Improved exome prioritization of disease genes through cross-species phenotype comparison.
Improved exome prioritization of disease genes through cross-species phenotype comparison.
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DOI：
10.1101/gr.160325.113
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发表时间：
2014-02
期刊：
影响因子：
7
通讯作者：
Smedley D
中科院分区：
文献类型：
--
作者：
Robinson PN;Köhler S;Oellrich A;Sanger Mouse Genetics Project;Wang K;Mungall CJ;Lewis SE;Washington N;Bauer S;Seelow D;Krawitz P;Gilissen C;Haendel M;Smedley D
在过去的几年里，通过全外显子组测序研究，已经发现了许多新的疾病基因关联。然而，许多病例仍然没有解决，因为在常用的过滤策略(如去除低质量和常见的变异以及那些被认为不太可能致病的变异)后，仍有大量候选变异保留下来。观察到，我们的每个基因组包含大约100个真正的功能丧失变体，这使得单独使用这些策略时识别致病突变是有问题的。我们建议使用从模式生物研究中已经存在的表型数据来评估这些外显子变异体的潜在影响。在这里，我们介绍了Exomiser工具中的Exomiser工具中的表型解释(PHIVE)，这是一种算法，它将计算人类疾病和转基因小鼠模型之间的表型相似性与根据等位基因频率、致病性和遗传方式评估变异相结合。使用100,000个包含已知突变的外显子进行的PHIVE分析的大规模验证表明，与纯粹基于变异(频率和致病性)的方法相比，有实质性的改进(高达54.1倍)，正确的基因在高达83%的样本中被称为最高命中率，对应于ROC曲线下95%的区域。我们的结论是，表型数据的整合可以在翻译生物信息学中发挥重要作用，并建议外显子组测序项目应该系统地捕获临床表型，以利用这里提出的策略。
Numerous new disease-gene associations have been identified by whole-exome sequencing studies in the last few years. However, many cases remain unsolved due to the sheer number of candidate variants remaining after common filtering strategies such as removing low quality and common variants and those deemed unlikely to be pathogenic. The observation that each of our genomes contains about 100 genuine loss-of-function variants makes identification of the causative mutation problematic when using these strategies alone. We propose using the wealth of genotype to phenotype data that already exists from model organism studies to assess the potential impact of these exome variants. Here, we introduce PHenotypic Interpretation of Variants in Exomes (PHIVE), an algorithm that integrates the calculation of phenotype similarity between human diseases and genetically modified mouse models with evaluation of the variants according to allele frequency, pathogenicity, and mode of inheritance approaches in our Exomiser tool. Large-scale validation of PHIVE analysis using 100,000 exomes containing known mutations demonstrated a substantial improvement (up to 54.1-fold) over purely variant-based (frequency and pathogenicity) methods with the correct gene recalled as the top hit in up to 83% of samples, corresponding to an area under the ROC curve of >95%. We conclude that incorporation of phenotype data can play a vital role in translational bioinformatics and propose that exome sequencing projects should systematically capture clinical phenotypes to take advantage of the strategy presented here.
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DOI：
10.1126/science.1215040
发表时间：
2012-02-17
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
MacArthur DG;Balasubramanian S;Frankish A;Huang N;Morris J;Walter K;Jostins L;Habegger L;Pickrell JK;Montgomery SB;Albers CA;Zhang ZD;Conrad DF;Lunter G;Zheng H;Ayub Q;DePristo MA;Banks E;Hu M;Handsaker RE;Rosenfeld JA;Fromer M;Jin M;Mu XJ;Khurana E;Ye K;Kay M;Saunders GI;Suner MM;Hunt T;Barnes IH;Amid C;Carvalho-Silva DR;Bignell AH;Snow C;Yngvadottir B;Bumpstead S;Cooper DN;Xue Y;Romero IG;1000 Genomes Project Consortium;Wang J;Li Y;Gibbs RA;McCarroll SA;Dermitzakis ET;Pritchard JK;Barrett JC;Harrow J;Hurles ME;Gerstein MB;Tyler-Smith C
通讯作者：
Tyler-Smith C
影响因子：
30.8
作者：
通讯作者：
--
来自1,092个人基因组的遗传变异的综合图。
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
3.9
作者：
Amberger, Joanna;Bocchini, Carol;Hamosh, Ada
通讯作者：
Hamosh, Ada
影响因子：
4.3
作者：
Doelken SC;Köhler S;Mungall CJ;Gkoutos GV;Ruef BJ;Smith C;Smedley D;Bauer S;Klopocki E;Schofield PN;Westerfield M;Robinson PN;Lewis SE
通讯作者：
Lewis SE