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Statistical considerations for the analysis of massively parallel reporter assays data.
Genetic Epidemiology ( IF 2.1 ) Pub Date : 2020-07-18 , DOI: 10.1002/gepi.22337
Dandi Qiao 1 , Corwin M Zigler 2 , Michael H Cho 1, 3 , Edwin K Silverman 1, 3 , Xiaobo Zhou 1 , Peter J Castaldi 1, 4 , Nan H Laird 5
Affiliation  

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.

中文翻译:

分析大规模并行报告分析数据的统计考虑。

非编码DNA含有改变基因表达的基因调控元件,这些元件的功能可以通过遗传变异来改变。大规模平行报告基因分析 (MPRA) 能够对功能性遗传变异进行高通量鉴定和表征,但在 MPRA 数据中鉴定等位基因效应的统计方法尚未完全开发。在这项研究中,我们展示了 MPRA 文库中的基线等位基因失衡如何产生有偏差的结果,并且我们提出了一种新的、非参数的、自适应的测试方法,该方法对这种偏差具有鲁棒性。我们将这种方法与其他常用方法的性能进行了比较,我们证明了我们的新型自适应方法可以在广泛的场景中控制 I 类错误,同时保持出色的功率。我们已经在 MPRA 分析工具集 (@MPRA) 中实施了这些测试以及用于模拟 MPRA 数据的例程,这是一个用于设计和分析 MPRA 实验的 R 包。它在 http://github.com/redaq/atMPRA 上公开可用。
更新日期:2020-09-11
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