five

SUPERSEDED - Summary statistics for three depression phenotypes in UK Biobank

收藏
DataCite Commons2023-04-27 更新2025-04-17 收录
下载链接:
https://datashare.ed.ac.uk/handle/10283/2951
下载链接
链接失效反馈
官方服务:
资源简介:
## This item has been replaced by the one which can be found at https://doi.org/10.7488/ds/2314 ## Depression is a polygenic trait that causes extensive periods of disability and increases the risk of suicide, a leading cause of death in young people. Previous genetic studies have identified a number of common risk variants which have increased in number in line with increasing sample sizes. We conducted a genome-wide association study (GWAS) in the largest single population-based cohort to date, UK Biobank. This allowed us to estimate the effects of ≈ 8 million genetic variants in 320,000 people for three depression phenotypes: broad depression, probable major depressive disorder (MDD), and International Classification of Diseases (ICD, version 9 or 10)-coded MDD. Each phenotype was found to be significantly genetically correlated with the results from a previous independent study of clinically defined MDD. We identified 14 independent loci that were significantly associated (P < 5 × 10-8) with broad depression, two independent variants for probable MDD, and one independent variant for ICD-coded MDD. Gene-based analysis of our GWAS results with MAGMA revealed 46 regions significantly associated (P < 2.77 × 10-6) with broad depression, two significant regions for probable MDD and one significant region for ICD-coded MDD. Gene region-based analysis of our GWAS results with MAGMA revealed 59 regions significantly associated (P < 6.02 × 10-6) with broad depression, of which 27 were also detected by gene-based analysis. Variants for broad depression were enriched in pathways for excitatory neurotransmission, mechanosensory behavior, postsynapse, neuron spine and dendrite. This study provides a number of novel genetic risk variants that can be leveraged to elucidate the mechanisms of MDD and low mood.
提供机构:
University of Edinburgh. Centre for Clinical Brain Sciences.
创建时间:
2017-11-24
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作