Detection of cell-type-specific risk-CpG sites in epigenome-wide association studies

CeDAR: incorporating cell type hierarchy improves cell type-specific differential analyses in bulk omics data

Bulk high-throughput omics data contain signals from a mixture of cell types. Recent developments of deconvolution methods facilitate cell type-specific inferences from bulk data. Our real data exploration suggests that differential expression or methylation status is often correlated among cell types. Based on this observation, we develop a novel statistical method named CeDAR to incorporate the cell type hierarchy in cell type-specific differential analyses of bulk data. Extensive simulation and real data analyses demonstrate that this approach significantly improves the accuracy and power in detecting cell type-specific differential signals compared with existing methods, especially in low-abundance cell types.

Nucleated red blood cells explain most of the association between DNA methylation and gestational age

Determining if specific cell type(s) are responsible for an association between DNA methylation (DNAm) and a given phenotype is important for understanding the biological mechanisms underlying the association. Our EWAS of gestational age (GA) in 953 newborns from the Norwegian MoBa study identified 13,660 CpGs significantly associated with GA (p_Bonferroni<0.05) after adjustment for cell type composition. When the CellDMC algorithm was applied to explore cell-type specific effects, 2,330 CpGs were significantly associated with GA, mostly in nucleated red blood cells [nRBCs; n = 2,030 (87%)]. Similar patterns were found in another dataset based on a different array and when applying an alternative algorithm to CellDMC called Tensor Composition Analysis (TCA). Our findings point to nRBCs as the main cell type driving the DNAm-GA association, implicating an epigenetic signature of erythropoiesis as a likely mechanism. They also explain the poor correlation observed between epigenetic age clocks for newborns and those for adults.

MiXcan: a framework for cell-type-aware transcriptome-wide association studies with an application to breast cancer

Human bulk tissue samples comprise multiple cell types with diverse roles in disease etiology. Conventional transcriptome-wide association study approaches predict genetically regulated gene expression at the tissue level, without considering cell-type heterogeneity, and test associations of predicted tissue-level expression with disease. Here we develop MiXcan, a cell-type-aware transcriptome-wide association study approach that predicts cell-type-level expression, identifies disease-associated genes via combination of cell-type-level association signals for multiple cell types, and provides insight into the disease-critical cell type. As a proof of concept, we conducted cell-type-aware analyses of breast cancer in 58,648 women and identified 12 transcriptome-wide significant genes using MiXcan compared with only eight genes using conventional approaches. Importantly, MiXcan identified genes with distinct associations in mammary epithelial versus stromal cells, including three new breast cancer susceptibility genes. These findings demonstrate that cell-type-aware transcriptome-wide analyses can reveal new insights into the genetic and cellular etiology of breast cancer and other diseases.

A comprehensive assessment of cell type-specific differential expression methods in bulk data

Accounting for cell type compositions has been very successful at analyzing high-throughput data from heterogeneous tissues. Differential gene expression analysis at cell type level is becoming increasingly popular, yielding biomarker discovery in a finer granularity within a particular cell type. Although several computational methods have been developed to identify cell type-specific differentially expressed genes (csDEG) from RNA-seq data, a systematic evaluation is yet to be performed. Here, we thoroughly benchmark six recently published methods: CellDMC, CARseq, TOAST, LRCDE, CeDAR and TCA, together with two classical methods, csSAM and DESeq2, for a comprehensive comparison. We aim to systematically evaluate the performance of popular csDEG detection methods and provide guidance to researchers. In simulation studies, we benchmark available methods under various scenarios of baseline expression levels, sample sizes, cell type compositions, expression level alterations, technical noises and biological dispersions. Real data analyses of three large datasets on inflammatory bowel disease, lung cancer and autism provide evaluation in both the gene level and the pathway level. We find that csDEG calling is strongly affected by effect size, baseline expression level and cell type compositions. Results imply that csDEG discovery is a challenging task itself, with room to improvements on handling low signal-to-noise ratio and low expression genes.

