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 kern

SUBMITTER: Brucker A 

PROVIDER: S-EPMC7224564 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature