Muin Khoury, director of the Office of Public Health Genomics at the U.S. Centers for Disease Control and Prevention, who was not involved in the new study, notes that it is solid work and that the twin data is an excellent source.
At the same time, he says, "whole-genome sequencing is a great tool, but it's not ready for prime time—for a number of reasons."
Among those reasons, he notes, is that we have definitive genetic correlates for very few diseases—most of which are relatively rare in the general population. Most diseases are not inherently genetic in nature, and even if they seem to have some associated genetic hallmarks, those are not strong enough to be able to say for certain that a person will or will not get the disease at some point in his or her lifetime.
As Vogelstein notes, these genetic risk predictions are unlikely to get much stronger. Because they conducted their mathematical analysis based on an ideal scientific world, in which we already knew how gene variants were connected to diseases, even "1,000 years from now, with intense research, these numbers wouldn't change," he says. That is, they created a model that was as generous as possible in terms of genetic correlates to disease in order to create a sort of upper bounds of the utility of these tests.
Not all in the genes
One of the keys to improving disease risk prediction will be to collect even more comprehensive information from study subjects and individuals. "We need to integrate nongenetic factors because most factors are nongenetic anyway," Khoury notes. Variables such as diet, exposures to carcinogens (such as via first- or secondhand smoke) and family history have a strong influence on many disease risks. "That's part of the challenge we face," he says. The genome "really doesn't mean much by itself"—and for people seeking the most accurate picture of their risk for various diseases, a more nuanced and integrated picture is needed.
As Vogelstein and his co-authors pointed out in the study, most identical twins do not die from—or even get—the same diseases, which should be a first clue that genetics can only go so far in determining pathological outcomes.
Khoury cautions consumers that even if the price of a whole genome sequence becomes reasonable for them, it is worth spending some time asking, in turn, what they are going to get from it. Although the extra genetic information might not hurt, for an individual who is looking for educated estimations of disease risk, having strong family history and personal health and lifestyle information are some of the most valuable data points one can take to the doctor.
But that does not mean that whole-genome sequencing will not be useful in the future—or that is not already for some higher-risk individuals or well-characterized rare conditions. "I'm an optimist—but also a realist," Khoury says. "We'd all like for whole-genome testing to succeed—I think it's already succeeded in some areas. But for population health, I think we have a long way to go."
By better understanding the limitations of genome-wide sequencing, Vogelstein notes, researchers and policymakers might be better able to direct funding and efforts to areas, such as Alzheimer's disease, where a person's genetic profile might have a very real effect on their likelihood of getting the disease. With that information comes an opportunity to make a much larger impact on public health in general—and a way to ease consumers' minds as well.