Research from Baylor College of Medicine is improving the ability to identify the genetic cause of undiagnosed conditions.1

“About nine of every 10 patients that are referred to us have neurological conditions, such as developmental delay and intellectual disability, for which they don’t have a diagnosis,” says Sarah Elsea, PhD, professor of molecular and human genetics at Baylor and corresponding author of the work.

To identify the genetic cause of undiagnosed conditions, the researchers look for potentially defective genes in the patient’s genome. They use whole-exome sequencing, which analyzes all the genes that encode proteins. A gene may have many variants that encode slightly different versions of the same protein that still carry their function normally. But some variants may encode defective proteins that can cause disease. The challenging part is determining whether the variant of a particular gene that is found in a patient is causing the disease.

“In some cases, the variant is missing all or a large portion of the gene, which results in a nonfunctional protein. This suggests that the variant is involved in the disease. However, most genetic variants involve changes in a single building block of the DNA. That one ‘misspelled’ gene sequence may or may not result in a defective or less functional protein, and we need other mechanisms, such as untargeted metabolomics, to determine if that genetic change causes disease,” says Elsea, who also is the senior director of biochemical genetics at Baylor Genetics.

Elsea and her colleagues used untargeted metabolomics to provide an additional level of information to help them determine whether the genetic variant they found in the patient was actually causing the condition.

In the current study, the researchers integrated whole-exome sequencing and targeted metabolomics to analyze the data of a group of 170 patients. They found that the metabolomics data informed 44% of the cases.

“The analysis let us reclassify nine variants as likely benign, 15 variants as likely causing disease, and three as disease-causing variants. Metabolomics data confirmed a clinical diagnosis in 21 cases,” Elsea says. “Our analysis is extremely helpful not only for confirming that a variant causes the condition, but also to rule out variants as the cause of disease. Having a more accurate diagnosis may help identify a better treatment for the condition and also provides important information for the family regarding recurrence risk.”

This analysis also aids with the diagnosis of patients that may have a mild form of a disease, because the analysis is broad and very sensitive and shows the effects of the variant in entire metabolic pathways.

The researchers hope that their integrated multiomics analysis will help other patients by providing a diagnosis, clarifying previous suspected diagnoses, or monitoring their treatment.


1.Alaimo JT, Glinton KE, Liu N, Xiao J, Yang Y, Sutton VR, Elsea SH. Integrated analysis of metabolomic profiling and exome data supplements sequence variant interpretation, classification, and diagnosis. Genet Med.Epub May 22, 2020. doi: 10.1038/s41436-020-0827-0.