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MAGMA HBA Analysis

I applied MAGMA to the DecodeME GWAS1 using scRNAseq data from the Human Brain Atlas2 (HBA) as a reference.

Results

The results are plotted below:

decode-me-hba-magma

The x-axis corresponds to HBA cluster number2, while the y-axis corresponds to the \(-\log_{10}(p)\) score generated by MAGMA. Clusters are colored according to their HBA supercluster. The dotted line denotes the Bonferroni significance threshold. I used a conditional analysis approach based on the one described in Wanatabe et al.3 to identify independent clusters. These 3 independent clusters are labeled in plot. I have also listed them in the table below, together with some cluster-annotations from Duncan et al.4.

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
Cluster234 4.01e-06 Eccentric medium spiny neuron NEUR GABA CCK CHGA CHGB NAMPT NUCB PENK SCG UBL VGF proSAAS MSN-D1 Amygdala: 75.9%, Cerebral cortex: 14.6%, Thalamus: 5.4% NPFFR2, ZNF736P9Y, GABRQ, LMNTD1, EYA2, AC012078.2, AC087516.2, PCDH11Y, LINC00354, NMBR
Cluster419 8.2057e-06 Amygdala excitatory NEUR VGLUT1 VGLUT2 ADCYAP CART CCK CHGA CHGB NAMPT NUCB NXPH SCG UBL VGF proSAAS 0 Amygdala: 78.9%, Cerebral cortex: 14.0%, Thalamus: 5.1% AC025244.1, AC096759.1, SCN5A, FAM9B, GABRQ, LINC01920, VWA5B1, CYP19A1, CARM1P1, LINC02498
Cluster136 1.2765e-05 Deep-layer intratelencephalic NEUR VGLUT1 VGLUT2 ADCYAP CBLN CCK CHGA CHGB CRH NAMPT NUCB PYY SCG UBL UCN VGF proSAAS 0 Amygdala: 54.6%, Cerebral cortex: 36.2%, Hypothalamus: 8.6% AC099517.1, LINC02196, AC079380.1, LINC02465, AL138927.1, AC073578.2, AL450352.1, ARHGAP15, TNNT2, LINC02378

  1. 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. ↩

  2. 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. ↩↩

  3. 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. ↩

  4. Laramie E Duncan, Tayden Li, Madeleine Salem, Will Li, Leili Mortazavi, Hazal Senturk, Naghmeh Shahverdizadeh, Sam Vesuna, Hanyang Shen, Jong Yoon, and others. Mapping the cellular etiology of schizophrenia and complex brain phenotypes. Nature Neuroscience, 28(2):248–258, 2025. URL: https://www.nature.com/articles/s41593-024-01834-w. ↩