Kai Peter Voss-Fels

Genomic mate-allocation strategies exploiting additive and non-additive genetic effects to maximise total clonal performance in sugarcane

Publisher: Cold Spring Harbor Laboratory

Date: 20-12-2022

DOI: 10.1101/2022.12.19.521119

Abstract: Mate-allocation in breeding programs can improve progeny performance by exploiting non-additive effects. Breeding decisions in the mate-allocation approach are based on predicted progeny merit rather than parental breeding value. This is particularly attractive when non-additive effects are significant, and the best-predicted progeny can be clonally propagated, for ex le sugarcane. We compared mate-allocation strategies that leverage non-additive and heterozygosity effects to maximise sugarcane clonal performance to schemes that use only additive effects to maximise breeding value. We used phenotypes and genotypes from a population of 2,909 clones phenotyped in Australia’s sugarcane breeding program’s final assessment trials for three traits: tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and fibre. The clones from the last generation of this data set were used as parents to simulate families from all possible crosses (1,225), each with 50 progenies. The breeding and clonal values of progeny were predicted using GBLUP (considering only additive effects) and the e-GBLUP model (incorporating additive, non-additive and heterozygosity effects). Integer linear programming was used to identify the optimal mate-allocation among the selected parents. Compared to the breeding value, the predicted progeny value of allocated crossing pairs based on clonal performance (e-GBLUP) increased by 57%, 12%, and 16% for TCH, CCS, and fibre, respectively. In our study, the mate-allocation strategy exploiting non-additive and heterozygosity effects resulted in better clonal performance. However, there was a noticeable decline in additive gain, particularly for TCH, which might have been caused by the presence of large epistatic effects. When crosses were chosen based on clonal performance for TCH, progenies’ inbreeding coefficients were found significantly lower than for random mating, indicating that utilising non-additive and heterozygosity effects has advantages for controlling inbreeding depression. Therefore, mate-allocation is recommended in clonal crops to improve clonal performance and reduce inbreeding.

Physiological and Genetic Drivers Underpinning Canopy Development are Associated with Durum Wheat Yield in Rainfed Environments

Publisher: Cold Spring Harbor Laboratory

Date: 21-08-2021

DOI: 10.1101/2021.08.21.457180

Abstract: New durum wheat ( Triticum turgidum L. ssp. Durum ) cultivars with improved adaptation to variable rainfall environments are required to sustain productivity in the face of climate change. Physiological traits related to canopy development underpin the production of biomass and yield, as they interact with solar radiation and affect the timing of water use throughout the growing season. This study explored the temporal canopy dynamics of durum wheat using a nested-association mapping population evaluated for longitudinal normalized difference vegetation index (NDVI) measurements. Association mapping was performed to identify quantitative trait loci (QTL) for time-point NDVI and spline-smoothed NDVI trajectory traits. Yield effects associated with QTL for canopy development were investigated using data from four rainfed field trials. Four QTL associated with slower canopy closure, improved yield in specific environments, and notably, were not associated with a yield penalty in any environment. This was likely due to optimised timing of water-use and pleiotropic effects on yield component traits, including spike number and spike length. Overall, this study suggests that slower canopy closure is beneficial for durum wheat production in rainfed environments. Selection for traits or loci associated with canopy development may improve yield stability of durum wheat in water limited environments.

Deletion of a Stay-Green Gene Associates with Adaptive Selection in Brassica napus

Publisher: Elsevier BV

Date: 12-2016

DOI: 10.1016/J.MOLP.2016.10.017

Abstract: Chlorophyll levels provide important information about plant growth and physiological plasticity in response to changing environments. The stay-green gene NON-YELLOWING 1 (NYE1) is believed to regulate chlorophyll degradation during senescence, concomitantly affecting the disassembly of the light-harvesting complex and hence indirectly influencing photosynthesis. We identified Brassica napus accessions carrying an NYE1 deletion associated with increased chlorophyll content, and with upregulated expression of light-harvesting complex and photosynthetic reaction center (PSI and PSII) genes. Comparative analysis of the seed oil content of accessions with related genetic backgrounds revealed that the B. napus NYE1 gene deletion (bnnye1) affected oil accumulation, and linkage disequilibrium signatures suggested that the locus has been subject to artificial selection by breeding in oilseed B. napus forms. Comparative analysis of haplotype ersity groups (haplogroups) between three different ecotypes of the allopolyploid B. napus and its A-subgenome diploid progenitor, Brassica rapa, indicated that introgression of the bnnye1 deletion from Asian B. rapa into winter-type B. napus may have simultaneously improved its adaptation to cooler environments experienced by autumn-sown rapeseed.

Subgenomic Diversity Patterns Caused by Directional Selection in Bread Wheat Gene Pools

Publisher: Wiley

Date: 07-2015

DOI: 10.3835/PLANTGENOME2015.03.0013

Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity

Publisher: Springer Science and Business Media LLC

Date: 18-03-2021

DOI: 10.1007/S00122-021-03812-3

Abstract: Climate change and Genotype-by-Environment-by-Management interactions together challenge our strategies for crop improvement. Research to advance prediction methods for breeding and agronomy is opening new opportunities to tackle these challenges and overcome on-farm crop productivity yield-gaps through design of responsive crop improvement strategies. Genotype-by-Environment-by-Management (G × E × M) interactions underpin many aspects of crop productivity. An important question for crop improvement is “How can breeders and agronomists effectively explore the erse opportunities within the high dimensionality of the complex G × E × M factorial to achieve sustainable improvements in crop productivity?” Whenever G × E × M interactions make important contributions to attainment of crop productivity, we should consider how to design crop improvement strategies that can explore the potential space of G × E × M possibilities, reveal the interesting Genotype–Management (G–M) technology opportunities for the Target Population of Environments (TPE), and enable the practical exploitation of the associated improved levels of crop productivity under on-farm conditions. Climate change adds additional layers of complexity and uncertainty to this challenge, by introducing directional changes in the environmental dimension of the G × E × M factorial. These directional changes have the potential to create further conditional changes in the contributions of the genetic and management dimensions to future crop productivity. Therefore, in the presence of G × E × M interactions and climate change, the challenge for both breeders and agronomists is to co-design new G–M technologies for a non-stationary TPE. Understanding these conditional changes in crop productivity through the relevant sciences for each dimension, Genotype, Environment, and Management, creates opportunities to predict novel G–M technology combinations suitable to achieve sustainable crop productivity and global food security targets for the likely climate change scenarios. Here we consider critical foundations required for any prediction framework that aims to move us from the current unprepared state of describing G × E × M outcomes to a future responsive state equipped to predict the crop productivity consequences of G–M technology combinations for the range of environmental conditions expected for a complex, non-stationary TPE under the influences of climate change.

