Title: Parametric modeling of whole-genome sequencing data for CNV identification.
Authors: Vardhanabhuti, Saran; Jeng, X Jessie; Wu, Yinghua; Li, Hongzhe
Published In Biostatistics, (2014 Jul)
Abstract: Copy number variants (CNVs) constitute an important class of genetic variants in human genome and are shown to be associated with complex diseases. Whole-genome sequencing provides an unbiased way of identifying all the CNVs that an individual carries. In this paper, we consider parametric modeling of the read depth (RD) data from whole-genome sequencing with the aim of identifying the CNVs, including both Poisson and negative-binomial modeling of such count data. We propose a unified approach of using a mean-matching variance stabilizing transformation to turn the relatively complicated problem of sparse segment identification for count data into a sparse segment identification problem for a sequence of Gaussian data. We apply the optimal sparse segment identification procedure to the transformed data in order to identify the CNV segments. This provides a computationally efficient approach for RD-based CNV identification. Simulation results show that this approach often results in a small number of false identifications of the CNVs and has similar or better performances in identifying the true CNVs when compared with other RD-based approaches. We demonstrate the methods using the trio data from the 1000 Genomes Project.
PubMed ID: 24478395
MeSH Terms: DNA Copy Number Variations/genetics*; Genome, Human/genetics*; Humans; Models, Statistical*; Sequence Analysis, DNA/methods*