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. 2020 Dec 11;11(1):98.
doi: 10.1186/s13229-020-00403-9.

Gene functional networks and autism spectrum characteristics in young people with intellectual disability: a dimensional phenotyping study

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Gene functional networks and autism spectrum characteristics in young people with intellectual disability: a dimensional phenotyping study

Diandra Brkić et al. Mol Autism. .

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 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 suggest 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.

Keywords: Anxiety; Autism dimensions; Genetics; Hyperactivity; Intellectual disability.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Relative importance of predictors for each ASC component, averaged across the set of candidate models. The importance of predictors was calculated by summing the Akaike weights over the subset of candidate models in which the predictor is present. The arbitrary threshold of 0.8 was applied as cut-off for identifying the most relevant predictors (i.e. estimates that appear in more than 80% of models). FNG = Functional Network Group (synaptic or chromatin)
Fig. 2
Fig. 2
Effect size plots of estimated coefficients for each ASC. This figure illustrates model averaged coefficients and their effect sizes. The white dot is the beta coefficient value for each estimate, the thick lines are the confidence intervals (95% CI), and the lighter lines are the standard errors (SE). FNG = Functional Network Group refers to the synaptic–chromatin grouping
Fig. 3
Fig. 3
Relationships between ASC and non-ASC behavioural characteristics, within each Functional Network Group. The fitted lines are ASC PCA factor scores predicted by behavioural standardised coefficients, for each FNG (blue = chromatin, orange = synaptic). Individual data points (orange triangles = synaptic; blue circles = chromatin) are partial residuals, i.e. observed data accounting for all the variables included in the models (age, gender, FNG, general ability, and interactions with behavioural measures). The shaded areas, respectively, in blue and orange for each FNG, are confidence intervals at 95%

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