Greenwood Tiffany A
Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
Curr Top Behav Neurosci. 2023;63:291-314. doi: 10.1007/7854_2022_388.
Schizophrenia is a severe and debilitating psychotic disorder that is highly heritable and relatively common in the population. The clinical heterogeneity associated with schizophrenia is substantial, with patients exhibiting a broad range of deficits and symptom severity. Large-scale genomic studies employing a case-control design have begun to provide some biological insight. However, this strategy combines individuals with clinically diverse symptoms and ignores the genetic risk that is carried by many clinically unaffected individuals. Consequently, the majority of the genetic architecture underlying schizophrenia remains unexplained, and the pathways by which the implicated variants contribute to the clinically observable signs and symptoms are still largely unknown. Parsing the complex, clinical phenotype of schizophrenia into biologically relevant components may have utility in research aimed at understanding the genetic basis of liability. Cognitive dysfunction is a hallmark symptom of schizophrenia that is associated with impaired quality of life and poor functional outcome. Here, we examine the value of quantitative measures of cognitive dysfunction to objectively target the underlying neurobiological pathways and identify genetic variants and gene networks contributing to schizophrenia risk. For a complex disorder, quantitative measures are also more efficient than diagnosis, allowing for the identification of associated genetic variants with fewer subjects. Such a strategy supplements traditional analyses of schizophrenia diagnosis, providing the necessary biological insight to help translate genetic findings into actionable treatment targets. Understanding the genetic basis of cognitive dysfunction in schizophrenia may thus facilitate the development of novel pharmacological and procognitive interventions to improve real-world functioning.
精神分裂症是一种严重且使人衰弱的精神障碍，具有高度遗传性且在人群中相对常见。与精神分裂症相关的临床异质性很大，患者表现出广泛的缺陷和症状严重程度。采用病例对照设计的大规模基因组研究已开始提供一些生物学见解。然而，这种策略将具有临床症状各异的个体合并在一起，忽略了许多临床未受影响个体所携带的遗传风险。因此，精神分裂症潜在的大部分遗传结构仍无法解释，而且所涉及的变异体导致临床可观察到的体征和症状的途径在很大程度上仍然未知。将精神分裂症复杂的临床表型解析为生物学相关成分可能有助于旨在理解易感性遗传基础的研究。认知功能障碍是精神分裂症的一个标志性症状，与生活质量受损和功能预后不良相关。在此，我们研究认知功能障碍定量测量的价值，以客观地针对潜在的神经生物学途径，并识别导致精神分裂症风险的遗传变异和基因网络。对于一种复杂疾病，定量测量也比诊断更有效，能够用较少的受试者识别相关的遗传变异。这样一种策略补充了对精神分裂症诊断的传统分析，提供了必要的生物学见解，以帮助将遗传发现转化为可操作的治疗靶点。因此，了解精神分裂症认知功能障碍的遗传基础可能有助于开发新的药物和促认知干预措施，以改善实际功能。
