Discovering pair-wise genetic interactions: an information theory-based approach.
Discovering pair-wise genetic interactions: an information theory-based approach.
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DOI:
10.1371/journal.pone.0092310
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发表时间:
2014
期刊:
影响因子:
3.7
通讯作者:
Galas DJ
中科院分区:
文献类型:
--
作者:
Ignac TM;Skupin A;Sakhanenko NA;Galas DJ
Phenotypic variation, including that which underlies health and disease in humans, results in part from multiple interactions among both genetic variation and environmental factors. While diseases or phenotypes caused by single gene variants can be identified by established association methods and family-based approaches, complex phenotypic traits resulting from multi-gene interactions remain very difficult to characterize. Here we describe a new method based on information theory, and demonstrate how it improves on previous approaches to identifying genetic interactions, including both synthetic and modifier kinds of interactions. We apply our measure, called interaction distance, to previously analyzed data sets of yeast sporulation efficiency, lipid related mouse data and several human disease models to characterize the method. We show how the interaction distance can reveal novel gene interaction candidates in experimental and simulated data sets, and outperforms other measures in several circumstances. The method also allows us to optimize case/control sample composition for clinical studies.
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影响因子:
4.4
作者:
DEVLIN, B;RISCH, N
通讯作者:
RISCH, N
DOI:
10.1126/science.1166426
发表时间:
2009-01-23
期刊:
Science (New York, N.Y.)
影响因子:
--
作者:
Gerke J;Lorenz K;Cohen B
通讯作者:
Cohen B
影响因子:
9.8
作者:
Gorlov, Ivan P.;Gorlova, Olga Y.;Amos, Christopher I.
通讯作者:
Amos, Christopher I.
影响因子:
--
作者:
Tabangin ME;Woo JG;Martin LJ
通讯作者:
Martin LJ
影响因子:
2.5
作者:
Li, M;Chen, X;Vitányi, PMB
通讯作者:
Vitányi, PMB