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.
journal_name
Genet Epidemioljournal_title
Genetic epidemiologyauthors
Qiao D,Zigler CM,Cho MH,Silverman EK,Zhou X,Castaldi PJ,Laird NHdoi
10.1002/gepi.22337subject
Has Abstractpub_date
2020-10-01 00:00:00pages
785-794issue
7eissn
0741-0395issn
1098-2272journal_volume
44pub_type
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