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

• Activity Number: 209 - Statistical methods for genomic and epigenetic data analysis
• Type: Contributed
• Date/Time: Tuesday, August 10, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Amanda Brucker* and Jung-Ying Tzeng and Wenbin Lu and Rachel Marceau West and Qi-You Yu and Chuhsing Kate Hsiao and Tzu-Hung Hsiao and Ching-Heng Lin and Patrik K. E. Magnusson and Patrick F Sullivan and Jin P. Szatkiewicz and Tzu-Pin Lu
• Companies: Duke University and North Carolina State University and North Carolina State University and North Carolina State University and National Taiwan University and National Taiwan University and Taichung Veterans General Hospital and Taichung Veterans General Hospital and Karolinska Institutet and University of North Carolina at Chapel Hill and University of North Carolina at Chapel Hill and National Taiwan University
• Keywords: copy number variants; kernel machine regression

Abstract

Kernel-based methods have been shown to be a powerful tool for detecting rare copy number variant (CNV) associations due to their flexibility in modeling the aggregate CNV effects, ability to capture effects from different CNV features, and accommodation of effect heterogeneity. In this work, we develop a new kernel-based test called CONCUR that 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.