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develop precise, targeted, “heat-seeking missile” type therapies, even if for just a subset of all patients. Or even better, that the data would allow pre-identification of folks likely to have disease so that pre-treatment might be possible before irreversible damage occurred.
When I looked at my own genome with the latest in genetic meta-analysis data, I realized I might have entertained the conclusion that I had MS. Now obviously, it wouldn’t be “healthy” for anyone to be on immunosuppressive therapies for 20 years if such treatment was not necessary. In my instance this would have proven to be the case. So we are left with a key question: How much more data (and what kinds of data) would we need to collect to better differentiate the genomes like mine, which (so far) have proved unaffected, while there are similar risk factors in genomes like that of my cousin, who developed the disease at 28 years old?
My fear is that for most complex diseases there are not enough patients on earth (in extant generations) to differentiate fully between individuals who will develop disease and those who will not. In fact, current research suggests that we’ve now sampled enough of the complex genetic-disease patient population to be able to definitively rule out the possibility for many diseases. Moreover, the data suggests that while we may be able to eventually describe all the alleles that confer risk to disease, we will never be able to pinpoint for most patients, even related patients, the precise set of variants that gave them their disease. Or to quote a Boston sage: “we will never be able to differentiate casual from causal” at the level of the patient.
In some ways this is easiest to explain using that original example I gave of DR2. Forty percent of MS patients have a DR2 allele, 20 percent of unaffected individuals have this allele. Clearly DR2 variants increase your risk for disease. However, it is entirely possible that while DR2 is involved in the driving MS disease for some of DR2 positive patients, it may actually play no role in other patients (similar to the 20 percent of the unaffected population who are DR2 positive). It could simply be a case of “true, true, and unrelated.”
To be clear, I would love to be wrong about this. And I hope that the response to this article is that statistical geneticists take up arms to destroy my hypothesis that a futility analysis is likely to be positive for most complex genetic diseases.
But in the near term, I’m banking on the continued determination of my colleagues in drug discovery, who work every day to try to improve efficacy and lessen side effects on their candidate drugs, be it by clever delivery, thoughtful structural drug design, or thorough preclinical and clinical assessments. These folks are some of the hardest working people I know, and the natural architecture of the genome is not making their jobs any easier. I’m also banking on the ingenuity of my colleagues in genomics and proteomics as they collaborate with industry to find a path to bring to bear the genome on drug development insomuch as it is possible today.
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