Integrating a growth degree-days based reaction norm methodology and multi-trait modeling for genomic prediction in wheat.

Multi-trait and multi-environment analyses can improve genomic prediction by exploiting between-trait correlations and genotype-by-environment interactions. In the context of reaction norm models, genotype-by-environment interactions can be described as functions of high-dimensional sets of markers and environmental covariates. However, comprehensive multi-trait reaction norm models accounting for marker × environmental covariates interactions are lacking. In this article, we propose to extend a reaction norm model incorporating genotype-by-environment interactions through (co)variance structures of markers and environmental covariates to a multi-trait reaction norm case. To do that, we propose a novel methodology for characterizing the environment at different growth stages based on growth degree-days (GDD). The proposed models were evaluated by variance components estimation and predictive performance for winter wheat grain yield and protein content in a set of 2,015 F6-lines. Cross-validation analyses were performed using leave-one-year-location-out (CV1) and leave-one-breeding-cycle-out (CV2) strategies. The modeling of genomic [SNPs] × environmental covariates interactions significantly improved predictive ability and reduced the variance inflation of predicted genetic values for grain yield and protein content in both cross-validation schemes. Trait-assisted genomic prediction was carried out for multi-trait models, and it significantly enhanced predictive ability and reduced variance inflation in all scenarios. The genotype by environment interaction modeling via genomic [SNPs] × environmental covariates interactions, combined with trait-assisted genomic prediction, boosted the benefits in predictive performance. The proposed multi-trait reaction norm methodology is a comprehensive approach that allows capitalizing on the benefits of multi-trait models accounting for between-trait correlations and reaction norm models exploiting high-dimensional genomic and environmental information.

Improvement of genomic prediction in advanced wheat breeding lines by including additive-by-additive epistasis.

KEY MESSAGE: Including additive and additive-by-additive epistasis in a NOIA parametrization did not yield orthogonal partitioning of genetic variances, nevertheless, it improved predictive ability in a leave-one-out cross-validation for wheat grain yield. Additive-by-additive epistasis is the principal non-additive genetic effect in inbred wheat lines and is potentially useful for developing cultivars based on total genetic merit; nevertheless, its practical benefits have been highly debated. In this article, we aimed to (i) evaluate the performance of models including additive and additive-by-additive epistatic effects for variance components (VC) estimation of grain yield in a wheat-breeding population, and (ii) to investigate whether including additive-by-additive epistasis in genomic prediction enhance wheat grain yield predictive ability (PA). In total, 2060 sixth-generation (F6) lines from Nordic Seed A/S breeding company were phenotyped in 21 year-location combinations in Denmark, and genotyped using a 15 K-Illumina-BeadChip. Three models were used to estimate VC and heritability at plot level: (i) "I-model" (baseline), (ii) "I + GA-model", extending I-model with an additive genomic effect, and (iii) "I + GA + GAA-model", extending I + GA-model with an additive-by-additive genomic effects. The I + GA-model and I + GA + GAA-model were based on the Natural and Orthogonal Interactions Approach (NOIA) parametrization. The I + GA + GAA-model failed to achieve orthogonal partition of genetic variances, as revealed by a change in estimated additive variance of I + GA-model when epistasis was included in the I + GA + GAA-model. The PA was studied using leave-one-line-out and leave-one-breeding-cycle-out cross-validations. The I + GA + GAA-model increased PA significantly (16.5%) compared to the I + GA-model in leave-one-line-out cross-validation. However, the improvement due to including epistasis was not observed in leave-one-breeding-cycle-out cross-validation. We conclude that epistatic models can be useful to enhance predictions of total genetic merit. However, even though we used the NOIA parameterization, the variance partition into orthogonal genetic effects was not possible.

Strategies Using Genomic Selection to Increase Genetic Gain in Breeding Programs for Wheat.

Conventional wheat-breeding programs involve crossing parental lines and subsequent selfing of the offspring for several generations to obtain inbred lines. Such a breeding program takes more than 8 years to develop a variety. Although wheat-breeding programs have been running for many years, genetic gain has been limited. However, the use of genomic information as selection criterion can increase selection accuracy and that would contribute to increased genetic gain. The main objective of this study was to quantify the increase in genetic gain by implementing genomic selection in traditional wheat-breeding programs. In addition, we investigated the effect of genetic correlation between different traits on genetic gain. A stochastic simulation was used to evaluate wheat-breeding programs that run simultaneously for 25 years with phenotypic or genomic selection. Genetic gain and genetic variance of wheat-breeding program based on phenotypes was compared to the one with genomic selection. Genetic gain from the wheat-breeding program based on genomic estimated breeding values (GEBVs) has tripled compared to phenotypic selection. Genomic selection is a promising strategy for improving genetic gain in wheat-breeding programs.

