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XUECAI ZHANG Zefeng Yang Yang Xu (2022, [Artículo])
Genotyping by Sequencing Genomic Selection SNP Array Marker Density CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MARKER-ASSISTED SELECTION MAIZE SINGLE NUCLEOTIDE POLYMORPHISM HYBRIDS
Enhancement of plant variety protection and regulation using molecular marker technology
Yunbi Xu Jian Zhang Jiansheng LI (2022, [Artículo])
Plant Variety Protection Distinctness-Uniformity-Stability Essentially Derived Variety Molecular Markers Molecular Diagnostics Genetic Similarity CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENETICS GENETIC MARKERS PLANT BREEDING VARIETIES
Melaku Gedil Ana Luisa Garcia-Oliveira Nnanna Unachukwu Cesar Petroli Sarah Hearne Abebe Menkir (2023, [Artículo])
Genetic Relationship Desirable Target Traits Parental Selection CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENETIC STRUCTURES INBRED LINES MAIZE BREEDING PROGRAMMES
Editorial: Genomic selection: Lessons learned and perspectives
Johannes Martini Sarah Hearne Valentin Wimmer Fernando Henrique Toledo (2022, [Artículo])
Genomic Selection Selection Gain Breeding Schemes CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BREEDING PROGRAMMES MARKER-ASSISTED SELECTION GENOTYPE ENVIRONMENT INTERACTION PLANT BREEDING
Results from rapid-cycle recurrent genomic selection in spring bread wheat
Susanne Dreisigacker Paulino Pérez-Rodríguez Leonardo Abdiel Crespo Herrera Alison Bentley Jose Crossa (2023, [Artículo])
Genomic-Assisted Breeding Molecular Markers Pedigree Information Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENOMICS GENETIC MARKERS WHEAT BREEDING PROGRAMMES
Population genetic structure of the maize weevil, Sitophilus zeamais, in southern Mexico
Michael Jones Martha Willcox (2023, [Artículo])
Maize Weevil Genetic Structure CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRICULTURAL WORKERS FILTRATION GENE FLOW MAIZE SINGLE NUCLEOTIDE POLYMORPHISM SITOPHILUS ZEAMAIS CURCULIONIDAE
MAHENDAR THUDI Abhishek Bohra Spurthi Nayak Trushar Shah R. Varma Penmetsa Nepolean Thirunavukkarasu Pooran Gaur Pawan Kulwal Hari Upadhyaya Polavarapu Kavi Kishor Rajeev Varshney (2011, [Artículo])
Molecular Markers Recombinant Inbred Lines CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENETIC MARKERS MICROSATELLITES PLANTS DNA CHICKPEAS ARRAYS TECHNOLOGY CHROMOSOME MAPPING GENETIC VARIATION GENOTYPES MOLECULAR CLONING
Manish Pandey Trushar Shah David Bertioli Rajeev Varshney (2012, [Artículo])
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BACKCROSSING GENOTYPES GROUNDNUTS MICROSATELLITES ARACHIS HYPOGAEA CHROMOSOME MAPPING PLANTS GENETIC MARKERS QUANTITATIVE TRAIT LOCI TETRAPLOIDY
UTTAM KUMAR Rajeev Ranjan Kumar Philomin Juliana Sundeep Kumar (2022, [Artículo])
Genomic Selection Single-Trait Genomic Selection Multi-Trait Genomic Selection Genomic Estimated Breeding Value Climate-Resilient Crops CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MARKER-ASSISTED SELECTION CLIMATE CHANGE STRESS CLIMATE RESILIENCE CROPS ABIOTIC STRESS BIOTIC STRESS
Osval Antonio Montesinos-Lopez ABELARDO MONTESINOS LOPEZ RICARDO ACOSTA DIAZ Rajeev Varshney Jose Crossa ALISON BENTLEY (2022, [Artículo])
Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to
the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs.
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA Bayes Theorem Genome Inflammatory Bowel Diseases Models, Genetic Plant Breeding