Deconvolution of bulk blood eQTL effects into immune cell subpopulations

Aguirre-Gamboa, Raúl; Klein, Niek de; Tommaso, Jennifer di; Claringbould, Annique; Võsa, Urmo; Zorro, Maria M.; Chu, Xiaojing; Bakker, Olivier B.; Borek, Z; Ricaño-Ponce, Isis; Deelen, Patrick; Xu, Cheng‐Jian; Swertz, Morris A.; Jonkers, Iris; Withoff, Sebo; Joosten, Irma; Kumar, Vinod; Koenen, Hans J. P. M.; Joosten, Leo A. B.; Netea, Mihai G.; Wijmenga, Cisca; Franke, Lude; Li, Y.

doi:10.1101/548669

Cited by 26 publications

(38 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Our downstream eQTL analysis in CBCS breast tumor expression also provided some insight into gene regulation, similar to recent work into deconvolving immune subpopulation eQTL signals from bulk blood eQTLs (101). In breast cancer, Geeleher et al previously showed that a similarly implemented interaction eQTL model gave better mapping of compartment-specific eQTLs (8,9).…”

Section: Unlike Traditional Reference-based Methods That Require Compsupporting

confidence: 68%

“…We investigated how incorporating estimated compartment proportions affect cis-expression quantitative trait loci (cis-eQTL) mapping in breast tumors, a common application of deconvolution methods in assessing sources of variation in gene regulation (9,101). In previous eQTL studies using CBCS expression, several bulk breast tumor cis-eGenes (i.e.…”

Section: Incorporating Compartment Proportions Into Eqtl Models Detecmentioning

confidence: 99%

See 1 more Smart Citation

DeCompress: tissue compartment deconvolution of targeted mRNA expression panels using compressed sensing

Bhattacharya

Hamilton

Troester

et al. 2020

Preprint

View full text Add to dashboard Cite

Targeted mRNA expression panels, measuring up to 800 genes, are used in academic and clinical settings due to low cost and high sensitivity for archived samples. Most samples assayed on targeted panels originate from bulk tissue comprised of many cell types, and cell-type heterogeneity confounds biological signals. Reference-free methods are used when cell-type-specific expression references are unavailable, but limited feature spaces render implementation challenging in targeted panels. Here, we present DeCompress, a semi-reference-free deconvolution method for targeted panels. DeCompress leverages a reference RNA-seq or microarray dataset from similar tissue to expand the feature space of targeted panels using compressed sensing. Ensemble reference-free deconvolution is performed on this artificially expanded dataset to estimate cell-type proportions and gene signatures. In simulated mixtures, four public cell line mixtures, and a targeted panel (1199 samples; 406 genes) from the Carolina Breast Cancer Study, DeCompress recapitulates cell-type proportions with less error than reference-free methods and finds biologically relevant compartments. We integrate compartment estimates into cis-eQTL mapping in breast cancer, identifying a tumor-specific cis-eQTL for CCR3 (C-C Motif Chemokine Receptor 3) at a risk locus. DeCompress improves upon reference-free methods without requiring expression profiles from pure cell populations, with applications in genomic analyses and clinical settings.

show abstract

Section: Unlike Traditional Reference-based Methods That Require Compsupporting

confidence: 68%

Section: Incorporating Compartment Proportions Into Eqtl Models Detecmentioning

confidence: 99%

DeCompress: tissue compartment deconvolution of targeted mRNA expression panels using compressed sensing

Bhattacharya

Hamilton

Troester

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Technical covariates significantly associated with at least one PC (sequencing batch, RIN, age) were included in the model. In addition, because blood cell type composition can impact gene expression estimates in bulk RNA samples, we used DeconCell v. 0.1.0 23 to estimate the proportion of CD8T, CD4T, NK, B cells, monocytes and granulocytes in each sample (Supplementary table 2), and tested these for association with the first 10 PCs as described above. All covariates were significantly associated with at least one PC and were included in the differential expression model.…”

Section: Methodsmentioning

confidence: 99%

Genome-wide DNA methylation and gene expression patterns reflect genetic ancestry and environmental differences across the Indonesian archipelago

