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Barley is the fourth largest grain crop globally with varieties suited to temperate, subarctic, and subtropical areas. The identification and subsequent selection of superior varieties are complicated by genotype-by-environment interactions. The main objective of this study was to use parametric and non-parametric stability measures along with a GGE biplot model to identify high-yielding stable barley genotypes in Iran. Eighteen barley genotypes (16 new genotypes and two control varieties) were evaluated in a randomized complete block design with four replications at five locations over three growing seasons (2013–2014, 2014–2015, 2015–2016). The combined analysis of variance indicated that the environment main effect accounted for > 69% of all variation, compared with < 31% for the combined genotype (G) and genotype-by-environment interaction effects. The mean grain yield of each genotype across the five test sites and three seasons ranged from 1900 to 2302 kg ha−1. Using Spearman’s rank correlation and principal component analyses, the stability measures were divided into three groups: the first included mean yield, TOP and b, which are related to the dynamic concept of stability, the second comprised θi, Wi2, σi2, CVi, \\[S_{di}^{2}\\], KR, and the non-parametric measures, S(i) and NP(i), which are related to the static concept of stability, and the third included θi and R2. The GGE biplot analysis indicated that, of the five test locations, Gonbad and Moghan had the most discriminating and representative environments. Hence, these locations are recommended as ideal test locations in Iran for the selection of superior genotypes. The numerical and graphical methods both produced similar results, identifying genotypes G12, G13, and G17 as the best material for rainfed conditions in Iran; these genotypes should be promoted for commercial production.

• Leaves interact with a wealth of microorganisms. Among these, fungi are highly diverse and are known to contribute to plant health, leaf senescence and early decomposition. However, patterns and drivers of the seasonal dynamics of foliar fungal communities are poorly understood. • We used a multifactorial experiment to investigate the influence of warming and tree genotype on the foliar fungal community on the pedunculate oak Quercus robur across one growing season. • Fungal species richness increased, evenness tended to decrease, and community composition strongly shifted during the growing season. Yeasts increased in relative abundance as the season progressed, while putative fungal pathogens decreased. Warming decreased species richness, reduced evenness and changed community composition, especially at the end of the growing season. Warming also negatively affected putative fungal pathogens. We only detected a minor imprint of tree genotype and warming × genotype interactions on species richness and community composition. • Overall, our findings demonstrate that warming plays a larger role than plant genotype in shaping the seasonal dynamics of the foliar fungal community on oak. These warming-induced shifts in the foliar fungal community may have a pronounced impact on plant health, plant–fungal interactions and ecosystem functions.

Whole -genome sequencing projects of millions of subjects contain enormous genotypes, entailing a huge memory burden and time for computation. Here, we present GBC, a toolkit for rapidly compressing large-scale genotypes into highly addressable byte-encoding blocks under an optimized parallel framework. We demonstrate that GBC is up to 1000 times faster than state-of-the-art methods to access and manage compressed large-scale genotypes while maintaining a competitive compression ratio. We also showed that conventional analysis would be substantially sped up if built on GBC to access genotypes of a large population. GBC’s data structure and algorithms are valuable for accelerating large-scale genomic research.

• Latitudinal and elevational gradients provide valuable experimental settings for studies of the potential impact of global warming on forest tree species. The availability of long-term phenological surveys in common garden experiments for traits associated with climate, such as bud flushing for sessile oaks (Quercus petraea), provide an ideal opportunity to investigate this impact. • We sequenced 18 sessile oak populations and used available sequencing data for three other closely related European white oak species (Quercus pyrenaica, Quercus pubescens, and Quercus robur) to explore the evolutionary processes responsible for shaping the genetic variation across latitudinal and elevational gradients in extant sessile oaks. We used phenotypic surveys in common garden experiments and climatic data for the population of origin to perform genome-wide scans for population differentiation and genotype–environment and genotype–phenotype associations. • The inferred historical relationships between Q. petraea populations suggest that interspecific gene flow occurred between Q. robur and Q. petraea populations from cooler or wetter areas. A genome-wide scan of differentiation between Q. petraea populations identified single nucleotide polymorphisms (SNPs) displaying strong interspecific relative divergence between these two species. These SNPs followed genetic clines along climatic or phenotypic gradients, providing further support for the likely contribution of introgression to the adaptive divergence of Q. petraea populations. • Overall, the results indicate that outliers and associated SNPs are Q. robur ancestry-informative. We discuss the results of this study in the framework of the postglacial colonization scenario, in which introgression and diversifying selection have been proposed as essential drivers of Q. petraea microevolution.

Plant biology is experiencing a renewed interest in the mechanistic underpinnings and evolution of phenotypic plasticity that calls for a re-evaluation of how we analyse phenotypic responses to a rapidly changing climate. We suggest that dissecting plant plasticity in response to increasing temperature needs an approach that can represent plasticity over multiple environments, and considers both population-level responses and the variation between genotypes in their response. Here, we outline how a random regression mixed model framework can be applied to plastic traits that show linear or nonlinear responses to temperature. Random regressions provide a powerful and efficient means of characterising plasticity and its variation. Although they have been used widely in other fields, they have only recently been implemented in plant evolutionary ecology. We outline their structure and provide an example tutorial of their implementation.

