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 assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials

Key message

Early generation genomic selection is superior to conventional phenotypic selection in line breeding and can be strongly improved by including additional information from preliminary yield trials.

Abstract

The selection of lines that enter resource-demanding multi-environment trials is a crucial decision in every line breeding program as a large amount of resources are allocated for thoroughly testing these potential varietal candidates. We compared conventional phenotypic selection with various genomic selection approaches across multiple years as well as the merit of integrating phenotypic information from preliminary yield trials into the genomic selection framework. The prediction accuracy using only phenotypic data was rather low (r = 0.21) for grain yield but could be improved by modeling genetic relationships in unreplicated preliminary yield trials (r = 0.33). Genomic selection models were nevertheless found to be superior to conventional phenotypic selection for predicting grain yield performance of lines across years (r = 0.39). We subsequently simplified the problem of predicting untested lines in untested years to predicting tested lines in untested years by combining breeding values from preliminary yield trials and predictions from genomic selection models by a heritability index. This genomic assisted selection led to a 20% increase in prediction accuracy, which could be further enhanced by an appropriate marker selection for both grain yield (r = 0.48) and protein content (r = 0.63). The easy to implement and robust genomic assisted selection gave thus a higher prediction accuracy than either conventional phenotypic or genomic selection alone. The proposed method took the complex inheritance of both low and high heritable traits into account and appears capable to support breeders in their selection decisions to develop enhanced varieties more efficiently.

     

Improving the baking quality of bread wheat by genomic selection in early generations

Key message

Genomic selection shows great promise for pre-selecting lines with superior bread baking quality in early generations, 3 years ahead of labour-intensive, time-consuming, and costly quality analysis.

Abstract

The genetic improvement of baking quality is one of the grand challenges in wheat breeding as the assessment of the associated traits often involves time-consuming, labour-intensive, and costly testing forcing breeders to postpone sophisticated quality tests to the very last phases of variety development. The prospect of genomic selection for complex traits like grain yield has been shown in numerous studies, and might thus be also an interesting method to select for baking quality traits. Hence, we focused in this study on the accuracy of genomic selection for laborious and expensive to phenotype quality traits as well as its selection response in comparison with phenotypic selection. More than 400 genotyped wheat lines were, therefore, phenotyped for protein content, dough viscoelastic and mixing properties related to baking quality in multi-environment trials 2009–2016. The average prediction accuracy across three independent validation populations was r = 0.39 and could be increased to r = 0.47 by modelling major QTL as fixed effects as well as employing multi-trait prediction models, which resulted in an acceptable prediction accuracy for all dough rheological traits (r = 0.38–0.63). Genomic selection can furthermore be applied 2–3 years earlier than direct phenotypic selection, and the estimated selection response was nearly twice as high in comparison with indirect selection by protein content for baking quality related traits. This considerable advantage of genomic selection could accordingly support breeders in their selection decisions and aid in efficiently combining superior baking quality with grain yield in newly developed wheat varieties.

     

Construction of an ultra-high density consensus genetic map,and enhancement of the physical map from genome sequencing in <Emphasis Type="Italic">Lupinus angustifolius</Emphasis>

Key message

An ultra-high density genetic map containing 34,574 sequence-defined markers was developed in Lupinus angustifolius. Markers closely linked to nine genes of agronomic traits were identified. A physical map was improved to cover 560.5 Mb genome sequence.

Abstract

Lupin (Lupinus angustifolius L.) is a recently domesticated legume grain crop. In this study, we applied the restriction-site associated DNA sequencing (RADseq) method to genotype an F₉ recombinant inbred line population derived from a wild type × domesticated cultivar (W × D) cross. A high density linkage map was developed based on the W × D population. By integrating sequence-defined DNA markers reported in previous mapping studies, we established an ultra-high density consensus genetic map, which contains 34,574 markers consisting of 3508 loci covering 2399 cM on 20 linkage groups. The largest gap in the entire consensus map was 4.73 cM. The high density W × D map and the consensus map were used to develop an improved physical map, which covered 560.5 Mb of genome sequence data. The ultra-high density consensus linkage map, the improved physical map and the markers linked to genes of breeding interest reported in this study provide a common tool for genome sequence assembly, structural genomics, comparative genomics, functional genomics, QTL mapping, and molecular plant breeding in lupin.

