Statistical considerations for the analysis of massively parallel reporter assays data

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Noncoding DNA harbors gene regulatory elements crucial for gene expression control.
• Genetic variations can significantly alter the function of these regulatory elements.
• Massively Parallel Reporter Assays (MPRA) are powerful tools for high-throughput functional genetic variant identification.

Purpose of the Study:

• To address the lack of fully developed statistical methods for analyzing allelic effects in MPRA data.
• To propose a novel statistical method robust to baseline allelic imbalance in MPRA libraries.
• To provide a reliable tool for the design and analysis of MPRA experiments.

Main Methods:

• Demonstrated how baseline allelic imbalance in MPRA libraries can introduce bias.
• Proposed a novel, nonparametric, adaptive testing method designed to be robust to bias.
• Compared the performance of the novel method against commonly used methods.

Main Results:

• The novel adaptive method effectively controls Type I error across diverse scenarios.
• The method maintains excellent statistical power for detecting allelic effects.
• The developed method is implemented in the @MPRA R package.

Conclusions:

• The proposed nonparametric, adaptive testing method offers a robust solution for analyzing MPRA data.
• This method corrects for baseline allelic imbalance, improving the accuracy of identifying functional genetic variants.
• The @MPRA R package provides a comprehensive tool for MPRA experimental design and analysis.