Search datasets
21 datasets found
-
Prediction of multiple-trait and multiple-environment genomic data using reco... N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaIn genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. In current genomic selection, while researchers have a large amount of information and appropriate statistical models to process it,...Created January 20, 2025 • Updated January 20, 2025 -
Rapid cycling genomic selection in a multi-parental tropical maize population N3 | TTL | RDF/XML | JSON-LD
CIMMYT Ethiopia^^Created January 20, 2025 • Updated January 20, 2025 -
Genomic-enabled prediction in maize using kernel models with genotype × envir... N3 | TTL | RDF/XML | JSON-LD
CIMMYT Ethiopia^^Created January 20, 2025 • Updated January 20, 2025 -
HTMA MPS2 Cycle 2 genotyping for GEBV estimation N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaCycle 2 formed by inter mating selected Cycle1 genotypes genotyped with 93 SNPs for GEBV estimation for grain yieldCreated January 20, 2025 • Updated January 20, 2025 -
HTMA MPS2 Cycle 1 Genotyping for GEBV estimation N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaCycle 1 formed by inter mating selected S2 families genotypes genotyped with 84 SNPs for GEBV estimation for grain yieldCreated January 20, 2025 • Updated January 20, 2025 -
HTMA MPS1 Cycle 2 genotyping for GEBV estimation N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaCycle 2 formed by inter mating selected Cycle1 genotypes genotyped with 93 SNPs for GEBV estimation for grain yieldCreated January 20, 2025 • Updated January 20, 2025 -
HTMA MPS1 Cycle 1 Genotyping for GEBV estimation N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaCycle1 formed by inter mating selected S2 families genotypes genotyped with 84 SNPs for GEBV estimation for grain yieldCreated January 20, 2025 • Updated January 20, 2025 -
Genomic Selection in Plant Breeding: Advances and Perspectives N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaN/ACreated January 20, 2025 • Updated January 20, 2025 -
Replication Data for: Genome-Wide Association Mapping and Genomic Prediction ... N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaTo dissect the genetic architecture of grain yield and flowering traits under drought stress, a genome-wide association study (GWAS) was conducted using 236 test-crossed maize lines. The materials were evaluated under managed drought and optimal growing conditions in multiple environments using seven multi-locus GWAS models (mrMLM, FASTmrMLM, FASTmrEMMA,...Created January 20, 2025 • Updated January 20, 2025 -
Replication Data for: Exploiting genomic tools for genetic dissection and imp... N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaFusarium stalk rot (FSR) poses a threat to maize cultivation around the world. Phenotypic selection to improve FSR resistance cannot meet this challenge alone. Genome-wide association studies (GWAS) and genomic prediction (GP) can also help to uncover genetic determinants of FSR resistance that could be used in breeding. This study provides genotypic and...Created January 20, 2025 • Updated January 20, 2025 -
Replication Data for: Optimizing Genomic-Enabled Prediction: A Feature Weight... N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaThis study provides supplemental data to support the study on Optimizing Genomic-Enabled Prediction: A Feature Weighting Approach for Enhancing within Family Accuracy.Created January 20, 2025 • Updated January 20, 2025 -
Supplemental data for Genomic Prediction of Resistance to Tan Spot, Spot Blot... N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaThis study provides supplemental data to support the study on Genomic Prediction of Resistance to Tan Spot, Spot Blotch and Septoria Nodorum Blotch on Synthetics Hexaploid Wheat.Created January 20, 2025 • Updated January 20, 2025 -
Replication Data for: Allocation of wheat lines in sparse testing for genome-... N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaSparse 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.Created January 20, 2025 • Updated January 20, 2025 -
Replication Data for: A multivariate Poisson deep learning model for genomic ... N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaGenomic selection (GS) is an important method used in plant and animal breeding. The experimental data provided in this study contain counting data. These datasets were used to support research on efficient methodologies for multivariate count data outcomes including a multivariate Poisson deep neural network (MPDN) model, a conventional multivariate...Created January 20, 2025 • Updated January 20, 2025 -
Replication Data for: Multi-trait Bayesian decision for parental selection N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaThe files included in this study contains the data used with three promising multivariate loss functions: Kullback-Leibler (KL); the Energy Score; and the Multivariate Asymmetric Loss (MALF); to select the best performing parents for the next breeding cycle in two extensive real wheat data sets.Created January 20, 2025 • Updated January 20, 2025 -
Sparse designs for genomic selection using multi-environment data N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaThis 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...Created January 20, 2025 • Updated January 20, 2025 -
Genomic selection models based on integration of GWAS loci and epistatic inte... N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaThe potential to integrate consistent associations identified from GWAS as fixed variables in GP models to improve prediction accuracy for complex traits (for example, grain yield) has not been investigated comprehensively in wheat. Here, we untangled the genetic architecture of grain yield and yield stability by haplotypes-based GWAS and epistatic scan...Created January 20, 2025 • Updated January 20, 2025 -
A Bayesian genomic multi-output regressor stacking model for predicting multi... N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaA new statistical model is presented for genomic prediction on maize and wheat data comprising multi-trait, multi-environment data.Created January 20, 2025 • Updated January 20, 2025 -
New deep learning genomic prediction model for multi-traits with mixed binary... N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaThe seven data sets are wheat data from CIMMYT Global Wheat Breeding program. They comprise different traits, like days to heading, days to maturity, grain yield, grain color, different type of leaf and stripe rust in wheat. Also the trials were run in different environments.Created January 20, 2025 • Updated January 20, 2025 -
Deep learning genomic-enabled prediction of plant traits N3 | TTL | RDF/XML | JSON-LD
CIMMYT EthiopiaMachine learning (ML) is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (i.e., progressively improve performance on a specific task) from data, without being explicitly programmed to do this. ML is closely related to (and often overlaps with) computational statistics, which also focuses on...Created January 20, 2025 • Updated January 20, 2025
Can't find it?
Request a Dataset