Better understand autism

What are the exact causes for autism? How can we improve prenatal screening and early diagnoses of autism? These are the questions Israeli scientist, Osnat Penn, is trying to answer.

The trademark of Osnat Penn’s research is a multidisciplinary approach combining bioinformatics, genomics, statistics and molecular evolution. Having used this approach during her PhD research in Israel to gain a better understanding of the molecular evolution of different subtypes of the HIV virus, she now plans to tackle the challenge of identifying the underlying genetic causes of autism spectrum disorders by fine-scale analysis of the massive quantity of data produced by high-throughput sequencing of patient genomes.

Autism is a debilitating disorder affecting 1% of the population. Patients suffer from deficits in language development and social interaction and display repetitive behaviors. Whilst autism and associated disorders are known to have a heritable component, it has so far been difficult to identify the precise genetic causes.

In order to identify where the spontaneous genomic variants associated with autism occur, Osnat will compare protein-coding regions of the genomes of thousands of autistic children with similar regions in the genomes of their unaffected parents, with thousands of unaffected people from different populations around the world. To be able to analyze the large amounts of data generated by this “family trio” approach, she will develop innovative computational methods to help her detect both large variations and much smaller, more subtle variations which may not be detected by other means.

Osnat believes that two different types of variants may contribute to autism: common variants with minor effects and rare variants with major effects, and she plans to focus on the rare and more severe ones. She hypothesizes that if such common variants exist and have not been eliminated through evolution, they may be linked to alleles which carry some evolutionary advantage. Using advanced computational tools to filter the list of potential candidates, Osnat will trace back the molecular ancestry of the most significant of these human variations and rank the relative importance in autism of each candidate variation and their combined effects.

Her statistical approach will enable validation of predictive models which could be used in prenatal molecular screening and early diagnosis of autism in children as well as providing novel targets for the development of new treatments. On her return to Israel, she plans to set up a new laboratory where she can apply this novel approach to other human disorders.

For Women in Science

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