1932

Abstract

Clinical genetic variant classification science is a growing subspecialty of clinical genetics and genomics. The field's continued improvement is essential for the success of precision medicine in both germline (hereditary) and somatic (oncology) contexts. This review focuses on variant classification for DNA next-generation sequencing tests. We first summarize current limitations in variant discovery and definition, and then describe the current five- and four-tier classification systems outlined in dominant standards and guideline publications for germline and somatic tests, respectively. We then discuss measures of variant classification discordance and the field's bias for positive results, as well as considerations for panel size and population screening in the context of estimates of positive predictive value thatincorporate estimated variant classification imperfections. Finally, we share opinions on the current state of variant classification from some of the authors of the most widely used standards and guideline publications and from other domain experts.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-genom-121620-082709
2021-08-31
2024-04-20
Loading full text...

Full text loading...

/deliver/fulltext/genom/22/1/annurev-genom-121620-082709.html?itemId=/content/journals/10.1146/annurev-genom-121620-082709&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Abnizova I, te Boekhorst R, Orlov YL. 2017. Computational errors and biases in short read next generation sequencing. J. Proteom. Bioinform. 10:1
    [Google Scholar]
  2. 2. 
    ACMG Lab. Pract. Comm. Work. Group 2000. ACMG recommendations for standards for interpretation of sequence variations. Genet. Med. 2:302–3
    [Google Scholar]
  3. 3. 
    Agarwala V, Khozin S, Singal G, O'Connell C, Kuk D et al. 2018. Real-world evidence in support of precision medicine: clinico-genomic cancer data as a case study. Health Aff. 37:765–72
    [Google Scholar]
  4. 4. 
    Genet Ambry 2020. CustomNext-Cancer®. Ambry Genetics https://www.ambrygen.com/providers/genetic-testing/2/oncology/customnext-cancer
    [Google Scholar]
  5. 5. 
    Amendola LM, Muenzen K, Biesecker LG, Bowling KM, Cooper GM et al. 2020. Variant classification concordance using the ACMG-AMP variant interpretation guidelines across nine genomic implementation research studies. Am. J. Hum. Genet. 107:932–41
    [Google Scholar]
  6. 6. 
    Balmaña J, Digiovanni L, Gaddam P, Walsh MF, Joseph V et al. 2016. Conflicting interpretation of genetic variants and cancer risk by commercial laboratories as assessed by the prospective registry of multiplex testing. J. Clin. Oncol. 34:4071–78
    [Google Scholar]
  7. 7. 
    Bennett C. 2019. Precision medicine adopts cancer variant guidelines, but the work isn't over. Clin. OMICs 6:16–17
    [Google Scholar]
  8. 8. 
    Brnich SE, Abou Tayoun AN, Couch FJ, Cutting GR, Greenblatt MS et al. 2019. Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework. Genome Med 12:3
    [Google Scholar]
  9. 9. 
    Broad Inst. MIT Harv 2020. ATM: ATM serine/threonine kinase. Genome Aggreg. Database (gnomAD) version 2.1.1. https://gnomad.broadinstitute.org/gene/ENSG00000149311?dataset=gnomad_r2_1
    [Google Scholar]
  10. 10. 
    Broad Inst. MIT Harv 2020. Deletion (3 bases): 7-117199644-ATCT-A (GRCh37). Genome Aggreg. Database (gnomAD), version 2.1.1. https://gnomad.broadinstitute.org/variant/7-117199644-ATCT-A?dataset=gnomad_r2_1
  11. 11. 
    Chen D. 2020. Myriad: using RNA testing in variant classification since 2015. Myriad myRisk Jan. 2. https://myriadmyrisk.com/using-rna-in-variant-classification
  12. 12. 
    Chen Z, Yuan Y, Chen X, Chen J, Lin S et al. 2020. Systematic comparison of somatic variant calling performance among different sequencing depth and mutation frequency. Sci. Rep. 10:3501
    [Google Scholar]
  13. 13. 
    ClinGen (Clin. Genome Resour.) 2020. FDA recognized human variant database. ClinGen https://www.clinicalgenome.org/about/fda-recognition
    [Google Scholar]
  14. 14. 
