Author: Yoseph Beyene

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

CIMMYT Eastern Africa 2019 Regional Trial Report

MacDonald Jumbo Yoseph Beyene Dan Makumbi Prasanna Boddupalli (2021)

This dataset contains the results from three regional trials carried out in Eastern Africa in 2019. Each report focuses on a different product profile. Two reports present data for product profile EA-PP1, It focuses on includes early/intermediate-maturing white maize with multiple stress tolerance (drought, low N, MLN, MSV, TLB, GLS, ear rots) for the Eastern African rainfed mid-altitude dry/wet agro-ecologies. The third report presents data for product profile EA-PP2. It focuses on late-maturing, white maize varieties with multiple stress tolerance (drought, low N, GLS, TLB, MSV, ear rots, Striga) for the Eastern African rainfed upper mid-altitude region.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices

Marco Lopez-Cruz Yoseph Beyene Manje Gowda Jose Crossa Paulino Pérez-Rodríguez Gustavo de los Campos (2021)

Genomic prediction models may be used in plant breeding pipelines. They are often calibrated using multi-generation data and there is an open question of whether all available data or a subset of it should be used to calibrate genomic prediction models. Therefore, a study was undertaken to determine whether combining sparse selection indexes (SSIs) and kernel methods could further improve prediction accuracy when training genomic models using multi-generation data. This dataset contains the genotypic and phenotypic data from CIMMYT maize doubled haploid lines that were used to perform the analyses. The results of the analyses are presented in the accompanying article.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: Genetic Dissection of Resistance to Gray Leaf Spot by Combining Genome-Wide Association, Linkage Mapping and Genomic Prediction in Tropical Maize Germplasm

Sudha Nair Biswanath Das MacDonald Jumbo Dan Makumbi Suresh L.M. Yoseph Beyene Michael Olsen Prasanna Boddupalli Manje Gowda (2020)

Gray leaf spot (GLS) is a major maize foliar disease in sub-Saharan Africa that can substantially reduce yields for farmers. This dataset contains supporting phenotypic and genotypic data used in the analysis of the genetic architecture of GLS resistance in maize. The data in this paper relate to several different sets of germplasm: (1) The IMAS (Improved Maize for Africa Soils) panel with 430 lines assembled from diverse breeding programs from Africa, Asia and Latin America. (2) A DH population developed from crossing CML550 with CML494; (3) A DH population developed from crossing CML550 with CML504; (4) A DH population developed from crossing CML550 with CML511; and (5) several F3 populations The IMAS panel is a good source for genetic studies on resistance for several maize diseases and also for abiotic stress like low N conditions. The DH populations are a good source for low N discovery studies as well as for MLN and other disease resistance studies.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

CIMMYT Maize Regional Trial Data for Eastern Africa 2017

MacDonald Jumbo Yoseph Beyene Dan Makumbi Lewis Machida Suresh L.M. Amsal Tarekegne Manje Gowda Vijay Chaikam Prasanna Boddupalli (2020)

The summary results of the Regional Trials for CIMMYT Maize Hybrids in Eastern Africa for 2017. The trials include: EHYB17-Set I – Early/extra-early maturing elite pre-commercial hybrids regional trials (including external and internal checks); IHYB17-Set I – Intermediate maturing elite pre-commercial hybrids regional trial (including external and internal checks); ILHYB17 – Intermediate-Late maturing elite pre-released and released hybrids regional trials (including external and internal checks); EHYB17-Set II – Early maturing elite pre-commercial hybrids regional trials; ILHYB17 Set II – Intermediate/late maturing elite pre-commercial hybrids regional trials.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

CIMMYT Maize Regional Trial Data for Eastern Africa 2016

MacDonald Jumbo Yoseph Beyene Dan Makumbi Suresh L.M. Amsal Tarekegne Tsedeke Abate Manje Gowda Vijay Chaikam Prasanna Boddupalli (2020)

The summary results of the Regional Trials for CIMMYT Maize Hybrids in Eastern Africa for 2016. The trials are: EHYB16 - Early/extra-early maturing elite pre-commercial hybrids regional trials, EIHYB16 – Early-intermediate maturing elite pre-commercial hybrids regional trial, IHYB16 (Set 1) – Intermediate maturing elite pre-commercial hybrids regional trial, IHYB16 (Set 2) – Intermediate/Late maturing elite pre-commercial hybrids regional trial, and MLN tolerant hybrids – MLN trials of promising candidate hybrids.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA