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This study revealed that the relative superiority of the RF method increases as more interacting SNPs are added to the model.
Also, all methods tend to detect more interacting SNPs with strong main effects than those with weak main effects (power of all the methods on models 2 and 3 is generally higher than on models 1, 4, and 5).
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This means that many more potentially interacting SNPs are likely to occur in the data set.
We derive an analogous result for a scenario involving two interacting SNPs under a simple interaction model.
By applying this methodology the analysis was capable to identify in most cases two of the three interacting SNPs among the top ranking interactions (figure 3).
As Chen and colleagues discussed, the problem with their interaction importance measure was that two interacting SNPs need to be jointly selected in a tree branch relatively often.
Here, we assume that whenever two given proteins possess one or more interacting domains between them, those proteins will interact.
Indirect interactions potentially emerge in any community of three or more interacting species [1], [2].
To ensure that interacting SNPs on the same chromosome were independent, we only tested interactions among those intra-chromosomal SNP pairs with an r < 0.01, as measured in the samples for that particular tissue.
First, while some SNPs in interactions with strong effects are successfully detected, most of the methods miss many interacting SNPs at an acceptable rate of false positives.
No more interacting.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com