    ClinGen (Clin. Genome Resour.) 2020. Variant pathogenicity curation. ClinGen https://www.clinicalgenome.org/curation-activities/variant-pathogenicity
    [Google Scholar]
  15. 15. 
    Color 2019. Hereditary cancer test. Fact Sheet, Color, Burlingame, CA. https://static.getcolor.com/pdfs/providers/Color_Hereditary_Cancer_Test_Fact_Sheet.pdf
  16. 16. 
    Curtin C. 2020. Tumor, germline testing of cancer patients can give discordant results GenomeWeb Nov. 23. https://www.genomeweb.com/cancer/tumor-germline-testing-cancer-patients-can-give-discordant-results
  17. 17. 
    Dohm JC, Lottaz C, Borodina T, Himmelbauer H. 2008. Substantial biases in ultra-short read data sets from high-throughput DNA sequencing. Nucleic Acids Res 36:e105
    [Google Scholar]
  18. 18. 
    Dolinsky JS, Hruska KS, Pesaran T, Richardson ME, Klein RT et al. 2017. Efforts toward consensus variant interpretation by commercial laboratories. J. Clin. Oncol. 35:1261–62
    [Google Scholar]
  19. 19. 
    Duncavage EJ, Abel HJ, Pfeifer JD. 2017. In silico proficiency testing for clinical next-generation sequencing. J. Mol. Diagn. 19:35–42
    [Google Scholar]
  20. 20. 
    Eggington JM, Bowles KR, Moyes K, Manley S, Esterling L et al. 2014. A comprehensive laboratory-based program for classification of variants of uncertain significance in hereditary cancer genes. Clin. Genet. 86:229–37
    [Google Scholar]
  21. 21. 
    Emami NC, Leong L, Wan E, Van Blarigan EL, Cooperberg MR et al. 2017. Tissue sources for accurate measurement of germline DNA genotypes in prostate cancer patients treated with radical prostatectomy. Prostate 77:425–34
    [Google Scholar]
  22. 22. 
    Esterling L, Wijayatunge R, Brown K, Morris B, Hughes E et al. 2020. Impact of a cancer gene variant reclassification program over a 20-year period. JCO Precis. Oncol. 4:944–54
    [Google Scholar]
  23. 23. 
    FDA (Food Drug Adm.) 2017. CDRH'S approach to tumor profiling next generation sequencing tests. Fact Sheet, FDA, Silver Spring, MD. https://www.fda.gov/media/109050/download
  24. 24. 
    Fenton AT, Anderson EC, Scharnetzki E, Reed K, Edelman E et al. 2020. Differences in cancer patients’ and clinicians’ preferences for disclosure of uncertain genomic tumor testing results. Patient Educ. Couns. 104:3–11
    [Google Scholar]
  25. 25. 
    Fox EJ, Reid-Bayliss KS, Emond MJ, Loeb LA. 2014. Accuracy of next generation sequencing platforms. J. Next Gener. Seq. Appl. 1:1
    [Google Scholar]
  26. 26. 
    Ganguly P. 2019. NHGRI funds centers for advancing the reference sequence of the human genome. News Release, Sept. 24 Natl. Hum. Genome Res. Inst. Bethesda, MD. https://www.genome.gov/news/news-release/NIH-funds-centers-for-advancing-sequence-of-human-genome-reference
    [Google Scholar]
  27. 27. 
    Gao P, Zhang R, Li Z, Ding J, Xie J, Li J. 2019. Challenges of providing concordant interpretation of somatic variants in non-small cell lung cancer: a multicenter study. J. Cancer 10:1814–24
    [Google Scholar]
  28. 28. 
    GeneDx 2020. OncoGeneDx: comprehensive common cancer panel. Test Inf. Fact Sheet GeneDX, Gaithersburg, MD. https://www.genedx.com/wp-content/uploads/2013/09/B275.Comprehensive-Common-Cancer-Panel-TIS.v3-1_17_20.pdf
    [Google Scholar]
  29. 29. 
    GenomeDenmark 2020. GenomeDenmark platforms. GenomeDenmark http://www.genomedenmark.dk
    [Google Scholar]
  30. 30. 
