从RNA测序读取数据评估多个组织中的等位基因特异性表达。
Assessing allele-specific expression across multiple tissues from RNA-seq read data.
作者信息
Pirinen Matti, Lappalainen Tuuli, Zaitlen Noah A, Dermitzakis Emmanouil T, Donnelly Peter, McCarthy Mark I, Rivas Manuel A
机构信息
Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
Department of Genetic Medicine and Development and, Institute for Genetics and Genomics in Geneva (iG3), University of Geneva, Geneva, Switzerland, Swiss Institute of Bioinformatics, Geneva, Switzerland, Department of Genetics, Stanford University, Palo Alto, CA, USA, New York Genome Center, New York, NY, USA, Department of Systems Biology, Columbia University, New York, NY, USA.
出版信息
Bioinformatics. 2015 Aug 1;31(15):2497-504. doi: 10.1093/bioinformatics/btv074. Epub 2015 Mar 27.
MOTIVATION
RNA sequencing enables allele-specific expression (ASE) studies that complement standard genotype expression studies for common variants and, importantly, also allow measuring the regulatory impact of rare variants. The Genotype-Tissue Expression (GTEx) project is collecting RNA-seq data on multiple tissues of a same set of individuals and novel methods are required for the analysis of these data.
RESULTS
We present a statistical method to compare different patterns of ASE across tissues and to classify genetic variants according to their impact on the tissue-wide expression profile. We focus on strong ASE effects that we are expecting to see for protein-truncating variants, but our method can also be adjusted for other types of ASE effects. We illustrate the method with a real data example on a tissue-wide expression profile of a variant causal for lipoid proteinosis, and with a simulation study to assess our method more generally.
动机
RNA测序能够进行等位基因特异性表达（ASE）研究，这为常见变异的标准基因型表达研究提供了补充，并且重要的是，还能用于测量罕见变异的调控影响。基因型-组织表达（GTEx）项目正在收集同一组个体多个组织的RNA测序数据，因此需要新的方法来分析这些数据。
结果
我们提出了一种统计方法，用于比较不同组织间ASE的不同模式，并根据遗传变异对全组织表达谱的影响对其进行分类。我们重点关注预期会在截短蛋白变异中出现的强ASE效应，但我们的方法也可针对其他类型的ASE效应进行调整。我们通过一个关于类脂蛋白沉积症致病变异的全组织表达谱的真实数据示例，以及一项更全面评估我们方法的模拟研究，对该方法进行了说明。
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