Frontiers of Mathematics in China

, Volume 6, Issue 6, pp 1203–1216

A study of biases of DNA copy number estimation based on PICR model

  • Quan Wang
  • Jianghan Qu
  • Xiaoxing Cheng
  • Yongjian Kang
  • Lin Wan
  • Minping Qian
  • Minghua Deng
Research Article

DOI: 10.1007/s11464-011-0125-x

Cite this article as:
Wang, Q., Qu, J., Cheng, X. et al. Front. Math. China (2011) 6: 1203. doi:10.1007/s11464-011-0125-x

Abstract

Affymetrix single-nucleotide polymorphism (SNP) arrays have been widely used for SNP genotype calling and copy number variation (CNV) studies, both of which are dependent on accurate DNA copy number estimation significantly. However, the methods for copy number estimation may suffer from kinds of difficulties: probe dependent binding affinity, crosshybridization of probes, and the whole genome amplification (WGA) of DNA sequences. The probe intensity composite representation (PICR) model, one former established approach, can cope with most complexities and achieve high accuracy in SNP genotyping. Nevertheless, the copy numbers estimated by PICR model still show array and site dependent biases for CNV studies. In this paper, we propose a procedure to adjust the biases and then make CNV inference based on both PICR model and our method. The comparison indicates that our correction of copy numbers is necessary for CNV studies.

Keywords

single-nucleotide polymorphism (SNP) arraycopy number variation (CNV)cross-hybridization

MSC

62P1068U0192D20

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Quan Wang
    • 1
  • Jianghan Qu
    • 2
  • Xiaoxing Cheng
    • 3
  • Yongjian Kang
    • 2
  • Lin Wan
    • 1
    • 3
    • 4
  • Minping Qian
    • 1
    • 3
  • Minghua Deng
    • 1
    • 3
    • 5
  1. 1.Center for Theoretical BiologyPeking UniversityBeijingChina
  2. 2.Yuanpei CollegePeking UniversityBeijingChina
  3. 3.School of Mathematical SciencesPeking UniversityBeijingChina
  4. 4.Molecular and Computational BiologyUniversity of Southern CaliforniaLos AngelesUSA
  5. 5.Center for Statistical SciencesPeking UniversityBeijingChina