Genomic-enabled Prediction Accuracies Increased by Modeling Genotype × Environment Interaction in Durum Wheat

Genomic prediction studies incorporating genotype × environment (G×E) interaction effects are limited in durum wheat. We tested the genomic-enabled prediction accuracy (PA) of Genomic Best Linear Unbiased Predictor (GBLUP) models—six non-G × E and three G × E models—on three basic cross-validation (CV) schemes— in predicting incomplete field trials (CV2), new lines (CV1), and lines in untested environments (CV0)— in a durum wheat panel grown under yield potential, drought stress, and heat stress conditions. For CV0, three scenarios were considered: (i) leave-one environment out (CV0-Env); (ii) leave one site out (CV0- Site); and (iii) leave 1 yr out (CV0-Year). The reaction norm models with G × E effects showed higher PA than the non-G × E models. Among the CV schemes, CV2 and CV0-Env had higher PA (0.58 each) than the CV1 scheme (0.35). When the average of all the models and CV schemes were considered, among the eight traits— grain yield, thousand grain weight, grain number, days to anthesis, days to maturity, plant height, and normalized difference vegetation index at vegetative (NDVIvg) and grain filling (NDVIllg)—, plant height had the highest PA (0.68) and moderate values were observed for grain yield (0.34). The results indicated that genomic selection models incorporating G × E interaction show great promise for forward prediction and application in durum wheat breeding to increase genetic gains

Genomic Prediction with Pedigree and Genotype x Environment Interaction in Spring Wheat Grown in South and West Asia, North Africa, and Mexico

Developing genomic selection (GS) models is an important step in applying GS to accelerate the rate of genetic gain in grain yield in plant breeding. In this study, seven genomic prediction models under two cross-validation (CV) scenarios were tested on 287 advanced elite spring wheat lines phenotyped for grain yield (GY), thousand-grain weight (GW), grain number (GN), and thermal time for flowering (TTF) in 18 international environments (year-location combinations) in major wheat-producing countries in 2010 and 2011. Prediction models with genomic and pedigree information included main effects and interaction with environments. Two random CV schemes were applied to predict a subset of lines that were not observed in any of the 18 environments (CV1), and a subset of lines that were not observed in a set of the environments, but were observed in other environments (CV2). Genomic prediction models, including genotype x environment (GxE) interaction, had the highest average prediction ability under the CV1 scenario for GY (0.31), GN (0.32), GW (0.45), and TTF (0.27). For CV2, the average prediction ability of the model including the interaction terms was generally high for GY (0.38), GN (0.43), GW (0.63), and TTF (0.53). Wheat lines in siteyear combinations in Mexico and India had relatively high prediction ability for GY and GW. Results indicated that prediction ability of lines not observed in certain environments could be relatively high for genomic selection when predicting GxE interaction in multi-environment trials

Multi-environment QTL analysis using an updated genetic map of a widely distributed Seri × Babax spring wheat population

Seri/Babax spring wheat RIL population was developed to minimize the confounding effect of phenology in the genetic dissection of abiotic stress traits. An existing linkage map (< 500 markers) was updated with 6470 polymorphic Illumina iSelect 90K array and DArTseq SNPs to a genetic map of 5576.5 cM with 1748 non-redundant markers (1165 90K SNPs, 207 DArTseq SNPs, 183 AFLP, 111 DArT array, and 82 SSR) assigned to 31 linkage groups. We conducted QTL mapping for yield and related traits phenotyped in several major wheat growing areas in Egypt, Sudan, Iran, India, and Mexico (nine environments: heat, drought, heat plus drought, and yield potential). QTL analysis identified 39 (LOD 2.5–23.6; PVE 4.8–21.3%), 36 (LOD 2.5–15.4; PVE 2.9–21.4%), 30 (LOD 2.5–13.1; PVE 3.6–26.8%), 39 (LOD 2.7–14.4; PVE 2.6–15.9%), and 22 (LOD 2.8–4.8; PVE 6.8–12.9%) QTLs for grain yield, thousand-grain weight, grain number, days to heading, and plant height, respectively. The present study confirmed QTLs from previous studies and identified novel QTLs. QTL analysis based on high-yielding and low-yielding environmental clusters identified 11 QTLs (LOD 2.6–14.9; PVE 2.7–19.7%). The updated map thereby provides a better genome coverage (3.5-fold) especially on the D genome (4-fold), higher density (1.1-fold), and a good collinearity with the IWGSC RefSeq v1.0 genome, and increased the number of detected QTLs (5-fold) compared with the earlier map. This map serves as a useful genomic resource for genetic analyses of important traits on this wheat population that was widely distributed around the world.info:eu-repo/semantics/acceptedVersio

