斯坦福大学Stephen B. Montgomery研究团队的一项最新研究，提出了利用血液转录组测序和大型对照组鉴定罕见病基因的方法。 2019年6月出版的《Nature Medicine》发表了这项成果。

该课题组试图评估血液中的RNA-seq作为诊断不同病理生理学罕见疾病工具的效用。研究人员从94名患有16种不同疾病类别的未确诊罕见疾病的患者身上提取了全血进行RNA-seq。他们开发了一种稳健的方法，将这些个体的数据与对照(n=1,594个不相关对照和n=49个家庭成员)的大量RNA-seq数据进行比较，并展示了表达、剪接、基因和变异筛选策略对疾病基因识别的影响。在研究的整个队列中，课题组观察到RNA-seq的诊断率为7.5%，而随着候选基因分辨率的提高，其诊断率又增加了16.7%。

研究人员表示，全球约有3.5亿人患有罕见疾病，主要是由单一基因突变引起的。目前的分子诊断率估计为50%，其中全外显子组测序(WES)是最成功的方法之一。对于不能提供WES信息的患者，RNA测序(RNA-seq)已显示出对特定组织和疾病的诊断价值。包括来自未确诊的罕见肌肉疾病患者的肌肉活检，以及来自线粒体疾病患者的培养成纤维细胞。然而，对许多人来说，活检不利于临床护理，并且组织难以获得。

附：英文原文

Title: Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts

Author: Laure Frsard, Craig Smail, Nicole M. Ferraro, Nicole A. Teran, Xin Li, Kevin S. Smith, Devon Bonner, Kristin D. Kernohan, Shruti Marwaha, Zachary Zappala, Brunilda Balliu, Joe R. Davis, Boxiang Liu, Cameron J. Prybol, Jennefer N. Kohler, Diane B. Zastrow, Chloe M. Reuter, Dianna G. Fisk, Megan E. Grove, Jean M. Davidson, Taila Hartley, Ruchi Joshi, Benjamin J. Strober, Sowmithri Utiramerur, Lars Lind, Erik Ingelsson, Alexis Battle, Gill Bejerano, Jonathan A. Bernstein, Euan A. Ashley, Kym M. Boycott, Jason D. Merker, Matthew T. Wheeler, Stephen B. Montgomery

Issue&Volume:Volume 25 Issue 6，June 2019

Abstract: It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene1. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches25. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases68. This includes muscle biopsies from patients with undiagnosed rare muscle disorders6,9, and cultured fibroblasts from patients with mitochondrial disorders7. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n=1,594 unrelated controls and n=49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution.

DOI: 10.1038/s41591-019-0457-8

Source: https://www.nature.com/articles/s41591-019-0457-8