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

TY - JOUR

T1 - Cell-type heterogeneity in adipose tissue is associated with complex traits and reveals disease-relevant cell-specific eQTLs

AU - Glastonbury, Craig A

AU - Couto Alves, Alexessander

AU - El-Sayed Moustafa, Julia S.

AU - Small, Kerrin S.

PY - 2019/6/6

Y1 - 2019/6/6

N2 - Adipose tissue is an important endocrine organ with a role in many cardiometabolic diseases. It is comprised of a heterogeneous collection of cell-types which 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 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 (BMI), but find a stronger BMI-independent association with 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 eQTLs in 20 genes, including 4 autoimmune disease 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.

AB - Adipose tissue is an important endocrine organ with a role in many cardiometabolic diseases. It is comprised of a heterogeneous collection of cell-types which 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 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 (BMI), but find a stronger BMI-independent association with 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 eQTLs in 20 genes, including 4 autoimmune disease 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.

KW - GTEx

KW - TwinsUK

KW - adipose

KW - cell type composition

KW - eQTL

KW - genetics

KW - genomics

KW - interactions

KW - obesity

KW - transcriptomics

UR - http://www.scopus.com/inward/record.url?scp=85066439845&partnerID=8YFLogxK

U2 - 10.1016/j.ajhg.2019.03.025

DO - 10.1016/j.ajhg.2019.03.025

M3 - Article

SN - 0002-9297

VL - 104

SP - 1013

EP - 1024

JO - American Journal of Human Genetics

JF - American Journal of Human Genetics

IS - 6

ER -