Adaptive Traits to Improve Durum Wheat Yield in Drought and Crown Rot Environments

Publisher: MDPI AG

Date: 24-07-2020

DOI: 10.3390/IJMS21155260

Abstract: Durum wheat (Triticum turgidum L. ssp. durum) production can experience significant yield losses due to crown rot (CR) disease. Losses are usually exacerbated when disease infection coincides with terminal drought. Durum wheat is very susceptible to CR, and resistant germplasm is not currently available in elite breeding pools. We hypothesize that deploying physiological traits for drought adaptation, such as optimal root system architecture to reduce water stress, might minimize losses due to CR infection. This study evaluated a subset of lines from a nested association mapping population for stay-green traits, CR incidence and yield in field experiments as well as root traits under controlled conditions. Weekly measurements of normalized difference vegetative index (NDVI) in the field were used to model canopy senescence and to determine stay-green traits for each genotype. Genome-wide association studies using DArTseq molecular markers identified quantitative trait loci (QTLs) on chromosome 6B (qCR-6B) associated with CR tolerance and stay-green. We explored the value of qCR-6B and a major QTL for root angle QTL qSRA-6A using yield datasets from six rainfed environments, including two environments with high CR disease pressure. In the absence of CR, the favorable allele for qSRA-6A provided an average yield advantage of 0.57 t·ha−1, whereas in the presence of CR, the combination of favorable alleles for both qSRA-6A and qCR-6B resulted in a yield advantage of 0.90 t·ha−1. Results of this study highlight the value of combining above- and belowground physiological traits to enhance yield potential. We anticipate that these insights will assist breeders to design improved durum varieties that mitigate production losses due to water deficit and CR.

A linkage disequilibrium-based approach to position unmapped SNPs in crop species

Publisher: Springer Science and Business Media LLC

Date: 29-10-2021

DOI: 10.1186/S12864-021-08116-W

Abstract: High-density SNP arrays are now available for a wide range of crop species. Despite the development of many tools for generating genetic maps, the genome position of many SNPs from these arrays is unknown. Here we propose a linkage disequilibrium (LD)-based algorithm to allocate unassigned SNPs to chromosome regions from sparse genetic maps. This algorithm was tested on sugarcane, wheat, and barley data sets. We calculated the algorithm’s efficiency by masking SNPs with known locations, then assigning their position to the map with the algorithm, and finally comparing the assigned and true positions. In the 20-fold cross-validation, the mean proportion of masked mapped SNPs that were placed by the algorithm to a chromosome was 89.53, 94.25, and 97.23% for sugarcane, wheat, and barley, respectively. Of the markers that were placed in the genome, 98.73, 96.45 and 98.53% of the SNPs were positioned on the correct chromosome. The mean correlations between known and new estimated SNP positions were 0.97, 0.98, and 0.97 for sugarcane, wheat, and barley. The LD-based algorithm was used to assign 5920 out of 21,251 unpositioned markers to the current Q208 sugarcane genetic map, representing the highest density genetic map for this species to date. Our LD-based approach can be used to accurately assign unpositioned SNPs to existing genetic maps, improving genome-wide association studies and genomic prediction in crop species with fragmented and incomplete genome assemblies. This approach will facilitate genomic-assisted breeding for many orphan crops that lack genetic and genomic resources.

Designer Roots for Future Crops

Publisher: Elsevier BV

Date: 11-2018

DOI: 10.1016/J.TPLANTS.2018.08.004

Abstract: Despite the importance of roots, they have largely been ignored by modern crop research and breeding. We discuss important progress in crop root research and highlight how the context-dependent optimisation of below- and above-ground plant components provides opportunities to improve future crops in the face of increasing environmental fluctuations.

genomicSimulation: fast R functions for stochastic simulation of breeding programs

Publisher: Oxford University Press (OUP)

Date: 02-09-2022

DOI: 10.1093/G3JOURNAL/JKAC216

Abstract: Simulation tools are key to designing and optimizing breeding programs that are multiyear, high-effort endeavors. Tools that operate on real genotypes and integrate easily with other analysis software can guide users toward crossing decisions that best balance genetic gains and genetic ersity required to maintain gains in the future. Here, we present genomicSimulation, a fast and flexible tool for the stochastic simulation of crossing and selection based on real genotypes. It is fully written in C for high execution speeds, has minimal dependencies, and is available as an R package for the integration with R’s broad range of analysis and visualization tools. Comparisons of a simulated recreation of a breeding program to a real data set demonstrate the simulated offspring from the tool correctly show key population features, such as genomic relationships and approximate linkage disequilibrium patterns. Both versions of genomicSimulation are freely available on GitHub: The R package version at llrs/genomicSimulation/ and the C library version at llrs/genomicSimulationC/.

“SpeedGS” to Accelerate Genetic Gain in Spring Wheat

Publisher: Elsevier

Date: 2019

DOI: 10.1016/B978-0-08-102163-7.00014-4

A chickpea genetic variation map based on the sequencing of 3,366 genomes

Publisher: Springer Science and Business Media LLC

Date: 10-11-2021

DOI: 10.1038/S41586-021-04066-1

Abstract: Zero hunger and good health could be realized by 2030 through effective conservation, characterization and utilization of germplasm resources 1 . So far, few chickpea ( Cicer arietinum ) germplasm accessions have been characterized at the genome sequence level 2 . Here we present a detailed map of variation in 3,171 cultivated and 195 wild accessions to provide publicly available resources for chickpea genomics research and breeding. We constructed a chickpea pan-genome to describe genomic ersity across cultivated chickpea and its wild progenitor accessions. A ergence tree using genes present in around 80% of in iduals in one species allowed us to estimate the ergence of Cicer over the last 21 million years. Our analysis found chromosomal segments and genes that show signatures of selection during domestication, migration and improvement. The chromosomal locations of deleterious mutations responsible for limited genetic ersity and decreased fitness were identified in elite germplasm. We identified superior haplotypes for improvement-related traits in landraces that can be introgressed into elite breeding lines through haplotype-based breeding, and found targets for purging deleterious alleles through genomics-assisted breeding and/or gene editing. Finally, we propose three crop breeding strategies based on genomic prediction to enhance crop productivity for 16 traits while avoiding the erosion of genetic ersity through optimal contribution selection (OCS)-based pre-breeding. The predicted performance for 100-seed weight, an important yield-related trait, increased by up to 23% and 12% with OCS- and haplotype-based genomic approaches, respectively.

