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.

Genomic studies have been a major approach to elucidating disease etiology and to exploring potential targets for treatments of many complex diseases. Statistical analyses in these studies often face the challenges of multiplicity, weak signals, and the nature of dependence among genetic markers. This situation becomes even more complicated when multi-omics data are available. To integrate the data from different platforms, various integrative analyses have been adopted, ranging from the direct union or intersection operation on sets derived from different single-platform analysis to complex hierarchical multi-level models. The former ignores the biological relationship between molecules while the latter can be hard to interpret. We propose in this study an integrative approach that combines both single nucleotide variants (SNVs) and copy number variations (CNVs) in the same genomic unit to co-localize the concurrent effect and to deal with the sparsity due to rare variants. This approach is illustrated with simulation studies to evaluate its performance and is applied to low-density lipoprotein cholesterol and triglyceride measurements from Taiwan Biobank. The results show that the proposed method can more effectively detect the collective effect from both SNVs and CNVs compared to traditional methods. For the biobank analysis, the identified genetic regions including the gene VNN2 could be novel and deserve further investigation.