An analysis pipeline for detecting copy number variations with a low false discovery rate in microarray data
Sprache des Titels:
Englisch
Original Buchtitel:
12th International Congress of Human Genetics and the American Society of Human Genetics
Original Kurzfassung:
A low false discovery rate (FDR) at the detection of copynumber
aberrations (CNAs) in microarray data ensures sufficient detection
power and prevents failures in CNA-disease association studies. A high
FDR means many falsely discovered aberrations, which are not associated
with the disease, though correction for multiple testing must take them into
account. Thus, a high FDR not only decreases the discovery power of
studies but also the significance level of the remaining discoveries after
correction for multiple testing. Methods: We obtain a low FDR at the detection
of CNAs in microarray data by a probabilistic latent variable model, called
?cn.FARMS?. The model is optimized by Bayesian maximum a posteriori
approach, where a Laplace prior prefers models, which represent the null
hypothesis of observing a constant copy number 2 for all samples. The
posterior can only deviate from this prior by strong (deviation from copy
number 2 intensities) and consistent signals in the data, which hints at a
CNA - the alternative hypothesis. The information gain of the posterior over
the prior gives the informative/non-informative (I/NI) call that serves as a
filter for CNA candidate regions. I/NI call filtering reduces the FDR, because
a region with a large I/NI call is unlikely to be a falsely detected CNA, which
would neither have strong nor consistent measurements. It can be shown
that the I/NI call filter applied to null hypotheses of the association study is
independent of the test statistic which in turn guarantees that a type I error
rate control by correction for multiple testing is still possible after filtering.
I/NI-calls perform well for the usually rare CNAs that are seen at few samples
only, where variance-based filtering approaches fail. Results: cn.FARMS
clearly outperformed prevalent methods for CNA detection with respect to
sensitivity and especially with respect to FDR on different HapMap benchmark
data sets.