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21st Century Cures Act[edit]

Medical Software Regulation[edit]

Medical software is regulated as a medical device by the FDA in the Federal Food, Drug, and Cosmetic Act.[1] Section 3060 of the 21st Century Cures Act was created as an amendment to section 520 of the FD&C Act, which addressed how medical devices are defined.[2][3] It outlined software functions that would be exempt from FDA regulation, such as those used for administrative purposes, encouraging a healthy lifestyle, electronic health records, clinical laboratory test results and related information, and clinical decision tools.[4][5]

https://www.congress.gov/114/plaws/publ255/PLAW-114publ255.pdf

https://www.fda.gov/regulatory-information/search-fda-guidance-documents/changes-existing-medical-software-policies-resulting-section-3060-21st-century-cures-act

https://www.fda.gov/media/109622/download

Genetic privacy[edit]

Other Edits[edit]

  • "Federal Regulation"
    • NHGRI Genomic Data Sharing (GDS) Policy (2015)[6]
    • Common Rule[6]
    • HIPAA Privacy Rule[7][8]

Risks[edit]

Privacy Breaches[edit]

Studies have shown that genomic data is not immune to adversary attacks.[6][8][9] A study conducted in 2013 revealed vulnerabilities in the security of public databases that contain genetic data.[10] As a result, research subjects could sometimes be identified by their DNA alone.[11] Although reports of premeditated breaches outside of experimental research are disputed, researchers suggest the liability is still important to study.[12]

While accessible genomic data has been pivotal in advancing biomedical research, it also escalates the possibility of exposing sensitive information.[6][8][9][12][13] A common practice in genomic medicine to protect patient anonymity involves removing patient identifiers.[6][7][8][9] However, de-identified data is not subject to the same privileges as the research subjects.[7][9] Furthermore, there is an increasing ability to re-identify patients and their genetic relatives from their genetic data.[6][8][9][13]

One study demonstrated re-identification by piecing together genomic data from short tandem repeats (e.g. CODIS), SNP allele frequencies (e.g. ancestry testing), and whole-genome sequencing.[6] They also hypothesize using a patient's genetic information, ancestry testing, and social media to identify relatives.[6] Other studies have echoed the risks associated with linking genomic information with public data like social media, including voter registries, web searches, and personal demographics,[8] or with controlled data, like personal medical records.[9]

There is also controversy regarding the responsibility a DNA testing company has to ensure that leaks and breaches do not happen. Determining who legally owns the genomic data, the company or the individual, is of legal concern. There have been published examples of personal genome information being exploited, as well as indirect identification of family members.[6][14] Additional privacy concerns, related to, e.g., genetic discrimination, loss of anonymity, and psychological impacts, have been increasingly pointed out by the academic community[14][15] as well as government agencies.[16]

Law Enforcement[edit]

Additionally, for criminal justice and privacy advocates, the use of genetic information in identifying suspects for criminal investigations proves worrisome under the Fourth Amendment—especially when an indirect genetic link connects an individual to crime scene evidence.[17] Since 2018, law enforcement officials have been harnessing the power of genetic data to revisit cold cases with DNA evidence.[18] Suspects discovered through this process are not directly identified by the input of their DNA into established criminal databases, like CODIS. Instead, suspects are identified as the result of familial genetic sleuthing by law enforcement, submitting crime scene DNA evidence to genetic database services that link users whose DNA similarity indicates a family connection.[18][19]  Officers can then track the newly identified suspect in person, waiting to collect discarded trash that might carry DNA in order to confirm the match.[18]

Despite the privacy concerns of suspects and their relatives, this procedure is likely to survive Fourth Amendment scrutiny.[20] Much like donors of biological samples in cases of genetic research,[21][22] criminal suspects do not retain property rights in abandoned waste; they can no longer assert an expectation of privacy in the discarded DNA used to confirm law enforcement suspicions, thereby eliminating their Fourth Amendment protection in that DNA.[20]Additionally, the genetic privacy of relatives is likely irrelevant under current caselaw since Fourth Amendment protection is “personal” to criminal defendants.[20]

Psychological Impact[edit]

In a systematic review of perspectives toward genetic privacy, researchers highlight some of the concerns individuals hold regarding their genetic information, such as the potential dangers and effects on themselves and family members.[12] Academics note that participating in biomedical research or genetic testing has implications beyond the participant; it can also reveal information about genetic relatives.[6][11][12][14] The study also found that people expressed concerns as to which body controls their information and if their genetic information could be used against them.[12]

