Direct Testing for Allele-Specific Expression Differences Between Conditions.
Direct Testing for Allele-Specific Expression Differences Between Conditions.
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DOI：
10.1534/g3.117.300139
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发表时间：
2018-02-02
期刊：
影响因子：
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通讯作者：
Marroni F
中科院分区：
文献类型：
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作者：
León-Novelo L;Gerken AR;Graze RM;McIntyre LM;Marroni F
等位基因不平衡（AI）表明顺式调控区存在功能变异。使用AI检测顺式调节差异很普遍，但没有正式的统计方法来测试AI是否在不同条件下存在差异。在这里，我们提出了一个新的模型，并使用贝叶斯可信区间正式测试AI在不同条件下的差异。该方法通过环境（G×E）相互作用测试AI，并可用于测试环境，基因型，性别和任何其他条件之间的AI。我们将偏见纳入建模过程。允许偏差在条件之间变化，使得模型的公式化具有一般性。由于基因表达影响AI检测的能力，并且由于表达可能在条件之间变化，因此该模型明确考虑了覆盖率。该模型在几种情况下具有较低的I型和II型误差，并且对条件之间的覆盖率差异很大具有鲁棒性。我们重新分析了来自果蝇种群面板的RNA-seq数据，F1基因型，以比较交配和处女雌蝇之间的AI水平，我们表明AI ×基因型相互作用也可以进行测试。为了证明使用该模型来测试遗传差异和相互作用，在两个F1之间进行了正式测试，显示AI的预期差异为20%。该模型允许G×E和G×G的正式测试，并重申了以前的发现，顺式调节是强大的环境之间。
Allelic imbalance (AI) indicates the presence of functional variation in cis regulatory regions. Detecting cis regulatory differences using AI is widespread, yet there is no formal statistical methodology that tests whether AI differs between conditions. Here, we present a novel model and formally test differences in AI across conditions using Bayesian credible intervals. The approach tests AI by environment (G×E) interactions, and can be used to test AI between environments, genotypes, sex, and any other condition. We incorporate bias into the modeling process. Bias is allowed to vary between conditions, making the formulation of the model general. As gene expression affects power for detection of AI, and, as expression may vary between conditions, the model explicitly takes coverage into account. The proposed model has low type I and II error under several scenarios, and is robust to large differences in coverage between conditions. We reanalyze RNA-seq data from a Drosophila melanogaster population panel, with F1 genotypes, to compare levels of AI between mated and virgin female flies, and we show that AI × genotype interactions can also be tested. To demonstrate the use of the model to test genetic differences and interactions, a formal test between two F1s was performed, showing the expected 20% difference in AI. The proposed model allows a formal test of G×E and G×G, and reaffirms a previous finding that cis regulation is robust between environments.