Biology subjects

Crossa, J.

Publications and source records attributed to Crossa, J..

12 recordsLinked to original sources

bioRxiv2026

An axiomatic approach to cultivar ranking in multi-environment trials

AO_SCPLOWBSTRACTC_SCPLOWMulti-environment trials are central to cultivar evaluation because they reveal how candidate cultivars perform across locations, years, management conditions, and stress environments. The resulting yield matrix is a rich source of data on genotype-by-environment interaction, and a wide literature on estimation, decomposition, visualisation, and prediction of yield potential and stability has flourished. However the ultimate question of which cultivar to recommend on the basis of such a matrix is often left implicit. The question is far from trivial, and in this paper we formulate cultivar recommendation as an axiomatic ranking problem. This framework is rich enough to encompass the existing literature on stability indices, as well as any other deterministic ranking procedure. We show that many commonly used stability-based procedures can violate minimal criteria of efficiency or consistency. The result of such violations is that a cultivar with uniformly high yield could be ranked below a cultivar with uniformly low yield, or the relative ranks of two cultivars could depend on whether or not a third cultivar is present in the matrix. Our results prove that under a small number of such criteria the space of admissible rules collapses to the family of power means and their limiting cases. If we further wish to allow multiplication normalisation of yield, we are left with the geometric mean as the unique solution.

genetics↗

bioRxiv2026

A novel matrix multiplication framework for modeling genotype-by-environment interaction in genomic prediction

Accurate modeling of genotype-by-environment (GxE) interaction is critical for genomic prediction in plant breeding but remains challenging due to complex interaction structures. Conventional models often use the Hadamard product of genotype and environment covariance matrices to capture joint similarity, which may not fully represent GxE complexity. Here we propose a novel framework that derives covariance structures from the matrix multiplication of genotype and environment kernels, decomposing these into symmetric components incorporated as random effects in mixed models. Evaluated for 11 wheat and rice multi-environment datasets and across, this approach consistently outperformed the traditional Hadamard-based model, improving prediction accuracy by up to 13.2% in Pearsons correlation and enhancing top-selection accuracy. Combining both methods yielded the highest performance, indicating complementary information capture. This framework offers a flexible, interpretable, and computationally feasible extension for modeling GxE interaction, potentially enhancing genomic selection effectiveness under diverse environmental conditions.

genetics↗

bioRxiv2024

Improving genomic selection in hexaploid wheat with sub-genome additive and epistatic models

The goal of wheat breeding is the development of superior cultivars tailored to specific environments, and the identification of promising crosses is crucial for the success of breeding programs. Although genomic estimated breeding values were developed to estimate additive effects of genotypes before testing as parents, application has focused on predicting performance of candidate lines, ignoring non-additive genetic effects. However, non-additive genetic effects are hypothesized to be especially importance in allopolyploid species due to the interaction between homeologous genes. The objectives of this study were to model additive and additive-by-additive epistatic effects to better delineate the genetic architecture of grain yield in wheat and to the improve accuracy of genomewide predictions. The dataset utilized consisted of 3740 F5:6 experimental lines tested in the K-State wheat breeding program across the years 2016 and 2018. Covariance matrices were calculated based on whole and sub-genome marker data and the natural and orthogonal interaction approach (NOIA) was used to estimate variance components for additive and additive-by-additive epistatic effects. Incorporating epistatic effects in additive models resulted in non-orthogonal partitioning of genetic effects but increased total genetic variance and reduced deviance information criteria. Estimation of sub-genome effects indicated that genotypes with the greatest whole genome effects often combine sub-genomes with intermediate to high effects, suggesting potential for crossing parental lines which have complementary sub-genome effects. Modeling epistasis in either whole-genome or sub-genome models led to a marginal (3%) but significant improvement in genomic prediction accuracy, which could result in significant genetic gains across multiple cycles of breeding.

genetics↗

bioRxiv2023

Investigating genomic prediction strategies for grain carotenoid traits in a tropical/subtropical maize panel

