The genetic architecture of multimodal human brain age

Junhao Wen; Bingxin Zhao; Zhijian Yang; Guray Erus; Ioanna Skampardoni; Elizabeth Mamourian; Yuhan Cui; Gyujoon Hwang; Jingxuan Bao; Aleix Boquet-Pujadas; Zhen Zhou; Yogasudha Veturi; Marylyn D. Ritchie; Haochang Shou; Paul M. Thompson; Li Shen; Arthur W. Toga; Christos Davatzikos

doi:10.1038/s41467-024-46796-6

Nature Communications (Mar 2024)

The genetic architecture of multimodal human brain age

Junhao Wen,
Bingxin Zhao,
Zhijian Yang,
Guray Erus,
Ioanna Skampardoni,
Elizabeth Mamourian,
Yuhan Cui,
Gyujoon Hwang,
Jingxuan Bao,
Aleix Boquet-Pujadas,
Zhen Zhou,
Yogasudha Veturi,
Marylyn D. Ritchie,
Haochang Shou,
Paul M. Thompson,
Li Shen,
Arthur W. Toga,
Christos Davatzikos

Affiliations

Junhao Wen: Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California
Bingxin Zhao: Department of Statistics and Data Science, University of Pennsylvania
Zhijian Yang: Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
Guray Erus: Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
Ioanna Skampardoni: Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
Elizabeth Mamourian: Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
Yuhan Cui: Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
Gyujoon Hwang: Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
Jingxuan Bao: Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine
Aleix Boquet-Pujadas: Biomedical Imaging Group, EPFL
Zhen Zhou: Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
Yogasudha Veturi: Department of Biobehavioral Health and Statistics, Penn State University
Marylyn D. Ritchie: Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania
Haochang Shou: Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania
Paul M. Thompson: Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California
Li Shen: Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine
Arthur W. Toga: Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California
Christos Davatzikos: Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania

DOI: https://doi.org/10.1038/s41467-024-46796-6
Journal volume & issue: Vol. 15, no. 1
pp. 1 – 15

Abstract

Read online

Abstract The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10−8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine .

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

About the journal