Author Correction: A chickpea genetic variation map based on the sequencing of 3,366 genomes

Publisher: Springer Science and Business Media LLC

Date: 25-03-2022

DOI: 10.1038/S41586-022-04660-X

Discovering new alleles for yellow spot resistance in the Vavilov wheat collection

Publisher: Springer Science and Business Media LLC

Date: 16-10-2019

DOI: 10.1007/S00122-018-3204-5

Abstract: GWAS detected 11 yellow spot resistance QTL in the Vavilov wheat collection. Promising adult-plant resistance loci could provide a sustainable genetic solution to yellow spot in modern wheat varieties. Yellow spot, caused by the fungal pathogen Pyrenophora tritici-repentis (Ptr), is the most economically damaging foliar disease of wheat in Australia. Genetic resistance is considered to be the most sustainable means for disease management, yet the genomic regions underpinning resistance to Ptr, particularly adult-plant resistance (APR), remain vastly unknown. In this study, we report results of a genome-wide association study using 295 accessions from the Vavilov wheat collection which were extensively tested for response to Ptr infections in glasshouse and field trials at both seedling an adult growth stages. Combining phenotypic datasets from multiple experiments in Australia and Russia with 25,286 genome-wide, high-quality DArTseq markers, we detected a total of 11 QTL, of which 5 were associated with seedling resistance, 3 with all-stage resistance, and 3 with APR. Interestingly, the novel APR QTL were effective even in the presence of host sensitivity gene Tsn1. These genomic regions could offer broad-spectrum yellow spot protection, not just to ToxA but also other pathogenicity or virulence factors. Vavilov wheat accessions carrying APR QTL combinations displayed enhanced levels of resistance highlighting the potential for QTL stacking through breeding. We propose that the APR genetic factors discovered in our study could be used to improve resistance levels in modern wheat varieties and contribute to the sustainable control of yellow spot.

Strategies and considerations for implementing genomic selection to improve traits with additive and non-additive genetic architectures in sugarcane breeding

Publisher: Springer Science and Business Media LLC

Date: 15-02-2021

DOI: 10.1007/S00122-021-03785-3

Revolutionizing agriculture with synthetic biology

Publisher: Springer Science and Business Media LLC

Date: 18-11-2019

DOI: 10.1038/S41477-019-0539-0

Abstract: Synthetic biology is here to stay and will transform agriculture if given the chance. The huge challenges facing food, fuel and chemical production make it vital to give synthetic biology that chance-notwithstanding the shifts in mindset, training and infrastructure investment this demands. Here, we assess opportunities for agricultural synthetic biology and ways to remove barriers to their realization.

Improved genomic prediction of clonal performance in sugarcane by exploiting non-additive genetic effects

Publisher: Springer Science and Business Media LLC

Date: 26-04-2021

DOI: 10.1007/S00122-021-03822-1

Abstract: Non-additive genetic effects seem to play a substantial role in the expression of complex traits in sugarcane. Including non-additive effects in genomic prediction models significantly improves the prediction accuracy of clonal performance. In the recent decade, genetic progress has been slow in sugarcane. One reason might be that non-additive genetic effects contribute substantially to complex traits. Dense marker information provides the opportunity to exploit non-additive effects in genomic prediction. In this study, a series of genomic best linear unbiased prediction (GBLUP) models that account for additive and non-additive effects were assessed to improve the accuracy of clonal prediction. The reproducible kernel Hilbert space model, which captures non-additive genetic effects, was also tested. The models were compared using 3,006 genotyped elite clones measured for cane per hectare (TCH), commercial cane sugar (CCS), and Fibre content. Three forward prediction scenarios were considered to investigate the robustness of genomic prediction. By using a pseudo-diploid parameterization, we found significant non-additive effects that accounted for almost two-thirds of the total genetic variance for TCH. Average heterozygosity also had a major impact on TCH, indicating that directional dominance may be an important source of phenotypic variation for this trait. The extended-GBLUP model improved the prediction accuracies by at least 17% for TCH, but no improvement was observed for CCS and Fibre. Our results imply that non-additive genetic variance is important for complex traits in sugarcane, although further work is required to better understand the variance component partitioning in a highly polyploid context. Genomics-based breeding will likely benefit from exploiting non-additive genetic effects, especially in designing crossing schemes. These findings can help to improve clonal prediction, enabling a more accurate identification of variety candidates for the sugarcane industry.

Modelling selection response in plant breeding programs using crop models as mechanistic gene-to-phenotype (CGM-G2P) multi-trait link functions

Publisher: Cold Spring Harbor Laboratory

Date: 14-10-2020

DOI: 10.1101/2020.10.13.338301

Abstract: Plant breeding programs are designed and operated over multiple cycles to systematically change the genetic makeup of plants to achieve improved trait performance for a Target Population of Environments (TPE). Within each cycle, selection applied to the standing genetic variation within a structured reference population of genotypes (RPG) is the primary mechanism by which breeding programs make the desired genetic changes. Selection operates to change the frequencies of the alleles of the genes controlling trait variation within the RPG. The structure of the RPG and the TPE has important implications for the design of optimal breeding strategies. The breeder’s equation, together with the quantitative genetic theory behind the equation, informs many of the principles for design of breeding programs. The breeder’s equation can take many forms depending on the details of the breeding strategy. Through the genetic changes achieved by selection, the cultivated varieties of crops (cultivars) are improved for use in agriculture. From a breeding perspective, selection for specific trait combinations requires a quantitative link between the effects of the alleles of the genes impacted by selection and the trait phenotypes of plants and their breeding value. This gene-to-phenotype link function provides the G2P map for one to many traits. For complex traits controlled by many genes, the infinitesimal model for trait genetic variation is the dominant G2P model of quantitative genetics. Here we consider motivations and potential benefits of using the hierarchical structure of crop models as CGM-G2P trait link functions in combination with the infinitesimal model for the design and optimisation of selection in breeding programs.

