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Structural models of genome-wide covariance identify multiple common dimensions in autism

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De Hoyos,  Lucía
Language and Genetics Department, MPI for Psycholinguistics, Max Planck Society;
Population genetics of human communication, MPI for Psycholinguistics, Max Planck Society;
International Max Planck Research School for Language Sciences, MPI for Psycholinguistics, Max Planck Society;

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Barendse,  M. T.
Language and Genetics Department, MPI for Psycholinguistics, Max Planck Society;
Academic Centre for Dentistry Amsterdam (ACTA);

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Schlag,  Fenja
Language and Genetics Department, MPI for Psycholinguistics, Max Planck Society;
Population genetics of human communication, MPI for Psycholinguistics, Max Planck Society;

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Van Donkelaar,  Marjolein M. J.
Language and Genetics Department, MPI for Psycholinguistics, Max Planck Society;

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Verhoef,  Ellen
Language and Genetics Department, MPI for Psycholinguistics, Max Planck Society;
Population genetics of human communication, MPI for Psycholinguistics, Max Planck Society;

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Fisher,  Simon E.
Language and Genetics Department, MPI for Psycholinguistics, Max Planck Society;
Donders Institute for Brain, Cognition and Behaviour, External Organizations;

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St Pourcain,  Beate
Language and Genetics Department, MPI for Psycholinguistics, Max Planck Society;
University of Bristol;
Donders Institute for Brain, Cognition and Behaviour, External Organizations;
Population genetics of human communication, MPI for Psycholinguistics, Max Planck Society;

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Citation

De Hoyos, L., Barendse, M. T., Schlag, F., Van Donkelaar, M. M. J., Verhoef, E., Shapland, C. Y., et al. (2024). Structural models of genome-wide covariance identify multiple common dimensions in autism. Nature Communications, 15: 1770. doi:10.1038/s41467-024-46128-8.


Cite as: https://hdl.handle.net/21.11116/0000-000B-58BD-E
Abstract
Common genetic variation has been associated with multiple symptoms in Autism Spectrum Disorder (ASD). However, our knowledge of shared genetic factor structures contributing to this highly heterogeneous neurodevelopmental condition is limited. Here, we developed a structural equation modelling framework to directly model genome-wide covariance across core and non-core ASD phenotypes, studying autistic individuals of European descent using a case-only design. We identified three independent genetic factors most strongly linked to language/cognition, behaviour and motor development, respectively, when studying a population-representative sample (N=5,331). These analyses revealed novel associations. For example, developmental delay in acquiring personal-social skills was inversely related to language, while developmental motor delay was linked to self-injurious behaviour. We largely confirmed the three-factorial structure in independent ASD-simplex families (N=1,946), but uncovered simplex-specific genetic overlap between behaviour and language phenotypes. Thus, the common genetic architecture in ASD is multi-dimensional and contributes, in combination with ascertainment-specific patterns, to phenotypic heterogeneity.