Genetic determination of cortex structure The human cerebral cortex is important for cognition, and it is of interest to see how genetic variants affect its structure. Grasby et al. combined genetic data with brain magnetic resonance imaging from more than 50,000 people to generate a genome-wide analysis of how human genetic variation influences human cortical surface area and thickness. From this analysis, they identified variants associated with cortical structure, some of which affect signaling and gene expression. They observed overlap between genetic loci affecting cortical structure, brain development, and neuropsychiatric disease, and the correlation between these phenotypes is of interest for further study. Science, this issue p. eaay6690 Common genetic variation is associated with interindividual variation in the structure of the cortex of the human brain. INTRODUCTION The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS We identified 306 nominally genome-wide significant loci (P < 5 × 10−8) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 299 loci for which replication data were available, 241 loci influencing surface area and 14 influencing thickness remained significant after replication, with 199 loci passing multiple testing correction (P < 8.3 × 10−10; 187 influencing surface area and 12 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation (rG = −0.32, SE = 0.05, P = 6.5 × 10−12), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 10 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function. Identifying genetic influences on human cortical structure. (A) Measurement of cortical surface area and thickness from MRI. (B) Genomic locations of common genetic variants that influence global and regional cortical structure. (C) Our results support the radial unit hypothesis that the expansion of cortical surface area is driven by proliferating neural progenitor cells. (D) Cortical surface area shows genetic correlation with psychiatric and cognitive traits. Error bars indicate SE. IMAGE CREDITS: (A) K. COURTNEY; (C) M. R. GLASS The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson’s disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.

中文翻译：

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人类大脑皮层的遗传结构


皮质结构的遗传决定人类大脑皮层对于认知非常重要，了解遗传变异如何影响其结构很有意义。格拉斯比等人。将超过 50,000 人的遗传数据与脑部磁共振成像相结合，对人类遗传变异如何影响人类皮质表面积和厚度进行全基因组分析。通过这项分析，他们发现了与皮质结构相关的变异，其中一些影响信号传导和基因表达。他们观察到影响皮质结构、大脑发育和神经精神疾病的遗传位点之间的重叠，这些表型之间的相关性值得进一步研究。科学，本期第 14 页。 eaay6690 常见的遗传变异与人脑皮质结构的个体差异有关。简介 大脑皮层是我们复杂认知能力的基础。人类皮质表面积和厚度的变化与神经、心理和行为特征相关，并且可以通过磁共振成像（MRI）在体内测量。对模式生物的研究已经确定了影响皮质结构的基因，但对影响人类皮质结构的常见遗传变异知之甚少。基本原理为了在全球和区域水平上识别与人类皮质结构相关的遗传变异，我们对 60 个队列中 51,665 名个体的脑 MRI 数据进行了全基因组关联荟萃分析。我们分析了整个皮质和 34 个具有已知功能特化的皮质区域的表面积和平均厚度。 结果 我们在 33,992 名欧洲血统参与者的发现样本中确定了 306 个名义上全基因组显着位点 (P < 5 × 10−8) 与皮质结构相关。在可获得复制数据的 299 个位点中，241 个影响表面积和 14 个影响厚度的位点在复制后仍然显着，其中 199 个位点通过了多重检验校正（P < 8.3 × 10−10；187 个影响表面积和 12 个影响厚度） ）。常见的遗传变异解释了总表面积变异的 34% (SE = 3%) 和平均厚度变异的 26% (SE = 2%)；表面积和厚度呈现负遗传相关性（rG = -0.32，SE = 0.05，P = 6.5 × 10−12），这表明遗传影响对表面积和厚度具有相反的影响。生物信息学分析表明，总表面积受到遗传变异的影响，遗传变异改变了胎儿发育过程中神经祖细胞的基因调控活性。相比之下，平均厚度受到成人大脑样本中活跃调节元件的影响，这可能反映了胎儿中期发育后发生的过程，例如髓鞘形成、分支或修剪。当综合考虑时，这些结果支持径向单位假说，即不同的发育机制促进表面积扩张和厚度增加。为了确定对各个皮质区域的特定遗传影响，我们在区域分析中控制了全局测量（总表面积或平均厚度）。经过多次测试校正，我们确定了 175 个影响区域表面积的位点和 10 个影响区域厚度的位点。 影响区域表面积的基因座聚集在参与 Wnt 信号通路的基因附近，已知该通路会影响区域同一性。我们观察到总表面积与一般认知功能和教育程度之间存在显着的正向遗传相关性和双向因果关系的证据。我们发现总表面积与帕金森病之间存在额外的正向遗传相关性，但没有发现因果关系的证据。总表面积与失眠、注意力缺陷多动障碍、抑郁症状、重度抑郁症和神经质之间存在明显的负遗传相关性。结论 这项大规模的合作工作增强了我们对人类大脑皮层遗传结构及其区域模式的理解。皮质的高度多基因结构表明不同的基因参与特定皮质区域的发育。此外，我们发现证据表明，大脑结构是导致从遗传变异到一般认知功能差异的因果途径中的关键表型。识别遗传对人类皮质结构的影响。 (A) 通过 MRI 测量皮质表面积和厚度。 (B) 影响全球和区域皮质结构的常见遗传变异的基因组位置。 (C) 我们的结果支持径向单位假设，即皮质表面积的扩张是由增殖的神经祖细胞驱动的。 (D) 皮质表面积显示与精神和认知特征的遗传相关性。误差线表示SE。图片来源：(A) K. COURTNEY； (三)先生 大脑皮层是我们复杂认知能力的基础，但我们对影响人类皮层结构的特定基因位点知之甚少。为了识别影响皮质结构的遗传变异，我们对 51,665 名个体的脑磁共振成像数据进行了全基因组关联荟萃分析。我们分析了整个皮层和 34 个已知功能特化区域的表面积和平均厚度。我们鉴定了 199 个显着位点，并发现在产前皮质发育期间活跃的调节元件中影响总表面积的位点显着富集，支持径向单位假说。影响区域表面积的基因座聚集在 Wnt 信号通路中的基因附近，从而影响祖细胞扩张和区域同一性。皮质结构的变异与认知功能、帕金森病、失眠、抑郁、神经质和注意力缺陷多动障碍有遗传相关性。