It is like trying to find a needle in a haystack. And to make it more stark, we are not expecting to have hundreds of such individuals for any of the illnesses we are targeting, which might have made it easer to find these protective factors. Instead, we will have isolated individuals with a variation that has occurred somewhere in their massive genome. So it won’t be easy, but methods have emerged in the last five years to allow us to attempt it.
What are those methods?
One is called network biology filtering. Instead of treating every piece in the genomic haystack as one of a million to look through, we can focus more narrowly in the network connected to a faulty gene.
The other approach involves using gene-editing techniques. We can create the primary mutation, and other possible buffering mutations one or two at a time, in a cell line in the lab, then work through them to find what offers protection.
Has the idea of finding treatments by studying those who don’t get sick been tried before?
Yes, but not in the systematic genetic way that we are doing, which was too costly until now. There are two earlier efforts that encouraged us. One is a genetic approach: For most inherited diseases, scientists and clinicians have studied an extended family with a particular mutation, looking for individuals who carried the faulty gene but had fewer symptoms. The other approach uses observable traits. For example, Helen Hobbs at the Howard Hughes Medical Institute in Maryland sought patients with high lipid levels who did not get heart disease. She discovered mutations in a gene that result in reduced levels of “bad” LDL cholesterol. That is now being built as a therapy for heart disease.
How have you prepared for the project?
Before we launched, we thought it would be a good idea to look at existing samples in anonymized databanks, such as 23andMe, to get an idea of whether there would be any unexpected heroes. About half of the disease-linked genes we were interested in analyzing were represented. We screened half a million samples and found dozens of unexpected heroes—people that appear to have, for example, the genetic fault that causes cystic fibrosis, but are generally healthy.
You are hoping to have volunteers from around the globe. Why is that so important?
Resilience in the populations we have looked at so far is very infrequent, occurring in one in 35,000 individuals. So how might you increase the chances of finding these unexpected heroes? We think there are several ways. One is to go to regions of the world where first cousins marry first cousins; that brings up the frequency of alterations. In the Middle East, for example, if we enroll 100,000 people, there is a chance that we will find more unexpected heroes than in the U.S. We are also hoping that there are environmental niche populations, whether in the Amazon or in the Arctic, that may carry protective factors genetically or as a result of environmental influences that will again be of benefit for finding resilience.
Is it possible to pinpoint environmental factors that might help protect against these diseases?
That is much harder. Most environmental studies rely on large numbers. We are not going to have those numbers. The reality is that for the nongenetic factors, entirely separate studies would need to be done afterwards to try and find hamlets or villages where there is a disproportionate effect, where you might be able to identify an environmental cause.
When might the project yield new treatments?
Finding unexpected heroes is likely to take a few years, but I don’t think it should take more than a year to decipher their data. Then, to turn an intriguing target that we hopefully find into a possible treatment, I think it would be another five years to get to clinical trials. It pretty much adds up to a decade.
But to get there, most of all we need individuals to step up and volunteer. It just takes a swab of DNA and a willingness to wonder, “What’s inside me?”
This article originally appeared in New Scientist.
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