Discovering new alleles for yellow spot resistance in the Vavilov wheat collection

Publisher: Springer Science and Business Media LLC

Date: 16-10-2019

DOI: 10.1007/S00122-018-3204-5

Abstract: GWAS detected 11 yellow spot resistance QTL in the Vavilov wheat collection. Promising adult-plant resistance loci could provide a sustainable genetic solution to yellow spot in modern wheat varieties. Yellow spot, caused by the fungal pathogen Pyrenophora tritici-repentis (Ptr), is the most economically damaging foliar disease of wheat in Australia. Genetic resistance is considered to be the most sustainable means for disease management, yet the genomic regions underpinning resistance to Ptr, particularly adult-plant resistance (APR), remain vastly unknown. In this study, we report results of a genome-wide association study using 295 accessions from the Vavilov wheat collection which were extensively tested for response to Ptr infections in glasshouse and field trials at both seedling an adult growth stages. Combining phenotypic datasets from multiple experiments in Australia and Russia with 25,286 genome-wide, high-quality DArTseq markers, we detected a total of 11 QTL, of which 5 were associated with seedling resistance, 3 with all-stage resistance, and 3 with APR. Interestingly, the novel APR QTL were effective even in the presence of host sensitivity gene Tsn1. These genomic regions could offer broad-spectrum yellow spot protection, not just to ToxA but also other pathogenicity or virulence factors. Vavilov wheat accessions carrying APR QTL combinations displayed enhanced levels of resistance highlighting the potential for QTL stacking through breeding. We propose that the APR genetic factors discovered in our study could be used to improve resistance levels in modern wheat varieties and contribute to the sustainable control of yellow spot.

Breeding improves wheat productivity under contrasting agrochemical input levels

Publisher: Springer Science and Business Media LLC

Date: 17-06-2019

DOI: 10.1038/S41477-019-0445-5

Abstract: The world cropping area for wheat exceeds that of any other crop, and high grain yields in intensive wheat cropping systems are essential for global food security. Breeding has raised yields dramatically in high-input production systems however, selection under optimal growth conditions is widely believed to diminish the adaptive capacity of cultivars to less optimal cropping environments. Here, we demonstrate, in a large-scale study spanning five decades of wheat breeding progress in western Europe, where grain yields are among the highest worldwide, that breeding for high performance in fact enhances cultivar performance not only under optimal production conditions but also in production systems with reduced agrochemical inputs. New cultivars incrementally accumulated genetic variants conferring favourable effects on key yield parameters, disease resistance, nutrient use efficiency, photosynthetic efficiency and grain quality. Combining beneficial, genome-wide haplotypes could help breeders to more efficiently exploit available genetic variation, optimizing future yield potential in more sustainable production systems.

Overcoming polyploidy pitfalls: a user guide for effective SNP conversion into KASP markers in wheat

Publisher: Springer Science and Business Media LLC

Date: 04-06-2020

DOI: 10.1007/S00122-020-03608-X

Abstract: Conversion of SNP chip assays into locus-specific KASP markers requires adapted strategies in polyploid species with high genome homeology. Procedures are exemplified by QTL-associated SNPs in hexaploid wheat. Kompetitive allele-specific PCR (KASP) markers are commonly used in marker-assisted commercial plant breeding due to their cost-effectiveness and throughput for high s le volumes. However, conversion of trait-linked SNP markers from array-based SNP detection technologies into KASP markers is particularly challenging in polyploid crop species, due to the presence of highly similar homeologous and paralogous genome sequences. We evaluated strategies and identified key requirements for successful conversion of Illumina Infinium assays from the wheat 90 K SNP array into robust locus-specific KASP markers. Numerous ex les showed that commonly used software for semiautomated KASP primer design frequently fails to achieve locus-specificity of KASP assays in wheat. Instead, alignment of SNP probes with multiple reference genomes and Sanger sequencing of relevant genotypes, followed by visual KASP primer placement, was critical for locus-specificity. To identify KASP assays resulting in false calling of heterozygous in iduals, validation of KASP assays using extended reference genotype sets including heterozygous genotypes is strongly advised for polyploid crop species. Applying this strategy, we developed highly reproducible, stable KASP assays that are predictive for root biomass QTL haplotypes from highly homoeologous wheat chromosome regions. Due to their locus-specificity, these assays predicted root biomass considerably better than the original trait-associated markers from the Illumina array.

Heterosis for Biomass and Grain Yield Facilitates Breeding of Productive Dual‐Purpose Winter Barley Hybrids

Publisher: Wiley

Date: 30-06-2017

DOI: 10.2135/CROPSCI2016.10.0872

Exploring and harnessing haplotype diversity to improve yield stability in crops

Publisher: Frontiers Media SA

Date: 05-09-2017

DOI: 10.3389/FPLS.2017.01534

Wheat root systems as a breeding target for climate resilience

Publisher: Springer Science and Business Media LLC

Date: 26-04-2021

DOI: 10.1007/S00122-021-03819-W

Abstract: In the coming decades, larger genetic gains in yield will be necessary to meet projected demand, and this must be achieved despite the destabilizing impacts of climate change on crop production. The root systems of crops capture the water and nutrients needed to support crop growth, and improved root systems tailored to the challenges of specific agricultural environments could improve climate resiliency. Each component of root initiation, growth and development is controlled genetically and responds to the environment, which translates to a complex quantitative system to navigate for the breeder, but also a world of opportunity given the right tools. In this review, we argue that it is important to know more about the ‘hidden half’ of crop plants and hypothesize that crop improvement could be further enhanced using approaches that directly target selection for root system architecture. To explore these issues, we focus predominantly on bread wheat ( Triticum aestivum L.), a staple crop that plays a major role in underpinning global food security. We review the tools available for root phenotyping under controlled and field conditions and the use of these platforms alongside modern genetics and genomics resources to dissect the genetic architecture controlling the wheat root system. To contextualize these advances for applied wheat breeding, we explore questions surrounding which root system architectures should be selected for, which agricultural environments and genetic trait configurations of breeding populations are these best suited to, and how might direct selection for these root ideotypes be implemented in practice.

Evolutionary computing to assemble standing genetic diversity and achieve long-term genetic gain

Publisher: Cold Spring Harbor Laboratory

Date: 07-05-2023

DOI: 10.1101/2023.05.05.539510

Abstract: Loss of genetic ersity in elite crop breeding pools can severely limit long-term genetic gains, and limits gain for new traits that are becoming important as the climate changes, such as heat tolerance. Introgression of specific traits and pre-breeding germplasm is an alternative to introduce new ersity, however, introgression is typically slow and challenging. Here we use a genetic algorithm (GA) to select sets of parents which between them contain chromosome segments with very high segment breeding values for the target trait, as an alternative to truncation selection on genomic estimated breeding values (GEBVs). Repeated recurrent selection and intercrossing for a yield trait, beginning with a founder population of wheat genotypes and guided by the GA, was compared to repeated recurrent selection and to optimal contribution selection (OCS) on yield GEBV. After 100 generations of selection and intercrossing, truncation selection had exhausted the genetic ersity, while considerable ersity remained in the GA population. In some situations, particularly where the number of parents crossed each generation was relatively small and the number of progeny per cross was large, gain from the GA exceeded that from truncation selection. Compared to OCS, selection under the GA maintained more useful ersity, i.e. erse segments with high yield GEBV, which allowed it to maintain higher rates of gain for longer. These results indicate that GA-based parent selection could be a promising method to maintain erse alleles with higher genetic merit in breeding populations with little additional effort to a regular genomic selection program. A segment-stacking algorithm can outperform optimal contribution selection and truncation selection in long-term crop breeding programs, particularly with small populations, while also maintaining more useful ersity.

