Li James J, He Quanfa, Dorn Stephen, Wang Zihang, Lu Qiongshi
Department of Psychology, University of Wisconsin-Madison, 1202 W. Johnson Street, Madison, WI, 53706, USA.
Waisman Center, University of Wisconsin-Madison, Madison, WI, USA.
J Neurodev Disord. 2025 Jun 9;17(1):32. doi: 10.1186/s11689-025-09620-w.
Polygenic scores (PGS) are widely used in psychiatric genetic associations studies due to their predictive power for focal outcomes. However, they lack discriminatory power, in part due to the high degree of genetic overlap between psychiatric disorders. The lack of prediction specificity limits the clinical utility of psychiatric PGS, particularly for diagnostic applications. The goal of the study was to enhance the discriminatory power of psychiatric PGS for two highly comorbid and genetically correlated neurodevelopmental disorders in ADHD and autism spectrum disorder (ASD).
Genomic structural equation modeling (GenomicSEM) was used to generate novel PGS for ADHD and ASD by accounting for the genetic overlap between these disorders (and eight others) to achieve greater discriminatory power in non-focal outcome predictions. PGS associations were tested in two large independent samples - the Philadelphia Neurodevelopmental Cohort (N = 4,789) and the Simons Foundation Powering Autism Research for Knowledge (SPARK) ASD and sibling controls (N = 5,045) cohort.
PGS from GenomicSEM achieved superior discriminatory power in terms of showing significantly attenuated associations with non-focal outcomes relative to traditionally computed PGS for these disorders. Additionally, genetic correlations between GenomicSEM PGS for ASD and ADHD were significantly attenuated in cross-trait associations with other psychiatric disorders and outcomes.
Psychiatric PGS associations are likely inflated by the high degree of genetic overlap between the psychiatric disorders. Methods such as GenomicSEM can be used to refine PGS signals to be more disorder-specific, thereby enhancing their discriminatory power for future diagnostic applications.
多基因分数（PGS）因其对特定结局的预测能力而广泛应用于精神疾病遗传关联研究。然而，它们缺乏区分能力，部分原因是精神疾病之间存在高度的遗传重叠。预测特异性的缺乏限制了精神疾病PGS的临床应用，特别是在诊断方面。本研究的目的是提高精神疾病PGS对注意力缺陷多动障碍（ADHD）和自闭症谱系障碍（ASD）这两种高度共病且基因相关的神经发育障碍的区分能力。
采用基因组结构方程模型（GenomicSEM），通过考虑ADHD和ASD（以及其他八种疾病）之间的遗传重叠，生成针对ADHD和ASD的新型PGS，以在非特定结局预测中获得更大的区分能力。在两个大型独立样本中测试了PGS关联——费城神经发育队列（N = 4789）以及西蒙斯基金会自闭症研究促进知识（SPARK）ASD及同胞对照队列（N = 5045）。
相对于传统计算的PGS，GenomicSEM生成的PGS在与非特定结局的关联方面表现出显著减弱，从而具有更强的区分能力。此外，在与其他精神疾病及结局的跨性状关联中，GenomicSEM针对ASD和ADHD的PGS之间的遗传相关性显著减弱。
精神疾病之间高度的遗传重叠可能夸大了精神疾病PGS关联。诸如GenomicSEM这样的方法可用于优化PGS信号，使其更具疾病特异性，从而增强其在未来诊断应用中的区分能力。
