Dataset Information

Deconvolution of bulk blood eQTL effects into immune cell subpopulations.

ABSTRACT:

Background

Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type-context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, current methods to do this are labor-intensive and expensive. We introduce a new method, Decon2, as a framework for estimating cell proportions using expression profiles from bulk blood samples (Decon-cell) followed by deconvolution of cell type eQTLs (Decon-eQTL).

Results

The estimated cell proportions from Decon-cell agree with experimental measurements across cohorts (R???0.77). Using Decon-cell, we could predict the proportions of 34 circulating cell types for 3194 samples from a population-based cohort. Next, we identified 16,362 whole-blood eQTLs and deconvoluted cell type interaction (CTi) eQTLs using the predicted cell proportions from Decon-cell. CTi eQTLs show excellent allelic directional concordance with eQTL (? 96-100%) and chromatin mark QTL (?87-92%) studies that used either purified cell subpopulations or single-cell RNA-seq, outperforming the conventional interaction effect.

Conclusions

Decon2 provides a method to detect cell type interaction effects from bulk blood eQTLs that is useful for pinpointing the most relevant cell type for a given complex disease. Decon2 is available as an R package and Java application (https://github.com/molgenis/systemsgenetics/tree/master/Decon2) and as a web tool (www.molgenis.org/deconvolution).

SUBMITTER: Aguirre-Gamboa R

PROVIDER: S-EPMC7291428 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Publications

Deconvolution of bulk blood eQTL effects into immune cell subpopulations.

Aguirre-Gamboa Raúl R de Klein Niek N di Tommaso Jennifer J Claringbould Annique A van der Wijst Monique Gp MG de Vries Dylan D Brugge Harm H Oelen Roy R Võsa Urmo U Zorro Maria M MM Chu Xiaojin X Bakker Olivier B OB Borek Zuzanna Z Ricaño-Ponce Isis I Deelen Patrick P Xu Cheng-Jiang CJ Swertz Morris M Jonkers Iris I Withoff Sebo S Joosten Irma I Sanna Serena S Kumar Vinod V Koenen Hans J P M HJPM Joosten Leo A B LAB Netea Mihai G MG Wijmenga Cisca C Franke Lude L Li Yang Y

BMC bioinformatics 20200612 1

<h4>Background</h4>Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type-context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, current methods to do this are labor-intensive and ...[more]

PMID: 32532224

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Project description:BackgroundTo facilitate the investigation of the pathogenic roles played by various immune cells in complex tissues such as tumors, a few computational methods for deconvoluting bulk gene expression profiles to predict cell composition have been created. However, available methods were usually developed along with a set of reference gene expression profiles consisting of imbalanced replicates across different cell types. Therefore, the objective of this study was to create a new deconvolution method equipped with a new set of reference gene expression profiles that incorporate more microarray replicates of the immune cells that have been frequently implicated in the poor prognosis of cancers, such as T helper cells, regulatory T cells and macrophage M1/M2 cells.MethodsOur deconvolution method was developed by choosing ε-support vector regression (ε-SVR) as the core algorithm assigned with a loss function subject to the L1-norm penalty. To construct the reference gene expression signature matrix for regression, a subset of differentially expressed genes were chosen from 148 microarray-based gene expression profiles for 9 types of immune cells by using ANOVA and minimizing condition number. Agreement analyses including mean absolute percentage errors and Bland-Altman plots were carried out to compare the performances of our method and CIBERSORT.ResultsIn silico cell mixtures, simulated bulk tissues, and real human samples with known immune-cell fractions were used as the test datasets for benchmarking. Our method outperformed CIBERSORT in the benchmarks using in silico breast tissue-immune cell mixtures in the proportions of 30:70 and 50:50, and in the benchmark using 164 human PBMC samples. Our results suggest that the performance of our method was at least comparable to that of a state-of-the-art tool, CIBERSORT.ConclusionsWe developed a new cell composition deconvolution method and the implementation was entirely based on the publicly available R and Python packages. In addition, we compiled a new set of reference gene expression profiles, which might allow for a more robust prediction of the immune cell fractions from the expression profiles of cell mixtures. The source code of our method could be downloaded from https://github.com/holiday01/deconvolution-to-estimate-immune-cell-subsets.

Dataset Information

Deconvolution of bulk blood eQTL effects into immune cell subpopulations.

Background

Results

Conclusions

Publications

Deconvolution of bulk blood eQTL effects into immune cell subpopulations.

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