Enrique Autrique

Increased Prediction Accuracy in Wheat Breeding Trials Using a Marker × Environment Interaction Genomic Selection Model

Genomic selection (GS) models use genome-wide genetic information to predict genetic values of candidates of selection. Originally, these models were developed without considering genotype × environment interaction(G×E). Several authors have proposed extensions of the single-environment GS model that accommodate G×E using either covariance functions or environmental covariates. In this study, we model G×E using a marker × environment interaction (M×E) GS model; the approach is conceptually simple and can be implemented with existing GS software. We discuss how the model can be implemented by using an explicit regression of phenotypes on markers or using co-variance structures (a genomic best linear unbiased prediction-type model). We used the M×E model to analyze three CIMMYT wheat data sets (W1, W2, and W3), where more than 1000 lines were genotyped using genotyping-by-sequencing and evaluated at CIMMYT’s research station in Ciudad Obregon, Mexico, under simulated environmental conditions that covered different irrigation levels, sowing dates and planting systems. We compared the M×E model with a stratified (i.e., within-environment) analysis and with a standard (across-environment) GS model that assumes that effects are constant across environments (i.e., ignoring G×E). The prediction accuracy of the M×E model was substantially greater of that of an across-environment analysis that ignores G×E. Depending on the prediction problem, the M×E model had either similar or greater levels of prediction accuracy than the stratified analyses. The M×E model decomposes marker effects and genomic values into components that are stable across environments (main effects) and others that are environment-specific (interactions). Therefore, in principle, the interaction model could shed light over which variants have effects that are stable across environments and which ones are responsible for G×E. The data set and the scripts required to reproduce the analysis are publicly available as Supporting Information.

Comparison of two crossing and four selection schemes for yield, yield traits, and slow rusting resistance to leaf rust in wheat

The most important breeding objectives in crop improvement are improving grain yield, grain quality, and resistances to various biotic and abiotic stresses. The objectives of our study were to compare two crossing and four selection schemes for grain yield, yield traits, and slow rusting resistance to leaf rust (Puccinia recondita) based on additive genes in wheat (Triticum aestivum), and to identify the most efficient crossing and selection methodologies in terms of genetic gains and cost efficiency. Segregating populations were derived from 18 simple crosses and the same number of top (three-way) crosses. Half of the crosses were derived from Yecora 70 and the other half from Veery #10 as the common leaf rust susceptible parents. The four selection schemes were: pedigree, modified bulk (F2 and F1top as pedigree, selected lines in F3, F4, F2-top, F3-top as bulk; and pedigree in F5 and F4-top populations), selected bulk (selected plants in F2, F3, F4, F1top, F2-top and F3-top as bulk; and pedigree in F5 and F4-top populations), and nonselected bulk (bulk in F2, F3, F4, F1top, F2-top and F3-top; and pedigree in F5 and F4-top populations). A total of 320 progeny lines, parents and checks were tested for grain yield, other agronomic traits and leaf rust resistance during the 1992/93 and 1993/94 seasons in Ciudad Obregon (Sonora State, Mexico) which represents a typical high yielding irrigated site. The influence of the type of cross and the selection scheme on the mean grain yield and other traits of the progenies was minimal. The selection of parents was the most important feature in imparting yield potential and other favourable agronomic traits. Moreover, the highest yielding lines were distributed equally. Progeny lines derived from Veery #10 crosses had significantly higher mean grain yield compared to those derived from the Yecora 70 crosses. Furthermore, a large proportion of the highest yielding lines also originated from Veery #10 crosses. Mean leaf rust severity of the top cross progenies was lower than that of the simple cross progenies possibly because two parents contributed resistance to top cross progenies. Mean leaf rust severity of the nonselected bulk derivatives was twice that of lines derived from the other three schemes. Selected bulk appears to be the most attractive selection scheme in terms of genetic gains and cost efficiency.

Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat

Genomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative to phenotypic selection. Traits measured using high-throughput phenotyping based on proximal or remote sensing could be useful for improving pedigree and genomic prediction model accuracies for traits not yet possible to phenotype directly. We tested if using aerial measurements of canopy temperature, and green and red normalized difference vegetation index as secondary traits in pedigree and genomic best linear unbiased prediction models could increase accuracy for grain yield in wheat, Triticum aestivum L., using 557 lines in five environments. Secondary traits on training and test sets, and grain yield on the training set were modeled as multivariate, and compared to univariate models with grain yield on the training set only. Cross validation accuracies were estimated within and across-environment, with and without replication, and with and without correcting for days to heading. We observed that, within environment, with unreplicated secondary trait data, and without correcting for days to heading, secondary traits increased accuracies for grain yield by 56% in pedigree, and 70% in genomic prediction models, on average. Secondary traits increased accuracy slightly more when replicated, and considerably less when models corrected for days to heading. In across-environment prediction, trends were similar but less consistent. These results show that secondary traits measured in high-throughput could be used in pedigree and genomic prediction to improve accuracy. This approach could improve selection in wheat during early stages if validated in early-generation breeding plots.

