I am contacting you as the corresponding author for the paper: "GRAM: A generalized model to predict the molecular effect of a non-coding variant in a cell-type specific manner." PLoS genetics 15.8 (2019): e1007860.
I would like to express my thanks to you and your group for developing & publishing GRAM. I have recently tested it out and the results have been most interesting.
I have begun to work with eQTL analysis only recently and as a result, I was wondering what you would recommend as a multiple testing correction method for GRAM score based eQTL analysis?
From the literature I have seen that standard multiple testing correction methods such as Bonferroni & Benjamini-Hochberg have be considered too conservative for regular eQTL analysis as they do not take linkage disequilibrium into account, and several permutation testing based approaches have been published specifically for eQTL as a result (e.g. eigenMT). However, as you have demonstrated GRAM score based eQTL to be able to differentiate the regulatory effects of variants in linkage disequilibrium, I am unsure whether such methods would be appropriate here.
One of the application scenarios of using GRAM is fine-mapping, which suppose that you have a list of eQTL and its LD associated mutations. If you don’t have eQTL and want to try it on eQTL identification, maybe one way is you compare the gram score with a normally distributed background (use tens of thousands of background/random selected mutations) and infer a p-value of the GRAM score of a variant relative to the background, then use BH or FDR method to do the multi-testing correction.
Frankly speaking, this is a very great point to extend our GRAM. We may also consider testing this recently. The most computation-intensive part of this to calculate deepbind score for background variants, which will take a long time if we want to test millions of background variants. If you have any feedback, further questions or preliminary findings regarding this, please feel free to let us know.