Statistical considerations for the analysis of massively parallel reporter assays data.
Genet Epidemiol
; 44(7): 785-794, 2020 10.
Article
en En
| MEDLINE
| ID: mdl-32681690
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.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
ADN
/
Expresión Génica
/
Secuencias Reguladoras de Ácidos Nucleicos
/
ARN no Traducido
/
Secuenciación de Nucleótidos de Alto Rendimiento
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Año:
2020
Tipo del documento:
Article