Bayesian Joint Estimation of CN and LOH Aberrations

  • Paola M. V. Rancoita
  • Marcus Hutter
  • Francesco Bertoni
  • Ivo Kwee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5518)

Abstract

SNP-microarrays are able to measure simultaneously both copy number and genotype at several single nucleotide polymorphism positions. Combining the two data, it is possible to better identify genomic aberrations. For this purpose, we propose a Bayesian piecewise constant regression which infers the type of aberration occurred, taking into account all the possible influence in the microarray detection of the genotype, resulting from an altered copy number level. Namely, we model the distributions of the detected genotype given a specific genomic alteration and we estimate the hyper-parameters used on public reference datasets.

Keywords

Bayesian regression piecewise constant function change point problem DNA copy number estimation LOH estimation 

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References

  1. 1.
    Bacolod, M.D., et al.: The Signatures of Autozygosity among Patients with Colorectal Cancer. Cancer Research 68, 2610–2621 (2008)CrossRefGoogle Scholar
  2. 2.
    Bea, S., et al.: Uniparental disomies, homozygous deletions, amplifications and target genes in mantle cell lymphoma revealed by integrative high-resolution whole genome profiling. Blood (2008)Google Scholar
  3. 3.
    Beroukhim, R., et al.: Inferring Loss-of-Heterozygosity from Unpaired Tumors Using High-Density Oligonucleotide SNP Arrays. PLOS Computational Biology 2, 323–332 (2006)CrossRefGoogle Scholar
  4. 4.
    Bertoni, F., et al.: Genome wide-DNA profiling of Richter’s syndrome-diffuse large B-cell lymphoma (RS-DLBCL): differences with de novo DLBCL and possible mechanisms of transformation from chronic lymphocytic leukemia (CLL). Blood (ASH annual meeting abstracts) 112(11), 720 (2008)Google Scholar
  5. 5.
    Forconi, F., et al.: High density genome-wide DNA profiling reveals a remarkably stable profile in hairy cell leukaemia. British Journal of Haematology 141, 622–630 (2008)CrossRefGoogle Scholar
  6. 6.
    The international HapMap Consortium: A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–862 (2007)Google Scholar
  7. 7.
    Hodgson, G., et al.: Genome scanning with array CGH delineates regional alterations in mouse islet carcinomas. Nature Genetics 29, 459–464 (2001)CrossRefGoogle Scholar
  8. 8.
    Newton, M.A., Lee, Y.: Inferring the Location and Effect of Tumor Suppressor Genes by Instability-Selection Modelling of Allelic-Loss Data. Biometrics 56, 1088–1097 (2000)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Olshen, A.B., Venkatraman, E.S., Lucito, R., Wigler, M.: Circular Binary Segmentation for the Analysis of Array-based DNA Copy Number Data. Biostatistics 4, 557–572 (2004)CrossRefMATHGoogle Scholar
  10. 10.
    Rancoita, P.M.V., Hutter, M., Bertoni, F., Kwee, I.: Bayesian DNA copy number analysis. BMC Bioinformatics 10(10) (2009)Google Scholar
  11. 11.
    Rancoita, P.M.V., Hutter, M., Bertoni, F., Kwee, I.: An integrated Bayesian analysis of genotyping and copy number data (in preparation)Google Scholar
  12. 12.
    Scharpf, R.B., Parmigiani, G., Pevsner, J., Ruczinski, I.: Hidden Markov models for the assessment of chromosomal alterations using high-throughput SNP arrays. Annals of Applied Statistics 2, 687–713 (2008)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Zhao, X., et al.: An Integrated View of Copy Number and Allelic Alterations in the Cancer Genome Using Single Nucleotide Polymorphism Arrays. Cancer Research 64, 3060–3071 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Paola M. V. Rancoita
    • 1
    • 2
    • 3
  • Marcus Hutter
    • 4
  • Francesco Bertoni
    • 2
  • Ivo Kwee
    • 1
    • 2
  1. 1.Istituto Dalle Molle di Studi sull’Intelligenza ArtificialeMannoSwitzerland
  2. 2.Laboratory of Experimental OncologyOncology Institute of Southern SwitzerlandBellinzonaSwitzerland
  3. 3.Dipartimento di MatematicaUniversità degli Studi di MilanoMilanoItaly
  4. 4.RSISE, ANU and SML, NICTACanberraAustralia

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