: the ACMG recommendations on the reporting of incidental findings in clinical whole genome and whole exome sequencing. Trends Biotechnol [Internet]. 2013 Aug;31(8):439–41. https://doi.org/10.1016/j.tibtech.2013.04.006

Johnson SB, Slade I, Giubilini A, Graham M. Rethinking the ethical principles of genomic medicine services. Eur J Hum Genet [Internet]. 2020 Feb;28(2):147–54. https://doi.org/10.1038/s41431-019-0507-1

PGP-UK Consortium. Personal Genome Project UK (PGP-UK): a research and citizen science hybrid project in support of personalized medicine. BMC Med Genomics [Internet]. 2018 Nov 27;11(1):108. https://doi.org/10.1186/s12920-018-0423-1

Chervova O, Conde L, Guerra-Assunção JA, et al. The Personal Genome Project-UK, an open access resource of human multi-omics data. Sci Data [Internet]. 2019 Oct 31;6(1):257. https://doi.org/10.1038/s41597-019-0205-4

Choquet H, Wiggs JL, Khawaja AP. Clinical implications of recent advances in primary open-angle glaucoma genetics. Eye [Internet]. 2020 Jan;34(1):29–39. https://doi.org/10.1038/s41433-019-0632-7

The Human Genome Project [Internet]. [cited 2021 Jan 22]. Available from: https://www.genome.gov/human-genome-project

Green ED, Watson JD, Collins FS. Human Genome Project: Twenty-five years of big biology. Nature [Internet]. 2015 Oct 1;526(7571):29–31. Available from: https://doi.org/10.1038/526029a

Humphries C. A Moore’s Law for Genetics [Internet]. [cited 2021 Jan 22]. Available from: https://www.technologyreview.com/2010/02/23/205915/a-moores-law-for-genetics/

Wetterstrand KA. DNA Sequencing Costs: Data [Internet]. National Human Genome Research Institute. [cited 2021 Mar 5]. Available from: https://www.genome.gov/about-genomics/fact-sheets/DNA-Sequencing-Costs-Data

115 PDF
1