BIOINFORMATIC CORE

生物信息学核心

基本信息

批准号：
8143196
负责人：
PATRICIA K DONAHOE
金额：
$ 8.21万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-07-01 至 2016-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8143196
关键词：
Accounting Algorithms Bioinformatics Biological Comorbidity Complex Congenital Abnormality Congenital diaphragmatic hernia Data Data Set Development Diaphragmatic Hernia Disease Doctor of Philosophy Family member Gene Mutation Genes Genetic Variation Genome Heart Human Knowledge Loss of Heterozygosity Lung Methods Modeling Molecular Ontology Pathway interactions Patients Phenotype Proteins Proteome Proteomics Reporting Research Design Risk Factors Sequence Analysis Source System base design exome genome wide association study genome-wide knockout gene novel programs protein protein interaction tool

Accounting Algorithms Bioinformatics Biological Comorbidity Complex Congenital Abnormality Congenital diaphragmatic hernia Data Data Set Development Diaphragmatic Hernia Disease Doctor of Philosophy Family member Gene Mutation Genes Genetic Variation Genome Heart Human Knowledge Loss of Heterozygosity Lung Methods Modeling Molecular Ontology Pathway interactions Patients Phenotype Proteins Proteome Proteomics Reporting Research Design Risk Factors Sequence Analysis Source System base design exome genome wide association study genome-wide knockout gene novel programs protein protein interaction tool

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项目摘要

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.

介绍和目标：已经表明，参与类似Mendelian和复杂表型的蛋白质具有在人类蛋白质相互作用网络中直接和物理相互作用的强烈趋势。特别是，已经在许多方法中探索了一阶相互作用，以优先考虑与特定表型相关的链接区域中的候选人。但是，当基因座变得太大时，这些策略会失去力量，也许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高阶物理相互作用。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级的一般方法，据我们所知，尤其是在寻找出生缺陷的分子原因时。在众多复杂疾病中，基因组广泛的关联研究（GWAS）在同一疾病中具有犯罪基因，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，或者因为我们没有对其串扰的所有生物学途径或知识的完整概述。如果后一个原因是正确的，则在构建系统使用途径关系来确定复杂疾病中的新成分的模型时，必须考虑有关许多途径的精确组成的知识。在这里，我们提出了一个模型，该模型在给定疾病中确定候选者是否与已知疾病显着相互作用，从而导致蛋白质在高阶相互作用网络中。该模型的一个组成部分是精制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众所周知的途径。这样，我们的模型反映了全基因组关联研究中的途径独立发现。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。