Vitamin A deficiency remains prevalent on a global scale, including in regions where maize constitutes a high percentage of human diets. One solution for alleviating this deficiency has been to increase grain concentrations of provitamin A carotenoids in maize (Zea mays ssp. mays L.)--an example of biofortification. The International Maize and Wheat Improvement Center (CIMMYT) developed a Carotenoid Association Mapping panel of 380 inbred lines adapted to tropical and subtropical environments that have varying grain concentrations of provitamin A and other health-beneficial carotenoids. This project assesses the accuracy of several genomic prediction (GP) strategies for these traits ({beta}-carotene, {beta}-cryptoxanthin, provitamin A, lutein, and zeaxanthin) within and between four environments in Mexico. Methods employing Ridge Regression-Best Linear Unbiased Prediction, Elastic Net, or Reproducing Kernel Hilbert Spaces had high accuracy in predicting all tested provitamin A carotenoid traits and outperformed Least Absolute Shrinkage and Selection Operator. Furthermore, prediction accuracies were higher when using genome-wide markers rather than only the markers proximal to two previously identified carotenoid-related genes that have been used in marker-assisted selection, suggesting that GP is worthwhile for these traits, even though key genes have already been identified. Prediction accuracy was maintained for all traits (except lutein) in between-environment prediction. The TASSEL (Trait Analysis by aSSociation, Evolution, and Linkage) Genomic Selection plugin performed as well as other more computationally intensive methods for within-environment prediction. The findings observed herein indicate the utility of GP methods for these traits and could inform their resource-efficient implementation in biofortification breeding programs.

plant biology↗

bioRxiv2022

ENVIROME-WIDE ASSOCIATIONS ENHANCE MULTI-YEAR GENOME-BASED PREDICTION OF HISTORICAL WHEAT BREEDING DATA

Linking high-throughput environmental data (enviromics) into genomic prediction (GP) is a cost-effective strategy for increasing selection intensity under genotype-by-environment interactions (GxE). This study developed a data-driven approach based on Environment-Phenotype Associations (EPA) aimed at recycling important GxE information from historical breeding data. EPA was developed in two applications: (1) scanning a secondary source of genetic variation, weighted from the shared reaction-norms of past-evaluated genotypes; (2) pinpointing weights of the similarity among trial-sites (locations), given the historical impact of each envirotyping data variable for a given site. Then, the EPA outcomes were integrated into multi-environment GP models through a new single-step GBLUP. The wheat trial data used included 36 locations, 8 years and 3 target populations of environments (TPE) in India. Four prediction scenarios and 6 kernel-models within/across TPEs were tested. Our results suggest that the conventional GBLUP, without enviromic data or when omitting EPA, is inefficient in predicting the performance of wheat lines in future years. However, when EPA was introduced as an intermediary learning step to reduce the dimensionality of the GxE kernels while connecting phenotypic and environmental-wide variation, a significant enhancement of GxE prediction accuracy was evident. EPA revealed that the effect of seasonality makes strategies such as "covariable selection" unfeasible because GxE is year-germplasm specific. We propose that the EPA effectively serves as a "reinforcement learner" algorithm capable of uncovering the effect of seasonality over the reaction-norms, with the benefits of better forecasting the similarities between past and future trialing sites. EPA combines the benefits of dimensionality reduction while reducing the uncertainty of genotype-by-year predictions and increasing the resolution of GP for the genotype-specific level.

genetics↗

bioRxiv2022

Modeling genotype environment interaction for single- and multi-trait genomic prediction in potato (Solanum tuberosum L.)

In this study we extend research on genomic prediction (GP) to polysomic polyploid plant species with the main objective to investigate single trait (ST) versus multi-trait (MT) for multi-environment (ME) models for the combination of three locations in Sweden (Helgeg[a]rden [HEL], Mosslunda [MOS], Ume[a] [UM]) over two year-trials (2020, 2021) of 253 potato cultivars and breeding clones for five tuber weight traits and two tuber flesh quality characteristics. This research investigated the GP of four genome-based prediction models with genotype xenvironment interactions (GE): (1) single trait reaction norm model (M1), (2) single trait model considering covariances between environments (M2), (3) single trait M2 extended to include a random vector that utilizes the environmental covariances (M3) and (4) multi-trait model with GE (M4). Several prediction problems were analyzed for each of the GP accuracy of the four models. Results of the prediction of traits in HEL, the high yield potential testing site in 2021, show that the best predicted traits were tuber flesh starch (%), weight of tuber above 60 or below 40 mm in size, and total tuber weight. In terms of GP, accuracy model M4 gave the best prediction accuracy in three traits, namely tuber weight of 40-50 or above 60 mm in size, and total tuber weight and very similar in the starch trait. For MOS in 2021, the best predictive traits were starch, weight of tuber above 60, 50-60, or below 40 mm in size, and total tuber weight. MT model M4 was the best GP model based on its accuracy when some cultivars are observed in some traits. For GP accuracy of traits in UM in 2021, the best predictive traits were weight of tuber above 60, 50-60, or below 40 mm in size and the best model was MT M4 followed by models ST M3 and M2.

