Sub-tissue type eQTL analysis of GTEx data Open Access
Yu, Qi (Spring 2020)
Background: Large-scale expression quantitative trait loci (eQTL) studies have been carried out recently to provide insights on how single-nucleotide polymorphisms associated with the expression of known genes. However, most of such studies ignored cell type mixing. Recent studies showed there are differences in gene expression patterns among different cell types. Thus tissue-specific eQTL studies with tissues of mixed cell type may suffer from false positives problems. In this work, we provide a new tool for sub- tissue type eQTL analysis with a recently published method (TOAST) for identifying cell- type specific effects.
Materials and Methods: Here we only consider the case of whole blood in GTEx project. We test both reference-based methods--CYBERSORTx and PRC with reference LM22 and a reference-free method--TOAST for deconvolution. We then conduct sub-tissue type specific eQTL analysis using TOAST.
Results: Deconvolution analysis show that there is a significant difference in the proportions of the six major cell types found in whole blood (CD4 T Cells, CD8 T Cells, B cells, Monocytes, Neutrophils and NK cells) across samples. Cell-type specific eQTL analysis on gene MARK4 with its significant associated SNPs in whole blood shows that eQTLs of MARK4 are more significant for Neutrophils than that for other cell types in the mixture.
Conclusion: We present a novel sub-tissue type eQTL analysis tool, can be applied to expression data measured from whole tissues provided knowledge of the reference cell types of the tissue is known. Our study reveals that eQTL analysis can be conducted at the sub-tissue type level when a reference is available.
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|Sub-tissue type eQTL analysis of GTEx data ()||2020-04-24 00:27:45 -0400||