Statistical considerations for the analysis of massively parallel reporter assays data. Issue 7 (18th July 2020)

• Record Type: Journal Article
• Authors: Qiao, Dandi; Zigler, Corwin M.; Cho, Michael H.; Silverman, Edwin K.; Zhou, Xiaobo; Castaldi, Peter J.; Laird, Nan H.
• Is Part Of: Genetic epidemiology. Volume 44:Issue 7(2020)
• Page Start: 785
• Page End: 794
• Publication Date: 2020-07-18
• Subjects: functional variants -- gene expression -- MPRA -- reporter assays; Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.22337
• Languages: English
• ISSNs: 0741-0395

Abstract

Abstract: Noncoding DNA contains gene regulatory elements that alter gene expression, and the function of these elements can be modified by genetic variation. Massively parallel reporter assays (MPRA) enable high‐throughput identification and characterization of functional genetic variants, but the statistical methods to identify allelic effects in MPRA data have not been fully developed. In this study, we demonstrate how the baseline allelic imbalance in MPRA libraries can produce biased results, and we propose a novel, nonparametric, adaptive testing method that is robust to this bias. We compare the performance of this method with other commonly used methods, and we demonstrate that our novel adaptive method controls Type I error in a wide range of scenarios while maintaining excellent power. We have implemented these tests along with routines for simulating MPRA data in the Analysis Toolset for MPRA (@MPRA), an R package for the design and analyses of MPRA experiments. It is publicly available at http://github.com/redaq/atMPRA .