Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior

Autor: Philip R. Jansen, Patrick J. F. Groenen, Cornelius A. Rietveld, Ronald de Vlaming, Alain Dagher, Philipp Koellinger, Eric A. W. Slob
Přispěvatelé: de Vlaming, Ronald [0000-0001-6416-6067], Jansen, Philip R [0000-0003-1550-2444], Dagher, Alain [0000-0002-0945-5779], Groenen, Patrick JF [0000-0001-6683-8971], Rietveld, Cornelius A [0000-0003-4053-1861], Apollo - University of Cambridge Repository, Groenen, Patrick J F [0000-0001-6683-8971], Applied Economics, Child and Adolescent Psychiatry / Psychology, Econometrics, Erasmus School of Economics, Economics, Amsterdam Neuroscience - Complex Trait Genetics, Complex Trait Genetics, Jansen, Philip R. [0000-0003-1550-2444], Groenen, Patrick J. F. [0000-0001-6683-8971], Rietveld, Cornelius A. [0000-0003-4053-1861], Human genetics, APH - Aging & Later Life, APH - Mental Health
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Communications Biology
de Vlaming, R, Slob, E A W, Jansen, P R, Dagher, A, Koellinger, P D, Groenen, P J F & Rietveld, C A 2021, ' Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior ', Communications biology, vol. 4, 1180, pp. 1-9 . https://doi.org/10.1038/s42003-021-02712-y
Communications Biology, 4(1):1180. Springer Nature
Communications biology, 4:1180, 1-9. Nature Research
Communications Biology, Vol 4, Iss 1, Pp 1-9 (2021)
de Vlaming, R, Slob, E A W, Jansen, P R, Dagher, A, Koellinger, P D, Groenen, P J F & Rietveld, C A 2021, ' Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior ', Communications Biology, vol. 4, no. 1, 1180 . https://doi.org/10.1038/s42003-021-02712-y
de Vlaming, R, Slob, E A W, Jansen, P R, Dagher, A, Koellinger, P, Groenen, P J F & Rietveld, C A 2021 ' Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior ' bioRxiv, bioRxiv . https://doi.org/10.1101/2021.04.19.440478
ISSN: 2399-3642
Popis: 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.
Databáze: OpenAIRE
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