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MAGMA HBA Analysis

I applied MAGMA2 to the lupus GWAS of Bentham et al.3 using scRNAseq data from the Human Brain Atlas4 (HBA) as a reference.

Results

The results are plotted below:

Result of HBA MAGMA applied Bentham et al.'s Lupus GWAS. The x-axis corresponds to HBA cluster number, while the y-axis corresponds to the negative log p value generated by MAGMA. Clusters are colored according to their HBA supercluster. The dotted line denotes the Bonferroni significance threshold.

I also used a conditional analysis approach based on the one described in Wanatabe et al.5 to identify independent clusters. The two independent clusters are listed in table below and labeled in the subsequent plot.

Retained_clusters P Supercluster Class auto-annotation Neurotransmitter auto-annotation Neuropeptide auto-annotation Subtype auto-annotation Transferred MTG Label Top three regions Top Enriched Genes
Cluster0 2.4048e-11 Miscellaneous BCELL 0 0 0 Midbrain: 21.0%, Basal forebrain: 19.0%, Pons: 14.3% IGHM, MS4A1, FCRL1, AC244205.1, IGLL5, IGHA1, BLK, IGLC3, IGLC2, IKZF3
Cluster7 4.7581e-07 Microglia MGL 0 0 0 Micro-PVM Basal forebrain: 32.0%, Midbrain: 19.4%, Pons: 12.5% CD74, CX3CR1, APBB1IP, HLA-DRA, LNCAROD, C3, ITGAX, FYB1, DOCK8, PTPRC

lupus_hba_magma_indep_clusters

The two independent clusters are a B-cell cluster and a microglial cluster.

  • Lupus is well known to be an immunoglobulin-driven disease. Since B-cells produce immunoglobulin, it is highly reasonable that the most strongly associated MAGMA cell type should be a group of B-cells1.
  • There has also been some research suggesting a role of microglial activation in lupus6. Thus it is possible that the microglial signal indicates the real importance of microglia to the pathogenesis of lupus. On the other hand, it is possible that the signal is a artifact of the similarity between microglial gene expression programs and the gene expression programs of other immune cells that truly drive lupus.

  1. See Janeway's Immunobiology7 Chapter 15. ↩

  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. James Bentham, David L Morris, Deborah S Cunninghame Graham, Christopher L Pinder, Philip Tombleson, Timothy W Behrens, Javier Martín, Benjamin P Fairfax, Julian C Knight, Lingyan Chen, and others. Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus. Nature Genetics, 47(12):1457–1464, 2015. URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC4668589/. ↩

  4. Kimberly Siletti, Rebecca Hodge, Alejandro Mossi Albiach, Ka Wai Lee, Song-Lin Ding, Lijuan Hu, Peter Lönnerberg, Trygve Bakken, Tamara Casper, Michael Clark, and others. Transcriptomic diversity of cell types across the adult human brain. Science, 382(6667):eadd7046, 2023. URL: https://www.science.org/doi/abs/10.1126/science.add7046. ↩

  5. Kyoko Watanabe, Maša Umićević Mirkov, Christiaan A de Leeuw, Martijn P van den Heuvel, and Danielle Posthuma. Genetic mapping of cell type specificity for complex traits. Nature Communications, 10(1):3222, 2019. URL: https://www.nature.com/articles/s41467-019-11181-1. ↩

  6. Erica Moore, Michelle W Huang, and Chaim Putterman. Advances in the diagnosis, pathogenesis and treatment of neuropsychiatric systemic lupus erythematosus. Current Opinion in Rheumatology, 32(2):152–158, 2020. ↩

  7. Kenneth Murphy, Casey Weaver, and Leslie Berg. Janeway's Immunobiology. Norton, 2022. URL: https://wwnorton.com/books/9780393884890. ↩