Omics-informed CNV calls reduce false-positive rates and improve power for CNV-trait associations.

ABSTRACT: Copy-number variations (CNV) are believed to play an important role in a wide range of complex traits, but discovering such associations remains challenging. While whole-genome sequencing (WGS) is the gold-standard approach for CNV detection, there are several orders of magnitude more samples with available genotyping microarray data. Such array data can be exploited for CNV detection using dedicated software (e.g., PennCNV); however, these calls suffer from elevated false-positive and -negative rates. In this study, we developed a CNV quality score that weights PennCNV calls (pCNVs) based on their likelihood of being true positive. First, we established a measure of pCNV reliability by leveraging evidence from multiple omics data (WGS, transcriptomics, and methylomics) obtained from the s

SUBMITTER: Lepamets M 

PROVIDER: S-EPMC9399386 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature