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MAGMA (GTEx)

I applied MAGMA1 to the DECODE meta-GWAS of seronegative 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. Thus even though S-LDSC failed to identify heritability enrichment in immune cells, gene-level analysis does point to immune etiology.

Contrasting the above graph with the corresponding one for seropositive RA we observe both conditions produce spikes in the HLA region of chromosome 6, but the seropositive RA Manhattan plot has more significant genes outside of chromosome 6, and so is less concentrated at that locus.

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 Bonferroni-corrected significance threshold.

Interestingly, and consistent with the earlier S-LDSC analysis, no tissue types are significant. This may be a consequence of the lack of strong signal outside of the HLA region, in contrast to seropositive RA.

  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. ↩