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Association Tests Using Copy Number Profile Curves (CONCUR) Enhances Power in Rare Copy Number Variant Analysis

By Amanda Brucker, Wenbin Lu, Rachel Marceau West, Qi-You Yu, Chuhsing Kate Hsiao, Tzu-Hung Hsiao, Ching-Heng Lin, Patrik KE Magnusson, Patrick F Sullivan, Jin P Szatkiewicz, Tzu-Pin Lu, Jung-Ying Tzeng

Posted 10 Jun 2019
bioRxiv DOI: 10.1101/666875 (published DOI: 10.1371/journal.pcbi.1007797)

Copy number variants (CNVs) are the gain or loss of DNA segments in the genome that can vary in dosage and length. CNVs comprise a large proportion of variation in human genomes and impact health conditions. To detect rare CNV association, kernel-based methods have been shown to be a powerful tool because their flexibility in modeling the aggregate CNV effects, their ability to capture effects from different CNV features, and their ability to accommodate effect heterogeneity. To perform a kernel association test, a CNV locus needs to be defined so that locus-specific effects can be retained during aggregation. However, CNV loci are arbitrarily defined and different locus definitions can lead to different performance depending on the underlying effect patterns. In this work, we develop a new kernel-based test called CONCUR (i.e., COpy Number profile CURve-based association test) that is free from a definition of locus and evaluates CNV-phenotype association by comparing individuals' copy number profiles across the genomic regions. CONCUR is built on the proposed concepts of \``copy number profile curves" to describe the CNV profile of an individual, and the \``common area under the curve (cAUC) kernel" to model the multi-feature CNV effects. Compared to existing methods, CONCUR captures the effects of CNV dosage and length, accounts for the continuous nature of copy number values, and accommodates between- and within-locus etiological heterogeneities without the need to define artificial CNV loci as required in current kernel methods. In a variety of simulation settings, CONCUR shows comparable and improved power over existing approaches. Real data analyses suggest that CONCUR is well powered to detect CNV effects in gene pathways associated with phenotypes using data from the Swedish Schizophrenia Study and the Taiwan Biobank.

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