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Genome-wide analysis of brain age identifies 59 loci and unveils links to mental and physical health

Poster No:

687

Submission Type:

Abstract Submission

Authors:

Philippe Jawinski¹, Helena Forstbach¹, Holger Kirsten², Frauke Beyer³, Arno Villringer³, Veronica Witte⁴, Markus Scholz², Stephan Ripke⁵, Sebastian Markett¹

Institutions:

¹Humboldt-Universität zu Berlin, Berlin, Germany, ²Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Germany, Leipzig, Germany, ³Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, ⁴Cognitive Neurology, University of Leipzig Medical Center & Department of Neurology, Max Planck Inst, Leipzig, Germany, ⁵Stanley Center for Psychiatric Research, Broad Institute of the Massachusetts Institute of Technolog, Cambridge, MA

First Author:

Philippe Jawinski
Humboldt-Universität zu Berlin
Berlin, Germany

Co-Author(s):

Helena Forstbach
Humboldt-Universität zu Berlin
Berlin, Germany

Holger Kirsten
Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Germany
Leipzig, Germany

Frauke Beyer
Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences
Leipzig, Germany

Arno Villringer
Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences
Leipzig, Germany

Veronica Witte
Cognitive Neurology, University of Leipzig Medical Center & Department of Neurology, Max Planck Inst
Leipzig, Germany

Markus Scholz
Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Germany
Leipzig, Germany

Stephan Ripke
Stanley Center for Psychiatric Research, Broad Institute of the Massachusetts Institute of Technolog
Cambridge, MA

Sebastian Markett
Humboldt-Universität zu Berlin
Berlin, Germany

Introduction:

Neuroimaging and machine learning are opening up new opportunities in studying biological aging mechanisms. In this field, 'brain age gap' has emerged as promising MRI-based biomarker quantifying the deviation between an individual's biological and chronological age of the brain – an indicator of accelerated/decelerated aging. Here we conducted a genome-wide association study (GWAS) of brain age gap to discover associated genomic loci and to examine pleiotropic relationships with other complex traits, i.e., the links with mental and physical health.

Methods:

We present findings from the largest brain age GWAS to date, with genetic effects derived from up to 56,622 individuals. We estimate grey matter (GM), white matter (WM), and combined grey and white matter (GWM) brain age gap by applying supervised machine learning techniques to structural T1-weighted MRI scans. In a first step, we identify associated loci in a discovery sample of n = 32,634 UK Biobank individuals and replicate our findings in another n = 23,988 individuals. In a second step, we conduct a meta-analysis across discovery and replication cohort to discover additional loci and perform a broad range of GWAS follow-up analyses to prioritize relevant genes. Moreover, we compute genetic correlations with more than 1000 health traits using LD Score Regression, establish evidence for causal relationships via two-sample Mendelian Randomization, and use genetic effect size distribution analysis to estimate the degree of polygenicity. We make our analysis pipeline publicly available on GitHub: https://github.com/pjawinski/ukb_brainage

Results:

Our study demonstrates that brain age has a substantial genetic component, with heritability estimates (h2SNP) ranging from 23% to 29% (SE: 2.0%). In the discovery sample, we identify 25 associated loci, all showing sign concordance, and 19 achieving nominal significance in the replication sample. Our meta-analysis across both discovery and replication samples highlights 59 independently associated loci (40 novel). For the strongest signal at 17q21.31 (p = 9e-83), we prioritize MAPT encoding the tau protein central to Alzheimer's disease. Genetic correlations unveil relationships with several traits, including substance use (e.g., drinks per week), mental health (e.g., frequency of unenthusiasm), physical health (e.g., diabetes), and lifespan variables (e.g., mother's age at death). Using Mendelian Randomization analyses, we demonstrate a causal role of enhanced blood pressure and diabetes on accelerated brain aging. Genetic effect size distribution analyses suggest a relatively low degree of polygenicity with ~11.5k contributing variants.

Conclusions:

In conclusion, our study refines the genetic architecture of brain age gap and its implications for other health traits. We identify 59 variants (40 new) and prioritize plausible genes such as MAPT and APOC1 involved in neurodegenerative diseases, and CARF linked to premature senescence. The observed genomic signals unveil several enriched biological pathways, e.g., immune-system-related processes as well as the binding of small GTPases, prompting further mechanistic exploration.

Genetics:

Genetic Association Studies ¹

Lifespan Development:

Aging ²

Neuroanatomy, Physiology, Metabolism and Neurotransmission:

Normal Development

Keywords:

Aging

Development

Machine Learning

Neurological

STRUCTURAL MRI

Other - brain aging; genetics; mental health

^1|2Indicates the priority used for review

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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

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Structural MRI

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