Characterizing genetic interactions in human disease association studies using statistical epistasis networks.
Characterizing genetic interactions in human disease association studies using statistical epistasis networks.
复制标题
使用统计上居性网络在人类疾病关联研究中表征遗传相互作用。
DOI：
10.1186/1471-2105-12-364
复制
发表时间：
2011-09-12
影响因子：
3
通讯作者：
Moore JH
中科院分区：
文献类型：
--
作者：
Hu T;Sinnott-Armstrong NA;Kiralis JW;Andrew AS;Karagas MR;Moore JH
上位性普遍存在于疾病易感性等复杂性状的遗传结构中。在模式生物中的实验研究已经揭示了基因间生物相互作用的广泛证据。与此同时，人类群体的统计和计算研究表明，遗传变异对复杂性状的非加性影响。尽管这些研究为理解复杂性状的遗传结构提供了基础，但迄今为止，他们只考虑了少数遗传变异之间的相互作用。我们的目标是使用网络科学来确定非加性相互作用在遗传变异的小子集之外存在的程度。我们推断统计上位性网络来表征全球空间的成对相互作用之间的约1500单核苷酸多态性（SNP）跨越近500个癌症易感基因在一个大的人群为基础的研究膀胱癌。统计上位性网络是通过连接成对的SNP来建立的，如果它们的成对相互作用强于系统推导的阈值。它的拓扑结构清楚地将这个真实数据网络与在基因型和表型之间不存在关联的零假设下从相同数据的排列获得的网络区分开来。该网络具有显著更高数量的中心SNP，有趣的是，这些中心SNP不一定具有高主效应。该网络具有39个SNP的最大连接组件，这在任何其他置换数据网络中都不存在。此外，该网络的顶点度明显遵循近似幂律分布，其拓扑结构呈现无标度特性。与许多现有的专注于高主效应SNP或几个相互作用的SNP模型的技术相比，我们的网络方法表征了基于群体的遗传数据中基因-基因相互作用的全局图像。该网络是使用成对相互作用构建的，其独特的网络拓扑结构和大的连接组件表明了大量SNP的联合效应。我们的观察结果表明，这种特殊的统计上位性网络捕获了以前没有描述过的膀胱癌遗传结构的重要特征。
Epistasis is recognized ubiquitous in the genetic architecture of complex traits such as disease susceptibility. Experimental studies in model organisms have revealed extensive evidence of biological interactions among genes. Meanwhile, statistical and computational studies in human populations have suggested non-additive effects of genetic variation on complex traits. Although these studies form a baseline for understanding the genetic architecture of complex traits, to date they have only considered interactions among a small number of genetic variants. Our goal here is to use network science to determine the extent to which non-additive interactions exist beyond small subsets of genetic variants. We infer statistical epistasis networks to characterize the global space of pairwise interactions among approximately 1500 Single Nucleotide Polymorphisms (SNPs) spanning nearly 500 cancer susceptibility genes in a large population-based study of bladder cancer. The statistical epistasis network was built by linking pairs of SNPs if their pairwise interactions were stronger than a systematically derived threshold. Its topology clearly differentiated this real-data network from networks obtained from permutations of the same data under the null hypothesis that no association exists between genotype and phenotype. The network had a significantly higher number of hub SNPs and, interestingly, these hub SNPs were not necessarily with high main effects. The network had a largest connected component of 39 SNPs that was absent in any other permuted-data networks. In addition, the vertex degrees of this network were distinctively found following an approximate power-law distribution and its topology appeared scale-free. In contrast to many existing techniques focusing on high main-effect SNPs or models of several interacting SNPs, our network approach characterized a global picture of gene-gene interactions in a population-based genetic data. The network was built using pairwise interactions, and its distinctive network topology and large connected components indicated joint effects in a large set of SNPs. Our observations suggested that this particular statistical epistasis network captured important features of the genetic architecture of bladder cancer that have not been described previously.
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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
DOI：
10.1056/nejmra0808700
发表时间：
2009-04-23
期刊：
The New England journal of medicine
影响因子：
--
作者：
Hardy J;Singleton A
通讯作者：
Singleton A
影响因子：
64.8
作者：
Jeong, H;Mason, SP;Oltvai, ZN
通讯作者：
Oltvai, ZN
影响因子：
10.4
作者：
Karagas, MR;Tosteson, TD;Klaue, B
通讯作者：
Klaue, B
影响因子：
5.8
作者：
Hahn, LW;Ritchie, MD;Moore, JH
通讯作者：
Moore, JH