小柯机器人

新型工具可使用不同的分布模式来区分疾病候选基因的优先级
2019-12-25 11:00

英国曼彻斯特大学May Tassabehji研究小组开发了一个名为GeVIR的工具,这是一种连续的基因水平指标,其使用不同的分布模式来区分疾病候选基因的优先级。相关论文2019年12月23日在线发表在《自然—遗传学》上。

利用来自138632个外显子组和基因组序列的数据,研究人员开发了一个名为GeVIR(gene variation intolerance rank)的工具。这是一种连续的基因水平指标,可检测19361个基因,能够优先检测孟德尔疾病的显性和隐性疾病基因,优于错义约束指标,并且对功能丧失(LOF)约束度量标准具有可比性 (而且同时具有互补性)。GeVIR还能够对短基因进行优先排序,对于这些短基因,LOF约束则无法可靠地估计。在这项研究中鉴定的大多数最不耐受的基因没有明确的表型,并且是严重显性疾病的候选基因。

据了解,随着大规模人群测序项目的发展,目前需要一种可以提高疾病基因优先级的策略。提供有关基因及其耐受蛋白质改变变异能力信息的度量标准可有助于人类基因组的临床解释,并可促进疾病基因的发现。先前报道的方法分析了基因中的总变体负荷,但没有分析基因中变体的分布模式。

附:英文原文

Title: GeVIR is a continuous gene-level metric that uses variant distribution patterns to prioritize disease candidate genes

Author: Nikita Abramovs, Andrew Brass, May Tassabehji

Issue&Volume: 2019-12-23

Abstract: With large-scale population sequencing projects gathering pace, there is a need for strategies that advance disease gene prioritization1,2. Metrics that provide information about a gene and its ability to tolerate protein-altering variation can aid in clinical interpretation of human genomes and can advance disease gene discovery1,2,3,4. Previous reported methods analyzed the total variant load in a gene1,2,3,4, but did not analyze the distribution pattern of variants within a gene. Using data from 138,632 exome and genome sequences2, we developed gene variation intolerance rank (GeVIR), a continuous gene-level metric for 19,361 genes that is able to prioritize both dominant and recessive Mendelian disease genes5, that outperforms missense constraint metrics3 and that is comparable—but complementary—to loss-of-function (LOF) constraint metrics2. GeVIR is also able to prioritize short genes, for which LOF constraint cannot be estimated with confidence2. The majority of the most intolerant genes identified here have no defined phenotype and are candidates for severe dominant disorders.

DOI: 10.1038/s41588-019-0560-2

Source: https://www.nature.com/articles/s41588-019-0560-2

Nature Genetics:《自然—遗传学》,创刊于1992年。隶属于施普林格·自然出版集团,最新IF:41.307
官方网址:https://www.nature.com/ng/
投稿链接:https://mts-ng.nature.com/cgi-bin/main.plex


本期文章:《自然—遗传学》:Online/在线发表

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