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Inferring relevant cell types for complex traits using single-cell gene expression

View ORCID ProfileDiego Calderon, Anand Bhaskar, View ORCID ProfileDavid A. Knowles, David Golan, Towfique Raj, View ORCID ProfileAudrey Q. Fu, View ORCID ProfileJonathan K. Pritchard
doi: https://doi.org/10.1101/136283
Diego Calderon
1Program in Biomedical Informatics, Stanford University, Stanford, CA, 94305, USA
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Anand Bhaskar
2Department of Genetics, Stanford University, Stanford, CA, 94305, USA
3Howard Hughes Medical Institute, Stanford University, Stanford, CA, 94305, USA
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David A. Knowles
2Department of Genetics, Stanford University, Stanford, CA, 94305, USA
4Department of Radiology, Stanford University, Stanford, CA, 94305, USA
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David Golan
5Faculty of Industrial Engineering & Management, Technion, Haifa, Israel
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Towfique Raj
6Department of Neuroscience, Mount Sinai School of Medicine, New York, NY, 10029, USA
7Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY, 10029, USA
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Audrey Q. Fu
8Department of Statistical Science, University of Idaho, Moscow, ID, 83844, USA
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Jonathan K. Pritchard
2Department of Genetics, Stanford University, Stanford, CA, 94305, USA
3Howard Hughes Medical Institute, Stanford University, Stanford, CA, 94305, USA
9Department of Biology, Stanford University, Stanford, CA, 94305, USA
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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 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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 10, 2017.
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Inferring relevant cell types for complex traits using single-cell gene expression
Diego Calderon, Anand Bhaskar, David A. Knowles, David Golan, Towfique Raj, Audrey Q. Fu, Jonathan K. Pritchard
bioRxiv 136283; doi: https://doi.org/10.1101/136283
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Inferring relevant cell types for complex traits using single-cell gene expression
Diego Calderon, Anand Bhaskar, David A. Knowles, David Golan, Towfique Raj, Audrey Q. Fu, Jonathan K. Pritchard
bioRxiv 136283; doi: https://doi.org/10.1101/136283

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