Natri

Bobowik

Kusuma

et al. 2019

Preprint

View full text Add to dashboard Cite

34Indonesia is the world's fourth most populous country, host to striking levels of human diversity, regional 35 patterns of admixture, and varying degrees of introgression from both Neanderthals and Denisovans. 36 However, it has been largely excluded from the human genomics sequencing boom of the last decade. 37 To serve as a benchmark dataset of molecular phenotypes across the region, we generated genome-wide 38 CpG methylation and gene expression measurements in over 100 individuals from three locations that 39 capture the major genomic and geographical axes of diversity across the Indonesian archipelago. 40Investigating between-and within-island differences, we find up to 10% of tested genes are differentially 41 expressed between the islands of Mentawai (Sumatra) and New Guinea. Variation in gene expression is 42 closely associated with DNA methylation, with expression levels of 9.7% of genes strongly correlating 43 with nearby CpG methylation, and many of these genes being differentially expressed between islands. 44Genes identified in our differential expression and methylation analyses are enriched in pathways 45 involved in immunity, highlighting Indonesia tropical role as a source of infectious disease diversity and 46 the strong selective pressures these diseases have exerted on humans. Finally, we identify robust within-47 island variation in DNA methylation and gene expression, likely driven by very local environmental 48 differences across sampling sites. Together, these results strongly suggest complex relationships between 49 DNA methylation, transcription, archaic hominin introgression and immunity, all jointly shaped by the 50 environment. This has implications for the application of genomic medicine, both in critically 51 understudied Indonesia and globally, and will allow a better understanding of the interacting roles of 52 genomic and environmental factors shaping molecular and complex phenotypes. 53 54 55 56 57 58 59 60 61 Modern human genomics does not equitably represent the full breadth of humanity. While genome 62 sequences for people of European descent now number a million or more, most of the world is deeply 63 understudied 1 . This is particularly true of Indonesia 2 , a country geographically as large as continental 64 Europe and the world's fourth largest by population. Genomic diversity in Indonesia is strikingly 65 different to other well-characterized East Asian populations, such as Han Chinese and Japanese, but this 66 diversity is not captured in large global datasets like the 1000 Genomes Project 3 or the Simons Genome 67 Diversity Project 4 . The first Indonesian genome sequences were only reported in 2016 5 with the first 68 representative survey of diversity across the archipelago only appearing in 2019 6 . This extreme lack of 69 representation extends to molecular phenotypes. To our knowledge, only one genome-wide gene 70 expression study has been published 7 from the region, focused exclusively on host-pathogen interactions 71 with P. falciparum. There are...

show abstract

“…In order to simultaneously analyze two scales, the linear scale for heterogeneous cell mixing and the log/logit scale for trait effects, we applied nonlinear regression in omicwas (equations (4) and (5)). To cope with the multicollinearity of interaction terms, we applied ridge regularization (equations (9) and (10) The simulation data was generated from real datasets of DNA methylation and gene expression. The original cell type composition was retained for all samples, and the case-control status was randomly assigned.…”

Section: Evaluation In Simulated Datamentioning

confidence: 99%

Nonlinear Ridge Regression Improves Robustness of Cell-Type-Specific Differential Expression Studies

Takeuchi

Kato

2020

Preprint

View full text Add to dashboard Cite

Abstract Background: Epigenome-wide association studies (EWAS) and differential gene expression analyses are generally performed on tissue samples, which consist of multiple cell types. Cell-type-specific effects of a trait, such as disease, on the omics expression are of interest but difficult or costly to measure experimentally. By measuring omics data for the bulk tissue, cell type composition of a sample can be inferred statistically. Subsequently, cell-type-specific effects are estimated by linear regression that includes terms representing the interaction between the cell type proportions and the trait. This approach involves two issues, scaling and multicollinearity.Results: First, although cell composition is analyzed in linear scale, differential methylation/expression is analyzed suitably in the logit/log scale. To simultaneously analyze two scales, we developed nonlinear regression. Second, we show that the interaction terms are highly collinear, which is obstructive to ordinary regression. To cope with the multicollinearity, we applied ridge regularization. In simulated and real data, the improvement was modest by nonlinear regression and substantial by ridge regularization. Conclusion: Nonlinear ridge regression performed cell-type-specific association test on bulk omics data more robustly than previous methods. The omicwas package for R implements nonlinear ridge regression for cell-type-specific EWAS, differential gene expression and QTL analyses. The software is freely available from https://github.com/fumi-github/omicwas

show abstract

Deconvolution of bulk blood eQTL effects into immune cell subpopulations

Cited by 26 publications

References 41 publications

DeCompress: tissue compartment deconvolution of targeted mRNA expression panels using compressed sensing

DeCompress: tissue compartment deconvolution of targeted mRNA expression panels using compressed sensing

Genome-wide DNA methylation and gene expression patterns reflect genetic ancestry and environmental differences across the Indonesian archipelago

Nonlinear Ridge Regression Improves Robustness of Cell-Type-Specific Differential Expression Studies

Contact Info

Product

Resources

About