Application of neural networks with back-propagation to genome-enabled prediction of complex traits in Holstein-Friesian and German Fleckvieh cattle

Background

Recently, artificial neural networks (ANN) have been proposed as promising machines for marker-based genomic predictions of complex traits in animal and plant breeding. ANN are universal approximators of complex functions, that can capture cryptic relationships between SNPs (single nucleotide polymorphisms) and phenotypic values without the need of explicitly defining a genetic model. This concept is attractive for high-dimensional and noisy data, especially when the genetic architecture of the trait is unknown. However, the properties of ANN for the prediction of future outcomes of genomic selection using real data are not well characterized and, due to high computational costs, using whole-genome marker sets is difficult. We examined different non-linear network architectures, as well as several genomic covariate structures as network inputs in order to assess their ability to predict milk traits in three dairy cattle data sets using large-scale SNP data. For training, a regularized back propagation algorithm was used. The average correlation between the observed and predicted phenotypes in a 20 times 5-fold cross-validation was used to assess predictive ability. A linear network model served as benchmark.

Results

Predictive abilities of different ANN models varied markedly, whereas differences between data sets were small. Dimension reduction methods enhanced prediction performance in all data sets, while at the same time computational cost decreased. For the Holstein-Friesian bull data set, an ANN with 10 neurons in the hidden layer achieved a predictive correlation of r=0.47 for milk yield when the entire marker matrix was used. Predictive ability increased when the genomic relationship matrix (r=0.64) was used as input and was best (r=0.67) when principal component scores of the marker genotypes were used. Similar results were found for the other traits in all data sets.

Conclusion

Artificial neural networks are powerful machines for non-linear genome-enabled predictions in animal breeding. However, to produce stable and high-quality outputs, variable selection methods are highly recommended, when the number of markers vastly exceeds sample size.     

The impact of population structure on genomic prediction in stratified populations

Key message

Impacts of population structure on the evaluation of genomic heritability and prediction were investigated and quantified using high-density markers in diverse panels in rice and maize.

Abstract

Population structure is an important factor affecting estimation of genomic heritability and assessment of genomic prediction in stratified populations. In this study, our first objective was to assess effects of population structure on estimations of genomic heritability using the diversity panels in rice and maize. Results indicate population structure explained 33 and 7.5 % of genomic heritability for rice and maize, respectively, depending on traits, with the remaining heritability explained by within-subpopulation variation. Estimates of within-subpopulation heritability were higher than that derived from quantitative trait loci identified in genome-wide association studies, suggesting 65 % improvement in genetic gains. The second objective was to evaluate effects of population structure on genomic prediction using cross-validation experiments. When population structure exists in both training and validation sets, correcting for population structure led to a significant decrease in accuracy with genomic prediction. In contrast, when prediction was limited to a specific subpopulation, population structure showed little effect on accuracy and within-subpopulation genetic variance dominated predictions. Finally, effects of genomic heritability on genomic prediction were investigated. Accuracies with genomic prediction increased with genomic heritability in both training and validation sets, with the former showing a slightly greater impact. In summary, our results suggest that the population structure contribution to genomic prediction varies based on prediction strategies, and is also affected by the genetic architectures of traits and populations. In practical breeding, these conclusions may be helpful to better understand and utilize the different genetic resources in genomic prediction.     

Genomic prediction ability for yield-related traits in German winter barley elite material

Key message

Genomic prediction was evaluated in German winter barley breeding lines. In this material, prediction ability is strongly influenced by population structure and main determinant of prediction ability is the close genetic relatedness of the breeding material.

