Author: Jose Crossa

A general Bayesian estimation method of linear-bilinear models applied to plant breeding trials with genotype × environment interaction

Jose Crossa (2012)

Statistical analyses of two-way tables with interaction arise in many different fields of research. This study proposes the von Mises?Fisher distribution as a prior on the set of orthogonal matrices in a linear?bilinear model for studying and interpreting interaction in a two-way table. Simulated and empirical plant breeding data were used for illustration; the empirical data consist of a multi-environment trial established in two consecutive years. For the simulated data, vague but proper prior distributions were used, and for the real plant breeding data, observations from the first year were used to elicit a prior for parameters of the model for data of the second year trial. Bivariate Highest Posterior Density (HPD) regions for the posterior scores are shown in the biplots, and the significance of the bilinear terms was tested using the Bayes factor. Results of the plant breeding trials show the usefulness of this general Bayesian approach for breeding trials and for detecting groups of genotypes and environments that cause significant genotype × environment interaction. The present Bayes inference methodology is general and may be extended to other linear?bilinear models by fixing certain parameters equal to zero and relaxing some model constraints.


CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA Bayesian inference Bilinear interaction terms Two-way table with interaction von Mises-Fisher

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

Jose Crossa (2017)

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.



META: A suite of sas programs to analyze multienvironment breeding trials

Jose Crossa (2013)

Multi-environment trials (METs) enable the evaluation of the same genotypes in a variety of environments and management conditions. We present here META (Multi Environment Trial Analysis), a suite of 31 SAS programs that analyze METs with complete or incomplete block designs, with or without adjustment by a covariate. The entire program is run through a graphical user interface. The program can produce boxplots or histograms for all traits, as well as univariate statistics. It also calculates Best Linear Unbiased Estimators (BLUEs) and Best Linear Unbiased Predictors (BLUPs) for the main response variable and BLUEs for all other traits. For all traits it calculates variance components by Restricted Maximum Likelihood (REML), Least Significant Differences (LSD), Coefficient of Variation (CV), and broad-sense heritability using PROC MIXED. The program can analyze each location separately, combine the analysis by management conditions, or combine all locations. The flexibility and simplicity of use of this program makes it a valuable tool for the analysis of METs in breeding and agronomy. The META program can be used by researcher knowing few principles of SAS.



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

Jose Crossa (2016)

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.



Identification of superior quality protein maize hybrids for different mega-environments using the biplot methodology

Jose Crossa (2006)

The utilization of site regression models (SREG) on multilocation testing allow the detection of significant differences in the genotype x environment interaction, even though these may not be detected by the analysis of variance (ANOVA). The results can be graphically displayed using the Biplot technique, revealing the additive effects on the genotypes and the genotype x environment interaction across years. Thus, the objectives of this work were to identify mega-environments, superior maize hybrids for each environment and mega-environment, stable maize hybrids with good performance across environments, and the most suitable environments for evaluation as well. A total of 66 field trials were grouped in five sets of experiments. An individual SREG analysis for each set of experiments and their combined analysis were conducted to assist in the graphic representation by the Biplot methodology. Results revealed that the constructed Biplots, graphically allowed the identification of superior maize hybrids, and the proper environments to conduct maize hybrid evaluation trials; however, it was not a reliable option for grouping test-sites in mega-environments.