When faster supercomputers aren’t enough

When a million veterans shared their genetic data by enrolling in the Department of Veterans Affairs' “Million Veteran Program,” they expected nothing back.

This did not surprise Ravi Madduri, a senior scientist with the Data Science and Learning Division at the U.S.

Department of Energy’s (DOE) Argonne National Laboratory.

He is abundantly clear about the terms under which these veterans donated their data: “Even after giving everything to the country, they found it in their hearts to give more without expecting anything in return.” That gift, which constituted the nation’s largest biorepository and most ancestrally varied cohorts of veteran data in the world, created a debt that Madduri, who also holds a joint appointment in the Department of Human Genetics at the University of Chicago, is working to repay.

“What I feel when I look at that data is that I have an obligation to deliver to the veterans on their intentions in doing this,” he says.

Those intentions, as he reads it, are that others, veteran or not, can benefit from whatever the data can reveal.

“We cannot dilly dally,” Madduri says.

After arriving at Argonne as a graduate student in 2001, Madduri has spent the last two decades at the intersection of supercomputing and biomedicine, much of the time building the computing that turns the veterans’ data into results impacting areas from post-traumatic stress disorder to heart disease to cancer.

He explains that machines have largely held up their end, but the obstacles between precious data and delivered results are not always solved with faster machines.

Understanding the challenges is the path toward better science.

What the Math Was Unable to Solve Any two human genomes differ at millions of specific positions.

A genome-wide association study asks a seemingly simple question across an enormous volume of data.

For example, does a given genetic variant travel together with a particular medical condition? On its own, the question is simple.

Multiplied across every variant worth testing and every condition recorded in a patient's chart, it becomes an enormous math problem, which is what supercomputers exist to solve.