Hepatitis B virus (HBV) genotypes E to J are understudied genotypes. Genotype E is found almost exclusively in West Africa. Genotypes F and H are found in America and are rare in other parts of the world. The distribution of genotype G is not completely known. Genotypes I and J are found in Asia and probably result from recombination events with other genotypes. The number of reported sequences for HBV genotypes E to J is small compared to other genotypes, which could impact phylogenetic and pairwise distance analyses. Genotype F is the most divergent of the HBV genotypes and is subdivided into six subgenotypes F1 to F6. Genotype E may be a recent genotype circulating almost exclusively in sub-Saharan Africa. Genotype J is a putative genotype originating from a single Japanese patient. The paucity of data from sub-Saharan Africa and Latin America is due to the under-representation of these regions in clinical and research cohorts. The purpose of this review is to highlight the need for further research on HBV genotypes E to J, which appear to be overlooked genotypes.

Multi-environment trials have a fundamental role in the selection of the best genotypes across different environments before its commercial release. This study was carried out to identify high-yielding stable sunflower hybrids using the graphical method of the GGE biplot. For this purpose, 11 new hybrids along with four hybrid cultivars were evaluated in a randomized complete block design with four replications across 8 environments (combination of years and locations) during the 2018–2020 growing seasons. The mean oil yield of the environments varied from 833 kg ha−1 in E4 to 1565 kg ha−1 in E5 and the oil yield of hybrids ranged from 1085 kg ha−1 in hybrid H9 to 1565 kg ha−1 in hybrid H8. The results indicated that genotype (G), environment (E) and genotype × environment (G × E) effects were significant for oil yield. The G, E, and G × E interaction effects accounted for 64.83, 11.86, and 23.31% of the total variation, respectively. Results of biplot analysis showed that the first and second principal components accounted for 45.9% and 20.4%, respectively, and in total 66.3% of oil yield variance. GGE biplot analysis indicated two major mega-environments of sunflower testing locations in Iran. Based on the hypothetical ideal genotype biplot, the hybrids H3 and H5 were better than the other hybrids in terms of oil yield and stability, which had the highest general adaptation to all of the environments. Based on the ideal genotype from the most desirable to the most undesirable the hybrids were ranked as follows: H5 > H3 > H8 > H14 > H6 > H2 > H13 > H12 > H10 > H11 > H1 > H7 > H4 > H15 > H9. Furthermore, ranking of the environments based on the ideal environment introduced Sari as the best environment. Therefore, Sari can be used as a suitable test location for selecting superior sunflower hybrids in Iran.

Experiments measuring the interaction between genotypes and environments measure the spatial (e.g., locations) and temporal (e.g., years) separation and/or combination of these factors. The genotype-by-environment interaction (GEI) is very important in plant breeding programs. Over the past six decades, the propensity to model the GEI led to the development of several models and mathematical methods for deciphering GEI in multi-environmental trials (METs) called “stability analyses”. However, its size is hidden by the contribution of improved management in the yield increase, and for this reason comparisons of new with old varieties in a single experiment could reveal its real size. Due to the existence of inherent differences among proposed methods and analytical models, it is necessary for researchers that calculate stability indices, and ultimately select the superior genotypes, to dissect their usefulness. Thus, we have collected statistics, as well as models and their equations, to explore these methods further. This review introduces a complete set of parametric and non-parametric methods and models with a selection pattern based on each of them. Furthermore, we have aligned each method or statistic with a matched software, macro codes, and/or scripts.

Over the last two decades, we have extensively studied the genetics of congenital adrenal hyperplasia caused by 21-hydroxylase deficiency (CAH) and have performed 8,290 DNA analyses of the CYP21A2 gene on members of 4,857 families at risk for CAH—the largest cohort of CAH patients reported to date. Of the families studied, 1,507 had at least one member affected with one of three known forms of CAH, namely salt wasting, simple virilizing, or nonclassical CAH. Here, we report the genotype and phenotype of each affected patient, as well as the ethnic group and country of origin for each patient. We showed that 21 of 45 genotypes yielded a phenotypic correlation in our patient cohort. In particular, contrary to what is generally reported in the literature, we found that certain mutations, for example, the P30L, I2G, and I172N mutations, yielded different CAH phenotypes. In salt wasting and nonclassical CAH, a phenotype can be attributed to a genotype; however, in simple virilizing CAH, we observe wide phenotypic variability, particularly with the exon 4 I172N mutation. Finally, there was a high frequency of homozygous I2G and V281L mutations in Middle Eastern and Ashkenazi Jewish populations, respectively. By identifying the predominant phenotype for a given genotype, these findings should assist physicians in prenatal diagnosis and genetic counseling of parents who are at risk for having a child with CAH.