Additionally, the American Society of Human Genetics has expressed issues about genetic tests in children.[23] They infer that testing could lead to negative consequences for the child. For example, if a child's likelihood for adoption was influenced by genetic testing, the child might suffer from self esteem issues. A child's well-being might also suffer due to paternity testing or custody battles that require this type of information.[24]

Variant of uncertain significance[edit]

https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/cfrsearch.cfm?fr=866.5950

https://blueprintgenetics.com/wp-content/uploads/2019/04/Variant_Classification_WP_VARA41-05-1.pdfhttps://www.acgs.uk.com/media/10791/evaluation_and_reporting_of_sequence_variants_bpgs_june_2013_-_finalpdf.pdfhttps://www.phgfoundation.org/documents/Variant%20classification%20and%20identification%20June%202017.pdf

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6502951/

https://academic.oup.com/jlb/article/4/3/648/4820755

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5707196/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5446800/#R6

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4908185/#bib6

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4544753/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7313390/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7062975/

https://journals.sagepub.com/doi/pdf/10.1177/1073110520916995/https://journals.sagepub.com/doi/full/10.1177/1073110520916995

History[edit]

This blood smear shows a white blood cell and several "sickled" cells (one at the tip of the arrow) mixed in with many normal red blood cells.

Sickle cell anemia is widely considered to be the first "molecular disease".[25] From earlier protein biochemistry, it was known that the disease was caused by a mutation in the β-globin gene. In 1977, in the third of a series of 3 research papers published in The Journal of Biological Chemistry, this mutation was identified as a single base transversion of adenosine to uridine.[26]

In 2001, an initial draft of the human genome was published by the International Human Genome Sequencing Consortium.[27] With the development of next-generation sequencing, the cost of sequencing has plummeted and the number of human genomes and exomes sequenced each year is increasing dramatically.[28] As of 2017, the cost of a quality whole genome sequence is $1,000 or less.[29] If the ratio of approximately 20 DNA sequence variants per gene[30] holds over the entire genome (with approximately 20,000 genes) that means that every person who elects to have their genome sequenced will be provided with almost half a million Variants of Unknown Significance. To assist people to understand the meaning of all these variants, classification is a first step.

Classification[edit]

Since the Human Genome Project first sequenced the human genome in 2001 at a cost of US$100 million, costs have fallen precipitously, outpacing even Moore's law, and were ≈US$1,000 in 2015. More widely available genome sequencing has led to more available data on variants of uncertain significance.

In 2015, the American College of Medical Genetics and Genomics (ACMG), the Association for Molecular Pathology (AMP), and the College of American Pathologists (CAP) published a third revision of their guidelines on variant interpretation in Mendelian disorders.[31] The publication recommended the following categories: pathogenic, likely pathogenic, uncertain significance, likely benign, and benign. This guideline is one of many resources published by the ACMG in hopes of improving standardization of variant interpretation and reporting.[32][33][34][35][36]

As of 2020, there continues to be limited involvement from federal agencies to regulate the clinical validity (accuracy) and utility (risks and benefits) of genetic testing.[37][38][39] Variant interpretation and classification is notably subjective, as laboratories developed their own criteria prior to the ACMG-AMP guidelines. [33][34][40] This subjectiveness is further problematic when there is evidence that variant significance changes over time.[39] Due to the lack of consistency in official guidelines, the genomics community is left struggling to efficiently categorize genetic variants.[41][42][43]

Pathogenic[edit]

This category is for variants that are well-documented to cause disease. There is conclusive evidence from

Likely pathogenic[edit]

This category is for variants where the evidence is compelling, but not definitive, to cause disease.

Uncertain significance[edit]

This category is for variants where there is unknown clinical significance. Additional evidence is needed in order to determine whether or not the variant is causative for a particular disease.

Likely benign[edit]

This category is for variants that are not causative for a disease.

Benign[edit]

This category is for variants that are not causative for a disease. Benign variants are usually seen previously in higher frequencies and in silico analysis predicts a benign effect on the encoded protein.

