Genetic mapping of phenotypic traits generally focuses on a single time point, but biomass accumulates continuously during plant development. Resolution of the temporal dynamics that affect biomass ...recently became feasible using non-destructive imaging.
With the aim to identify key genetic factors for vegetative biomass formation from the seedling stage to flowering, we explored growth over time in a diverse collection of two-rowed spring barley accessions. High heritabilities facilitated the temporal analysis of trait relationships and identification of quantitative trait loci (QTL). Biomass QTL tended to persist only a short period during early growth. More persistent QTL were detected around the booting stage. We identified seven major biomass QTL, which together explain 55% of the genetic variance at the seedling stage, and 43% at the booting stage. Three biomass QTL co-located with genes or QTL involved in phenology. The most important locus for biomass was independent from phenology and is located on chromosome 7HL at 141 cM. This locus explained ~20% of the genetic variance, was significant over a long period of time and co-located with HvDIM, a gene involved in brassinosteroid synthesis.
Biomass is a dynamic trait and is therefore orchestrated by different QTL during early and late growth stages. Marker-assisted selection for high biomass at booting stage is most effective by also including favorable alleles from seedling biomass QTL. Selection for dynamic QTL may enhance genetic gain for complex traits such as biomass or, in the future, even grain yield.
Genomic selection exploits dense genome-wide marker data to predict breeding values. In this study we used a large sugar beet population of 924 lines representing different germplasm types present in ...breeding populations: unselected segregating families and diverse lines from more advanced stages of selection. All lines have been intensively phenotyped in multi-location field trials for six agronomically important traits and genotyped with 677 SNP markers.
We used ridge regression best linear unbiased prediction in combination with fivefold cross-validation and obtained high prediction accuracies for all except one trait. In addition, we investigated whether a calibration developed based on a training population composed of diverse lines is suited to predict the phenotypic performance within families. Our results show that the prediction accuracy is lower than that obtained within the diverse set of lines, but comparable to that obtained by cross-validation within the respective families.
The results presented in this study suggest that a training population derived from intensively phenotyped and genotyped diverse lines from a breeding program does hold potential to build up robust calibration models for genomic selection. Taken together, our results indicate that genomic selection is a valuable tool and can thus complement the genomics toolbox in sugar beet breeding.
To extend agricultural productivity by knowledge-based breeding and tailor varieties adapted to specific environmental conditions, it is imperative to improve our ability to assess the dynamic ...changes of the phenome of crops under field conditions. To this end, we have developed a precision phenotyping platform that combines various sensors for a non-invasive, high-throughput and high-dimensional phenotyping of small grain cereals. This platform yielded high prediction accuracies and heritabilities for biomass of triticale. Genetic variation for biomass accumulation was dissected with 647 doubled haploid lines derived from four families. Employing a genome-wide association mapping approach, two major quantitative trait loci (QTL) for biomass were identified and the genetic architecture of biomass accumulation was found to be characterized by dynamic temporal patterns. Our findings highlight the potential of precision phenotyping to assess the dynamic genetics of complex traits, especially those not amenable to traditional phenotyping.
Genome-wide association studies (GWAS) have gained central importance for the identification of candidate loci underlying complex traits. Single nucleotide polymorphism (SNP) markers are mostly used ...as genetic variants for the analysis of genotype-phenotype associations in populations, but closely linked SNPs that are grouped into haplotypes are also exploited. The benefit of haplotype-based GWAS approaches
SNP-based approaches is still under debate because SNPs in high linkage disequilibrium provide redundant information. To overcome some constraints of the commonly-used haplotype-based GWAS in which only consecutive SNPs are considered for haplotype construction, we propose a new method called functional haplotype-based GWAS (FH GWAS). FH GWAS is featured by combining SNPs into haplotypes based on the additive and epistatic effects among SNPs. Such haplotypes were termed functional haplotypes (FH). As shown by simulation studies, the FH GWAS approach clearly outperformed the SNP-based approach unless the minor allele frequency of the SNPs making up the haplotypes is low and the linkage disequilibrium between them is high. Applying FH GWAS for the trait flowering time in a large
population with whole-genome sequencing data revealed its potential empirically. FH GWAS identified all candidate regions which were detected in SNP-based and two other haplotype-based GWAS approaches. In addition, a novel region on chromosome 4 was solely detected by FH GWAS. Thus both the results of our simulation and empirical studies demonstrate that FH GWAS is a promising method and superior to the SNP-based approach even if almost complete genotype information is available.
Key message
Genomic prediction of genebank accessions benefits from the consideration of additive-by-additive epistasis and subpopulation-specific marker effects.
Wheat (
Triticum aestivum
L.) and ...other species of the
Triticum
genus are well represented in genebank collections worldwide. The substantial genetic diversity harbored by more than 850,000 accessions can be explored for their potential use in modern plant breeding. Characterization of these large number of accessions is constrained by the required resources, and this fact limits their use so far. This limitation might be overcome by engaging genomic prediction. The present study compared ten different genomic prediction approaches to the prediction of four traits, namely flowering time, plant height, thousand grain weight, and yellow rust resistance, in a diverse set of 7745 accession samples from Germany’s Federal ex situ genebank at the Leibniz Institute of Plant Genetics and Crop Plant Research in Gatersleben. Approaches were evaluated based on prediction ability and robustness to the confounding influence of strong population structure. The authors propose the wide application of extended genomic best linear unbiased prediction due to the observed benefit of incorporating additive-by-additive epistasis. General and subpopulation-specific additive ridge regression best linear unbiased prediction, which accounts for subpopulation-specific marker-effects, was shown to be a good option if contrasting clusters are encountered in the analyzed collection. The presented findings reaffirm that the trait’s genetic architecture as well as the composition and relatedness of the training set and test set are major driving factors for the accuracy of genomic prediction.
Genome-wide prediction is a powerful tool in breeding. Initial results suggest that genome-wide approaches are also promising for enhancing the use of the genebank material: predicting the ...performance of plant genetic resources can unlock their hidden potential and fill the information gap in genebanks across the world and, hence, underpin prebreeding programs. As a proof of concept, we evaluated the power of across-genebank prediction for extensive germplasm collections relying on historical data on flowering/heading date, plant height, and thousand kernel weight of 9,344 barley (
Hordeum vulgare
L.) plant genetic resources from the German Federal Ex situ Genebank for Agricultural and Horticultural Crops (IPK) and of 1,089 accessions from the International Center for Agriculture Research in the Dry Areas (ICARDA) genebank. Based on prediction abilities for each trait, three scenarios for predictive characterization were compared: 1) a benchmark scenario, where test and training sets only contain ICARDA accessions, 2) across-genebank predictions using IPK as training and ICARDA as test set, and 3) integrated genebank predictions that include IPK with 30% of ICARDA accessions as a training set to predict the rest of ICARDA accessions. Within the population of ICARDA accessions, prediction abilities were low to moderate, which was presumably caused by a limited number of accessions used to train the model. Interestingly, ICARDA prediction abilities were boosted up to ninefold by using training sets composed of IPK plus 30% of ICARDA accessions. Pervasive genotype × environment interactions (GEIs) can become a potential obstacle to train robust genome-wide prediction models across genebanks. This suggests that the potential adverse effect of GEI on prediction ability was counterbalanced by the augmented training set with certain connectivity to the test set. Therefore, across-genebank predictions hold the promise to improve the curation of the world’s genebank collections and contribute significantly to the long-term development of traditional genebanks toward biodigital resource centers.
Genebank genomics promises to unlock valuable diversity for plant breeding but first, one key question is which marker system is most suitable to fingerprint entire genebank collections. Using wheat ...as model species, we tested for the presence of an ascertainment bias and investigated its impact on estimates of genetic diversity and prediction ability obtained using three marker platforms: simple sequence repeat (SSR), genotyping-by-sequencing (GBS), and array-based SNP markers. We used a panel of 378 winter wheat genotypes including 190 elite lines and 188 plant genetic resources (PGR), which were phenotyped in multi-environmental trials for grain yield and plant height. We observed an ascertainment bias for the array-based SNP markers, which led to an underestimation of the molecular diversity within the population of PGR. In contrast, the marker system played only a minor role for the overall picture of the population structure and precision of genome-wide predictions. Interestingly, we found that rare markers contributed substantially to the prediction ability. This combined with the expectation that valuable novel diversity is most likely rare suggests that markers with minor allele frequency deserve careful consideration in the design of a pre-breeding program.
