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MAGMA (GTEx)

I applied MAGMA1 to the DECODE meta-GWAS of seropositive rheumatoid arthritis2.

MAGMA Gene Analysis

I used MAGMA's SNP-wise mean model to run gene-level analysis.

In this step:

  • Data from the 1000 genomes projects was downloaded from the MAGMA website and used as a linkage disequilibrium reference.
  • Build 151 of dbSNP was used to assign RSIDs to SNPs.
  • Magma's default proximity-based rules were used to assign SNPs to genes.

The plot below shows the results of MAGMA gene-level analysis

It is interesting to note the p-value spike in the HLA region of chromosome 6, consistent with immune pathogenesis.

MAGMA Gene Property Analysis

I next applied MAGMA's gene property analysis module to seropositive RA. This step combined the gene analysis results above with tissue-specific RNA expression values from the GTEx project3.

The results are plotted below:

In this plot, the y-axis corresponds to \(-\log_{10}(p)\) values, the x-axis corresponds to tissue type (only tissues with the smallest p values are shown), and bars are colored according to whether their p value meets the Bonferroni-corrected significance threshold,

The tissue types Whole blood, Spleen, and EBV-transformed lymphocytes make sense, given that RA is an immune-related condition. The other tissues are less clear. The presence of lung tissue can be explained by the observation that post-mortem GTEx lung samples may contain blood 4. The gut-related tissue could reflect presence of important immune-related cells in the gut.

  1. Christiaan A De Leeuw, Joris M Mooij, Tom Heskes, and Danielle Posthuma. MAGMA: generalized gene-set analysis of GWAS data. PLoS Computational Biology, 11(4):e1004219, 2015. URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004219. ↩

  2. Saedis Saevarsdottir, Lilja Stefansdottir, Patrick Sulem, Gudmar Thorleifsson, Egil Ferkingstad, Gudrun Rutsdottir, Bente Glintborg, Helga Westerlind, Gerdur Grondal, Isabella C Loft, and others. Multiomics analysis of rheumatoid arthritis yields sequence variants that have large effects on risk of the seropositive subset. Annals of the rheumatic diseases, 81(8):1085–1095, 2022. URL: https://www.sciencedirect.com/science/article/pii/S0003496724209766. ↩

  3. GTEx Consortium. The GTEx consortium atlas of genetic regulatory effects across human tissues. Science, 369(6509):1318–1330, 2020. URL: https://www.science.org/doi/full/10.1126/science.aaz1776. ↩

  4. Hilary K Finucane, Yakir A Reshef, Verneri Anttila, Kamil Slowikowski, Alexander Gusev, Andrea Byrnes, Steven Gazal, Po-Ru Loh, Caleb Lareau, Noam Shoresh, and others. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nature Genetics, 50(4):621–629, 2018. URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC5896795/. ↩