An integrative framework for circular RNA quantitative trait locus discovery with application in human T cells

Molecular quantitative trait locus (QTL) mapping of genetic variants with intermediate molecular phenotypes has proven to be a powerful approach for prioritizing genetic regulatory variants and causal genes identified by Genome-wide association studies (GWAS). Recently, this success has been extended to circular RNA (circRNA), a potential group of RNAs that can serve as markers for the diagnosis, prognosis, or therapeutic targets of cancer, cardiovascular, and autoimmune diseases. However, the detection of circRNA QTL (circQTL) currently is heavily reliant on a single circRNA detection algorithm for circRNA annotation and quantification which implies limitations in both sensitivity and specificity. In this study, we show that circQTL results produced by different circRNA calling tools are extremely divergent, making difficulties in interpretation. To resolve this issue, we develop an integrative method for circQTL mapping and implement it as an automated, reproducible, and scalable, and easy-to-use framework based on Nextflow, named cscQTL. Compared to the existing approach, the new method effectively identify circQTLs with an increase of 20-100% circQTLs detected and recovered all circQTLs that are highly supported by the single method approach. We apply the new method to a dataset of human T cells and discover genetic variants that control the expression of 55 circRNAs. By collocation analysis, we further identify circBACH2 and circYY1AP1 as potential candidates for immune disease regulation. cscQTL is freely available at: https://github.com/datngu/cscQTL.