Abstract
The amygdala is a small but critical multi-nucleus structure for emotion, cognition and neuropsychiatric disorders. Although genetic associations with amygdala volumetric traits have been investigated in sex-combined European populations, cross-ancestry and sex-stratified analyses are lacking. Here we conducted cross-ancestry and sex-stratified genome-wide association analyses for 21 amygdala volumetric traits in 6,923 Chinese and 48,634 European individuals. We identified 191 variant–trait associations (P < 2.38 × 10−9), including 47 new associations (12 new loci) in sex-combined univariate analyses and seven additional new loci in sex-combined and sex-stratified multivariate analyses. We identified 12 ancestry-specific and two sex-specific associations. The identified genetic variants include 16 fine-mapped causal variants and regulate amygdala and fetal brain gene expression. The variants were enriched for brain development and colocalized with mood, cognition and neuropsychiatric disorders. These results indicate that cross-ancestry and sex-stratified genetic association analyses may provide insight into the genetic architectures of amygdala and subnucleus volumes.
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Data availability
GWAS summary statistics for M-Bi in 13,160 ENIGMA participants were downloaded from https://enigma.ini.usc.edu. Summary statistics for sex-combined and sex-stratified univariate EAS-GWASs, EUR-GWASs and cross-ancestry GWAS meta-analyses are available in the NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/home) under accession numbers GCST90474869–GCST90475057. Additional statistics, including sex-combined and sex-stratified multivariate EAS-GWASs, EUR-GWASs and cross-ancestry GWAS meta-analyses as well as univariate EUR-GWAS meta-analysis and cross-ancestry meta-analysis with 55,557 samples for M-Bi are accessible via figshare (https://doi.org/10.6084/m9.figshare.27933333.v1)117. The cis-eQTL data of fetal brain samples were derived from previously published work65 (https://doi.org/10.1186/s13059-018-1567-1). The variants were mapped to the GRCh37 assembly using the chain file available at https://hgdownload.soe.ucsc.edu/goldenPath/hg38/liftOver/hg38ToHg19.over.chain.gz. GWAS summary statistics of brain-related phenotypes used in this study are available in the MRC Integrative Epidemiology Unit (IEU) OpenGWAS database (https://gwas.mrcieu.ac.uk).
Code availability
The code for analysis in this paper is accessible on Zenodo (https://doi.org/10.5281/zenodo.14272663)118 and the software used in this study is publicly available. The sMRI process and amygdala and subnucleus segmentation were performed using FreeSurfer (v.7.1.1) (https://surfer.nmr.mgh.harvard.edu) and harmonization was conducted using Combat harmonization (v.1.0.1) (https://github.com/Jfortin1/ComBatHarmonization). Outliers were detected by isolation forest (https://ww2.mathworks.cn/help/stats/isolationforest.html). Genetic data processing involved PLINK (v.2.0) (http://zzz.bwh.harvard.edu/plink), SHAPEIT2 (v.2.r904) (https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html), IMPUTE2 (v.2.3.2) (http://mathgen.stats.ox.ac.uk/impute/impute_v2.html) and qctool (v.2) (https://www.well.ox.ac.uk/~gav/qctool_v2). The R package ukbtools (v.0.11.3) (https://kenhanscombe.github.io/ukbtools) was used to estimate the relatedness of UKB participants. Univariate GWASs were performed using BGENIE (v.1.3) (https://jmarchini.org/bgenie) and multivariate GWASs were performed using C-GWAS (v.0.9.3) (https://github.com/Fun-Gene/CGWAS). Cross-ancestry meta-analyses were conducted using METASOFT (v.2.0.0) (http://genetics.cs.ucla.edu/meta). EAS-GWAS summary statistics were also obtained using MAMA (v.1.0.0) (https://github.com/JonJala/mama) by combining EAS-GWAS and EUR-GWAS summary statistics. The post-GWAS analyses mainly involved LDSC (v.1.0.1) (https://github.com/bulik/ldsc), Popcorn (v.1.0) (https://github.com/brielin/Popcorn), FINEMAP (v.1.4.1) (http://www.christianbenner.com/), Ensembl VEP tool (v.110) (http://www.ensembl.org/info/docs/tools/vep/index.html), ToppGene (v.1.0.0) (https://toppgene.cchmc.org), LiftOver (Jan.2022 release, https://genome.ucsc.edu/cgi-bin/hgLiftOver), SMR (v.1.3.1) (https://yanglab.westlake.edu.cn/software/smr/), coloc (v.5.1.0) (https://chr1swallace.github.io/coloc/index.html), gwasglue (v.0.0.0.9000) (https://mrcieu.github.io/gwasglue) and PRSice-2 (v.2.3.5) (https://github.com/choishingwan/PRSice). All statistical analyses in this study were conducted using R (v.4.3.1) (https://www.r-project.org) and Python (v.3.11.4) (https://www.python.org).
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Acknowledgements
This research was conducted using the UKB Resource under application number 75556. This study was covered by ethical approval from UKB, which was granted by the National Information Governance Board for Health and Social Care and the NHS North West Multicenter Research Ethics Committee. All participants provided their written informed consent. We express our gratitude to all participants from CHIMGEN, UKB and EMIGMA as well as to the dedicated researchers who collected and contributed valuable data. This work was supported by the National Natural Science Foundation of China (82430063, 82030053 and 81425013 to C.Y., 82330059 to B.Z. and 82371924 to J.X.), the National Key Research and Development Program of China (2018YFC1314300 to C.Y.), the National Science and Technology Innovation 2030–Major program of ‘Brain Science and Brain-Like Research’ (2022ZD0211800 to B.Z.) and the China National Postdoctoral Program for Innovative Talents (BX20240255 to N.L.).
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C.Y. and Y.J. designed the study and wrote the paper. All authors critically reviewed the paper. Y.J., N.L., Y. Yang, M.W., J.C., W.Z., S.Q., M.J.L., W.Q., F.L., M.L., S.W., Q.X., J. Xu, J.F. and C.Y. were the principal investigators. Y. Yang, M.W., J.C., W.Z., S.Q., Z.G., G.C., Y. Yu, W. Liao, H.Z., B.G., X.X., T.H., Z. Yao, Q.Z., P.Z., W. Li, D.S., C.W., S.L., Z. Yan, F.C., J.Z., W.S., Y.M., D.W., J-H.G., X.Z., K.X., X-N.Z., L.Z., Z. Ye, J. Xian, B.Z. and C.Y. acquired the data. C.Y., B.Z. and J. Xian supervised the work. Y.J. analyzed the data.
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Ji, Y., Liu, N., Yang, Y. et al. Cross-ancestry and sex-stratified genome-wide association analyses of amygdala and subnucleus volumes. Nat Genet 57, 839–850 (2025). https://doi.org/10.1038/s41588-025-02136-y
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DOI: https://doi.org/10.1038/s41588-025-02136-y
