Genome-wide analysis of subcortical aging identifies a spatially structured pattern of genetic associations.

Nicholas J Kim, Ayati Mishra, Jeremy S Yu, Owen M Vega, Nikhil N Chaudhari, Fangyun C Liu, Andrei Irimia

Abstract read

In one paragraph

Article in GeroScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Nicholas J KimAlfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.

Ayati MishraEthel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.

Jeremy S YuEthel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.

Owen M VegaEthel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.

Nikhil N ChaudhariAlfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.

Fangyun C LiuEthel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.

Andrei IrimiaAlfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA. irimia@usc.edu.ORCID http://orcid.org/0000-0002-9254-9388

Funding

Interpretable machine learning to synergize brain age estimation and neuroimaging geneticsR01AG079957 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Andrei Irimia · 2023 to 2026

$3.2M

NIA NIH HHS R01 AG 079957

6 · The paper itself

Abstract

Local brain age (LBA) is a spatially resolved biomarker of brain aging that captures regional deviations from chronological age, yet its genetic architecture in the subcortex remains unexplored. Here, we present the first genome-wide association study (GWAS) of subcortical LBA, estimated using a deep neural network applied to T1-weighted MRI scans from 41,957 cognitively normal participants in the UK Biobank. We computed LBA across 14 subcortical structures and identified 14 significant single-nucleotide polymorphisms (SNPs) across nine independent loci. These variants map to genes involved in cellular homeostasis, gene regulation, and synaptic and developmental signaling. A prominent signal emerged at the 17q21.31 haplotype, encompassing MAPT-related regulatory architecture, with significant associations across all subcortical regions. Across loci, we observed a recurring spatial pattern in which effect sizes are relatively larger in metabolically central structures such as the pallidum and thalamus compared to limbic regions. Together, these findings support a spatially structured pattern of genetic associations in subcortical brain aging. This work supports subcortical LBA as a genetically informed phenotype and provides a framework for linking common genetic variation to region-specific vulnerability and resilience in neurodegenerative disease.

Indexed as

Brain ageDeep learningGWASMRI

Identifiers

PMID42541656

DOI10.1038/s41592-021-01186-4

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.