Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Science. 2020 Sep 11;369(6509). doi: 10.1126/science.aaz5900. Epub 2020 Sep 10.
Rare genetic variants are abundant across the human genome, and identifying their function and phenotypic impact is a major challenge. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants (RVs). Here, we expanded detection of genetically driven transcriptome abnormalities by analyzing gene expression, allele-specific expression, and alternative splicing from multitissue RNA-sequencing data, and demonstrate that each signal informs unique classes of RVs. We developed Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function, validated these predictions in additional cohorts and through experimental assays, and used them to assess RVs in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. Our results link thousands of RVs to diverse molecular effects and provide evidence to associate RVs affecting the transcriptome with human traits.
人类基因组中存在大量罕见的遗传变异，鉴定它们的功能和表型影响是一个主要挑战。测量异常基因表达有助于识别功能强大的罕见变异（RVs）。在这里，我们通过分析来自多组织 RNA-seq 数据的基因表达、等位基因特异性表达和选择性剪接，扩展了对遗传驱动转录组异常的检测，并证明每种信号都提供了独特的 RV 类别。我们开发了 Watershed，这是一种概率模型，可整合多种基因组和转录组信号来预测变体功能，通过额外的队列和实验检测对这些预测进行了验证，并将其用于评估英国生物库、百万退伍军人计划和杰克逊心脏研究中的 RV。我们的结果将数千个 RV 与多种分子效应联系起来，并提供证据将影响转录组的 RV 与人类特征联系起来。