  1. ^ Health, Center for Devices and Radiological (2020-09-09). "What are examples of Software as a Medical Device?". FDA.
  2. ^ Guidance for Industry and Food and Drug Administration Staff. "Changes to Existing Medical Software Policies Resulting from Section 3060 of the 21st Century Cures Act". Food and Drug Administration.{{cite web}}: CS1 maint: url-status (link)
  3. ^ Health, Center for Devices and Radiological (2019-09-30). "Changes to Existing Medical Software Policies Resulting from Section 3060 of the 21st Century Cures Act". U.S. Food and Drug Administration. Retrieved 2020-12-05.
  4. ^ H.R. 34 Division A—21st Century Cures
  5. ^ Evans, Barbara; Ossorio, Pilar (2018-08-14). "The Challenge of Regulating Clinical Decision Support Software After 21st Century Cures". American Journal of Law & Medicine. 44 (2–3): 237–251. doi:10.1177/0098858818789418. S2CID 52006753.
  6. ^ a b c d e f g h i j Schwab, Abraham P.; Luu, Hung S.; Wang, Jason; Park, Jason Y. (December 2018). "Genomic Privacy". Clinical Chemistry. 64 (12): 1696–1703. doi:10.1373/clinchem.2018.289512. ISSN 1530-8561. PMID 29991478. S2CID 51612181.
  7. ^ a b c Rights (OCR), Office for Civil (2012-09-07). "Methods for De-identification of PHI". HHS.gov. Retrieved 2020-10-31.
  8. ^ a b c d e f Shi, Xinghua; Wu, Xintao (January 2017). "An overview of human genetic privacy". Annals of the New York Academy of Sciences. 1387 (1): 61–72. doi:10.1111/nyas.13211. ISSN 0077-8923. PMC 5697154. PMID 27626905.
  9. ^ a b c d e f Wang, Shuang; Jiang, Xiaoqian; Singh, Siddharth; Marmor, Rebecca; Bonomi, Luca; Fox, Dov; Dow, Michelle; Ohno-Machado, Lucila (January 2017). "Genome privacy: challenges, technical approaches to mitigate risk, and ethical considerations in the United States". Annals of the New York Academy of Sciences. 1387 (1): 73–83. doi:10.1111/nyas.13259. ISSN 0077-8923. PMC 5266631. PMID 27681358.
  10. ^ "Genetic privacy". Nature. 493 (7433): 451. 24 January 2013. doi:10.1038/493451a. PMID 23350074.
  11. ^ a b Kolata, Gina (16 June 2013). "Poking Holes in Genetic Privacy". The New York Times. Retrieved 29 December 2016.
  12. ^ a b c d e Clayton, Ellen W.; Halverson, Colin M.; Sathe, Nila A.; Malin, Bradley A. (2018-10-31). "A systematic literature review of individuals' perspectives on privacy and genetic information in the United States". PLOS ONE. 13 (10): e0204417. doi:10.1371/journal.pone.0204417. ISSN 1932-6203. PMC 6209148. PMID 30379944.
  13. ^ a b Shringarpure, Suyash; Bustamante, Carlos (November 2015). "Privacy Risks from Genomic Data-Sharing Beacons". American Journal of Human Genetics. 97 (5): 631–646. doi:10.1016/j.ajhg.2015.09.010. ISSN 0002-9297. PMC 4667107. PMID 26522470.
  14. ^ a b c De Cristofaro, Emiliano (2012-10-17). "Whole Genome Sequencing: Innovation Dream or Privacy Nightmare?". arXiv:1210.4820 [cs.CR].
  15. ^ Curtis, Caitlin; Hereward, James (December 4, 2017). "It's time to talk about who can access your digital genomic data". The Conversation. Retrieved May 21, 2018.
  16. ^ Research, Center for Drug Evaluation and (2018-11-03). "Direct-to-Consumer Tests". FDA.
  17. ^ Ram, Natalie (2015). "DNA by the Entirety". Columbia Law Review. 115: 923.
  18. ^ a b c Murphy, Heather (2019-04-25). "Sooner or Later Your Cousin's DNA Is Going to Solve a Murder". The New York Times. ISSN 0362-4331. Retrieved 2020-05-19.
  19. ^ Hill, Kashmir; Murphy, Heather (2019-11-05). "Your DNA Profile is Private? A Florida Judge Just Said Otherwise". The New York Times. ISSN 0362-4331. Retrieved 2020-05-19.
  20. ^ a b c Ram, Natalie (July 2018). "Incidental Informants: Police Can Use Genealogy Databases to Help Identify Criminal Relatives--But Should They?". Maryland Bar Journal.
