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MAGMA HBA Analysis

I applied MAGMA1 to the GWAS of Human Herpesvirus 7 of Kamitaki et al.2 using scRNAseq data from the Human Brain Atlas3 as a reference.

The results are plotted below:

Result of HBA MAGMA applied to Kamitaki et al.'s HHV7 DNA 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 Watanabe et al.4 to identify independent clusters. The two independent clusters are listed in the table below:

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
Cluster7 6.3343e-11 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
Cluster1 1.8631e-09 Miscellaneous TCELL 0 0 0 Midbrain: 15.0%, Basal forebrain: 14.0%, Pons: 13.2% CD2, IL7R, PTPRC, SLFN12L, IL32, CCL5, GRAP2, RUNX3, CD69, CD3E

HHV7 infections are highly prevalent. As is the case with EBV DNA levels, it is likely that HHV7 DNA levels are primarily a function of the extent to which a person's immune system is able to contain their infection, and maintain it in a dormant state. For this reason, it makes sense that T-cells are a key cell type determining HHV7 DNA levels.

The other independent cluster is a microglial cell type. The interpretation of this cluster is less clear. Rather than reflecting a true role for microglia in determining HHV7 DNA levels, it may reflect shared transcriptional programs between key innate immune cells and microglia.

Reproducing Analysis

To reproduce the above, run the HHV7 Analysis Script.

  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. Nolan Kamitaki, David Tang, Steven A McCarroll, and Po-Ru Loh. Genes and environment profoundly affect the human virome. bioRxiv, pages 2025–09, 2025. URL: https://www.biorxiv.org/content/10.1101/2025.09.08.674901. ↩

  3. 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. ↩

  4. 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. ↩