Author: Jose Crossa

Additive genetic variance and covariance between relatives in wheat crosses with variable parental ploidy levels

Jose Crossa (2020)

Synthetic hexaploid wheat was developed and used in breeding to introduce new genetic diversity into bread wheat, through interspecific hybridization of T. tauschii (diploid) and durum wheat T. turgidum (tetraploid) to produce synthetic derivatives. Therefore, one may infer that the genetic variances of native wild populations vs. improved wheat may be different due differential origin and evolutionary history. We investigate this idea by partitioning the additive variance of grain yield with respect to breed origin using data from a synthetic derivative. Such information is needed to predict breeding values of synthetic derivatives and their parental populations. A mixed model with a heterogeneous covariance structure for breeding values was employed to estimate variance components using a program written by us. Data originated in a multi-year multi-location field trial of synthetic derivatives from the International Maize and Wheat Improvement Center (CIMMYT). Bayesian estimates of additive variances of grain yield from each population were similar for T. turgidum (0.0225) and T. tauschii (0.0208), but they were strikingly different from the one of T. aestivum (0.0131). Segregation variances were higher than zero, indicating differences in gene frequencies between pure breeds. Broad-sense heritability of the 25% synthetic derivative breed group was estimated to be equal to 0,66. Overall, our results support the suitability of models with heterogeneous additive genetic variances to predict breeding values in wheat crosses with variable ploidy levels.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Supplemental Materials for The Relative Efficiency of Three Constrained Multistage Linear Phenotypic Selection Indices

Jose Crossa (2018)

This dataset provides supplemental information related to an investigation of constrained multistage linear phenotypic selection indices.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Sparse designs for genomic selection using multi-environment data

Yoseph Beyene Juan Burgueño Jose Crossa (2020)

This research study the genomic-enabled prediction accuracy of the composition of the following sparse testing allocation design: (1) all non-overlapping (0 overlapping) lines in environments, (2) all overlapping (0 non-overlapping) lines tested in all the environments, and (3) combinations of the two previous cases where certain numbers of non-overlapping (NO)/overlapping (O) lines were distributed in the environments. We also studied cases where the size of the testing population was decreased. The study used two large maize data sets (T1 and T2). Four different genomic-enabled prediction models were studied, two models (M1 and M2) that do not include the genomic × environment interaction (GE), whereas models M3 and M4 incorporate two forms of modeling GE. The results show that genome-based models including GE (M3 and M4) captured more genetic variability with the GE component than the other models for both data sets. Also, models M3 and M4 provide higher prediction accuracy than models M1 and M2 for the different allocation designs comprising different combinations of NO/O lines in environments. Results indicate that substantial savings of testing resources can be achieved by optimizing the allocation design using genome-based models including genomic × environment interaction.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Combined Multistage Linear Genomic Selection Indices to Predict the Net Genetic Merit in Plant Breeding

J. Jesús Cerón Rojas Jose Crossa (2019)

Multistage selection is a cost-saving strategy for improving several traits because it is not necessary to measure all traits at each stage. A combined linear genomic selection index is a linear combination of phenotypic and genomic estimated breeding values useful to predict the individual net genetic merit, which in turn is a linear combination of the true unobservable breeding values of the traits weighted by their respective economic values. The main combined multistage linear genomic selection indices are the optimum and decorrelated indices. Using real and simulated data, we compared the efficiency of both indices to predict the net genetic merit in plants in a two-stage breeding context. The criteria used to compare the efficiency of both indices were that the total selection response of each index must be lower than or equal to the single-stage combined linear genomic selection index response and that the correlation of each index with the net genetic merit should be maximum. Using four different total proportions for the real data set, the total decorrelated and optimum index selection responses explained 90% and 97.5%, respectively, of the estimated single-stage combined selection response. In addition, at stage two, the correlation of the optimum and decorrelated indices with the net genetic merit were 0.84 and 0.63, respectively.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

A singular value decomposition Bayesian multiple-trait and multiple-environment genomic model

Osval Antonio Montesinos-Lopez Jose Crossa (2018)

In this paper, we propose a two-stage analysis for multiple-trait data; in the first stage, we perform a singular value decomposition (SVD) on the resulting matrix of traits responses, and in the second stage, multiple trait analysis on transformed responses is performed. We use simulated as well as wheat and maize data sets

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA