Fusion of large-scale genomic knowledge and frequency data computationally prioritizes variants in epilepsy.
Fusion of large-scale genomic knowledge and frequency data computationally prioritizes variants in epilepsy.
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DOI：
10.1371/journal.pgen.1003797
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发表时间：
2013
期刊：
影响因子：
4.5
通讯作者：
Shaw CA
中科院分区：
文献类型：
--
作者：
Campbell IM;Rao M;Arredondo SD;Lalani SR;Xia Z;Kang SH;Bi W;Breman AM;Smith JL;Bacino CA;Beaudet AL;Patel A;Cheung SW;Lupski JR;Stankiewicz P;Ramocki MB;Shaw CA
通过全基因组检测确定的拷贝数变异的管理和解释受到每个人基因组中大量事件的挑战。传统的表型相关性测定依赖于受影响个体与对照组相比频率更高的模式；然而，越来越多的确定变异是罕见的或私人的氏族。因此，频率数据在区分致病与良性方面的效用较小。一种解决方案是针对疾病的算法，利用基因知识和变异频率来帮助确定优先级。我们利用大规模资源，包括基因本体、蛋白-蛋白相互作用和其他注释系统，以及83个已知与癫痫相关的基因，构建了表型的致病性评分。我们评估了所有注释的人类基因的评分，并应用贝叶斯方法将衍生的致病性评分与我们诊断实验室的频率信息结合起来。分析确定了每个基因的贝叶斯因子和后验分布。我们将我们的方法应用于染色体微阵列结果异常的受试者和电子病历审查收集的癫痫确诊诊断。与非神经系统适应症患者相比，癫痫患者基因缺失的致病性评分和贝叶斯因子明显更高。我们还应用我们的分数在一个复杂的基因组区域中确定了一个最近验证的癫痫基因，并揭示了癫痫的候选基因。我们建议在全基因组筛选的背景下，我们的结果在临床决策支持中的潜在用途。我们的方法展示了整合数据在医学基因组学中的效用。测序和微阵列技术的改进提高了基因检测的分辨率和范围。结果，在不相关个体的每个个人基因组中发现了数百万个变异。在遗传疾病检测的背景下，在如此众多的候选者中确定导致疾病的变体是困难的。识别致病变异的传统研究依赖于受影响患者与健康个体之间更高频率的模式。然而，引起人类疾病的往往是最罕见的变异，这使得单独的频率信息不太有用。许多研究小组已经转向计算分析来帮助解释遗传变异。癫痫是一种有用的疾病，因为只有一小部分疑似遗传性癫痫的患者有特定的遗传诊断。为了帮助改善癫痫的变异解释，我们使用计算分析将来自大型云信息源的基因知识与来自诊断实验室的突变频率相结合，对所有基因进行评分，以确定它们与癫痫相关的可能性。我们使用这些分数来确定癫痫可能的候选基因，并探索其他下游应用。
Curation and interpretation of copy number variants identified by genome-wide testing is challenged by the large number of events harbored in each personal genome. Conventional determination of phenotypic relevance relies on patterns of higher frequency in affected individuals versus controls; however, an increasing amount of ascertained variation is rare or private to clans. Consequently, frequency data have less utility to resolve pathogenic from benign. One solution is disease-specific algorithms that leverage gene knowledge together with variant frequency to aid prioritization. We used large-scale resources including Gene Ontology, protein-protein interactions and other annotation systems together with a broad set of 83 genes with known associations to epilepsy to construct a pathogenicity score for the phenotype. We evaluated the score for all annotated human genes and applied Bayesian methods to combine the derived pathogenicity score with frequency information from our diagnostic laboratory. Analysis determined Bayes factors and posterior distributions for each gene. We applied our method to subjects with abnormal chromosomal microarray results and confirmed epilepsy diagnoses gathered by electronic medical record review. Genes deleted in our subjects with epilepsy had significantly higher pathogenicity scores and Bayes factors compared to subjects referred for non-neurologic indications. We also applied our scores to identify a recently validated epilepsy gene in a complex genomic region and to reveal candidate genes for epilepsy. We propose a potential use in clinical decision support for our results in the context of genome-wide screening. Our approach demonstrates the utility of integrative data in medical genomics. Improvements in sequencing and microarray technologies have increased the resolution and scope of genetic testing. As a result, millions of variations are identified in each personal genome of unrelated individuals. In the context of testing for genetic diseases, identifying the variant or variants contributing to illness among such a large number of candidates is difficult. Conventional studies to identify causative variants have relied on patterns of higher frequency in affected patients compared with individuals that are well. However, it is often the rarest variations that cause human disease, making frequency information alone less useful. Many groups have turned to computational analysis to aid in interpretation of genetic variants. Epilepsy is a disease where such tools would be useful, as only a fraction of patients with suspected genetic epilepsy have a specific genetic diagnosis. To help improve variant interpretation in epilepsy, we used computational analysis to combine knowledge about genes from large cloud information sources with mutation frequency from our diagnostic laboratory to score all genes as to how likely they are to be associated with epilepsy. We use these scores to identify possible candidate genes in epilepsy, and explore other downstream applications.
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影响因子：
30.8
作者：
Helbig I;Mefford HC;Sharp AJ;Guipponi M;Fichera M;Franke A;Muhle H;de Kovel C;Baker C;von Spiczak S;Kron KL;Steinich I;Kleefuss-Lie AA;Leu C;Gaus V;Schmitz B;Klein KM;Reif PS;Rosenow F;Weber Y;Lerche H;Zimprich F;Urak L;Fuchs K;Feucht M;Genton P;Thomas P;Visscher F;de Haan GJ;Møller RS;Hjalgrim H;Luciano D;Wittig M;Nothnagel M;Elger CE;Nürnberg P;Romano C;Malafosse A;Koeleman BP;Lindhout D;Stephani U;Schreiber S;Eichler EE;Sander T
通讯作者：
Sander T
影响因子：
14.9
作者：
Blake JA;Bult CJ;Kadin JA;Richardson JE;Eppig JT;Mouse Genome Database Group
通讯作者：
Mouse Genome Database Group
影响因子：
3.7
作者：
Jia P;Ewers JM;Zhao Z
通讯作者：
Zhao Z
影响因子：
10.5
作者：
Gonzaga-Jauregui C;Lupski JR;Gibbs RA
通讯作者：
Gibbs RA
影响因子：
2.2
作者：
Banerjee, Poonam Nina;Filippi, David;Hauser, W. Allen
通讯作者：
Hauser, W. Allen