Wheat quality improvement at CIMMYT and the use of genomic selection on it

The International Center for Maize and Wheat Improvement (CIMMYT) leads the Global Wheat Program, whose main objective is to increase the productivity of wheat cropping systems to reduce poverty in developing countries. The priorities of the program are high grain yield, disease resistance, tolerance to abiotic stresses (drought and heat), and desirable quality. The Wheat Chemistry and Quality Laboratory has been continuously evolving to be able to analyze the largest number of samples possible, in the shortest time, at lowest cost, in order to deliver data on diverse quality traits on time to the breeders for making selections for advancement in the breeding pipeline. The participation of wheat quality analysis/selection is carried out in two stages of the breeding process: evaluation of the parental lines for new crosses and advanced lines in preliminary and elite yield trials. Thousands of lines are analyzed which requires a big investment in resources. Genomic selection has been proposed to assist in selecting for quality and other traits in breeding programs. Genomic selection can predict quantitative traits and is applicable to multiple quantitative traits in a breeding pipeline by attaining historical phenotypes and adding high-density genotypic information. Due to advances in sequencing technology, genome-wide single nucleotide polymorphism markers are available through genotyping-by-sequencing at a cost conducive to application for genomic selection. At CIMMYT, genomic selection has been applied to predict all of the processing and end-use quality traits regularly tested in the spring wheat breeding program. These traits have variable levels of prediction accuracy, however, they demonstrated that most expensive traits, dough rheology and baking final product, can be predicted with a high degree of confidence. Currently it is being explored how to combine both phenotypic and genomic selection to make more efficient the genetic improvement for quality traits at CIMMYT spring wheat breeding program.

Genetic yield gains in CIMMYT’s international elite Spring Wheat yield trials by modeling the Genotype X environment interaction

We calculated the annual genetic gains for grain yield (GY) of wheat (Triticum aestivum L.) achieved over 8 yr of international Elite Spring Wheat Yield Trials (ESWYT), from 2006–2007 (27th ESWYT) to 2014–2015 (34th ESWYT). In total, 426 locations were classified within three main megaenvironments (MEs): ME1 (optimally irrigated environments), ME4 (drought-stressed environments), and ME5 (heat-stressed environments). By fitting a factor analytical structure for modeling the genotype × environment (G × E) interaction, we measured GY gains relative to the widely grown cultivar Attila (GYA) and to the local checks (GYLC). Genetic gains for GYA and GYLC across locations were 1.67 and 0.53% (90.1 and 28.7 kg ha–1 yr–1), respectively. In ME1, genetic gains were 1.63 and 0.72% (102.7 and 46.65 kg ha–1 yr–1) for GYA and GYLC, respectively. In ME4, genetic gains were 2.7 and 0.41% (88 and 15.45 kg ha–1 yr–1) for GYA and GYLC, respectively. In ME5, genetic gains were 0.31 and 1.0% (11.28 and 36.6 kg ha–1 yr–1) for GYA and GYLC, respectively. The high GYA in ME1 and ME4 can be partially attributed to yellow rust races that affect Attila. When G × E interactions were not modeled, genetic gains were lower. Analyses showed that CIMMYT’s location at Ciudad Obregon, Mexico, is highly correlated with locations in other countries in ME1. Lines that were top performers in more than one ME and more than one country were identified. CIMMYT’s breeding program continues to deliver improved and widely adapted germplasm for target environments.

Identification of genomic regions for grain yield and yield stability and their epistatic interactions

The task of identifying genomic regions conferring yield stability is challenging in any crop and requires large experimental data sets in conjunction with complex analytical approaches. We report findings of a first attempt to identify genomic regions with stable expression and their individual epistatic interactions for grain yield and yield stability in a large elite panel of wheat under multiple environments via a genome wide association mapping (GWAM) approach. Seven hundred and twenty lines were genotyped using genotyping-by-sequencing technology and phenotyped for grain yield and phenological traits. High gene diversity (0.250) and a moderate genetic structure (five groups) in the panel provided an excellent base for GWAM. The mixed linear model and multi-locus mixed model analyses identified key genomic regions on chromosomes 2B, 3A, 4A, 5B, 7A and 7B. Further, significant epistatic interactions were observed among loci with and without main effects that contributed to additional variation of up to 10%. Simple stepwise regression provided the most significant main effect and epistatic markers resulting in up to 20% variation for yield stability and up to 17% gain in yield with the best allelic combination.

Grain yield, adaptation and progress in breeding for early-maturing and heat-tolerant wheat lines in South Asia

Highlights • Each year from 2009 to 2014, 28 newly developed early-maturing high-yielding CIMMYT wheat lines were evaluated across locations in South Asia.• Maximum temperatures in ME5 (continual high temperature stress regions) and minimum temperature in ME1 (terminal high temperature stress regions) had significant impact on grain yield in South Asia.• Significant negative genetic correlations of grain yield with days to heading.• Early maturity has the potential to improve adaptation and maintenance of genetic gains in South Asia.

Single-step genomic and pedigree genotype × environment interaction models for predicting wheat lines in international environments

Genomic prediction accuracy models have been commonly used in plant breeding but only in reduced datasets comprising a few hundred genotyped individual plants. In this study we used pedigree and genomic data from 58,798 wheat lines evaluated in different environments. We use pedigree and genomic information in a model that incorporates genotype × environment interactions to predict wheat line performance in environments in South Asia.