Abstract

To ensure breeding progress under changing environmental conditions the implementation and evaluation of new breeding methods is of crucial importance. Modern breeding approaches like genomic selection may significantly accelerate breeding progress. We assessed the potential of genomic prediction in a training population of 750 genotypes, consisting of multiple six-rowed winter barley (Hordeum vulgare L.) elite material families and old cultivars, which reflect the breeding history of barley in Germany. Crosses of parents selected from the training set were used to create a set of double-haploid families consisting of 750 genotypes. Those were used to confirm prediction ability estimates based on a cross-validation with the training set material using 11 different genomic prediction models. Population structure was inferred with dimensionality reduction methods like discriminant analysis of principle components and the influence of population structure on prediction ability was investigated. In addition to the size of the training set, marker density is of crucial importance for genomic prediction. We used genome-wide linkage disequilibrium and persistence of linkage phase as indicators to estimate that 11,203 evenly spaced markers are required to capture all QTL effects. Although a 9k SNP array does not contain a sufficient number of polymorphic markers for long-term genomic selection, we obtained fairly high prediction accuracies ranging from 0.31 to 0.71 for the traits earing, hectoliter weight, spikes per square meter, thousand kernel weight and yield and show that they result from the close genetic relatedness of the material. Our work contributes to designing long-term genetic prediction programs for barley breeding.

     

Optimizing the allocation of resources for genomic selection in one breeding cycle

Key message

We developed a universally applicable planning tool for optimizing the allocation of resources for one cycle of genomic selection in a biparental population. The framework combines selection theory with constraint numerical optimization and considers genotype? ×? environment interactions.

Abstract

Genomic selection (GS) is increasingly implemented in plant breeding programs to increase selection gain but little is known how to optimally allocate the resources under a given budget. We investigated this problem with model calculations by combining quantitative genetic selection theory with constraint numerical optimization. We assumed one selection cycle where both the training and prediction sets comprised double haploid (DH) lines from the same biparental population. Grain yield for testcrosses of maize DH lines was used as a model trait but all parameters can be adjusted in a freely available software implementation. An extension of the expected selection accuracy given by Daetwyler et al. (2008) was developed to correctly balance between the number of environments for phenotyping the training set and its population size in the presence of genotype?×?environment interactions. Under small budget, genotyping costs mainly determine whether GS is superior over phenotypic selection. With increasing budget, flexibility in resource allocation increases greatly but selection gain leveled off quickly requiring balancing the number of populations with the budget spent for each population. The use of an index combining phenotypic and GS predicted values in the training set was especially beneficial under limited resources and large genotype × environment interactions. Once a sufficiently high selection accuracy is achieved in the prediction set, further selection gain can be achieved most efficiently by massively expanding its size. Thus, with increasing budget, reducing the costs for producing a DH line becomes increasingly crucial for successfully exploiting the benefits of GS.     

Validating the prediction accuracies of marker-assisted and genomic selection of Fusarium head blight resistance in wheat using an independent sample

Key message

Compared with independent validation, cross-validation simultaneously sampling genotypes and environments provided similar estimates of accuracy for genomic selection, but inflated estimates for marker-assisted selection.

Abstract

Estimates of prediction accuracy of marker-assisted (MAS) and genomic selection (GS) require validations. The main goal of our study was to compare the prediction accuracies of MAS and GS validated in an independent sample with results obtained from fivefold cross-validation using genomic and phenotypic data for Fusarium head blight resistance in wheat. In addition, the applicability of the reliability criterion, a concept originally developed in the context of classic animal breeding and GS, was explored for MAS. We observed that prediction accuracies of MAS were overestimated by 127% using cross-validation sampling genotype and environments in contrast to independent validation. In contrast, prediction accuracies of GS determined in independent samples are similar to those estimated with cross-validation sampling genotype and environments. This can be explained by small population differentiation between the training and validation sets in our study. For European wheat breeding, which is so far characterized by a slow temporal dynamic in allele frequencies, this assumption seems to be realistic. Thus, GS models used to improve European wheat populations are expected to possess a long-lasting validity. Since quantitative trait loci information can be exploited more precisely if the predicted genotype is more related to the training population, the reliability criterion is also a valuable tool to judge the level of prediction accuracy of individual genotypes in MAS.

     

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

Key Message

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.

