Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior.
Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior.
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DOI：
10.1038/s42003-021-02712-y
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发表时间：
2021-10-12
影响因子：
5.9
通讯作者：
Rietveld CA
中科院分区：
文献类型：
--
作者：
de Vlaming R;Slob EAW;Jansen PR;Dagher A;Koellinger PD;Groenen PJF;Rietveld CA
人类大脑形态和行为的变化是相关的，并且是高度遗传的。然而，大脑形态和行为的特定特征在多大程度上与基因相关，这在很大程度上是未知的。在这里，我们引入了一种计算效率高的基于多变量基因组相关性的限制最大似然（MGREML）方法，以同时估计大量表型之间的遗传相关性。使用来自UK Biobank的个人水平数据（N = 20,190），我们提供了大脑中74个感兴趣区域（roi）灰质体积遗传性的估计，并绘制了这些roi与健康相关行为结果（包括智力）之间的遗传相关性。我们在大脑中发现了四个基因上不同的集群，它们与神经科学中的标准解剖细分相一致。行为特征与大脑形态具有明显的遗传相关性，这表明roi与性状特异性相关。这些实证结果说明了MGREML如何用于估计大型数据集中内部一致和高维遗传相关矩阵。Ronald de Vlaming和Eric Slob等人提出了MGREML，这是一种多变量工具，用于估计多个性状之间的成对遗传相关性。他们将MGREML应用于UK Biobank的74种脑成像表型和8种行为特征数据，证明这些表型与脑形态具有明显的遗传相关性。
Human variation in brain morphology and behavior are related and highly heritable. Yet, it is largely unknown to what extent specific features of brain morphology and behavior are genetically related. Here, we introduce a computationally efficient approach for multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) to estimate the genetic correlation between a large number of phenotypes simultaneously. Using individual-level data (N = 20,190) from the UK Biobank, we provide estimates of the heritability of gray-matter volume in 74 regions of interest (ROIs) in the brain and we map genetic correlations between these ROIs and health-relevant behavioral outcomes, including intelligence. We find four genetically distinct clusters in the brain that are aligned with standard anatomical subdivision in neuroscience. Behavioral traits have distinct genetic correlations with brain morphology which suggests trait-specific relevance of ROIs. These empirical results illustrate how MGREML can be used to estimate internally consistent and high-dimensional genetic correlation matrices in large datasets. Ronald de Vlaming and Eric Slob et al. present MGREML, a multivariate tool to estimate pairwise genetic correlations between multiple traits. They apply MGREML to UK Biobank data for 74 brain imaging phenotypes and 8 behavioral traits, demonstrating that these phenotypes have distinct genetic correlations with brain morphology.
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影响因子：
64.8
作者：
Elliott LT;Sharp K;Alfaro-Almagro F;Shi S;Miller KL;Douaud G;Marchini J;Smith SM
通讯作者：
Smith SM
基因发现和多基因预测，从基因组全基因组协会的教育程度研究中，有110万个人。
影响因子：
30.8
作者：
Lee JJ;Wedow R;Okbay A;Kong E;Maghzian O;Zacher M;Nguyen-Viet TA;Bowers P;Sidorenko J;Karlsson Linnér R;Fontana MA;Kundu T;Lee C;Li H;Li R;Royer R;Timshel PN;Walters RK;Willoughby EA;Yengo L;23andMe Research Team;COGENT (Cognitive Genomics Consortium);Social Science Genetic Association Consortium;Alver M;Bao Y;Clark DW;Day FR;Furlotte NA;Joshi PK;Kemper KE;Kleinman A;Langenberg C;Mägi R;Trampush JW;Verma SS;Wu Y;Lam M;Zhao JH;Zheng Z;Boardman JD;Campbell H;Freese J;Harris KM;Hayward C;Herd P;Kumari M;Lencz T;Luan J;Malhotra AK;Metspalu A;Milani L;Ong KK;Perry JRB;Porteous DJ;Ritchie MD;Smart MC;Smith BH;Tung JY;Wareham NJ;Wilson JF;Beauchamp JP;Conley DC;Esko T;Lehrer SF;Magnusson PKE;Oskarsson S;Pers TH;Robinson MR;Thom K;Watson C;Chabris CF;Meyer MN;Laibson DI;Yang J;Johannesson M;Koellinger PD;Turley P;Visscher PM;Benjamin DJ;Cesarini D
通讯作者：
Cesarini D
影响因子：
30.8
作者：
Bulik-Sullivan, Brendan K.;Loh, Po-Ru;Finucane, Hilary K.;Ripke, Stephan;Yang, Jian;Patterson, Nick;Daly, Mark J.;Price, Alkes L.;Neale, Benjamin M.
通讯作者：
Neale, Benjamin M.
影响因子：
9.8
作者：
Maier R;Moser G;Chen GB;Ripke S;Cross-Disorder Working Group of the Psychiatric Genomics Consortium;Coryell W;Potash JB;Scheftner WA;Shi J;Weissman MM;Hultman CM;Landén M;Levinson DF;Kendler KS;Smoller JW;Wray NR;Lee SH
通讯作者：
Lee SH
DOI：
10.1016/j.dadm.2015.11.008
发表时间：
2016-01-01
期刊：
Alzheimer's & dementia (Amsterdam, Netherlands)
影响因子：
--
作者：
Hwang, Jihye;Kim, Chan Mi;Na, Duk L
通讯作者：
Na, Duk L