Identification of cell-type-specific spatially variable genes accounting for excess zeros

MOTIVATION

Spatial transcriptomic techniques can profile gene expressions while retaining the spatial information, thus offering unprecedented opportunities to explore the relationship between gene expression and spatial locations. The spatial relationship may vary across cell types, but there is a lack of statistical methods to identify cell-type-specific spatially variable (SV) genes by simultaneously modeling excess zeros and cell-type proportions.

RESULTS

We develop a statistical approach CTSV to detect cell-type-specific SV genes. CTSV directly models spatial raw count data and considers zero-inflation as well as overdispersion using a zero-inflated negative binomial distribution. It then incorporates cell-type proportions and spatial effect functions in the zero-inflated negative binomial regression framework. The R package pscl is employed to fit the model. For robustness, a Cauchy combination rule is applied to integrate P-values from multiple choices of spatial effect functions. Simulation studies show that CTSV not only outperforms competing methods at the aggregated level but also achieves more power at the cell-type level. By analyzing pancreatic ductal adenocarcinoma spatial transcriptomic data, SV genes identified by CTSV reveal biological insights at the cell-type level.

AVAILABILITY AND IMPLEMENTATION

The R package of CTSV is available at https://bioconductor.org/packages/devel/bioc/html/CTSV.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Cell-type heterogeneity: Why we should adjust for it in epigenome and biomarker studies

Most studies aiming to identify epigenetic biomarkers do so from complex tissues that are composed of many different cell-types. By definition, these cell-types vary substantially in terms of their epigenetic profiles. This cell-type specific variation among healthy cells is completely independent of the variation associated with disease, yet it dominates the epigenetic variability landscape. While cell-type composition of tissues can change in disease and this may provide accurate and reproducible biomarkers, not adjusting for the underlying cell-type heterogeneity may seriously limit the sensitivity and precision to detect disease-relevant biomarkers or hamper our understanding of such biomarkers. Given that computational and experimental tools for tackling cell-type heterogeneity are available, we here stress that future epigenetic biomarker studies should aim to provide estimates of underlying cell-type fractions for all samples in the study, and to identify biomarkers before and after adjustment for cell-type heterogeneity, in order to obtain a more complete and unbiased picture of the biomarker-landscape. This is critical, not only to improve reproducibility and for the eventual clinical application of such biomarkers, but importantly, to also improve our molecular understanding of disease itself.

The Effect of Model Directionality on Cell-Type-Specific Differential DNA Methylation Analysis

Calling differential methylation at a cell-type level from tissue-level bulk data is a fundamental challenge in genomics that has recently received more attention. These studies most often aim at identifying statistical associations rather than causal effects. However, existing methods typically make an implicit assumption about the direction of effects, and thus far, little to no attention has been given to the fact that this directionality assumption may not hold and can consequently affect statistical power and control for false positives. We demonstrate that misspecification of the model directionality can lead to a drastic decrease in performance and increase in risk of spurious findings in cell-type-specific differential methylation analysis, and we discuss the need to carefully consider model directionality before choosing a statistical method for analysis.

Testing cell-type-specific mediation effects in genome-wide epigenetic studies

Epigenome-wide mediation analysis aims to identify DNA methylation CpG sites that mediate the causal effects of genetic/environmental exposures on health outcomes. However, DNA methylations in the peripheral blood tissues are usually measured at the bulk level based on a heterogeneous population of white blood cells. Using the bulk level DNA methylation data in mediation analysis might cause confounding bias and reduce study power. Therefore, it is crucial to get fine-grained results by detecting mediation CpG sites in a cell-type-specific way. However, there is a lack of methods and software to achieve this goal. We propose a novel method (Mediation In a Cell-type-Specific fashion, MICS) to identify cell-type-specific mediation effects in genome-wide epigenetic studies using only the bulk-level DNA methylation data. MICS follows the standard mediation analysis paradigm and consists of three key steps. In step1, we assess the exposure-mediator association for each cell type; in step 2, we assess the mediator-outcome association for each cell type; in step 3, we combine the cell-type-specific exposure-mediator and mediator-outcome associations using a multiple testing procedure named MultiMed [Sampson JN, Boca SM, Moore SC, et al. FWER and FDR control when testing multiple mediators. Bioinformatics 2018;34:2418-24] to identify significant CpGs with cell-type-specific mediation effects. We conduct simulation studies to demonstrate that our method has correct FDR control. We also apply the MICS procedure to the Normative Aging Study and identify nine DNA methylation CpG sites in the lymphocytes that might mediate the effect of cigarette smoking on the lung function.

