Dysregulation of mitochondrial and proteolysosomal genes in Parkinson’s disease myeloid cells

Navarro, Elisa; Udine, Evan; Lopes, Kátia de Paiva; Parks, Madison; Riboldi, Giulietta; Schilder, Brian M; Humphrey, Jack; Snijders, Gijsje; Vialle, Ricardo Assunção; Zhuang, Maojuan; Sikder, Tamjeed; Argyrou, Charalambos; Allan, Amanda; Chao, Michael J.; Farrell, Kurt; Henderson, Brooklyn; Simon, Sarah; Raymond, Deborah; Elango, Sonya; Ortega, Roberto A.; Shanker, Vicki; Swan, Matthew; Zhu, Carolyn W.; Ramdhani, Ritesh A.; Walker, Ruth H.; Tse, Winona; Sano, Mary; Pereira, Ana C.; Ahfeldt, Tim; Goate, Alison; Bressman, Susan; Crary, John F.; Witte, Lotje D. De; Frucht, Steven J.; Saunders‐Pullman, Rachel; Raj, Towfique

doi:10.1038/s43587-021-00110-x

Cited by 26 publications

(35 citation statements)

References 97 publications

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“…3 – 4 ). No colocalization was observed in any other QTL dataset, though we note that USP6NL is expressed five-fold higher in microglia than in monocytes (MiGA median TPM = 15.77; MyND 42 monocyte median TPM = 3.13). Fine-mapping of the ECHDC3 locus suggested three additional SNPs as well as the lead GWAS SNP (rs7920721) and lead QTL SNP (rs7912495).…”

Section: Resultsmentioning

confidence: 57%

“…5b ), with only a few known loci in AD and PD ( BIN1 , PICALM, CHRNB1 ), presumably due to lower power in the Young et al data compared to our multi-tissue meta-analysis. Overall, 11% of MiGA eQTL colocalizations could be reproduced in the Young et al data, and 15% of the Young et al colocalizations could be found in the MiGA data, at a relaxed PP4 > 0.5, whereas sharing between the two monocyte datasets 41 , 42 was 17–24% with the same parameters ( Supplementary Fig. 13 ).…”

Section: Resultsmentioning

confidence: 79%

“…To assess eQTL reproducibility and cell-type specificity, we compared MiGA eQTLs with four other external eQTL datasets, including microglia 26 , monocytes 41 , 42 , and bulk brain dorsolateral prefrontal cortex (DLPFC) 43 using the q-value π 1 metric 44 . We found that eQTL sharing was both cell type- and region-dependent ( Fig.…”

Section: Resultsmentioning

confidence: 99%

“…We performed a cross-study eQTL meta-analysis (MiGA, Young 26 , MyND 42 , and Fairfax 41 ) using METASOFT 45 ) to assess the sharing of effects between distinct cell types. We focused on genes associated with AD 23 , 46 through METASOFT’s m-value, which is the posterior probability that the effect exists in a particular study.…”

Section: Resultsmentioning

confidence: 99%

“…We focused on genes associated with AD 23 , 46 through METASOFT’s m-value, which is the posterior probability that the effect exists in a particular study. The comparison of m-values between microglia (MiGA) and monocytes (MyND 42 , and Fairfax 41 ) showed that a large number of eQTLs in AD loci have shared effects between these two cell types, for example, MS4A6A , RABEP1 , CD33 , FCER1G , and ABCA7 . However, there were eQTLs with specific microglial effects that were absent in monocytes (e.g., BIN1 , PICALM , USP6NL and GNGT2 ; Fig.…”

Section: Resultsmentioning

confidence: 99%

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Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies

et al. 2022

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Microglia have emerged as important players in brain aging and pathology. To understand how genetic risk for neurological and psychiatric disorders is related to microglial function, large transcriptome studies are essential. Here, we describe the transcriptome analysis of 255 primary human microglia samples isolated at autopsy from multiple brain regions of 100 human subjects. We performed systematic analyses to investigate various aspects of microglial heterogeneities, including brain region and aging. We mapped expression and splicing quantitative trait loci and showed that many neurological disease susceptibility loci are mediated through gene expression or splicing in microglia. Fine-mapping of these loci nominated candidate causal variants that are within microglia-specific enhancers, finding associations with microglia expression of USP6NL for Alzheimer’s disease and P2RY12 for Parkinson’s disease. We have built the most comprehensive catalog to date of genetic effects on the microglia transcriptome and propose candidate functional variants in neurological and psychiatric disorders.

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Section: Resultsmentioning

confidence: 57%

Section: Resultsmentioning

confidence: 79%