Six Degrees of Epistasis: Statistical Network Models for GWAS.
Six Degrees of Epistasis: Statistical Network Models for GWAS.
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DOI：
10.3389/fgene.2011.00109
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发表时间：
2011
影响因子：
3.7
通讯作者：
Pajewski NM
中科院分区：
文献类型：
--
作者：
McKinney BA;Pajewski NM
越来越多的证据表明，要解释复杂表型的遗传性，需要比以前认为的更多的基因组。最近的研究表明，来自整个基因组的许多常见变异解释了部分遗传变异，催生了各种研究途径，旨在解释剩余的遗传性。这种多基因结构也是越来越多地应用途径和基因集富集技术的动机，这些技术已经产生了有希望的结果。这些发现表明，已知发生在基因调控水平的途径中的基因协调也可以在群体水平上检测到。虽然这些网络中的基因以复杂的方式相互作用，但大多数人口研究都集中在常见变异的附加贡献和罕见变异解释额外变异的潜力上。在这篇简短的综述中，我们讨论了通过聚集多个基因-基因相互作用以及网络范式中常见变异的主要影响来解释额外遗传变异的潜力。就像单基因座贡献的情况一样，我们预计网络中的每个基因-基因相互作用边都有很小的影响，但这些影响可能会通过网络中的枢纽和其他连接结构得到加强。我们讨论了网络方法分析全基因组关联研究（GWAS）的一些机遇和挑战，如枢纽和基序的研究，以及整合其他类型的变异和环境相互作用。这样的网络方法可能会揭示隐藏在GWAS中的变异，提高对疾病机制的理解，并可能适合进化遗传学的网络范式。
There is growing evidence that much more of the genome than previously thought is required to explain the heritability of complex phenotypes. Recent studies have demonstrated that numerous common variants from across the genome explain portions of genetic variability, spawning various avenues of research directed at explaining the remaining heritability. This polygenic structure is also the motivation for the growing application of pathway and gene set enrichment techniques, which have yielded promising results. These findings suggest that the coordination of genes in pathways that are known to occur at the gene regulatory level also can be detected at the population level. Although genes in these networks interact in complex ways, most population studies have focused on the additive contribution of common variants and the potential of rare variants to explain additional variation. In this brief review, we discuss the potential to explain additional genetic variation through the agglomeration of multiple gene–gene interactions as well as main effects of common variants in terms of a network paradigm. Just as is the case for single-locus contributions, we expect each gene–gene interaction edge in the network to have a small effect, but these effects may be reinforced through hubs and other connectivity structures in the network. We discuss some of the opportunities and challenges of network methods for analyzing genome-wide association studies (GWAS) such as the study of hubs and motifs, and integrating other types of variation and environmental interactions. Such network approaches may unveil hidden variation in GWAS, improve understanding of mechanisms of disease, and possibly fit into a network paradigm of evolutionary genetics.
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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：
10.1126/science.1193032
发表时间：
2010-08-13
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Genovese G;Friedman DJ;Ross MD;Lecordier L;Uzureau P;Freedman BI;Bowden DW;Langefeld CD;Oleksyk TK;Uscinski Knob AL;Bernhardy AJ;Hicks PJ;Nelson GW;Vanhollebeke B;Winkler CA;Kopp JB;Pays E;Pollak MR
通讯作者：
Pollak MR
影响因子：
9.8
作者：
Liu, Jimmy Z.;Mcrae, Allan F.;Macgregor, Stuart
通讯作者：
Macgregor, Stuart
影响因子：
10.6
作者：
Moskvina, Valentina;Craddock, Nick;Mueller-Myhsok, Bertram;Kam-Thong, Tony;Green, Elaine;Holmans, Peter;Owen, Michael J.;O'Donovan, Michael C.
通讯作者：
O'Donovan, Michael C.
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.