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A comparison of cosegregation analysis methods for the clinical setting

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Abstract

Quantitative cosegregation analysis can help evaluate the pathogenicity of genetic variants. However, genetics professionals without statistical training often use simple methods, reporting only qualitative findings. We evaluate the potential utility of quantitative cosegregation in the clinical setting by comparing three methods. One thousand pedigrees each were simulated for benign and pathogenic variants in BRCA1 and MLH1 using United States historical demographic data to produce pedigrees similar to those seen in the clinic. These pedigrees were analyzed using two robust methods, full likelihood Bayes factors (FLB) and cosegregation likelihood ratios (CSLR), and a simpler method, counting meioses. Both FLB and CSLR outperform counting meioses when dealing with pathogenic variants, though counting meioses is not far behind. For benign variants, FLB and CSLR greatly outperform as counting meioses is unable to generate evidence for benign variants. Comparing FLB and CSLR, we find that the two methods perform similarly, indicating that quantitative results from either of these methods could be combined in multifactorial calculations. Combining quantitative information will be important as isolated use of cosegregation in single families will yield classification for less than 1% of variants. To encourage wider use of robust cosegregation analysis, we present a website (http://www.analyze.myvariant.org) which implements the CSLR, FLB, and Counting Meioses methods for ATM, BRCA1, BRCA2, CHEK2, MEN1, MLH1, MSH2, MSH6, and PMS2. We also present an R package, CoSeg, which performs the CSLR analysis on any gene with user supplied parameters. Future variant classification guidelines should allow nuanced inclusion of cosegregation evidence against pathogenicity.

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Aknowledgements

This study was supported by the Damon Runyon Cancer Research Foundation (DRR-33-15), the National Human Genome Research Institute (R21HG008513), and the Fred Hutch/University of Washington Cancer Consortium (NCI 5P30 CA015704-39).

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Authors and Affiliations

  1. Department of Laboratory Medicine, University of Washington, Seattle, WA, 98105, USA

    John Michael O. Rañola & Brian H. Shirts

  2. Department of Bioengineering, University of Washington, Seattle, WA, 98105, USA

    Quanhui Liu

  3. Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA, 98105, USA

    Elisabeth A. Rosenthal

Authors
  1. John Michael O. Rañola
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  2. Quanhui Liu
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  3. Elisabeth A. Rosenthal
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  4. Brian H. Shirts
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Corresponding author

Correspondence to John Michael O. Rañola.

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Rañola, J.M.O., Liu, Q., Rosenthal, E.A. et al. A comparison of cosegregation analysis methods for the clinical setting. Familial Cancer 17, 295–302 (2018). https://doi.org/10.1007/s10689-017-0017-7

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