Multiple diseases that start early in life appear to be linked to changes in the same genes. Recent research looking at these changes in children found that those genes were not present in the parents.
It also demonstrated a connection between congenital heart defects and autism.
However, sequencing these gene changes is expensive, so small studies of individual diseases have limited power to identify genes that increase a person’s risk of the disease.
In the new study, researchers developed an algorithm called M-DATA (Multi-trait De novo mutation Association Test with Annotations) that combines sequencing data from people with related conditions to identify genes that contribute to disease.
They applied the new method to genetic data from people with congenital heart disease or autism and successfully identified 23 genes for congenital heart disease, including 12 that were previously unknown.
Results conclude that M-DATA is more effective at identifying genes that increase a person’s risk than analyses focusing on a single disease.
This is because instead of analyzing a small number of genomes from affected individuals, M-DATA analyzes a larger number of combined genomes from multiple groups of people.
This new method may help researchers identify previously unknown genes linked to disease and improve our understanding of the cause and potential treatment for different conditions.
By jointly analyzing unknown genetic changes from congenital heart disease (CHD) and autism, they were able to identify novel genes that may play an important role in explaining the shared genetic etiology of CHD and autism.
Source: Medindia
