Cool, I am looking forward to an improved version.
I would be interested in your rationale behind how the public would/could use this kind of data. I think the infino.me/health seems to be an example, pointing out major risk factors behind cancer and CVD. But don't you think this is already common knowledge?
Or is the primary goal to get people to voluntarily share their health and genotype data to get a big dataset for analysis, and maybe eventually provide a sort of personalized risk assessment? I wonder how the FDA views that sort of thing.
In short, I built a search engine for my genome. Im letting the rest of the world use it ;)
I did my PhD work in diabetes genetics. It runs in my family, and Im kinda pissed that its killing off a large fraction of us. The eventual goal is to make this into some kind of communal science effort where people can contribute open source analysis pipelines. I need the right kind of organizational structure however to keep the data safe and centralized but still allow open source research.
So, some kind of platform where algorithms can get in, results can get out, but raw data stays locked up. I want a world where this kind of research happens in the open, and not privately in biomedical corporations.
It is a good idea in general, but the idea of infosec for genetic data is a minefield. I deal with similar limitations daily -- one of my areas is large-scale meta-analysis of expression data, which was dandy when that data was collected using microarrays. Now, it's RNA-seq, so a lot of that data indeed stays locked up and you have to apply for special permission to access individual datasets from dbGaP, making large-scale studies difficult/impossible.
But although "algorithms in/results out" sounds good in principle, I think it will be hard to implement in practice. You would have to make algorithms run without network access to prevent a bulk_send_data_to_ip() type of function from being written, but that would hamper complex programs requiring external data.
In general I think the only realistic way forward is to take the 1000 genomes approach of finding people who are willing to take the privacy risks of truly open-sourcing their data. But it sounds like an interesting idea and I hope I'm wrong and your approach turns out to be workable.
I like your thoughts here! Indeed I have been hoping to mostly attract people who are willing to be fully open with their data. If I ever get big enough to implement this 'algorithms in/results out' approach I intend to re-engage the whole userbase and have people opt-in to crowdsourced scientific analysis. To prevent data leaking I figured we would start with full code review, and indeed air-gapped analysis.
Its a continually evolving thing. I imagine it would be years before I get to that stage. Depends on if I find funding or university help.
Hit me up at info@infino.me if you'd like to chat more.
What pisses me off is how much can be done to prevent it and how little people are willing to do so, especially when their genes can tell them point blank that they really ought to be doing something and doing it right now before the very bad thing happens that will negatively impact them forever.
It's more genetic than people realize. In my PhD I focused heavily on the key gene responsible. Your stem cells are literally predisposed to become adipose tissue instead of muscle from the start. It sucks.
So what happens when such a person eats relatively healthily and exercises regularly? Does this do any significant good or should they just accept the inevitable?
It always helps. Some people just need to try harder.
Much deeper, and more valuable, would be information on the optimal time of day to eat, exercise, etc, and more information on the specifics of the exercise needed (in terms of intensity or heart rate zones).
TLDR, exercise matters, so does genome, but what we really want is more specifics. This might inform better lifestyle adjustments.
I'm all for optimizing the last 20%, but unless I'm mistaken, in America, ~80% of this is due to a sedentary lifestyle. And that bias arises from knowing people genetically predisposed to type 2 who have close relatives who died young from it, yet persuading them to exercise is like pulling teeth.
Which is to say while I think you could build a billion dollar company out of that last 20% (and you can correct me on the percentages here, really, please do so), it would have to not only provide such information, but also present it in such a way that the recipient got up and did something with it. And that's the hard part, no?
I would be interested in your rationale behind how the public would/could use this kind of data. I think the infino.me/health seems to be an example, pointing out major risk factors behind cancer and CVD. But don't you think this is already common knowledge?
Or is the primary goal to get people to voluntarily share their health and genotype data to get a big dataset for analysis, and maybe eventually provide a sort of personalized risk assessment? I wonder how the FDA views that sort of thing.