Skip to content

MAGMA

I applied MAGMA1 to the Neale lab GWAS of self-reported CFS in the UK Biobank. I used GTEx bulk RNAseq reference data2. Since this study's phenotype is based on a single-question self-report, it is less precise than the detailed phenotyping of DecodeME. Nevertheless, it is still interesting and worthwhile to analyze this data.

Results are plotted below:

Results of applying MAGMA to the Neale Lab GWAS of self-reported CFS in UK Biobank. y-axis is negative log of p value. x-axis corresponds to GTEx tissue type.

As might be expected given the low case number and noisy phenotype, there are no significant tissues. Nevertheless, it is interesting to observe that the tissues with the lowest p-values (brain_cortex, etc) roughly correspond to the significant tissues found when MAGMA was run on DecodeME

  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. 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. ↩