Parametric modeling of whole-genome sequencing data for CNV identification.
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.
Harvard School of Public Health, 651 Huntington Avenue, Boston, MA 02115, USA.
Saran
S
Vardhanabhuti
Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA.
X Jessie
XJ
Jeng
Division of Biostatistics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Yinghua
Y
Wu
Division of Biostatistics, University of Pennsylvania, Philadelphia, PA 19104, USA hongzhe@upenn.edu.
Hongzhe
H
Li
D056915:DNA Copy Number Variations / Q000235:genetics*; D015894:Genome, Human / Q000235:genetics*; D006801:Humans; D015233:Models, Statistical; D017422:Sequence Analysis, DNA / Q000379:methods*
Natural exponential family; Sparse segment identification; Variance stabilization