PDF(Pubmed)
Abstract：
Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to map the genetic architecture of cortical surface area (SA) and cortical thickness (CT) for the 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance, which we are denoting genetically informed brain networks (GIBNs). Genomic SEM can fit a multivariate GWAS from summary statistics for each of the GIBNs, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA identified 6 GIBNs and CT identified 4 GIBNs. The multivariate GWASs of these GIBNs identified 74 genome-wide significant (GWS) loci (p<5×10-8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of GIBN GWASs found that SA-derived GIBNs had a positive genetic correlation with bipolar disorder (BPD), and cannabis use disorder, indicating genetic predisposition to a larger SA in the specific GIBN is associated with greater genetic risk of these disorders. A negative genetic correlation was observed with attention deficit hyperactivity disorder (ADHD), major depressive disorder (MDD), and insomnia, indicating genetic predisposition to a larger SA in the specific GIBN is associated with lower genetic risk of these disorders. CT GIBNs displayed a negative genetic correlation with alcohol dependence. Jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across phenotypes offers a new vantage point for mapping the cortex into genetically informed networks.
摘要：
对人类皮质结构的遗传贡献表现出普遍的多效性。可以利用这种多效性来识别皮质的独特遗传信息部分，这些部分在神经生物学上与功能不同，细胞建筑学，或其他皮质分裂方案。我们通过应用基因组结构方程模型（SEM）对ENIGMA皮质GWAS中最近报道的34个大脑区域的皮质表面积（SA）和皮质厚度（CT）的遗传结构进行了研究。基因组SEM使用从GWAS汇总统计数据和LD评分回归（LDSC）估计的经验遗传协方差来发现遗传协方差的潜在因素，我们表示的是基因知情的大脑网络(GIBN)。基因组SEM可以从每个GIBN的汇总统计中拟合多变量GWAS，随后可用于LD评分回归(LDSC)。我们发现皮质SA的最佳拟合模型鉴定了6个GIBN，CT鉴定了4个GIBN。这些GIBN的多变量GWAS鉴定出74个全基因组显著(GWS)基因座(p<5×10-8)，包括许多以前与神经影像学表型有关的，行为特征，和精神病。GIBNGWASs的LDSC发现，SA来源的GIBN与双相情感障碍（BPD）具有正遗传相关性，和大麻使用障碍，表明在特定GIBN中对较大SA的遗传易感性与这些疾病的更大遗传风险相关。与注意缺陷多动障碍（ADHD）呈负相关，抑郁症（MDD），失眠，表明在特定GIBN中对较大SA的遗传易感性与这些疾病的较低遗传风险相关。CTGIBNs与酒精依赖呈负相关。联合建模复杂性状的遗传结构并研究表型之间的多变量遗传联系为将皮质映射到遗传信息网络提供了新的有利位置。