Use of multiple traits genomic prediction, genotype by environment interactions and spatial effect to improve prediction accuracy in yield data.

Genomic selection has been extensively implemented in plant breeding schemes. Genomic selection incorporates dense genome-wide markers to predict the breeding values for important traits based on information from genotype and phenotype records on traits of interest in a reference population. To date, most relevant investigations have been performed using single trait genomic prediction models (STGP). However, records for several traits at once are usually documented for breeding lines in commercial breeding programs. By incorporating benefits from genetic characterizations of correlated phenotypes, multiple trait genomic prediction (MTGP) may be a useful tool for improving prediction accuracy in genetic evaluations. The objective of this study was to test whether the use of MTGP and including proper modeling of spatial effects can improve the prediction accuracy of breeding values in commercial barley and wheat breeding lines. We genotyped 1,317 spring barley and 1,325 winter wheat lines from a commercial breeding program with the Illumina 9K barley and 15K wheat SNP-chip (respectively) and phenotyped them across multiple years and locations. Results showed that the MTGP approach increased correlations between future performance and estimated breeding value of yields by 7% in barley and by 57% in wheat relative to using the STGP approach for each trait individually. Analyses combining genomic data, pedigree information, and proper modeling of spatial effects further increased the prediction accuracy by 4% in barley and 3% in wheat relative to the model using genomic relationships only. The prediction accuracy for yield in wheat and barley yield trait breeding, were improved by combining MTGP and spatial effects in the model.

Multi-Parental Populations Suitable for Identifying Sources of Resistance to Powdery Mildew in Winter Wheat.

Wheat (Triticum aestivum L.) is one of the world's staple food crops and one of the most devastating foliar diseases attacking wheat is powdery mildew (PM). In Denmark only a few specific fungicides are available for controlling PM and the use of resistant cultivars is often recommended. In this study, two Chinese wheat landraces and two synthetic hexaploid wheat lines were used as donors for creating four multi-parental populations with a total of 717 individual lines to identify new PM resistance genetic variants. These lines and the nine parental lines (including the elite cultivars used to create the populations) were genotyped using a 20 K Illumina SNP chip, which resulted in 8,902 segregating single nucleotide polymorphisms for assessment of the population structure and whole genome association study. The largest genetic difference among the lines was between the donors and the elite cultivars, the second largest genetic difference was between the different donors; a difference that was also reflected in differences between the four multi-parental populations. The 726 lines were phenotyped for PM resistance in 2017 and 2018. A high PM disease pressure was observed in both seasons, with severities ranging from 0 to >50%. Whole genome association studies for genetic variation in PM resistance in the populations revealed significant markers mapped to either chromosome 2A, B, or D in each of the four populations. However, linkage disequilibrium between these putative quantitative trait loci (QTL) were all above 0.80, probably representing a single QTL. A combined analysis of all the populations confirmed this result and the most associated marker explained 42% of the variation in PM resistance. This study gives both knowledge about the resistance as well as molecular tools and plant material that can be utilised in marker-assisted selection. Additionally, the four populations produced in this study are highly suitable for association studies of other traits than PM resistance.

ADAM-Plant: A Software for Stochastic Simulations of Plant Breeding From Molecular to Phenotypic Level and From Simple Selection to Complex Speed Breeding Programs.

Making decisions on plant breeding programs require plant breeders to be able to test different breeding strategies by taking into account all the crucial factors affecting crop genetic improvement. Due to the complexity of the decisions, computer simulation serves as an important tool for researchers and plant breeders. This paper describes ADAM-plant, which is a computer software that models breeding schemes for self-pollinated and cross-pollinated crop plants using stochastic simulation. The program simulates a population of plants and traces the genetic changes in the population under different breeding scenarios. It takes into account different population structures, genomic models, selection (strategies and units) and crossing strategies. It also covers important features e.g., allowing users to perform genomic selection (GS) and speed breeding, simulate genotype-by-environment interactions using multiple trait approach, simulate parallel breeding cycles and consider plot sizes. In addition, the software can be used to simulate datasets produced from very complex breeding program in order to test new statistical methodology to analyze such data. As an example, three wheat-breeding strategies were simulated in the current study: (1) phenotypic selection, (2) GS, and (3) speed breeding with genomic information. The results indicate that the genetic gain can be doubled by GS compared to phenotypic selection and genetic gain can be further increased considerably by speed breeding. In conclusion, ADAM-plant is an important tool for comparing strategies for plant breeding and for estimating the effects of allocation of different resources to the breeding program. In the current study, it was used to compare different methodologies for utilizing genomic information in cereal breeding programs for selection of best-fit breeding strategy as per available resources.

