Molparia Bhuvan, Pham Phillip H, Torkamani Ali
Scripps Translational Science Institute, La Jolla, California, USA.
Department of Integrative Structural and Computational Biology, Scripps Research Institute, La Jolla, California, USA.
Genet Med. 2015 Nov;17(11):859-65. doi: 10.1038/gim.2014.202. Epub 2015 Jan 15.
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/pinrank/.
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.
罕见基因变异是孟德尔疾病的主要病因，但仅有一半已描述的遗传疾病与某一基因存在因果关联。此外，预计罕见遗传疾病的总数将远多于已描述的疾病数量。对患者进行全基因组测序并随后进行基因和功能分析是描述这些基因异常的有效方法。然而，这种方法会产生数十到数百个候选致病基因，而且由于疾病罕见和表型异质性，很难识别出患有相同疾病的其他个体。
我们描述了一种基于遗传网络的方法，用于对在基于家系的测序研究中鉴定出的候选基因进行排名，称为表型信息网络（PIN）排名。此外，我们展示了一个案例研究，作为PIN排名方法的扩展，其中疾病症状驱动网络排名和致病基因的鉴定。
通过模拟，我们证明我们的方法在大多数情况下能够识别出正确的致病基因。PIN排名可在https://genomics.scripps.edu/pinrank/获取。
我们开发了一种基于症状对候选致病基因进行优先级排序的方法，这对于候选基因的优先级排序和其他受试者的识别都将是有用的。