Treatment of synthetic textile wastewater containing dye mixtures with microcosms

The aim was to assess the ability of microcosms (laboratory-scale shallow ponds) as a post polishing stage for the remediation of artificial textile wastewater comprising two commercial dyes (basic red 46 (BR46) and reactive blue 198 (RB198)) as a mixture. The objectives were to evaluate the impact of Lemna minor L. (common duckweed) on the water quality outflows; the elimination of dye mixtures, organic matter, and nutrients; and the impact of synthetic textile wastewater comprising dye mixtures on the L. minor plant growth. Three mixtures were prepared providing a total dye concentration of 10 mg/l. Findings showed that the planted simulated ponds possess a significant (p &lt; 0.05) potential for improving the outflow characteristics and eliminate dyes, ammonium-nitrogen (NH4-N), and nitrate-nitrogen (NO3-N) in all mixtures compared with the corresponding unplanted ponds. The removal of mixed dyes in planted ponds was mainly due to phyto-transformation and adsorption of BR46 with complete aromatic amine mineralisation. For ponds containing 2 mg/l of RB198 and 8 mg/l of BR46, removals were around 53%, which was significantly higher than those for other mixtures: 5 mg/l of RB198 and 5 mg/l of BR46 and 8 mg/l of RB198 and 2 mg/l of BR46 achieved only 41 and 26% removals, respectively. Dye mixtures stopped the growth of L. minor, and the presence of artificial wastewater reduced their development

Elasmobranch conservation, challenges and management strategy in India: recommendations from a national consultative meeting

Historically, India has been projected as one of the major elasmobranch fishing nations in the world. However, management and conservation efforts are not commensurate with this trend. Along with the Wildlife (Protection) Act, 1972, several generic conservation measures are in place at the regional/local level. But India is still a long way from meeting global conservation commitments. We present here the status of elasmobranch management and conservation in India, with the specific objec-tive of identifying the gaps in the existing set-up. We also present recommendations based on a national consultative workshop held at the Central Marine Fisheries Research Institute, Kochi, in February 2020. We recommend the implementation of a National Plan of Action (NPOA-Sharks) and more in-clusive governance and policymaking for elasmobranch conservation in India

Supplementary File for Capturing wheat phenotypes at the genome level

Supplementary S1: Yield and related traits in bread wheat. Table S1: Examples of genomic regions, candidate and cloned genes for yield and related traits in bread wheat. Supplementary S2: Drought tolerance. Table S2: Examples of genomic regions and candidate genes for drought tolerance. Supplementary S3: Heat tolerance. Table S3. Examples of genomic regions and candidate genes for heat tolerance. Supplementary S4: salinity tolerance in bread wheat. Table S4. Examples of genomic regions and candidate genes for salinity tolerance in bread wheat. Supplementary S5: Frost tolerance. Supplementary S6: Disease resistance. Table S5. Examples of genomic regions, candidate and cloned genes mapped for disease resistance in wheat species. Supplementary S7 insect and mite resistance. Table S6. Examples of genomic regions and candidate genes mapped for insect and mite resistance. Supplementary S8: Quality traits. Table S7. Examples of genomic regions, candidate and cloned genes for quality traits.Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world’s most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public–private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.Peer reviewe

