M-DATA Identifies Genes Linked to Autism and Heart Defects

Researchers identified almost two dozen genes that contribute to heart defects by studying genetic data from people born with congenital heart disease or autism.  The scientists from Yale University developed a new algorithm to analyze genetic data from related conditions, which they reported in PLOS Genetics.

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.

The researchers 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. The 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.

Lead researcher Hongyu Zhao says, “By jointly analyzing de novo mutations from congenital heart disease (CHD) and autism, we identified novel genes that may play an important role in explaining the shared genetic etiology of CHD and autism.”