Milo2.0 unlocks population genetic analyses of cell state abundance using a count-based mixed model

Cell type proportions vary between individuals and are heritable, as demonstrated by statistical genetic analysis of flow cytometry data [1,2]. Higher-resolution cell states can be identified by single-cell RNA-sequencing, the scalability of which now makes it applicable to population-scale cohorts. However, the integration of statistical genetic analysis of cell states using cohort-scale single-cell data requires appropriate algorithms to account for and model the genetic relationships and complex batch-processing inherent to these studies. We describe Milo2.0, which enables the discovery of cell state quantitative trait loci (csQTL), scaling to millions of cells across hundreds of individuals. We identify > 500 csQTLs across peripheral blood immune states and investigate their relationship with the genetic regulation of gene expression. Moreover, we colocalise immune csQTLs with human traits and identify links between immune regulators, cell state abundance and immune-mediated disease.