  21. ^ Moore v. Regents of the Univ. of Cal., 793 P.2d 479 (Cal. 1990).
  22. ^ Greenberg v. Miami Children’s Hosp. Research Inst., Inc., 264 F.Supp.2d 1064 (S.D. Fla. 2003).
  23. ^ Md 20852 (2015-07-02). "ASHG". ASHG. Retrieved 2020-11-07.{{cite web}}: CS1 maint: numeric names: authors list (link)
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  25. ^ Serjeant GR (2001). "The emerging understanding of sickle cell disease". British Journal of Haematology. 112 (1): 3–18. doi:10.1046/j.1365-2141.2001.02557.x. PMID 11167776. S2CID 40008032.
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  27. ^ International Human Genome Sequencing Consortium (February 2001). "Initial sequencing and analysis of the human genome" (PDF). Nature. 409 (6822): 860–921. Bibcode:2001Natur.409..860L. doi:10.1038/35057062. PMID 11237011.
  28. ^ "Next Generation Sequencing Market Size, Share, Analysis Report". Grand View Research, Inc. Retrieved 2015-07-26.
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  30. ^ Mucaki EJ, Caminsky NG, Perri AM, Lu R, Laederach A, Halvorsen M, Knoll JH, Rogan PK (2016). "A unified analytic framework for prioritization of non-coding variants of uncertain significance in heritable breast and ovarian cancer". primary. BMC Medical Genomics. 9: 19. doi:10.1186/s12920-016-0178-5. PMC 4828881. PMID 27067391.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  31. ^ Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW, Hegde M, Lyon E, Spector E, Voelkerding K, Rehm HL (2015). "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". guideline. Genetics in Medicine. 17 (5): 405–24. doi:10.1038/gim.2015.30. PMC 4544753. PMID 25741868.
  32. ^ "Medical Genetics Practice Resources". www.acmg.net. Retrieved 2020-11-23.
  33. ^ a b Kim, Young-Eun; Ki, Chang-Seok; Jang, Mi-Ae (September 2019). "Challenges and Considerations in Sequence Variant Interpretation for Mendelian Disorders". Annals of Laboratory Medicine. 39 (5): 421–429. doi:10.3343/alm.2019.39.5.421. ISSN 2234-3806. PMC 6502951. PMID 31037860.
  34. ^ a b Amendola, Laura; Jarvik, Gail; Leo, Michael; McLaughlin, Heather; Akkari, Yassmine; Amaral, Michelle; Berg, Jonathan; Biswas, Sawona; Bowling, Kevin; Conlin, Laura; Cooper, Greg (June 2016). "Performance of ACMG-AMP Variant-Interpretation Guidelines among Nine Laboratories in the Clinical Sequencing Exploratory Research Consortium". American Journal of Human Genetics. 98 (6): 1067–1076. doi:10.1016/j.ajhg.2016.03.024. ISSN 0002-9297. PMC 4908185. PMID 27181684.
  35. ^ Richards, Sue; Aziz, Nazneen; Bale, Sherri; Bick, David; Das, Soma; Gastier-Foster, Julie; Grody, Wayne W.; Hegde, Madhuri; Lyon, Elaine; Spector, Elaine; Voelkerding, Karl (May 2015). "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". Genetics in Medicine : Official Journal of the American College of Medical Genetics. 17 (5): 405–424. doi:10.1038/gim.2015.30. ISSN 1098-3600. PMC 4544753. PMID 25741868.
  36. ^ Riggs, Erin Rooney; Andersen, Erica F.; Cherry, Athena M.; Kantarci, Sibel; Kearney, Hutton; Patel, Ankita; Raca, Gordana; Ritter, Deborah I.; South, Sarah T.; Thorland, Erik C.; Pineda-Alvarez, Daniel (February 2020). "Technical standards for the interpretation and reporting of constitutional copy number variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen)". Genetics in Medicine : Official Journal of the American College of Medical Genetics. 22 (2): 245–257. doi:10.1038/s41436-019-0686-8. ISSN 1098-3600. PMC 7313390. PMID 31690835.
  37. ^ "Regulation of Genetic Tests". NIH. National Human Genome Research Institute. Retrieved 20 January 2017.{{cite web}}: CS1 maint: url-status (link)
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