Explore the vast range of titles available.

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.

In plant breeding and agricultural research, biplot analysis has become an important statistical technique. The goal of this study was to find the winning genotype(s) for the test settings in a part of the Southwest region of Nigeria, as well as to investigate the nature and extent of genotype × environment interaction (GEI) effects on Bambara groundnut (BGN) production. The experiment was carried out in four environments (two separate sites, Ibadan and Ikenne, for two consecutive years, 2018 and 2019) with ninety-five BGN accessions. According to the combined analysis of variance over environments, genotypes and GEI both had a substantial (p < 0.001) impact on BGN yield. The results revealed that BGN accessions performed differently in different test conditions, indicating that the interaction was crossover in nature. The results revealed that BGN accessions performed differently in different test conditions, indicating that the interaction was crossover in nature. To examine and show the pattern of the interaction components, biplots with the genotype main effect and genotype × environment interaction (GEI) were used. The first two PCs explained 80% of the total variation of the GGE model (i.e., G + GE) (PC1 = 48.59%, PC2 = 31.41%). The accessions that performed best in each environment based on the “which-won-where” polygon were TVSu-2031, TVSu-1724, TVSu-1742, TVSu-2022, TVSu-1943, TVSu-1892, TVSu-1557, TVSu-2060, and TVSu-2017. Among these accessions, TVSu-2017, TVSu-1557, TVSu-2060, TVSu-1892, and TVSu-1943 were among the highest-yielding accessions on the field. The adaptable accessions were TVSu-1763, TVSu-1899, TVSu-2019, TVSu-1898, TVSu-1957, TVSu-2021, and TVSu-1850, and the stable accessions were TVSu-1589, TVSu-1905, and TVSu-2048. In terms of discriminating and representativeness for the environments, Ibadan 2019 is deemed to be a superior environment. The selected accessions are recommended as parental lines in breeding programs for grain yield improvement in Ibadan or Ikenne or similar agro-ecological zones.

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.

To investigate associations of single nucleotide polymorphisms (SNPs) rs2228314 of sterol regulatory element-binding protein-2 (SREBP-2) or rs11039155 of liver X receptor [alpha] (LXR[alpha]) with susceptibility to polycystic ovary syndrome (PCOS) in a Chinese Han population. SREBP-2 rs2228314 and LXR[alpha] rs11039155 polymorphisms were genotyped in patients with PCOS and age- and sex-matched PCOS-free controls from a Chinese Han population. A total of 605 patients with PCOS and 615 controls were recruited in this study. We found that GC and CC genotypes of rs2228314, and variant C, were associated with a significantly increased risk of PCOS. In addition, GA and AA genotypes of rs11039155, as well as variant A, were also associated with a significantly increased risk of PCOS. Our results showed that SREBP-2 rs2228314 G to C change and variant C genotype as well as LXR[alpha] rs11039155 G to A change and variant A may contribute to PCOS in Chinese Han population.[PUBLICATION ABSTRACT]

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.

Survivin, an apoptotic inhibitor, is overexpressed in various types of cancer. It has been shown that single nucleotide polymorphisms in the survivin gene promoter can modulate survivin expression and consequently influence the risk for some cancers. The aims of the present study were to: analyze by means of PCR/RFLP, genotype and allele frequencies for the promoter –31 G/C polymorphism in the survivin gene of 59 Wilms tumour (WT) patients and 82 controls, determine cytoplasmic and nuclear survivin expression in WTs using immunohistochemical methods. The frequencies of alleles and genotypes were significantly different between patients and controls for the –31 G/C polymorphism. Individuals with CC and CG genotypes had significantly decreased risk of WT compared to GG individuals (OR 0.26, 95% CI 0.07–0.96; OR 0.30, 95% CI 0.15–0.60). A statistically significant difference in cytoplasmic survivin expression between lower and higher grades tumours has been detected as well (p=0.000), but without correlation with the genotypes. Our findings suggest that both survivin genotypes and survivin expression, though not showing direct relationship, represent relevant risk/prognostic markers for WT in Serbian population.

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.