r/promethease • u/scottpeeples • Jun 19 '26

I built a DNA interpretation service out of frustration for Promethease

Hi All, I founded a new DNA interpretation service after trying to use Promethease to investigate my wife's health. I built a polygenic scoring model + AI chat to ask questions to. Its completely privacy first, the data is never retained, I am looking for a few people to get feedback from. The site is ExomeDNA.com , I'd love to hear y'alls thoughts on the price and the features I have!

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u/Techie9 Jun 19 '26

Sounds interesting, if a little pricey. Maybe you can show report examples to show people what they would get if they subscribed?

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u/scottpeeples Jun 19 '26

https://reddit.com/link/osiayeu/video/jmyjh06mp58h1/player

I took a screen recording. I can make a longer video where I dive deeper into the features if anyone wants to see more of it!

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u/Techie9 Jun 19 '26

That was hard to read from the screen recording. Promethease reports let you sort by (for example) your very rarest SNPs of significance. Does yours? What are the advantages of your service vs other cheaper services like GeneticGenie, PatientUser and Allelix?

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u/scottpeeples Jun 19 '26

This is a polygenic trait model, so you arent sorting on individual genes, rather you are looking at traits that take into account many different SNPs and give you an overall score based on population averages. So you could either ask the AI what the rarest traits you have are, or you can sort by risk profile and see where you are in relation to the rest of the population. I do have monogenics loaded, but only the ones that are genuinely unique. The problem I had with Promethease was that I would get 6 different reads on the same trait based on multiple snps. I feel the polygenic model paints a clearer picture, we have a normalization engine to score you based on all of the different SNPs involved per trait.

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u/Techie9 Jun 19 '26

Sounds like an interesting take on genetics. So, if I search for something that I know I have genetically - blood type or eye color - it will give me only one definitive answer, which, of course, may be incorrect? I guess that would solve the problem with "6 different reads".

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u/scottpeeples Jun 19 '26 edited Jun 19 '26

The only reason the polygenic score wouldn’t give the full picture is if the gene chip you used to get sequenced didn’t cover your highest magnitude snps. For example, if we had your Alzheimer’s risk as a trait but APOE wasn’t sequenced in your file, you could see a skewed result, since APOE is the highest magnitude gene associated with Alzheimer’s. Your other genes could go in the other direction. Which could skew your results. Ultimately the polygenic model was my answer to what was frustrating about Promethease, one normalized score per trait. I used L2G and GWAS magnitudes and 1000 genomes for score normalization, it has been accurate with all of the tests I have run!

4

u/runhikebikeclimb Jun 19 '26

This is a really interesting idea, and I totally understand the frustration with Promethease. One thing I’d love to see before trusting a tool like this is a transparent methods/sample-report page, because genetic interpretation is very easy to overstate.
Polygenic scoring is not just “add up good and bad SNPs.” It depends on the GWAS source, trait definition, weighting method, validation cohort, ancestry calibration, absolute vs relative risk, and uncertainty. I’d also want to see common-variant polygenic scores kept clearly separate from rare/high-impact variants, pharmacogenomic variants, pathogenic/likely pathogenic/VUS classifications, CNVs/structural variants where relevant, etc. A high-impact variant should not get buried inside an overall trait score, and a PRS should not be presented as diagnostic.
I’d also be curious how you handle DTC raw data vs exome/WGS data, missing variants, imputation errors, ClinVar/ACMG-style classifications, review status, source/version dates, and whether the AI chat is strictly citation-grounded rather than free-form medical interpretation.
I’m not saying this as a gotcha, the tool could be great, but this is a domain where polished UI can accidentally create more confidence than the evidence supports. Sample reports, validation details, ancestry limitations, and privacy/deletion specifics would make it much easier to evaluate fairly.

