In grafted plants, such as grapevine, increasing the diversity of rootstocks available to growers is an ideal strategy for helping plants to adapt to climate change. The rootstocks used for grapevine ...are hybrids of various American Vitis, including V. berlandieri. The rootstocks currently use in vineyards are derived from breeding programs involving very small numbers of parental individuals. We investigated the structure of a natural population of V. berlandieri and the association of genetic diversity with environmental variables. In this study, we collected seeds from 78 wild V. berlandieri plants in Texas after open fertilization. We genotyped 286 individuals to describe the structure of the population, and environmental information collected at the sampling site made it possible to perform genome–environment association analysis (GEA). De novo long‐read whole‐genome sequencing was performed on V. berlandieri and a STRUCTURE analysis was performed. We identified and filtered 104,378 SNPs. We found that there were two subpopulations associated with differences in elevation, temperature, and rainfall between sampling sites. GEA identified three QTL for elevation and 15 QTL for PCA coordinates based on environmental parameter variability. This original study is the first GEA study to be performed on a population of grapevines sampled in natural conditions. Our results shed new light on rootstock genetics and could open up possibilities for introducing greater diversity into genetic improvement programs for grapevine rootstocks.
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 diversity 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 diversity 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.
Roots are of immense importance for environmental adaptation but are largely unexplored in major crops. Via high‐resolution linkage disequilibrium mapping, we discovered strong linkage drag in European wheat between a haplotype variant controlling heading date and two flanking loci carrying alleles that constrain root biomass. Investigation of genes within the region of interest could help breeders to recover root diversity for future food production in fluctuating environments.
Key message
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 diverse 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.
Farmers around the world have recently experienced significant crop losses due to severe heat and drought. Such extreme weather events and the need to feed a rapidly growing population have raised ...concerns for global food security. While plant breeding has been very successful and has delivered today's highly productive crop varieties, the rate of genetic improvement must double to meet the projected future demands. Here we discuss basic principles and features of crop breeding and how modern technologies could efficiently be explored to boost crop improvement in the face of increasingly challenging production conditions.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Genomic selection in sugarcane faces challenges due to limited genomic tools and high genomic complexity, particularly because of its high and variable ploidy. The classification of genotypes for ...single nucleotide polymorphisms (SNPs) becomes difficult due to the wide range of possible allele dosages. Previous genomic studies in sugarcane used pseudo‐diploid genotyping, grouping all heterozygotes into a single class. In this study, we investigate the use of continuous genotypes as a proxy for allele‐dosage in genomic prediction models. The hypothesis is that continuous genotypes could better reflect allele dosage at SNPs linked to mutations affecting target traits, resulting in phenotypic variation. The dataset included genotypes of 1318 clones at 58K SNP markers, with about 26K markers filtered using standard quality controls. Predictions for tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and fiber content (Fiber) were made using parametric, non‐parametric, and Bayesian methods. Continuous genotypes increased accuracy by 5%–7% for CCS and Fiber. The pseudo‐diploid parametrization performed better for TCH. Reproducing kernel Hilbert spaces model with Gaussian kernel and AK4 (arc‐cosine kernel with hidden layer 4) kernel outperformed other methods for TCH and CCS, suggesting that non‐additive effects might influence these traits. The prevalence of low‐dosage markers in the study may have limited the benefits of approximating allele‐dosage information with continuous genotypes in genomic prediction models. Continuous genotypes simplify genomic prediction in polyploid crops, allowing additional markers to be used without adhering to pseudo‐diploid inheritance. The approach can particularly benefit high ploidy species or emerging crops with unknown ploidy.
Core Ideas
Continuous genotypes offer a practical, computationally efficient, and biologically realistic approach for modeling genomic prediction in highly polyploid crops such as sugarcane, effectively accommodating their inherent complexity.
Continuous genotypes result in more accurate predictions, demonstrating slightly improved prediction accuracy compared to diploid parameterization.
The effect of continuous genotypes on prediction accuracy may vary depending on the specific trait being predicted. It is crucial to consider the trait‐specific variation when building prediction models.
Key message 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 x E x M) interactions underpin many aspects of crop productivity. An important question for crop improvement is "How can breeders and agronomists effectively explore the diverse opportunities within the high dimensionality of the complex G x E x M factorial to achieve sustainable improvements in crop productivity?" Whenever G x E x 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 x E x 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 x E x 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 x E x 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 x E x 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.
Core Ideas
Efficiency in genomic selection is not particularly based on detailed genotype profiling facilitated by maximum marker density.
Extensive genome‐wide linkage disequilibrium is a common ...characteristic of breeding pools in many crop species.
Every quantitative trait locus across the genome can be captured by one or a few representative markers.
Fewer representative markers selected in respect of linkage disequilibrium (LD) can capture the association between a genomic region and a phenotypic trait.
Low‐density marker sets enable genomic prediction accuracies in breeding populations with strong LD comparable to those achieved with high‐density genotyping.
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.
The optimal root system architecture (RSA) of a crop is context dependent and critical for efficient resource capture in the soil. Narrow root growth angle promoting deeper root growth is often ...associated with improved access to water and nutrients in deep soils during terminal drought. RSA, therefore is a drought-adaptive trait that could minimize yield losses in regions with limited rainfall. Here, GWAS for seminal root angle (SRA) identified seven marker-trait associations clustered on chromosome 6A, representing a major quantitative trait locus (
) which also displayed high levels of pairwise LD (
= 0.67). Subsequent haplotype analysis revealed significant differences between major groups. Candidate gene analysis revealed loci related to gravitropism, polar growth and hormonal signaling. No differences were observed for root biomass between lines carrying hap1 and hap2 for
, highlighting the opportunity to perform marker-assisted selection for the
locus and directly select for wide or narrow RSA, without influencing root biomass. Our study revealed that the genetic predisposition for deep rooting was best expressed under water-limitation, yet the root system displayed plasticity producing root growth in response to water availability in upper soil layers. We discuss the potential to deploy root architectural traits in cultivars to enhance yield stability in environments that experience limited rainfall.
Designer Roots for Future Crops Voss-Fels, Kai P.; Snowdon, Rod J.; Hickey, Lee T.
Trends in plant science,
November 2018, 2018-11-00, 20181101, Letnik:
23, Številka:
11
Journal Article
Recenzirano
Odprti dostop
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.
Loss of genetic diversity in elite crop breeding pools can severely limit long‐term genetic gains and limit ability to make gains in new traits, like heat tolerance, that are becoming important as ...the climate changes. Here, we investigate and propose potential breeding program applications of optimal haplotype stacking (OHS), a selection method that retains useful diversity in the population. OHS selects sets of candidates containing, between them, haplotype segments with very high segment breeding values for the target trait. We compared the performance of OHS, a similar method called optimal population value (OPV), truncation selection on genomic estimated breeding values (GEBVs), and optimal contribution selection (OCS) in stochastic simulations of recurrent selection on founder wheat genotypes. After 100 generations of intercrossing and selection, OCS and truncation selection had exhausted the genetic diversity, while considerable diversity remained in the OHS population. Gain under OHS in these simulations ultimately exceeded that from truncation selection or OCS. OHS achieved faster gains when the population size was small, with many progeny per cross. A promising hybrid strategy, involving a single cycle of OHS in the first generation followed by recurrent truncation selection, substantially improved long‐term gain compared with truncation selection and performed similarly to OCS. The results of this study provide initial insights into where OHS could be incorporated into breeding programs.
Core Ideas
We investigate potential uses of a haplotype‐stacking strategy, optimal haplotype stacking (OHS).
Several selection strategies were compared in stochastic simulations of recurrent selection in wheat.
OHS maintained more useful diversity than optimal cross selection or truncation‐based genomic selection.
Rates of gain from OHS are competitive in small populations.
One generation of OHS in a truncation selection program can increase short‐ and long‐term genetic gain.
Plain Language Summary
Breeders use selection strategies based on genetic and phenotypic information to choose parents that will improve agriculturally relevant traits (e.g., grain yield) in their progeny. Generally, this involves estimating breeding values (scores) for each candidate parent. This study investigated an alternative “haplotype stacking” approach called optimal haplotype stacking (OHS), which instead estimates breeding values for each unique genome segment in the population, then selects a group of parents who, between them, carry the haplotypes with the highest estimated breeding value at each chromosomal segment. In simulations, OHS gives improvements close to existing methods when populations are small and outperforms them in the long term (100+ generations). Using just one generation of OHS boosts the performance of other methods in the short and long term. Breeders might consider adopting haplotype stacking in their programs, once techniques to do so are established.
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