Genomic selection (GS) is transforming the field of plant breeding and implementing models that improve prediction accuracy for complex traits is needed. Analytical methods for complex datasets traditionally used in other disciplines represent an opportunity for improving prediction accuracy in GS. Deep learning (DL) is a branch of machine learning (ML) which focuses on densely connected networks using artificial neural networks for training the models. The objective of this research was to evaluate the potential of DL models in the Washington State University spring wheat breeding program. We compared the performance of two DL algorithms, namely multilayer perceptron (MLP) and convolutional neural network (CNN), with ridge regression best linear unbiased predictor (rrBLUP), a commonly used GS model. The dataset consisted of 650 recombinant inbred lines (RILs) from a spring wheat nested association mapping (NAM) population planted from 2014–2016 growing seasons. We predicted five different quantitative traits with varying genetic architecture using cross-validations (CVs), independent validations, and different sets of SNP markers. Hyperparameters were optimized for DL models by lowering the root mean square in the training set, avoiding model overfitting using dropout and regularization. DL models gave 0 to 5% higher prediction accuracy than rrBLUP model under both cross and independent validations for all five traits used in this study. Furthermore, MLP produces 5% higher prediction accuracy than CNN for grain yield and grain protein content. Altogether, DL approaches obtained better prediction accuracy for each trait, and should be incorporated into a plant breeder’s toolkit for use in large scale breeding programs.

BackgroundGenomic selection has the potential to increase genetic gains by using molecular markers as predictors of breeding values of individuals. This study evaluated the accuracy of predictions for grain yield, heading date, plant height, and yield components in soft red winter wheat under different prediction scenarios. Response to selection for grain yield was also compared across different selection strategies- phenotypic, marker-based, genomic, combination of phenotypic and genomic, and random selections.ResultsGenomic selection was implemented through a ridge regression best linear unbiased prediction model in two scenarios- cross-validations and independent predictions. Accuracy for cross-validations was assessed using a diverse panel under different marker number, training population size, relatedness between training and validation populations, and inclusion of fixed effect in the model. The population in the first scenario was then trained and used to predict grain yield of biparental populations for independent validations. Using subsets of significant markers from association mapping increased accuracy by 64–70% for grain yield but resulted in lower accuracy for traits with high heritability such as plant height. Increasing size of training population resulted in an increase in accuracy, with maximum values reached when ~ 60% of the lines were used as a training panel. Predictions using related subpopulations also resulted in higher accuracies. Inclusion of major growth habit genes as fixed effect in the model caused increase in grain yield accuracy under a cross-validation procedure. Independent predictions resulted in accuracy ranging between − 0.14 and 0.43, dependent on the grouping of site-year data for the training and validation populations. Genomic selection was “superior” to marker-based selection in terms of response to selection for yield. Supplementing phenotypic with genomic selection resulted in approximately 10% gain in response compared to using phenotypic selection alone.ConclusionsOur results showed the effects of different factors on accuracy for yield and agronomic traits. Among the factors studied, training population size and relatedness between training and validation population had the greatest impact on accuracy. Ultimately, combining phenotypic with genomic selection would be relevant for accelerating genetic gains for yield in winter wheat.

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In order to meet the goal of doubling wheat yield by 2050, breeders must work to improve breeding program efficiency while also implementing new and improved technologies in order to increase genetic gain. Genomic selection (GS) is an expansion of marker assisted selection which uses a statistical model to estimate all marker effects for an individual simultaneously to determine a genome estimated breeding value (GEBV). Breeders are thus able to select for performance based on GEBVs in the absence of phenotypic data. In wheat, genomic selection has been successfully implemented for a number of key traits including grain yield, grain quality and quantitative disease resistance, such as that for Fusarium head blight. For this review, we focused on the ways to modify genomic selection to maximize prediction accuracy, including prediction model selection, marker density, trait heritability, linkage disequilibrium, the relationship between training and validation sets, population structure, and training set optimization methods. Altogether, the effects of these different factors on the accuracy of predictions should be thoroughly considered for the successful implementation of GS strategies in wheat breeding programs.

