In the summer of 2000, the Human Genome Project successfully concluded with the first fully sequenced human genome. To commemorate this accomplishment the White House hosted an epic celebration. In his remarks on that June day, President Clinton echoed the hopes of scientists from all over the world when he said that:

In coming years, doctors increasingly will be able to cure diseases like Alzheimer's, Parkinson's, diabetes and cancer by attacking their genetic roots…In fact, it is now conceivable that our children's children will know the term cancer only as a constellation of stars.

While cancer survival rates have increased in the nearly 20 years since that day, why is it that at the same time we have not succeeded in curing (or even improving treatments for) Alzheimer’s disease, Parkinson’s disease and diabetes by attacking their genetic roots? The answer does not lie in a failure of effort.

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Since 1990, grantees of the National Institute of Diabetes, Digestive and Kidney Disease have won 11 Nobel Prizes. Nor is it purely a question of resources; in 2018 alone, the NIH allocated over 1.8 billion to research on Alzheimer’s. Why is it that unlocking the human genetic code did not transform the treatment of disease in the way President Clinton, and the rest of us, had anticipated?

To some degree the answer is that many of us, although generally not the wiser older heads who built the genome project, were naïve. Genes do not generally encode our fate. Rather, they capture something critical about our inborn risks. Consider identical twins; two individuals who share exactly the same genome.

If twin number one suffers diabetes, there is only about an 25 percent chance that twin number two will suffer diabetes in her lifetime, a 25 percent twin concordance rate. For Parkinson’s this risk drops to something near 15 percent, though for Alzheimer’s it may be as high as 45 percent.

Consider what this means. When we find an individual genome that actually produced Alzheimer’s disease for its carrier, that does not mean that anyone with that genome will get Alzheimer’s. Indeed, it is more likely than not that the second twin, a carrier of that very same genome, will not get Alzheimer’s.

So how do the risks embedded in our genes become the diseases, the so-called phenotypes, we seek to cure or prevent? They are realized (or remediated) by interactions with the foods we eat, the compounds to which we are exposed, the daily choices we make. Depression, to take a particularly pressing example, has about a 30 percent twin concordance rate, and is hugely influenced by our social interactions.

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It is not just nature, but also nurture, which leads to disease. This is something that we have known for centuries, but which we seem to have conveniently forgotten in our rush to embrace the technology of genetics. In 1990 the only thing we could measure comprehensively was genetics, so we did it. But why did we stop there?

Traditionally it has been challenging to characterize the nurture influences, but today the digital revolution is leading many scholars like myself to wonder if the true aspirations of the Human Genome Project are finally within reach — if only we are brave enough. In the wake of the Human Genome Project, many scientists believe we should launch a national c that would fill-in the missing pieces, the nurture, we need to understand our nature. (I have been working on a smaller, University-based version of this idea in New York City.

The data each of us produces (and gives away to giant corporations) with our credit cards, our smartphones, our educational records, our geo-tracks, our browsing history, our medical records, with the in-home sensors we purchase (like smart thermostats or smart speakers), provides an almost unimaginably complete record of the environments in which we each live. We know that it is the interaction of these finely grained records of exposures and behaviors with our genomes which determine our phenotypes, but we make little effort on behalf of the public to harvest this data for good.

Building the requisite catalogs of data — building a Human Phenome Project — to complete the work of the Human Genome Project, seems of little interest to legislators and their constituents. At the same time that huge corporations gather more and more detailed and fragmented digital data about each of us, government seems to be devoting little effort to the study of aggregated phenotype.

The successor to the Human Genome Project, the NIH’s All of Us, is focused on gathering one million genomes. That genetic treasure trove, however, will be paired primarily with electronic medical records of what diseases each of those million people has experienced. In a project sometimes forecast to cost over ten billion dollars, less than 50 million is expected to be spent aggregating phenotypic data.

With the advent of large-scale databases that measure everything from credit-worthiness (a strong predictor of overall health), to individual social networks (a predictor of mental health), to air quality (a predictor of asthma), comprehensive pheno-typing is becoming practical. If we were to pair that existing big data with smartphone-based survey instruments, low-cost in-home passive sensors and traditional medical measurements, we could construct nearly complete phenotypic maps of an individual.

Ironically, we could do that for about the same cost-per-participant as the genome mapping proposed for All of Us. Many researchers see this as a lost opportunity. We have invested tens of billions of dollars in the genetic revolution. The technology of today’s digital transformation finally puts the goal of that original genetic revolution within our grasp. We have only to decide to reach for it.

Paul Glimcher holds the Julius Silver, Roslyn S. Silver and Enid Silver Winslow Chair of Neural Science at New York University (NYU) in the School of Arts and Sciences where he also holds professorial appointments in Economics and Psychology and in Neuroscience and Physiology in NYU's School of Medicine. He is also president and chief executive officer of Datacubed Health, a technology company focused on health and life sciences.