Stanford Researchers Mapping the Genetic Markers of Autism and Related Conditions
In an effort to improve the diagnostic process for autism and other developmental delays, the Wall Lab at Stanford University is launching a survey to parents to augment its high-level genetic research.
January 29, 2016
At Stanford University, a group of biologists is developing novel ways to decipher the molecular pathology of autism spectrum disorder and related neurological disorders. In other words, they are tracing the biological roots of autism in order to better — and more quickly — recognize and treat it in children.
“My lab is also comparing what is known about the genetics of autism with the genetic systems of other behaviorally related neurological disorders,” writes Dennis Wall, associate professor of pediatrics at Stanford and principal investigator for The Wall Lab. “One basic hope is that we will find ‘usual suspects’ that have significant implications for neurological malfunction. A grander hope is that the work will result in a clearer genotype-phenotype map for autism, i.e. that it will enable us to circumscribe sections of the genetic landscape of autism that cause epilepsy, seizure disorder, etc., thereby leading to a set of genetic markers that can be used for diagnosis/prognosis.
“We are also hoping to level the playing field to enable all autism researchers to tap into the benefits of computational systems biology for deciphering the genetic map of autism — by making our informatics approaches and results accessible through various web applications.”
The implications of The Wall Lab’s research remain unknown, but it’s potential is undoubtedly great. To learn more and to contribute your family’s experience to The Wall Lab’s growing database, click the link below to complete a short 15-question online survey about your child’s day-to-day behavior. The survey is designed for parent’s of children ages 2 to 17, and should take no more than 10 minutes to complete.
With these survey results in hand, the researchers hope they’ll have more insight into the diagnostic process of common developmental delays — and, ultimately, provide a blueprint for earlier and more efficient detection of these disorders.