     

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.

     

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.

     

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.

     

Simultaneous estimation of saponin contents from soybean seed using Fourier transform infrared spectroscopy and high-performance liquid chromatography analysis

The aim of this study is to determine the feasibility of Fourier transform infrared (FT-IR) spectroscopy for simultaneous determination of saponin contents in different soybean cultivars. In cross validation between predicted content of saponin by PLS regression modeling from FT-IR spectra and measured content by HPLC, total saponin contents were predicted with good accuracy (R ² ≥ 0.71). In external validation, saponin group Ab (R ² = 0.88), saponin DDMP-group βg (R ² = 0.85), total saponin group B (R ² = 0.88), and total saponin content (R ² = 0.87) were predicted with good accuracy, while prediction for saponin group Aa (R ² = 0.58), saponin group Bb′ (R ² = 0.58), and total saponin group A (R ² = 0.25) had relatively lower accuracy. Considering these results, we suggest that the PLS prediction system for saponin contents using FT-IR analysis could be applied as a novel screening tool for high yielding lines in soybean breeding.     

Mapping stripe rust resistance gene <Emphasis Type="Italic">YrZH22</Emphasis> in Chinese wheat cultivar Zhoumai 22 by bulked segregant RNA-Seq (BSR-Seq) and comparative genomics analyses

Key message

A stripe rust resistance gene YrZH22 was mapped by combined BSR-Seq and comparative genomics analyses to a 5.92 centimorgan (cM) genetic interval spanning a 4 Mb physical genomic region on wheat chromosome 4BL1.

Abstract

Stripe rust, caused by Puccinia striiformis f. sp. tritici (PST), is one of the most destructive diseases of wheat and severely threatens wheat production worldwide. The widely grown Chinese wheat cultivar Zhoumai 22 is highly resistant to the current prevailing PST race CYR34 (V26). Genetic analysis of F_5:6 and F_6:7 recombinant inbred line (RIL) populations indicated that adult-plant stripe rust resistance in Zhoumai 22 is controlled by a single gene, temporarily designated YrZH22. By applying bulked segregant RNA-Seq (BSR-Seq), 7 SNP markers were developed and SNP mapping showed that YrZH22 is located between markers WGGB105 and WGGB112 on chromosome arm 4BL. The corresponding genomic regions of the Chinese Spring 4BL genome assembly and physical map of Aegilops tauschii 4DL were selected for comparative genomics analyses to develop nine new polymorphic markers that were used to construct a high-resolution genetic linkage map of YrZH22. YrZH22 was delimited in a 5.92 cM genetic interval between markers WGGB133 and WGGB146, corresponding to 4.1 Mb genomic interval in Chinese Spring 4BL and a 2.2 Mb orthologous genomic region in Ae. tauschii 4DL. The genetic linkage map of YrZH22 will be valuable for fine mapping and positional cloning of YrZH22, and can be used for marker-assisted selection in wheat breeding.

     

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.     

Genome-based prediction of maize hybrid performance across genetic groups,testers, locations,and years

Key message

The calibration data for genomic prediction should represent the full genetic spectrum of a breeding program. Data heterogeneity is minimized by connecting data sources through highly related test units.

