This paper develops and benchmarks experimental and computational approaches for allele-specific expression (ASE) analysis from single-nucleus RNA-seq—leveraging intronic reads, read length, and hybrid selection—and demonstrates that ASE has greater power than eQTL analysis in a Parkinson's disease…

By improving cell-type–resolved detection of regulatory effects of genetic variants in PD, these methods help prioritize causal genes/variants and refine target and biomarker discovery pipelines, increasing translational value even though the work is primarily methodological.

Combining allele-specific expression (ASE) analysis with single-cell RNA-seq can elucidate how genomic variation affects RNA expression at the single-cell level. We explore how experimental and computational choices impact the power of ASE-based methods and develop a suite of single-cell ASE computational tools. With single-nucleus RNA-Seq, we extract more ASE information from reads in intronic than exonic regions. We show how read length can increase power and that hybrid selection improves power to detect ASE in targeted genes. We apply our methods to a Parkinson's disease cohort and show that ASE analysis has more power than eQTL analysis.