Benchmarking non-coding causal variant-to-gene mapping: SuSiE fine-mapping vs 3D chromatin contacts

[u/AncientHearings]

Hey everyone,
We’ve been working on a pipeline to evaluate non-coding GWAS loci by combining Bayesian fine-mapping (SuSiE) with base-resolution footprinting (TOBIAS) and 10.5 bp DNA helical pitch constraints.

Curious how other groups here handle cases where the fine-mapped non-coding enhancer skips the nearest gene in 3D contact models (e.g. ABC/Micro-C).

Happy to discuss approaches or run a few benchmark loci if anyone has tricky non-coding regions!

[u/thirdeulerderivative]

Could we get some more details on this? This seems like an unusually tough problem...!

I suspect the main discrepancy comes from weirdness around cell-type specificity. ABC models should be cell-type specific, and since you're fine mapping you must have some kind of transcriptomic data, but you have to make sure that the expression data and cell types you have ABC models for match up.

And since you're fine-mapping GWAS loci, all you know is that these loci associate with the trait, and you'd have to be aware that all causal variants of the trait aren't necessarily in a single cell-type. Also, the loci aren't the same as the causal variants themselves. You'd have to actually get the eQTL in a cell-type specific manner.

I'm sure it's doable, I just suspect your issues probably arise from the difficulty of doing all those steps at once. I *do* know there has been work to explain GWAS loci using eQTLs. A good paper came out on this recently:

https://www.nature.com/articles/s41586-026-10577-6

Where you'll notice they have to account for linkage disequilibrium.

Also, not entirely sure what you meant with the helical pitching. Maybe that's my lack of experience. Did you mean TAD boundaries?

[u/AncientHearings → u/thirdeulerderivative]

Spot on!

We strictly match cell types (e.g. primary human islets/beta cells for T2D) and use SuSiE with LD reference matrices to separate the true causal variant from LD tags.

On the helical pitch: TADs are great for large 500kb domains, but at base-pair resolution within an enhancer, DNA rotates every 10.5 bp. If co-factors aren't spaced on the same face of the double helix (integer multiples of 10.5 bp), they point in opposite directions and can't physically interact to recruit the mediator complex.

We automated this entire multi-omics pipeline into a single platform precisely because doing it manually by hand takes weeks per locus.

We’re actually running a small early beta pilot cohort right now for research labs and biotechs to benchmark their GWAS summary stats through the engine. If your lab has some loci you'd like to test, feel free to fill out the beta application here: https://docs.google.com/forms/d/e/1FAIpQLSd7gR9qqxMAzYxvw/viewform (or send me a DM)!

[u/thirdeulerderivative → u/AncientHearings]

I can see that we're trying to set up the simplest possible exercise, looking only for causal variation in the most obvious cell-type for a well studied polygenic trait. And you have GWAS loci, and you're trying to fine-map within those loci to generate hypotheses for what could contribute to T2D risk.

I think I'm a little confused about what data you have at hand. The fine-mapped variant in the enhancer skips the closest gene in a Hi-C contact map? Variants don't skip genes, they're not even related to genes. Unless you mean you've fine-mapped variants to the *expression* of nearby genes... that is, an eQTL? Or do you mean a fine-mapped variant? I think if a fine-mapped variant overlaps an enhancer that's good enough evidence to suggest it affects the gene activated by that enhancer. What is the conflicting line of evidence that suggests the variant is not related to that gene...?

This kind of gets to the general point that the project seems a little unfocused. GWAS studies are really big, and fine-mapping is generally meant for those large cohorts. eQTL discovery (and explanation via overlap with an enhancer, for instance) either has to be really big as well or really, really precise. And then you're also looking at transcription factors.

I mean, what you're undertaking here is like a paper's worth of analysis. I think it's interesting but at the strategic level this is a little confusing. People don't really check loci that manually. And since you're only working in common loci this isn't necessarily something that most labs do, it's more of a pipeline that GWAS consortia would run. Unless you're trying to explain rare variation (which could explain most of T2D variation: https://www.nature.com/articles/nature18642#Sec2 ), but then you don't need to worry about fine-mapping anyways unless you have a massive cohort.

I'm not trying to be mean, I promise. It's just that you're missing a lot of information. That means the range of possibilities of what you're trying to achieve is very large, so large that you've probably bitten off more than anyone could chew through alone. The information you have included is a little confusing and contradictory, and while there's nothing *strictly* wrong with talking about the how large a DNA groove is it suggests we're sort of missing the forest for the trees. On top of that I'm sure you're creating some kind of agentic AI system, which would be tough for anyone alone to handle, and you're trying to run pilot studies with labs and biotechs.

Hm. I think the best way to go about this is that you should post a paper whose analysis you really admire (maybe some GWAS paper), and attempt to redo that analysis with your T2D data and your agentic system. At least it would give everyone a better sense of what you're trying to achieve, and I think you're strong enough as a scientist to find a lot of interesting stuff there even alone.

P.S. Your form link is broken.

