Harvard biostatistician Peter Kraft (co-author of an excellent recent article on genetic risk prediction in the New England Journal of Medicine) has just added an interesting comment on his experience of this week’s Consumer Genetics Show:
I just wanted to share what for me were two stand-out moments at the
CGS. First was Zak Kohane’s discussion of the “Incidentalome”–a great
turn of phrase that captures something I’ve been mulling over myself.
(A less eloquent statement of this idea made it into the recent Nick
Wade NYT article on genetic risk prediction). Basically, the idea is
that even if you have great tests with high clinical validity
[accuracy]–which is not the case right now by a long shot with genetic
risk models–if you do lots of tests, then your chance of at least one
false positive goes up–and so does the risk of unnecessary
From Zak’s JAMA article on the “Incidentalome” (12jul2006):
“Physicians know that as the number of tests increases, the chance
that a spurious abnormal test result will arise also increases. They
also know that it is difficult to ignore abnormal findings, and they
often must embark on a sequence of more expensive tests to investigate
This was a theme of several presentations: that, despite the
potential of “personalized medicine” to reduce costs and improve
health, its immediate impact may be to increase costs by leading to
increased testing [or just increased demands on clinicians’ time
explaining why these tests are not clinically useful] without a
decrease in disease burden. [See “Raiding the medical commons,” JAMA.
Kari Stefansson pushed back a little, saying [if I understood
correctly] that the risk of false positives is not a problem, as what
DecodeMe [etc.] give the consumer are estimates of their risk [not
binary tests of high/low risk], and that these estimates are unbiased.
I’ll concede the last point here, modulo the fact that these estimates
are based on current knowledge which is incomplete and changing quickly
[cf my NEJM article with David Hunter]. But I don’t think providing
unbiased estimates of relative or absolute risk solves the problem of
the Incidentalome. To me, everybody [after discussions with their
clinician, of course] has a set of risk thresholds above which they
will take action [if there’s an action available]. So in practice you
end up with a set of decision rules: do nothing, watch more carefully,
The term “incidentalome” will probably sound somewhat familiar to clinicians; it’s derived from the term “incidentaloma“,
which refers to “a tumour found by coincidence without clinical
symptoms or suspicion”, usually as a result of a whole-body scan. Such
tumours can often be perfectly benign, but nonetheless result in a whole
series of additional (and often invasive) tests to determine their
Any test that generates large amounts of potentially health-relevant data is prone to incidental findings (both genuine unexpected findings and spurious artefacts), and this will certainly be the case for whole-genome sequencing.
The first type of finding will be complete technical artefacts – false positives due to sequencing error – which will be a non-trivial problem over the next few years as the wrinkles are ironed out of rapid sequencing technologies, but will reduce in number as accuracy improves. Once sequencing accuracy is high enough these sorts of findings can be ruled out fairly easily through downstream validation assays (although there certainly is a need to design faster, cheaper and more readily customisable assays for novel sequence variants).
A much larger problem will be genuine sequence changes that would be predicted to seriously mess with the function of an important gene, but for which the health consequences are unknown. Accurate functional assays don’t exist for most genes (and are cumbersome and imperfect even for well-studied genes such as BRCA1; hence the large numbers of BRCA mutations ending up in the “variants of uncertain significance” category), so it will often be difficult, expensive or simply downright impossible to get a handle on the effects of a newly discovered variant on disease risk.
Is this really a good argument against widespread genome sequencing, however, as some people have suggested? I don’t think it’s a compelling one; rather, it’s a strong incentive for the medical establishment to start thinking hard about developing evidence-based strategies for dealing with uncertain genetic data and deciding which of the three strategies Kraft notes is most appropriate: do nothing, watch more carefully,
or intervene aggressively. Preventing people from getting access to genetic information is obviously not a productive long-term solution to the problem of incidental findings.
The other high point for me was RC Green’s talk on the results of
the REVEAL (Risk Evaluation and Education for Alzheimer’s Disease)
study. This is one of the few studies [that I know of] that has
measured how folks react to genetic risk testing–whether intensive
counseling pre- and post-test minimizes adverse psychological effects
or maximizes information recall/understanding, how folks interpret and
act on risk estimates. Lots of ink has been spilled about these things,
but there has not been much empirical research along these
lines–although that is sure to change soon. In his keynote, Francis
Collins highlighted this [empirical research into how best to convey
information from genetic tests, how these tests are used by physicians
and patients] as an important area for future research.
This is indeed an incredibly important (and astonishingly under-studied) field. I recently saw a presentation by Theresa Marteau describing her currently unpublished systematic literature review of studies looking at the effects of genetic testing results on behaviour. I was shocked at how little literature actually exists on this topic, but also intrigued by the general findings of the studies done so far: it seems as though testing results – even for serious diseases – actually have virtually no impact on long-term behaviour or quality of life.
There’s clearly much more research to be done here, but if this general finding is confirmed it would be both good and bad news for personal genomics companies: good news in that it means that the wilder claims of critics (customers jumping off bridges after receiving news of an increased Alzheimer’s risk) are overblown, but obviously bad news in that one of the primary stated motivations of companies like 23andMe is to motivate customers to improve their lifestyle.
Anyway, it sounds as though we will soon have a much clearer idea one way or the other about the effects of genetic data on behaviour. It will be good to see the discussion on this issue driven by data rather than protectionistic fear-mongering on one hand and commercial hype on the other.