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Cell-type heterogeneity in adipose tissue is associated with complex traits and reveals disease-relevant cell-specific eQTLs

Cell-type heterogeneity in adipose tissue is associated with complex traits and reveals disease-relevant cell-specific eQTLs
Cell-type heterogeneity in adipose tissue is associated with complex traits and reveals disease-relevant cell-specific eQTLs
Adipose tissue is an important endocrine organ with a role in many cardiometabolic diseases. It is comprised of a heterogeneous collection of cell types that can differentially impact disease phenotypes. Cellular heterogeneity can also confound -omic analyses but is rarely taken into account in analysis of solid-tissue transcriptomes. Here, we investigate cell-type heterogeneity in two population-level subcutaneous adipose-tissue RNA-seq datasets (TwinsUK, n = 766 and the Genotype-Tissue Expression project [GTEx], n = 326) by estimating the relative proportions of four distinct cell types (adipocytes, macrophages, CD4+ T cells, and micro-vascular endothelial cells). We find significant cellular heterogeneity within and between the TwinsUK and GTEx adipose datasets. We find that adipose cell-type composition is heritable and confirm the positive association between adipose-resident macrophage proportion and obesity (high BMI), but we find a stronger BMI-independent association with dual-energy X-ray absorptiometry (DXA) derived body-fat distribution traits. We benchmark the impact of adipose-tissue cell composition on a range of standard analyses, including phenotype-gene expression association, co-expression networks, and cis-eQTL discovery. Our results indicate that it is critical to account for cell-type composition when combining adipose transcriptome datasets in co-expression analysis and in differential expression analysis with obesity-related traits. We applied gene expression by cell-type proportion interaction models (G × Cell) to identify 26 cell-type-specific expression quantitative trait loci (eQTLs) in 20 genes, including four autoimmune disease genome-wide association study (GWAS) loci. These results identify cell-specific eQTLs and demonstrate the potential of in silico deconvolution of bulk tissue to identify cell-type-restricted regulatory variants.
0002-9297
103 - 1024
Glastonbury, C.A.
56e68d76-6f47-4999-a0b5-df87f4266b38
Couto Alves, A.
87b9179e-abde-4ca5-abfc-4b7c5ac8b03b
El-Sayed Moustafa, J.S.
7f9041cf-1d54-48d2-b593-167fd43bf0a0
Small, K.S.
9d852d98-b93e-4c2b-ba74-1649db3689c3
Glastonbury, C.A.
56e68d76-6f47-4999-a0b5-df87f4266b38
Couto Alves, A.
87b9179e-abde-4ca5-abfc-4b7c5ac8b03b
El-Sayed Moustafa, J.S.
7f9041cf-1d54-48d2-b593-167fd43bf0a0
Small, K.S.
9d852d98-b93e-4c2b-ba74-1649db3689c3

Glastonbury, C.A., Couto Alves, A., El-Sayed Moustafa, J.S. and Small, K.S. (2019) Cell-type heterogeneity in adipose tissue is associated with complex traits and reveals disease-relevant cell-specific eQTLs. American Journal of Human Genetics, 103 - 1024. (doi:10.1016/j.ajhg.2019.03.025).

Record type: Article

Abstract

Adipose tissue is an important endocrine organ with a role in many cardiometabolic diseases. It is comprised of a heterogeneous collection of cell types that can differentially impact disease phenotypes. Cellular heterogeneity can also confound -omic analyses but is rarely taken into account in analysis of solid-tissue transcriptomes. Here, we investigate cell-type heterogeneity in two population-level subcutaneous adipose-tissue RNA-seq datasets (TwinsUK, n = 766 and the Genotype-Tissue Expression project [GTEx], n = 326) by estimating the relative proportions of four distinct cell types (adipocytes, macrophages, CD4+ T cells, and micro-vascular endothelial cells). We find significant cellular heterogeneity within and between the TwinsUK and GTEx adipose datasets. We find that adipose cell-type composition is heritable and confirm the positive association between adipose-resident macrophage proportion and obesity (high BMI), but we find a stronger BMI-independent association with dual-energy X-ray absorptiometry (DXA) derived body-fat distribution traits. We benchmark the impact of adipose-tissue cell composition on a range of standard analyses, including phenotype-gene expression association, co-expression networks, and cis-eQTL discovery. Our results indicate that it is critical to account for cell-type composition when combining adipose transcriptome datasets in co-expression analysis and in differential expression analysis with obesity-related traits. We applied gene expression by cell-type proportion interaction models (G × Cell) to identify 26 cell-type-specific expression quantitative trait loci (eQTLs) in 20 genes, including four autoimmune disease genome-wide association study (GWAS) loci. These results identify cell-specific eQTLs and demonstrate the potential of in silico deconvolution of bulk tissue to identify cell-type-restricted regulatory variants.

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Published date: 6 June 2019

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Local EPrints ID: 494831
URI: http://eprints.soton.ac.uk/id/eprint/494831
ISSN: 0002-9297
PURE UUID: d0d174f8-4538-49ae-8757-66f3fddda1a8
ORCID for A. Couto Alves: ORCID iD orcid.org/0000-0001-8519-7356

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Date deposited: 16 Oct 2024 16:43
Last modified: 19 Oct 2024 02:14

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Contributors

Author: C.A. Glastonbury
Author: A. Couto Alves ORCID iD
Author: J.S. El-Sayed Moustafa
Author: K.S. Small

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