    Green RE, Krause J, Briggs AW, Maricic T, Stenzel U et al. 2010. A draft sequence of the Neandertal genome. Science 328:710–22
    [Google Scholar]
  31. 31. 
    Hagenkord J, Funke B, Qian E, Hegde M, Jacobs KB et al. 2020. Design and reporting considerations for genetic screening tests. J. Mol. Diagn. 22:599–609
    [Google Scholar]
  32. 32. 
    Harrison SM, Dolinksy JS, Chen W, Collins CD, Das S et al. 2018. Scaling resolution of variant classification differences in ClinVar between 41 clinical laboratories through an outlier approach. Hum. Mutat. 39:1641–49
    [Google Scholar]
  33. 33. 
    Hehir-Kwa JY, Marschall T, Kloosterman WP, Francioli LC, Baaijens JA et al. 2016. A high-quality human reference panel reveals the complexity and distribution of genomic structural variants. Nat. Commun. 7:12989
    [Google Scholar]
  34. 34. 
    Ioannidis JPA. 2005. Why most published research findings are false. PLOS Med 2:e124
    [Google Scholar]
  35. 35. 
    Jennings LJ, Arcila ME, Corless C, Kamel-Reid S, Lubin IM et al. 2017. Guidelines for validation of next-generation sequencing–based oncology panels. J. Mol. Diagn. 19:341–65
    [Google Scholar]
  36. 36. 
    Karam R, LaDuca H, Richardson ME, Pesaran T, Chao E. 2020. RNA-seq analysis is a useful tool in variant classification. JCO Precis. Oncol. 4:1226–27
    [Google Scholar]
  37. 37. 
    Kehr B, Helgadottir A, Melsted P, Jonsson H, Helgason H et al. 2017. Diversity in non-repetitive human sequences not found in the reference genome. Nat. Genet. 49:588–93
    [Google Scholar]
  38. 38. 
    Kehr B, Melsted P, Halldórsson BV. 2016. PopIns: population-scale detection of novel sequence insertions. Bioinformatics 32:961–67
    [Google Scholar]
  39. 39. 
    Kelley DR, Schatz MC, Salzberg SL. 2010. Quake: quality-aware detection and correction of sequencing errors. Genome Biol 11:R116
    [Google Scholar]
  40. 40. 
    Kerr ID, Cox HC, Moyes K, Evans B, Burdett BC et al. 2017. Assessment of in silico protein sequence analysis in the clinical classification of variants in cancer risk genes. J. Community Genet. 8:87–95
    [Google Scholar]
  41. 41. 
    Koboldt DC. 2020. Best practices for variant calling in clinical sequencing. Genome Med 12:91
    [Google Scholar]
  42. 42. 
    Landrum MJ, Kattman BL. 2018. ClinVar at five years: delivering on the promise. Hum. Mutat. 39:1623–30
    [Google Scholar]
  43. 43. 
    Lawrence MS, Stojanov P, Polak P, Kryukov GV, Cibulskis K et al. 2013. Mutational heterogeneity in cancer and the search for new cancer genes. Nature 499:214–18
    [Google Scholar]
  44. 44. 
    Li MM, Datto M, Duncavage EJ, Kulkarni S, Lindeman NI et al. 2017. Standards and guidelines for the interpretation and reporting of sequence variants in cancer: a joint consensus recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists. J. Mol. Diagn 19:4–23
    [Google Scholar]
  45. 45. 
    Lincoln SE, Yang S, Cline MS, Kobayashi Y, Zhang C et al. 2017. Consistency of BRCA1 and BRCA2 variant classifications among clinical diagnostic laboratories. JCO Precis. Oncol. 1: https://doi.org/10.1200/PO.16.00020
    [Google Scholar]
  46. 46. 
    Logsdon GA, Vollger MR, Eichler EE. 2020. Long-read human genome sequencing and its applications. Nat. Rev. Genet. 21:597–614
    [Google Scholar]
  47. 47. 
    Ma X, Shao Y, Tian L, Flasch DA, Mulder HL et al. 2019. Analysis of error profiles in deep next-generation sequencing data. Genome Biol 20:50
    [Google Scholar]
  48. 48. 
