A new technique, developed by researchers at Scripps Institute, studies gene expression levels to identify the cause of rare genetic diseases.

The technique called as, ANEVA-DOT, by researchers studies the difference in gene expression levels of the two alleles of each gene. The new technique compares the activity levels of maternal and paternal alleles by studying all genes across the genome. They do it by a technique called the whole transcriptome analysis to compare the gene activity levels. It detects when the activity of an allele lies far enough outside of the normal range to be a plausible cause of disease.

To support their invention, researchers successfully demonstrated the use of this technique to reveal disease causing genes in patients with Muscular Dystrophy disease. It is a disease that causes loss of muscle mass and results in inability to perform normal activities and may result into death.

“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,” says study first author Pejman Mohammadi, PhD, an assistant professor in the Department of Integrative Structural and Computational Biology at Scripps Research.

The team focussed on finding a better way to detect and identify rare genetic diseases which emerge early in life and may progress into life threatening situations.

Standard methods of sequencing gene and transcripts- applied to the patient and family members can detect gene mutations affecting the disease. But, this can detect on the gene mutations which are obvious ones that result in missing or damaged proteins.

At least half of rare genetic diseases have more subtle causes that effectively can’t be detected using standard methods, Mohammadi says. 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, ANEVA-DOT (analysis of expression variation – dosage outlier test) uses gene transcription data to detect differences in the two alleles of the gene with a same individual.  It can be used to screen a handful of genes with abnormal activity levels in one allele. 

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.

“Even if you had an identical twin, the fact that the twin ate a burger this morning and you didn’t would create differences between you in the activity levels of many genes,” Mohammadi says

To correctly identify if the allele activity is abnormal, the method uses the normal reference ranges difference between maternal and paternal alleles, available in public databases like NCBI, for each gene.

“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.

 demonstrated the ANEVA-DOT method by applying it to a group of patients with muscular dystrophy-type genetic diseases. They successfully detected the disease-linked genes in cases where there was already a diagnosis and an expected major imbalance in allele activity. In many of the undiagnosed cases, the ANEVA-DOT technique uncovered a short list of plausible disease-linked, muscle-related genes. In one case that was resolved by the time the researchers submitted their paper, a suspect gene uncovered by ANEVA-DOT was confirmed as the disease gene.

The scientists now are using ANEVA-DOT to help a San Diego children’s hospital diagnose genetic disease in newborns.


Pejman Mohammadi, Stephane E Castel, Beryl B Cummings, Jonah Einson, Christina Sousa, Paul Hoffman, Sandra Donkervoort, Zhuoxun Jiang, Payam Mohassel, A Reghan Foley, Heather E Wheeler, Hae Kyung Im, Carsten G Bonnemann, Daniel G MacArthur, Tuuli Lappalainen.
Genetic regulatory variation in populations informs transcriptome analysis in rare disease.
Science, 10 Oct 2019. doi: 10.1126/science.aay0256

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