Investigating the shared genetic architecture between depression and subcortical volumes

Depression, a widespread and highly heritable mental health condition, profoundly affects millions of individuals worldwide. Neuroimaging studies have consistently revealed volumetric abnormalities in subcortical structures associated with depression. However, the genetic underpinnings shared between depression and subcortical volumes remain inadequately understood. Here, we investigate the extent of polygenic overlap using the bivariate causal mixture model (MiXeR), leveraging summary statistics from the largest genome-wide association studies for depression (N = 674,452) and 14 subcortical volumetric phenotypes (N = 33,224). Additionally, we identify shared genomic loci through conditional/conjunctional FDR analyses. MiXeR shows that subcortical volumetric traits share a substantial proportion of genetic variants with depression, with 44 distinct shared loci identified by subsequent conjunctional FDR analysis. These shared loci are predominantly located in intronic regions (58.7%) and non-coding RNA intronic regions (25.4%). The 269 protein-coding genes mapped by these shared loci exhibit specific developmental trajectories, with the expression level of 55 genes linked to both depression and subcortical volumes, and 30 genes linked to cognitive abilities and behavioral symptoms. These findings highlight a shared genetic architecture between depression and subcortical volumetric phenotypes, enriching our understanding of the neurobiological underpinnings of depression.

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Publication Details

Journal
Nature Communications
Published
2024-09-02
DOI
https://doi.org/10.1038/s41467-024-52121-y
Citations
28
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
15.41

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article

Investigating the shared genetic architecture between depression and subcortical volumes

Haoyang Dong, Sijia Wang, Mengge Liu, Yayuan Chen et al.
28 citations
Nature Communications
Genetic Associations and Epidemiology
15.41
article

Investigating the shared genetic architecture between depression and subcortical volumes

Haoyang Dong, Sijia Wang, Mengge Liu, Yayuan Chen, Shaoying Wang, Lu Wang, Feng Liu, Zhihui Zhang, Nannan Zhang, Guoshu Zhao, Caihong Wang, Qian Qian, Minghuan Lei, Yujie Zhang, Qiyu Zhao
article en
28 citations

Abstract

Depression, a widespread and highly heritable mental health condition, profoundly affects millions of individuals worldwide. Neuroimaging studies have consistently revealed volumetric abnormalities in subcortical structures associated with depression. However, the genetic underpinnings shared between depression and subcortical volumes remain inadequately understood. Here, we investigate the extent of polygenic overlap using the bivariate causal mixture model (MiXeR), leveraging summary statistics from the largest genome-wide association studies for depression (N = 674,452) and 14 subcortical volumetric phenotypes (N = 33,224). Additionally, we identify shared genomic loci through conditional/conjunctional FDR analyses. MiXeR shows that subcortical volumetric traits share a substantial proportion of genetic variants with depression, with 44 distinct shared loci identified by subsequent conjunctional FDR analysis. These shared loci are predominantly located in intronic regions (58.7%) and non-coding RNA intronic regions (25.4%). The 269 protein-coding genes mapped by these shared loci exhibit specific developmental trajectories, with the expression level of 55 genes linked to both depression and subcortical volumes, and 30 genes linked to cognitive abilities and behavioral symptoms. These findings highlight a shared genetic architecture between depression and subcortical volumetric phenotypes, enriching our understanding of the neurobiological underpinnings of depression.

Nature CommunicationsVol. 15(1)
Tianjin Medical University General Hospital (CN), First Affiliated Hospital of Zhengzhou University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Tianjin City
Openalex Percentile: Top 1%
Genetic Associations and Epidemiology
15.41
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