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

I also thank you for your thought-provoking posts.

I may be able to answer one of your questions from my Sunday morning arm-chair position. You asked why rs1333049 was not included in the deCODEme report. DeCODEme uses the Illumina BeadChip array, and that particular SNP is not included. (23andMe also uses Illumina, with a smaller set of SNPs). The NEJM article used an array from Illumina’s competitor, Affymetrix, which has selected a different (but partially overlapping)set of SNPs for its product.

It’s quite possible that the Illumina set has a different SNP that correlates strongly with the Affymetrix SNP. Neither SNP need be causally related to the medical effect — they just happened to be in the neighborhood of the actual location where one person had the critical mutation, and they go along for the ride in that person’s descendants.

Both deCODEme and 23andMe are being fairly conservative in their interpretive reports, since many initial “discoveries” turn out to be false positives with attempts to replicate them in different populations. The Illumina FAQ alludes briefly to their criteria; 23andME has a more detailed white paper about the issue.

https://www.23andmeobjects.com/res/1926/pdf/23-03_Vetting_Genetic_Associations.pdf