Is there anything similar to GSEA for locus-based (instead of of gene-based) data? As the question states, I am interested in an analysis similar to Gene Set Enrichment Analysis (ranked gene sets) but focused on locus-level data instead of genes. 

To explain in greater detail: I have a set of genomic coordinates from DNA methylation data that are scored based on their contribution to components from a non-negative matrix factorization. I am interested in better understanding the biological underpinnings of what these NMF components represent.  I know I can use Locus Overlap Analysis tools like GREAT or LOLA to tell me what GO/Functions are enriched in a particular BED file, but this type of analysis doesn't take into account the scores/rank of the loci in the BED file as would be done in an analysis akin to GSEA. 

Does anyone know of any tools/methods for analyzing functional enrichment while taking advantage of feature/locus ranks/scores with locus-level data?

I know I probably didn't explain this very clearly, so please let me know if I can clarify anything for you.  Thanks
I tinkered with a program GSEA-SNP quite a few years back, which claims that it does a similar ranking procedure with SNPs. It carries out its procedure by first linking SNPs with genes, then running an algorithm similar to GSEA. There's a bit more detail in the paper.

Unfortunately this is a space I've got a bit further away from in recent years, so I don't know if anyone has carried out the legwork to generalise the algorithm for sets of any type where each item within each set fits a continuous (but not necessarily normal) distribution.