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

TY - JOUR

T1 - BrainXcan identifies brain features associated with behavioral and psychiatric traits using large-scale genetic and imaging data

AU - Liang, Yanyu

AU - Nyasimi, Festus

AU - Melia, Owen

AU - Carroll, Timothy J.

AU - Brettin, Thomas

AU - Brown, Andrew

AU - Im, Hae Kyung

N1 - Publisher Copyright: © 2025 The Authors

PY - 2025/6

Y1 - 2025/6

N2 - 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.

AB - 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.

KW - Association study

KW - Complex phenotype genetics

KW - Genetic architecture

KW - Genetic prediction

KW - Image derived phenotypes

KW - Mendelian Randomization

UR - https://www.scopus.com/pages/publications/105000060077

U2 - 10.1016/j.dcn.2025.101542

DO - 10.1016/j.dcn.2025.101542

M3 - Article

C2 - 40101670

AN - SCOPUS:105000060077

SN - 1878-9293

VL - 73

JO - Developmental Cognitive Neuroscience

JF - Developmental Cognitive Neuroscience

M1 - 101542

ER -