BIOINFORMATIC CORE

简介和目标： 研究表明，参与类似孟德尔和复杂表型的蛋白质在人类蛋白质相互作用网络中具有直接和物理相互作用的强烈倾向。特别是，已经在许多方法中探索了一级相互作用，用于对与特定表型相关的连锁区域中的候选者进行优先级排序。然而，当基因座变得太大时，这些策略就会失去作用，可能是因为它们仅限于使用直接的一阶相互作用或使用基因本体和表达数据来预测更高阶的物理相互作用。据我们所知，基于蛋白质相互作用网络数据的表型中候选者的基因组规模优先排序的一般方法尚未被报道，特别是在寻找出生缺陷的分子原因方面。 在许多复杂的疾病中，全基因组关联研究 (GWAS) 已将同一疾病中的基因归咎于这些基因，而这些基因并不明显参与相同的细胞途径。这可能是因为不存在连接基因的途径关系，或者因为我们没有对所有生物途径的完整概述或对其串扰的了解。如果后一个原因是正确的，那么在构建系统地使用通路关系来确定复杂疾病中的新成分的模型时，必须考虑到对许多通路的精确组成缺乏了解。 在这里，我们提出了一个模型，在给定的疾病中，确定候选者是否与高阶相互作用网络中已知的致病蛋白显着相互作用。该模型的一个组成部分是精炼的大规模蛋白质组学数据，这意味着它不局限于或偏向现有的众所周知的途径。通过这种方式，我们的模型反映了全基因组关联研究中独立于途径的发现。这个 该模型能够对复杂表型中的风险因素进行准确的全基因组预测。

INTRODUCTION and OBJECTIVES: It has been shown that proteins involved in similar Mendelian and complex phenotypes have a strong tendency to interact directly and physically in human protein interaction networks. In particular, first order interactions have been explored in a number of methods for prioritizing candidates in linkage regions associated with a particular phenotype. However, these strategies lose their power when the loci become too big, perhaps because they are confined to using direct first order interactions or use gene ontology and expression data, to predict higher order physical interactions. General methods for genome-scale prioritization of candidates in a phenotype based on protein interaction network data, have to our knowledge not been reported, particularly in the search for molecular causes of birth defects. In numerous complex disorders, Genome Wide Association studies (GWAS) have incriminated genes in the same disease that are not known to obviously participate in the same cellular pathway. This could be because there is no pathway relationship connecting the genes or because we do not have a complete overview of all biological pathways or knowledge of their crosstalk. If the latter reason is correct, a lack of knowledge on the precise composition of many pathways must be taken into account when constructing models that systematically uses pathway relationships to determine novel components in complex disorders. Here, we present a model that in a given disease, determines if a candidate significantly interacts with known disease causing proteins in higher order interaction networks. A component of this model is refined large-scale proteomics data, meaning it is not confined to or biased towards existing well-known pathways. In this way, our model mirrors the pathway independent discovery in genome-wide association studies. This model has the power to make accurate genome-wide predictions of risk factors in a complex phenotype.