An atlas of cell-type dependent and phenome-wide associations at cancer and autoimmune disease loci

Summary

This web application can be used to investigate results from a genome-wide analysis of 16 cancer and autoimmune disease outcomes using gene-based scores analysed with a Mendelian randomization (MR) approach which accounts for the local linkage disequilibrium structure of weakly correlated instruments¹. The cell-type specificity of top findings can be explored using results from in-depth genetic colocalization analyses based on data from 18 immune cell-type datasets^2,3. Lastly, we conducted phenome-wide analyses on top hits by applying MR analyses to 321 complex traits and diseases and these can also be investigated using interactive plots to explore pervasive pleiotropy.

Instructions

To query results from our atlas of results please select parameters on the left hand side of this page before clicking the 'Search Atlas' button. This will generate results in the Tables of Results tab which can then be downloaded using the 'Download' button once queries have been run.

There are 3 ways to query the results of our atlas:

Genome-wide analysis - A genome-wide association analysis for a selected autoimmune disease or cancer outcome. Select a Disease outcome of interest from the drop-down menu before clicking the 'Search Atlas' button. This will evaluate all genome-wide MR results for your outcome and display these interactively using a Manhattan plot⁴.

Cell-type effects - In-depth genetic colocalization analyses to evaluate shared causal variants between immune-cell gene expression and disease outcoems. Select a Gene and then Disease outcome from the drop-down menus before clicking the 'Search Atlas' button. This will display evidence of that the expression of your gene of interest shares a causal variants with this disease outcome using 15 immune-cell datasets. Coefficients are posterior probabilities of association (PPA) derived using the 'coloc' R package⁵ where a PPA>0.8 is considered evidence of a shared causal variant in this study. Comparisons across immune cell-types are visualised using barplot using the 'ggplot2' R package⁶.

Phenome-wide analysis - Phenome-wide association study for genetically predicted gene regulatory on X traits and outcomes. Select a Gene and click the 'Search Atlas' button. This will query genetically predicted effects for your gene of interest on all phenome-wide outcomes from our atlas and plot them interactively based on subcategories⁷.

Citation

Integrative multiomics analysis highlights immune-cell regulatory mechanisms and shared genetic architecture for 14 immune-associated diseases and cancer outcomes by Claire Prince, Ruth E Mitchell and Tom G Richardson. American Journal of Human Genetics (2021) doi: https://doi.org/10.1016/j.ajhg.2021.10.003

About

For any queries please contact:

Tom.G.Richardson@bristol.ac.uk

References

1. The eQTLGen consortium

2. Chen et al, Cell, 167(5):1398-1414

3. Schmiedel et al, Cell, 175(6):1701-1715

4. Manhattanly R package

5. Giambartolomei et al, PLoS Genetics, 10(5):e1004383

6. ggplot2 R package

7. Highcharter R package