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H-MAGMA

I applied H-MAGMA2 to the DecodeME3 GWAS-1 summary statistics. H-MAGMA operates identically to standard MAGMA4, except that variants are assigned to genes not using proximity, but according to the results of Hi-C chromatin interaction experiments performed on a variety of neural and glial cells.

Results

I used the 6 standard variant-to-gene assignment maps provided by the authors of H-MAGMA. Like in the other MAGMA analyses, I used the European 1000-genomes linkage disequilibrium reference. The Manhattan plots below illustrate the gene-level MAGMA results.

Adult Brain

Fetal Brain

Cortical Neuron

Midbrain

IPSC-derived Astrocyte

IPSC-derived Neuron

Discussion

  • As would be expected, switching from MAGMA to H-MAGMA preserved the broad pattern of significance across the genome.
  • On the other hand, some of the individual significant genes change. For instance, BTN1A1 is significant in a number of the H-MAGMA analyses, but was not significant in the original MAGMA analysis1.

  1. The authors of H-MAGMA included non-protein-coding RNA transcripts in their annotation file, in addition to protein-coding genes. This results in around 54k gene-like entities, and a Bonferoni threshold of \(0.05/54000 \approx 9 \times 10^{-7}\). The significance threshold differs slightly between analyses because the number of annotated genes differs, which in turn affects the Bonferroni correction. ↩

  2. Nancy YA Sey, Benxia Hu, Won Mah, Harper Fauni, Jessica Caitlin McAfee, Prashanth Rajarajan, Kristen J Brennand, Schahram Akbarian, and Hyejung Won. A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles. Nature Neuroscience, 23(4):583–593, 2020. URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC7131892/. ↩

  3. Genetics Delivery Team, Thibaud Boutin, Andrew D Bretherick, Joshua J Dibble, Esther Ewaoluwagbemiga, Emma Northwood, Gemma L Samms, Veronique Vitart, Project, Cohort Delivery Team, Øyvind Almelid, and others. Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome. medRxiv, pages 2025–08, 2025. URL: https://www.medrxiv.org/content/10.1101/2025.08.06.25333109v1. ↩

  4. 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. ↩