Nucleotide diversity and linkage disequilibrium of nine genes with putative effects on flowering time in perennial ryegrass (Lolium perenne L.).

Optimization of flowering is an important breeding goal in forage and turf grasses, such as perennial ryegrass (Lolium perenne L.). Nine floral control genes including Lolium perenne CONSTANS (LpCO), SISTER OF FLOWERING LOCUS T (LpSFT), TERMINAL FLOWER1 (LpTFL1), VERNALIZATION1 (LpVRN1, identical to LpMADS1) and five additional MADS-box genes, were analyzed for nucleotide diversity and linkage disequilibrium (LD). For each gene, about 1 kb genomic fragments were isolated from 10 to 20 genotypes of perennial ryegrass of diverse origin. Four to twelve haplotypes per gene were observed. On average, one single nucleotide polymorphism (SNP) was present per 127 bp between two randomly sampled sequences for the nine genes (π = 0.00790). Two MADS-box genes, LpMADS1 and LpMADS10, involved in timing of flowering showed high nucleotide diversity and rapid LD decay, whereas MADS-box genes involved in floral organ identity were found to be highly conserved and showed extended LD. For LpMADS4, LpMADS5, LpCO, LpSFT and LpTFL1, LD extended over the entire region analyzed. The results are compared to previously published results on resistance genes within the same collection of genotypes and the prospects for association mapping of floral control in perennial ryegrass are discussed.

Fructan accumulation and transcription of candidate genes during cold acclimation in three varieties of Poa pratensis.

Poa pratensis, a type species for the grass family (Poaceae), is an important cool season grass that accumulates fructans as a polysaccharide reserve. We studied fructan contents and expression of candidate fructan metabolism genes during cold acclimation in three varieties of P. pratensis adapted to different environments: Northern Norway, Denmark, and the Netherlands. Fructan content increased significantly during cold acclimation and varieties showed significant differences in the level of fructan accumulation. cDNA sequences of putative fructosyltransferase (FT), fructan exohydrolase (FEH), and cold acclimation protein (CAP) genes were identified and cloned. In agreement with a function in fructan biosynthesis, transcription of a putative sucrose:fructan 6-fructosyltransferase (Pp6-SFT) gene was induced during cold acclimation and fructan accumulation in all three P. pratensis varieties. Transcription of putative PpFEH and PpCAP genes was also induced by cold acclimation; however, transcription of these two genes was several-fold higher in the variety from Norway compared to the other two varieties. The results presented here suggest that Pp6-SFT is involved in fructan biosynthesis in P. pratensis. FEHs have previously been suggested to be involved in fructan biosynthesis and freezing tolerance, and induced expression of PpFEH during fructan accumulation could also suggest a role in fructan biosynthesis. However, based on the different PpFEH transcription rates among varieties and similar expression of PpFEH and PpCAP, we suggest that PpFEH is more likely to be involved in mediating freezing tolerance, e.g., by regulating the cell osmotic potential through fructan degradation.

Polymorphisms in monolignol biosynthetic genes are associated with biomass yield and agronomic traits in European maize (Zea mays L.).

BACKGROUND: Reduced lignin content leads to higher cell wall digestibility and, therefore, better forage quality and increased conversion of lignocellulosic biomass into ethanol. However, reduced lignin content might lead to weaker stalks, lodging, and reduced biomass yield. Genes encoding enzymes involved in cell wall lignification have been shown to influence both cell wall digestibility and yield traits. RESULTS: In this study, associations between monolignol biosynthetic genes and plant height (PHT), days to silking (DTS), dry matter content (DMC), and dry matter yield (DMY) were identified by using a panel of 39 European elite maize lines. In total, 10 associations were detected between polymorphisms or tight linkage disequilibrium (LD) groups within the COMT, CCoAOMT2, 4CL1, 4CL2, F5H, and PAL genomic fragments, respectively, and the above mentioned traits. The phenotypic variation explained by these polymorphisms or tight LD groups ranged from 6% to 25.8% in our line collection. Only 4CL1 and F5H were found to have polymorphisms associated with both yield and forage quality related characters. However, no pleiotropic polymorphisms affecting both digestibility of neutral detergent fiber (DNDF), and PHT or DMY were discovered, even under less stringent statistical conditions. CONCLUSION: Due to absence of pleiotropic polymorphisms affecting both forage yield and quality traits, identification of optimal monolignol biosynthetic gene haplotype(s) combining beneficial quantitative trait polymorphism (QTP) alleles for both quality and yield traits appears possible within monolignol biosynthetic genes. This is beneficial to maximize forage and bioethanol yield per unit land area.

