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MAGMA GTEx (Pulit et al, BMI)

I applied MAGMA2 to the Pulit et al. meta-GWAS of BMI3.

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.
  • Magma's default proximity-based rules were used to assign SNPs to genes.

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

MAGMA Gene Property Analysis

I next applied MAGMA's gene property analysis module to the BMI GWAS. This step combines the gene analysis results above with tissue-specific RNA expression values from the GTEx project4. The resulting per-tissue p values are plotted below:

The strong enrichment of brain tissue is consistent the known importance of neural pathways in obesity1.

  1. See Yeo 20185 for a readable popular science account of the genetics of eating. ↩

  2. 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. ↩

  3. Sara L Pulit, Charli Stoneman, Andrew P Morris, Andrew R Wood, Craig A Glastonbury, Jessica Tyrrell, Loïc Yengo, Teresa Ferreira, Eirini Marouli, Yingjie Ji, and others. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of european ancestry. Human molecular genetics, 28(1):166–174, 2019. URL: https://academic.oup.com/hmg/article/28/1/166/5098227. ↩

  4. 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. ↩

  5. Giles Yeo. Gene eating: the story of human appetite. Orion Spring, 2018. URL: https://a.co/d/0bxNrVJZ. ↩