Perspectives on Applications of Hierarchical Gene-To-Phenotype (G2P) Maps to Capture Non-stationary Effects of Alleles in Genomic Prediction

Publisher: Frontiers Media SA

Date: 04-06-2021

DOI: 10.3389/FPLS.2021.663565

Abstract: Genomic prediction of complex traits across environments, breeding cycles, and populations remains a challenge for plant breeding. A potential explanation for this is that underlying non-additive genetic (GxG) and genotype-by-environment (GxE) interactions generate allele substitution effects that are non-stationary across different contexts. Such non-stationary effects of alleles are either ignored or assumed to be implicitly captured by most gene-to-phenotype (G2P) maps used in genomic prediction. The implicit capture of non-stationary effects of alleles requires the G2P map to be re-estimated across different contexts. We discuss the development and application of hierarchical G2P maps that explicitly capture non-stationary effects of alleles and have successfully increased short-term prediction accuracy in plant breeding. These hierarchical G2P maps achieve increases in prediction accuracy by allowing intermediate processes such as other traits and environmental factors and their interactions to contribute to complex trait variation. However, long-term prediction remains a challenge. The plant breeding community should undertake complementary simulation and empirical experiments to interrogate various hierarchical G2P maps that connect GxG and GxE interactions simultaneously. The existing genetic correlation framework can be used to assess the magnitude of non-stationary effects of alleles and the predictive ability of these hierarchical G2P maps in long-term, multi-context genomic predictions of complex traits in plant breeding.

Speed breeding for multiple quantitative traits in durum wheat

Publisher: Springer Science and Business Media LLC

Date: 14-05-2018

DOI: 10.1186/S13007-018-0302-Y

Investigations into the emergent properties of gene-to-phenotype networks across cycles of selection: A case study of shoot branching in plants

Publisher: Cold Spring Harbor Laboratory

Date: 05-02-2022

DOI: 10.1101/2022.02.03.479064

Abstract: Predictive breeding is now widely practised in crop improvement programs and has accelerated selection response (i.e., the amount of genetic gain between breeding cycles) for complex traits. However, world food production needs to increase further to meet the demands of the growing human population. The prediction of complex traits with current methods can be inconsistent across different genetic, environmental, and agronomic management contexts because the complex relationships between genomic and phenotypic variation are not well accounted for. Therefore, developing gene-to-phenotype network models for traits that integrate the knowledge of networks from systems biology, plant and crop physiology with population genomics has been proposed to close this gap in predictive modelling. Here, we develop a gene-to-phenotype network for shoot branching, a critical developmental pathway underpinning harvestable yield for many crop species, as a case study to explore the value of developing gene-to-phenotype networks to enhance understanding of selection responses. We observed that genetic canalization is an emergent property of the complex interactions among shoot branching gene-to-phenotype network components, leading to the accumulation of cryptic genetic variation, reduced selection responses, and large variation in selection trajectories across populations. As genetic canalization is expected to be pervasive in traits, such as grain yield, that result from interactions among multiple genes, traits, environments, and agronomic management practices, the need to model traits in crop improvement programs as outcomes of gene-to-phenotype networks is highlighted as an emerging opportunity to advance our understanding of selection response and the efficiency of developing resilient crops for future climates.

High-resolution mapping of rachis nodes per rachis, a critical determinant of grain yield components in wheat

Publisher: Springer Science and Business Media LLC

Date: 28-06-2019

DOI: 10.1007/S00122-019-03383-4

Abstract: Exploring large genomic data sets based on the latest reference genome assembly identifies the rice ortholog APO1 as a key candidate gene for number of rachis nodes per spike in wheat. Increasing grain yield in wheat is a key breeding objective worldwide. Several component traits contribute to grain yield with spike attributes being among the most important. In this study, we performed a genome-wide association analysis for 12 grain yield and component traits measured in field trials with contrasting agrochemical input levels in a panel of 220 hexaploid winter wheats. A highly significant, environmentally consistent QTL was detected for number of rachis nodes per rachis (NRN) on chromosome 7AL. The five most significant SNPs formed a strong linkage disequilibrium (LD) block and tagged a 2.23 Mb region. Using pairwise LD for exome SNPs located across this interval in a large worldwide hexaploid wheat collection, we reduced the genomic region for NRN to a 258 Kb interval containing four of the original SNP and six high-confidence genes. The ortholog of one (TraesCS7A01G481600) of these genes in rice was ABBERANT PANICLE ORGANIZATION1 (APO1), which is known to have significant effects on panicle attributes. The APO1 ortholog was the best candidate for NRN and was associated with a 115 bp promoter deletion and two amino acid (C47F and D384 N) changes. Using a large worldwide collection of tetraploid and hexaploid wheat, we found 12 haplotypes for the NRN QTL and evidence for positive enrichment of two haplotypes in modern germplasm. Comparison of five QTL haplotypes in Australian yield trials revealed their relative, context-dependent contribution to grain yield. Our study provides diagnostic SNPs and value propositions to support deployment of the NRN trait in wheat breeding.

genomicSimulation: fast R functions for stochastic simulation of breeding programs

Publisher: Cold Spring Harbor Laboratory

Date: 13-12-2021

DOI: 10.1101/2021.12.12.472291

Abstract: Simulation tools are key to designing and optimising breeding programs that are multi-year, high-effort endeavours. Tools that operate on real genotypes and integrate easily with other analysis software are needed to guide users to crossing decisions that best balance genetic gains and ersity to maintain gains in the future. This paper presents genomicSimulation, a fast and flexible tool for the stochastic simulation of crossing and selection on real genotypes. It is fully written in C for high execution speeds, has minimal dependencies, and is available as an R package for integration with R’s broad range of analysis and visualisation tools. Comparisons of a simulated recreation of a breeding program to the real data shows that the tool’s simulated offspring correctly show key population features, such as linkage disequilibrium patterns and genomic relationships. Both versions of genomicSimulation are freely available on GitHub: The R package version at llrs/genomicSimulation/ and the C library version at llrs/genomicSimulationC/

Accelerating Genetic Gain in Sugarcane Breeding Using Genomic Selection

Publisher: MDPI AG

Date: 19-04-2020

DOI: 10.3390/AGRONOMY10040585

Abstract: Sugarcane is a major industrial crop cultivated in tropical and subtropical regions of the world. It is the primary source of sugar worldwide, accounting for more than 70% of world sugar consumption. Additionally, sugarcane is emerging as a source of sustainable bioenergy. However, the increase in productivity from sugarcane has been small compared to other major crops, and the rate of genetic gains from current breeding programs tends to be plateauing. In this review, some of the main contributors for the relatively slow rates of genetic gain are discussed, including (i) breeding cycle length and (ii) low narrow-sense heritability for major commercial traits, possibly reflecting strong non-additive genetic effects involved in quantitative trait expression. A general overview of genomic selection (GS), a modern breeding tool that has been very successfully applied in animal and plant breeding, is given. This review discusses key elements of GS and its potential to significantly increase the rate of genetic gain in sugarcane, mainly by (i) reducing the breeding cycle length, (ii) increasing the prediction accuracy for clonal performance, and (iii) increasing the accuracy of breeding values for parent selection. GS approaches that can accurately capture non-additive genetic effects and potentially improve the accuracy of genomic estimated breeding values are particularly promising for the adoption of GS in sugarcane breeding. Finally, different strategies for the efficient incorporation of GS in a practical sugarcane breeding context are presented. These proposed strategies hold the potential to substantially increase the rate of genetic gain in future sugarcane breeding.

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

Publisher: Springer Science and Business Media LLC

Date: 06-03-2021

DOI: 10.1007/S00122-021-03788-0

Accuracy of genomic prediction of complex traits in sugarcane

Publisher: Springer Science and Business Media LLC

Date: 15-02-2021

DOI: 10.1007/S00122-021-03782-6

Need for speed: manipulating plant growth to accelerate breeding cycles

Publisher: Elsevier BV

Date: 04-2021

DOI: 10.1016/J.PBI.2020.101986

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

Publisher: Springer Science and Business Media LLC

Date: 28-10-2017

DOI: 10.1007/S00122-017-3002-5

Abstract: 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. Due to the phenomenon of heterosis, per se performance is not a reliable indicator for F

A Major Root Architecture QTL Responding to Water Limitation in Durum Wheat

Publisher: Frontiers Media SA

Date: 10-04-2019

DOI: 10.3389/FPLS.2019.00436

Mining the Vavilov wheat diversity panel for new sources of adult plant resistance to stripe rust

Publisher: Springer Science and Business Media LLC

Date: 03-02-2022

DOI: 10.1007/S00122-022-04037-8

Abstract: Multi-year evaluation of the Vavilov wheat ersity panel identified new sources of adult plant resistance to stripe rust. Genome-wide association studies revealed the key genomic regions influencing resistance, including seven novel loci. Wheat stripe rust (YR) caused by Puccinia striiformis f. sp . tritici ( Pst ) poses a significant threat to global food security. Resistance genes commonly found in many wheat varieties have been rendered ineffective due to the rapid evolution of the pathogen. To identify novel sources of adult plant resistance (APR), 292 accessions from the N.I. Vavilov Institute of Plant Genetic Resources, Saint Petersburg, Russia, were screened for known APR genes (i.e. Yr18 , Yr29 , Yr46 , Yr33 , Yr39 and Yr59 ) using linked polymerase chain reaction (PCR) molecular markers. Accessions were evaluated against Pst (pathotype 134 E16 A + Yr17 + Yr27) at seedling and adult plant stages across multiple years (2014, 2015 and 2016) in Australia. Phenotypic analyses identified 132 lines that potentially carry novel sources of APR to YR. Genome-wide association studies (GWAS) identified 68 significant marker–trait associations ( P 0.001) for YR resistance, representing 47 independent quantitative trait loci (QTL) regions. Fourteen genomic regions overlapped with previously reported Yr genes, including Yr29 , Yr56 , Yr5 , Yr43 , Yr57 , Yr30 , Yr46, Yr47 , Yr35 , Yr36 , Yrxy1 , Yr59 , Yr52 and YrYL . In total, seven QTL (positioned on chromosomes 1D, 2A, 3A, 3D, 5D, 7B and 7D) did not collocate with previously reported genes or QTL, indicating the presence of promising novel resistance factors. Overall, the Vavilov ersity panel provides a rich source of new alleles which could be used to broaden the genetic bases of YR resistance in modern wheat varieties.

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

Publisher: Wiley

Date: 07-2018

DOI: 10.3835/PLANTGENOME2017.09.0084

Abstract: 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. With extensive genome‐wide linkage disequilibrium (LD) being a common characteristic of breeding pools, fewer representative markers from available high‐density genotyping platforms could be sufficient to capture the association between a genomic region and a phenotypic trait. To examine the effects of reduced marker density on genomic prediction accuracy, we collected data on three traits across 2 yr in a panel of 203 homozygous Chinese semiwinter rapeseed ( Brassica napus L.) inbred lines, broadly encompassing allelic variability in the Asian B. napus genepool. We investigated two approaches to selecting subsets of markers: a trait‐dependent strategy based on genome‐wide association study (GWAS) significance thresholds and a trait‐independent method to detect representative tag SNPs. Prediction accuracies were evaluated using cross‐validation with ridge‐regression best linear unbiased predictions (rrBLUP). With semiwinter rapeseed as a model species, we demonstrate that low‐density marker sets comprising a few hundred to a few thousand markers enable high prediction accuracies in breeding populations with strong LD comparable to those achieved with high‐density arrays. Our results are valuable for facilitating routine application of cost‐efficient GS in breeding programs.

Linkage drag constrains the roots of modern wheat

Publisher: Wiley

Date: 18-02-2017

DOI: 10.1111/PCE.12888

Abstract: Roots, the hidden half of crop plants, are essential for resource acquisition. However, knowledge about the genetic control of below‐ground plant development in wheat, one of the most important small‐grain crops in the world, is very limited. The molecular interactions connecting root and shoot development and growth, and thus modulating the plant's demand for water and nutrients along with its ability to access them, are largely unexplored. Here, we demonstrate that linkage drag in European bread wheat, driven by strong selection for a haplotype variant controlling heading date, has eliminated a specific combination of two flanking, highly conserved haplotype variants whose interaction confers increased root biomass. Reversing this inadvertent consequence of selection could recover root ersity that may prove essential for future food production in fluctuating environments. Highly conserved synteny to rice across this chromosome segment suggests that adaptive selection has shaped the ersity landscape of this locus across different, globally important cereal crops. By mining wheat gene expression data, we identified root‐expressed genes within the region of interest that could help breeders to select positive variants adapted to specific target soil environments.

