Editorial: Genome wide association studies and genomic selection for crop improvement in the era of Big Data

Alison Bentley Charles Chen Nunzio D'Agostino (2022)

Artículo

Allele Mining High-Throughput Phenotyping Genomic Estimated Breeding Value CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CROP IMPROVEMENT DNA CHROMOSOME MAPPING GENETIC LINKAGE GENOMES GENOTYPING GERMINATION HEAT STRESS QUALITY CONTROL SINGLE NUCLEOTIDE POLYMORPHISM TRITICUM AESTIVUM GENETIC DIVERSITY (AS RESOURCE) HIGH-THROUGHPUT SEQUENCING

Maintenance of UK bread baking quality: Trends in wheat quality traits over 50 years of breeding and potential for future application of genomic-assisted selection

Nick Fradgley Alison Bentley Keith Gardner Stéphanie M. Swarbreck (2023)

Artículo

Sustainable Food Systems Genomic Prediction Genome-Wide Association Analysis CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT BREEDING MARKER-ASSISTED SELECTION VARIETIES FOOD SYSTEMS QUALITY

Replication Data for: Sparse testing using genomic predication improves selection for breeding targets in elite spring wheat

Ravi Singh Kelly Robbins Jose Crossa Alison Bentley (2022)

In multi-environment yield trials, the use of sparse testing genomic selection enables increasing selection intensity or testing environments. The data presented in this dataset were used in the evaluation of different sparse testing genomic selection strategies in the early yield testing stage of CIMMYT spring wheat breeding pipeline. Phenotypic, genotypic, and coefficient of parentage data are provided. The germplasm is made up of multiple populations each with small family sizes. The findings of the study are detailed in an accompanying article.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Trade-offs in the genetic control of functional and nutritional quality traits in UK winter wheat

Nick Fradgley Keith Gardner Stéphanie M. Swarbreck Alison Bentley (2022)

Artículo

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT GENETIC CONTROL NUTRITIVE VALUE QUANTITATIVE TRAIT LOCI

Results from rapid-cycle recurrent genomic selection in spring bread wheat

Susanne Dreisigacker Leonardo Abdiel Crespo Herrera Alison Bentley Jose Crossa (2022)

Empirical studies of early generation genomic selection strategies for parental selection or population improvement are still lacking in wheat and other major crops. We show the potential of rapid-cycle recurrent GS to increase genetic gain for grain yield in wheat. We show a consistent realized genetic gain for grain yield after three cycles of recombination of bi-parental F1’s, when summarized across two years of phenotyping.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Balancing quality with quantity: a case study of UK bread wheat

Nick Fradgley Keith Gardner Stéphanie M. Swarbreck Alison Bentley (2023)

Artículo

Grain Protein Content Environmental Sustainability End-Use Quality Modern Bread Baking Methods CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GRAIN PROTEIN CONTENT HISTORY QUALITY WHEAT YIELDS

Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.)

Filippo Maria Bassi Alison Bentley Rodomiro Ortiz Jose Crossa (2016)

Artículo

Marker-Aided Breeding CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BREEDING VALUE GENETIC GAIN GENOTYPE ENVIRONMENT INTERACTION QUANTITATIVE TRAIT LOCI

Sowing the wheat seeds of Afghanistan's future

Nigel Poole Rajiv Sharma Orzala Nemat Jason Donovan Alison Bentley (2022)

Artículo

Humanitarian Intervention CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA FOOD SECURITY IRRIGATION NUTRITION PLANT BREEDING SEED SYSTEMS

Development of a next generation SNP genotyping array for wheat

Simon Griffiths Susanne Dreisigacker Alison Bentley Gina Brown-Guedira (2023)

Artículo

Axiom Array High Throughput Genotyping Hexaploid Wheat Copy Number Variation Analysis CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SINGLE NUCLEOTIDE POLYMORPHISMS WHEAT GENOTYPING GENETIC DIVERSITY (AS RESOURCE)

Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding

Osval Antonio Montesinos-Lopez ABELARDO MONTESINOS LOPEZ RICARDO ACOSTA DIAZ Rajeev Varshney Jose Crossa ALISON BENTLEY (2022)

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.

Artículo

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA Bayes Theorem Genome Inflammatory Bowel Diseases Models, Genetic Plant Breeding