BackgroundA range of resistance loci against different races of Xanthomonas oryzae pv. oryzae (Xoo), the pathogen causing bacterial blight (BB) disease of rice, have been discovered and characterized. Several have been deployed in modern varieties, however, due to rapid evolution of Xoo, a number have already become ineffective. The continuous “arms race” between Xoo and rice makes it imperative to discover new resistance loci to enable durable deployment of multiple resistance genes in modern breeding lines. Rice diversity panels can be exploited as reservoirs of useful genetic variation for bacterial blight (BB) resistance. This study was conducted to identify loci associated to BB resistance, new genetic donors and useful molecular markers for marker-assisted breeding.ResultsA genome-wide association study (GWAS) of BB resistance using a diverse panel of 285 rice accessions was performed to identify loci that are associated with resistance to nine Xoo strains from the Philippines, representative of eight global races. Single nucleotide polymorphisms (SNPs) associated with differential resistance were identified in the diverse panel and a subset of 198 indica accessions. Strong associations were found for novel SNPs linked with known bacterial blight resistance Xa genes, from which high utility markers for tracking and selection of resistance genes in breeding programs were designed. Furthermore, significant associations of SNPs in chromosomes 6, 9, 11, and 12 did not overlap with known resistance loci and hence might prove to be novel sources of resistance. Detailed analysis revealed haplotypes that correlated with resistance and analysis of putative resistance alleles identified resistant genotypes as potential donors of new resistance genes.ConclusionsThe results of the GWAS validated known genes underlying resistance and identified novel loci that provide useful targets for further investigation. SNP markers and genetic donors identified in this study will help plant breeders in improving and diversifying resistance to BB.Electronic supplementary materialThe online version of this article (doi:10.1186/s12284-017-0147-4) contains supplementary material, which is available to authorized users.

Secondary traits from high-throughput phenotyping could be used to select for complex target traits to accelerate plant breeding and increase genetic gains. This study aimed to evaluate the potential of using spectral reflectance indices (SRI) for indirect selection of winter-wheat lines with high yield potential and to assess the effects of including secondary traits on the prediction accuracy for yield. A total of five SRIs were measured in a diversity panel, and F5 and doubled haploid wheat breeding populations planted between 2015 and 2018 in Lind and Pullman, WA. The winter-wheat panels were genotyped with 11,089 genotyping-by-sequencing derived markers. Spectral traits showed moderate to high phenotypic and genetic correlations, indicating their potential for indirect selection of lines with high yield potential. Inclusion of correlated spectral traits in genomic prediction models resulted in significant (p < 0.001) improvement in prediction accuracy for yield. Relatedness between training and test populations and heritability were among the principal factors affecting accuracy. Our results demonstrate the potential of using spectral indices as proxy measurements for selecting lines with increased yield potential and for improving prediction accuracy to increase genetic gains for complex traits in US Pacific Northwest winter wheat.

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The aim of this study was to identify quantitative trait locus (QTL) associated with grain yield (GY) in a recombinant inbred line (RIL) population from a cross between two elite soft red winter wheat (SRWW) cultivars ('Pioneer 26R61' and 'AGS2000'). The RIL population was grown from 2011 to 2014 in 12 site-year combinations throughout the southeastern US. Overall, AGS2000 was the higher yielding parental line, out-performing 26R61 in seven of the 12 environments. Mean GY for the RILs ranged from 3.39 to 7.16 t ha -1 with significant genotype, environment and genotype by environmental interaction effects. Nine stable QTL were detected for yield, explaining up to 53 % of the phenotypic variation when fit into a multiple-QTL model. The QTL with the largest effect was detected at the Vrn-B1 locus with the short vernalization winter allele from AGS2000 favorable for yield. In addition, vrn-B1 acted additively with a region on chromosome 2B near the Ppd-B1 locus, indicating that a shorter vernalization requirement combined with the Ppd-B1b allele for photoperiod sensitivity may play a key role in adaptation of SRWW to the southern US. Single nucleotide polymorphism markers linked to additional QTL on chromosomes 3A and 3B were in agreement with a previous genome-wide association study in spring wheat, confirming the importance of these regions for yield across environments and germplasm pools. Overall the stable QTL were more predictive of GY compared to individual site-year QTL, indicating that a targeted QTL approach can be utilized by breeding programs to enrich for favorable loci.Electronic supplementary material The online version of this article (