Neurodevelopmental disorders (NDs) include intellectual disabilities, severe learning disabilities, autism spectrum disorder (ASD) are frequent with a prevalence of 1%, 6%, and 1,4% respectively. They represent a significant health burden with handicaps present throughout life. The genetic contribution to NDs is as high as 80%. With the routine implementation of genetic testing in the neurodevelopmental clinic, rare mutations that contribute significantly to neurodevelopmental symptoms are identified in 10 to 20 % of children with NDs.
However, for most "pathogenic" mutations reported back to patients, we have little or no data to estimate their quantitative impact on neurodevelopment. It is therefore difficult for clinicians to estimate the extent to which a genetic variant may contribute to the neurodevelopmental symptoms in a patient. This is due to 2 major issue: 1) Over 75% of "pathogenic" mutations are very rare and observed only once or a few times in patients. It is therefore impossible to conduct individual association studies and 2) Most studies have focussed on associating mutations with complex categorical diagnoses such as ASD or intellectual disabilities. The cognitive mechanisms underlying these associations remain unknown.
To address this issue of undocumented mutations, we propose a novel strategy to understand the effects of CNVs genome-wide, on cognitive and brain measures involved in neurodevelopmental disorders.
The deliverables are algorithms estimating the effect size of any CNV or SNV on cognitive and behavioral traits assessed along a continuum. This will, allow clinicians to estimate the level of contribution of one or several rare mutations to the neurodevelopmental symptoms in a given patient. It will also provide insight into the mechanisms by which mutations may lead to neurodevelopmental disorders.