Literature DB >> 32821946

Integrative analysis of scRNA-seq and GWAS data pinpoints periportal hepatocytes as the relevant liver cell types for blood lipids.

Xingjie Hao1, Kai Wang1, Chengguqiu Dai1, Zeyang Ding2, Wei Yang3, Chaolong Wang1,4, Shanshan Cheng1.   

Abstract

Liver, a heterogeneous tissue consisting of various cell types, is known to be relevant for blood lipid traits. By integrating summary statistics from genome-wide association studies (GWAS) of lipid traits and single-cell transcriptome data of the liver, we sought to identify specific cell types in the liver that were most relevant for blood lipid levels. We conducted differential expression analyses for 40 cell types from human and mouse livers in order to construct the cell-type specifically expressed gene sets, which we refer to as construction of the liver cell-type specifically expressed gene sets (CT-SEGS). Under the assumption that CT-SEGS represented specific functions of each cell type, we applied stratified linkage disequilibrium score regression to determine cell types that were most relevant for complex traits and diseases. We first confirmed the validity of this method (of delineating functionally relevant cell types) by identifying the immune cell types as relevant for autoimmune diseases. We further showed that lipid GWAS signals were enriched in the human and mouse periportal hepatocytes. Our results provide important information to facilitate future cellular studies of the metabolic mechanism affecting blood lipid levels.
© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Year:  2020        PMID: 32821946     DOI: 10.1093/hmg/ddaa188

Source DB:  PubMed          Journal:  Hum Mol Genet        ISSN: 0964-6906            Impact factor:   6.150


  1 in total

1.  Leveraging Single-Cell RNA-seq Data to Uncover the Association Between Cell Type and Chronic Liver Diseases.

Authors:  Xiangyu Ye; Julong Wei; Ming Yue; Yan Wang; Hongbo Chen; Yongfeng Zhang; Yifan Wang; Meiling Zhang; Peng Huang; Rongbin Yu
Journal:  Front Genet       Date:  2021-03-08       Impact factor: 4.599

  1 in total

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