Autor: Manje Gowda

Replication Data for: Combination of Linkage and Association Mapping with Genomic Prediction to Infer QTL Regions Associated with Gray Leaf Spot and Northern Corn Leaf Blight Resistance in Tropical Maize

Manje Gowda Yoseph Beyene Suresh L.M. David Berger (2023)

Gray Leaf Spot (GLS) and Northern Corn Leaf Blight (NCLB) are two pathogens with high genetic diversity that can reduce grain yield in infected maize plants.To identify population-based quantitative trait loci (QTL) for GLS and NCLB resistance, a biparental population and an association mapping panel were genotyped and were also phenotyped across multi-environments in western Kenya. This dataset includes the analyzed BLUES of the collected phenotypic data for Gray Leaf Spot GLS and NCLB resistance from the DH population and diversity panel, as well as the GBS genotypic data. The results of the analysis are reported in the accompanying article.

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: Discovery of Genomic Regions Associated with Grain Yield and Agronomic Traits in Bi-parental Populations of Maize (Zea mays. L) under Optimum and Low Nitrogen Conditions

Noel Ndlovu Vijay Chaikam Berhanu Tadesse Ertiro Biswanath Das Yoseph Beyene Charles Spillane Manje Gowda (2023)

Low soil nitrogen stress can contribute to food insecurity, malnutrition, and rural poverty in maize-dependent smallholder communities of sub-Saharan Africa (SSA). Enhanced selection for improved varieites may result from a better understanding of the genomic regions associated with low nitrogen tolerance. Four F3 maize populations were used to study the genetic architecture of grain yield (GY) and its associated traits (anthesis-silking interval (ASI), anthesis date (AD), plant height (PH), ear position (EPO), and ear height (EH)) under different soil nitrogen regimes in Kenya and Zimbabwe. Information about the populations and the genotypic data used in the analyses are provided in this dataset. The results of the analysis are reported in the related journal article.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

2022 CIMMYT Maize Eastern Africa Product Profile 3 Product Announcement

Dagne Wegary Gissa Yoseph Beyene Suresh L.M. Manje Gowda Walter Chivasa Aparna Das Prasanna Boddupalli (2022)

New and improved maize hybrids, developed by the CIMMYT Global Maize Program, are available for uptake by public and private sector partners, especially those interested in marketing or disseminating hybrid maize seed across upper altitudes of Eastern Africa and similar agro-ecologies in other regions. Following a rigorous trialing and a stage-gate advancement process culminating in the 2021 Stage 5 trials, CIMMYT advanced a total of 1 new elite maize hybrid for highlands in Eastern Africa in 2022. Phenotypic data collected in Stage 4 and Stage 5 trials for the selected hybrid as well as information about the trial sites are provided in this dataset. These trials were conducted through a network of partners, including NARS and private seed companies, in Eastern Africa under various management and environmental conditions.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA