| Application: | 4844, STratifying Resilience and Depression Longitudinally (STRADL) |
| Title: | Genome-wide meta-analysis of depression in 807,553 individuals identifies 102 independent variants with replication in a further 1,507,153 individuals |
| Size: | 820375 B |
| Archived: | 9 Dec 2020 |
| Stability: | Complete |
| Personal: | No individual-level data |
Return 3049
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Notes
Major depression is a debilitating psychiatric illness that is typically associated with low mood, anhedonia and a range of comorbidities. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximise sample size, we meta-analysed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 gene-sets associated with depression, including both genes and gene-pathways associated with synaptic structure and neurotransmission. Further evidence of the importance of prefrontal brain regions in depression was provided by an enrichment analysis. In an independent replication sample of 1,507,153 individuals (474,574 cases and 1,032,579 controls), 87 of the 102 associated variants were significant following multiple testing correction. Based on the putative genes associated with depression this work also highlights several potential drug repositioning opportunities. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding aetiology and developing new treatment approaches.Application 4844
STratifying Resilience and Depression Longitudinally (STRADL)
Progress in understanding the causes of major depressive disorder has been slow. Dividing depression into subtypes, a process called stratification, could ultimately lead to faster progress. We will stratify or divide individuals with MDD and depressive syndromes into more similar groups of people in UK Biobank. Our aims are to: 1. Identify and describe specific subtypes of depression 2. Identify the causes underlying different types of depression using GWAS and MRI 3. Test whether resistance to depression (i.e. resilience) to depression can be accurately measured. 4. Identify the mechanisms underlying resilience using genetic and brain imaging data. This research seeks to use the medical, cognitive, imaging and genetic data from UKBiobank to study the mechanisms of common medical conditions and use them as a platform to better diagnosis. These aims are consistent with UK Biobank's. Providing this information will help to identify new drug targets for depression. Stratifying depression into more homogenous categories will provide better 'disease' targets for other research studies because there will be less lumping together of individuals with different causes for their illness within the same broad category of depression. We will test whether these sub-classes of depression and depressive symptom have neurobiological associations in UKbiobank by comparing them with depressed individuals as w whole, as well as controls, using MRI and genetic data. We will firstly examine the associations of depression with cognition (baseline measures and web-based measures of attention and memory, for example), brain structure, function and connection strength (MRI). We will examine the association of different depression types with biological intermediates (measurable variables important in the causation of depression) using a technique called polygenic profiling. We will also compare resilient and non-resilient individuals. We are interested in the full UKbiobank cohort for most analyses - and the subgroup of UKbiobank with genetic and imaging (brain MRI) data for more detailed analysis. We appreciate the time scale for the availability of genotyping and imaging data.
| Lead investigator: | Professor Andrew McIntosh |
| Lead institution: | University of Edinburgh |
6 related Returns
| Return ID | App ID | Description | Archive Date |
|---|---|---|---|
| 1641 | 4844 | A Genome-Wide Association Study Finds Genetic Associations with Broadly-Defined Headache in UK Biobank (N = 223,773) | 19 Jul 2019 |
| 2921 | 4844 | Automated classification of depression from structural brain measures across two independent community-based cohorts | 3 Dec 2020 |
| 1664 | 4844 | Genetic and Environmental Risk for Chronic Pain and the Contribution of RiskVariants for Major Depressive Disorder: A Family-Based Mixed-Model Analysis | 23 Jul 2019 |
| 3050 | 4844 | Genome-Wide Association Study Meta-Analysis of the Alcohol Use Disorders Identification Test (AUDIT) in Two Population-Based Cohorts | 9 Dec 2020 |
| 749 | 4844 | Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N=122117) | 17 Oct 2017 |
| 1878 | 4844 | Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. | 16 Jan 2020 |
1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 3050 | Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions | David M. Howard (+37) | 2019 | Nature Neuroscience |
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