(UPDATED: See below.)
As I discussed a few weeks ago with respect to deCODEme — a “personal genomics” service hurriedly launched last November by Iceland’s deCODE Genetics in an apparent attempt to beat 23andMe to market (it succeeded by a day or so) — these sorts of services can awfully dense and difficult to navigate. The deCODEme service appears to be particularly bad in that respect, both in terms of its design and even the underlying science used to justify the genetic information displayed in a demonstration user account.
So it’s a pleasant surprise to report that 23andMe, which over the weekend began allowing people to set up demonstration accounts itself, appears to have made the process of understanding your genetic inheritance about as simple and intuitive as it can probably get. The demo accounts don’t display your own genetic information, of course — instead, they show a profile for the fictional Greg and Lilly Mendel and their immediate relatives. (The family name is inspired by Gregor Mendel, a nineteenth-century monk known as the “father of genetics” for his studies on the inheritance of pea plants; the profile uses actual data from an anonymous European family.)
These sorts of demo accounts are particularly useful given that 23andMe and its competitors are charging customers roughly $1,000 for a genetic analysis, which is a lot to shell out when you don’t have any real idea what you’re getting for your money. To sign up for a 23andMe demo account, click here.
Like all these services — of which there are currently at least four, counting the new DNATraits project launched recently by Family Tree DNA — 23andMe takes a genetic sample (here from having users spit repeatedly into a tube) and checks 600,000 or so individual DNA “letters,” or bases, known to vary between people. After analyzing those letters, the company posts your genetic information on a Web site where you can see what your particular genetic pattern says about inherited traits such as your susceptibility to cancer or heart disease, longevity and even eye and hair color. Not only can you spin through the data in as much or as little detail as you like, you can share it with relatives or friends and search for others with similar traits.
One of the first things a new visitor will see is the clean and uncluttered look of 23andMe’s “gene journal,” which lets you scroll through various genetic traits and then dive in to see how you — well, your Mendel stand-in — fare compared to the population at large. (See a screenshot of Greg Mendel’s gene journal using the thumbnail above and to the left.)
More after the jump:
The contrast with deCODEme couldn’t be more striking. Where the Icelandic service offers minimal data and requires lots of clicking just to see how your genes stack up against those of the general population, 23andMe puts almost all that information out front. Each condition is rated according to the reliability of the data that links particular genetic variations (technically known as “single nucleotide polymorphisms,” or SNPs) — I’ve blown up the ratings in the graphic to the left; click for a larger version — which itself is a big step, since deCODEme appears to limit what it shows customers to the most conservative data available. Yes, it’s always possible that people will misinterpret their disease risk based on sketchy data, but here such mistakes are particularly difficult to make, given that the site deploys simple icons and contant boxed reminders whenever gene-disease links are considered preliminary as opposed to established.
You can also sort disease conditions by the affected region of the body — a feature that actually works here, as opposed to a similar feature at deCODEme when I last beamed in.
The individual disease-information pages open with some general information about the condition, but quickly get to the good stuff (here, I’ve chosen heart attack; click the thumbnail at left for a larger image). The site quickly presents you with your general risk versus that of a comparable population — in this example, European males aged 45-54, although you can adjust those settings via drop-down menus — then outlines the extent to which genes, as opposed to environment, are thought to contribute to the danger of heart attack.
Next — and this struck me as particularly cool — you get a graph of the individual SNPs 23andMe is using to make its calculations, which shows the positive and negative effects of each. Clicking on any particular SNP loads a panel immediately below that describes what’s known about it and lists citations from the scientific literature for more background. I could have sworn that the individual SNPs were also linked to a 23andMe database, allowing you to browse more deeply if you care to, but at the moment, if the disease pages permit that, I can’t find it again. You can click on a “technical report” link to the left in order to see the actual SNPs on which 23andMe bases its finding, but those aren’t linked either.
Fortnately, unlike deCODEme, the 23andMe demo accounts are fully functional. The site’s GenomeExplorer lets you browse through your raw data, either in a graphical format displayed by chromosome or by searching on particular genes or SNPs. The output of such searches doesn’t strike me as terribly useful in its current incarnation, but that will presumably improve as time goes by. Instead, though, you can download “your” genetic data for use in other programs designed to read that format (such as this one). Don’t count on reading it directly without special help, though; I now have a 14MB file of “Greg Mendel’s” data sitting on my hard drive that’s simply too large to open with any common text-editing program.
You can also compare your genetics with others who share their information, although from my perspective, this feature is somewhat disappointing. Comparing Greg Mendel to his daughter, for instance, tells me only that they’re 84.15 percent similar across their entire genomes (at least that portion measured by 23andMe), or that across genes related to a limited number of characteristics — muscle endurance and circadian rhythm, for instance — they’re between 85 percent and 89 percent similar. This is probably more fun if you’re comparing yourself to your friends, assuming you’re open to that sort of thing, but I’m still at a loss as to exactly how this sort of stuff can prove useful.
I’ve sidestepped the entire ancestry section for now, although I’ll try to check back later if I have more time to look at it.
The 23andMe service isn’t without flaws, obviously. In many conditions, it also seems to limit the number of SNPs it reports on, much the same as I found at deCODEme. Heart attack is a prime example, since 23andMe omits the very same SNP strongly correlated with coronary-artery disease — rs1333049 — that I criticized deCODEme for skipping. And as noted above, some of the genetic exploration and comparison tools could probably use work. Overall, though, the service as displayed here is a pretty impressive effort.
But don’t take my word for it: Feel free to check out the 23andMe demo account yourself.
UPDATE: Review completed and rewritten throughout.
