Author: Juan Burgueño

Evaluation of Maize Landraces for Drought Tolerance in 2015

Terence Molnar Juan Burgueño (2018)

Maize landraces were evaluated for drought tolerance in three locations in 2015.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Phenotypic evaluation of the Seeds of Discovery - MasAgro Biodiversidad Linked Topcross Population 1 (LTP1)

Juan Burgueño deepmala sehgal (2019)

Phenotypic evaluation of the Linked Topcross Population 1 (LTP1) from the MasAgro Biodiversidad - Seeds of Discovery Initiative under drought, heat, and irrigated conditions.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Phenotypic data for "Single-gene resolution of locally adaptive genetic variation in Mexican maize.

Martha Willcox Juan Burgueño Sarah Hearne (2020)

Phenotypic data for: Daniel J Gates, Dan Runcie, Garrett M. Janzen, Alberto Romero Navarro, Martha Willcox, Kai Sonder, Samantha J. Snodgrass, Fausto Rodríguez-Zapata, Ruairidh J. H. Sawers, Rubén Rellán-Álvarez, Edward S. Buckler, Sarah Hearne, Matthew B. Hufford, Jeffrey Ross-Ibarra. Single-gene resolution of locally adaptive genetic variation in Mexican maize. doi: https://doi.org/10.1101/706739.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

CIMMYT Maize Genetic Resource Lines

Terence Molnar Juan Burgueño Jose Crossa (2020)

CIMMYT makes available to the public a set of maize inbred lines called CIMMYT Maize Genetic Resource Lines (CMGRL). The CMGRLs are derived from crosses between elite CIMMYT lines and landrace accessions, populations or synthetics from the CIMMYT Germplasm Bank. CMGRLs are intended to be used by breeders as sources of novel alleles for traits of economic importance. These lines should also be of interest to maize researchers who are not breeders but are studying the underlying genetic mechanisms of abiotic and biotic traits. The inaugural group of CMGRLs includes five subtropical-adapted lines with tolerance to drought during flowering and grain-fill and four tropical adapted lines for Tar Spot Complex resistance.

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

Wheat Linked Topcross Population Information from the Seeds of Discovery - MasAgro Biodiversidad Project

Prashant Vikram Carolina Saint Pierre Juan Burgueño Carolina Sansaloni (2017)

The Linked Topcross Population (LTP) was generated to introgress useful traits from wheat germplasm bank accessions, including synthetic hexaploids and landraces, into elite wheat varieties. In addition to generating pre-breeding materials selected for important traits such as heat and drought tolerance, this population has been used to generate data that can be useful for several applications, including genome-wide association studies.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

BGGE: A new package for genomic prediction incorporating genotype by environments models

Italo Granato Jaime Cuevas Francisco Javier Luna Vázquez Jose Crossa Juan Burgueño Roberto Fritsche-Neto (2018)

One of the major issues in plant breeding is the occurrence of genotype by environment (GE) interaction. Several models have been created to understand this phenomenon and explore it by selecting the most stable genotypes. In the genomic era, several models were employed to simultaneously improve selection by using markers and account for GE interaction. Some of these models use special genetic covariance matrices. In addition, multi-environment trials scales are getting larger, and this increases the computational challenges. In this context, we propose an R package that, in general, allows building GE genomic covariance matrices and fitting linear mixed models, in particular, to a few genome GE models. Here we propose a function to create the genomic kernels needed to fit these models. This function makes genome predictions through a Bayesian linear mixed model approach. A particular treatment is given for structured dispersed covariance matrices; in particular, those structured as a block diagonal that are present in some GE models in order to decrease the computational demand. In empirical comparisons with Bayesian Genomic Linear Regression (BGLR), accuracies and the mean squared error were similar; however, the computational time was up to five times lower than when using the classic approach. Bayesian Genomic Genotype × Environment Interaction (BGGE) is a fast, efficient option to create genome GE kernels and make genomic predictions.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Evaluation of maize pre-breeding materials under the Seeds of Discovery initiative in 2018

Terence Molnar Marcela Carvalho Juan Burgueño Jose Crossa Cesar Petroli Sarah Hearne (2021)

These data describe the evaluation of landrace-derived pre-breeding materials for biotic and abiotic stress resistance as well as for blue maize production in 2018. Populations of interest for drought stress during flowering time, heat stress during flowering time, Tar Spot tolerance, and blue maize production were evaluated for yield potential and response to the stresses with support from the MasAgro Biodiversidad project and the CGIAR Research Program on Maize.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA