The causes of many complex human diseases are still largely unknown. Genetics plays an important role in uncovering the molecular mechanisms of complex human diseases. A key step to characterize the genetics of a complex human disease is to unbiasedly identify disease-associated gene transcripts on a whole-genome scale. Confounding factors could cause false positives. Paired design, such as measuring gene expression before and after treatment for the same subject, can reduce the effect of known confounding factors. However, not all known confounding factors can be controlled in a paired/match design. Model-based clustering, such as mixtures of hierarchical models, has been proposed to detect gene transcripts differentially expressed between paired samples. To the best of our knowledge, no model-based gene clustering methods have the capacity to adjust for the effects of covariates yet. In this article, we proposed a novel mixture of hierarchical models with covariate adjustment in identifying differentially expressed transcripts using high-throughput whole-genome data from paired design. Both simulation study and real data analysis show the good performance of the proposed method.

SUMMARY: Genome-wide association studies (GWAS) have revealed thousands of genetic loci for common diseases. One of the main challenges in the post-GWAS era is to understand the causality of the genetic variants. Expression quantitative trait locus (eQTL) analysis is an effective way to address this question by examining the relationship between gene expression and genetic variation in a sufficiently powered cohort. However, it is frequently a challenge to determine the sample size at which a variant with a specific allele frequency will be detected to associate with gene expression with sufficient power. This is a particularly difficult task for single-cell RNAseq studies. Therefore, a user-friendly tool to estimate statistical power for eQTL analyses in both bulk tissue and single-cell data is needed. Here, we presented an R package called powerEQTL with flexible functions to estimate power, minimal sample size or detectable minor allele frequency for both bulk tissue and single-cell eQTL analysis. A user-friendly, program-free web application is also provided, allowing users to calculate and visualize the parameters interactively. AVAILABILITY AND IMPLEMENTATION: The powerEQTL R package source code and online tutorial are freely available at CRAN: https://cran.r-project.org/web/packages/powerEQTL/. The R shiny application is publicly hosted at https://bwhbioinfo.shinyapps.io/powerEQTL/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Recently differential variability has been showed to be valuable in evaluating the association of DNA methylation to the risks of complex human diseases. The statistical tests based on both differential methylation level and differential variability can be more powerful than those based only on differential methylation level. Anh and Wang (2013) proposed a joint score test (AW) to simultaneously detect for differential methylation and differential variability. However, AW's method seems to be quite conservative and has not been fully compared with existing joint tests. RESULTS: We proposed three improved joint score tests, namely iAW.Lev, iAW.BF, and iAW.TM, and have made extensive comparisons with the joint likelihood ratio test (jointLRT), the Kolmogorov-Smirnov (KS) test, and the AW test. Systematic simulation studies showed that: 1) the three improved tests performed better (i.e., having larger power, while keeping nominal Type I error rates) than the other three tests for data with outliers and having different variances between cases and controls; 2) for data from normal distributions, the three improved tests had slightly lower power than jointLRT and AW. The analyses of two Illumina HumanMethylation27 data sets GSE37020 and GSE20080 and one Illumina Infinium MethylationEPIC data set GSE107080 demonstrated that three improved tests had higher true validation rates than those from jointLRT, KS, and AW. CONCLUSIONS: The three proposed joint score tests are robust against the violation of normality assumption and presence of outlying observations in comparison with other three existing tests. Among the three proposed tests, iAW.BF seems to be the most robust and effective one for all simulated scenarios and also in real data analyses.

It is well known that excessive drinking could result in human diseases (e.g., liver disease and cardiovascular disease). To investigate the underlying mechanisms of the effects of alcohol consumption, dental pulp stem cells have been used due to their self-renewal capability, multi-lineage differentiation, and clonogenic efficiency. Researchers usually focus on detecting genes having different mean expression levels between different conditions. However, the variance of gene expression might also provide useful information. In this study, we applied the Brown. Forsythe (BF) test for equal variance based on a human dental pulp stem cells dataset to explore the role of alcohol on the variation of gene expression. We identified two differential variable (DV) gene probes, 213597_s_at near gene CTDSPL (FDR-adjusted p-value = 1.50x10-5) and 213993_at near gene SPON1 (FDR-adjusted p-value=0.042). To best of our knowledge, no studies have found yet the associations of CTDSPL and/or SPON1 to alcohol consumption. GeneMANIA showed that these two genes are related to 20 genes. Web based gene set analysis toolkit (WebGestalt) showed that these 22 genes are enriched in 4 alcohol-related KEGG pathways: adherens junction, TGF-beta signaling pathway, signaling pathways regulating pluripotency of stem cells, and Hippo signaling pathway.

Moderate alcohol consumption has health benefits of adults, while excessive alcohol has poisonous effects on numerous different organs. However, the underlying molecular mechanisms of this phenomenon are still largely unclear yet. Human dental pulp stem cells (hDPSCs) have been used as models to investigate the effect of alcohol (EtOH) on gene expression. It has been demonstrated that both EtOH dosage and exposure time affects gene expression. However, the investigation into whether there exists synergistic effect of EtOH dosage and exposure time on gene expression is rare. In the present study, we reported that the effect of interaction between EtOH dosage and exposure time is significantly associated with the expression of genes SLC25A36 and ZFAND5 based on an hDPSC experiment. This result is novel in that genes SLC25A36 and ZFAND5 have not been reported yet to be associated with alcohol consumption in the existing literature.

Dental pulp stem cells (DPSCs) are stem cellspresent in the dental pulp and have same renewal anddifferentiating properties as bone marrow cells. Hence, DPSCs canbe used for future regenerative therapies of various diseases.Previous studies show that DPSCs would lose their differentiationcapability during long-term passage when they were cultured inmedium promoting differentiation. Researchers have beenworking to explore the key genes that influence their differentiation capability. However, the differentiation of dental pulp stem cells is accompanied by a complex biological process involving multiple genes and multi-element interactions. In this study, we used the weighted gene co-expression network (WGCNA) to obtain gene network modules based on a gene microarray data set from a DPSCs experiment, which is available at the Gene Expression Omnibus (GEO) public database. We identified two modules (yellow module and salmon module) significantly associated with DPSC passage.

Oral squamous cell carcinomas (OSCC) is the most common head and neck cancer worldwide, with more than 300,000 new cases being diagnosed annually. Studies have shown that miRNAs are involved in the process of growth, differentiation, apoptosis, invasion and metastasis of OSCC tumor cells. How miRNAs work together to contribute to this process is still largely unknown. The goal of our study was to characterize the coexpression network of miRNAs and to identify the miRNA subnetworks (modules) that were significantly associated with the OSCC cancer status. We also searched hub miRNAs that might play a vital role in the development of OSCC. We applied the weighted gene co-expression network analysis (WGCNA) to the miRNA expression profile data from a paired design study contributed by Shiah et al. To account for the within-pair correlation, a linear mixed model (LMM) was constructed to test the associations of miRNA modules to cancer status. Two significant modules (turquoise module with 254 miRNAs and grey module with 309 miRNAs) were identified. The miRNA miR-let-7c was the hub miRNA in the turquoise module in terms of node degree. Finally, we used miRsystem to perform the target gene prediction and KEGG pathway enrichment analysis of miRNAs within the two modules. Interestingly, the two modules have similar sets of target genes so that the top 6 enriched KEGG pathways for the 2 modules were the same. Compared with the probe-wise test used by Shiah et al., we took the network approach and identified significant OSCC-associated miRNA modules, which could help uncover the mechanism that miRNAs interplay each other to contribute to OSCC.