Dataset Information

Estimation of Cell-Type Composition Including T and B Cell Subtypes for Whole Blood Methylation Microarray Data.

ABSTRACT: DNA methylation levels vary markedly by cell-type makeup of a sample. Understanding these differences and estimating the cell-type makeup of a sample is an important aspect of studying DNA methylation. DNA from leukocytes in whole blood is simple to obtain and pervasive in research. However, leukocytes contain many distinct cell types and subtypes. We propose a two-stage model that estimates the proportions of six main cell types in whole blood (CD4+ T cells, CD8+ T cells, monocytes, B cells, granulocytes, and natural killer cells) as well as subtypes of T and B cells. Unlike previous methods that only estimate overall proportions of CD4+ T cell, CD8+ T cells, and B cells, our model is able to estimate proportions of naïve, memory, and regulatory CD4+ T cells as well as naïve and memory CD8+ T cells and naïve and memory B cells. Using real and simulated data, we are able to demonstrate that our model is able to reliably estimate proportions of these cell types and subtypes. In studies with DNA methylation data from Illumina's HumanMethylation450k arrays, our estimates will be useful both for testing for associations of cell type and subtype composition with phenotypes of interest as well as for adjustment purposes to prevent confounding in epigenetic association studies. Additionally, our method can be easily adapted for use with whole genome bisulfite sequencing (WGBS) data or any other genome-wide methylation data platform.

SUBMITTER: Waite LL

PROVIDER: S-EPMC4757643 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Estimation of Cell-Type Composition Including T and B Cell Subtypes for Whole Blood Methylation Microarray Data.

Waite Lindsay L LL Weaver Benjamin B Day Kenneth K Li Xinrui X Roberts Kevin K Gibson Andrew W AW Edberg Jeffrey C JC Kimberly Robert P RP Absher Devin M DM Tiwari Hemant K HK

Frontiers in genetics 20160218

DNA methylation levels vary markedly by cell-type makeup of a sample. Understanding these differences and estimating the cell-type makeup of a sample is an important aspect of studying DNA methylation. DNA from leukocytes in whole blood is simple to obtain and pervasive in research. However, leukocytes contain many distinct cell types and subtypes. We propose a two-stage model that estimates the proportions of six main cell types in whole blood (CD4+ T cells, CD8+ T cells, monocytes, B cells, gr ...[more]

PMID: 26925097

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Project description:BackgroundPeripheral blood samples have been subjected to comprehensive gene expression profiling to identify biomarkers for a wide range of diseases. However, blood samples include red blood cells, white blood cells, and platelets. White blood cells comprise polymorphonuclear leukocytes, monocytes, and various types of lymphocytes. Blood is not distinguishable, irrespective of whether the expression profiles reflect alterations in (a) gene expression patterns in each cell type or (b) the proportion of cell types in blood. CD4+ Th cells are classified into two functionally distinct subclasses, namely Th1 and Th2 cells, on the basis of the unique characteristics of their secreted cytokines and their roles in the immune system. Th1 and Th2 cells play an important role not only in the pathogenesis of human inflammatory, allergic, and autoimmune diseases, but also in diseases that are not considered to be immune or inflammatory disorders. However, analyses of minor cellular components such as CD4+ cell subpopulations have not been performed, partly because of the limited number of these cells in collected samples.Methodology/principal findingsWe describe fluorescently activated cell sorting followed by microarray (FACS-array) technology as a useful experimental strategy for characterizing the expression profiles of specific immune cells in the circulation. We performed reproducible gene expression profiling of Th1 and Th2, respectively. Our data suggest that this procedure provides reliable information on the gene expression profiles of certain small immune cell populations. Moreover, our data suggest that GZMK, GZMH, EOMES, IGFBP3, and STOM may be novel markers for distinguishing Th1 cells from Th2 cells, whereas IL17RB and CNTNAP1 can be Th2-specific markers.Conclusions/significanceOur approach may help in identifying aberrations and novel therapeutic or diagnostic targets for diseases that affect Th1 or Th2 responses and elucidating the involvement of a subpopulation of immune cells in some diseases.

Project description:The determination of the relationship between a pair of individuals is a fundamental application of genetics. Previously, we and others have demonstrated that identity-by-descent (IBD) information generated from high-density single-nucleotide polymorphism (SNP) data can greatly improve the power and accuracy of genetic relationship detection. Whole-genome sequencing (WGS) marks the final step in increasing genetic marker density by assaying all single-nucleotide variants (SNVs), and thus has the potential to further improve relationship detection by enabling more accurate detection of IBD segments and more precise resolution of IBD segment boundaries. However, WGS introduces new complexities that must be addressed in order to achieve these improvements in relationship detection. To evaluate these complexities, we estimated genetic relationships from WGS data for 1490 known pairwise relationships among 258 individuals in 30 families along with 46 population samples as controls. We identified several genomic regions with excess pairwise IBD in both the pedigree and control datasets using three established IBD methods: GERMLINE, fastIBD, and ISCA. These spurious IBD segments produced a 10-fold increase in the rate of detected false-positive relationships among controls compared to high-density microarray datasets. To address this issue, we developed a new method to identify and mask genomic regions with excess IBD. This method, implemented in ERSA 2.0, fully resolved the inflated cryptic relationship detection rates while improving relationship estimation accuracy. ERSA 2.0 detected all 1(st) through 6(th) degree relationships, and 55% of 9(th) through 11(th) degree relationships in the 30 families. We estimate that WGS data provides a 5% to 15% increase in relationship detection power relative to high-density microarray data for distant relationships. Our results identify regions of the genome that are highly problematic for IBD mapping and introduce new software to accurately detect 1(st) through 9(th) degree relationships from whole-genome sequence data.

Dataset Information

Estimation of Cell-Type Composition Including T and B Cell Subtypes for Whole Blood Methylation Microarray Data.

Publications

Estimation of Cell-Type Composition Including T and B Cell Subtypes for Whole Blood Methylation Microarray Data.

Similar Datasets

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