Network models of genome-wide association studies uncover the topological centrality of protein interactions in complex diseases.
Network models of genome-wide association studies uncover the topological centrality of protein interactions in complex diseases.
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DOI：
10.1136/amiajnl-2012-001519
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发表时间：
2013-07
期刊：
影响因子：
--
通讯作者：
Lussier YA
中科院分区：
文献类型：
--
作者：
Lee Y;Li H;Li J;Rebman E;Achour I;Regan KE;Gamazon ER;Chen JL;Yang XH;Cox NJ;Lussier YA
虽然复杂性状的全基因组关联研究（GWAS）迄今为止已经揭示了数千种可重复的遗传关联，但这些基因座共同赋予其各自疾病的遗传性很少，并且通常对我们理解潜在疾病生物学贡献不大。物理蛋白质相互作用已被用来增加我们对人类孟德尔疾病基因座的理解，但尚未充分利用复杂的性状。我们假设GWAS研究结果的蛋白质相互作用建模可以突出重要的疾病相关位点，并揭示其网络拓扑结构在复杂遗传疾病的遗传结构中的作用。对国家人类基因组研究所复杂性状GWAS目录中基因内单核苷酸多态性相关蛋白质的网络建模显示，复杂性状相关基因座更可能是现有网络中的枢纽和瓶颈基因，尽管网络不完整（OR=1.59，Fisher精确检验p<2.24×10−12）。网络建模还优先考虑了来自芬兰-美国非胰岛素依赖型糖尿病遗传学调查和Wellcome Trust GWAS数据的新型2型糖尿病（T2 D）遗传变异，并证明了优先T2 D GWAS基因中的枢纽和瓶颈的富集。T2 D枢纽和瓶颈基因的潜在生物学相关性通过其与已知T2 D基因的一级蛋白质相互作用的数量增加而揭示，根据几个独立的来源（p<0.01，是已知T2 D基因的第一相互作用物的概率）。事实上，所有常见疾病都是复杂的人类特征，因此复杂特征基因的蛋白质网络的拓扑中心性在遗传学、个人基因组学和治疗中具有重要意义。
While genome-wide association studies (GWAS) of complex traits have revealed thousands of reproducible genetic associations to date, these loci collectively confer very little of the heritability of their respective diseases and, in general, have contributed little to our understanding the underlying disease biology. Physical protein interactions have been utilized to increase our understanding of human Mendelian disease loci but have yet to be fully exploited for complex traits. We hypothesized that protein interaction modeling of GWAS findings could highlight important disease-associated loci and unveil the role of their network topology in the genetic architecture of diseases with complex inheritance. Network modeling of proteins associated with the intragenic single nucleotide polymorphisms of the National Human Genome Research Institute catalog of complex trait GWAS revealed that complex trait associated loci are more likely to be hub and bottleneck genes in available, albeit incomplete, networks (OR=1.59, Fisher's exact test p<2.24×10−12). Network modeling also prioritized novel type 2 diabetes (T2D) genetic variations from the Finland–USA Investigation of Non-Insulin-Dependent Diabetes Mellitus Genetics and the Wellcome Trust GWAS data, and demonstrated the enrichment of hubs and bottlenecks in prioritized T2D GWAS genes. The potential biological relevance of the T2D hub and bottleneck genes was revealed by their increased number of first degree protein interactions with known T2D genes according to several independent sources (p<0.01, probability of being first interactors of known T2D genes). Virtually all common diseases are complex human traits, and thus the topological centrality in protein networks of complex trait genes has implications in genetics, personal genomics, and therapy.
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影响因子：
9.8
作者：
Heinzen EL;Ge D;Cronin KD;Maia JM;Shianna KV;Gabriel WN;Welsh-Bohmer KA;Hulette CM;Denny TN;Goldstein DB
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von Mering C
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通讯作者：
Oltvai, ZN
影响因子：
56.9
作者：
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通讯作者：
Cho, Judy H.
稀有变体会产生整个基因组的关联。
影响因子：
9.8
作者：
Dickson SP;Wang K;Krantz I;Hakonarson H;Goldstein DB
通讯作者：
Goldstein DB