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Gene functional networks and autism spectrum characteristics in young people with intellectual disability: a dimensional phenotyping study
Authors:
BAKER, K., BRKIC, D, Ng-Cordell, E., O’BRIEN, S., Scerif, G., ASTLE, D.
Reference:
Molecular Autism
Year of publication:
In Press
CBU number:
8589
Abstract:
Background The relationships between specific genetic aetiology and phenotype in neurodevelopmental disorders are complex and hotly contested. Genes associated with Intellectual disability (ID) can be grouped into networks according to gene function. This study explored whether individuals with ID show differences in autism spectrum characteristics (ASC), depending on the functional network membership of their rare, pathogenic de novo genetic variants. Methods Children and young people with ID of known genetic origin were allocated to two broad functional network groups: synaptic physiology (n=29) or chromatin regulation (n=23). We applied principle components analysis to the Social Responsiveness Scale to map the structure of ASC in this population, and identified three components – Inflexibility, Social Understanding and Social Motivation. We then used Akaike Information Criterion (AIC) to test the best fitting models for predicting ASC components, including demographic factors (age, gender), non-ASC behavioural factors (global adaptive function, anxiety, hyperactivity, inattention) and gene functional networks. Results We found that, when other factors are accounted for, the chromatin regulation group showed higher levels of Inflexibility. We also observed contrasting predictors of ASC within each network group. Within the chromatin regulation group, Social Understanding was associated with inattention, and Social Motivation was predicted by hyperactivity. Within the synaptic group, Social Understanding was associated with hyperactivity, and Social Motivation was linked to anxiety. Limitations Functional network definitions were manually curated based on multiple sources of evidence, but a data-driven approach to classification may be more robust. Sample sizes for rare genetic diagnoses remain small, mitigated by our network-based approach to group comparisons. This is a cross-sectional study across a wide age range, and longitudinal data within focused age groups will be informative of developmental trajectories across network groups. Conclusion We report that gene functional networks can predict Inflexibility, but not other ASC dimensions. Contrasting behavioural associations within each group suggests network-specific developmental pathways from genomic variation to autism. Simple classification of neurodevelopmental disorder genes as high risk or low risk for autism is unlikely to be valid or useful.
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