Wheat (Triticum aestivum L.) cultivars must possess suitable enduse quality for release and consumer acceptability. However, breeding for quality traits is often considered a secondary target relative to yield largely because of amount of seed needed and expense. Without testing and selection, many undesirable materials are advanced, expending additional resources. Here, we develop and validate whole-genome prediction models for end-use quality phenotypes in the CIMMYT bread wheat breeding program. Model accuracy was tested using forward prediction on breeding lines (n = 5520) tested in unbalanced yield trials from 2009 to 2015 at Ciudad Obregon, Sonora, Mexico. Quality parameters included test weight, 1000-kernel weight, hardness, grain and flour protein, flour yield, sodium dodecyl sulfate sedimentation, Mixograph and Alveograph performance, and loaf volume. In general, prediction accuracy substantially increased over time as more data was available to train the model. Reflecting practical implementation of genomic selection (GS) in the breeding program, forward prediction accuracies (r) for quality parameters were assessed in 2015 and ranged from 0.32 (grain hardness) to 0.62 (mixing time). Increased selection intensity was possible with GS since more entries can be genotyped than phenotyped and expected genetic gain was 1.4 to 2.7 times higher across all traits than phenotypic selection. Given the limitations in measuring many lines for quality, we conclude that GS is a powerful tool to facilitate early generation selection for end-use quality in wheat, leaving larger populations for selection on yield during advanced testing and leading to better gain for both quality and yield in bread wheat breeding programs.

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This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H) for the highly heritable traits, days to heading (DTH), and days to maturity (DTM). Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G × E). Two alternative prediction strategies were studied: (1) random cross-validation of the data in 20% training (TRN) and 80% testing (TST) (TRN20-TST80) sets, and (2) two types of core sets, “diversity” and “prediction”, including 10% and 20%, respectively, of the total collections. Accounting for population structure decreased prediction accuracy by 15–20% as compared to prediction accuracy obtained when not accounting for population structure. Accounting for population structure gave prediction accuracies for traits evaluated in one environment for TRN20-TST80 that ranged from 0.407 to 0.677 for Mexican landraces, and from 0.166 to 0.662 for Iranian landraces. Prediction accuracy of the 20% diversity core set was similar to accuracies obtained for TRN20-TST80, ranging from 0.412 to 0.654 for Mexican landraces, and from 0.182 to 0.647 for Iranian landraces. The predictive core set gave similar prediction accuracy as the diversity core set for Mexican collections, but slightly lower for Iranian collections. Prediction accuracy when incorporating G × E for DTH and DTM for Mexican landraces for TRN20-TST80 was around 0.60, which is greater than without the G × E term. For Iranian landraces, accuracies were 0.55 for the G × E model with TRN20-TST80. Results show promising prediction accuracies for potential use in germplasm enhancement and rapid introgression of exotic germplasm into elite materials.

Kernel methods are flexible and easy to interpret and have been successfully used in genomicenabled prediction of various plant species. Kernel methods used in genomic prediction comprise the linear genomic best linear unbiased predictor (GBLUP or GB) kernel, and the Gaussian kernel (GK). In general, these kernels have been used with two statistical models: single-environment and genomic • environment (GE) models. Recently near infrared spectroscopy (NIR) has been used as an inexpensive and non-destructive high-throughput phenotyping method for predicting unobserved line performance in plant breeding trials. In this study, we used a non-linear arc-cosine kernel (AK) that emulates deep learning artificial neural networks. We compared AK prediction accuracy with the prediction accuracy of GB and GK kernel methods in four genomic data sets, one of which also includes pedigree and NIR information. Results show that for all four data sets, AK and GK kernels achieved higher prediction accuracy than the linear GB kernel for the single-environment and GE multi-environment models. In addition, AK achieved similar or slightly higher prediction accuracy than the GK kernel. For all data sets, the GE model achieved higher prediction accuracy than the single-environment model. For the data set that includes pedigree, markers and NIR, results show that the NIR wavelength alone achieved lower prediction accuracy than the genomic information alone; however, the pedigree plus NIR information achieved only slightly lower prediction accuracy than the marker plus the NIR high-throughput data. KEYWORDS Genomic based prediction Genomic Best Unbiased Predictor (GBLUP, GB linear and nonlinear kernel methods) near infrared (NIR) highthroughput phenotype single-environment model deep learning genomic • environment interaction model Genomic Prediction GenPred Shared Data Resources In genomic selection (GS) (Meuwissen et al. 2001), Bayesian models were introduced in the context of whole-genome regression and since then have become common in genomic prediction (Gianola 2013). Genomic-assisted selection has the advantage over phenotypic selection that it saves time and resources when making selection by adopting predictive methods and models for complex traits, along with information

