I recommend that you create decision rule based on your understanding of the literature, then validate it against pathways that are very well known to be D.R. in your disease state.

Of course, it goes without saying that some decision rules will be better than others. For instance, you could choose all genes within Y bases of X. Or, you could do a lot more work and reason out which genes you think are the most likely to be differentially regulated based on the alteration to chromatin structure you are seeing in your data. How would you do this? Well, you are free to use your understanding of the literature and any available data. So, while it is true what ATPoint says (motif or correlation based) theres no reason why you can't attempt to find mQTL data in your disease of interest, for instance, and use that to help you make the gene list ... in either event, there is a ton of useful data of different kinds out there that can help you.

Here's an example. https://pubmed.ncbi.nlm.nih.gov/24390342/#&gid=article-figures&pid=figure-2-uid-1 - Check out Figure 2. What did they have? Well, a list of genomic coordinates. What did they obtain? Well, a list of prioritized genes near to each association signal. From there, nothing is to stop them from feeding, say, their most highly prioritized gene in each locus into a pathway analysis program...

Never forget that, ultimately, the most important question is, "what will you use the gene list for." And, are there pathways that you definitely expect to find. If you do, and one approach does not identify any of the "positive control pathways" so to speak, it may not have been a good method for gene selection. Of course you can't know that a priori, but if you have good quality data and you iterate your analysis enough, in my experience you can usually leverage available data to solidify your choices.