Just to be clear, we are not ourselves working on private home sequencing.
I'm aware of people working on at-home devices but that is not our own approach.
We are just working on a mixing service to anonymize data user identities as their samples and data move through labs, logistics, etc.
Legal protections around genetic data can easily be rolled back in several ways, both incremental and drastic.
While it's true someone's DNA can easily be collected by a sufficiently motivated party, there is still a need at scale for something regular people can use to investigate their ancestry and predispositions without becoming datasets for unheeding organizations.
The 23andMe hacks, for example, should be a sobering realization that personal genomics is a systemic risk for several classes of people.
We have shown that we can batch user samples and then send them anonymously to the lab. Users can then use an MPC protocol to securely fetch their data without losing anonymity.
We plan to roll out a product called Batcher which does this soon.
You're right, most analyses are done using biobanks, etc right now. The sentence is an oversimplification but people still implicitly contribute their data to insecure and centralized sources.
If we can enable people to get sequenced not just when they are sick but for altruistic or financial reasons without leaking their DNA data, we could unlock much bigger datasets than currently available, especially from populations that are underrepresented or ignored.
If you look at a previous experiment we did, the costs for getting genotyped are pretty moderate (<$80) and going down further. Blended genome-exome (<$150) and full genome sequencing (<$500) are getting cheaper as well.
Through our Explorer product (https://explorer.monadicdna.com/) we let people glean information about their genome using GWAS Catalog data.
This PoC addresses the next step, i.e. how to keep genetic data private at the user level while still allowing aggregate studies to happen, with or without financial incentives.
This post describes a local-first approach to personal genomics. Large-scale analysis (processing raw genotype data against the GWAS Catalog, >1M traits) runs entirely on the user’s device.
LLMs are used only for exploration and summarization of selected results and they never see raw genetic data. Users can choose different LLM backends depending on their preferences, including a TEE-based option ( nilAI), local models via Ollama, or hosted models via HuggingFace.
Happy to answer technical questions about the pipeline, privacy tradeoffs, or limitations.
A privacy-first genomics tool that lets you explore hundreds of thousands of genetic traits from GWAS Catalog data and analyze your genetic information entirely in your browser, with optional secure LLM analysis.
I'm building Monadic DNA explorer, a tool to explore thousands of genetic traits from GWAS Catalog in your browser and plug in your own DNA data from 23andMe, Ancestry, etc. All processing happens locally on your machine and AI insights are run in a private LLM inside a TEE.
I'm giving you general reasons why this is the case. For our own app, we hope to build a protocol where third parties can operate async on user data (with consent).
You may wish to build a protocol where third parties can asynchronously operate on user data. You may also want to have separation between the end app and the compute layer for legal or practical purposes. Finally, you may not want to store large payloads on client devices.
We address this point in the article. On the default path with existing rules and regulations some degree of trust in labs will always be required.
At-home sequencing could be a game changer.
The other reply also mentions molecular cryptography which could provide really strong anonymity and privacy guarantees. We hope to do a PoC accordingly some time in the neat future.
I'm aware of people working on at-home devices but that is not our own approach.
We are just working on a mixing service to anonymize data user identities as their samples and data move through labs, logistics, etc.
Legal protections around genetic data can easily be rolled back in several ways, both incremental and drastic.
While it's true someone's DNA can easily be collected by a sufficiently motivated party, there is still a need at scale for something regular people can use to investigate their ancestry and predispositions without becoming datasets for unheeding organizations.
The 23andMe hacks, for example, should be a sobering realization that personal genomics is a systemic risk for several classes of people.