The starch fraction, comprising about 70% of the total dry matter in the wheat grain, can greatly affect the end-use quality of products made from wheat kernels, especially Asian noodles. Starch is associated with the shelf life and nutritional value (glycaemic index) of different wheat products. Starch quality is closely associated with the ratio of amylose to amylopectin, the two main macromolecules forming starch. In this review, we briefly summarise the discovery of waxy proteins-shown to be the sole enzymes responsible for amylose synthesis in wheat. The review particularly focuses on the different variants of these proteins, together with their molecular characterisation and evaluation of their effects on starch composition. There have been 19 different waxy protein variants described using protein electrophoresis; and at a molecular level 19, 15 and seven alleles described for Wx-A1, Wx-B1 and Wx-D1, respectively. This large variability, found in modern wheat and genetic resources such as wheat ancestors and wild relatives, is in some cases not properly ordered. The proper ordering of all the data generated is the key to enhancing use in breeding programmes of the current variability described, and thus generating wheat with novel starch properties to satisfy the demand of industry and consumers for novel high-quality processed food.

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This work was conducted to optimize the extraction conditions for the best recovery of antioxidant compounds from peanut skins. The extracts from the peanut skins were obtained by different extraction methods. The extraction conditions were: different ethanol proportions as the solvent (0, 30, 50, 70 and 96% v/v in distilled water), different peanut skin particle sizes (0-1, 1-2 and 2-10 mm and noncrushed skins), different proportions of solvent/skins (20, 30, 40, 50 and 60 ml g −1 ), different extraction times (by maceration and shaking) and different numbers of extractions. The different extracts obtained under different extraction conditions were compared with special regard to yield, total phenolic compounds and radical scavenging activity. The results showed that the best delivery of phenolic compounds was reached using 70% ethanol, non-crushed peanut skins, ratio of solvent/solid of 20 ml g −1 , at 10 min shaking and three extractions. The maximum yield of 0.118 g g −1 was recorded for phenolic compounds when extracted at the optimum conditions.

Current trends in population growth and consumption patterns continue to increase the demand for wheat, a key cereal for global food security. Further, multiple abiotic challenges due to climate change and evolving pathogen and pests pose a major concern for increasing wheat production globally. Triticeae species comprising of primary, secondary, and tertiary gene pools represent a rich source of genetic diversity in wheat. The conventional breeding strategies of direct hybridization, backcrossing and selection have successfully introgressed a number of desirable traits associated with grain yield, adaptation to abiotic stresses, disease resistance, and bio-fortification of wheat varieties. However, it is time consuming to incorporate genes conferring tolerance/resistance to multiple stresses in a single wheat variety by conventional approaches due to limitations in screening methods and the lower probabilities of combining desirable alleles. Efforts on developing innovative breeding strategies, novel tools and utilizing genetic diversity for new genes/alleles are essential to improve productivity, reduce vulnerability to diseases and pests and enhance nutritional quality. New technologies of high-throughput phenotyping, genome sequencing and genomic selection are promising approaches to maximize progeny screening and selection to accelerate the genetic gains in breeding more productive varieties. Use of cisgenic techniques to transfer beneficial alleles and their combinations within related species also offer great promise especially to achieve durable rust resistance.