Researchers have developed a new tool that enables the diagnosis of rare diseases by reusing existing data

A team of researchers from group  Machine Learning in Biomedicine at Cima of Navarra has developed a novel tool , UPDhmm, that enables the diagnosis of rare diseases by reusing existing data .

Uniparental disomy (UPD) is a Genetics subject Genetics a child inherits both copies of a chromosome from a single parent instead of one from each. "It is an error in chromosome segregation that can cause serious diseases because it disrupts the genetic balance necessary for development . These abnormalities can be the cause of rare diseases and neurodevelopmental disorders such as autism, but they often go undetected by conventional tests," notesCarlos Ruiz Arenas, the study’s lead author. 

Until now, there was no tool detect this based on the data obtained during internship . “Current methods for detecting UPD are experimental, so they are only used when UPD is already suspected,” adds the researcher at Cima.

The new tool data based on trios (father, mother, and child) and models inheritance patterns to detect these variants. “UPDhmm uses the model Markov model (HMM), model machine learning model specialized in predicting sequences. Specifically, the algorithm analyzes the inheritance patterns of family trios to determine whether inheritance was biparental or whether there is clear evidence of uniparental disomy.”

To demonstrate its effectiveness, the team applied UPDhmm to a public dataset of nearly 2,400 families with cases of Autism Spectrum Disorder. As Carlos Ruiz Arenas explains, “the software not only detected known events but also identified a case of paternal isodisomy of chromosome 8, meaning the child inherited both copies of the same chromosome from the father.” This event had not been previously reported, so this tool be the missing piece needed to confirm the diagnosis of many children with “unnamed” diseases. The results have been published in the scientific journalBioinformatics.

“The great advantage of UPDhmm is that it doesn’t require additional testing; instead, we can make use of data generated during routine internship , even though there were previously no tools available to detect these disomies,” concludes the researcher at Cima. 

The study, conducted in partnership Pompeu Fabra University, received public funding from the Ministry of Science, Innovation, and Universities.

reference letter :
Bioinformatics.March 2026;42(3)
UPDhmm: detecting uniparental disomy from NGS trio data