Abstract

One of the major challenges of genome-enabled prediction in plant breeding lies in the optimum design of the population employed in model training. With highly interconnected breeding cycles staggered in time the choice of data for model training is not straightforward. We used cross-validation and independent validation to assess the performance of genome-based prediction within and across genetic groups, testers, locations, and years. The study comprised data for 1,073 and 857 doubled haploid lines evaluated as testcrosses in 2 years. Testcrosses were phenotyped for grain dry matter yield and content and genotyped with 56,110 single nucleotide polymorphism markers. Predictive abilities strongly depended on the relatedness of the doubled haploid lines from the estimation set with those on which prediction accuracy was assessed. For scenarios with strong population heterogeneity it was advantageous to perform predictions within a priori defined genetic groups until higher connectivity through related test units was achieved. Differences between group means had a strong effect on predictive abilities obtained with both cross-validation and independent validation. Predictive abilities across subsequent cycles of selection and years were only slightly reduced compared to predictive abilities obtained with cross-validation within the same year. We conclude that the optimum data set for model training in genome-enabled prediction should represent the full genetic and environmental spectrum of the respective breeding program. Data heterogeneity can be reduced by experimental designs that maximize the connectivity between data sources by common or highly related test units.     

Genomic models with genotype × environment interaction for predicting hybrid performance: an application in maize hybrids

Key message

A new genomic model that incorporates genotype?×?environment interaction gave increased prediction accuracy of untested hybrid response for traits such as percent starch content, percent dry matter content and silage yield of maize hybrids.

Abstract

The prediction of hybrid performance (HP) is very important in agricultural breeding programs. In plant breeding, multi-environment trials play an important role in the selection of important traits, such as stability across environments, grain yield and pest resistance. Environmental conditions modulate gene expression causing genotype?×?environment interaction (G?×?E), such that the estimated genetic correlations of the performance of individual lines across environments summarize the joint action of genes and environmental conditions. This article proposes a genomic statistical model that incorporates G?×?E for general and specific combining ability for predicting the performance of hybrids in environments. The proposed model can also be applied to any other hybrid species with distinct parental pools. In this study, we evaluated the predictive ability of two HP prediction models using a cross-validation approach applied in extensive maize hybrid data, comprising 2724 hybrids derived from 507 dent lines and 24 flint lines, which were evaluated for three traits in 58 environments over 12 years; analyses were performed for each year. On average, genomic models that include the interaction of general and specific combining ability with environments have greater predictive ability than genomic models without interaction with environments (ranging from 12 to 22%, depending on the trait). We concluded that including G?×?E in the prediction of untested maize hybrids increases the accuracy of genomic models.

     

Evaluation of QTLs for Shoot Fly (<Emphasis Type="Italic">Atherigona soccata</Emphasis>) Resistance Component Traits of Seedling Leaf Blade Glossiness and Trichome Density on Sorghum (<Emphasis Type="Italic">Sorghum bicolor</Emphasis>) Chromosome SBI-10L

Shoot fly is a major insect pest of sorghum damaging early crop growth, establishment and productivity. Host plant resistance is an efficient approach to minimize yield losses due to shoot fly infestation. Seedling leaf blade glossiness and trichome density are morphological traits associated with shoot fly resistance. Our objective was to identify and evaluate QTLs for glossiness and trichome density using- i) 1894 F₂s, ii) a sub-set of 369 F₂-recombinants, and iii) their derived 369 F_2:3 progenies, from a cross involving introgression lines RSG04008-6 (susceptible)?×?J2614-11 (resistant). The QTLs were mapped to a 37–72 centimorgan (cM) or 5–15 Mb interval on the long arm of sorghum chromosome 10 (SBI-10L) with flanking markers Xgap001 and Xtxp141. One QTL each for glossiness (QGls10) and trichome density (QTd10) were mapped in marker interval Xgap001-Xnhsbm1044 and Xisep0630-Xtxp141, confirming their loose linkage, for which phenotypic variation accounted for ranged from 2.29 to 11.37 % and LOD values ranged from 2.03 to 24.13, respectively. Average physical map positions for glossiness and trichome density QTLs on SBI-10 from earlier studies were 4 and 2 Mb, which in the present study were reduced to 2 Mb and 800 kb, respectively. Candidate genes Glossy15 (Sb10g025053) and ethylene zinc finger protein (Sb10g027550) falling in support intervals for glossiness and trichome density QTLs, respectively, are discussed. Also we identified a sub-set of recombinant population that will facilitate further fine mapping of the leaf blade glossiness and trichome density QTLs on SBI-10.     

