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bioRxiv · 10.1101/136283

Inferring Relevant Cell Types For Complex Traits Using Single-Cell Gene Expression

Calderon, D.·Bhaskar, A.·Knowles, D.·Golan, D.·Raj, T.·Fu, A.·Pritchard, J. K.
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Abstract

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 RolyPolys accuracy through simulation and validate previously known tissue-trait associations. We discover a significant association between microglia and late-onset Alzheimers 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 Alzheimers 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.

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Calderon, D., Bhaskar, A., Knowles, D., Golan, D., Raj, T., Fu, A., Pritchard, J. K.. 2017-05-10. Inferring Relevant Cell Types For Complex Traits Using Single-Cell Gene Expression. https://doi.org/10.1101/136283

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