4.7 Article

Variegation of autism related traits across seven neurogenetic disorders

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TRANSLATIONAL PSYCHIATRY
卷 12, 期 1, 页码 -

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SPRINGERNATURE
DOI: 10.1038/s41398-022-01895-0

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资金

  1. National Institute of Mental Health Intramural Research Program [1ZIAMH002949-04, 89-M-0006]
  2. Division of Intramural Research, National Human Genome Research Institute, NIH [Z1D-HG200352, 01-HG-0109]
  3. Division of Intramural Research from the National Heart, Lung, and Blood Institute [ZIA HL006212]
  4. Eunice Kennedy Shriver National Institute of Child Health and Human Development [R21 HD100997, R21 HD106164]

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This research examines the impact of different gene dosage disorders on autism-related traits. The study found that different disorders have varying effects on these traits, and these effects are influenced by the subtype of the disorder and the dimension of the traits.
Gene dosage disorders (GDDs) constitute a major class of genetic risks for psychopathology, but there is considerable debate regarding the extent to which different GDDs induce different psychopathology profiles. The current research speaks to this debate by compiling and analyzing dimensional measures of several autism-related traits (ARTs) across seven diverse GDDs. The sample included 350 individuals with one of 7 GDDs, as well as reference idiopathic autism spectrum disorder (ASD; n = 74) and typically developing control (TD; n = 171) groups. The GDDs were: Down, Williams-Beuren, and Smith-Magenis (DS, WS, SMS) syndromes, and varying sex chromosome aneuploidies (plusX, plusXX, plusY, plusXY). The Social Responsiveness Scale (SRS-2) was used to measure ARTS at different levels of granularity-item, subscale, and total. General linear models were used to examine ART profiles in GDDs, and machine learning was used to predict genotype from SRS-2 subscales and items. These analyses were completed with and without covariation for cognitive impairment. Twelve of all possible 21 pairwise GDD group contrasts showed significantly different ART profiles (7/21 when co-varying for IQ, all Bonferroni-corrected). Prominent GDD-ART associations in post hoc analyses included relatively preserved social motivation in WS and relatively low levels of repetitive behaviors in plusX. Machine learning revealed that GDD group could be predicted with plausible accuracy (similar to 60-80%) even after controlling for IQ. GDD effects on ARTs are influenced by GDD subtype and ART dimension. This observation has consequences for mechanistic, clinical, and translational aspects of psychiatric neurogenetics.

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