The American Journal of Human GeneticsGeneticsPeer-reviewed journal article
Single-cell RNA-seq of mouse dopaminergic neurons informs candidate gene selection for sporadic Parkinson’s disease
Paul W. HookiD, Sarah A. McClymontiD, Gabrielle Heather CannoniD, William Law, A. Jennifer MortoniD, Loyal A. GoffiD, Andrew S. McCallioniD
Abstract
Parkinsons disease (PD) is caused by the collapse of substantia nigra (SN) dopaminergic (DA) neurons of the midbrain (MB), while other DA populations remain relatively intact. Common variation influencing susceptibility to sporadic PD has been primarily identified through genome wide association studies (GWAS). However, like many other common genetic diseases, the genes impacted by common PD-associated variation remain to be elucidated. Here, we used single-cell RNA-seq to characterize DA neuron populations in the mouse brain at embryonic and early postnatal timepoints. These data allow for the unbiased identification of DA neuron subpopulations, including a novel postnatal neuroblast population and SN DA neurons. Comparison of SN DA neurons with other DA neurons populations in the brain reveals a unique transcriptional profile, novel marker genes, and specific gene regulatory networks. By integrating these cell population specific data with published GWAS, we develop a scoring system for prioritizing candidate genes in PD-associated loci. With this, we prioritize candidate genes in all 32 GWAS intervals implicated in sporadic PD risk, the first such systematically generated list. From this we confirm that the prioritized candidate gene CPLX1 disrupts the nigrostriatal pathway when knocked out in mice. Ultimately, this systematic rationale leads to the identification of biologically pertinent candidates and testable hypotheses for sporadic PD that will inform a new era of PD genetic research.
- organism: mouse
- assay: scRNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: benchmarking
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 136
- FWCI
- 9.66
- Field percentile
- top 1%
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