Diversity and path coefficient analysis of Southern African maize hybrids

Detailed knowledge on genetic diversity among germplasm is important for hybrid maize ( Zea mays L.) breeding. The objective of the study was to determine genetic diversity in widely grown hybrids in Southern Africa, and compare effectiveness of phenotypic analysis models for determining genetic distances between hybrids. Fifty hybrids were evaluated at one site with two replicates. The experiment was a randomized complete block design. Phenotypic and genotypic data were analyzed using SAS and Power Marker respectively. There was significant (p < 0.01) variation and diversity among hybrid brands but small within brand clusters. Polymorphic Information Content (PIC) ranged from 0.07 to 0.38 with an average of 0.34 and genetic distance ranged from 0.08 to 0.50 with an average of 0.43. SAH23 and SAH21 (0.48) and SAH33 and SAH3 (0.47) were the most distantly related hybrids. Both single nucleotide polymorphism (SNP) markers and phenotypic data models were effective for discriminating genotypes according to genetic distance. SNP markers revealed nine clusters of hybrids. The 12-trait phenotypic analysis model, revealed eight clusters at 85%, while the five-trait model revealed six clusters. Path analysis revealed significant direct and indirect effects of secondary traits on yield. Plant height and ear height were negatively correlated with grain yield meaning shorter hybrids gave high yield. Ear weight, days to anthesis, and number of ears had highest positive direct effects on yield. These traits can provide good selection index for high yielding maize hybrids. Results confirmed that diversity of hybrids is small within brands and also confirm that phenotypic trait models are effective for discriminating hybrids.

Prevalence of preterm labor in Iran: A systematic review and meta-analysis

Background: Preterm labor, which defines as live-birth delivery before 37 weeks of gestation is a main determinant of neonatal morbidity and mortality around the world. Objective: The aim of this study was to determine the prevalence of preterm labor in Iran by a meta-analysis study, to be as a final measure for policy makers in this field. Materials and Methods: In this meta-analysis, the databases of Thomson database (Web of Knowledge), PubMed/Medline, Science Direct, Scopus, Google Scholar, Iranmedex, Scientific Information Database (SID), Magiran, and Medlib were searched for articles in English and Persian language published between 1995 and 2014. Among the studies with regard to the inclusion and exclusion criteria, 14 studies (out of 1370 publications) were selected. Data were analyzed by using Stata software version 11. The heterogeneity of reported prevalence among studies was evaluated by the Chi-square based Q test and I2 statistics. Results: The results of Chi-square based on Q test and I2 statistics revealed severe heterogeneity (Q=2505.12, p-value < 0.001 and I2= 99.5%) and consequently, the random effect model was used for the meta-analysis. Based on the random effect model, the overall estimated prevalence of preterm in Iran was 9.2% (95% CI: 7.6 – 10.7). Conclusion: Present study summarized the results of previous studies and provided a comprehensive view about the preterm delivery in Iran. In order to achieve a more desirable level and its reduction in the coming years, identifying affecting factor and interventional and preventive actions seem necessary.