Does root plasticity contribute to crop tolerance to abiotic stresses?

Poster that presents the experiments, the main results and take home messages

Genome-Wide Association Mapping and Genomic Prediction of Anther Extrusion in CIMMYT Hybrid Wheat Breeding Program via Modeling Pedigree, Genomic Relationship, and Interaction With the Environment

Anther extrusion (AE) is the most important male floral trait for hybrid wheat seed production. AE is a complex quantitative trait that is difficult to phenotype reliably in field experiments not only due to high genotype-by-environment effects but also due to the short expression window in the field condition. In this study, we conducted a genome-wide association scan (GWAS) and explored the possibility of applying genomic prediction (GP) for AE in the CIMMYT hybrid wheat breeding program. An elite set of male lines (n = 603) were phenotype for anther count (AC) and anther visual score (VS) across three field experiments in 2017–2019 and genotyped with the 20K Infinitum is elect SNP array. GWAS produced five marker trait associations with small effects. For GP, the main effects of lines (L), environment (E), genomic (G) and pedigree relationships (A), and their interaction effects with environments were used to develop seven statistical models of incremental complexity. The base model used only L and E, whereas the most complex model included L, E, G, A, and G × E and A × E. These models were evaluated in three cross-validation scenarios (CV0, CV1, and CV2). In cross-validation CV0, data from two environments were used to predict an untested environment; in random cross-validation CV1, the test set was never evaluated in any environment; and in CV2, the genotypes in the test set were evaluated in only a subset of environments. The prediction accuracies ranged from −0.03 to 0.74 for AC and −0.01 to 0.54 for VS across different models and CV schemes. For both traits, the highest prediction accuracies with low variance were observed in CV2, and inclusion of the interaction effects increased prediction accuracy for AC only. In CV0, the prediction accuracy was 0.73 and 0.45 for AC and VS, respectively, indicating the high reliability of across environment prediction. Genomic prediction appears to be a very reliable tool for AE in hybrid wheat breeding. Moreover, high prediction accuracy in CV0 demonstrates the possibility of implementing genomic selection across breeding cycles in related germplasm, aiding the rapid breeding cycle

Genomic selection in wheat breeding using genotyping-by-sequencing

Citation: Poland, Jesse, Jeffrey Endelman, Julie Dawson, Jessica Rutkoski, Shuangye Wu, Yann Manes, Susanne Dreisigacker, et al. “Genomic Selection in Wheat Breeding Using Genotyping-by-Sequencing.” The Plant Genome 5, no. 3 (2012): 103–13. https://doi.org/10.3835/plantgenome2012.06.0006.Genomic selection (GS) uses genomewide molecular markers to predict breeding values and make selections of individuals or breeding lines prior to phenotyping. Here we show that genotyping-by-sequencing (GBS) can be used for de novo genotyping of breeding panels and to develop accurate GS models, even for the large, complex, and polyploid wheat (Triticum aestivum L.) genome. With GBS we discovered 41,371 single nucleotide polymorphisms (SNPs) in a set of 254 advanced breeding lines from CIMMYT’s semiarid wheat breeding program. Four different methods were evaluated for imputing missing marker scores in this set of unmapped markers, including random forest regression and a newly developed multivariate-normal expectation-maximization algorithm, which gave more accurate imputation than heterozygous or mean imputation at the marker level, although no signifi cant differences were observed in the accuracy of genomic-estimated breeding values (GEBVs) among imputation methods. Genomic-estimated breeding value prediction accuracies with GBS were 0.28 to 0.45 for grain yield, an improvement of 0.1 to 0.2 over an established marker platform for wheat. Genotyping-bysequencing combines marker discovery and genotyping of large populations, making it an excellent marker platform for breeding applications even in the absence of a reference genome sequence or previous polymorphism discovery. In addition, the flexibility and low cost of GBS make this an ideal approach for genomics-assisted breeding

Particle accumulation in ureteral stents is governed by fluid dynamics: <i>in vitro</i> study using a “stent-on-chip” model

Objective: To investigate the correlation between fluid dynamic processes and deposition of encrusting particles in ureteral stents.Materials and Methods: Microfluidic models (referred to as “stent-on-chip” or SOC) were developed to replicate relevant hydrodynamic regions of a stented ureter, including drainage holes and the cavity formed by a ureteral obstruction. Computational fluid dynamic simulations were performed to determine the wall shear stress (WSS) field over the solid surfaces of the model, and the computational flow field was validated experimentally. Artificial urine was conveyed through the SOCs to measure the temporal evolution of encrustation through optical microscopy.Results: It was revealed that drainage holes located well downstream of the obstruction had almost stagnant flow and low WSS (average 0.01 Pa, at 1 mL/min), and thus suffered from higher encrustation rates. On the contrary, higher levels of WSS in holes proximal to the obstruction (average ∼0.04 Pa, at 1 mL/min) resulted in lower encrustation rates in these regions. The cavity located nearby the obstruction was characterized by high levels of encrustation, because of the low WSS (average 1.6 × 10−4 Pa, at 1 mL/min) and the presence of flow vortices. Increasing the drainage flow rate from 1 to 10 mL/min resulted in significantly lower deposition of encrusting crystals.Conclusion: This study demonstrated an inverse correlation between deposition of encrusting bodies and the local WSS in a stented ureter model. Critical regions with low WSS and susceptible to encrustation were identified, including “inactive” side holes (i.e., with minimal or absent flow exchange between stent and ureter) and the cavity formed by a ureteral occlusion. Findings from this study can open new avenues for improving the stent's design through fluid dynamic optimization

