Multivariate genome-wide association study on tissue-sensitive diffusion metrics highlights pathways that shape the human brain

Chun Chieh Fan (), Robert Loughnan, Carolina Makowski, Diliana Pecheva, Chi-Hua Chen, Donald J. Hagler, Wesley K. Thompson, Nadine Parker, Dennis van der Meer, Oleksandr Frei, Ole A. Andreassen and Anders M. Dale
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Chun Chieh Fan: University of California, San Diego
Robert Loughnan: University of California, San Diego
Carolina Makowski: University of California, San Diego
Diliana Pecheva: University of California, San Diego
Chi-Hua Chen: University of California, San Diego
Donald J. Hagler: University of California, San Diego
Wesley K. Thompson: University of California, San Diego
Nadine Parker: University of Oslo
Dennis van der Meer: University of Oslo
Oleksandr Frei: University of Oslo
Ole A. Andreassen: University of Oslo
Anders M. Dale: University of California, San Diego

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: Abstract The molecular determinants of tissue composition of the human brain remain largely unknown. Recent genome-wide association studies (GWAS) on this topic have had limited success due to methodological constraints. Here, we apply advanced whole-brain analyses on multi-shell diffusion imaging data and multivariate GWAS to two large scale imaging genetic datasets (UK Biobank and the Adolescent Brain Cognitive Development study) to identify and validate genetic association signals. We discover 503 unique genetic loci that have impact on multiple regions of human brain. Among them, more than 79% are validated in either of two large-scale independent imaging datasets. Key molecular pathways involved in axonal growth, astrocyte-mediated neuroinflammation, and synaptogenesis during development are found to significantly impact the measured variations in tissue-specific imaging features. Our results shed new light on the biological determinants of brain tissue composition and their potential overlap with the genetic basis of neuropsychiatric disorders.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30110-3

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DOI: 10.1038/s41467-022-30110-3

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