美国纽约基因组中心Tuuli Lappalainen研究团队最新研究探明，人群中遗传调控的差异为罕见病的转录组分析提供了信息。 相关论文在线发表于2019年10月10日的《科学》。

转录组数据可以帮助解释稀有遗传变异的影响。在这项新研究中，研究人员利用ANEVA（表达变异分析）根据人群中的等位基因表达（AE）数据量化基因组中的遗传变异。 ANEVA在基因型-组织表达（GTEx）数据中的应用表明，这种方差估计是可靠的，并且与基因中选择性限制相关。

在对70名孟德尔肌肉病患者的AE数据进行剂量离群值测试（ANEVA-DOT）时，使用这种方差估计可以准确地检测出已解决病例中具有的致病变异基因，并导致了一项已确认和数种潜在的新诊断。 利用从GTEx数据获得的参考估计值，可以将ANEVA-DOT纳入罕见病诊断流程，以便更有效地使用RNA测序数据。

附：英文原文

Title: Genetic regulatory variation in populations informs transcriptome analysis in rare disease

Author: Pejman Mohammadi, Stephane E. Castel, Beryl B. Cummings, Jonah Einson, Christina Sousa, Paul Hoffman, Sandra Donkervoort, Zhuoxun Jiang, Payam Mohassel, A. Reghan Foley, Heather E. Wheeler, Hae Kyung Im, Carsten G. Bonnemann, Daniel G. MacArthur, Tuuli Lappalainen

Issue&Volume: 2019/10/10

Abstract: 

Transcriptome data can facilitate the interpretation of the effects of rare genetic variants. Here, we introduce ANEVA (analysis of expression variation) to quantify genetic variation in gene dosage from allelic expression (AE) data in a population. Application of ANEVA to the Genotype-Tissues Expression (GTEx) data showed that this variance estimate is robust and correlated with selective constraint in a gene. Using these variance estimates in a dosage outlier test (ANEVA-DOT) applied to AE data from 70 Mendelian muscular disease patients showed accuracy in detecting genes with pathogenic variants in previously resolved cases and led to one confirmed and several potential new diagnoses. Using our reference estimates from GTEx data, ANEVA-DOT can be incorporated in rare disease diagnostic pipelines to use RNA-sequencing data more effectively.

DOI: 10.1126/science.aay0256

Source: https://science.sciencemag.org/content/early/2019/10/09/science.aay0256