Estimating the cis -heritability of gene expression using single cell expression profiles controls false positive rate of eGene detection.

Abstract

For gene expression traits, cis -genetic heritability can quantify the strength of genetic regulation in particular cell types, elucidating the cell-type-specificity of disease variants and genes. To estimate gene expression heritability, standard models require a single gene expression value per individual, forcing data from single cell RNA-sequencing (scRNA-seq) experiments to be "pseudobulked". Here, we show that applying standard heritability models to pseudobulk data overestimates gene expression heritability and produces inflated false positive rates for detecting cis -heritable genes. Therefore, we introduce a new method called scGeneHE ( s ingle c ell Gene expression H eritability E stimation), a Poisson mixed-effects model that quantifies the cis -genetic component of gene expression using individual cellular profiles. In simulations, scGeneHE has a consistently well-calibrated false positive rate for eGene detection and unbiasedly estimates cis -heritability at many parameter settings. We applied scGeneHE to scRNA-seq data from 969 individuals, 11 immune cell types, and 822,552 cells from the OneK1K cohort to infer cell-type-specificity of genetic regulation at risk genes for immune-mediated diseases and trace the fluctuation of cis -heritability across cellular populations of varying resolution. In summary, we developed a new statistical method that resolves the analytical challenge of estimating gene expression cis -heritability from native scRNA-seq data.