Accurate detection of copy number variations in next generation sequencing data by a latent variable model
Sprache des Titels:
12th International Congress of Human Genetics and the American Society of Human Genetics
The quantitative analysis of next generation sequencing (NGS) data like
the detection of copy number variations (CNVs) is still challenging. Current
methods detect CNVs as changes of read densities along chromosomes,
therefore they are prone to a high false discovery rate (FDR) because of
technological or genomic read count variations, even after GC correction.
A high FDR means many wrongly detected CNVs that are not associated
with the disease considered in a study, though correction for multiple testing
must take them into account and thereby decreases the study's discovery
power. We propose ?Copy Number estimation by a Mixture Of PoissonS?
(cn.MOPS) for CNV detection from NGS data, which constructs a model
across samples at each genomic position, therefore it is not affected by read
count variations along chromosomes. In a Bayesian framework, cn.MOPS
decomposes read variations across samples into integer copy numbers and
noise by its mixture components and Poisson distributions, respectively.
The more the data drives the posterior away from a Dirichlet prior corresponding
to copy number two, the more likely the data is caused by a CNV,
and, the larger is the informative/non-informative (I/NI) call. cn.MOPS detects
a CNV in the DNA of an individual by a region with large I/NI calls. I/NI call
based CNV detection gurantees a low FDR because wrong detections are
less likely for large I/NI calls. We compare cn.MOPS with the five most
popular CNV detection methods for NGS data at three benchmark data
sets: (1) artificial, (2) NGS data from a male HapMap individual with
implanted CNVs from the X chromosome, and (3) the HapMap phase 2
individuals with known CNVs. At all benchmark data sets cn.MOPS outperformed
its five competitors with respect to precision (1-FDR) and recall both
at gains and losses.