IDEAS home Printed from https://ideas.repec.org/a/plo/pgen00/1007746.html
   My bibliography  Save this article

Association analysis using somatic mutations

Author

Listed:
  • Yang Liu
  • Qianchan He
  • Wei Sun

Abstract

Somatic mutations drive the growth of tumor cells and are pivotal biomarkers for many cancer treatments. Genetic association analysis using somatic mutations is an effective approach to study the functional impact of somatic mutations. However, standard regression methods are not appropriate for somatic mutation association studies because somatic mutation calls often have non-ignorable false positive rate and/or false negative rate. While large scale association analysis using somatic mutations becomes feasible recently—thanks for the improvement of sequencing techniques and the reduction of sequencing cost—there is an urgent need for a new statistical method designed for somatic mutation association analysis. We propose such a method with computationally efficient software implementation: Somatic mutation Association test with Measurement Errors (SAME). SAME accounts for somatic mutation calling uncertainty using a likelihood based approach. It can be used to assess the associations between continuous/dichotomous outcomes and individual mutations or gene-level mutations. Through simulation studies across a wide range of realistic scenarios, we show that SAME can significantly improve statistical power than the naive generalized linear model that ignores mutation calling uncertainty. Finally, using the data collected from The Cancer Genome Atlas (TCGA) project, we apply SAME to study the associations between somatic mutations and gene expression in 12 cancer types, as well as the associations between somatic mutations and colon cancer subtype defined by DNA methylation data. SAME recovered some interesting findings that were missed by the generalized linear model. In addition, we demonstrated that mutation-level and gene-level analyses are often more appropriate for oncogene and tumor-suppressor gene, respectively.Author summary: Cancer is a genetic disease that is driven by the accumulation of somatic mutations. Association studies using somatic mutations is a powerful approach to identify the potential impact of somatic mutations on molecular or clinical features. One challenge for such tasks is the non-ignorable somatic mutation calling errors. We have developed a statistical method to address this challenge and applied our method to study the gene expression traits associated with somatic mutations in 12 cancer types. Our results show that some somatic mutations affect gene expression in several cancer types. In particular, we show that the associations between gene expression traits and TP53 gene level mutation reveal some similarities across a few cancer types.

Suggested Citation

  • Yang Liu & Qianchan He & Wei Sun, 2018. "Association analysis using somatic mutations," PLOS Genetics, Public Library of Science, vol. 14(11), pages 1-18, November.
  • Handle: RePEc:plo:pgen00:1007746
    DOI: 10.1371/journal.pgen.1007746
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1007746
    Download Restriction: no

    File URL: https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1007746&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pgen.1007746?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. Yi-Juan Hu & Peizhou Liao & H Richard Johnston & Andrew S Allen & Glen A Satten, 2016. "Testing Rare-Variant Association without Calling Genotypes Allows for Systematic Differences in Sequencing between Cases and Controls," PLOS Genetics, Public Library of Science, vol. 12(5), pages 1-19, May.
    2. Lin, D.Y. & Zeng, D., 2006. "Likelihood-Based Inference on Haplotype Effects in Genetic Association Studies," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 89-104, March.
    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. Jinbo Chen & Dongyu Lin & Hagit Hochner, 2012. "Semiparametric Maximum Likelihood Methods for Analyzing Genetic and Environmental Effects with Case-Control Mother–Child Pair Data," Biometrics, The International Biometric Society, vol. 68(3), pages 869-877, September.
    2. Jinbo Chen & Carmen Rodriguez, 2007. "Conditional Likelihood Methods for Haplotype-Based Association Analysis Using Matched Case–Control Data," Biometrics, The International Biometric Society, vol. 63(4), pages 1099-1107, December.
    3. Iryna Lobach & Raymond J. Carroll & Christine Spinka & Mitchell H. Gail & Nilanjan Chatterjee, 2008. "Haplotype‐Based Regression Analysis and Inference of Case–Control Studies with Unphased Genotypes and Measurement Errors in Environmental Exposures," Biometrics, The International Biometric Society, vol. 64(3), pages 673-684, September.
    4. Wu Song & Yang Jie & Wu Rongling, 2010. "Mapping Quantitative Trait Loci in a Non-Equilibrium Population," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-21, August.
    5. French Benjamin & Lumley Thomas & Cappola Thomas P. & Mitra Nandita, 2012. "Non-Iterative, Regression-Based Estimation of Haplotype Associations with Censored Survival Outcomes," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-24, February.
    6. Yulia V. Marchenko & Raymond K. Carroll & Danyu Y. Lin & Christopher I. Amos & Roberto G. Gutierrez, 2008. "Semiparametric analysis of case–control genetic data in the presence of environmental factors," Stata Journal, StataCorp LLC, vol. 8(3), pages 305-333, September.
    7. Sinha Samiran & Gruber Stephen B & Mukherjee Bhramar & Rennert Gad, 2008. "Inference of the Haplotype Effect in a Matched Case-Control Study Using Unphased Genotype Data," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-28, May.
    8. Wei Sun & Chong Jin & Jonathan A. Gelfond & Ming‐Hui Chen & Joseph G. Ibrahim, 2020. "Joint analysis of single‐cell and bulk tissue sequencing data to infer intratumor heterogeneity," Biometrics, The International Biometric Society, vol. 76(3), pages 983-994, September.
    9. X. Li & B. N. Thomas & S. M. Rich & D. Ecker & J. K. Tumwine & A. S. Foulkes, 2009. "Estimating and testing haplotype–trait associations in non‐diploid populations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(5), pages 663-678, December.
    10. Hua Yun Chen & Daniel E. Rader & Mingyao Li, 2015. "Likelihood Inferences on Semiparametric Odds Ratio Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1125-1135, September.
    11. Tianying Wang & Alex Asher, 2021. "Improved Semiparametric Analysis of Polygenic Gene–Environment Interactions in Case–Control Studies," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(3), pages 386-401, December.
    12. Bhramar Mukherjee & Nilanjan Chatterjee, 2008. "Exploiting Gene‐Environment Independence for Analysis of Case–Control Studies: An Empirical Bayes‐Type Shrinkage Estimator to Trade‐Off between Bias and Efficiency," Biometrics, The International Biometric Society, vol. 64(3), pages 685-694, September.
    13. Thomas H. Scheike & Torben Martinussen & Jeremy D. Silver, 2010. "Estimating Haplotype Effects for Survival Data," Biometrics, The International Biometric Society, vol. 66(3), pages 705-715, September.

    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:pgen00:1007746. 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: plosgenetics (email available below). General contact details of provider: https://journals.plos.org/plosgenetics/ .

    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.