Transcript expression-aware annotation improves rare variant interpretation
Transcript expression-aware annotation improves rare variant interpretation
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DOI：
10.1038/s41586-020-2329-2
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发表时间：
2020-05-28
期刊：
影响因子：
64.8
通讯作者：
MacArthur, Daniel G.
中科院分区：
文献类型：
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作者：
Cummings, Beryl B.;Karczewski, Konrad J.;MacArthur, Daniel G.
患者和人群研究样本中DNA测序的加速导致了广泛的人类遗传变异目录，但对罕见遗传变异的解释仍然存在问题。这一挑战的一个显著例子是在剂量敏感的疾病基因中存在破坏性变异，即使在表面上健康的人中也是如此。在这里，通过手动对基因组聚合数据库(GnomAD)(1)中单倍体不足的疾病基因的假定功能丧失(PLoF)变体进行筛选，我们证明了对这一悖论的一种解释涉及到mRNA的选择性剪接，这允许一个基因的外显子在不同类型的细胞中以不同的水平表达。目前，没有现有的注释工具系统地将有关外显子表达的信息合并到变体的解释中。我们开发了一种转录水平的注释度量，称为“转录本之间表达的比例”，它对变体的异构体表达进行量化。我们使用来自基因类型组织表达(GTEx)项目的11,706个组织样本计算了这一指标(2)，并表明它可以区分进化上高度保守和弱保守的外显子，这是功能重要性的代理。我们证明了基于表达的注释选择性地过滤了在gnomAD单倍体不足的疾病基因中发现的22.8%的错误注释的pLoF变体，同时删除了同一基因中不到4%的高置信度致病变体。最后，我们将我们的表达过滤器应用于自闭症谱系障碍和智能障碍或发育障碍患者的从头变体的分析，结果表明，弱表达区域的pLoF变体与同义变体的效应大小相似，而高表达外显子的pLoF变体在病例之间最为丰富。我们的注释是快速、灵活和可泛化的，使得任何变体文件都可以用任何异构体表达数据集进行注释，这将对罕见疾病的遗传诊断、复杂疾病中罕见变量负担的分析以及按基因召回研究中变体的整理和优先排序有价值。
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) project(2) 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.