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Genetic risk for autism stems mostly from common genes
Date:7/20/2014

Led by Roeder, the researchers used new statistical methods such as machine learning techniques and dimension reduction tools that allowed them to more reliably sort out the inheritability of the disorder. In addition, they were able to compare their results with a parallel family-based study in the Swedish population, which took into account data from twins, cousins, and factors like age of the father at birth and parents' psychiatric history. A best-fit statistical model took form, based mostly on additive genetic and non-shared environmental effects.

"Thanks to the boost in statistical power that comes with ample sample size, autism geneticists can now see the forest for the trees," said Thomas R. Insel, director of the National Institute of Mental Health (NIMH). "Knowing the nature of the genetic risk will help focus the search for clues to the molecular roots of the disorder."

Thomas Lehner, chief of the NIMH's Genomics Research Branch, agreed and added, "This is a different kind of analysis than employed in previous studies. Data from genome-wide association studies was used to identify a genetic model instead of focusing just on pinpointing genetic risk factors. The researchers were able to pick from all of the cases of illness within a population-based registry."

Now that the genetic architecture is better understood, the researchers are identifying specific genetic risk factors detected in the sample, such as deletions and duplications of genetic material and spontaneous mutations. The researchers said even though such rare spontaneous mutations accounted for only a small fraction of autism risk, the potentially large effects of these glitches make them important clues to understanding the molecular underpinnings of the disorder.

"Within a given family, the mutations could be a critical determinant that leads to the manifestation of ASD in a particular family member," said Joseph Buxbaum, the study's first author
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Contact: Shilo Rea
shilo@cmu.edu
412-268-6094
Carnegie Mellon University
Source:Eurekalert  

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