Integration of estimated regional gene expression with neuroimaging and clinical phenotypes at biobank scale.

Autor: Hoang N; Department of Computer Science, Vanderbilt University, Nashville, Tennessee, United States of America., Sardaripour N; Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America., Ramey GD; Biological and Medical Informatics Division, University of California, San Francisco, California, United States of America.; Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America., Schilling K; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, Tennessee, United States of America.; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America., Liao E; Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America., Chen Y; Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America., Park JH; Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America., Bledsoe X; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America., Landman BA; Department of Computer Science, Vanderbilt University, Nashville, Tennessee, United States of America.; Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America.; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, Tennessee, United States of America.; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America., Gamazon ER; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America., Benton ML; Department of Computer Science, Baylor University, Waco, Texas, United States of America., Capra JA; Department of Computer Science, Vanderbilt University, Nashville, Tennessee, United States of America.; Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America.; Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America.; Bakar Computational Health Sciences Institute, University of California, San Francisco, California, United States of America., Rubinov M; Department of Computer Science, Vanderbilt University, Nashville, Tennessee, United States of America.; Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America.; Department of Psychology, Vanderbilt University, Nashville, Tennessee, United States of America.; Howard Hughes Medical Institute Janelia Research Campus, Ashburn, Virginia, United States of America.
Jazyk: angličtina
Zdroj: PLoS biology [PLoS Biol] 2024 Sep 13; Vol. 22 (9), pp. e3002782. Date of Electronic Publication: 2024 Sep 13 (Print Publication: 2024).
DOI: 10.1371/journal.pbio.3002782
Abstrakt: An understanding of human brain individuality requires the integration of data on brain organization across people and brain regions, molecular and systems scales, as well as healthy and clinical states. Here, we help advance this understanding by leveraging methods from computational genomics to integrate large-scale genomic, transcriptomic, neuroimaging, and electronic-health record data sets. We estimated genetically regulated gene expression (gr-expression) of 18,647 genes, across 10 cortical and subcortical regions of 45,549 people from the UK Biobank. First, we showed that patterns of estimated gr-expression reflect known genetic-ancestry relationships, regional identities, as well as inter-regional correlation structure of directly assayed gene expression. Second, we performed transcriptome-wide association studies (TWAS) to discover 1,065 associations between individual variation in gr-expression and gray-matter volumes across people and brain regions. We benchmarked these associations against results from genome-wide association studies (GWAS) of the same sample and found hundreds of novel associations relative to these GWAS. Third, we integrated our results with clinical associations of gr-expression from the Vanderbilt Biobank. This integration allowed us to link genes, via gr-expression, to neuroimaging and clinical phenotypes. Fourth, we identified associations of polygenic gr-expression with structural and functional MRI phenotypes in the Human Connectome Project (HCP), a small neuroimaging-genomic data set with high-quality functional imaging data. Finally, we showed that estimates of gr-expression and magnitudes of TWAS were generally replicable and that the p-values of TWAS were replicable in large samples. Collectively, our results provide a powerful new resource for integrating gr-expression with population genetics of brain organization and disease.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Hoang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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