UPDATE REDUX: For the technically inclined, physician-turned-DNA enthusiast Ann Turner offers a comparative review of 23andMe and deCODEme over at Eye on DNA. (For what it’s worth, Turner also commented on my earlier deCODEme coverage here.)
Separately, 23andMe product manager Brian Naughton wrote to clarify that while 23andMe doesn’t use the rs1333049 SNP, it does use another SNP in the same chromosomal region that correlates highly (80 to 90 percent in Europeans) with rs1333049.
FURTHER UPDATE: deCODEme replies in comments. I’ll let their missive stand for itself, so take a look and see what you think.
Tags: co:23andme, demo-account, personal-genomics, personalized-medicineOne Comment
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The deCODEme team said:
Dear David P. Hamilton,
We read your recent review of deCODEme.com, posted in VentureBeat on Jan 23, 2008, with great interest. We noted that you had some concerns that the disease risk modeling provided in the deCODEme service was based on a limited number of genetic variants (SNPs), even though as you put it “… most diseases are thought to be influenced by tens or hundreds of different genes.” As examples, you specifically mention Alzheimer’s disease and heart attack, citing two references that seem to report more SNPs than are used to predict disease risk in the deCODEme service.
Obviously, the opinion of someone like you is valuable, as it can help us to further improve the quality of our service and the information we provide about that service. However, we would like to point out that your concerns, although clearly well-intentioned, are unfounded. We hope the following explanation will shed some light on this matter.
It is hypothesized (and very likely true) that the risk of developing any one common disease may be affected by numerous genetic variants, most of which are presently unidentified. Obviously, genetic variants cannot be used to estimate disease risk until they are discovered. This is a limitation faced equally by deCODEme and its competitors. Even though not all genetic risk variants have been discovered, there is considerable value and predictive power in the risk estimates provided by deCODEme based on the current set of identified and verified disease associated genetic variants. When deCODEme reports the relative genetic risk, it is assumed that the impact of the still undiscovered or unconfirmed variants is the same for every person. This is equivalent to saying; if you don’t know a person’s cholesterol level, family history, or other currently known risk factors for heart attack, then your best estimate for his risk is the population’s average risk for heart attack.
It is encouraging to note that, in many cases, the genetic risk variants that have already been discovered and are used in the deCODEme service will be those that contribute the greatest risk of the disease in the population – because these tend to be the easiest variants to detect. A good example of this is the variant in the TCF7L2 gene associated to type 2 diabetes (discovered by deCODE genetics in 2005), which is likely to be the single most important genetic risk factor in this disease in most populations. Indeed, many of the new genetic risk variants are being discovered by the scientists at deCODE genetics (www.decode.com), some of whom are involved in bringing such new discoveries to the public through the deCODEme service.
It is imperative to note that deCODEme only reports risk based on well validated genetic variants (SNPs). Not only does deCODEme require that the association between genetic variant and a disease is truly statistically significant, it also requires that the association has been replicated in at least two independent studies. To include risk estimates based on unverified variants (i.e. those based on marginal evidence) is not only questionable from the point of view of our customers, it is scientifically unsound.
In some cases, variants with a verified disease association are excluded from the genetic risk estimates in deCODEme service. This is done when multiple variants from the same chromosomal region are strongly correlated and therefore redundant. In such instances deCODEme uses the minimum number of SNPs that capture all the risk conferred by the full set of correlated SNPs. In this case no information about genetic risk is lost, even though some variants are not used in the risk prediction. When there is redundancy due to correlation between SNPs, quantity does not translate into quality! Thus, it is not the case, as you stated, that deCODEme “overlooks 13 other SNPs linked to heart disease in the same study”. Rather, some SNPs are excluded either because they are redundant and covered by other SNPs that are included in the risk estimate or they cannot be used because they are unverified. Significantly, the genetic variants that are used both by deCODEme and others to assess risk of the disease were discovered by scientists at deCODE genetics. These same scientists used their specialist knowledge to select the most informative subset of SNPs to estimate the genetic risk of heart attack for the deCODEme service. You can rest assured that they did a good job.
In relation to Alzheimer’s disease, you state that deCODEme overlooks four variants that meet statistical criteria according to a paper cited in relation to the well established apolipoprotein E (APOE) variant. In fact, these other SNPs must be classified as unverified. They have only nominal significance based on a specific genetic model, such that the authors themselves point out that the effect is weak (p-values of 0.04 to 0.001) and that further evaluation is needed. In comparison, the disease association of the APOE variant is beyond any criticism. Indeed, it is the most cited and significant (p-value of 2.0×10E-44) association to a common human disease. As previously explained, it would be scientifically unsound and irresponsible to jump the gun by including unverified genetic variants in disease risk assessments.
Given your obvious interest in the number of SNPs used to estimate the genetic risk of diseases, we were somewhat disappointed to note that your review did not mention the fact that for most of the diseases, deCODEme uses more SNPs than the competitor 23andMe (which you seem to favour). Moreover, as the deCODEme service is based on over 1 million SNPs, compared to only about 650 thousand measured by 23andme, it is considerably more likely that future genetic discoveries will be efficiently covered by the deCODEme service than by that of this competitor.
Furthermore, deCODE genetics has contributed more than any other institution in the world to the recent surge of discoveries of genetic variants conferring risk of common diseases (www.decode.com/publications). Hence, when deCODE genetics scientists convert these discoveries into components of the deCODEme service, we are exploiting our core expertise and unique position in this scientific field. We are confident that we know what we are doing, but we welcome constructive criticism, because we are eager to do even better.
The deCODEme team
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