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EPIC: Inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing

More than a decade of genome-wide association studies (GWASs) have identified genetic risk variants that are significantly associated with complex traits. Emerging evidence suggests that the function of trait-associated variants likely acts in a tissue- or cell-type-specific fashion. Yet, it remains...

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Detalles Bibliográficos
Autores principales: Wang, Rujin, Lin, Dan-Yu, Jiang, Yuchao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9242467/
https://www.ncbi.nlm.nih.gov/pubmed/35709291
http://dx.doi.org/10.1371/journal.pgen.1010251
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author Wang, Rujin
Lin, Dan-Yu
Jiang, Yuchao
author_facet Wang, Rujin
Lin, Dan-Yu
Jiang, Yuchao
author_sort Wang, Rujin
collection PubMed
description More than a decade of genome-wide association studies (GWASs) have identified genetic risk variants that are significantly associated with complex traits. Emerging evidence suggests that the function of trait-associated variants likely acts in a tissue- or cell-type-specific fashion. Yet, it remains challenging to prioritize trait-relevant tissues or cell types to elucidate disease etiology. Here, we present EPIC (cEll tyPe enrIChment), a statistical framework that relates large-scale GWAS summary statistics to cell-type-specific gene expression measurements from single-cell RNA sequencing (scRNA-seq). We derive powerful gene-level test statistics for common and rare variants, separately and jointly, and adopt generalized least squares to prioritize trait-relevant cell types while accounting for the correlation structures both within and between genes. Using enrichment of loci associated with four lipid traits in the liver and enrichment of loci associated with three neurological disorders in the brain as ground truths, we show that EPIC outperforms existing methods. We apply our framework to multiple scRNA-seq datasets from different platforms and identify cell types underlying type 2 diabetes and schizophrenia. The enrichment is replicated using independent GWAS and scRNA-seq datasets and further validated using PubMed search and existing bulk case-control testing results.
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spelling pubmed-92424672022-06-30 EPIC: Inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing Wang, Rujin Lin, Dan-Yu Jiang, Yuchao PLoS Genet Research Article More than a decade of genome-wide association studies (GWASs) have identified genetic risk variants that are significantly associated with complex traits. Emerging evidence suggests that the function of trait-associated variants likely acts in a tissue- or cell-type-specific fashion. Yet, it remains challenging to prioritize trait-relevant tissues or cell types to elucidate disease etiology. Here, we present EPIC (cEll tyPe enrIChment), a statistical framework that relates large-scale GWAS summary statistics to cell-type-specific gene expression measurements from single-cell RNA sequencing (scRNA-seq). We derive powerful gene-level test statistics for common and rare variants, separately and jointly, and adopt generalized least squares to prioritize trait-relevant cell types while accounting for the correlation structures both within and between genes. Using enrichment of loci associated with four lipid traits in the liver and enrichment of loci associated with three neurological disorders in the brain as ground truths, we show that EPIC outperforms existing methods. We apply our framework to multiple scRNA-seq datasets from different platforms and identify cell types underlying type 2 diabetes and schizophrenia. The enrichment is replicated using independent GWAS and scRNA-seq datasets and further validated using PubMed search and existing bulk case-control testing results. Public Library of Science 2022-06-16 /pmc/articles/PMC9242467/ /pubmed/35709291 http://dx.doi.org/10.1371/journal.pgen.1010251 Text en © 2022 Wang et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wang, Rujin
Lin, Dan-Yu
Jiang, Yuchao
EPIC: Inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing
title EPIC: Inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing
title_full EPIC: Inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing
title_fullStr EPIC: Inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing
title_full_unstemmed EPIC: Inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing
title_short EPIC: Inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing
title_sort epic: inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell rna sequencing
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9242467/
https://www.ncbi.nlm.nih.gov/pubmed/35709291
http://dx.doi.org/10.1371/journal.pgen.1010251
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