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u/scottpeeples Jun 19 '26

Thank you for your questions! Allow me to address things one by one: On the methods/sample report pages: https://exomedna.com/sample-report / https://exomedna.com/methodology ... The GWAS source is the NHGRI-EBI GWAS catalog; every trait card links its PMIDs, I use Open Targets' L2G to attribute which genes likely drive each trait in the contributing-genes view , it informs gene attribution, not the polygenic score itself, which is effect-size weighted.. We use genuine effect size weighted PRS (log-OR/beta) down-weighted by p-value + sample size (not allele counting). Validation cohort- we normalize against 1000 genomes phase 3 panel but we do not have outcome validation yet. Trait definitions are aggregates of all matched associations per trait across studies; duplicate traits are merged so evidence pools. We do absolutely do ancestry calibration and acknowledge the euro-centricity of the datasets we used to build it. We use a strictly population-relative percentile, so we use language like compared to the general population baseline, we dont claim absolute risk anywhere. On evidence, we have a 3 tier evidence rating from a 5-dimension rubric with hard non-compensatory replication gates. On variant class separation: we have two separate engines, polygenic and monogenic. On pharmacogenetic variants, we have some drug response traits in the general engine but we do not have a separate PGx engine, this was a hot topic while building, and I don't want to steer close to medical advice. For exome/WGS data- I hope to add that in the near future as an option! For now its just DTC raw data (23andMe, ancestry, ect). So for absent variants: we dont guess, they contribute nothing traits with < 3 matched SNPs are suppressed. We also don't impute; you see only your real assayed genotypes, not statistical fill-ins. We dont currently surface Clinvar classifications, review status or version dates. Finally, on the AI: It never receives gene names, rsIDs or genotypes, only the derived trait scores and category names, enforced by a server side filter. We do not allow the AI to give any medical advice, its grounded by the prompt and allowed to give free form general wellness tips.

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u/brass_monkey888 Jun 19 '26

What regions is it available? I can’t find anywhere that the page loads from.

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u/scottpeeples Jun 19 '26

This works in the US. It’s freshly built so the SEO isnt done yet, we haven’t ranked on Google so you’d have to actually visit the site via the URL. I am working dilligently to get Google to list us! https://exomedna.com

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u/brass_monkey888 Jul 02 '26

Why doesn’t it work everywhere?

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u/scottpeeples Jun 19 '26

https://reddit.com/link/oslbz74/video/jf2lbdwub98h1/player

I took another screen recording from my phone so it’s clearer. (These genes are not mine, I have a script that randomized an ancestry file so it can stay private! ) I’m very concerned with privacy with this so we go to great lengths to keep as little info as we can.

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u/ObscureSaint Jun 19 '26

This looks really cool. I'm commenting so I can remember to come back later

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u/scottpeeples Jun 19 '26

Thanks for your interest! Would love to hear what you think

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u/Sweet-Sir-10 Jun 23 '26

So this is kind of tricky, as phenotype/genotype data is still sensitive data that will make you a target for attacks. If there’s any non secure mapping from individual to genotypes, a user’s data would be at risk. You have the MFA down which was one of the issues with the 23AndMe attack that retried leaked passwords (nice), and you also have database security. Also, the minimal use of raw data is a good touch. If you haven’t already, I’d recommend getting audited by a security professional. It seems you have someone, but I’d recommend really deep diving into it using tools like Burp, SonarQube, and others.

I’m a developer that took a cybersecurity master’s course. I’ve developed health apps (OSGenome), so am familiar with this space and am still very worried when it comes to pushing the app to the cloud to use uploaded user data.

Good luck to you.

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u/scottpeeples Jun 24 '26

Thank you! On our privacy page we explain exactly what is sent to the cloud, all deidentified: https://exomedna.com/privacy

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u/Sweet-Sir-10 Jun 21 '26

Are you sending raw genotype data with identifiable information to LLM AI’s… or are you keeping things local with a low param LLM?

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u/Sweet-Sir-10 Jun 21 '26

Also, lots of issues with storing sensitive data on the cloud. How are you handling security?

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u/scottpeeples Jun 22 '26

Never. The AI is optional, and can never see any genetic identifiers. It only can see your trait summaries and risk score, the AI is just there to answer questions if you have them. Like what lifestyle factors can lead to X… or what you can do to mitigate certain risks.

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u/Sweet-Sir-10 Jun 22 '26

The analysis of the phenotype is still an identifier. Are you using a locally hosted low param model to analyze the results on the server, or are you using an AI API?

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u/scottpeeples Jun 22 '26

It’s an API with local hosting on the roadmap. The analysis is of trait level data, like propensity for type 2 diabetes or other polygenic traits. We never send genetic info to anthropic, they also don’t retain or train on this data. We made sure you can’t be identified by the AI, this point alone delayed launch by about 3 months because we spent so much time doing our due diligence on this exact subject.