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Outcomes of 92 patient-driven family studies for reclassification of variants of uncertain significance

Genetics in Medicine (2018) | Download Citation




Family studies are an important but underreported source of information for reclassification of variants of uncertain significance (VUS). We evaluated outcomes of a patient-driven framework that offered familial VUS reclassification analysis to any adult with any clinically ascertained VUS from any laboratory in the United States.


With guidance from, participants recruited their own relatives for study participation. We genotyped relatives, calculated quantitative cosegregation likelihood ratios, and evaluated variant classifications using Tavtigian’s unified framework for Bayesian analysis with American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) criteria. We report participation and VUS reclassification rates from the 50 families enrolled for at least one year and reclassification results for 112 variants from the larger 92-family cohort.


For the 50-family cohort, 6.7 relatives per family were invited to participate and 67% of relatives returned samples for genotyping. Sixty-one percent of VUS were reclassified, 84% of which were classified as benign or likely benign. Genotyping relatives identified a de novo variant, phase variants, and relatives with phenotypes highly specific for or incompatible with specific classifications.


Motivated families can contribute to successful VUS reclassification at substantially higher rates than those previously published. Clinical laboratories could consider offering family studies to all patients with VUS.

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  1. 1.

    Shirts BH, Pritchard CC, Walsh T. Family-specific variants and the limits of human genetics. Trends Mol Med. 2016;22:925–934.

  2. 2.

    Moreno L, Linossi C, Esteban I, et al. Germline BRCA testing is moving from cancer risk assessment to a predictive biomarker for targeting cancer therapeutics. Clin Transl Oncol.2016;18:981–987.

  3. 3.

    Murray ML, Cerrato F, Bennett RL, Jarvik GP. Follow-up of carriers of BRCA1 and BRCA2 variants of unknown significance: variant reclassification and surgical decisions. Genet Med. 2011;13:998–1005.

  4. 4.

    Welsh JL, Hoskin TL, Day CN, et al. Clinical decision-making in patients with variant of uncertain significance in BRCA1 or BRCA2 genes. Ann Surg Oncol.2017;24:3067–3072.

  5. 5.

    Macklin SK, Jackson JL, Atwal PS, Hines SL. Physician interpretation of variants of uncertain significance. Fam Cancer. 2018 May 2;[Epub ahead of print].

  6. 6.

    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.2015;5:30.

  7. 7.

    National Cancer Comprehensive Network. Genetic/familial high risk assessment: colorectal version 3.2017. 2017. Accessed 11 May 2018.

  8. 8.

    National Cancer Comprehensive Network. Genetic/familial high risk assessment: breast and ovarian version 1.2018. 2017. Accessed 11 May 2018.

  9. 9.

    Macklin S, Durand N, Atwal P, Hines S. Observed frequency and challenges of variant reclassification in a hereditary cancer clinic. Genet Med. 2018;20:346–350.

  10. 10.

    Petrucelli N, Lazebnik N, Huelsman KM, Lazebnik RS. Clinical interpretation and recommendations for patients with a variant of uncertain significance in BRCA1 or BRCA2: a survey of genetic counseling practice. Genet Test. 2002;6:107–113.

  11. 11.

    Eccles BK, Copson E, Maishman T, Abraham JE, Eccles DM. Understanding of BRCA VUS genetic results by breast cancer specialists. BMC Cancer. 2015;15:936.

  12. 12.

    Culver JO, Brinkerhoff CD, Clague J, et al. Variants of uncertain significance in BRCA testing: evaluation of surgical decisions, risk perception, and cancer distress. Clin Genet. 2013;84:464–472.

  13. 13.

    van Dijk S, van Asperen CJ, Jacobi CE, et al. Variants of uncertain clinical significance as a result of BRCA1/2 testing: impact of an ambiguous breast cancer risk message. Genet Test. 2004;8:235–239.

  14. 14.

    Vos J, Otten W, van Asperen C, Jansen A, Menko F, Tibben A. The counsellees’ view of an unclassified variant in BRCA1/2: recall, interpretation, and impact on life. Psychooncology.2008;17:822–830.

  15. 15.

    Garcia C, Lyon L, Littell RD, Powell CB. Comparison of risk management strategies between women testing positive for a BRCA variant of unknown significance and women with known BRCA deleterious mutations. Genet Med.2014;16:896–902.

  16. 16.

    Teng J, Risch N. The relative power of family-based and case-control designs for linkage disequilibrium studies of complex human diseases. II. Individual genotyping. Genome Res. 1999;9:234–241.

  17. 17.

    Thornton T, McPeek MS. Case-control association testing with related individuals: a more powerful quasi-likelihood score test. Am J Hum Genet.2007;81:321–337.

  18. 18.

    Shirts BH, Jacobson A, Jarvik GP, Browning BL. Large numbers of individuals are required to classify and define risk for rare variants in known cancer risk genes. Genet Med.2013;19:187.

  19. 19.

    Thompson D, Easton DF, Goldgar DE. A full-likelihood method for the evaluation of causality of sequence variants from family data. Am J Hum Genet. 2003;73:652–655.