VERNALIZATION1 Modulates Root System Architecture in Wheat and Barley

Publisher: Elsevier BV

Date: 2018

DOI: 10.1016/J.MOLP.2017.10.005

Understanding and utilizing crop genome diversity via high‐resolution genotyping

Publisher: Wiley

Date: 19-08-2015

DOI: 10.1111/PBI.12456

Abstract: High-resolution genome analysis technologies provide an unprecedented level of insight into structural ersity across crop genomes. Low-cost discovery of sequence variation has become accessible for all crops since the development of next-generation DNA sequencing technologies, using erse methods ranging from genome-scale resequencing or skim sequencing, reduced-representation genotyping-by-sequencing, transcriptome sequencing or sequence capture approaches. High-density, high-throughput genotyping arrays generated using the resulting sequence data are today available for the assessment of genomewide single nucleotide polymorphisms in all major crop species. Besides their application in genetic mapping or genomewide association studies for dissection of complex agronomic traits, high-density genotyping arrays are highly suitable for genomic selection strategies. They also enable description of crop ersity at an unprecedented chromosome-scale resolution. Application of population genetics parameters to genomewide ersity data sets enables dissection of linkage disequilibrium to characterize loci underlying selective sweeps. High-throughput genotyping platforms simultaneously open the way for targeted ersity enrichment, allowing rejuvenation of low- ersity chromosome regions in strongly selected breeding pools to potentially reverse the influence of linkage drag. Numerous recent ex les are presented which demonstrate the power of next-generation genomics for high-resolution analysis of crop ersity on a subgenomic and chromosomal scale. Such studies give deep insight into the history of crop evolution and selection, while simultaneously identifying novel ersity to improve yield and heterosis.

Modelling selection response in plant-breeding programs using crop models as mechanistic gene-to-phenotype (CGM-G2P) multi-trait link functions

Publisher: Oxford University Press (OUP)

Date: 28-12-2021

DOI: 10.1093/INSILICOPLANTS/DIAA016

Abstract: Plant-breeding programs are designed and operated over multiple cycles to systematically change the genetic makeup of plants to achieve improved trait performance for a Target Population of Environments (TPE). Within each cycle, selection applied to the standing genetic variation within a structured reference population of genotypes (RPG) is the primary mechanism by which breeding programs make the desired genetic changes. Selection operates to change the frequencies of the alleles of the genes controlling trait variation within the RPG. The structure of the RPG and the TPE has important implications for the design of optimal breeding strategies. The breeder’s equation, together with the quantitative genetic theory behind the equation, informs many of the principles for design of breeding programs. The breeder’s equation can take many forms depending on the details of the breeding strategy. Through the genetic changes achieved by selection, the cultivated varieties of crops (cultivars) are improved for use in agriculture. From a breeding perspective, selection for specific trait combinations requires a quantitative link between the effects of the alleles of the genes impacted by selection and the trait phenotypes of plants and their breeding value. This gene-to-phenotype link function provides the G2P map for one to many traits. For complex traits controlled by many genes, the infinitesimal model for trait genetic variation is the dominant G2P model of quantitative genetics. Here we consider motivations and potential benefits of using the hierarchical structure of crop models as CGM-G2P trait link functions in combination with the infinitesimal model for the design and optimization of selection in breeding programs.

Unlocking new alleles for leaf rust resistance in the Vavilov wheat collection

Publisher: Springer Science and Business Media LLC

Date: 04-10-2017

DOI: 10.1007/S00122-017-2990-5

Abstract: Thirteen potentially new leaf rust resistance loci were identified in a Vavilov wheat ersity panel. We demonstrated the potential of allele stacking to strengthen resistance against this important pathogen. Leaf rust (LR) caused by Puccinia triticina is an important disease of wheat (Triticum aestivum L.), and the deployment of genetically resistant cultivars is the most viable strategy to minimise yield losses. In this study, we evaluated a ersity panel of 295 bread wheat accessions from the N. I. Vavilov Institute of Plant Genetic Resources (St Petersburg, Russia) for LR resistance and performed genome-wide association studies (GWAS) using 10,748 polymorphic DArT-seq markers. The ersity panel was evaluated at seedling and adult plant growth stages using three P. triticina pathotypes prevalent in Australia. GWAS was applied to 11 phenotypic data sets which identified a total of 52 significant marker-trait associations representing 31 quantitative trait loci (QTL). Among them, 29 QTL were associated with adult plant resistance (APR). Of the 31 QTL, 13 were considered potentially new loci, whereas 4 co-located with previously catalogued Lr genes and 14 aligned to regions reported in other GWAS and genomic prediction studies. One seedling LR resistance QTL located on chromosome 3A showed pronounced levels of linkage disequilibrium among markers (r

Genetic characterization of adult-plant resistance to tan spot (syn, yellow spot) in wheat

Publisher: Springer Science and Business Media LLC

Date: 06-2021

DOI: 10.1007/S00122-021-03861-8

Abstract: QTL mapping identified key genomic regions associated with adult-plant resistance to tan spot, which are effective even in the presence of the sensitivity gene Tsn1, thus serving as a new genetic solution to develop disease-resistant wheat cultivars. Improving resistance to tan spot (Pyrenophora tritici-repentis Ptr) in wheat by eliminating race-specific susceptibility genes is a common breeding approach worldwide. The potential to exploit variation in quantitative forms of resistance, such as adult-plant resistance (APR), offers an alternative approach that could lead to broad-spectrum protection. We previously identified wheat landraces in the Vavilov ersity panel that exhibited high levels of APR despite carrying the sensitivity gene Tsn1. In this study, we characterised the genetic control of APR by developing a recombinant inbred line population fixed for Tsn1, but segregating for the APR trait. Linkage mapping using DArTseq markers and disease response phenotypes identified a QTL associated with APR to Ptr race 1 (producing Ptr ToxA- and Ptr ToxC) on chromosome 2B (Qts.313-2B), which was consistently detected in multiple adult-plant experiments. Additional loci were also detected on chromosomes 2A, 3D, 5A, 5D, 6A, 6B and 7A at the seedling stage, and on chromosomes 1A and 5B at the adult stage. We demonstrate that Qts.313-2B can be combined with other adult-plant QTL (i.e. Qts.313-1A and Qts.313-5B) to strengthen resistance levels. The APR QTL reported in this study provide a new genetic solution to tan spot in Australia and could be deployed in wheat cultivars, even in the presence of Tsn1, to decrease production losses and reduce the application of fungicides.

