ForestPMPlot: A Flexible Tool for Visualizing Heterogeneity Between Studies in Meta-analysis.

Meta-analysis has become a popular tool for genetic association studies to combine different genetic studies. A key challenge in meta-analysis is heterogeneity, or the differences in effect sizes between studies. Heterogeneity complicates the interpretation of meta-analyses. In this paper, we describe ForestPMPlot, a flexible visualization tool for analyzing studies included in a meta-analysis. The main feature of the tool is visualizing the differences in the effect sizes of the studies to understand why the studies exhibit heterogeneity for a particular phenotype and locus pair under different conditions. We show the application of this tool to interpret a meta-analysis of 17 mouse studies, and to interpret a multi-tissue eQTL study

The benefits of selecting phenotype-specific variants for applications of mixed models in genomics

Applications of linear mixed models (LMMs) to problems in genomics include phenotype prediction, correction for confounding in genome-wide association studies, estimation of narrow sense heritability, and testing sets of variants (e.g., rare variants) for association. In each of these applications, the LMM uses a genetic similarity matrix, which encodes the pairwise similarity between every two individuals in a cohort. Although ideally these similarities would be estimated using strictly variants relevant to the given phenotype, the identity of such variants is typically unknown. Consequently, relevant variants are excluded and irrelevant variants are included, both having deleterious effects. For each application of the LMM, we review known effects and describe new effects showing how variable selection can be used to mitigate them.National Institute on Aging (Brain eQTL Study (dbGaP phs000249.v1.p1)

A powerful and efficient set test for genetic markers that handles confounders

Approaches for testing sets of variants, such as a set of rare or common variants within a gene or pathway, for association with complex traits are important. In particular, set tests allow for aggregation of weak signal within a set, can capture interplay among variants, and reduce the burden of multiple hypothesis testing. Until now, these approaches did not address confounding by family relatedness and population structure, a problem that is becoming more important as larger data sets are used to increase power. Results: We introduce a new approach for set tests that handles confounders. Our model is based on the linear mixed model and uses two random effects-one to capture the set association signal and one to capture confounders. We also introduce a computational speedup for two-random-effects models that makes this approach feasible even for extremely large cohorts. Using this model with both the likelihood ratio test and score test, we find that the former yields more power while controlling type I error. Application of our approach to richly structured GAW14 data demonstrates that our method successfully corrects for population structure and family relatedness, while application of our method to a 15,000 individual Crohn's disease case-control cohort demonstrates that it additionally recovers genes not recoverable by univariate analysis. Availability: A Python-based library implementing our approach is available at http://mscompbio.codeplex.comComment: * denotes equal contribution

Cocoa polyphenols suppress TNF-α-induced vascular endothelial growth factor expression by inhibiting phosphoinositide 3-kinase (PI3K) and mitogen-activated protein kinase kinase-1 (MEK1) activities in mouse epidermal cells

Cocoa polyphenols have antioxidant and anti-inflammatory effects. TNF-α is a pro-inflammatory cytokine that has a vital role in the pathogenesis of inflammatory diseases such as cancer and psoriasis. Vascular endothelial growth factor (VEGF) expression is associated with tumorigenesis, CVD, rheumatoid arthritis and psoriasis. We tested whether cocoa polyphenol extract (CPE) inhibited TNF-α-induced VEGF expression in promotion-sensitive JB6 mouse epidermal cells. CPE significantly inhibited TNF-α-induced up-regulation of VEGF via reducing TNF-α-induced activation of the nuclear transcription factors activator protein-1 (AP-1) and NF-κB, which are key regulators of VEGF expression. CPE also inhibited TNF-α-induced phosphorylation of protein kinase B (Akt) and extracellular signal-regulated kinase. CPE blocked activation of their downstream kinases, p70 kDa ribosomal protein S6 kinase and p90 kDa ribosomal protein S6 kinase. CPE suppressed phosphoinositide 3-kinase (PI3K) activity via binding PI3K directly. CPE did not affect TNF-α-induced phosphorylation of mitogen-activated protein kinase kinase-1 (MEK1) but suppressed TNF-α-induced MEK1 activity. Collectively, these results indicate that CPE reduced TNF-α-induced up-regulation of VEGF by directly inhibiting PI3K and MEK1 activities, which may contribute to its chemopreventive potentia

Time-resolved pathogenic gene expression analysis of the plant pathogen Xanthomonas oryzae pv. oryzae

Virulence of wild-type and mutant Xoo strains on rice. (DOCX 16 kb

VGLL3 expression is associated with macrophage infiltration and predicts poor prognosis in epithelial ovarian cancer

Background/objectiveHigh-grade serous ovarian carcinoma (HGSOC) is the most common histologic type of epithelial ovarian cancer (EOC). Due to its poor survival outcomes, it is essential to identify novel biomarkers and therapeutic targets. The hippo pathway is crucial in various cancers, including gynaecological cancers. Herein, we examined the expression of the key genes of the hippo pathway and their relationship with clinicopathological significance, immune cells infiltration and the prognosis of HGSOC.MethodsThe Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data were curated to analyse the mRNA expression as well as the clinicopathological association and correlation with immune cell infiltration in HGSOC. The protein levels of significant genes in the HGSOC tissue were analysed using Tissue Microarray (TMA)-based immunohistochemistry. Finally, DEGs pathway analysis was performed to find the signalling pathways associated with VGLL3.ResultsVGLL3 mRNA expression was significantly correlated with both advanced tumor stage and poor overall survival (OS) (p=0.046 and p=0.003, respectively). The result of IHC analysis also supported the association of VGLL3 protein with poor OS. Further, VGLL3 expression was significantly associated with tumor infiltrating macrophages. VGLL3 expression and macrophages infiltration were both found to be independent prognostic factors (p=0.003 and p=0.024, respectively) for HGSOC. VGLL3 was associated with four known and three novel cancer-related signalling pathways, thus implying that VGLL3 is involved in the deregulation of many genes and pathways.ConclusionOur study revealed that VGLL3 may play a distinct role in clinical outcomes and immune cell infiltration in patients with HGSOC and that it could potentially be a prognostic marker of EOC