The Genetic Architecture of Multimodal Human Brain Age

Overview

Journal bioRxiv

Date 2023 Jun 19

PMID 37333190

Authors

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

Soon will be listed here.

Abstract

The complex biological mechanisms underlying human brain aging remain incompletely understood, involving multiple body organs and chronic diseases. In this study, we used multimodal magnetic resonance imaging and artificial intelligence to examine the genetic architecture of the brain age gap (BAG) derived from gray matter volume (GM-BAG, =31,557 European ancestry), white matter microstructure (WM-BAG, =31,674), and functional connectivity (FC-BAG, =32,017). We identified sixteen genomic loci that reached genome-wide significance (P-value<5×10). 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 showed the highest heritability enrichment for genetic variants in conserved regions, whereas WM-BAG exhibited the highest heritability enrichment in the 5' untranslated regions; oligodendrocytes and astrocytes, but not neurons, showed significant heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several exposure variables on brain aging, such as type 2 diabetes on GM-BAG (odds ratio=1.05 [1.01, 1.09], P-value=1.96×10) and AD on WM-BAG (odds ratio=1.04 [1.02, 1.05], P-value=7.18×10). Overall, our results provide valuable insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at the MEDICINE knowledge portal: https://labs.loni.usc.edu/medicine.

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