Reducing deposition of encrustation in ureteric stents by changing the stent architecture: a microfluidic-based investigation

Ureteric stents are clinically deployed to retain ureteral patency in the presence of an obstruction of the ureter lumen. Despite the fact that multiple stent designs have been researched in recent years, encrustation and biofilm-associated infections remain significant complications of ureteral stenting, potentially leading to the functional failure of the stent. It has been suggested that ‘inactive’ side-holes of stents may act as anchoring sites for encrusting crystals, as they are associated with low wall shear stress (WSS) levels. Obstruction of side-holes due to encrustation is particularly detrimental to the function of the stent, since holes provide a path for urine to by-pass the occlusion. Therefore, there is an unmet need to develop novel stents to reduce deposition of encrusting particles at side-holes. In this study, we employed a stent-on-chip (SoC) microfluidic model of the stented and occluded ureter to investigate the effect of stent architecture on WSS distribution and encrustation over its surface. Variations in the stent geometry encompassed (i) the wall thickness and (ii) the shape of side-holes. Stent thickness was varied in the range 0.3-0.7 mm, while streamlined side-holes of triangular shape were evaluated (with vertex angle in the range 45-120°). Reducing the thickness of the stent increased WSS and thus reduced encrustation rate at side-holes. A further improvement in performance was achieved by using side-holes with triangular shape; notably, a 45° vertex angle showed superior performance compared to other angles investigated, resulting in a significant increase in WSS within ‘inactive’ side-holes. In conclusion, by combining the optimal stent thickness (0.3 mm) and hole vertex angle (45°) resulted in a ~90% reduction in encrustation rate within side-holes, compared to a standard design. If translated to a full-scale ureteric stent, this optimised architecture has the potential for significantly increasing the stent lifetime while reducing clinical complications

Hybrid Wheat Prediction Using Genomic, Pedigree, and Environmental Covariables Interaction Models

In this study, we used genotype × environment interactions (G×E) models for hybrid prediction, where similarity between lines was assessed by pedigree and molecular markers, and similarity between environments was accounted for by environmental covariables. We use five genomic and pedigree models (M1–M5) under four cross-validation (CV) schemes: prediction of hybrids when the training set (i) includes hybrids of all males and females evaluated only in some environments (T2FM), (ii) excludes all progenies from a randomly selected male (T1M), (iii) includes all progenies from 20% randomly selected females in combination with all males (T1F), and (iv) includes one randomly selected male plus 40% randomly selected females that were crossed with it (T0FM). Models were tested on a total of 1888 wheat ( L.) hybrids including 18 males and 667 females in three consecutive years. For grain yield, the most complex model (M5) under T2FM had slightly higher prediction accuracy than the less complex model. For T1F, the prediction accuracy of hybrids for grain yield and other traits of the most complete model was 0.50 to 0.55. For T1M, Model M3 exhibited high prediction accuracies for flowering traits (0.71), whereas the more complex model (M5) demonstrated high accuracy for grain yield (0.5). For T0FM, the prediction accuracy for grain yield of Model M5 was 0.61. Including genomic and pedigree gave relatively high prediction accuracy even when both parents were untested. Results show that it is possible to predict unobserved hybrids when modeling genomic general combining ability (GCA) and specific combining ability (SCA) and their interactions with environments

Towards a wheat phenome atlas and a phenome atlas toolbox: What are they? What progress?

A Phenome Map is a representation of all the regions of a genome that influence heritable phenotypic variation for a trait, and a Phenome Atlas consists of the integration of all available phenome maps with a description of the methodologies that were used to produce the maps. A Phenome Atlas Toolbox is a set of tools and methodologies for producing the Phenome Atlas. The Wheat Phenome Atlas (WPA) will be an integration of phenotypic data (17 million data points for 80 traits from 10,000 international field trials collected during more than 40 years) generated by CIMMYT and partners on approximately 13,000 wheat lines (for which pedigrees are known) with greater than 26 million DArT marker data points obtained by genotyping these lines. To generate this amount of phenotypic data would cost over $500 million today