Capturing pair-wise epistatic effects associated with three agronomic traits in barley

Genetic association mapping has been widely applied to determine genetic markers favorably associated with a trait of interest and provide information for marker-assisted selection. Many association mapping studies commonly focus on main effects due to intolerable computing intensity. This study aims to select several sets of DNA markers with potential epistasis to maximize genetic variations of some key agronomic traits in barley. By doing so, we integrated a MDR (multifactor dimensionality reduction) method with a forward variable selection approach. This integrated approach was used to determine single nucleotide polymorphism pairs with epistasis effects associated with three agronomic traits: heading date, plant height, and grain yield in barley from the barley Coordinated Agricultural Project. Our results showed that four, seven, and five SNP pairs accounted for 51.06, 45.66 and 40.42% for heading date, plant height, and grain yield, respectively with epistasis being considered, while corresponding contributions to these three traits were 45.32, 31.39, 31.31%, respectively without epistasis being included. The results suggested that epistasis model was more effective than non-epistasis model in this study and can be more preferred for other applications.     

Growth form and distribution of introduced plants in their native and non-native ranges in Eastern Asia and North America

There is a growing interest in understanding the influence of plant traits on their ability to spread in non-native regions. Many studies addressing this issue have been based on relatively small areas or restricted taxonomic groups. Here, we analyse a large data base involving 1567 plant species introduced between Eastern Asia and North America or from elsewhere to both regions. We related the extent of species distributions in each region to growth form and the distinction between upland and wetland habitats. We identified significant relationships between geographical distribution and plant traits in both native and exotic ranges as well as regional differences in the relationships. Range size was larger for herbaceous graminoids and forbs, especially annuals compared to perennials, than for woody species, and range size also was larger for plants of wetland compared to upland habitats. Distributions were more extensive in North America than in Eastern Asia, although native plants from both regions had broader distributions than non-natives, with exotics from elsewhere intermediate. Growth form and environment explained more of the variance in distribution of plants in North America than in Eastern Asia. The influence of growth form and habitat on distribution suggests that these traits might be related to tolerance of ecological conditions. In addition, the smaller extents of species in non-native compared to native areas suggest roles for dispersal limitation and adaptation to region-specific ecological conditions in determining distribution.     

Screening of significant biomarkers related with prognosis of liver cancer by lncRNA-associated ceRNAs analysis

This study aimed to identify significant biomarkers related to the prognosis of liver cancer using long noncoding RNA (lncRNA)-associated competing endogenous RNAs (ceRNAs) analysis. Differentially expressed mRNA and lncRNAs between liver cancer and paracancerous tissues were screened, and the functions of these mRNAs were predicted by gene ontology and pathway enrichment analyses. A ceRNA network consisting of differentially expressed mRNAs and lncRNAs was constructed. LncRNA FENDRR and lncRNA HAND2-AS1 were hub nodes in the ceRNA network. A risk score assessment model consisting of eight genes (PDE2A, ESR1, FBLN5, ALDH8A1, AKR1D1, EHHADH, ADRA1A, and GNE) associated with prognosis were developed. Multivariate Cox regression suggested that both pathologic_T and risk group could be regarded as independent prognostic factors. Furthermore, a nomogram model consisting of pathologic_T and risk group showed a good prediction ability for predicting the survival rate of liver cancer patients. The nomogram model consisting of pathologic_T and a risk score assessment model could be regarded as an independent factor for predicting prognosis of liver cancer.