A novel prognostic model of methylation-associated genes in acute myeloid leukemia.

BACKGROUND: There is growing evidence that methylation-associated genes (MAGs) play an important role in the prognosis of acute myeloid leukemia (AML) patients. Thus, the aim of this research was to investigate the impact of MAGs in predicting the outcomes of AML patients. METHODS: The expression profile and clinical information of patients were downloaded from public databases. A novel prognostic model based on 7 MAGs was established in the TCGA training cohort and validated in the GSE71014 dataset. To validate the clinical implications, the correlation between MAGs signature and drug sensitivity was further investigated. RESULTS: 76 genes were screened out by the univariate Cox regression and significantly enriched in multiple methylation-related pathways. After filtering variables using LASSO regression analysis, 7 MAGs were introduced to construct the predictive model. The survival analysis showed overall survival of patients with the high-risk score was considerably poorer than that with the low-risk score in both the training and validating cohorts (p < 0.01). Furthermore, the risk score system as a prognostic factor also worked in the intermediate-risk patients based on ELN-2017 classification. Importantly, the risk score was demonstrated to be an independent prognostic factor for AML in the univariate and multivariate Cox regression analysis. Interestingly, GSEA analysis revealed that multiple metabolism-related pathways were significantly enriched in the high-risk group. Drug sensitivity analysis showed there was a significant difference in sensitivity of some drugs between the two groups. CONCLUSION: We developed a robust and accurate prognostic model with 7 MAGs. Our findings might provide a reference for the clinical prognosis and management of AML.

Flood Footprint Assessment: A Multiregional Case of 2009 Central European Floods.

Hydrometeorological phenomena have increased in intensity and frequency in last decades, with Europe as one of the most affected areas. This accounts for considerable economic losses in the region. Regional adaptation strategies for costs minimization require a comprehensive assessment of the disasters' economic impacts at a multiple-region scale. This article adapts the flood footprint method for multiple-region assessment of total economic impact and applies it to the 2009 Central European Floods event. The flood footprint is an impact accounting framework based on the input-output methodology to economically assess the physical damage (direct) and production shortfalls (indirect) within a region and wider economic networks, caused by a climate disaster. Here, the model is extended through the capital matrix, to enable diverse recovery strategies. According to the results, indirect losses represent a considerable proportion of the total costs of a natural disaster, and most of them occur in nonhighly directly impacted industries. For the 2009 Central European Floods, the indirect losses represent 65% out of total, and 70% of it comes from four industries: business services, manufacture general, construction, and commerce. Additionally, results show that more industrialized economies would suffer more indirect losses than less-industrialized ones, in spite of being less vulnerable to direct shocks. This may link to their specific economic structures of high capital-intensity and strong interindustrial linkages.

Genomic Selection with Allele Dosage in Panicum maximum Jacq.

Genomic selection is an efficient approach to get shorter breeding cycles in recurrent selection programs and greater genetic gains with selection of superior individuals. Despite advances in genotyping techniques, genetic studies for polyploid species have been limited to a rough approximation of studies in diploid species. The major challenge is to distinguish the different types of heterozygotes present in polyploid populations. In this work, we evaluated different genomic prediction models applied to a recurrent selection population of 530 genotypes of Panicum maximum, an autotetraploid forage grass. We also investigated the effect of the allele dosage in the prediction, i.e., considering tetraploid (GS-TD) or diploid (GS-DD) allele dosage. A longitudinal linear mixed model was fitted for each one of the six phenotypic traits, considering different covariance matrices for genetic and residual effects. A total of 41,424 genotyping-by-sequencing markers were obtained using 96-plex and Pst1 restriction enzyme, and quantitative genotype calling was performed. Six predictive models were generalized to tetraploid species and predictive ability was estimated by a replicated fivefold cross-validation process. GS-TD and GS-DD models were performed considering 1,223 informative markers. Overall, GS-TD data yielded higher predictive abilities than with GS-DD data. However, different predictive models had similar predictive ability performance. In this work, we provide bioinformatic and modeling guidelines to consider tetraploid dosage and observed that genomic selection may lead to additional gains in recurrent selection program of P. maximum.

Quantitative trait loci mapping and the genetic basis of heterosis in maize and rice.

Despite its importance to agriculture, the genetic basis of heterosis is still not well understood. The main competing hypotheses include dominance, overdominance, and epistasis. NC design III is an experimental design that has been used for estimating the average degree of dominance of quantitative trait loci (QTL) and also for studying heterosis. In this study, we first develop a multiple-interval mapping (MIM) model for design III that provides a platform to estimate the number, genomic positions, augmented additive and dominance effects, and epistatic interactions of QTL. The model can be used for parents with any generation of selfing. We apply the method to two data sets, one for maize and one for rice. Our results show that heterosis in maize is mainly due to dominant gene action, although overdominance of individual QTL could not completely be ruled out due to the mapping resolution and limitations of NC design III. For rice, the estimated QTL dominant effects could not explain the observed heterosis. There is evidence that additive x additive epistatic effects of QTL could be the main cause for the heterosis in rice. The difference in the genetic basis of heterosis seems to be related to open or self pollination of the two species. The MIM model for NC design III is implemented in Windows QTL Cartographer, a freely distributed software.