    Maretty L, Jensen JM, Petersen B, Sibbesen JA, Liu S et al. 2017. Sequencing and de novo assembly of 150 genomes from Denmark as a population reference. Nature 548:87–91
    [Google Scholar]
  49. 49. 
    May A, Abeln S, Buijs MJ, Heringa J, Crielaard W, Brandt BW. 2015. NGS-eval: NGS Error analysis and novel sequence VAriant detection tooL. Nucleic Acids Res 43:W301–5
    [Google Scholar]
  50. 50. 
    Merker JD, Devereaux K, Iafrate AJ, Kamel-Reid S, Kim AS et al. 2019. Proficiency testing of standardized samples shows very high inter-laboratory agreement for clinical next generation sequencing-based oncology assays. Arch. Pathol. Lab. Med. 143:463–71
    [Google Scholar]
  51. 51. 
    Mersch J, Brown N, Pirzadeh-Miller S, Mundt E, Cox HC et al. 2018. Prevalence of variant reclassification following hereditary cancer genetic testing. JAMA 320:1266–74
    [Google Scholar]
  52. 52. 
    Mikhail FM, Biegel JA, Cooley LD, Dubuc AM, Hirsch B et al. 2019. Technical laboratory standards for interpretation and reporting of acquired copy-number abnormalities and copy-neutral loss of heterozygosity in neoplastic disorders: a joint consensus recommendation from the American College of Medical Genetics and Genomics (ACMG) and the Cancer Genomics Consortium (CGC). Genet. Med. 21:1903–16
    [Google Scholar]
  53. 53. 
    Moncur JT, Bartley AN, Bridge JA, Kamel-Reid S, Lazar AJ et al. 2019. Performance comparison of different analytic methods in proficiency testing for mutations in the BRAF, EGFR, and KRAS genes: a study of the College of American Pathologists Molecular Oncology Committee. Arch. Pathol. Lab. Med. 143:1203–11
    [Google Scholar]
  54. 54. 
    Morris B, Hughes E, Rosenthal E, Gutin A, Bowles KR. 2016. Classification of genetic variants in genes associated with Lynch syndrome using a clinical history weighting algorithm. BMC Genet 17:99
    [Google Scholar]
  55. 55. 
    MyriadGenet 2020. Myriad myRisk® Hereditary Cancer technical specifications. Tech. Specif., Myriad Genet. Salt Lake City, UT. https://s3.amazonaws.com/myriad-library/technical-specifications/myRisk+Hereditary+Cancer+Tech+Specs.pdf
    [Google Scholar]
  56. 56. 
    Natl. Cent. Biotechnol. Inf 2019. Invitae multi-cancer panel: performance characteristics. Genet. Test. Regist., updated Oct. 15. https://www.ncbi.nlm.nih.gov/gtr/tests/528909/performance-characteristics
  57. 57. 
    Natl. Cent. Biotechnol. Inf 2021. NM_000492.3(CFTR):c.1521_1523delCTT (p.Phe508delPhe). ClinVar, Access. VCV000007105.25, updated Jan. 16. https://www.ncbi.nlm.nih.gov/clinvar/variation/7105
  58. 58. 
    Nykamp K, Anderson M, Powers M, Garcia J, Herrera B et al. 2017. Sherloc: a comprehensive refinement of the ACMG–AMP variant classification criteria. Genet. Med. 19:1105–17
    [Google Scholar]
  59. 59. 
    Pfeiffer F, Gröber C, Blank M, Händler K, Beyer M et al. 2018. Systematic evaluation of error rates and causes in short samples in next-generation sequencing. Sci. Rep. 8:10950
    [Google Scholar]
  60. 60. 
    Pirooznia M, Kramer M, Parla J, Goes FS, Potash JB et al. 2014. Validation and assessment of variant calling pipelines for next-generation sequencing. Hum. Genom. 8:14
    [Google Scholar]
  61. 61. 
    Plon SE, Eccles DM, Easton D, Foulkes WD, Genuardi M et al. 2008. Sequence variant classification and reporting: recommendations for improving the interpretation of cancer susceptibility genetic test results. Hum. Mutat. 29:1282–91
    [Google Scholar]
  62. 62. 
