Genotype copy number variations using Gaussian mixture models: Theory and algorithms

Chang Yun Lin, Yungtai Lo, Kenny Q. Ye

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Copy number variations (CNVs) are important in the disease association studies and are usually targeted by most recent microarray platforms developed for GWAS studies. However, the probes targeting the same CNV regions could vary greatly in performance, with some of the probes carrying little information more than pure noise. In this paper, we investigate how to best combine measurements of multiple probes to estimate copy numbers of individuals under the framework of Gaussian mixture model (GMM). First we show that under two regularity conditions and assume all the parameters except the mixing proportions are known, optimal weights can be obtained so that the univariate GMM based on the weighted average gives the exactly the same classification as the multivariate GMM does. We then developed an algorithm that iteratively estimates the parameters and obtains the optimal weights, and uses them for classification. The algorithm performs well on simulation data and two sets of real data, which shows clear advantage over classification based on the equal weighted average.

Original languageEnglish (US)
Article number5
JournalStatistical Applications in Genetics and Molecular Biology
Volume11
Issue number5
DOIs
StatePublished - Sep 1 2012

Keywords

  • Common copy number variants
  • EM algorithm
  • Microarray
  • Rate of correct classification

ASJC Scopus subject areas

  • Statistics and Probability
  • Molecular Biology
  • Genetics
  • Computational Mathematics

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