Credit: Casey Atkins

Core member and chair of the faculty, Broad Institute of MIT and Harvard; director, Klarman Cell Observatory, Broad Institute of MIT and Harvard; professor of biology, MIT; investigator, Howard Hughes Medical Institute; founding co-chair, Human Cell Atlas.

Credit: Mouka Studio/Alamy Stock Photo

For many years, biology and disease appeared ‘too big’ to tackle on a broad level: with millions of genome variants, tens of thousands of disease-associated genes, thousands of cell types and an almost unimaginable number of ways they can combine, we had to approximate a best starting point—choose one target, guess the cell, simplify the experiment.

But we are now on the cusp of an inflection point, where the ‘bigness’ of biomedicine turns into an advantage. We are beginning to see advances towards these goals already, in polygenic risk scores, in understanding the cell and modules of action of genes through genome-wide association studies (GWAS), and in predicting the impact of combinations of interventions. Going forward, our success in harnessing bigness will rely on our ability to leverage structure, prediction and expanded data scale. Disease is highly structured at the molecular, genetic, gene program, cell and tissue levels; acknowledging and understanding this structure can help us reduce the overwhelming lists of genes and variants to a manageable number of meaningful gene modules. We cannot test every possible combination, so we need algorithms to make better computational predictions of experiments we have never performed in the lab or in clinical trials. But only when data are truly big, scaled massively and rich in content, will we have the most effective structuring and prediction power towards building a much-needed Roadmap of Disease for patients.

To achieve this, we need to invest in building the right initiatives—like the Human Cell Atlas and the International Common Disease Alliance—and in new experimental platforms: data platforms and algorithms. But we also need a broader ecosystem of partnerships in medicine that engages interaction between clinical experts and mathematicians, computer scientists and engineers who together will bring new approaches to drive experiments and algorithms to build this Roadmap.