IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0056626.html
   My bibliography  Save this article

Assessing the Impact of Differential Genotyping Errors on Rare Variant Tests of Association

Author

Listed:
  • Morgan Mayer-Jochimsen
  • Shannon Fast
  • Nathan L Tintle

Abstract

Genotyping errors are well-known to impact the power and type I error rate in single marker tests of association. Genotyping errors that happen according to the same process in cases and controls are known as non-differential genotyping errors, whereas genotyping errors that occur with different processes in the cases and controls are known as differential genotype errors. For single marker tests, non-differential genotyping errors reduce power, while differential genotyping errors increase the type I error rate. However, little is known about the behavior of the new generation of rare variant tests of association in the presence of genotyping errors. In this manuscript we use a comprehensive simulation study to explore the effects of numerous factors on the type I error rate of rare variant tests of association in the presence of differential genotyping error. We find that increased sample size, decreased minor allele frequency, and an increased number of single nucleotide variants (SNVs) included in the test all increase the type I error rate in the presence of differential genotyping errors. We also find that the greater the relative difference in case-control genotyping error rates the larger the type I error rate. Lastly, as is the case for single marker tests, genotyping errors classifying the common homozygote as the heterozygote inflate the type I error rate significantly more than errors classifying the heterozygote as the common homozygote. In general, our findings are in line with results from single marker tests. To ensure that type I error inflation does not occur when analyzing next-generation sequencing data careful consideration of study design (e.g. use of randomization), caution in meta-analysis and using publicly available controls, and the use of standard quality control metrics is critical.

Suggested Citation

  • Morgan Mayer-Jochimsen & Shannon Fast & Nathan L Tintle, 2013. "Assessing the Impact of Differential Genotyping Errors on Rare Variant Tests of Association," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-10, March.
  • Handle: RePEc:plo:pone00:0056626
    DOI: 10.1371/journal.pone.0056626
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0056626
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0056626&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0056626?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Bo Eskerod Madsen & Sharon R Browning, 2009. "A Groupwise Association Test for Rare Mutations Using a Weighted Sum Statistic," PLOS Genetics, Public Library of Science, vol. 5(2), pages 1-11, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ruixue Fan & Shaw-Hwa Lo, 2013. "A Robust Model-free Approach for Rare Variants Association Studies Incorporating Gene-Gene and Gene-Environmental Interactions," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-14, December.
    2. Wan-Yu Lin & Xiang-Yang Lou & Guimin Gao & Nianjun Liu, 2014. "Rare Variant Association Testing by Adaptive Combination of P-values," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-7, January.
    3. Ai-Ru Hsieh & Dao-Peng Chen & Amrita Sengupta Chattopadhyay & Ying-Ju Li & Chien-Ching Chang & Cathy S J Fann, 2017. "A non-threshold region-specific method for detecting rare variants in complex diseases," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-16, November.
    4. Diana Chang & Alon Keinan, 2012. "Predicting Signatures of “Synthetic Associations” and “Natural Associations” from Empirical Patterns of Human Genetic Variation," PLOS Computational Biology, Public Library of Science, vol. 8(7), pages 1-9, July.
    5. Gaurav Bhatia & Vikas Bansal & Olivier Harismendy & Nicholas J Schork & Eric J Topol & Kelly Frazer & Vineet Bafna, 2010. "A Covering Method for Detecting Genetic Associations between Rare Variants and Common Phenotypes," PLOS Computational Biology, Public Library of Science, vol. 6(10), pages 1-12, October.
    6. ChangJiang Xu & Martin Ladouceur & Zari Dastani & J Brent Richards & Antonio Ciampi & Celia M T Greenwood, 2012. "Multiple Regression Methods Show Great Potential for Rare Variant Association Tests," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-10, August.
    7. Yao-Hwei Fang & Yen-Feng Chiu, 2013. "A Novel Support Vector Machine-Based Approach for Rare Variant Detection," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-9, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0056626. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.