Abstract

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 F₁-hybrid performance, and selection of well-paired parents requires the testing of large quantities of hybrid combinations in extensive field trials. However, the number of potential hybrids, in general, dramatically exceeds breeding capacity and budget. Integration of genomic selection (GS) could substantially increase the number of potential combinations that can be evaluated. GS models can be used to predict the performance of untested individuals based only on their genotypic profiles, using marker effects previously predicted in a training population. This allows for a preselection of promising genotypes, enabling a more efficient allocation of resources. In this study, we evaluated the usefulness of the Illumina Brassica 60 k SNP array for genomic prediction and compared three alternative approaches based on a homoscedastic ridge regression BLUP and three Bayesian prediction models that considered general and specific combining ability (GCA and SCA, respectively). A total of 448 hybrids were produced in a commercial breeding program from unbalanced crosses between 220 paternal doubled haploid lines and five male-sterile testers. Predictive ability was evaluated for seven agronomic traits. We demonstrate that the Brassica 60 k genotyping array is an adequate and highly valuable platform to implement genomic prediction of hybrid performance in oilseed rape. Furthermore, we present first insights into the application of established statistical models for prediction of important agronomical traits with contrasting patterns of polygenic control.

     

Improving the analysis of low heritability complex traits for enhanced genetic gain in potato

Key message

Best linear unbiased prediction (BLUP), which uses pedigree to estimate breeding values, can result in increased genetic gains for low heritability traits in autotetraploid potato.

Abstract

Conventional potato breeding strategies, based on outcrossing followed by phenotypic recurrent selection over a number of generations, can result in slow but steady improvements of traits with moderate to high heritability. However, faster gains, particularly for low heritability traits, could be made by selection on estimated breeding values (EBVs) calculated using more complete pedigree information in best linear unbiased prediction (BLUP) analysis. One complication in applying BLUP predictions of breeding value to potato breeding programs is the autotetraploid inheritance pattern of this species. Here we have used a large pedigree, dating back to 1908, to estimate heritability for nine key traits for potato breeding, modelling autotetraploid inheritance. We estimate the proportion of double reduction in potatoes from our data, and across traits, to be in the order of 10 %. Estimates of heritability ranged from 0.21 for breeder’s visual preference, 0.58 for tuber yield, to 0.83 for plant maturity. Using the accuracies of the EBVs determined by cross generational validation, we model the genetic gain that could be achieved by selection of genotypes for breeding on BLUP EBVs and demonstrate that gains can be greater than in conventional schemes.     

Genome-wide mapping and prediction suggests presence of local epistasis in a vast elite winter wheat populations adapted to Central Europe

Key message

Genome-wide association mapping as well as marker- and haplotype-based genome-wide selection unraveled a complex genetic architecture of grain yield with absence of large effect QTL and presence of local epistatic effects.

Abstract

The genetic architecture of grain yield determines to a large extent the optimum design of genomic-assisted wheat breeding programs. The main goal of our study was to examine the potential and limitations to dissect the genetic architecture of grain yield in wheat using a large experimental data set. Our study was based on phenotypic information and genomic data of 13,901 SNPs of a diverse set of 3816 elite wheat lines adapted to Central Europe. We applied genome-wide association mapping based on experimental and simulated data sets and performed marker- and haplotype-based genomic prediction. Computer simulations revealed for our mapping population a high power to detect QTL, even if they individually explained only 2.5% of the genetic variation. Despite this, we found no stable marker–trait associations when validating in independent subsets. A two-dimensional scan for marker–marker interactions indicated presence of local epistasis which was further supported by improved prediction abilities when shifting from marker- to haplotype-based genome-wide prediction approaches. We observed that marker effects estimated using genome-wide prediction approaches strongly varied across years albeit resulting in high prediction abilities. Thus, our results suggested that the prediction accuracy of genomic selection in wheat is mainly driven by relatedness rather than by exploiting knowledge of the genetic architecture.

     

Multiple-line cross QTL mapping for biomass yield and plant height in triticale (× Triticosecale Wittmack)

Key message

QTL mapping in multiple families identifies trait-specific and pleiotropic QTL for biomass yield and plant height in triticale.

Abstract

Triticale shows a broad genetic variation for biomass yield which is of interest for a range of purposes, including bioenergy. Plant height is a major contributor to biomass yield and in this study, we investigated the genetic architecture underlying biomass yield and plant height by multiple-line cross QTL mapping. We employed 647 doubled haploid lines from four mapping populations that have been evaluated in four environments and genotyped with 1710 DArT markers. Twelve QTL were identified for plant height and nine for biomass yield which cross-validated explained 59.6 and 38.2 % of the genotypic variance, respectively. A major QTL for both traits was identified on chromosome 5R which likely corresponds to the dominant dwarfing gene Ddw1. In addition, we detected epistatic QTL for plant height and biomass yield which, however, contributed only little to the genetic architecture of the traits. In conclusion, our results demonstrate the potential of genomic approaches for a knowledge-based improvement of biomass yield in triticale.     

