Association test using Copy Number Profile Curves (CONCUR) enhances power in rare copy number variant analysis

Abstract

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 associations, kernel-based methods have been shown to be a powerful tool due to their flexibility in modeling the aggregate CNV effects, their ability to capture effects from different CNV features, and their accommodation of 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 associations 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. The proposed method captures the effects of CNV dosage and length, accounts for the numerical nature of copy numbers, and accommodates between-and within-locus etiological heterogeneity without the need to define artificial CNV loci as required in current kernel methods. In a variety of simulation settings, CONCUR shows comparable or improved power over existing approaches. Real data analyses suggest that CONCUR is well powered to detect CNV effects in the Swedish Schizophrenia Study and the Taiwan Biobank.

Genetic and epigenetic signatures associated with plasma oxytocin levels in children and adolescents with autism spectrum disorder

“…Association test results at the probe or CpG‐level in our transcriptome and methylome datasets were only used for downstream pathway analyses because we were not adequately powered to detect signal from those datasets in this study. CNVs were tested through burden tests implemented in CONCUR (Brucker et al, 2020) and with generalized linear models in R, where burden was modeled as total number of CNVs and total length of CNVs in kilobases. All tests were performed within deletions and duplications combined, deletions‐only, and duplications‐only datasets.…”

Genetic and epigenetic signatures associated with plasma oxytocin levels in children and adolescents with autism spectrum disorder

“…CNVs were tested through burden tests implemented in CONCUR 72 and with generalized linear models in R, where burden was modeled as total number of CNVs and total length of CNVs in kilobases. All tests were performed within deletions and duplications combined, deletions-only, and duplications-only datasets.…”

An Integrative Co-localization (INCO) Analysis for SNV and CNV Genomic Features With an Application to Taiwan Biobank Data

“…One solution is to analyze multiple CNVs simultaneously. Algorithms based on multiple CNVs have been proposed, such as the CNV kernel association test (CKAT) ( Zhan et al, 2016 ), the CNV Collapsing Random Effects Test (CCRET) ( Tzeng et al, 2015 ) and the copy number profile curve-based association test (CONCUR) ( Brucker et al, 2020 ). Successful examples using multiple CNVs have been reported in obesity and psychiatric disorders ( Lee K.W.…”