Autism. The diagnosis didn’t even exist until the 1940s, though undoubtedly the disorder—well, actually a continuum of related disorders—did. Today, the Centers for Disease Control estimates that as many as 1 of every 110 children in the United States will be diagnosed with some form of what’s come to be called autism spectrum disorders,
or ASDs. That’s a considerable jump from the level of diagnoses as recently as the 1980s.
This seeming epidemic of ASD has raised a host of questions, not the least of which is the cause of the apparent explosion in autism rates. Does it stem from a true increase in affected individuals, or is it the product of changing diagnostic standards? If the former, what could be behind the increased incidence? For that matter, what is the underlying cause of autism, regardless of whether the incidence is growing or not? Is it exclusively genetic, or are there environmental co-factors? Also, how should ASD be treated, and what is the prognosis for those with ASD?
As always, data lies at the heart of answering these questions. Which brings me to the subject of this blog (long way around, I know!): the National Database for Autism Research (NDAR), a community-wide resource that was established by the National Institutes of Health. Under the direction of Dr. Michael F. Huerta, NDAR has assembled a
massive collection of autism information. As described on the program’s website, “NDAR is an extensible, scalable informatics platform for ASD relevant data at all levels of biological and behavioral organization (molecules, genes, neural tissue, behavioral, social and environmental interactions) and for all data types (text, numeric, image, time series, etc.).”
NDAR is designed not just to curate these data but also to facilitate the sharing of information, tools, and methodologies and to foster collaboration across the entire ASD community. It builds on the broad, common use of informatics platforms in the autism research community and promotes common data definitions and standards. It engages investigators, funding sources, and platform operators through workshops, meetings, talks, webinars, and tutorials. NDAR represents a new model of 21st-century biomedical research, combining three overlapping methodologies for doing science—high-volume data collection, computation and informatics, and collaborating laboratories. Michael Huerta calls this community science.
This community science endeavor has built a growing federation of partners who have agreed to share their data and adopted NDAR standards, including global unique identifiers, data definitions, validation tools, and an authentication scheme. Dr. Huerta estimates that the federation is now on track to provide most of the autism community access to these tools and data, and its efforts have resulted in harmonized technical and policy considerations and have generated great enthusiasm among researchers, funders,
and advocacy groups.
NDAR is truly pointing the way toward the future of collaborative, data-intensive biomedical research—community science at its best.