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 - http://www.scopus.com/inward/record.url?scp=105000060077&partnerID=8YFLogxK
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 -