Biodiversity, management, and commercialization of ornamental plants at nurseries in Fortin de las Flores, Veracruz

Objective: The aim of this study was to create an inventory of the biodiversity, management, and commercialization of ornamental plants at nurseries in the municipality of Fortín de las Flores in the State of Veracruz, Mexico. Design/methodology/approach: Semi-structured interviews were carried out with 150 nursery owners. Results: The respondents reported a total of 230 different plants. With regard to commercialization, 20 species were reported as the most sold. The most notable species were: Syagrus romanzoffiana (Cham.) Glassman, Caesalpinia echinata Lam., Anthurium andreanum Linden, and Phoenix roebelenii O’Brien. The biggest problem was the sale price; 65% of the nursery owners reported that they sold their plants to strangers, and that they did not belong to any organization or cooperative. Limitations on study/implications: The poor local technological development characterized by unspecialized small and medium-sized businesses that compete with each other with little trade organization is a result of the sociocultural context of the region. This context limits regional development. Therefore, more research regarding sociocultural aspects should be promoted. Findings/conclusions: We have concluded that there is an underutilization of the native agrobiodiversity, since species originating in other parts of the world are what is most often sold. This is evidenced in the main species sold as well as those that generate the highest profits for the nursery owners

A reaction norm model for genomic selection using high-dimensional genomic and environmental data

In most agricultural crops the effects of genes on traits are modulated by environmental conditions, leading to genetic by environmental interaction (G × E). Modern genotyping technologies allow characterizing genomes in great detail and modern information systems can generate large volumes of environmental data. In principle, G × E can be accounted for using interactions between markers and environmental covariates (ECs). However, when genotypic and environmental information is high dimensional, modeling all possible interactions explicitly becomes infeasible. In this article we show how to model interactions between high-dimensional sets of markers and ECs using covariance functions. The model presented here consists of (random) reaction norm where the genetic and environmental gradients are described as linear functions of markers and of ECs, respectively. We assessed the proposed method using data from Arvalis, consisting of 139 wheat lines genotyped with 2,395 SNPs and evaluated for grain yield over 8 years and various locations within northern France. A total of 68 ECs, defined based on five phases of the phenology of the crop, were used in the analysis. Interaction terms accounted for a sizable proportion (16 %) of the within-environment yield variance, and the prediction accuracy of models including interaction terms was substantially higher (17–34 %) than that of models based on main effects only. Breeding for target environmental conditions has become a central priority of most breeding programs. Methods, like the one presented here, that can capitalize upon the wealth of genomic and environmental information available, will become increasingly important

Scanning and filling : ultra-dense SNP genotyping combining genotyping-by-sequencing, SNP array and whole-genome resequencing data

Genotyping-by-sequencing (GBS) represents a highly cost-effective high-throughput genotyping approach. By nature, however, GBS is subject to generating sizeable amounts of missing data and these will need to be imputed for many downstream analyses. The extent to which such missing data can be tolerated in calling SNPs has not been explored widely. In this work, we first explore the use of imputation to fill in missing genotypes in GBS datasets. Importantly, we use whole genome resequencing data to assess the accuracy of the imputed data. Using a panel of 301 soybean accessions, we show that over 62,000 SNPs could be called when tolerating up to 80% missing data, a five-fold increase over the number called when tolerating up to 20% missing data. At all levels of missing data examined (between 20% and 80%), the resulting SNP datasets were of uniformly high accuracy (96– 98%). We then used imputation to combine complementary SNP datasets derived from GBS and a SNP array (SoySNP50K). We thus produced an enhanced dataset of >100,000 SNPs and the genotypes at the previously untyped loci were again imputed with a high level of accuracy (95%). Of the >4,000,000 SNPs identified through resequencing 23 accessions (among the 301 used in the GBS analysis), 1.4 million tag SNPs were used as a reference to impute this large set of SNPs on the entire panel of 301 accessions. These previously untyped loci could be imputed with around 90% accuracy. Finally, we used the 100K SNP dataset (GBS + SoySNP50K) to perform a GWAS on seed oil content within this collection of soybean accessions. Both the number of significant marker-trait associations and the peak significance levels were improved considerably using this enhanced catalog of SNPs relative to a smaller catalog resulting from GBS alone at 20% missing data. Our results demonstrate that imputation can be used to fill in both missing genotypes and untyped loci with very high accuracy and that this leads to more powerful genetic analyses

