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
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脂肪组织中的细胞类型异质性与复杂的性状相关，并揭示了与疾病相关的细胞特异性 eQTL
DOI：
10.1101/283929
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Glastonbury C
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文献类型：
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作者：
Glastonbury C
脂肪组织是重要的内分泌器官，在许多心脏代谢疾病中起作用。它由可以差异性影响疾病表型的细胞类型的异质集合组成。细胞异质性也可以混淆组学分析，但在固体组织转录组分析中很少考虑。在这里，我们通过估计四种不同细胞类型（脂肪细胞，巨噬细胞，CD 4 + T细胞和微血管内皮细胞）的相对比例，研究了两个人群水平皮下脂肪组织RNA-seq数据集（TwinsUK，n=766和基因型组织表达项目[GTEx]，n=326）中的细胞类型异质性。我们在TwinsUK和GTEx脂肪数据集内和之间发现了显著的细胞异质性。我们发现脂肪细胞类型组成是可遗传的，并证实了脂肪驻留巨噬细胞比例与肥胖（高BMI）之间的正相关性，但我们发现与双能X射线吸收测定法（DXA）衍生的体脂分布特征有更强的BMI独立相关性。我们对脂肪组织细胞组成的影响进行了一系列标准分析，包括表型-基因表达关联、共表达网络和顺式eQTL发现。我们的研究结果表明，在共表达分析和差异表达分析中结合脂肪转录组数据集与肥胖相关性状时，考虑细胞类型组成至关重要。我们应用基因表达细胞类型比例互作模型（G × Cell），在20个基因中鉴定了26个细胞类型特异性表达数量性状位点（eQTL），其中包括4个自身免疫性疾病全基因组关联研究（GWAS）位点。这些结果鉴定了细胞特异性eQTL，并证明了大量组织的计算机解卷积鉴定细胞类型限制性调控变体的潜力。
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, andcis-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 ofin silicodeconvolution of bulk tissue to identify cell-type-restricted regulatory variants.