Autism is defined based on a wide variety of behavioral symptoms, but it’s precisely this variation — along with a complex genetic background — that makes it tricky to connect behavior to the underlying genes1, 2.
A new algorithm may make this challenge a bit easier to solve. The algorithm, which employs a form of artificial intelligence that learns as it goes, analyzes behavioral data and has learned to recognize six genetic disorders associated with autism, according to research published 11 February in Molecular Autism3.
The researchers hope to use these behavioral signatures to hone their search for the genetic underpinnings of ‘idiopathic autism,’ for which there is no known cause.
“There was a sort of assumption that genetic risk factors probably lead to a distinguishable set of behavioral phenotypes, but people had never really formally tested that proposition before,” says lead investigator Patrick Bolton, professor of child and adolescent psychiatry at Kings College London. “That was the motivation for this project.”