Predictive ability of genomic selection models in a multi-population perennial ryegrass training set using genotyping-by-sequencing

Key message

Genomic prediction models for multi-year dry matter yield, via genotyping-by-sequencing in a composite training set, demonstrate potential for genetic gain improvement through within-half sibling family selection.

Abstract

Perennial ryegrass (Lolium perenne L.) is a key source of nutrition for ruminant livestock in temperate environments worldwide. Higher seasonal and annual yield of herbage dry matter (DMY) is a principal breeding objective but the historical realised rate of genetic gain for DMY is modest. Genomic selection was investigated as a tool to enhance the rate of genetic gain. Genotyping-by-sequencing (GBS) was undertaken in a multi-population (MP) training set of five populations, phenotyped as half-sibling (HS) families in five environments over 2 years for mean herbage accumulation (HA), a measure of DMY potential. GBS using the ApeKI enzyme yielded 1.02 million single-nucleotide polymorphism (SNP) markers from a training set of n = 517. MP-based genomic prediction models for HA were effective in all five populations, cross-validation-predictive ability (PA) ranging from 0.07 to 0.43, by trait and target population, and 0.40–0.52 for days-to-heading. Best linear unbiased predictor (BLUP)-based prediction methods, including GBLUP with either a standard or a recently developed (KGD) relatedness estimation, were marginally superior or equal to ridge regression and random forest computational approaches. PA was principally an outcome of SNP modelling genetic relationships between training and validation sets, which may limit application for long-term genomic selection, due to PA decay. However, simulation using data from the training experiment indicated a twofold increase in genetic gain for HA, when applying a prediction model with moderate PA in a single selection cycle, by combining among-HS family selection, based on phenotype, with within-HS family selection using genomic prediction.

     

QTL-seq for rapid identification of candidate genes for flowering time in broccoli × cabbage

Key message

A major QTL controlling early flowering in broccoli × cabbage was identified by marker analysis and next-generation sequencing, corresponding to GRF6 gene conditioning flowering time in Arabidopsis.

Abstract

Flowering is an important agronomic trait for hybrid production in broccoli and cabbage, but the genetic mechanism underlying this process is unknown. In this study, segregation analysis with BC₁P1, BC₁P2, F₂, and F_2:3 populations derived from a cross between two inbred lines “195” (late-flowering) and “93219” (early flowering) suggested that flowering time is a quantitative trait. Next, employing a next-generation sequencing-based whole-genome QTL-seq strategy, we identified a major genomic region harboring a robust flowering time QTL using an F₂ mapping population, designated Ef2.1 on cabbage chromosome 2 for early flowering. Ef2.1 was further validated by indel (insertion or deletion) marker-based classical QTL mapping, explaining 51.5% (LOD = 37.67) and 54.0% (LOD = 40.5) of the phenotypic variation in F₂ and F_2:3 populations, respectively. Combined QTL-seq and classical QTL analysis narrowed down Ef1.1 to a 228-kb genomic region containing 29 genes. A cabbage gene, Bol024659, was identified in this region, which is a homolog of GRF6, a major gene regulating flowering in Arabidopsis, and was designated BolGRF6. qRT-PCR study of the expression level of BolGRF6 revealed significantly higher expression in the early flowering genotypes. Taken together, our results provide support for BolGRF6 as a possible candidate gene for early flowering in the broccoli line 93219. The identified candidate genomic regions and genes may be useful for molecular breeding to improve broccoli and cabbage flowering times.

     

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.     

Optimization of multi-environment trials for genomic selection based on crop models

Key message

We propose a statistical criterion to optimize multi-environment trials to predict genotype × environment interactions more efficiently, by combining crop growth models and genomic selection models.

