Author: Philomin Juliana

Genotypic data from CIMMYT bread wheat breeding lines used in the Feed the Future Innovation Lab for Applied Wheat Genomics

Philomin Juliana Jose Crossa Jessica Rutkoski (2016)

Genetic profiling of wheat breeding lines from the CIMMYT bread wheat breeding program was carried out over several years.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Genotypic data for the South Asian panel with 184 lines

Xinyao He Philomin Juliana arun joshi Ravi Singh Pawan Singh (2021)

Genotypic data for the South Asian panel with 184 lines intended for multiple diseases resistance analysis

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: Genome-based prediction of multiple wheat quality traits in multiple years

Maria Itria Ibba Jose Crossa Osval Antonio Montesinos-Lopez Philomin Juliana Carlos Guzman Susanne Dreisigacker Jesse Poland (2020)

The use of genomic prediction could greatly help to increase the efficiency of selecting for wheat quality traits by reducing the cost and time required for this analysis. This study contains data used to evaluate the prediction performances of 13 wheat quality traits under two multi-trait models [Bayesian multi-trait multi-environment (BMTME) and multi-trait ridge regression (MTR)]. Separate files are provided for each year of data. An additional supplemental data file provides R code for running the analyses as well as a table describing the Average Pearson´s correlation (APC) and mean arctangent absolute percentage error (MAAPE) for the testing sets for each dataset and trait.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Deep kernel and deep learning for genomic-based prediction

Jose Crossa Paulino Pérez-Rodríguez Juan Burgueño Ravi Singh Philomin Juliana Osval Antonio Montesinos-Lopez Jaime Cuevas (2019)

Deep learning (DL) is a promising method in the context of genomic prediction for selecting individuals early in time without measuring their phenotypes. iI this paper we compare the performance in terms of genome-based prediction of the DL method, deep kernel (arc-cosine kernel, AK) method, Gaussian kernel (GK) method and the conventional kernel method (Genomic Best Linear Unbiased Predictor, GBLUP, GB). We used two real wheat data sets for the benchmarking of these methods. We found that the GK and deep kernel AK methods outperformed the DL and the conventional GB methods, although the gain in terms of prediction performance of AK and GK was not very large but they have the advantage that no tuning parameters are required. Furthermore, although AK and GK had similar genomic-based performance, deep kernel AK is easier to implement than the GK. For this reason, our results suggest that AK is an alternative to DL models with the advantage that no tuning process is required.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: Joint use of genome, pedigree and their interaction with environment for predicting the performance of wheat lines in new environments

Osval Antonio Montesinos-Lopez Philomin Juliana Ravi Singh Jesse Poland Paulino Pérez-Rodríguez Jose Crossa DIEGO JARQUIN (2019)

In this study, we evaluated genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree information in two different validation schemes. All models included main effects, and others also considered interactions between the different types of covariates via Hadamard products of similarity structures. The pedigree models always gave better results predicting new lines in observed environments than the genome-based models when only main effects were fitted. However, for all traits, the highest predictive abilities were obtained when interactions between pedigree, markers and environments were included. When new lines were predicted in unobserved environments in almost all trait/year combinations, the marker main-effects model was the best. These results provide strong evidence that the different sources of genetic information (molecular markers and pedigree) are not equally useful at different stages of the breeding pipelines, and can be employed differentially to improve the design of future breeding programs.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: Allocation of wheat lines in sparse testing for genome-based multi-environment prediction

Leonardo Abdiel Crespo Herrera Ravi Singh Suchismita Mondal Philomin Juliana DIEGO JARQUIN Jose Crossa (2021)

Sparse testing can be used in plant breeding and genome-based prediction. In sparse testing not all of the lines are sown in all environments. The phenotypic and genotypic data files provided in this dataset were used to execute an analysis of three general cases of the composition of the sparse testing allocation design for wheat breeding.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA