Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, 19104, USA.
Section on Developmental Neurogenomics, Human Genetics Branch, Division of Intramural Research at the National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA.
Transl Psychiatry. 2022 Apr 7;12(1):149. doi: 10.1038/s41398-022-01895-0.
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 (~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.
基因剂量障碍（GDD）是精神病理学遗传风险的主要类别，但不同 GDD 引起不同精神病理学特征的程度存在相当大的争议。本研究通过编译和分析七个不同 GDD 中几种自闭症相关特征（ART）的维度测量来解决这一争议。该样本包括 350 名患有七种 GDD 之一的个体，以及参考特发性自闭症谱系障碍（ASD；n=74）和典型发育对照（TD；n=171）组。GDD 是：唐氏综合征、威廉姆斯-贝伦综合征和史密斯-马根尼斯综合征（DS、WS 和 SMS）以及不同性染色体非整倍体（“加 X”、“加 XX”、“加 Y”、“加 XY”）。社会反应量表（SRS-2）用于测量不同粒度项目、子量表和总量表的 ART。使用一般线性模型检查 GDD 中的 ART 特征，并使用机器学习从 SRS-2 子量表和项目预测基因型。这些分析是在考虑和不考虑认知障碍的情况下完成的。在进行 Bonferroni 校正后，在所有可能的 21 对 GDD 组对比中，有 12 对显示出明显不同的 ART 特征（在考虑 IQ 时为 7/21）。事后分析中突出的 GDD-ART 关联包括 WS 中相对保留的社交动机和加 X 中相对较低的重复行为水平。机器学习表明，即使在控制 IQ 后，GDD 组也可以以合理的准确度（约 60-80%）进行预测。GDD 对 ARTs 的影响受 GDD 亚型和 ART 维度的影响。这一观察结果对精神神经遗传学的机制、临床和转化方面都有影响。
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