Genomic prediction for rust resistance in diverse wheat landraces

Key message

We have demonstrated that genomic selection in diverse wheat landraces for resistance to leaf, stem and strip rust is possible, as genomic breeding values were moderately accurate. Markers with large effects in the Bayesian analysis confirmed many known genes, while also discovering many previously uncharacterised genome regions associated with rust scores.

Abstract

Genomic selection, where selection decisions are based on genomic estimated breeding values (GEBVs) derived from genome-wide DNA markers, could accelerate genetic progress in plant breeding. In this study, we assessed the accuracy of GEBVs for rust resistance in 206 hexaploid wheat (Triticum aestivum) landraces from the Watkins collection of phenotypically diverse wheat genotypes from 32 countries. The landraces were genotyped for 5,568 SNPs using an Illumina iSelect 9 K bead chip assay and phenotyped for field-based leaf rust (Lr), stem rust (Sr) and stripe rust (Yr) responses across multiple years. Genomic Best Linear Unbiased Prediction (GBLUP) and a Bayesian Regression method (BayesR) were used to predict GEBVs. Based on fivefold cross-validation, the accuracy of genomic prediction averaged across years was 0.35, 0.27 and 0.44 for Lr, Sr and Yr using GBLUP and 0.33, 0.38 and 0.30 for Lr, Sr and Yr using BayesR, respectively. Inclusion of PCR-predicted genotypes for known rust resistance genes increased accuracy more substantially when the marker was diagnostic (Lr34/Sr57/Yr18) for the presence-absence of the gene rather than just linked (Sr2). Investigation of the impact of genetic relatedness between validation and reference lines on accuracy of genomic prediction showed that accuracy will be higher when each validation line had at least one close relationship to the reference lines. Overall, the prediction accuracies achieved in this study are encouraging, and confirm the feasibility of genomic selection in wheat. In several instances, estimated marker effects were confirmed by published literature and results of mapping experiments using Watkins accessions.     

Long-term perspective of hybrid versus line breeding in wheat based on quantitative genetic theory

Key message

The predicted future yield potential of hybrids was competitive with lines in the near future, but on a long term the competitiveness of hybrids depends on a number of factors.

Abstract

The change from line to hybrid breeding in autogamous crops is a recent controversial discussion among scientists and breeders. Our objectives were to employ wheat as a model to: (1) deliver a theoretical framework for the comparison of the selection gain of hybrid versus line breeding; (2) elaborate key parameters affecting selection gain in this comparison; (3) and evaluate the potential to modify these parameters in applied breeding programs. We developed a prediction model for future yield potential in both breeding methods as the sum of the population mean and the expected selection gain. The expected selection gain was smaller in hybrid than in line breeding and depended strongly on the hybrid seed production costs and the genetic variance available in hybrid versus line breeding. Owing to heterosis, the predicted future yield potential of hybrids was competitive with lines in the near future. On a long term, however, the competitiveness of hybrid compared to line breeding is questionable and depends on a number of factors. However, market specifications and political reasons might justify the current high interest in hybrid wheat breeding.     

Genomic prediction of starch content and chipping quality in tetraploid potato using genotyping-by-sequencing

Key message

Genomic prediction models for starch content and chipping quality show promising results, suggesting that genomic selection is a feasible breeding strategy in tetraploid potato.

