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

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 inform...

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Vydáno v:Statistical applications in genetics and molecular biology Ročník 11; číslo 5; s. 5
Hlavní autoři: Lin, Chang-Yun, Lo, Yungtai, Ye, Kenny Q
Médium: Journal Article
Jazyk:angličtina
Vydáno: Germany 12.10.2012
Témata:
Algorithms
Cluster Analysis
DNA Copy Number Variations - genetics
Gene Dosage
Genome
Genome, Human
Genome-Wide Association Study
Humans
Models, Genetic
Models, Statistical
Normal Distribution
Oligonucleotide Array Sequence Analysis
ISSN:1544-6115, 1544-6115
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Shrnutí: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.
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ISSN:1544-6115
1544-6115
DOI:10.1515/1544-6115.1725