| Application: | 18177, Multi-trait GWAS analyses in the UK Biobank |
| Title: | Classical Human Leukocyte Antigen Alleles and C4 Haplotypes Are Not Significantly Associated With Depression |
| Size: | 3.0 MB |
| Archived: | 29 Jan 2020 |
| Stability: | Complete |
| Personal: | Contains individual-level data |
Return 1921
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Notes
A bi-directional relationship between autoimmune diseases and depression has been reported in the literature, meaning that people with a history of depression are at increased risk for developing autoimmune diseases and vice-versa. The mechanisms underlying the bi-directional relationship are poorly understood, but one explanation could be that there is shared genetic risk for depression and autoimmune diseases.In our study we looked at a region of the genome called the Major Histocompatibility Complex (MHC). The MHC is important in human immunity and harbours genetic variation that increases risk for autoimmune diseases. We asked whether genetic variation in the MHC could also increase risk for depression.
We started by using information from the online Mental Health Questionnaire completed by some UK Biobank participants to identify people with a history of depression and those without a history of depression. We then tested whether different forms (alleles) of Human Leukocyte Antigen (HLA) genes located in the MHC were more common in people with a history of depression compared to those without a history of depression.
We did not find a statistically significant result, meaning that we found no evidence that the HLA alleles we tested are involved in depression. When we lowered the threshold for statistical significance, we found suggestive evidence that three HLA alleles were associated with very modest protection for depression. Overall, our results suggest that variation in HLA genes does not play a major role in risk for depression.
Application 18177
Multi-trait GWAS analyses in the UK Biobank
GWAS methods have been developed to analyse associations between SNPs and multiple phenotypes jointly. We have produced one such method (MultiPhen) and performed a simulation study finding that multivariate analyses can double the discovery of trait associated genetic variation compared with univariate analyses. Multi-trait analyses, such as polygenic risk scores, offer insights into shared and distinct aetiology among different phenotypes, such as ADHD, autism, schizophrenia, eating disorders and obesity. We will perform single and multi-trait analyses on the UK Biobank to boost discovery power of causal genetic variants, identify shared aetiology among phenotypes, and evaluate method performance on real data. Identifying shared genetic risk between physical and psychiatric phenotypes, in particular, could shed light on the aetiology of psychiatric disorders (which our group focuses on). Joint analysis of multiple traits may lead to substantially greater identification of novel genotype-phenotype associations and provide insights into the biological network underlying correlated phenotypes. Exposing the genetics responsible for comorbidity between phenotypes could also uncover possibilities for drug repositioning. By evaluating method performance we may find whether power is optimised by performing such analyses on clinically related phenotypes, those that are most highly correlated, or on sets of phenotypes with heterogeneous correlation structure. Genome-wide association study (GWAS) analyses will be performed on subsets of the UK Biobank phenotypes, allowing some traits to be analysed jointly for the first time. We will use several statistical approaches to investigate patterns of shared and distinct genetic risk between and within psychiatric and physical traits. We will use 'polygenic risk scoring' and 'linkage disequilibrium score regression' analyses software to infer genetic overlap and estimate genetic correlations between different phenotypes and outcomes, using our local computing facilities. We wish to include the full UK Biobank cohort in our analyses. In response to feedback from our approved preliminary application: we intend to use data from self-reported baseline information and health records to derive phenotypes. We acknowledge the complexity of looking across outcomes from different medical record sources and the difficulty in reliably ascertaining case status across multiple sources. We have undertaken preliminary analyses aimed at identifying case status in the UK Biobank using multivariate measures (self-report, hospital data, treatment status) as part of our collaboration on application 16577, and will extend this work with approval of this application.
| Lead investigator: | Professor Cathryn Lewis |
| Lead institution: | Icahn School of Medicine at Mount Sinai |
3 related Returns
| Return ID | App ID | Description | Archive Date |
|---|---|---|---|
| 1834 | 18177 | Association of Polygenic Risk for Attention-Deficit/Hyperactivity Disorder With Co-occurring Traits and Disorders | 29 Nov 2019 |
| 2891 | 18177 | Cannabis use, depression and self-harm: phenotypic and genetic relationships | 27 Nov 2020 |
| 2461 | 18177 | Depression with atypical neurovegetative symptoms shares genetic predisposition with immuno-metabolic traits and alcohol consumption | 15 Oct 2020 |
1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 1922 | Classical Human Leukocyte Antigen Alleles and C4 Haplotypes Are Not Significantly Associated With Depression | Glanville KP et al. | 2020 | Biol Psychiatry |
Enabling scientific discoveries that improve human health