Abstract

Genotype × environment interactions (GEI) are common in plant multi-environment trials (METs). In this context, models developed for genomic selection (GS) that refers to the use of genome-wide information for predicting breeding values of selection candidates need to be adapted. One promising way to increase prediction accuracy in various environments is to combine ecophysiological and genetic modelling thanks to crop growth models (CGM) incorporating genetic parameters. The efficiency of this approach relies on the quality of the parameter estimates, which depends on the environments composing this MET used for calibration. The objective of this study was to determine a method to optimize the set of environments composing the MET for estimating genetic parameters in this context. A criterion called OptiMET was defined to this aim, and was evaluated on simulated and real data, with the example of wheat phenology. The MET defined with OptiMET allowed estimating the genetic parameters with lower error, leading to higher QTL detection power and higher prediction accuracies. MET defined with OptiMET was on average more efficient than random MET composed of twice as many environments, in terms of quality of the parameter estimates. OptiMET is thus a valuable tool to determine optimal experimental conditions to best exploit MET and the phenotyping tools that are currently developed.

     

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.     

Performance of multi-trait genomic selection for <Emphasis Type="Italic">Eucalyptus robusta</Emphasis> breeding program

In forest tree genetic improvement, multi-trait genomic selection (GS) may have advantages in improving the accuracy of the genotype estimation and shortening selection cycles. For the breeding of Eucalyptus robusta, one of the most exotic planted species in Madagascar, volume at 49 months (V49), total lignin (TL), and holo-cellulose (Holo) were considered. For GS, 2919 single nucleotide polymorphisms (SNP) were used with the genomic best linear unbiased predictor (GBLUP) method, which was as efficient as the reproducing kernel Hilbert space (RKHS) and elastic net methods (EN), but more adapted to multi-trait modeling. The efficiency of individual I model, including the genomic data, was much higher than the provenance effect P model. For example, with V49, mean goodness-of-fit was: r_{I_Full} =?0.79, r_{P_Full} =?0.37 for I and P, respectively. The prediction accuracies using the cross-validation procedure were lower for V49: r_I =?0.29 r_P =?0.28. The genetic gains resulting from the indexes associating (V49, TL) and (V49, Holo) were higher using I than for the P model; for V49, the relative genetic gain was 37 and 20%, respectively, with 5% of selection intensity. The single-trait approach was as efficient as the multi-trait approach given the weak correlations between V49 and TL or Holo. The I model also brings greater diversity: for V49 the number of provenances represented in a selected population was two and three with the P model, and 6 and 16 with the I model.     

Characterization of a common wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.) high-tillering dwarf mutant

Key message

A novel high-tillering dwarf mutant in common wheat Wangshuibai was characterized and mapped to facilitate breeding for plant height and tiller and the future cloning of the causal gene.

Abstract

Tiller number and plant height are two major agronomic traits in cereal crops affecting plant architecture and grain yield. NAUH167, a mutant of common wheat landrace Wangshuibai induced by ethylmethyl sulfide (EMS) treatment, exhibits higher tiller number and reduced plant height. Microscope observation showed that the dwarf phenotype was attributed to the decrease in the number of cells and their length. The same as the wild type, the mutant was sensitive to exogenous gibberellins. Genetic analysis showed that the high-tillering number and dwarf phenotype were related and controlled by a partial recessive gene. Using a RIL_2:6 population derived from the cross NAUH167/Sumai3, a molecular marker-based genetic map was constructed. The map consisted of 283 loci, spanning a total length of 1007.98 cM with an average markers interval of 3.56 cM. By composite interval mapping, a stable major QTL designated QHt.nau-2D controlling both traits, was mapped to the short arm of chromosome 2D flanked by markers Xcfd11 and Xgpw361. To further map the QHt.nau-2D loci, another population consisted of 180 F₂ progeny from a cross 2011I-78/NAUH167 was constructed. Finally, QHt.nau-2D was located within a genetic region of 0.8 cM between markers QHT239 and QHT187 covering a predicted physical distance of 6.77 Mb. This research laid the foundation for map-based cloning of QHt.nau-2D and would facilitate the characterization of plant height and tiller number in wheat.