The cause of autism is thought to involve a combination of both genetic and environmental factors. One approach to understanding the genetic contribution to autism has been to use large samples to investigate multiple genes associated with the disorder. This approach, known as a genome-wide association study, has resulted in several interesting genetic ‘hits,’ — the discovery of genes that appear to be associated with autism.
However, similar ‘environment-wide’ approaches have never been applied to assess potential environmental factors associated with autism. An environment-wide approach includes testing multiple environmental factors all at one time, rather than evaluating each environmental factor individually. Such an approach offers the opportunity to test the interplay between different environmental factors. Determining environmental factors associated with autism is extremely important because some environmental factors may be directly modifiable. However, environment-wide studies require large populations with systematically collected data covering multiple environmental domains.
Tonya White and her team at Erasmus University Medical Center in the Netherlands are using a large, prenatal, population-based cohort to study child development in the Netherlands. Between 2002 and 2005, nearly 10,000 pregnant women in Rotterdam chose to participate in the study, and these mothers and their children have been closely followed since that time. Multiple environmental factors have been systematically collected since prenatal life, including toxins, heavy metals, pollutants, nutritional factors such as fatty acid and folate levels, obstetric complications and numerous other factors, totaling more than 200 relevant variables. The researchers obtained measures of autism symptoms when the children were 6 years old. In addition, as a part of this study, the researchers plan to use maternal blood obtained during pregnancy to assess markers of acute viral infections.
Using these environmental variables, White’s group plans to perform an environment-wide association study to assess environmental variables associated with autism. They also plan to use machine-learning techniques to derive measures to predict autism risk based on multiple genetic and environmental variables. To ensure that their findings are not the result of chance alone, they plan to replicate their results in an independent sample.