plant biology↗

bioRxiv2021

Enviromic-based Kernels Optimize Resource Allocation with Multi-trait Multi-environment Genomic Prediction for Tropical Maize

Genomic prediction (GP) success is directly dependent on establishing a training population, where incorporating envirotyping data and correlated traits may increase the GP accuracy. Therefore, we aimed to design optimized training sets for multi-trait for multi-environment trials (MTMET). For that, we evaluated the predictive ability of five GP models using the genomic best linear unbiased predictor model (GBLUP) with additive + dominance effects (M1) as the baseline and then adding genotype by environment interaction (G x E) (M2), enviromic data (W) (M3), W+G x E (M4), and finally W+G x W (M5), where G x W denotes the genotype by enviromic interaction. Moreover, we considered single-trait multi-environment trials (STMET) and MTMET for three traits: grain yield (GY), plant height (PH), and ear height (EH), with two datasets and two cross-validation schemes. Afterward, we built two kernels for genotype by environment by trait interaction (GET) and genotype by enviromic by trait interaction (GWT) to apply genetic algorithms to select genotype:environment:trait combinations that represent 98% of the variation of the whole dataset and composed the optimized training set (OTS). Using OTS based on enviromic data, it was possible to increase the response to selection per amount invested by 142%. Consequently, our results suggested that genetic algorithms of optimization associated with genomic and enviromic data efficiently design optimized training sets for genomic prediction and improve the genetic gains per dollar invested.

genetics↗

bioRxiv2020

EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture

Envirotyping is an essential technique used to unfold the non-genetic drivers associated with the phenotypic adaptation of living organisms. Here we introduce the EnvRtype R package, a novel toolkit developed to interplay large-scale envirotyping data (enviromics) into quantitative genomics. To start a user-friendly envirotyping pipeline, this package offers: (1) remote sensing tools for collecting (get_weather and extract_GIS functions) and processing ecophysiological variables (processWTH function) from raw environmental data at single locations or worldwide; (2) environmental characterization by typing environments and profiling descriptors of environmental quality (env_typing function), in addition to gathering environmental covariables as quantitative descriptors for predictive purposes (W_matrix function); and (3) identification of environmental similarity that can be used as an enviromic-based kernel (env_typing function) in whole-genome prediction (GP), aimed at increasing ecophysiological knowledge in genomic best-unbiased predictions (GBLUP) and emulating reaction norm effects (get_kernel and kernel_model functions). We highlight literature mining concepts in fine-tuning envirotyping parameters for each plant species and target growing environments. We show that envirotyping for predictive breeding collects raw data and processes it in an eco-physiologically-smart way. Examples of its use for creating global-scale envirotyping networks and integrating reaction-norm modeling in GP are also outlined. We conclude that EnvRtype provides a cost-effective envirotyping pipeline capable of providing high quality enviromic data for a diverse set of genomic-based studies, especially for increasing accuracy in GP across untested growing environments.

genetics↗

bioRxiv2020

Aerial High-Throughput Phenotyping Enabling Indirect Selection for Grain Yield at the Early-generation Seed-limited Stages in Breeding Programs

Breeding programs for wheat and many other crops require one or more generations of seed increase before replicated yield trials can be sown. Extensive phenotyping at this stage of the breeding cycle is challenging due to the small plot size and large number of lines under evaluation. Therefore, breeders typically rely on visual selection of small, unreplicated seed increase plots for the promotion of breeding lines to replicated yield trials. With the development of aerial high-throughput phenotyping technologies, breeders now have the ability to rapidly phenotype thousands of breeding lines for traits that may be useful for indirect selection of grain yield. We evaluated early generation material in the irrigated bread wheat (Triticum aestivum L.) breeding program at the International Maize and Wheat Improvement Center to determine if aerial measurements of vegetation indices assessed on small, unreplicated plots were predictive of grain yield. To test this approach, two sets of 1,008 breeding lines were sown both as replicated yield trials and as small, unreplicated plots during two breeding cycles. Vegetation indices collected with an unmanned aerial vehicle in the small plots were observed to be heritable and moderately correlated with grain yield assessed in replicated yield trials. Furthermore, vegetation indices were more predictive of grain yield than univariate genomic selection, while multi-trait genomic selection approaches that combined genomic information with the aerial phenotypes were found to have the highest predictive abilities overall. A related experiment showed that selection approaches for grain yield based on vegetation indices could be more effective than visual selection; however, selection on the vegetation indices alone would have also driven a directional response in phenology due to confounding between those traits. A restricted selection index was proposed for improving grain yield without affecting the distribution of phenology in the breeding population. The results of these experiments provide a promising outlook for the use of aerial high-throughput phenotyping traits to improve selection at the early-generation seed-limited stage of wheat breeding programs.

genetics↗

bioRxiv2020

Genomic Prediction with Genotype by Environment Interaction Analysis for Kernel Zinc Concentration in Tropical Maize Germplasm

Zinc (Zn) deficiency is a major risk factor for human health, affecting about 30% of the worlds population. To study the potential of genomic selection (GS) for maize with increased Zn concentration, an association panel and two doubled haploid (DH) populations were evaluated in three environments. Three genomic prediction models, M (M1: Environment + Line, M2: Environment + Line + Genomic, and M3: Environment + Line + Genomic + Genomic x Environment) incorporating main effects (lines and genomic) and the interaction between genomic and environment (G x E) were assessed to estimate the prediction ability (rMP) for each model. Two distinct cross-validation (CV) schemes simulating two genomic prediction breeding scenarios were used. CV1 predicts the performance of newly developed lines, whereas CV2 predicts the performance of lines tested in sparse multi-location trials. Predictions for Zn in CV1 ranged from -0.01 to 0.56 for DH1, 0.04 to 0.50 for DH2 and -0.001 to 0.47 for the association panel. For CV2, rMP values ranged from 0.67 to 0.71 for DH1, 0.40 to 0.56 for DH2 and 0.64 to 0.72 for the association panel. The genomic prediction model which included G x E had the highest average rMP for both CV1 (0.39 and 0.44) and CV2 (0.71 and 0.51) for the association panel and DH2 population, respectively. These results suggest that GS has potential to accelerate breeding for enhanced kernel Zn concentration by facilitating selection of superior genotypes.

genetics↗

bioRxiv2019

Genetic image-processing using regularized selection indices

High-throughput phenotyping (HTP) technologies can produce data on thousands of phenotypes per unit being monitored. These data can be used to breed for economically and environmentally relevant traits (e.g., drought tolerance); however, incorporating high-dimensional phenotypes in genetic analyses and in breeding schemes poses important statistical and computational challenges. To address this problem, we developed regularized selection indices; the methodology integrates techniques commonly used in high-dimensional phenotypic regressions (including penalization and rank-reduction approaches) into the selection index (SI) framework. Using extensive data from CIMMYTs (International Maize and Wheat Improvement Center) wheat breeding program we show that regularized SIs derived from hyper-spectral data offer consistently higher accuracy for grain yield than those achieved by canonical SIs, and by vegetation indices commonly used to predict agronomic traits. Regularized SIs offer an effective approach to leverage HTP data that is routinely generated in agriculture; the methodology can also be used to conduct genetic studies using high-dimensional phenotypes that are often collected in humans and model organisms including body images and whole-genome gene expression profiles.

genetics↗

bioRxiv2018

Use of Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat

Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype x environment (G x E) interactions across environments within a bread wheat (Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program, International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregon, Mexico over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker-and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker-and pedigree-based models were marginal when correcting for days to heading. Our results demonstrates the potential of hyperspectral imaging in predicting grain yield within a multi-environment context, it also supports further studies on integration of hyperspectral reflectance phenotyping in breeding programs.

genomics↗