Institute of Mental Health, Singapore, 539747, Singapore; Division of Psychiatry Research, The Zucker Hillside Hospital, Glen Oaks, NY 11004, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, Scotland, EH8 9JZ, United Kingdom; Department of Psychology, University of Edinburgh, Edinburgh, Scotland, EH8 9JZ, United Kingdom.
Am J Hum Genet. 2019 Aug 1;105(2):334-350. doi: 10.1016/j.ajhg.2019.06.012.
Susceptibility to schizophrenia is inversely correlated with general cognitive ability at both the phenotypic and the genetic level. Paradoxically, a modest but consistent positive genetic correlation has been reported between schizophrenia and educational attainment, despite the strong positive genetic correlation between cognitive ability and educational attainment. Here we leverage published genome-wide association studies (GWASs) in cognitive ability, education, and schizophrenia to parse biological mechanisms underlying these results. Association analysis based on subsets (ASSET), a pleiotropic meta-analytic technique, allowed jointly associated loci to be identified and characterized. Specifically, we identified subsets of variants associated in the expected ("concordant") direction across all three phenotypes (i.e., greater risk for schizophrenia, lower cognitive ability, and lower educational attainment); these were contrasted with variants that demonstrated the counterintuitive ("discordant") relationship between education and schizophrenia (i.e., greater risk for schizophrenia and higher educational attainment). ASSET analysis revealed 235 independent loci associated with cognitive ability, education, and/or schizophrenia at p < 5 × 10. Pleiotropic analysis successfully identified more than 100 loci that were not significant in the input GWASs. Many of these have been validated by larger, more recent single-phenotype GWASs. Leveraging the joint genetic correlations of cognitive ability, education, and schizophrenia, we were able to dissociate two distinct biological mechanisms-early neurodevelopmental pathways that characterize concordant allelic variation and adulthood synaptic pruning pathways-that were linked to the paradoxical positive genetic association between education and schizophrenia. Furthermore, genetic correlation analyses revealed that these mechanisms contribute not only to the etiopathogenesis of schizophrenia but also to the broader biological dimensions implicated in both general health outcomes and psychiatric illness.
精神分裂症的易感性与表型和遗传水平的一般认知能力呈负相关。矛盾的是，尽管认知能力和受教育程度之间存在很强的正遗传相关性，但在精神分裂症和受教育程度之间报告了适度但一致的正遗传相关性。在这里，我们利用已发表的认知能力、教育和精神分裂症的全基因组关联研究（GWAS）来解析这些结果背后的生物学机制。基于子集的关联分析（ASSET）是一种多效性荟萃分析技术，允许联合相关的基因座被识别和描述。具体来说，我们确定了在所有三种表型中以预期方向（即精神分裂症风险增加、认知能力降低和受教育程度降低）相关的基因子集；与表现出教育与精神分裂症之间反直觉关系的变体（即精神分裂症风险增加和受教育程度增加）形成对比。ASSET 分析揭示了 235 个与认知能力、教育和/或精神分裂症相关的独立基因座，p < 5 × 10。多效性分析成功地确定了 100 多个在输入 GWAS 中不显著的基因座。其中许多已经通过更大、更新的单表型 GWAS 得到了验证。利用认知能力、教育和精神分裂症的共同遗传相关性，我们能够分离出两种不同的生物学机制——早期神经发育途径，其特征是一致的等位基因变异，以及与教育和精神分裂症之间的正遗传关联相关的成年期突触修剪途径。此外，遗传相关性分析表明，这些机制不仅对精神分裂症的发病机制有贡献，而且对更广泛的生物学维度也有贡献，这些维度涉及一般健康结果和精神疾病。
