medRxivGenetic & Genomic MedicinePreprint
Integrating dorsolateral prefrontal cortex multi-omics and GWAS summary data enhances gene-mapping of Parkinson's disease
Qiang LiuiD, Shinya TasakiiD, David A. A BennettiD, Nicholas T. SeyfriediD, Philip L. De JageriD, Vilas MenoniD, Aron S. BuchmaniD, Jingjing YangiD
Abstract
Although dorsolateral prefrontal cortex (DLPFC) is not the main site of the Parkinson diseases (PD) defining pathology, DLPFC is still functionally and structurally relevant as PD progresses - particularly for the cognitive/executive dimension of PD in later stages. To enhance the gene-mapping of PD, we integrated xQTL weights derived from bulk RNA-seq (n=931), single-nucleus RNA-seq (n=415), and bulk proteomics (n=716) data of DLPFC with the largest available GWAS summary data of PD in European population. Through integrative Omnibus TWAS and PWAS analyses, we detected risk genes associated with PD through genetically predicted bulk or cell-type-aware gene expression, or bulk protein abundances in DLPFC. We detected 39 significant risk genes by bulk TWAS, 66 unique genes by cell-type-aware TWAS across six brain cell types, and 17 by bulk PWAS. Importantly, 57.9% (11/19) bulk and 62.5% (20/32) cell-type-aware independent TWAS risk genes had PWAS locus support. Comparing these TWAS/PWAS findings in DLPFC to significant PD TWAS risk genes detected using the bulk RNA-seq data of substantia nigra from the GTEx V10 (n=183) and the same GWAS summary data of PD, 53.8% (21/39) bulk TWAS risk genes, 36.4% (24/66) unique cell-type-aware TWAS genes, and 17.6% (3/17) PWAS genes in DLPFC were also found significant in substantia nigra. Protein-protein interaction analyses indicated connectivity of our detected risk genes in DLPFC with established PD-associated genes such as MAPT, SNCA, and LRRC37A. Cell-type-aware TWAS risk genes were enriched in apoptosis signaling, and T-cell activation was enriched across the bulk TWAS, cell-type-aware TWAS, and PWAS results.
- organism: human
- assay: bulk RNA-seq
- assay: proteomics
- research focus: gene regulatory network inference
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 0
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