Study Rationale:
Parkinson’s (PD) genome-wide association study (GWAS) with ~1.5 million subjects identified 90 PD risk signals with ~7,000 non-coding PD risk variants. Emerging evidences supported that these non-coding PD risk variants functionally regulate the expression of up- or down-stream target genes through specific brain cell type and region. By single cell expression quantitative trait (sn-eQTL) mapping of all data available on CRN cloud spanning ~1.96 million brain cells and nine brain regions, this study will identify gene expression regulation across cell types and brain regions for interpreting target genes of PD GWAS variants.
Hypothesis:
Parkinson’s disease (PD) genome-wide association study (GWAS) risk variants mediate disease risk by regulating the expression of up- or down-stream target genes through specific brain cell types and brain region.
Study Design:
This study will test how non-coding DNA mutations regulate gene expression across brain cell types and brain regions, by using generalized linear model and single cell data from ASAP CRN cloud to conduct the cell type meta eQTL analyses towards four brain regions which have sufficient subjects (N ≥ 60), including middle temporal gyrus (N =97), ACG (N = 64), IPL (N = 64) and substantia nigra (N = 141). This study then will construct a cell type- and brain region-gene regulation map by statistical integration and comparison of single cell eQTL across different cell types and brain regions.
Impact on Diagnosis/Treatment of Parkinson’s disease:
By constructing a cell type- and brain region-gene regulation map, this study will decode the PD GWAS functions across brain cell types and four brain regions, elucidates potential therapeutic targets, and provides a generally useful roadmap bridging GWAS to brain cell function for Parkinson’s disease.
Next Steps for Development:
The ultimate goal of this study is to construct a brain gene regulation map across brain cell types, brain cell spatial positions and brain regions. This study will refine the existing brain gene regulation map through the integration of newly accessible single-cell and spatial transcriptomic data from ASAP-CRN cloud.