Symptom-driven idiopathic disease gene identification.
Symptom-driven idiopathic disease gene identification.
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DOI：
10.1038/gim.2014.202
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发表时间：
2015-11
期刊：
影响因子：
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通讯作者：
Torkamani A
中科院分区：
文献类型：
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作者：
Molparia B;Pham PH;Torkamani A
罕见的基因变异是孟德尔疾病的主要原因，但在所描述的遗传病中，只有一半与基因有因果关系。此外，罕见遗传病的总数预计将远远超过已经描述的那些。对患者进行全基因组测序，然后进行遗传和功能分析，是描述这些基因异常的有效方法。然而，这种方法导致了数十到数百个候选致病基因，而且由于稀有性和表型异质性，识别其他患有相同疾病的个体可能很困难。我们描述了一种基于遗传网络的方法来对基于家族的测序研究中识别的候选基因进行排序，称为表型信息网络(PIN)排序。此外，我们提供了一个案例研究，作为PIN排名方法的扩展，在该方法中，疾病症状驱动网络排名和致病基因的识别。我们通过模拟证明，我们的方法能够在大多数情况下识别正确的致病基因。PIN-RANK可在https://genomics.scripps.edu/pin-rank/.上获得我们已经开发了一种方法，根据症状对候选致病基因进行优先排序，这对确定候选基因的优先顺序和确定其他受试者都是有用的。
Rare genetic variants are the major cause of Mendelian disorders, yet only half of described genetic diseases have been causally linked to a gene. In addition, the total number of rare genetic diseases is projected to be far greater than that of those already described. Whole-genome sequencing of patients with subsequent genetic and functional analysis is a powerful way to describe these gene anomalies. However, this approach results in tens to hundreds of candidate disease-causative genes, and the identification of additional individuals suffering from the same disorder can be difficult because of rarity and phenotypic heterogeneity. We describe a genetic network–based method to rank candidate genes identified in family-based sequencing studies, termed phenotype informed network (PIN) ranking. Furthermore, we present a case study as an extension of the PIN ranking method in which disease symptoms drive the network ranking and identification of the disease-causative gene. We demonstrate, through simulation, that our method is capable of identifying the correct disease-causative gene in a majority of cases. PIN-rank is available at https://genomics.scripps.edu/pin-rank/. We have developed a method to prioritize candidate disease-causative genes based on symptoms that would be useful for both the prioritization of candidates and the identification of additional subjects.