Large genomic databases hold clues linking genetic mutations to future disease risk

Now scientists from Harvard Medical School have

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developed a way to increase the predictive accuracy of genetic mutations as forerunners of diseases that may develop down the road. Using large-scale genomic analysis, the team has succeeded in illuminating patterns that can help predict symptoms likely to be caused by poorly understood disease-causing mutations and can help gauge the likelihood that a patient might develop clinical disease later on.

The researchers said their findings, published Aug. 16 in the journal Cell Systems, could help physicians better monitor future disease risk in patients with obvious symptoms affecting one organ but whose mutation may put them at risk for future disease affecting other organs and systems.

The analysis focused on rare genetic diseases because these tend to be easier to match to root-cause mutations, the researchers said. To pinpoint mutations common across multiple patients and link them to a specific disease, researchers typically must look at large numbers of sequenced genomes from people with the same diagnoses and then compare them against the genomes of people without such diagnoses. In the case of rare diseases—classified in the United States as those that affect fewer than 200,000 Americans—the pool for sequencing is limited to the genomes of only a handful of people.

On first blush, this may seem like a niche-value proposition, researchers note, but, in truth, the cumulative impact of characterizing the symptoms and future disease risk of many rare conditions could be profound. The National Institutes of Health estimate that as many as 30 million Americans have a rare disease.