Abstract
An integrative analysis combining genetic interactions and protein interactions can be used to identify candidate genes/proteins for type 1 diabetes and other complex diseases.
Highlights
Complex traits like type 1 diabetes (T1D) are generally believed to be under the influence of multiple genes interacting with each other to confer disease susceptibility and/or protection
Since all candidates identified in the present work were put in a functional context with other members of a network, the networks immediately offer clues on the Marginal markers In the total data set of 1,321 affected sibling pair families from the UK, the US and Scandinavia, data mining/decision tree analyses identified major T1D predictive signals corresponding to T1D linkage signals found by classic non-parametric linkage analysis [25]
PFrigouterien i2nteraction networks for predicted genetic interactions Protein interaction networks for predicted genetic interactions. (a) tumor necrosis factor alpha (TNFA)-D4S403, TNFA-D13S170 and TNFA-D2S177 are represented by one network, whereas TNFA-D1S229, TNFA-D16S287 and TNFA-D11S910 are represented by two or three networks
Summary
Complex traits like type 1 diabetes (T1D) are generally believed to be under the influence of multiple genes interacting with each other to confer disease susceptibility and/or protection. One of the strongest indications of functional association is the presence of a physical interaction between proteins [3] and several reports have shown that proteins involved in the same phenotype are likely to be part of the same functional module (that is, protein sub-network) [4,5,6] With this in mind, it seems reasonable to expect that, in many cases, components contributing to the same complex diseases will be members of the same functional modules, especially if the disease is associated with multiple genetic loci that show statistical indication for epistasis.
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