Genomic mapping for grain yield, stay green, and grain quality traits in sorghum

Doctor of PhilosophyDepartment of AgronomyJianming YuKnowledge of the genetic bases of grain quality traits will complement plant breeding efforts to improve the end use value of sorghum (Sorghum bicolor (L.) Moench). The objective of the first experiment was to assess marker-trait associations for 10 grain quality traits through candidate gene association mapping on a diverse panel of 300 sorghum accessions. The 10 grain quality traits were measured using the single kernel characterization system (SKCS) and near-infrared reflectance spectroscopy (NIRS). The analysis of the accessions through 1,290 genome-wide single nucleotide polymorphisms (SNPs) separated the panel into five subpopulations that corresponded to three major sorghum races (durra, kafir, and caudatum), one intermediate race (guinea-caudatum), and one working group (zerazera/caudatum). Association analysis between 333 SNPs in candidate genes/loci and grain quality traits resulted in eight significant marker-trait associations. A SNP in starch synthase IIa (SSIIa) gene was associated with kernel hardness (KH) with a likelihood ratio–based R[superscript]2 (R[subscript]L[subscript]R[superscript]2) value of 0.08. SNPs in starch synthase (SSIIb) gene (R[subscript]L[subscript]R[superscript]2 = 0.10) and loci pSB1120 (R[subscript]L[subscript]R[superscript]2 = 0.09) was associated with starch content. Sorghum is a crop well adapted to the semi arid regions of the world and my harbor genes for drought tolerance. The objective of second experiment was to identify quantitative trait loci (QTLs) for yield potential and drought tolerance. From a cross between Tx436 (food grain type) and 00MN7645 (drought tolerant) 248 recombinant inbred lines (RILs) was developed. Multi-location trials were conducted in 8 environments to evaluate agronomic performance of the RILs under favorable and drought stress conditions. The 248 RILs and their parents were genotyped by genotyping-by-sequencing (GBS). A subset of 800 SNPs was used for linkage map construction and QTL detection. Composite interval mapping identified a major QTLs for grain yield in chromosome 8 and QTL for flowering time in chromosome 9 under favorable conditions. Three major QTLs were detected for grain yield in chromosomes 1, 6, and 8 and two flowering time QTLs on chromosome 1 under drought conditions. Six QTLs were identified for stay green: two on chromosome 4; one each on chromosome 5, 6, 7, and 10 under drought conditions

Genomic-enabled Prediction Accuracies Increased by Modeling Genotype × Environment Interaction in Durum Wheat

Genomic prediction studies incorporating genotype × environment (G×E) interaction effects are limited in durum wheat. We tested the genomic-enabled prediction accuracy (PA) of Genomic Best Linear Unbiased Predictor (GBLUP) models—six non-G × E and three G × E models—on three basic cross-validation (CV) schemes— in predicting incomplete field trials (CV2), new lines (CV1), and lines in untested environments (CV0)— in a durum wheat panel grown under yield potential, drought stress, and heat stress conditions. For CV0, three scenarios were considered: (i) leave-one environment out (CV0-Env); (ii) leave one site out (CV0- Site); and (iii) leave 1 yr out (CV0-Year). The reaction norm models with G × E effects showed higher PA than the non-G × E models. Among the CV schemes, CV2 and CV0-Env had higher PA (0.58 each) than the CV1 scheme (0.35). When the average of all the models and CV schemes were considered, among the eight traits— grain yield, thousand grain weight, grain number, days to anthesis, days to maturity, plant height, and normalized difference vegetation index at vegetative (NDVIvg) and grain filling (NDVIllg)—, plant height had the highest PA (0.68) and moderate values were observed for grain yield (0.34). The results indicated that genomic selection models incorporating G × E interaction show great promise for forward prediction and application in durum wheat breeding to increase genetic gains