Bibliographic Details
| Title: |
Investigating the shared genetic architecture between depression and subcortical volumes |
| Authors: |
Mengge Liu, Lu Wang, Yujie Zhang, Haoyang Dong, Caihong Wang, Yayuan Chen, Qian Qian, Nannan Zhang, Shaoying Wang, Guoshu Zhao, Zhihui Zhang, Minghuan Lei, Sijia Wang, Qiyu Zhao, Feng Liu |
| Source: |
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024) |
| Publisher Information: |
Nature Portfolio, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Science |
| Subject Terms: |
Science |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2041-1723 |
| Relation: |
https://doaj.org/toc/2041-1723 |
| DOI: |
10.1038/s41467-024-52121-y |
| Access URL: |
https://doaj.org/article/b6721cd998564f99a83502d157e5c50f |
| Accession Number: |
edsdoj.b6721cd998564f99a83502d157e5c50f |
| Database: |
Directory of Open Access Journals |
