Zou Xue, Gomez Zachary W, Reddy Timothy E, Allen Andrew S, Majoros William H
Duke Center for Statistical Genetics and Genomics, Duke University, Durham, NC 27710, United States.
Department of Biostatistics & Bioinformatics, Duke University Medical School, Durham, NC 27710, United States.
Bioinformatics. 2025 Jun 2;41(6). doi: 10.1093/bioinformatics/btaf283.
Allele-specific expression (ASE) analyses aim to detect imbalanced expression of maternal versus paternal copies of an autosomal gene. Such allelic imbalance can result from a variety of cis-acting causes, including disruptive mutations within one copy of a gene that impact the stability of transcripts, as well as regulatory variants outside the gene that impact transcription initiation. Current methods for ASE estimation suffer from a number of shortcomings, such as relying on only one variant within a gene, assuming perfect phasing information across multiple variants within a gene, or failing to account for alignment biases and possible genotyping errors.
We developed BEASTIE, a Bayesian hierarchical model designed for precise ASE quantification at the gene level, based on given genotypes and RNA-Seq data. BEASTIE addresses the complexities of allelic mapping bias, genotyping error, and phasing errors by incorporating empirical phasing error rates derived from Genome-in-a-Bottle individual NA12878. BEASTIE surpasses existing methods in accuracy, especially in scenarios with high phasing errors. This improvement is critical for identifying rare genetic variants often obscured by such errors. Through rigorous validation on simulated data and application to real data from the 1000 Genomes Project, we establish the robustness of BEASTIE. These findings underscore the value of BEASTIE in revealing patterns of ASE across gene sets and pathways.
The software is freely available from Github (https://github.com/x811zou/BEASTIE); and Zendo (DOI: 10.5281/zenodo.15062124).
等位基因特异性表达（ASE）分析旨在检测常染色体基因的母本与父本拷贝的表达失衡。这种等位基因失衡可能由多种顺式作用原因导致，包括基因一个拷贝内的破坏性突变影响转录本的稳定性，以及基因外影响转录起始的调控变异。当前用于ASE估计的方法存在许多缺点，例如仅依赖基因内的一个变异，假设基因内多个变异的定相信息完美，或者未考虑比对偏差和可能的基因分型错误。
我们开发了BEASTIE，这是一种基于给定基因型和RNA测序数据，用于在基因水平精确量化ASE的贝叶斯层次模型。BEASTIE通过纳入源自“瓶中基因组”个体NA12878的经验性定相错误率，解决了等位基因映射偏差、基因分型错误和定相错误的复杂性。BEASTIE在准确性方面超越了现有方法，尤其是在定相错误率高的情况下。这种改进对于识别经常被此类错误掩盖的罕见遗传变异至关重要。通过对模拟数据的严格验证以及应用于千人基因组计划的真实数据，我们确立了BEASTIE的稳健性。这些发现强调了BEASTIE在揭示跨基因集和通路的ASE模式方面的价值。
该软件可从Github（https://github.com/x811zou/BEASTIE）和Zendo（DOI：10.5281/zenodo.15062124）免费获取。