  20. 20.

    Mohammadi L, Vreeswijk MP, Oldenburg R, et al. A simple method for co-segregation analysis to evaluate the pathogenicity of unclassified variants; BRCA1 and BRCA2 as an example. BMC Cancer.2009;9:211.

  21. 21.

    Jarvik GP, Browning BL. Consideration of cosegregation in the pathogenicity classification of genomic variants. Am J Hum Genet. 2016;98:1077–1081.

  22. 22.

    Gong G, Hannon N, Whittemore AS. Estimating gene penetrance from family data. Genet Epidemiol.2010;34:373–381.

  23. 23.

    Garrett LT, Hickman N, Jacobson A, et al. Family studies for classification of variants of uncertain classification: current laboratory clinical practice and a new web-based educational tool. J Genet Couns. 2016;25:1146–1156.

  24. 24.

    Landrum MJ, Lee JM, Riley GR, et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res.2014;42 Database issue:D980–985.

  25. 25.

    Eggington JM, Bowles KR, Moyes K, et al. A comprehensive laboratory-based program for classification of variants of uncertain significance in hereditary cancer genes. Clin Genet.2014;86:229–237.

  26. 26.

    Ramos E, Weissman SM. The dawn of consumer-directed testing. Am J Med Genet C Semin Med Genet.2018;178:89–97.

  27. 27.

    Solomon I, Harrington E, Hooker G, et al. Lynch syndrome limbo: patient understanding of variants of uncertain significance. J Genet Couns. 2017;26:866–877.

  28. 28.

    Xian Y, O’Brien EC, Fonarow GC, et al. Patient-centered research into outcomes stroke patients prefer and effectiveness research: implementing the patient-driven research paradigm to aid decision making in stroke care. Am Heart J.2015;170:36–45.

  29. 29.

    Barocas DA, Alvarez J, Resnick MJ, et al. Association between radiation therapy, surgery, or observation for localized prostate cancer and patient-reported outcomes after 3 years. JAMA. 2017;317:1126–1140.

  30. 30.

    University of Washington. 2015. Accessed 26 April 2018.

  31. 31.

    Harris PA, Taylor R, Thielke R, et al. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42:377–381.

  32. 32.

    Plon SE, Eccles DM, Easton D, et al. Sequence variant classification and reporting: recommendations for improving the interpretation of cancer susceptibility genetic test results. Hum Mutat.2008;29:1282–1291.

  33. 33.

    Tavtigian SV, Greenblatt MS, Harrison SM, et al. Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework. Genet Med. 2018 Jan 4;[Epub ahead of print].

  34. 34.

    Lek M, Karczewski KJ, Minikel EV, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536:285–291.

  35. 35.

    Thompson BA, Goldgar DE, Paterson C, et al. A multifactorial likelihood model for MMR gene variant classification incorporating probabilities based on sequence bioinformatics and tumor characteristics: a report from the Colon Cancer Family Registry. Hum Mutat.2013;34:200–209.

  36. 36.

    Rañola JMO, Liu Q, Rosenthal EA, Shirts BH. A comparison of cosegregation analysis methods for the clinical setting. Fam Cancer.2018;17:295–302.

  37. 37.

    Adzhubei IA, Schmidt S, Peshkin L, et al. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7:248–249.

  38. 38.

    Ng PC, Henikoff S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003;31:3812–3814.

  39. 39.

    Tavtigian SV, Deffenbaugh AM, Yin L, et al. Comprehensive statistical study of 452 BRCA1 missense substitutions with classification of eight recurrent substitutions as neutral. J Med Genet.2006;43:295–305.

  40. 40.

    Rosenthal EA, Ranola JMO, Shirts BH. Power of pedigree likelihood analysis in extended pedigrees to classify rare variants of uncertain significance in cancer risk genes. Fam Cancer.2017;16:611–620.

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We thank the many families who participated actively as partners in this work. We also thank the undergraduate students who assisted in genotyping: Kimberly Krupa, Sarah Upham, Yijun Sim, Lisa Williamson, Nela Novákova, and Sarah Helfen. We thank Eric Konnick, Moon Chung, David Fareti, Jailanie Kaganovsky, and Gynevill Villanueva for their assistance in processing samples. This study was supported by grants from the Damon Runyon Cancer Research Foundation (DRR-33-15), the National Human Genome Research Institute (NHGRI) (R21HG008513), and the Fred Hutch/University of Washington Cancer Consortium (NCI 5P30 CA015704-39).

Author information


  1. Department of Laboratory Medicine, University of Washington, Seattle, WA, USA

    • Ginger J. Tsai MS, LCGC
    • , John Michael O. Rañola PhD
    • , Christina Smith BS, MLS (ASCP)
    • , Lauren Thomas Garrett MS, LCGC
    •  & Brian H. Shirts MD, PhD
  2. Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA

    • Timothy Bergquist BS
  3. Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA

    • Silvia Casadei PhD
  4. Department of Bioethics and Humanities, University of Washington, Seattle, WA, USA

    • Deborah J. Bowen PhD


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Correspondence to Brian H. Shirts MD, PhD.

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