Genetic insights into underground responses to Fusarium graminearum infection in wheat

Publisher: Springer Science and Business Media LLC

Date: 03-09-2018

DOI: 10.1038/S41598-018-31544-W

Abstract: 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. Here, we present the first large-scale investigation of Fusarium root rot (FRR) resistance using a erse panel of 215 international wheat lines. We obtained data for a total of 21 resistance-related traits, including large-scale Real-time PCR experiments to quantify fungal spread. Association mapping and subsequent haplotype analyses discovered a number of highly conserved genomic regions associated with resistance, and revealed a significant effect of allele stacking on the stembase discoloration. Resistance alleles were accumulated in European winter wheat germplasm, implying indirect prior selection for improved FRR resistance in elite breeding programs. Our results give first insights into the genetic basis of FRR resistance in wheat and demonstrate how molecular parameters can successfully be explored in genomic prediction. Ongoing work will help to further improve our understanding of the complex interactions of genetic factors influencing FRR resistance.

Q&A: modern crop breeding for future food security

Publisher: Springer Science and Business Media LLC

Date: 25-02-2019

DOI: 10.1186/S12915-019-0638-4

Reduced response diversity does not negatively impact wheat climate resilience

Publisher: Proceedings of the National Academy of Sciences

Date: 28-05-2019

DOI: 10.1073/PNAS.1901882116

Accelerating crop genetic gains with genomic selection

Publisher: Springer Science and Business Media LLC

Date: 19-12-2019

DOI: 10.1007/S00122-018-3270-8

Abstract: Genomic prediction based on additive genetic effects can accelerate genetic gain. There are opportunities for further improvement by including non-additive effects that access untapped sources of genetic ersity. Several studies have reported a worrying gap between the projected global future demand for plant-based products and the current annual rates of production increase, indicating that enhancing the rate of genetic gain might be critical for future food security. Therefore, new breeding technologies and strategies are required to significantly boost genetic improvement of future crop cultivars. Genomic selection (GS) has delivered considerable genetic gain in animal breeding and is becoming an essential component of many modern plant breeding programmes as well. In this paper, we review the lessons learned from implementing GS in livestock and the impact of GS on crop breeding, and discuss important features for the success of GS under different breeding scenarios. We highlight major challenges associated with GS including rapid genotyping, phenotyping, genotype-by-environment interaction and non-additivity and give ex les for opportunities to overcome these issues. Finally, the potential of combining GS with other modern technologies in order to maximise the rate of crop genetic improvement is discussed, including the potential of increasing prediction accuracy by integration of crop growth models in GS frameworks.

A toolkit to rapidly modify root systems through single plant selection

Publisher: Springer Science and Business Media LLC

Date: 10-01-2022

DOI: 10.1186/S13007-021-00834-2

Abstract: The incorporation of root traits into elite germplasm is typically a slow process. Thus, innovative approaches are required to accelerate research and pre-breeding programs targeting root traits to improve yield stability in different environments and soil types. Marker-assisted selection (MAS) can help to speed up the process by selecting key genes or quantitative trait loci (QTL) associated with root traits. However, this approach is limited due to the complex genetic control of root traits and the limited number of well-characterised large effect QTL. Coupling MAS with phenotyping could increase the reliability of selection. Here we present a useful framework to rapidly modify root traits in elite germplasm. In this wheat exemplar, a single plant selection (SPS) approach combined three main elements: phenotypic selection (in this case for seminal root angle) MAS using KASP markers (targeting a root biomass QTL) and speed breeding to accelerate each cycle. To develop a SPS approach that integrates non-destructive screening for seminal root angle and root biomass, two initial experiments were conducted. Firstly, we demonstrated that transplanting wheat seedlings from clear pots (for seminal root angle assessment) into sand pots (for root biomass assessment) did not impact the ability to differentiate genotypes with high and low root biomass. Secondly, we demonstrated that visual scores for root biomass were correlated with root dry weight (r = 0.72), indicating that single plants could be evaluated for root biomass in a non-destructive manner. To highlight the potential of the approach, we applied SPS in a backcrossing program which integrated MAS and speed breeding for the purpose of rapidly modifying the root system of elite bread wheat line Borlaug100. Bi-directional selection for root angle in segregating generations successfully shifted the mean root angle by 30° in the subsequent generation ( P ≤ 0.05 ). Within 18 months, BC 2 F 4 :F 5 introgression lines were developed that displayed a full range of root configurations, while retaining similar above-ground traits to the recurrent parent. Notably, the seminal root angle displayed by introgression lines varied more than 30° compared to the recurrent parent, resulting in lines with both narrow and wide root angles, and high and low root biomass phenotypes. The SPS approach enables researchers and plant breeders to rapidly manipulate root traits of future crop varieties, which could help improve productivity in the face of increasing environmental fluctuations. The newly developed elite wheat lines with modified root traits provide valuable materials to study the value of different root systems to support yield in different environments and soil types.

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

Publisher: Springer Science and Business Media LLC

Date: 23-04-2020

DOI: 10.1186/S12864-020-6711-0

Abstract: 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. 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. Six haplotype regions were identified as significantly associated with oleic acid (C18:1) that mapped to chromosomes A02, A07, A08, C01, C02, and C03. Additionally, whole-genome sequencing of 50 rapeseed accessions revealed t hree genes (BnmtACP2-A02, BnABCI13-A02 and BnECI1-A02) in the A02 chromosome haplotype region and two genes ( BnFAD8-C02 and BnSDP1-C02) in the C02 chromosome haplotype region that were closely linked to oleic acid content phenotypic variation. Moreover, the co-expression network analysis uncovered candidate genes from these two different haplotype regions with potential regulatory interrelationships with oleic acid content accumulation. Our results suggest that several candidate genes are closely linked, which provides us with an opportunity to develop functional haplotype markers for the improvement of the oleic acid content in rapeseed.

Justus Liebig Universitat Giessen

Location: Germany

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University Of Queensland

Location: Australia

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Discovery Early Career Researcher Award - Grant ID: DE210101407

Start Date: 2021

End Date: 06-2021

Amount: $447,524.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP170100317

Start Date: 09-2018

End Date: 03-2023

Amount: $345,465.00

Funder: Australian Research Council

Industrial Transformation Training Centres - Grant ID: IC230100016

Start Date: 2023

End Date: 12-2027

Amount: $5,000,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP200200927

Start Date: 04-2022

End Date: 04-2026

Amount: $785,312.00

Funder: Australian Research Council