Phenotypic overlap in the contribution of individual genes to CNV pathogenicity revealed by cross-species computational analysis of single-gene mutations in humans, mice and zebrafish.
Phenotypic overlap in the contribution of individual genes to CNV pathogenicity revealed by cross-species computational analysis of single-gene mutations in humans, mice and zebrafish.
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DOI：
10.1242/dmm.010322
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发表时间：
2013-03
影响因子：
4.3
通讯作者：
Lewis SE
中科院分区：
文献类型：
--
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
许多疾病综合征与人类基因组中的拷贝数变异（CNV）区域相关，并且在大多数情况下，CNV的致病性被认为与受影响片段内所含基因的改变剂量有关。然而，确定单个基因对CNV综合征的总体致病性的贡献是困难的，并且通常依赖于通过手动检索文献和在线资源来鉴定潜在的候选者。我们在这里描述了一个计算框架的发展，全面搜索表型信息的模式生物和单基因人类遗传性疾病，从而加快解释复杂的CNV疾病的表型。目前有超过5000个人类基因，关于这些基因的表型还不清楚，但是可以获得小鼠和/或斑马鱼直系同源物的详细表型信息。在这里，我们提出了一种基于本体的方法来识别人类疾病的表现和突变表型的特征模式生物基因之间的相似性，因此，这种方法可以使用，即使在很少或没有信息的情况下，人类基因的功能。我们应用该算法检测了27种复发性CNV疾病的候选基因，并确定了802种基因-表型关联，其中约一半涉及先前报道的与个体表型特征相关的基因，其中一半是新的候选基因。仅根据模式生物表型数据，共进行了431次关联。此外，我们观察到一个惊人的，统计学上显着的趋势，个别疾病的表型与位于一个单一的CNV区域内的多个基因，一种现象，我们表示为表型聚类。许多簇在蛋白质-蛋白质相互作用网络内的蛋白质功能或邻近区域中也显示出统计学上显著的相似性。我们的研究结果为理解以前无法解释的致病性CNVs的基因型-表型相关性提供了基础，并为动员大量的模式生物表型数据提供了对人类遗传疾病的见解。
Numerous disease syndromes are associated with regions of copy number variation (CNV) in the human genome and, in most cases, the pathogenicity of the CNV is thought to be related to altered dosage of the genes contained within the affected segment. However, establishing the contribution of individual genes to the overall pathogenicity of CNV syndromes is difficult and often relies on the identification of potential candidates through manual searches of the literature and online resources. We describe here the development of a computational framework to comprehensively search phenotypic information from model organisms and single-gene human hereditary disorders, and thus speed the interpretation of the complex phenotypes of CNV disorders. There are currently more than 5000 human genes about which nothing is known phenotypically but for which detailed phenotypic information for the mouse and/or zebrafish orthologs is available. Here, we present an ontology-based approach to identify similarities between human disease manifestations and the mutational phenotypes in characterized model organism genes; this approach can therefore be used even in cases where there is little or no information about the function of the human genes. We applied this algorithm to detect candidate genes for 27 recurrent CNV disorders and identified 802 gene-phenotype associations, approximately half of which involved genes that were previously reported to be associated with individual phenotypic features and half of which were novel candidates. A total of 431 associations were made solely on the basis of model organism phenotype data. Additionally, we observed a striking, statistically significant tendency for individual disease phenotypes to be associated with multiple genes located within a single CNV region, a phenomenon that we denote as pheno-clustering. Many of the clusters also display statistically significant similarities in protein function or vicinity within the protein-protein interaction network. Our results provide a basis for understanding previously un-interpretable genotype-phenotype correlations in pathogenic CNVs and for mobilizing the large amount of model organism phenotype data to provide insights into human genetic disorders.
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影响因子：
30.8
作者：
Durand, Christelle M.;Betancur, Catalina;Bourgeron, Thomas
通讯作者：
Bourgeron, Thomas
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：
8
作者：
Battaglia, Agatino;Hoyme, H. Eugene;Carey, John C.
通讯作者：
Carey, John C.
影响因子：
2
作者：
Aradhya, Swaroop;Manning, Melanie A.;Cherry, Athena M.
通讯作者：
Cherry, Athena M.
影响因子：
39.2
作者：
Bellanné-Chantelot, C;Chauveau, D;Timsit, J
通讯作者：
Timsit, J