Department of Genetics, Dartmouth Medical School, Dartmouth College, Lebanon, NH, USA.
BMC Bioinformatics. 2011 Sep 12;12:364. doi: 10.1186/1471-2105-12-364.
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.
上位性在疾病易感性等复杂性状的遗传结构中普遍存在。模式生物的实验研究揭示了基因之间广泛存在的生物学相互作用。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影响不是加性的。尽管这些研究为理解复杂性状的遗传结构奠定了基础，但迄今为止，它们只考虑了少数遗传变异之间的相互作用。我们的目标是利用网络科学来确定非加性相互作用在多大程度上存在于遗传变异的小子集之外。我们推断统计上位性网络，以描述在膀胱癌的大型基于人群研究中，大约 1500 个单核苷酸多态性（SNP）跨越近 500 个癌症易感性基因之间的全局成对相互作用空间。
统计上位性网络是通过将对 SNP 对链接在一起构建的，如果它们的成对相互作用强于系统推导的阈值。其拓扑结构清楚地区分了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，从相同数据的置换中获得的网络。该网络具有显著更多的枢纽 SNP，有趣的是，这些枢纽 SNP 不一定具有高主效应。该网络具有一个不存在于任何其他置换数据网络中的 39 个 SNP 的最大连通分量。此外，该网络的顶点度数明显遵循近似幂律分布，其拓扑结构呈无标度特征。
与许多关注高主效 SNP 或几个相互作用 SNP 模型的现有技术不同，我们的网络方法描述了基于人群遗传数据中基因-基因相互作用的全局图景。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通分量表明了一组 SNP 的联合效应。我们的观察结果表明，这个特定的统计上位性网络捕捉到了膀胱癌遗传结构的重要特征，这些特征以前没有被描述过。
