Here is a nice review on the topic. from nature reviews in genetics.

hope this is of help

all the best,

 James

Article Detecting gene-gene interactions that underlie human diseases

Thank you Dr. James. may I have a full text of it as my university do not have this journal as subscription.

Regards,

Tara 

What methodology is available for you for setting out?

my question is that which methodology is recommended to prepare set up.

Dear Tahereh,

I have used the MDR method (www.multifactordimensionalityreduction.org/) to characterize gene-gene interactions in our articles (Please check their Methods for further details):

https://www.researchgate.net/publication/227175247_ENOS_and_BDKRB2_genotypes_affect_the_antihypertensive_responses_to_enalapril

https://www.researchgate.net/publication/224933151_Epistasis_among_eNOS_MMP-9_and_VEGF_maternal_genotypes_in_hypertensive_disorders_of_pregnancy

Best regards,

Marcelo

Article ENOS and BDKRB2 genotypes affect the antihypertensive respon...

Article Epistasis among eNOS, MMP-9 and VEGF maternal genotypes in h...

Thank you very much for advises... I will go through them and get back to you :) 

Greetings Tahereh

Its hard to comment on which is the best method but I can share my approach which I follow in pursuit of understanding gene-gene interactions.

The strategy used, depends on the sample size which is first to get affected during a stratified analysis and so the outcome of these analysis should be validated using other techniques as well.

I believe your trying to associate few SNPs with disease risk and also wish to identify plausible gene-gene interactions. In that case,  you must have entered your SNP data as a categorical variable into your database (say  created using SPSS). Now if you wish to identify interactions, you could convert your categorical data into numbers.

On how to convert categorical data into numerical,  I would suggest the link below

https://www.analyticsvidhya.com/blog/2015/11/easy-methods-deal-categorical-variables-predictive-modeling/

Initially doing such a thing may look like a mammoth task (if your sample size is huge and/or if you have many SNPs) but once done - this data can be statistically  analyzed in a number of ways to make sensible conclusions (This is my 1st approach).

But what if you dnt want to get into this trouble or want to get a preliminary idea ?

In that case, as suggested by Marcelo - you can use computational predictions (This is the second thing I do)

Under this header you can do either of the two things

1. MDR (Multifactorial dimension reduction) test - Very easy to do

If you have never done this before and want to start anew, please visit

http://www.epistasisblog.org/

This is a friendly site with all necessary information and download links. Dnt worry - there is no need to create a new database. The software will ask for  a 'tab-delimited' text file (having all data) which can be easily created from your SPSS/ xlsx/xls file.

2. I will also suggest using CART - classification and regression tree (There are may tutorial videos on YouTube)

To do this you can try 'Salford Predictive Modeler' which is a great software (requires a subscription) but I believe there will be free alternatives as well.

Why use this - It will stratifies data according to the most important classifiers, so when you study the classification tree, you will get an idea which SNPs are interacting.

3. Finally you can also use receiver operating characteristic ((ROC) curve to check for a statistical increase in the area under curve (AUC) to predict interaction. For instance the AUC will increase if two SNPs interact and elevate the risk of a disease as against their individual AUCs. There are many software platforms but I prefer SPSS and GraphPad Prism.

To check for statistical difference between ROCs, you can use this calculator

http://vassarstats.net/roc_comp.html

You may be also interested in checking linkage (Its wise to check for linkage when you  have multiple SNPs).

I will like to conclude by saying that this is my approach of investigating gene-gene & gene-environment interactions. There may be even simpler tricks, so keep looking.

I hope this is helpful

Best wishes.

Is there any possibility to look at the pathways interaction through gene expression study??

May, the mean of the Ct Value of a couple of a pathway coding genes, present the power of that particular pathway in comparison to other pathways??

Article Discerning molecular interactions: A comprehensive review on...

Please go through the paper. It may help you.