ResearchPad - chromosomal-duplications Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Association test using Copy Number Profile Curves (CONCUR) enhances power in rare copy number variant analysis]]> Copy number variants comprise a large proportion of variation in human genomes. Large rare CNVs, especially those disrupting genes or changing the dosages of genes, can carry relatively strong risks for neurodevelopmental and neuropsychiatric disorders. Kernel-based association methods have been developed for the analysis of rare CNVs and shown to be a valuable tool. Kernel methods model the collective effect of rare CNVs using flexible kernel functions that capture the characteristics of CNVs and measure CNV similarity of individual pairs. Typically kernels are created by summarizing similarity within an artificially defined “CNV locus” and then collapsing across all loci. In this work, we propose a new kernel-based test, CONCUR, that is based on the CNV location information contained in standard processing of the variants and which obviates the need for arbitrarily defined CNV loci. CONCUR quantifies similarity between individual pairs as the common area under their copy number profile curves and is designed to detect CNV dosage, length and dosage-length interaction effects. In simulation studies and real data analysis, we demonstrate the ability of the CONCUR test to detect CNV effects under diverse CNV architectures with power and robustness over existing methods.