Abstract

Genomic selection uses genome-wide molecular markers to predict performance of individuals and allows selections in the absence of direct phenotyping. It is regarded as a useful tool to accelerate genetic gain in breeding programs, and is becoming increasingly viable for crops as genotyping costs continue to fall. In this study, we have generated genomic prediction models for starch content and chipping quality in tetraploid potato to facilitate varietal development. Chipping quality was evaluated as the colour of a potato chip after frying following cold induced sweetening. We used genotyping-by-sequencing to genotype 762 offspring, derived from a population generated from biparental crosses of 18 tetraploid parents. Additionally, 74 breeding clones were genotyped, representing a test panel for model validation. We generated genomic prediction models from 171,859 single-nucleotide polymorphisms to calculate genomic estimated breeding values. Cross-validated prediction correlations of 0.56 and 0.73 were obtained within the training population for starch content and chipping quality, respectively, while correlations were lower when predicting performance in the test panel, at 0.30–0.31 and 0.42–0.43, respectively. Predictions in the test panel were slightly improved when including representatives from the test panel in the training population but worsened when preceded by marker selection. Our results suggest that genomic prediction is feasible, however, the extremely high allelic diversity of tetraploid potato necessitates large training populations to efficiently capture the genetic diversity of elite potato germplasm and enable accurate prediction across the entire spectrum of elite potatoes. Nonetheless, our results demonstrate that GS is a promising breeding strategy for tetraploid potato.

     

Haploid plants carrying a sodium azide-induced mutation (fdr1) produce fertile pollen grains due to first division restitution (FDR) in maize (Zea mays L.)

Key message

We induced a fdr1 mutation in maize which makes haploid plants male fertile due to first division restitution; the optimum sodium azide treatment on maize kernels has been identified.

Abstract

Sodium azide mutagenesis experiments were performed on haploid and diploid maize plants. Kernels with haploid embryos of maize inbred line B55 were induced by pollinating with RWS pollen. These kernels were treated with 0.2, 0.5, or 1.0 mM sodium azide solution for 2 h. The 0.5 mM solution was optimal for inducing numerous albino sectors on the treated plants without significant damage. Kernels of a maize hybrid, Oh43 × B55, were treated with sodium azide solutions at concentrations of 1.5, 2.0, 2.5, and 3.0 mM. Haploids were generated by pollinating RWS pollen. The highest rate of chlorophyll mutations in seedlings (15.3 % [13/85]) was recorded with the 2.5 mM concentration. A mutated haploid plant (PP1-50) with higher pollen fertility was isolated during the experiments. This haploid plant produced four kernels on the ear after selfing. These kernels were germinated and produced ears with full seed set after selfing. The haploid plants induced from PP1-50 diploids also exhibited high pollen fertility. In situ hybridization studies showed that meiocytes in PP1-50 haploid anthers underwent first division restitution at a rate of 48 % and produced equally divided dyads. We designated the genetic factor responsible for this high pollen fertility as fdr1. PP1-50 haploid ears exhibited high levels of sterility, as seen for regular haploids. Diploid PP1-50 meiocytes in the anther underwent normal meiosis, and all selfed progenies were normal diploids. We concluded that the fdr1 phenotype is only expressed in the anthers of haploid plants and not in the anthers of diploid plants.     

A fast genomic selection approach for large genomic data

Key message

We propose a novel computational method for genomic selection that combines identical-by-state (IBS)-based Haseman–Elston (HE) regression and best linear prediction (BLP), called HE-BLP.

Abstract

Genomic best linear unbiased prediction (GBLUP) has been widely used in whole-genome prediction for breeding programs. To determine the total genetic variance of a training population, a linear mixed model (LMM) should be solved via restricted maximum likelihood (REML), whose computational complexity is the cube of the sample size. We proposed a novel computational method combining identical-by-state (IBS)-based Haseman–Elston (HE) regression and best linear prediction (BLP), called HE-BLP. With this method, the total genetic variance can be estimated by solving a simple HE linear regression, which has a computational complex of the sample size squared; therefore, it is suitable for large-scale genomic data, except those with which environmental effects need to be estimated simultaneously, because it does not allow for this estimation. In Monte Carlo simulation studies, the estimated heritability based on HE was identical to that based on REML, and the prediction accuracy via HE-BLP and traditional GBLUP was also quite similar when quantitative trait loci (QTLs) were randomly distributed along the genome and their effects followed a normal distribution. In addition, the kernel row number (KRN) trait in a maize IBM population was used to evaluate the performance of the two methods; the results showed similar prediction accuracy of breeding values despite slightly different estimated heritability via HE and REML, probably due to the underlying genetic architecture. HE-BLP can be a future genomic selection method choice for even larger sets of genomic data in certain special cases where environmental effects can be ignored. The software for HE regression and the simulation program is available online in the Genetic Analysis Repository (GEAR; https://github.com/gc5k/GEAR/wiki).

