a new method for detecting associations with rare copynumber variants copy number variants cnvs play an important role in the etiology of many diseases such as cancers and psychiatric disorders due to a modest marginal effect size or the rarity of the cnvs collapsing rare cnvs together and collectively evaluating their effect serves as a key approach to evaluating the collective effect of rare cnvs on disease risk while a plethora of powerful collapsing methods are available for sequence variants eg snps in association analysis these methods cannot be directly applied to rare cnvs due to the cnvspecific challenges ie the multifaceted nature of cnv polymorphisms eg cnvs vary in size type dosage and details of gene disruption and etiological heterogeneity eg heterogeneous effects of duplications and deletions that occur within a locus or in different loci existing cnv collapsing analysis methods aka the burden test tend to have suboptimal performance due to the fact that these methods often ignore heterogeneity and evaluate only the marginal effects of a cnv feature we introduce ccret a random effects test for collapsing rare cnvs when searching for disease associations ccret is applicable to variants measured on a multicategorical scale collectively modeling the effects of multiple cnv features and is robust to etiological heterogeneity multiple confounders can be simultaneously corrected to evaluate the performance of ccret we conducted extensive simulations and analyzed largescale schizophrenia datasets we show that ccret has powerful and robust performance under multiple types of etiological heterogeneity and has performance comparable to or better than existing methods when there is no heterogeneity