Characterization of phenylpropanoid pathway genes within European maize (Zea mays L.) inbreds.

BACKGROUND: Forage quality of maize is influenced by both the content and structure of lignins in the cell wall. Biosynthesis of monolignols, constituting the complex structure of lignins, is catalyzed by enzymes in the phenylpropanoid pathway. RESULTS: In the present study we have amplified partial genomic fragments of six putative phenylpropanoid pathway genes in a panel of elite European inbred lines of maize (Zea mays L.) contrasting in forage quality traits. Six loci, encoding C4H, 4CL1, 4CL2, C3H, F5H, and CAD, displayed different levels of nucleotide diversity and linkage disequilibrium (LD) possibly reflecting different levels of selection. Associations with forage quality traits were identified for several individual polymorphisms within the 4CL1, C3H, and F5H genomic fragments when controlling for both overall population structure and relative kinship. A 1-bp indel in 4CL1 was associated with in vitro digestibility of organic matter (IVDOM), a non-synonymous SNP in C3H was associated with IVDOM, and an intron SNP in F5H was associated with neutral detergent fiber. However, the C3H and F5H associations did not remain significant when controlling for multiple testing. CONCLUSION: While the number of lines included in this study limit the power of the association analysis, our results imply that genetic variation for forage quality traits can be mined in phenylpropanoid pathway genes of elite breeding lines of maize.

Vernalization response in perennial ryegrass (Lolium perenne L.) involves orthologues of diploid wheat (Triticum monococcum) VRN1 and rice (Oryza sativa) Hd1.

Flowering time is important when adapting crop plants to different environments. While high feeding quality of forage grasses is facilitated by repression of flowering, flowering should also be inducible to facilitate grass seed production. Consequently, the identification and characterization of the genes controlling flowering time in forage grasses, including perennial ryegrass (Lolium perenne L.), is of great interest. In this study, three candidate genes for vernalization response genes in perennial ryegrass were identified based on DNA sequence homology to TmVRN1 and TmVRN2 of diploid wheat (Triticum monococcum), and Hd1 of rice (Oryza sativa). High sequence similarity between LpVRN1 and TmVRN1, co-localization of LpVRN1 with a major quantitative trait loci (QTL) for vernalization response in perennial ryegrass, synteny between map-positions of LpVRN1 and TmVRN1, mRNA expression analysis of LpVRN1 alleles during vernalization, and the correspondence between LpVRN1 mRNA expression levels and flowering time leads us to conclude that LpVRN1 is orthologous to TmVRN1 and that its function is conserved between diploid wheat and perennial ryegrass. Of the remaining two candidate genes, a putative Hd1 orthologue, LpCO, co-localized with a second QTL for vernlization response. LpCO has recently been shown to be involved in the photoperiodic regulation of flowering time. While epistasis, at the level of LpVRN1 transcription, was observed between the LpVRN1 and LpCO genomic regions, no differential expression of LpCO transcripts was observed during vernalization. While orthologous genes controlling flowering time can thus be identified, future allele sequencing efforts will reveal if causative polymorphisms are conserved across the grasses.

QTL mapping of vernalization response in perennial ryegrass (Lolium perenne L.) reveals co-location with an orthologue of wheat VRN1.

The objective of this study was to map quantitative trait loci (QTL) for the vernalization response in perennial ryegrass (Lolium perenne L.). The mapping population consisted of 184 F2 genotypes produced from a cross between one genotype of a synthetic perennial ryegrass variety "Veyo" and one genotype from the perennial ryegrass ecotype "Falster". Veyo and Falster were chosen among four different populations because of their contrasting vernalization requirements. In total, five QTL for the vernalization response, measured as days to heading, were identified and mapped to linkage groups (LG) LG2, LG4, LG6 and LG7. Individually, these QTL explained between 5.4 and 28.0% of the total phenotypic variation. The overall contribution of these five QTL was 80% of the total phenotypic variation. A putative orthologue of Triticum monococcum VRN1 was amplified from genomic DNA from perennial ryegrass. PCR fragments covering the proximal part of the promoter and the 5' end of the orthologue were subsequently PCR-amplified from both parents of the mapping population and shown to possess 95% DNA sequence identity to VRN1. Several polymorphisms were identified between Veyo and Falster in this fragment of the putative VRN1 orthologue. A CAPS marker, vrn-1, was developed and found to co-segregate with a major QTL on LG4 for the vernalization response. This indicates that the CAPS marker vrn-1 could be located in an orthologous gene of the wheat VRN1.