GeNA (Genotype-Neighborhood Associations) is a tool for identifying genetic variant associations to the abundance of cell states in single-cell datasets (cell state abundance quantitative trait loci, csaQTLs). In GeNA, we have adapted the framework we developed for Covarying Neighborhood Analysis in order to enable genome-wide csaQTL surveys in single-cell data. Instead of testing associations to predefined cell types, GeNA identifies the granular cell states whose abundance is most associated with genetic variants. The scripts required to run GeNA are stored in this repo.
We have evaluated GeNA in simulation to assess calibration and statistical power and we have applied GeNA in a genome-wide survey to scRNA-seq profiling from a cohort of 969 individuals. Scripts documenting our use of GeNA in these analyses for our manuscript are found in a separate repository, immunogenomics/GeNA-applied.
To use GeNA, you can clone this repository.
Dependencies:
- Python version 3.8.10
- R version 4.1.1
- PLINK version 2.00a2.3
- CNA version 0.1.6
- Rmpfr version 0.8-7
We illustrate how to use GeNA in a tutorial here. First, we demonstrate how to construct the single-cell data object format GeNA expects, then we summarize the arguments input to and files output from a call to GeNA. Finally, we illustrate basic characterization of example loci.
If you use GeNA in your work, you can cite our paper here
If you have questions about GeNA or require user support, please contact Laurie Rumker (Laurie_Rumker AT hms.harvard.edu) or post an issue on this repo.