Differentially expressed heterogeneous overdispersion genes testing for count data.

The mRNA-seq data analysis is a powerful technology for inferring information from biological systems of interest. Specifically, the sequenced RNA fragments are aligned with genomic reference sequences, and we count the number of sequence fragments corresponding to each gene for each condition. A gene is identified as differentially expressed (DE) if the difference in its count numbers between conditions is statistically significant. Several statistical analysis methods have been developed to detect DE genes based on RNA-seq data. However, the existing methods could suffer decreasing power to identify DE genes arising from overdispersion and limited sample size, where overdispersion refers to the empirical phenomenon that the variance of read counts is larger than the mean of read counts. We propose a new differential expression analysis procedure: heterogeneous overdispersion genes testing (DEHOGT) based on heterogeneous overdispersion modeling and a post-hoc inference procedure. DEHOGT integrates sample information from all conditions and provides a more flexible and adaptive overdispersion modeling for the RNA-seq read count. DEHOGT adopts a gene-wise estimation scheme to enhance the detection power of differentially expressed genes when the number of replicates is limited as long as the number of conditions is large. DEHOGT is tested on the synthetic RNA-seq read count data and outperforms two popular existing methods, DESeq2 and EdgeR, in detecting DE genes. We apply the proposed method to a test dataset using RNAseq data from microglial cells. DEHOGT tends to detect more differently expressed genes potentially related to microglial cells under different stress hormones treatments.

Differentially Expressed Heterogeneous Overdispersion Genes Testing for Count Data.

The mRNA-seq data analysis is a powerful technology for inferring information from biological systems of interest. Specifically, the sequenced RNA fragments are aligned with genomic reference sequences, and we count the number of sequence fragments corresponding to each gene for each condition. A gene is identified as differentially expressed (DE) if the difference in its count numbers between conditions is statistically significant. Several statistical analysis methods have been developed to detect DE genes based on RNA-seq data. However, the existing methods could suffer decreasing power to identify DE genes arising from overdispersion and limited sample size. We propose a new differential expression analysis procedure: heterogeneous overdispersion genes testing (DEHOGT) based on heterogeneous overdispersion modeling and a post-hoc inference procedure. DEHOGT integrates sample information from all conditions and provides a more flexible and adaptive overdispersion modeling for the RNA-seq read count. DEHOGT adopts a gene-wise estimation scheme to enhance the detection power of differentially expressed genes. DEHOGT is tested on the synthetic RNA-seq read count data and outperforms two popular existing methods, DESeq and EdgeR, in detecting DE genes. We apply the proposed method to a test dataset using RNAseq data from microglial cells. DEHOGT tends to detect more differently expressed genes potentially related to microglial cells under different stress hormones treatments.

DNA methylation of Nuclear Factor of Activated T Cells 1 mediates the prospective relation between exposure to different traumatic event types and post-traumatic stress disorder.

The mechanisms through which exposure to differing trauma types become biologically embedded to shape the risk for post-traumatic stress disorder (PTSD) is unclear. DNA methylation (5-mC), particularly in stress-relevant genes, may play a role in this relationship. Here, we conducted path analysis using generalized structural equation modeling to investigate whether blood-derived 5-mC in Nuclear Factor of Activated T Cells 1 (NFATC1) mediates the prospective association between each of five different trauma types ("assaultive violence", "other injury or shocking experience", "learning of trauma to loved one", "sudden, unexpected death of a close friend or relative", and "other") and lifetime PTSD. All five trauma types were significantly associated with reduced methylation at NFATC1 CpG site, cg17057218. Two of the five trauma types were significantly associated with increased methylation at NFATC1 CpG site, cg22324981. Moreover, methylation at cg17057218 significantly mediated 21-32% of the total effect for four of the five trauma types, while methylation at cg22324981 mediated 27-40% of the total effect for two of the five trauma types. These CpG sites were differentially associated with transcription factor binding sites and chromatin state signatures. NFATC1 5-mC may be a potential mechanism in the relationship between some trauma types and prospective risk for PTSD.

Leukocyte methylomic imprints of exposure to the genocide against the Tutsi in Rwanda: a pilot epigenome-wide analysis.

Aim & methods: We conducted a pilot epigenome-wide association study of women from Tutsi ethnicity exposed to the genocide while pregnant and their resulting offspring, and a comparison group of women who were pregnant at the time of the genocide but living outside of Rwanda.Results: Fifty-nine leukocyte-derived DNA samples survived quality control: 33 mothers (20 exposed, 13 unexposed) and 26 offspring (16 exposed, 10 unexposed). Twenty-four significant differentially methylated regions (DMRs) were identified in mothers and 16 in children. Conclusions:In utero genocide exposure was associated with CpGs in three of the 24 DMRs: BCOR, PRDM8 and VWDE, with higher DNA methylation in exposed versus unexposed offspring. Of note, BCOR and VWDE show significant correlation between brain and blood DNA methylation within individuals, suggesting these peripherally derived signals of genocide exposure may have relevance to the brain.

Lay abstract The 1994 Rwandan genocide against ethnic Tutsi has been associated with adverse mental health outcomes in survivors decades later, but the molecular mechanisms that contribute to this association remain poorly characterized. Epigenetic mechanisms such as DNA methylation regulate gene function and change in response to life experiences. We identified differentially methylated regions (DMRs) in genocide-exposed versus unexposed mothers and children. In utero genocide exposure was linked with methylation differences in three maternal DMRs, with higher methylation in exposed offspring. Two of three DMRs show correlation between brain and blood methylation within individuals, suggesting that peripherally derived signals of genocide exposure may be relevant to the brain.