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

TitleAssociation test using Copy Number Profile Curves (CONCUR) enhances power in rare copy number variant analysis.
Publication TypeJournal Article
Year of Publication2020
AuthorsBrucker, Amanda, Wenbin Lu, Rachel Marceau West, Qi-You Yu, Chuhsing Kate Hsiao, Tzu-Hung Hsiao, Ching-Heng Lin, Patrik K. E. Magnusson, Patrick F. Sullivan, Jin P. Szatkiewicz, Tzu-Pin Lu, and Jung-Ying Tzeng
JournalPLoS Comput Biol
Volume16
Issue5
Paginatione1007797
Date Published2020 05
ISSN1553-7358
KeywordsAlgorithms, Area Under Curve, Computational Biology, DNA Copy Number Variations, Genetic Predisposition to Disease, Genetic Variation, Genome, Human, Genome-Wide Association Study, Genomics, Humans, Polymorphism, Single Nucleotide, Spatial 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.

DOI10.1371/journal.pcbi.1007797
Alternate JournalPLoS Comput Biol
Original PublicationAssociation test using Copy Number Profile Curves (CONCUR) enhances power in rare copy number variant analysis.
PubMed ID32365089
PubMed Central IDPMC7224564
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
T32 GM081057 / GM / NIGMS NIH HHS / United States
R21 MH104831 / MH / NIMH NIH HHS / United States
R01 MH106611 / MH / NIMH NIH HHS / United States