There’s a new genetic sequencing company that wants you to get paid for donating your DNA data to science. Launched in December, Genos’s service costs around $500 and gives customers the ~3% of their genome sequence that codes for proteins, the “exome.” Additionally, Genos’s platform allows users to share their exome data with (academic and commercial) researchers…and get paid for it.
Let’s evaluate what is and isn’t novel about this proposition. First, people have been able to order direct-to-consumer genetic tests for over a decade. Exome sequencing is a little newer and shinier compared to the smaller scale microarray technology used by major players such as 23andMe and Ancestry DNA, but the general idea is well-precedented. Second, it’s not unusual for people to get paid for participating in research, genetic or otherwise. You take time to fill out a survey or participate in an interview, so why not get a token of appreciation (maybe $20 or $50) for your time and efforts. Note researchers and regulators do want these “incentives” to stop short of being coercive, meaning that it shouldn’t be so much money that a person who otherwise wouldn’t want to participate feels compelled to because they want/need the money.
Ok, so DTC data: not new; getting money for research participation: not new. What does seem to be raising some eyebrows (and piquing some purse strings) is, I think, the directly transactional nature of a customer sharing their — already generated — genetic data with researchers and getting paid, potentially much more than a typical participation incentive. Perhaps over and over again, each time they give Genos permission to share their slice of the company database with another researcher.
It seems a bit….smarmy. Over commercialized and over commodifying genetic data at the boundary of commerce and research — granted that research can be academic or commercial. Feeling conflicted about these developments myself, I was glad to see a recent Commentary on the topic in Nature Biotechnology. Written by legal experts, the Commentary reviews the strengths and weaknesses of a system where research participants are paid directly for their data. I’ll summarize the arguments here, in part because the full article is behind a paywall, and also because it allows me to weigh in on parts of the argument.
1. Respect for persons. Research can be lucrative so the people contributing data should be more valued, i.e. via direct compensation.
2. Uniqueness of information. Paying for genetic data recognizes its high value and importance to people.
My note: This is a vote for genetic exceptionalism – the idea that genetic data is somehow special and sacrosanct compared to other types of personal data. To this point I would counter maybe we shouldn’t be encouraging people to attach so much self worth and interest to their DNA sequence.
3. Promotes fairness and equality. Researchers profit and benefit from people’s data, so people should similarly profit and benefit.
My note: Valid point, but this goes against legal precedents in the US that people do not hold a financial stake in their biological specimens, and extracted data, once donated to research. But law and ethics are not the same, so there is definitely something here.
4. Greater good. The Genos model may encourage more people to participate in research, which benefits all.
1. Might decrease willingness to share. Studies have shown that when people would usually contribute out of altruism, offering them something in return actually decreases participation.
2. Undermines individual autonomy. Offering money may coerce people to participate, actually reducing their personal freedom.
My note: see earlier mention of researchers not wanting to offer so much money to would-be participants, for this every reason. Maybe a matter of “how much”, not “if”, when it comes to payout.
3. The problem of valuation. It may be difficult to assign value to an individual’s genome and thus what people are compensated may not reflect the true value.
My note: right, even giving someone $200 for their genome (the upper limit of the compensation range noted by Genos) could fall short of how much that individual’s genome actually benefits the researcher or company.
While the paper does present both sides, it comes off as saying “this is basically inevitable, so let’s strive for the best outcome possible.”
Genos is making people’s exome sequence available to them, as a personal resource, and also one they can “shop around” to different researchers. Part of this is line with an argument I made in Nature last fall, that genetics researchers should offer to give data back to participants if the participants want it. The reasoning being that more people might turn away from traditional research in favor of consumer genomics, which does make the “raw” data available. I wrote about good things that happen when people become “stewards of their genetic data.” With Genos it goes one step further, in that people become their own data brokers.