Author Correction: A chickpea genetic variation map based on the sequencing of 3,366 genomes

In Extended Data Fig. 1 of this Article, the labels ‘Market class’ and ‘Biological status’ were inadvertently swapped. In the corresponding figure legend, “Track 1: Biological status; Track 2: Market class;” should have been “Track 1: Market class; Track 2: Biological status;”. The original Article has been corrected online

Genome-based trait prediction in multi- environment breeding trials in groundnut

Genomic selection (GS) can be an efficient and cost-effective breeding approach which captures both small- and large-effect genetic factors and therefore promises to achieve higher genetic gains for complex traits such as yield and oil content in groundnut. A training population was constituted with 340 elite lines followed by genotyping with 58 K ‘Axiom_Arachis’ SNP array and phenotyping for key agronomic traits at three locations in India. Four GS models were tested using three different random cross-validation schemes (CV0, CV1 and CV2). These models are: (1) model 1 (M1 = E + L) which includes the main effects of environment (E) and line (L); (2) model 2 (M2 = E + L + G) which includes the main effects of markers (G) in addition to E and L; (3) model 3 (M3 = E + L + G + GE), a naïve interaction model; and (4) model 4 (E + L + G + LE + GE), a naïve and informed interaction model. Prediction accuracy estimated for four models indicated clear advantage of the inclusion of marker information which was reflected in better prediction accuracy achieved with models M2, M3 and M4 as compared to M1 model. High prediction accuracies (> 0.600) were observed for days to 50% flowering, days to maturity, hundred seed weight, oleic acid, rust@90 days, rust@105 days and late leaf spot@90 days, while medium prediction accuracies (0.400–0.600) were obtained for pods/plant, shelling %, and total yield/plant. Assessment of comparative prediction accuracy for different GS models to perform selection for untested genotypes, and unobserved and unevaluated environments provided greater insights on potential application of GS breeding in groundnut

Establishment of triple-negative breast cancer cells based on BMI: A novel model in the correlation between obesity and breast cancer

IntroductionObesity has been associated with an increased risk of biologically aggressive variants in breast cancer. Women with obesity often have tumors diagnosed at later stages of the disease, associated with a poorer prognosis and a different response to treatment. Human cell lines have been derived from specific subtypes of breast cancer and have served to define the cell physiology of corresponding breast cancer subtypes. However, there are no current cell lines for breast cancer specifically derived from patients with different BMIs. The availability of those breast cancer cell lines should allow to describe and unravel functional alterations linked to these comorbidities. MethodsCell cultures were established from tumor explants. Once generated, the triple negative subtype in a patient with obesity and a patient with a normal BMI were chosen for comparison. For cellular characterization, the following assays were conducted: proliferation assays, chemo – sensitivity assays for doxorubicin and paclitaxel, wound healing motility assays, matrix invasion assays, breast cancer cell growth to estradiol by chronic exposure to leptin, induction of endothelial permeability and tumorigenic potential in athymic mice with normo - versus hypercaloric diets with an evaluation of the epithelium – mesenchymal transformation proteins.ResultsTwo different cell lines, were established from patients with breast cancer: DSG-BC1, with a BMI of 21.9 kg/m2 and DSG-BC2, with a BMI of 31.5 kg/m2. In vitro, these two cell lines show differential growth rates, motility, chemosensitivity, vascular permeability, response to leptin with an activation of the JAK2/STAT3/AKT signaling pathway. In vivo, they displayed distinct tumorigenic potential. In particular, DSG-BC2, presented higher tumorigenicity when implanted in mice fed with a hypercaloric diet.DiscussionTo our knowledge, these primary cultures are the first in vitro representation of both breast cancer and obesity. DSG – BC2 presented a more aggressive in vivo and in vitro phenotype. These results support the hypothesis that breast cancer generated in an obese metabolic state may represent a contrasting variant within the same disease. This new model will allow both further comprehension, functional studies and the analysis of altered molecular mechanisms under the comorbidity of obesity and breast cancer