Key message
Genomic prediction with special weight of major genes is a valuable tool to populate bio-digital resource centers.
Phenotypic information of crop genetic resources is a prerequisite for ...an informed selection that aims to broaden the genetic base of the elite breeding pools. We investigated the potential of genomic prediction based on historical screening data of plant responses against the
Barley yellow mosaic viruses
for populating the bio-digital resource center of barley. Our study includes dense marker data for 3838 accessions of winter barley, and historical screening data of 1751 accessions for
Barley yellow mosaic virus
(BaYMV) and of 1771 accessions for
Barley mild mosaic virus
(BaMMV). Linear mixed models were fitted by considering combinations for the effects of genotypes, years, and locations. The best linear unbiased estimations displayed a broad spectrum of plant responses against BaYMV and BaMMV. Prediction abilities, computed as correlations between predictions and observed phenotypes of accessions, were low for the marker-assisted selection approach amounting to 0.42. In contrast, prediction abilities of genomic best linear unbiased predictions were high, with values of 0.62 for BaYMV and 0.64 for BaMMV. Prediction abilities of genomic prediction were improved by up to ~ 5% using W-BLUP, in which more weight is given to markers with significant major effects found by association mapping. Our results outline the utility of historical screening data and W-BLUP model to predict the performance of the non-phenotyped individuals in genebank collections. The presented strategy can be considered as part of the different approaches used in genebank genomics to valorize genetic resources for their usage in disease resistance breeding and research.
Genetic pathogen control is an economical and sustainable alternative to the use of chemicals. In order to breed resistant varieties, information about potentially unused genetic resistance ...mechanisms is of high value. We phenotyped 8,316 genotypes of the winter wheat collection of the
, for resistance to powdery mildew (PM),
, one of the most important biotrophic pathogens in wheat. To achieve this, we used a semi-automatic phenotyping facility to perform high-throughput detached leaf assays. This data set, combined with genotyping-by-sequencing (GBS) marker data, was used to perform a genome-wide association study (GWAS). Alleles of significantly associated markers were compared with SNP profiles of 171 widely grown wheat varieties in Germany to identify currently unexploited resistance conferring genes. We also used the Chinese Spring reference genome annotation and various domain prediction algorithms to perform a domain enrichment analysis and produced a list of candidate genes for further investigation. We identified 51 significantly associated regions. In most of these, the susceptible allele was fixed in the tested commonly grown wheat varieties. Eleven of these were located on chromosomes for which no resistance conferring genes have been previously reported. In addition to enrichment of leucine-rich repeats (LRR), we saw enrichment of several domain types so far not reported as relevant to PM resistance, thus, indicating potentially novel candidate genes for the disease resistance research and prebreeding in wheat.
Sweetpotato is a highly heterozygous hybrid, and populations of orange-fleshed sweetpotato (OFSP) have a considerable importance for food security and health. The objectives were to estimate ...heterosis increments and response to selection in three OFSP hybrid populations (H
) developed in Peru for different product profiles after one reciprocal recurrent selection cycle, namely, H
for wide adaptation and earliness (O-WAE), H
for no sweetness after cooking (O-NSSP), and H
for high iron (O-HIFE). The H
populations were evaluated at two contrasting locations together with parents, foundation (parents in H
), and two widely adapted checks. Additionally, O-WAE was tested under two environmental conditions of 90-day and a normal 120-day harvest. In each H
, the yield and selected quality traits were recorded. The data were analyzed using linear mixed models. The storage root yield traits exhibited population average heterosis increments of up to 43.5%. The quality traits examined have exhibited no heterosis increments that are worth exploiting. The storage root yield genetic gain relative to the foundation was remarkable: 118.8% for H
-O-WAE for early harvest time, 81.5% for H
-O-WAE for normal harvest time, 132.4% for H
-O-NSSP, and 97.1% for H
-O-HIFE. Population hybrid breeding is a tool to achieve large genetic gains in sweetpotato yield
more efficient population improvement and allows a rapid dissemination of globally true seed that is generated from reproducible elite crosses, thus, avoiding costly and time-consuming virus cleaning of elite clones typically transferred as vegetative plantlets. The population hybrid breeding approach is probably applicable to other clonally propagated crops, where potential for true seed production exists.
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