Cell type-aware analysis of RNA-seq data

Most tissue samples are composed of different cell types. Differential expression analysis without accounting for cell type composition cannot separate the changes due to cell type composition or cell type-specific expression. We propose a computational framework to address these limitations: Cell Type Aware analysis of RNA-seq (CARseq). CARseq employs a negative binomial distribution that appropriately models the count data from RNA-seq experiments. Simulation studies show that CARseq has substantially higher power than a linear model-based approach and it also provides more accurate estimate of the rankings of differentially expressed genes. We have applied CARseq to compare gene expression of schizophrenia/autism subjects versus controls, and identified the cell types underlying the difference and similarities of these two neuron-developmental diseases. Our results are consistent with the results from differential expression analysis using single cell RNA-seq data.

A cell-type deconvolution meta-analysis of whole blood EWAS reveals lineage-specific smoking-associated DNA methylation changes

Highly reproducible smoking-associated DNA methylation changes in whole blood have been reported by many Epigenome-Wide-Association Studies (EWAS). These epigenetic alterations could have important implications for understanding and predicting the risk of smoking-related diseases. To this end, it is important to establish if these DNA methylation changes happen in all blood cell subtypes or if they are cell-type specific. Here, we apply a cell-type deconvolution algorithm to identify cell-type specific DNA methylation signals in seven large EWAS. We find that most of the highly reproducible smoking-associated hypomethylation signatures are more prominent in the myeloid lineage. A meta-analysis further identifies a myeloid-specific smoking-associated hypermethylation signature enriched for DNase Hypersensitive Sites in acute myeloid leukemia. These results may guide the design of future smoking EWAS and have important implications for our understanding of how smoking affects immune-cell subtypes and how this may influence the risk of smoking related diseases.

Smoking-associated DNA methylation changes in whole blood have been reported by many EWAS. Here, the authors use a cell-type deconvolution algorithm to identify cell-type specific DNA methylation signals in seven EWAS, identifying lineage-specific smoking-associated DNA methylation changes.

Epigenetic Potential in Native and Introduced Populations of House Sparrows (Passer domesticus)

Epigenetic potential, defined as the capacity for epigenetically-mediated phenotypic plasticity, may play an important role during range expansions. During range expansions, populations may encounter relatively novel challenges while experiencing lower genetic diversity. Phenotypic plasticity via epigenetic potential might be selectively advantageous at the time of initial introduction or during spread into new areas, enabling introduced organisms to cope rapidly with novel challenges. Here, we asked whether one form of epigenetic potential (i.e., the abundance of CpG sites) in three microbial surveillance genes: Toll-like receptors (TLRs) 1B (TLR1B), 2A (TLR2A), and 4 (TLR4) varied between native and introduced house sparrows (Passer domesticus). Using an opportunistic approach based on samples collected from sparrow populations around the world, we found that introduced birds had more CpG sites in TLR2A and TLR4, but not TLR1B, than native ones. Introduced birds also lost more CpG sites in TLR1B, gained more CpG sites in TLR2A, and lost fewer CpG sites in TLR4 compared to native birds. These results were not driven by differences in genetic diversity or population genetic structure, and many CpG sites fell within predicted transcription factor binding sites (TFBS), with losses and gains of CpG sites altering predicted TFBS. Although we lacked statistical power to conduct the most rigorous possible analyses, these results suggest that epigenetic potential may play a role in house sparrow range expansions, but additional work will be critical to elucidating how epigenetic potential affects gene expression and hence phenotypic plasticity at the individual, population, and species levels.