BrainXcan identifies brain features associated with behavioral and psychiatric traits using large-scale genetic and imaging data

Advances in brain MRI have enabled many discoveries in neuroscience. Case-control comparisons of brain MRI features have highlighted potential causes of psychiatric and behavioral disorders. However, due to the cost and difficulty of collecting MRI data, most studies have small sample sizes, limiting their reliability. Furthermore, reverse causality complicates interpretation because many observed brain differences are the result rather than the cause of the disease. Here we propose a method (BrainXcan) that leverages the power of large-scale genome-wide association studies (GWAS) and reference brain MRI data to discover new mechanisms of disease etiology and validate existing ones. BrainXcan tests the association with genetic predictors of brain MRI-derived features and complex traits to pinpoint relevant brain-wide and region-specific features. Requiring only genetic data, BrainXcan allows us to test a host of hypotheses on mental illness, across many MRI modalities, using public data resources. For example, our method shows that reduced axonal density across the brain is associated with schizophrenia risk, consistent with the disconnectivity hypothesis. We also find that the hippocampus volume is associated with schizophrenia risk, highlighting the potential of our approach. Taken together, our results show the promise of BrainXcan to provide insights into the biology of GWAS traits.

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Publication Details

Journal
Developmental Cognitive Neuroscience
Published
2025-03-12
DOI
https://doi.org/10.1016/j.dcn.2025.101542
Citations
7
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
10.90

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article

BrainXcan identifies brain features associated with behavioral and psychiatric traits using large-scale genetic and imaging data

Timothy J. Carroll, Thomas Brettin, Andrew Brown, Owen Melia et al.
7 citations
Developmental Cognitive Neuroscience
Genetic Associations and Epidemiology
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article

BrainXcan identifies brain features associated with behavioral and psychiatric traits using large-scale genetic and imaging data

Timothy J. Carroll, Thomas Brettin, Andrew Brown, Owen Melia, Hae Kyung Im, Yanyu Liang, Festus M. Nyasimi
article en
7 citations

Abstract

Advances in brain MRI have enabled many discoveries in neuroscience. Case-control comparisons of brain MRI features have highlighted potential causes of psychiatric and behavioral disorders. However, due to the cost and difficulty of collecting MRI data, most studies have small sample sizes, limiting their reliability. Furthermore, reverse causality complicates interpretation because many observed brain differences are the result rather than the cause of the disease. Here we propose a method (BrainXcan) that leverages the power of large-scale genome-wide association studies (GWAS) and reference brain MRI data to discover new mechanisms of disease etiology and validate existing ones. BrainXcan tests the association with genetic predictors of brain MRI-derived features and complex traits to pinpoint relevant brain-wide and region-specific features. Requiring only genetic data, BrainXcan allows us to test a host of hypotheses on mental illness, across many MRI modalities, using public data resources. For example, our method shows that reduced axonal density across the brain is associated with schizophrenia risk, consistent with the disconnectivity hypothesis. We also find that the hippocampus volume is associated with schizophrenia risk, highlighting the potential of our approach. Taken together, our results show the promise of BrainXcan to provide insights into the biology of GWAS traits.

Developmental Cognitive NeuroscienceVol. 73
Argonne National Laboratory (US), University of Dundee (GB), University of Chicago (US), Genomics (United Kingdom) (GB)
National Institute on Alcohol Abuse and Alcoholism, National Institute of Diabetes and Digestive and Kidney Diseases
Gender equality
Openalex Percentile: Top 2%
Genetic Associations and Epidemiology
10.90
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