小柯机器人

澳大利亚新南威尔士大学Daniel G. MacArthur课题组取得一项新突破。他们的论文发现基因转录表达注释改善了对罕见突变的理解。这一研究成果于2020年5月27日在线发表在《自然》上。

通过人工处理基因组聚合数据库（gnomAD）中单倍体疾病基因中的假定功能丧失（pLoF）突变，研究人员发现对罕见遗传变异的一种解释涉及mRNA的可变剪接，其导致基因的外显子在不同细胞类型中以不同水平表达。当前，没有注释工具可以将有关外显子表达的信息系统地整合到变体的解释中。

研究人员开发了一种转录级别的注释工具，称为“跨转录表达的比率”，用于量化变体的同工型表达。研究人员使用来自基因型组织表达（GTEx）数据库的11,706个组织样品测试了该工具，并发现其可以区分弱和高度进化保守的外显子，这代表了功能的重要性。

研究人员证明基于表达的注释选择性地过滤了在gnomAD单倍型疾病基因中发现的pLoF变体错误注释的22.8％，同时删除了同一基因中高可信度不足4％的致病变体。最后，将表达过滤器应用于自闭症谱系障碍和智力障碍或发育障碍患者的从头变异体分析，研究人员发现弱表达区域的pLoF变体与同义变体具有相似的效应；而pLoF变体在高度表达区域，具有相似效应大小的外显子表达在病例中最丰富。

该方法快速、灵活且应用广泛，使任何变体都可以使用任何同工型表达数据集进行注释，并且对于稀有疾病的遗传诊断、复杂疾病中罕见变体的分析以及治疗具有宝贵的价值。

据介绍，对来自患者和种群研究样品中DNA的测序加速对导致人类遗传变异的分类，但是罕见遗传变异的解释仍然存在问题。这一问题的典型事例是剂量敏感性疾病基因中存在破坏性变异，即使在健康的个体中也是如此。

附：英文原文

Title: Transcript expression-aware annotation improves rare variant interpretation

Author: Beryl B. Cummings, Konrad J. Karczewski, Jack A. Kosmicki, Eleanor G. Seaby, Nicholas A. Watts, Moriel Singer-Berk, Jonathan M. Mudge, Juha Karjalainen, F. Kyle Satterstrom, Anne H. ODonnell-Luria, Timothy Poterba, Cotton Seed, Matthew Solomonson, Jessica Alfldi, Mark J. Daly, Daniel G. MacArthur

Issue&Volume: 2020-05-27

Abstract: The acceleration of DNA sequencing in samples from patients and population studies has resulted in extensive catalogues of human genetic variation, but the interpretation of rare genetic variants remains problematic. A notable example of this challenge is the existence of disruptive variants in dosage-sensitive disease genes, even in apparently healthy individuals. Here, by manual curation of putative loss-of-function (pLoF) variants in haploinsufficient disease genes in the Genome Aggregation Database (gnomAD)1, we show that one explanation for this paradox involves alternative splicing of mRNA, which allows exons of a gene to be expressed at varying levels across different cell types. Currently, no existing annotation tool systematically incorporates information about exon expression into the interpretation of variants. We develop a transcript-level annotation metric known as the ‘proportion expressed across transcripts’, which quantifies isoform expression for variants. We calculate this metric using 11,706 tissue samples from the Genotype Tissue Expression (GTEx) project2 and show that it can differentiate between weakly and highly evolutionarily conserved exons, a proxy for functional importance. We demonstrate that expression-based annotation selectively filters 22.8% of falsely annotated pLoF variants found in haploinsufficient disease genes in gnomAD, while removing less than 4% of high-confidence pathogenic variants in the same genes. Finally, we apply our expression filter to the analysis of de novo variants in patients with autism spectrum disorder and intellectual disability or developmental disorders to show that pLoF variants in weakly expressed regions have similar effect sizes to those of synonymous variants, whereas pLoF variants in highly expressed exons are most strongly enriched among cases. Our annotation is fast, flexible and generalizable, making it possible for any variant file to be annotated with any isoform expression dataset, and will be valuable for the genetic diagnosis of rare diseases, the analysis of rare variant burden in complex disorders, and the curation and prioritization of variants in recall-by-genotype studies.

DOI: 10.1038/s41586-020-2329-2

Source: https://www.nature.com/articles/s41586-020-2329-2

Nature：《自然》，创刊于1869年。隶属于施普林格·自然出版集团，最新IF：69.504
官方网址：http://www.nature.com/
投稿链接：http://www.nature.com/authors/submit_manuscript.html