Today, however, we’re mostly still stuck with manual analysis. Geneticists scour patients’ data for rare mutations known to cause disease, and for plausible suspects. When possible, children’s genes are also compared with their parents’ sequences, which can give extra clues. The process works best for kids whose diagnoses have already been discovered in someone else.

When it comes to identifying a new disease, successful diagnosis often hinges on serendipitous links between patients, whether they’re siblings like Tessa and Colton or strangers who find each other another way. In some cases, families of children with the same mutation have found each other on the internet and asked their doctors to confirm that the kids share the same symptoms and genetic changes.

The Undiagnosed Diseases Network

One early milestone in diagnosing rare, one-gene diseases came in May 2008, when a small team at the National Institutes of Health in Bethesda, Maryland, launched the NIH Undiagnosed Diseases Program. In its first six years, 3,100 children and adults with undiagnosed medical conditions applied to be evaluated by the program, and 750 were accepted; one of them was Tessa Nye. Unfortunately, her analysis didn’t provide her family an answer.

But overall, the program was a big success: The NIH estimates that 25 to 50 percent of the patients its team saw by mid-2014 were eventually diagnosed. And the number of people applying for evaluation kept growing.

In July 2014, Stanford was named as one of six additional clinical sites chosen for a national Undiagnosed Diseases Network, with Euan Ashley, MD, PhD, at the helm of the Stanford site.

“We’re working with patients who really have done everything they can,” Ashley says. “They’ve consulted so many different doctors, traipsed around the country, been on the internet every night for years and haven’t been able to find an answer.”

As part of the Undiagnosed Diseases Network, Stanford can offer whole-genome sequencing and other diagnostic tests that aren’t yet widely available or covered by insurance. (Once the UDN accepts a patient, the NIH covers the cost of his or her evaluation.) Stanford’s human immune monitoring core, for example, is beginning to yield information about previously unknown autoimmune and antibody-based diseases that can’t be detected by looking at the genes. Stanford researchers have developed ways to characterize the activity of certain categories of immune cells — as well as profiling patients’ cytokines and antibodies — to give strong clues about such diagnoses.

“These are investigational diagnostics that are not quite ready for prime time yet but are nonetheless very powerful,” says Ashley, who is an associate professor of medicine, of genetics and of biomedical data science. Once these tests are more widely used, he thinks they’ll help find answers for a sizeable share of the patients who aren’t diagnosed using genetic techniques.

Perhaps more importantly, the network provides an organized way for physicians all over the country to compare patients’ symptoms and genetic abnormalities. Since the network formed in 2014, a few dozen patients across the country have been diagnosed. UDN investigators have also discovered two new genetic diseases, described in recent publications in Human Molecular Genetics and The American Journal of Human Genetics.

The UDN’s work is taking place in an environment of broader efforts at Stanford to understand genetic problems and use the new findings to help patients. In a few cases, new genetic tools have begun to help some Stanford patients who aren’t enrolled in the UDN, and dozens of Stanford researchers continue to make advances in the laboratory, too.

For instance, Michael Snyder, MD, professor of genetics, is conducting research to understand the influence of gene mutations occurring outside the sequences that code directly for protein.

“We have a number of mutations outside our genes, in control sequences of DNA, and so far we’re very poor at identifying and understanding those,” Snyder says. “It’s an invisible part of our genetic picture but we think it counts for quite a bit.”

Remedying the near misses

Genetic testing faces a big challenge: figuring out the best way to harness the growing data deluge. “With each passing month, more of the world’s genetic diversity is represented in scientific databases, and each time more information is there, it’s easier to interpret the next thing you see,” says Jon Bernstein, MD, a clinical geneticist at Packard Children’s. That’s useful for new patients, but may not help children who have previously been told that their doctors can’t find a genetic diagnosis.

In July, Bernstein and Bejerano published a report in Genetics in Medicine about matching previously undiagnosed patients with new knowledge. The scientists tested whether computational tools that compare patients’ lists of mutated genes with current gene databases could yield diagnoses. They studied 40 people who had not received genetic diagnoses after their first round of analysis, and found that four could be diagnosed with recently discovered diseases. One patient, an 18-year-old from Stockton, California, named Shayla Haddock, was found to have a disease first described in the scientific literature in August 2012, only two weeks after her family had been told that her doctors could not identify a diagnosis. The researchers, whose tools have since solved dozens of other cases, want to end these near misses.