     

Historical changes in population structure during rice breeding programs in the northern limits of rice cultivation

Key message

The rice local population was clearly differentiated into six groups over the 100-year history of rice breeding programs in the northern limit of rice cultivation over the world.

Abstract

Genetic improvements in plant breeding programs in local regions have led to the development of new cultivars with specific agronomic traits under environmental conditions and generated the unique genetic structures of local populations. Understanding historical changes in genome structures and phenotypic characteristics within local populations may be useful for identifying profitable genes and/or genetic resources and the creation of new gene combinations in plant breeding programs. In the present study, historical changes were elucidated in genome structures and phenotypic characteristics during 100-year rice breeding programs in Hokkaido, the northern limit of rice cultivation in the world. We selected 63 rice cultivars to represent the historical diversity of this local population from landraces to the current breeding lines. The results of the phylogenetic analysis demonstrated that these cultivars clearly differentiated into six groups over the history of rice breeding programs. Significant differences among these groups were detected in five of the seven traits, indicating that the differentiation of the Hokkaido rice population into these groups was correlated with these phenotypic changes. These results demonstrated that breeding practices in Hokkaido have created new genetic structures for adaptability to specific environmental conditions and breeding objectives. They also provide a new strategy for rice breeding programs in which such unique genes in local populations in the world can explore the genetic potentials of the local populations.     

Genetic variation in wild Prunus L. subgen. Cerasus germplasm from Iran characterized by nuclear and chloroplast SSR markers

Key message

The selected material of Cerasus subgen. will be useful for conservation and management and important for Prunus breeding programs.

Abstract

Knowledge of relationships among the cultivated and wild species of Cerasus is important for recognizing gene pools in germplasm and developing effective conservation and management strategies. In this study, genetic and phylogenetic relationships of wild Cerasus subgenus species naturally growing in Iran, including P. avium (mazzard), P. mahaleb, P. brachypetala, P. incana, P. yazdiana, P. microcarpa subsp. microcarpa, P. microcarpa subsp. diffusa and P. pseudoprostrata and three commercial species, sweet cherry (P. avium), sour cherry (P. cerasus) and duke cherry (P. x gondouinii) was investigated based on 16 nuclear SSR and five chloroplast SSR. Very high level of polymorphism was detected among the studied species based these molecular markers, indicating high inter and intraspecific genetic variation. Inter and intraspecific genetic similarity coefficients varied from 0.00 to 1.00, indicating high genetic variation in studied germplasm. These two molecular markers types could distinguish differences between all species so that accessions of each species were placed into a single group. Based on molecular markers, a close correlation was observed between intraspecific variation and geographical distribution. Furthermore, based on nuSSR primers, most wild species showed 2–4 alleles and may be tetraploid. In conclusion, the conservation of these highly diverse native populations of Iranian wild Cerasus germplasm is recommended for future breeding activity.     

Dissecting genetic and non-genetic sources of long-term yield trend in German official variety trials

Key message

Long-term yield trends have genetic and non-genetic components which may be dissected by a linear mixed model with regression terms. Disease-resistance breakdown must be accounted for in the interpretation.

Abstract

Long-term yield trends of crop varieties may be studied to identify a genetic trend component due to breeding efforts and a non-genetic trend component due to advances in agronomic practices. Many such studies have been undertaken, and most of these inspect trends either by plotting means against years and/or by some kind of regression analysis based on such plots. Dissection of genetic and non-genetic trend components is a key challenge in such analyses. In the present paper, we consider mixed models with regression components for identifying different sources of trend. We pay particular attention to the effect of disease breakdown, which is shown to be confounded with long-term genetic and non-genetic trends, causing an over-estimation of genetic trends based on long-term yield trial data. The models are illustrated using German multi-environment trial data on yield, mildew and Septoria leaf blotch susceptibility for winter wheat and yield, mildew and net blotch susceptibility for spring barley.     

Hybrid wheat: quantitative genetic parameters and consequences for the design of breeding programs

Key message

Commercial heterosis for grain yield is present in hybrid wheat but long-term competiveness of hybrid versus line breeding depends on the development of heterotic groups to improve hybrid prediction.