    Pruss D, Morris B, Hughes E, Eggington JM, Esterling L et al. 2014. Development and validation of a new algorithm for the reclassification of genetic variants identified in the BRCA1 and BRCA2 genes. Breast Cancer Res. Treat. 147:119–32
    [Google Scholar]
  63. 63. 
    Reich D, Nalls MA, Kao WHL, Akylbekova EL, Tandon A et al. 2009. Reduced neutrophil count in people of African descent is due to a regulatory variant in the Duffy antigen receptor for chemokines gene. PLOS Genet 5:e1000360
    [Google Scholar]
  64. 64. 
    Richards CS, Aziz N, Bale S, Bick D, Das S et al. 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. Genet. Med. 17:405–24
    [Google Scholar]
  65. 65. 
    Richards CS, Bale S, Bellissimo DB, Das S, Grody WW et al.2008 ACMG recommendations for standards for interpretation and reporting of sequence variations: revisions; 2007. Genet. Med. 10:294–300
    [Google Scholar]
  66. 66. 
    Riggs ER, Andersen EF, Cherry AM, Kantarci S, Kearney H et al. 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). Genet. Med. 22:245–57
    [Google Scholar]
  67. 67. 
    Rosenthal ET, Bowles KR, Pruss D, van Kan A, Vail PJ et al. 2015. Exceptions to the rule: case studies in the prediction of pathogenicity for genetic variants in hereditary cancer genes. Clin. Genet. 88:533–41
    [Google Scholar]
  68. 68. 
    Rusk N. 2018. The UK Biobank. Nat. Methods 15:1001
    [Google Scholar]
  69. 69. 
    Shah N, Hou Y-CC, Yu H-C, Sainger R, Caskey CT et al. 2018. Identification of misclassified ClinVar variants via disease population prevalence. Am. J. Hum. Genet. 102:609–19
    [Google Scholar]
  70. 70. 
    Sherman RM, Forman J, Antonescu V, Puiu D, Daya M et al. 2019. Assembly of a pan-genome from deep sequencing of 910 humans of African descent. Nat. Genet. 51:30–35
    [Google Scholar]
  71. 71. 
    Spence T, Sukhai MA, Kamel-Reid S, Stockley TL. 2019. The Somatic Curation and Interpretation Across Laboratories (SOCIAL) project—current state of solid-tumour variant interpretation for molecular pathology in Canada. Curr. Oncol. 26:353–60
    [Google Scholar]
  72. 72. 
    Surrey LF, Oakley FD, Merker JD, Long TA, Vasalos P et al. 2019. Next-generation sequencing (NGS) methods show superior or equivalent performance to non-NGS methods on BRAF, EGFR, and KRAS proficiency testing samples. Arch. Pathol. Lab. Med. 143:980–84
    [Google Scholar]
  73. 73. 
    Thompson BA, Spurdle AB, Plazzer J-P, Greenblatt MS, Akagi K et al. 2014. Application of a 5-tiered scheme for standardized classification of 2,360 unique mismatch repair gene variants in the InSiGHT locus-specific database. Nat. Genet. 46:107–15
    [Google Scholar]
  74. 74. 
    Vail PJ, Morris B, van Kan A, Burdett BC, Moyes K et al. 2015. Comparison of locus-specific databases for BRCA1 and BRCA2 variants reveals disparity in variant classification within and among databases. J. Community Genet. 6:351–59
    [Google Scholar]
  75. 75. 
    Yang S, Lincoln SE, Kobayashi Y, Nykamp K, Nussbaum RL, Topper S. 2017. Sources of discordance among germ-line variant classifications in ClinVar. Genet. Med. 19:1118–26
    [Google Scholar]
  76. 76. 
    Yen JL, Garcia S, Montana A, Harris J, Chervitz S et al. 2017. A variant by any name: quantifying annotation discordance across tools and clinical databases. Genome Med 9:7
    [Google Scholar]
  77. 77. 
    Zhao S, Agafonov O, Azab A, Stokowy T, Hovig E. 2020. Accuracy and efficiency of germline variant calling pipelines for human genome data. Sci. Rep. 10:20222
    [Google Scholar]
/content/journals/10.1146/annurev-genom-121620-082709
Loading
/content/journals/10.1146/annurev-genom-121620-082709
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error