  Conducting genome-wide association studies (GWAS) in copy number variation
(CNV) level is a field where few people involves and little statistical
progresses have been achieved, traditional methods suffer from many problems
such as batch effects, heterogeneity across genome, leading to low power or
high false discovery rate. We develop a new robust method to find
disease-risking regions related to CNV's disproportionately distributed between
case and control samples, even if there are batch effects between them, our
test formula is robust to such effects. We propose a new empirical Bayes rule
to deal with overfitting when estimating parameters during testing, this rule
can be extended to the field of model selection, it can be more efficient
compared with traditional methods when there are too much potential models to
be specified. We also give solid theoretical guarantees for our proposed
method, and demonstrate the effectiveness by simulation and realdata analysis.
