BIOINFORMATIC CORE

引言和目标： 研究表明，在人类蛋白质相互作用网络中，具有相似孟德尔表型和复杂表型的蛋白质具有直接相互作用和物理相互作用的强烈倾向。具体地说，已经在许多方法中探索了一级相互作用，用于在与特定表型相关联的连锁区域中优先选择候选基因。然而，当基因座变得太大时，这些策略就会失去效力，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论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对表型候选进行基因组规模优先排序的一般方法尚未见报道，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，基因组广泛关联研究(Genome Wide Association Studs，GWAS)已经将一些未知的基因与相同疾病联系在一起，这些基因显然参与了相同的细胞途径。这可能是因为没有连接基因的途径关系，也可能是因为我们没有对所有生物途径的完整概述或对它们的串扰的了解。如果后一种理由是正确的，则在构建系统地使用途径关系来确定复杂疾病中的新成分的模型时，必须考虑到缺乏对许多途径的准确组成的知识。 在这里，我们提出了一个模型，在给定的疾病中，确定候选蛋白是否与高阶相互作用网络中的已知致病蛋白显著相互作用。这个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现有的众所周知的途径或对现有的众所周知的途径有偏见。通过这种方式，我们的模型反映了全基因组关联研究中的路径独立发现。这 模型有能力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.