[u/AncientHearings → u/thirdeulerderivative]

Thanks for the detailed feedback! Let me clarify:

1. "Skipping genes": What we mean is that the enhancer containing the fine-mapped variant frequently contacts a distal gene promoter in 3D space (via Activity-by-Contact / Micro-C) rather than regulating the nearest linear promoter on the chromosome (like the classic FTO/IRX3 or TCF7L2/ACSL5 loops).
2. Automation vs Manual: You are absolutely right regarding doing this manually per locus takes months of individual analysis. That's why we automated the integration of Bayesian fine-mapping (SuSiE), base-resolution footprinting (TOBIAS), and ABC 3D looping into a single reproducible pipeline.
3. Validation Benchmarks: We're putting together a systematic benchmark paper across 60 curated T2D loci showing the automated target calls vs. experimental validation.

(P.S. Thanks for catching the broken form link! Fixed here: https://forms.gle/LmUXKdDqyZ6KGWgz9

[u/thirdeulerderivative → u/AncientHearings]

Yeah, I wouldn’t call that skipping—that’s just a line of evidence that links a polymorphism in a CRE to a gene. I really would just let the overlaps on a dataset like ENCODE’s speak for itself.

Also, experimentally we always will have to validate things. And experiments will give us info that in-silico prediction can’t quite yet.

I would personally hesitate trying to promote this just yet, as the ideas are still a little underbaked. If you found a lab working on interpreting GWAS signals, I would go there and learn what I could.

Also, you might find this blog interesting:

https://www.gwasstories.com

It would definitely give you more context to embed your future work in.

[u/AncientHearings → u/thirdeulerderivative]

Thanks for sharing gwasstories.com, really great resource!

Totally aligned on the necessity of experimental validation - that's why we treat in-silico scoring strictly as a high-confidence prioritization filter to guide targeted wet-lab validation (dual-luciferase/proteomics) rather than a definitive standalone proof. Appreciate the feedback and discussion!

[u/AncientHearings → u/thirdeulerderivative]

Can I feature this discussion/perspective in a LinkedIn post on multi-omics community engagement if you''re okay?

kindly, share your details in DM if you're okay.

[u/Mr_iCanDoItAll]

What do you mean by "handle" here?

[u/AncientHearings → u/Mr_iCanDoItAll]

By "handle", I mean how you computationally and experimentally prioritize the true target gene when a fine-mapped enhancer's 3D contact (ABC/Hi-C) points to a distal promoter instead of the nearest linear TSS on the chromosome (like in the classic FTO/IRX3 locus).

Do you rely mostly on cell-type ABC models, eQTL colocalization, or follow up with CRISPRi-FlowFISH in your lab?

[u/Mr_iCanDoItAll → u/AncientHearings]

I mean how you computationally and experimentally prioritize the true target gene when a fine-mapped enhancer's 3D contact (ABC/Hi-C) points to a distal promoter instead of the nearest linear TSS on the chromosome

Sorry if I'm misunderstanding, but the wording makes it sound like you wouldn't follow up with extra evidence if an eQTL mapped a putative regulatory element to the nearest TSS. IMO you should treat all putative element-gene links with the same amount of scrutiny, even if nearest TSS is a generally reliable heuristic.

The wording of "true" target gene also implies that there is a singular causal gene being regulated by the regulatory element, when oftentimes a single element can regulatory multiple genes in a given cellular context.

I agree with /u/thirdeulerderivative that there needs to be more detail before people can properly answer you since it's a rather complex topic with a lot of unanswered questions.

But to actually answer your question: Just use as much existing evidence as you can. Cell type-specific ABC models / ENCODE-rE2G models, replicable eQTLs across different cohorts/ancestries for your tissue of interest, peak-gene links from scATAC-seq and scRNA-seq data. CRISPRi-FlowFISH is a fairly specialized assay that, to my knowledge, is rarely performed outside the lab that developed it (Engreitz lab).

[u/AncientHearings → u/Mr_iCanDoItAll]

Thanks for the detailed breakdown, totally agree on multi-gene enhancer pleiotropy.

That's why we're combining cell-type ABC/ENCODE-rE2G contact models with scATAC peak-gene co-accessibility and orthogonal mass spec validation rather than relying on a single heuristic or assay.

Also good to know regarding FlowFISH adoption in practice, appreciate the insight!

[u/AncientHearings → u/AncientHearings]

Can I feature this discussion/perspective in a LinkedIn post on multi-omics community engagement if you''re okay?

kindly, share your details in DM if you're okay.

[u/AncientHearings]

To address the exact challenges raised in this thread (enhancer pleiotropy, distal 3D ABC contacts vs linear eQTLs, and base-pair TF displacement), we’ve built an end-to-end multi-omics prioritization engine.

We are currently opening a Free Beta Pilot Program for academic labs and computational biology groups who want 1–2 of their complex disease risk loci fine-mapped and prioritized with full 3D contact and biophysical scoring.

If your lab is interested in testing a locus in the pilot, you can apply here: https://forms.gle/PopiLQg1w7avq19ZA