Assessing Population Level Genetic Instability via Moving Average

Samuel McDaniel, Jessica Minnier, Rebecca A. Betensky, Gayatry Mohapatra, Yiping Shen, James F. Gusella, David N. Louis, Tianxi Cai

Research output: Contribution to journalArticlepeer-review


Tumoral tissues tend to generally exhibit aberrations in DNA copy number that are associated with the development and progression of cancer. Genotyping methods such as array-based comparative genomic hybridization (aCGH) provide means to identify copy number variation across the entire genome. To address some of the shortfalls of existing methods of DNA copy number data analysis, including strong model assumptions, lack of accounting for sampling variability of estimators, and the assumption that clones are independent, we propose a simple graphical approach to assess population-level genetic alterations over the entire genome based on moving average. Furthermore, existing methods primarily focus on segmentation and do not examine the association of covariates with genetic instability. In our methods, covariates are incorporated through a possibly mis-specified working model and sampling variabilities of estimators are approximated using a resampling method that is based on perturbing observed processes. Our proposal, which is applicable to partial, entire or multiple chromosomes, is illustrated through application to aCGH studies of two brain tumor types, meningioma and glioma.

Original languageEnglish (US)
Pages (from-to)120-136
Number of pages17
JournalStatistics in Biosciences
Issue number2
StatePublished - Dec 2010


  • Gaussian process
  • Genomic data
  • Moving average
  • Perturbation method
  • aCGH data

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)


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