A network-based approach to prioritize results from genome-wide association studies.
A network-based approach to prioritize results from genome-wide association studies.
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DOI：
10.1371/journal.pone.0024220
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
McMahon FJ
中科院分区：
文献类型：
--
作者：
Akula N;Baranova A;Seto D;Solka J;Nalls MA;Singleton A;Ferrucci L;Tanaka T;Bandinelli S;Cho YS;Kim YJ;Lee JY;Han BG;Bipolar Disorder Genome Study (BiGS) Consortium;Wellcome Trust Case-Control Consortium;McMahon FJ
全基因组关联研究（GWAS）是了解复杂性状遗传基础的一种有价值的方法。GWAS的挑战之一是将遗传关联结果转化为适合在实验室中进一步研究的生物学假设。为了应对这一挑战，我们引入了多基因相互作用网络接口矿工（NIMMI），这是一种基于网络的方法，将GWAS数据与人类蛋白质-蛋白质相互作用数据（PPI）相结合。NIMMI构建了由连接性加权的生物网络，该连接性通过使用Google PageRank算法的修改来估计。然后将这些权重与来自GWAS的遗传关联p值相结合，产生我们所谓的“性状优先子网络”。作为原理的证明，NIMMI在先前针对身高（一种经典的多基因性状）分析的三个GWAS数据集上进行了测试。尽管样本量和血统存在差异，但NIMMI在排名前20%的子网络中捕获了95%的已知身高相关基因，远远优于单位点方法。前2%的NIMMI高度优先子网络显著富集了参与转录、信号转导、转运和基因表达的基因，以及核酸、磷酸盐、蛋白质和锌代谢的基因。在我们测试的所有三个高度GWAS数据集中，所有这些子网络都排名靠前。我们还测试了NIMMI的分类表型，克罗恩病。NIMMI优先考虑参与B和T细胞受体、趋化因子、白细胞介素和其他与克罗恩病的已知自身免疫性质一致的途径的子网络。NIMMI是一个简单、用户友好的开源软件工具，它有效地将遗传关联数据与生物网络相结合，将GWAS的发现转化为生物学假设。
Genome-wide association studies (GWAS) are a valuable approach to understanding the genetic basis of complex traits. One of the challenges of GWAS is the translation of genetic association results into biological hypotheses suitable for further investigation in the laboratory. To address this challenge, we introduce Network Interface Miner for Multigenic Interactions (NIMMI), a network-based method that combines GWAS data with human protein-protein interaction data (PPI). NIMMI builds biological networks weighted by connectivity, which is estimated by use of a modification of the Google PageRank algorithm. These weights are then combined with genetic association p-values derived from GWAS, producing what we call ‘trait prioritized sub-networks.’ As a proof of principle, NIMMI was tested on three GWAS datasets previously analyzed for height, a classical polygenic trait. Despite differences in sample size and ancestry, NIMMI captured 95% of the known height associated genes within the top 20% of ranked sub-networks, far better than what could be achieved by a single-locus approach. The top 2% of NIMMI height-prioritized sub-networks were significantly enriched for genes involved in transcription, signal transduction, transport, and gene expression, as well as nucleic acid, phosphate, protein, and zinc metabolism. All of these sub-networks were ranked near the top across all three height GWAS datasets we tested. We also tested NIMMI on a categorical phenotype, Crohn’s disease. NIMMI prioritized sub-networks involved in B- and T-cell receptor, chemokine, interleukin, and other pathways consistent with the known autoimmune nature of Crohn’s disease. NIMMI is a simple, user-friendly, open-source software tool that efficiently combines genetic association data with biological networks, translating GWAS findings into biological hypotheses.
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DOI：
10.1038/nrg2579
发表时间：
2009-06
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
Cordell HJ
通讯作者：
Cordell HJ
影响因子：
4.5
作者：
Evans, David M.;Marchini, Jonathan;Morris, Andrew P.;Cardon, Lon R.
通讯作者：
Cardon, Lon R.
影响因子：
5
作者：
Davis, N. A.;Crowe, J. E., Jr.;Pajewski, N. M.;McKinney, B. A.
通讯作者：
McKinney, B. A.
DOI：
10.1152/ajpendo.2000.279.2.e323
发表时间：
2000-08-01
影响因子：
5.1
作者：
Biolo, G;Iscra, F;Guarnieri, G
通讯作者：
Guarnieri, G
DOI：
10.1111/j.1532-5415.2000.tb03873.x
发表时间：
2000-12-01
影响因子：
6.3
作者：
Ferrucci, L;Bandinelli, S;Guralnik, JM
通讯作者：
Guralnik, JM