RT Journal Article SR Electronic T1 Inferring relevant cell types for complex traits using single-cell gene expression JF bioRxiv FD Cold Spring Harbor Laboratory SP 136283 DO 10.1101/136283 A1 Diego Calderon A1 Anand Bhaskar A1 David A. Knowles A1 David Golan A1 Towfique Raj A1 Audrey Q. Fu A1 Jonathan K. Pritchard YR 2017 UL http://biorxiv.org/content/early/2017/05/10/136283.abstract AB Previous studies have prioritized trait-relevant cell types by looking for an enrichment of GWAS signal within functional regions. However, these studies are limited in cell resolution by the lack of functional annotations from difficult-to-characterize or rare cell populations. Measurement of single-cell gene expression has become a popular method for characterizing novel cell types, and yet, hardly any work exists linking single-cell RNA-seq to phenotypes of interest. To address this deficiency, we present RolyPoly, a regression-based polygenic model that can prioritize trait-relevant cell types and genes from GWAS summary statistics and single-cell RNA-seq. We demonstrate RolyPoly’s accuracy through simulation and validate previously known tissue-trait associations. We discover a significant association between microglia and late-onset Alzheimer’s disease, and an association between oligodendrocytes and replicating fetal cortical cells with schizophrenia. Additionally, RolyPoly computes a trait-relevance score for each gene which reflects the importance of expression specific to a cell type. We found that differentially expressed genes in the prefrontal cortex of Alzheimer’s patients were significantly enriched for highly ranked genes by RolyPoly gene scores. Overall, our method represents a powerful framework for understanding the effect of common variants on cell types contributing to complex traits.