Background Racial/ethnic disparities in health reflect a combination of genetic and environmental causes, and DNA methylation may be an important mediator. We compared in an exploratory manner the blood DNA methylome of Japanese Americans (JPA) versus European Americans (EUA). Methods Genome-wide buffy coat DNA methylation was profiled among healthy Multiethnic Cohort participant women who were Japanese (JPA; n = 30) or European (EUA; n = 28) Americans aged 60–65. Differentially methylated CpGs by race/ethnicity (DM-CpGs) were identified by linear regression (Bonferroni-corrected P < 0.1) and analyzed in relation to corresponding gene expression, a priori selected single nucleotide polymorphisms (SNPs), and blood biomarkers of inflammation and metabolism using Pearson or Spearman correlations (FDR < 0.1). Results We identified 174 DM-CpGs with the majority of hypermethylated in JPA compared to EUA (n = 133), often in promoter regions (n = 48). Half (51%) of the genes corresponding to the DM-CpGs were involved in liver function and liver disease, and the methylation in nine genes was significantly correlated with gene expression for DM-CpGs. A total of 156 DM-CpGs were associated with rs7489665 (SH2B1). Methylation of DM-CpGs was correlated with blood levels of the cytokine MIP1B (n = 146). We confirmed some of the DM-CpGs in the TCGA adjacent non-tumor liver tissue of Asians versus EUA. Conclusion We found a number of differentially methylated CpGs in blood DNA between JPA and EUA women with a potential link to liver disease, specific SNPs, and systemic inflammation. These findings may support further research on the role of DNA methylation in mediating some of the higher risk of liver disease among JPA.

For many high-dimensional genomic and epigenomic datasets, the outcome of interest is ordinal. While these ordinal outcomes are often thought of as the observed cutpoints of some latent continuous variable, some ordinal outcomes are truly discrete and are comprised of the subjective combination of several factors. The nonlinear stereotype logistic model, which does not assume proportional odds, was developed for these ‘assessed’ ordinal variables. It has previously been extended to the frequentist high-dimensional feature selection setting, but the Bayesian framework provides some distinct advantages in terms of simultaneous uncertainty quantification and variable selection. Here, we review the stereotype model and Bayesian variable selection methods and demonstrate how to combine them to select genomic features associated with discrete ordinal outcomes. We compared the Bayesian and frequentist methods in terms of variable selection performance. We additionally applied the Bayesian stereotype method to an acute myeloid leukemia RNA-sequencing dataset to further demonstrate its variable selection abilities by identifying features associated with the European LeukemiaNet prognostic risk score.

The stage of cancer is a discrete ordinal response that indicates the aggressiveness of disease and is often used by physicians to determine the type and intensity of treatment to be administered. For example, the FIGO stage in cervical cancer is based on the size and depth of the tumor as well as the level of spread. It may be of clinical relevance to identify molecular features from high-throughput genomic assays that are associated with the stage of cervical cancer to elucidate pathways related to tumor aggressiveness, identify improved molecular features that may be useful for staging, and identify therapeutic targets. High-throughput RNA-Seq data and corresponding clinical data (including stage) for cervical cancer patients have been made available through The Cancer Genome Atlas Project (TCGA). We recently described penalized Bayesian ordinal response models that can be used for variable selection for over-parameterized datasets, such as the TCGA-CESC dataset. Herein, we describe our ordinalbayes R package, available from the Comprehensive R Archive Network (CRAN), which enhances the runjags R package by enabling users to easily fit cumulative logit models when the outcome is ordinal and the number of predictors exceeds the sample size, P>N, such as for TCGA and other high-throughput genomic data. We demonstrate the use of this package by applying it to the TCGA cervical cancer dataset. Our ordinalbayes package can be used to fit models to high-dimensional datasets, and it effectively performs variable selection.

Stage of cancer is a discrete ordinal response that indicates aggressiveness of disease and is often used by physicians to determine the type and intensity of treatment to be administered. For example, the FIGO stage in cervical cancer is based on the size and depth of the tumor as well as the level of spread. It may be of clinical relevance to identify molecular features from high-throughput genomic assays that are associated with stage of cervical cancer, to elucidate pathways related to tumor aggressiveness, identify improved molecular features that may be useful for staging, and identify therapeutic targets. High-throughput RNA-Seq data and corresponding clinical data (including stage) for cervical cancer patients has been made available through The Cancer Genome Atlas Project (TCGA). We recently described penalized Bayesian ordinal response models that can be used for variable selection for over-parameterized datasets such as the TCGA-CESC dataset. Herein, we describe our ordinalbayes R package, available from the Comprehensive R Archive Network (CRAN), which is capable of fitting cumulative logit models when the outcome is ordinal and the number of predictors exceeds the sample size, P&gt;N, such as for TCGA data. We demonstrate use of this package through application to TCGA cervical cancer dataset. Our ordinalbayes package can be used to fit models to high-dimensional dataset and effectively performs variable selection.