Abstract

Detailed knowledge of the amount of heterosis and quantitative genetic parameters are of paramount importance to assess the potential of hybrid breeding. Our objectives were to (1) examine the extent of midparent, better-parent and commercial heterosis in a vast population of 1,604 wheat (Triticum aestivum L.) hybrids and their parental elite inbred lines and (2) discuss the consequences of relevant quantitative parameters for the design of hybrid wheat breeding programs. Fifteen male lines were crossed in a factorial mating design with 120 female lines, resulting in 1,604 of the 1,800 potential single-cross hybrid combinations. The hybrids, their parents, and ten commercial wheat varieties were evaluated in multi-location field experiments for grain yield, plant height, heading time and susceptibility to frost, lodging, septoria tritici blotch, yellow rust, leaf rust, and powdery mildew at up to five locations. We observed that hybrids were superior to the mean of their parents for grain yield (10.7 %) and susceptibility to frost (?7.2 %), leaf rust (?8.4 %) and septoria tritici blotch (?9.3 %). Moreover, 69 hybrids significantly (P < 0.05) outyielded the best commercial inbred line variety underlining the potential of hybrid wheat breeding. The estimated quantitative genetic parameters suggest that the establishment of reciprocal recurrent selection programs is pivotal for a successful long-term hybrid wheat breeding.     

Capturing flavors from Capsicum baccatum by introgression in sweet pepper

Key message

Biochemical characterization in combination with genetic analyses in BC ₂ S ₁ plants and near-isogenic lines led to the detection and validation of C. baccatum loci affecting flavor, terpenoid content and Brix level.

Abstract

The species Capsicum baccatum includes the most common hot peppers of the Andean cuisine, known for their rich variation in flavors and aromas. So far the C. baccatum genetic variation remained merely concealed for Capsicum annuum breeding, due to post-fertilization genetic barriers encountered in interspecific hybridization. However, to exploit the potential flavor wealth of C. baccatum we combined interspecific crossing with embryo rescue, resulting in a multi-parent BC₂S₁ population. Volatile and non-volatile compounds plus some physical characters were measured in mature fruits, in combination with taste evaluation by a sensory panel. An enormous variation in biochemical composition and sensory attributes was found, with almost all traits showing transgression. A population-specific genetic linkage map was developed for QTL mapping. BC₂S₁ QTLs were validated in an experiment with near-isogenic lines, resulting in confirmed genetic effects for physical, biochemical and sensory traits. Three findings are described in more detail: (1) A small C. baccatum LG3 introgression caused an extraordinary effect on flavor, resulting in significantly higher scores for the attributes aroma, flowers, spices, celery and chives. In an attempt to identify the responsible biochemical compounds few consistently up- and down-regulated metabolites were detected. (2) Two introgressions (LG10.1 and LG1) had major effects on terpenoid content of mature fruits, affecting at least 15 different monoterpenes. (3) A second LG3 fragment resulted in a strong increase in Brix without negative effects on fruit size. The mapping strategy, the potential application of studied traits and perspectives for breeding are discussed.     

Increased genomic prediction accuracy in wheat breeding using a large Australian panel

Key message

Genomic prediction accuracy within a large panel was found to be substantially higher than that previously observed in smaller populations, and also higher than QTL-based prediction.

Abstract

In recent years, genomic selection for wheat breeding has been widely studied, but this has typically been restricted to population sizes under 1000 individuals. To assess its efficacy in germplasm representative of commercial breeding programmes, we used a panel of 10,375 Australian wheat breeding lines to investigate the accuracy of genomic prediction for grain yield, physical grain quality and other physiological traits. To achieve this, the complete panel was phenotyped in a dedicated field trial and genotyped using a custom Axiom^TM Affymetrix SNP array. A high-quality consensus map was also constructed, allowing the linkage disequilibrium present in the germplasm to be investigated. Using the complete SNP array, genomic prediction accuracies were found to be substantially higher than those previously observed in smaller populations and also more accurate compared to prediction approaches using a finite number of selected quantitative trait loci. Multi-trait genetic correlations were also assessed at an additive and residual genetic level, identifying a negative genetic correlation between grain yield and protein as well as a positive genetic correlation between grain size and test weight.