科學家提出鑑定罕見病基因新方法
作者：
小柯機器人發布時間：2019/7/9 13:30:34
史丹福大學Stephen B. Montgomery研究團隊的一項最新研究，提出了利用血液轉錄組測序和大型對照組鑑定罕見病基因的方法。 2019年6月出版的《Nature Medicine》發表了這項成果。
該課題組試圖評估血液中的RNA-seq作為診斷不同病理生理學罕見疾病工具的效用。研究人員從94名患有16種不同疾病類別的未確診罕見疾病的患者身上提取了全血進行RNA-seq。他們開發了一種穩健的方法，將這些個體的數據與對照(n=1,594個不相關對照和n=49個家庭成員)的大量RNA-seq數據進行比較，並展示了表達、剪接、基因和變異篩選策略對疾病基因識別的影響。在研究的整個隊列中，課題組觀察到RNA-seq的診斷率為7.5%，而隨著候選基因解析度的提高，其診斷率又增加了16.7%。
研究人員表示，全球約有3.5億人患有罕見疾病，主要是由單一基因突變引起的。目前的分子診斷率估計為50%，其中全外顯子組測序(WES)是最成功的方法之一。對於不能提供WES信息的患者，RNA測序(RNA-seq)已顯示出對特定組織和疾病的診斷價值。包括來自未確診的罕見肌肉疾病患者的肌肉活檢，以及來自線粒體疾病患者的培養成纖維細胞。然而，對許多人來說，活檢不利於臨床護理，並且組織難以獲得。
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