Monday, 11 February 2013
Genetics of cardiomyopathies: it's not easy...
Congratulation to Luis Lopes (PhD at the UCL Heart Hospital) who just published his paper (open access in the Journal of Medical Genetics) on a high throughput sequencing screen of a cohort of hypertrophic cardiomyopathy (HCM) patients. Luis (and others, including me) scanned 41 genes either known or candidates for being involved in more than 200 HCM cases and used these data to infer what variants are likely to be causal, and with what level of confidence.
Genetics of inherited cardiac disorders are really complex
This scan highlights the complexity associated with interpreting these data. The issue is that quite clearly not all of the likely causal variants are fully penetrant (ie. the disease may happen, but definitely not systematically). Relatively late onset is also not helping out the interpretation. In addition some of the associated genes are quite polymorphic. The literature has reported plenty of variants associated with HCM but it is hard to know what to do with these, and I suspect that many false positive have been published.
Lastly, it is plausible that not one but multiple variants, some common some rare, act together to define the phenotype and the disease outcome. We will need incredibly large panels to dissect this complexity. I am not completely convinced by this hypothesis, as the disease may show variable outcome due to factors what have nothing to do with genetics, but this is plausible.
The attitude of cardiologists toward genetic diagnosis is changing
This paper would definitely benefit from showing the stream of review, because it was not easy getting it published. Some of the comments were fair and I am in no way arguing that the reviews missed the point. But, while I am rather new to this disease area, my experience with this paper and my reading of the literature suggests that there is a bit of a turning point: in the past, clinicians have often relied on large pedigrees to obtain very reliable evidence that a variant is causal. While this is a worthy approach, it is often impractical in a clinical context, simply because relatives may not be available or the infrastructure of going through the cascade testing may simply be too complicated.
My point is that to be we need to be able to make statements on the basis of what we find in a single case if we want it to be relevant in the clinic. And that must mean not being completely sure in some cases, and being comfortable making probabilistic statements (such as: "on the basis of the data we see, we estimate that this variant is causing the disease with 80% probability").
Clinicians are, of course, not always happy with uncertainty. It's never very satisfying to tell a patient that we cannot be sure. But this is also not unusual: doctors always need to deal with "maybe" when making decisions. It is perhaps less accepted when it comes to genetic diagnosis because we have in mind a very deterministic view of what genetics can do. But uncertainty is unavoidable, so we will have to deal with it.
Last comment: thank you to the UK10K project
The control samples for this paper were provided by the UK10K, which is great because we need large panels of well matched and well phenotyped sequenced samples to understand what is going on in cases. I am pretty sure that there will be quite a few papers using the UK10K data as controls in the near future. Studies like ours would not be possible without these. As the sample size is growing and the phenotyping becomes more thorough, we plan to publish more on this theme and we will reliably continue using the UK10K as a ressource for controls. I am excited by the prospect of these data becoming readily available (which is not quite the case yet I think, as we had to fill a data acces request).