It begs the question: what is the right way to reciprocate and engage with research participants? The most traditional approach is to rely on the individuals’ altruism to improve the greater good, promising them no individual benefit in return. I don’t think this is the right model anymore, especially when current information infrastructures make it easier to give something back. But is Genos the right model? What about research models that offer people back some bits of interpretation in exchange for donating their genetic data? Sites such as DNA.land or openSNP, for example. Is that an even and fair exchange? I do wonder, especially in our current state of knowledge where genetic data is arguably more useful in the aggregate, to answer research questions using thousands of people’s data at once, versus an individual with their own genome trying to squeeze some drops of meaning from wringing out that double helix. I think such tools might still be leaning more heavily on people’s interests in general altruism than personal gain.
It will be interesting to see how the Genos roll-out goes over the next couple of months. Perhaps they are bringing us closer to Professor George Church’s vision, quoted on the company home page: “It should be our birthright that everybody on the planet should have access to their own genome.” (Oh, but p.s. New Yorkers – it’s not your birth right yet; Genos can’t ship a test kit to you…)
For my dissertation, I have been interviewing developers of third-party interpretation tools for consumer genomic data. These are tools such as Promethease, openSNP, and DNA.land, among many others, where people who have their genetic data file from consumer testing can seek further analysis and/or contribute their genome to research. Even though I’m only a few interviews in, I’m already seeing some interesting themes. This isn’t a formal analysis here, just some of my initial impressions — “field notes,” you might call it.
- Theme 1. Tools are heterogeneous in terms of their creators/developers, functions, purposes, and processes.
Just like with most studies, I have inclusion criteria to identify eligible tools. (Also called “scoping,” this is an essential process for any successful…and completable…graduate student project! We just can’t get enough of the scoping…). My three criteria for tool eligibility are:
- Must allow users to upload (or analyze locally) their genetic data file
- Must return some type of information or interpretation to the user
- Must be active at the time of my study (and there were some tools that failed on this – i.e., they are now defunct)
This is a very user or customer-facing set of inclusion criteria, which I did on purpose. That is, I thought of a user sitting at their computer, having just freshly downloaded their genetic data from 23andMe or AncestryDNA. What tools might they search for or stumble upon as they pursued further self-directed analysis of their genetics? Those were the tools I wanted to study.
At the outset, I was aware I might be grouping together a lot of very different tools, and indeed after months of closer study this is definitely the case. Some tools might not even want to be called “interpretation tools,” as they are more focused on users contributing data to research and just happen to give some tidbits of info in return. I also wasn’t expecting to include some DTC companies, but turns out there are some that also offer analysis of existing genetic data, i.e. from another testing company.
So I have a sort of motley crew of a dataset, but I’m optimistic it will make for an interesting and fruitful analytic substrate.
- Theme 2. Several developers built the tool they wanted for themselves.
This isn’t across the board, but I was struck at how many of the tools were born out of the developers’ own needs and desires. They had their own genetic data in hand and wanted to do something with it that existing tools couldn’t do, or couldn’t do in exactly the way they wanted. So with sufficient programming and bioinformatic skills, they build the tool they want and then expand it out for broader use. It reminds me of the phrase “If you build it, they will come.” Except here we have the developer saying something like: “If I build it, I will come…and then I’ll let everyone else who’s interested come, too.”
- Theme 3. The process of getting information is a source of information in and of itself.
I’ve been learning about tools by (1) studying the website and any associated papers or media coverage and (2) interviewing tool developers. The difficulty or ease of accessing these routes of information is sometimes telling about the tool itself. Some tools are run by companies where it’s very difficult to tease out any proprietary details of how they work (analytical tools and resources used, e.g.). Other tools are built on an idea of openness and are accordingly incredibly transparent about how they work, how many people are using it, etc. Reaching out to tool developers for interviews is similarly illuminating. But not always in the way I expected. Not surprisingly, several people are either ignoring or declining my inquiries, and it’s fair to say more companies fall into that category than not. But some companies are talking to me. And on the flip side, some academics are not. My goal is to get interviews with at least half of the tools I’m studying, which would be ~50% “response rate.” And the goal with qualitative interviews such as these is not to get to a statistically significant number, per se, but rather to gain some depth and texture to my understanding of who made these tools and why. Stay tuned for more!