Accelerating genetic gain through early-stage on-farm sparse testing.

Most African crop breeding programs conduct early-stage selection at very few research stations, which may not reflect smallholder farm conditions. Early-stage on-farm sparse testing utilizes genomic relationships to shift selection from research stations to hundreds of farms in the target population of environments, facilitating increased genetic gain in farmers' fields.

FieldSimR: an R package for simulating plot data in multi-environment field trials.

This paper presents a general framework for simulating plot data in multi-environment field trials with one or more traits. The framework is embedded within the R package FieldSimR, whose core function generates plot errors that capture global field trend, local plot variation, and extraneous variation at a user-defined ratio. FieldSimR's capacity to simulate realistic plot data makes it a flexible and powerful tool for a wide range of improvement processes in plant breeding, such as the optimisation of experimental designs and statistical analyses of multi-environment field trials.

Clonal diploid and autopolyploid breeding strategies to harness heterosis: insights from stochastic simulation.

Reciprocal recurrent selection sometimes increases genetic gain per unit cost in clonal diploids with heterosis due to dominance, but it typically does not benefit autopolyploids. Breeding can change the dominance as well as additive genetic value of populations, thus utilizing heterosis. A common hybrid breeding strategy is reciprocal recurrent selection (RRS), in which parents of hybrids are typically recycled within pools based on general combining ability.

Genomic selection strategies for clonally propagated crops.

For genomic selection in clonally propagated crops with diploid (-like) meiotic behavior to be effective, crossing parents should be selected based on genomic predicted cross-performance unless dominance is negligible. For genomic selection (GS) in clonal breeding programs to be effective, parents should be selected based on genomic predicted cross-performance unless dominance is negligible. Genomic prediction of cross-performance enables efficient exploitation of the additive and dominance value simultaneously.

Regional association analysis coupled with transcriptome analyses reveal candidate genes affecting seed oil accumulation in Brassica napus.

Regional association analysis of 50 re-sequenced Chinese semi-winter rapeseed accessions in combination with co-expression analysis reveal candidate genes affecting oil accumulation in Brassica napus. One of the breeding goals in rapeseed production is to enhance the seed oil content to cater to the increased demand for vegetable oils due to a growing global population. To investigate the genetic basis of variation in seed oil content, we used 60 K Brassica Infinium SNP array along with phenotype data of 203 Chinese semi-winter rapeseed accessions to perform a genome-wide analysis of haplotype blocks associated with the oil content.

Modeling Illustrates That Genomic Selection Provides New Opportunities for Intercrop Breeding.

Intercrop breeding programs using genomic selection can produce faster genetic gain than intercrop breeding programs using phenotypic selection. Intercropping is an agricultural practice in which two or more component crops are grown together. It can lead to enhanced soil structure and fertility, improved weed suppression, and better control of pests and diseases.

Multi-omics-based prediction of hybrid performance in canola.

Complementing or replacing genetic markers with transcriptomic data and use of reproducing kernel Hilbert space regression based on Gaussian kernels increases hybrid prediction accuracies for complex agronomic traits in canola. In plant breeding, hybrids gained particular importance due to heterosis, the superior performance of offspring compared to their inbred parents. Since the development of new top performing hybrids requires labour-intensive and costly breeding programmes, including testing of large numbers of experimental hybrids, the prediction of hybrid performance is of utmost interest to plant breeders.

How Population Structure Impacts Genomic Selection Accuracy in Cross-Validation: Implications for Practical Breeding.

Over the last two decades, the application of genomic selection has been extensively studied in various crop species, and it has become a common practice to report prediction accuracies using cross validation. However, genomic prediction accuracies obtained from random cross validation can be strongly inflated due to population or family structure, a characteristic shared by many breeding populations. An understanding of the effect of population and family structure on prediction accuracy is essential for the successful application of genomic selection in plant breeding programs.

GWAS and co-expression network combination uncovers multigenes with close linkage effects on the oleic acid content accumulation in Brassica napus.

Background: Strong artificial and natural selection causes the formation of highly conserved haplotypes that harbor agronomically important genes. GWAS combination with haplotype analysis has evolved as an effective method to dissect the genetic architecture of complex traits in crop species.

Results: We used the 60 K Brassica Infinium SNP array to perform a genome-wide analysis of haplotype blocks associated with oleic acid (C18:1) in rapeseed.

Strong temporal dynamics of QTL action on plant growth progression revealed through high-throughput phenotyping in canola.

A major challenge of plant biology is to unravel the genetic basis of complex traits. We took advantage of recent technical advances in high-throughput phenotyping in conjunction with genome-wide association studies to elucidate genotype-phenotype relationships at high temporal resolution. A diverse Brassica napus population from a commercial breeding programme was analysed by automated non-invasive phenotyping.

Genetic insights into underground responses to Fusarium graminearum infection in wheat.

The ongoing global intensification of wheat production will likely be accompanied by a rising pressure of Fusarium diseases. While utmost attention was given to Fusarium head blight (FHB) belowground plant infections of the pathogen have largely been ignored. The current knowledge about the impact of soil borne Fusarium infection on plant performance and the underlying genetic mechanisms for resistance remain very limited.

Effective Genomic Selection in a Narrow-Genepool Crop with Low-Density Markers: Asian Rapeseed as an Example.

Genomic selection (GS) has revolutionized breeding for quantitative traits in plants, offering potential to optimize resource allocation in breeding programs and increase genetic gain per unit of time. Modern high-density single nucleotide polymorphism (SNP) arrays comprising up to several hundred thousand markers provide a user-friendly technology to characterize the genetic constitution of whole populations and for implementing GS in breeding programs. However, GS does not build upon detailed genotype profiling facilitated by maximum marker density.

Genome-wide regression models considering general and specific combining ability predict hybrid performance in oilseed rape with similar accuracy regardless of trait architecture.

Genomic prediction using the Brassica 60 k genotyping array is efficient in oilseed rape hybrids. Prediction accuracy is more dependent on trait complexity than on the prediction model. In oilseed rape breeding programs, performance prediction of parental combinations is of fundamental importance.

Exploring and Harnessing Haplotype Diversity to Improve Yield Stability in Crops.

In order to meet future food, feed, fiber, and bioenergy demands, global yields of all major crops need to be increased significantly. At the same time, the increasing frequency of extreme weather events such as heat and drought necessitates improvements in the environmental resilience of modern crop cultivars. Achieving sustainably increase yields implies rapid improvement of quantitative traits with a very complex genetic architecture and strong environmental interaction.