Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy.
Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy.
Biol Psychiatry. 2023 Jul 15;94(2):121-130. doi: 10.1016/j.biopsych.2022.10.009. Epub 2022 Oct 28.
The clinically heterogeneous presentation of schizophrenia is compounded by the heterogeneity of risk factors and neurobiological correlates of the disorder. Genome-wide association studies in schizophrenia have uncovered a remarkably high number of genetic variants, but the biological pathways they impact upon remain largely unidentified. Among the diverse methodological approaches employed to provide a more granular understanding of genetic risk for schizophrenia, the use of biological labels, such as gene ontologies, regulome approaches, and gene coexpression have all provided novel perspectives into how genetic risk translates into the neurobiology of schizophrenia. Here, we review the salient aspects of parsing polygenic risk for schizophrenia into biological pathways. We argue that parsed scores, compared to standard polygenic risk scores, may afford a more biologically plausible and accurate physiological modeling of the different dimensions involved in translating genetic risk into brain mechanisms, including multiple brain regions, cell types, and maturation stages. We discuss caveats, opportunities, and pitfalls inherent in the parsed risk approach.
精神分裂症临床表现的异质性,加上该疾病的风险因素和神经生物学相关性的异质性,使得情况更加复杂。精神分裂症的全基因组关联研究已经发现了大量的遗传变异,但它们所影响的生物学途径在很大程度上仍未确定。在为更详细地了解精神分裂症的遗传风险而采用的各种方法中,使用生物标记,如基因本体、调控组方法和基因共表达,都为遗传风险如何转化为精神分裂症的神经生物学提供了新的视角。在这里,我们回顾了将精神分裂症的多基因风险分解成生物学途径的重要方面。我们认为,与标准多基因风险评分相比,分解后的评分可能更能合理地模拟将遗传风险转化为大脑机制的不同维度,包括多个脑区、细胞类型和成熟阶段,从而更准确地进行生理建模。我们讨论了解析风险方法所固有的注意事项、机会和陷阱。