Rare Daily Staff
Scientists at Scripps Research have developed a new technique for identifying disease-causing genes in patients with rare diseases by comparing the level of activity in genes inherited from an individual’s mother and father across the genome to determine when the activity of a gene lies far enough outside the normal range to be a plausible cause of disease.
The technique, which the researchers report in the journal Science, makes use of the fact that people inherit two copies or “alleles” of virtually every gene, one from the mother and one from the father. They demonstrated the technique by using it to reveal disease-causing genes in patients with rare muscular dystrophies.
“Adding this method to our toolkit should allow us to detect the causes of rare genetic diseases for some of the cases in which standard methods fail,” said study first author Pejman Mohammadi, an assistant professor in the Department of Integrative Structural and Computational Biology at Scripps Research.
The research team focused on finding a better way to identify rare genetic diseases that emerge early in life and can be significantly debilitating or even life-threatening. Standard methods of sequencing genes and their transcripts can reveal the cause, but only if the disease-driving mutations are obvious ones that result in missing or severely truncated proteins.
At least half of rare genetic diseases have more subtle causes that effectively can’t be detected using standard methods, Mohammadi said. For example, a mutation may affect a region of DNA that isn’t itself a gene but is involved in regulating the activity of a gene—and the resulting dysregulation of that gene’s activity can lead to disease.
The method developed by Mohammadi and his colleagues uses gene transcription data to detect differences in the activity levels of maternal and paternal alleles.
Many rare genetic diseases result from DNA mutations affecting a single allele of a gene. The researchers said comparing the activity of maternal and paternal alleles, which share the same molecular environment in the same cells in the same person, is a more sensitive approach than comparing one person’s gene activity to another’s since any two people will differ in many other confounding factors that affect gene activity besides their genetic backgrounds.
To help gauge when an allele’s activity is truly abnormal, the method includes a calculation, from publicly available gene transcription data, of the normal, healthy range of differences in maternal versus paternal allele activity of all genes.
The method, called ANEVA-DOT (analysis of expression variation-dosage outlier test), can be used to identify a handful of genes in each individual with apparently abnormal expression levels in one allele.
“It might tell you there are 10 or 20 genes with allele activity levels that are way off, and you can then follow up to determine which of those is causing the disease — but compared with other methods, it cuts down dramatically the number of genes you have to analyze in that way,” Mohammadi says.
The scientists now are using ANEVA-DOT to help a San Diego children’s hospital diagnose genetic disease in newborns.
Photo: Pejman Mohammadi, an assistant professor in the Department of Integrative Structural and Computational Biology at Scripps Research