From Solo Notes to a Full Symphony: Revealing Hidden Genetic Causes of Disease

How integrating multiple types of genomic data is helping solve previously undiagnosed cases

Imagine trying to understand a complex piece of music by listening to just one instrument at a time. You might hear the melody. Or the rhythm. But you’d miss how everything fits together, the harmony, the interplay, the whole composition.

For years, that’s been a persistent challenge in human genetics. Researchers have had powerful tools to detect different types of genetic variation, from small changes in DNA, like single-letter typos, to larger structural changes, like missing or duplicated sections. But these signals have often been analyzed separately, like listening to instruments in isolation.

Now, a new study, published in Genome Medicine and led by PNRI Associate Investigator Cláudia Carvalho, PhD, and her team along with collaborators at Baylor College of Medicine, offers a way to hear the full symphony.

A More Complete View of the Genome

Why does that matter?

Because many genetic diseases, especially rare, inherited conditions, aren’t caused  by a single mutation. Instead, they can arise from combinations of changes that only become clear when viewed together.

“For many families, the hardest part is not knowing,” said Dr. Carvalho. “When we can bring different types of genetic information together and see the genome more clearly, we have a better chance of finding answers that were previously missed. And those answers can change the course of someone’s care and their life.”

By bringing these signals together into a single view, the platform improves the chances of identifying disease-causing variants in patients who have gone undiagnosed.

The study introduces an integrated analysis platform that looks at multiple types of genetic variation at once, including single-nucleotide variants (SNVs) and structural variants such as copy number variations (CNVs).

Finding What Was Previously Invisible

One key advance in this work is improved detection of copy number variations–segments of DNA that are duplicated or deleted. These structural changes can have a major impact on health, but they are notoriously difficult to interpret, especially when they occur alongside other mutations.

This approach integrates multiple layers of genomic data, including read depth (how many times a stretch of DNA is sequenced) and allele balance (how evenly genetic variants are represented), to more accurately identify these changes and understand how they affect genes.

It also allows researchers to analyze family trios (child and parents) more effectively, helping researchers determine whether a mutation was inherited or arose spontaneously—a critical distinction in rare disease diagnosis.

From Data to Diagnosis

This study shows how integrating different types of genomic data can change that.

In one case, researchers analyzed a patient with a primary immunodeficiency who had remained undiagnosed after standard exome sequencing. Initial analysis identified a single rare mutation in the DOCK8 gene, but on its own, it wasn’t enough to explain the patient’s symptoms.

Using their integrated platform, the team uncovered a second, previously missed mutation: a small deletion affecting part of the same gene. By combining structural variation data with single-nucleotide variant analysis—and tracing how each variant was inherited—they showed the patient carried two different mutations in DOCK8, one from each parent. 

Together, these mutations disrupted the gene’s function, leading to a diagnosis of Hyper-IgE syndrome, a rare inherited immune disorder.

This type of finding is easy to miss when different kinds of genetic variation are analyzed separately. Here, seeing both changes at once made the diagnosis possible.

For families living with undiagnosed genetic conditions, advances like this can be profound. Even with genome sequencing, many cases remain unsolved because the underlying variants are complex or incomplete when viewed in isolation.

Why This Matters Beyond Rare Disease

While the study focuses on inherited conditions, the implications extend much further.

Structural variation plays a role in a wide range of diseases, including cancer and neurological disorders. More reliable ways to detect and interpret these changes can improve diagnosis and deepen our understanding of how the genome functions. Because the platform is designed to be accessible and scalable, it can be applied to large genomic datasets as sequencing becomes more widespread.

Like hearing a full orchestra instead of a single instrument, this approach brings us closer to understanding the true complexity of human genetics.