Recognizing the enormous potential of DNA markers in plant breeding, many agricultural research centers and plant breeding institutes have adopted the capacity for marker development and marker-assisted selection (MAS). However, due to rapid developments in marker technology, statistical methodology for identifying quantitative trait loci (QTLs) and the jargon used by molecular biologists, the utility of DNA markers in plant breeding may not be clearly understood by non-molecular biologists. This review provides an introduction to DNA markers and the concept of polymorphism, linkage analysis and map construction, the principles of QTL analysis and how markers may be applied in breeding programs using MAS. This review has been specifically written for readers who have only a basic knowledge of molecular biology and/or plant genetics. Its format is therefore ideal for conventional plant breeders, physiologists, pathologists, other plant scientists and students.

Key messageGenomic 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.AbstractPerennial 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.Electronic supplementary materialThe online version of this article (10.1007/s00122-017-3030-1) contains supplementary material, which is available to authorized users.

In this paper, we introduce a unique new plant breeding decision support software tool DeltaGen, implemented in R and its package Shiny. DeltaGen provides plant breeders with a single integrated solution for experimental design generation, data quality control, statistical and quantitative genetic analyses, breeding strategy evaluation, simulation, and cost analysis, pattern analysis, index selection, and underlying basic theory on quantitative genetics. Key analysis procedures in DeltaGen were demonstrated using three datasets generated from forage breeding trials in Australia, New Zealand, and the United States. Analyses of the perennial ryegrass seasonal growth data in Case Study 1 was based on residual maximum likelihood analysis and pattern analysis. A graphical summary of the performance of entries across locations was generated, and entries with specific and broad adaptation were identified. The quantitative genetic analysis and breeding method simulation procedures applied to the perennial ryegrass half‐sib (HS) family data in Case Study 2 enabled estimation of quantitative genetic parameters, prediction of genetic gain, and calculation of costs per selection cycle. These results enabled comparison of three breeding methods, which also included genomic selection, and their simulation. Data from Case Study 3 were analyzed to investigate a multivariate approach to identify HS families of switchgrass with breeding values that would enable an increase in biomass dry matter yield (DMY) and cell wall ethanol (CWE) and a decrease in Klason lignin (KL). The Smith–Hazel index developed enabled identification of HS families with genetic worth for increasing DMY and CWE and reducing KL, in contrast with individual trait selection. Analysis of the datasets in all three case studies provides a snapshot of the key analyses available within DeltaGen. This software tool could also be used as a teaching resource in plant breeding courses. DeltaGen is available as freeware at http://agrubuntu.cloudapp.net/PlantBreedingTool/

Apart from the importance of switchgrass (Panicum virgatum L.) as forage for livestock, it is useful as a high‐value cellulosic biofuel feedstock. Breeding for a biofuel crop is complicated by the existence of multiple platforms for conversion of biomass to energy. Our main objective was to investigate the relative merits of single‐trait selection, correlated response to selection, and Smith‐Hazel‐index‐based selection for the following traits: biomass dry matter yield (YLD), ethanol (ETOH), Klason lignin (KL), and high heating value (HHV). The genetic analysis was based on a 2‐yr data set generated from evaluation of 144 half‐sib (HS) families in sward‐plot trials at Arlington and Marshfield, WI. There was significant (P < 0.05) additive genetic variation among the families for all traits and for family × site interaction for the traits HHV and YLD. The estimates of narrow sense heritability on a HS family mean basis ranged from 0.37 for YLD to 0.51 for KL. Genetic correlation of YLD with ETOH, HHV, and KL were 0.38, 0.27, and 0.01, respectively. The index constructed to increase YLD and ETOH and reduce KL was most successful for a fermentation platform. This index enabled identification of families for enhancing ethanol production that would have been missed if selection was based solely on YLD. The index weighted to increase YLD and KL best suited a combustion platform. Both of these two indices had economic impacts superior to any other selection index evaluated.

A major challenge faced by today’s white clover breeder is how to manage resources within a breeding program. It is essential to utilise these resources with sufficient flexibility to build on past progress from conventional breeding strategies, but also take advantage of emerging opportunities from molecular breeding tools such as molecular markers and transformation. It is timely to review white clover breeding strategies. This background can then be used as a foundation for considering how to continue conventional plant improvement activities and complement them with molecular breeding opportunities. In this review, conventional white clover breeding strategies relevant to the Australian dryland target population environments are considered. Attention is given to: (i) availability of genetic variation, (ii) characterisation of germplasm collections, (iii) quantitative models for estimation of heritability, (iv) the role of multi-environment trials to accommodate genotype-by-environment interactions, (v) interdisciplinary research to understand adaptation to dryland environments, (vi) breeding and selection strategies, and (vii) cultivar structure. Current achievements in biotechnology with specific reference to white clover breeding in Australia are considered, and computer modelling of breeding programs is discussed as a useful integrative tool for the joint evaluation of conventional and molecular breeding strategies and optimisation of resource use in breeding programs. Four areas are identified as future research priorities: (i) capturing the potential genetic diversity among introduced accessions and ecotypes that are adapted to key constraints such as summer moisture stress and the use of molecular markers to assess the genetic diversity, (ii) understanding the underlying physiological/morphological root and shoot mechanisms involved in water use efficiency of white clover, with the objective of identifying appropriate selection criteria, (iii) estimation of quantitative genetic parameters of important morphological/physiological attributes to enable prediction of response to selection in target environments, and (iv) modelling white clover breeding strategies to evaluate the opportunities for integration of molecular breeding strategies with conventional breeding programs.

An outdoor study was conducted to examine relationships between plant productivity and stress-protective phenolic plant metabolites. Twenty-two populations of the pasture legume white clover were grown for 4½ months during spring and summer in Palmerston North, New Zealand. The major phenolic compounds identified and quantified by HPLC analysis were glycosides of the flavonoids quercetin and kaempferol. Multivariate analysis revealed a trade-off between flavonoid accumulation and plant productivity attributes. White clover populations with high biomass production, large leaves and thick tap roots showed low levels of quercetin glycoside accumulation and low quercetin:kaempferol ratios, while the opposite was true for less productive populations. The latter included stress-resistant ecotypes from Turkey and China, and the analysis also identified highly significant positive relationships of quercetin glycoside accumulation with plant morphology (root:shoot ratio). Importantly, a high degree of genetic variation was detected for most of the measured traits. These findings suggest merit for considering flavonoids such as quercetin as potential selection criteria in the genetic improvement of white clover and other crops.

11Forage nutritive value impacts animal nutrition, which underpins livestock productivity, reproduction 12 and health. Genetic improvement for nutritive traits has been limited, as they are typically expensive 13 and time-consuming to measure through conventional methods. Genomic selection is appropriate for 14 such complex and expensive traits, enabling cost-effective prediction of breeding values using genome-15 wide markers. The aims of the present study were to assess the potential of genomic selection for a 16 range of nutritive traits in a multi-population training set, and to quantify contributions of genotypic, Genomic selection for nutritive traits 2 39 Genomic selection for nutritive traits 4 program. From each population, 102 to 117 plants that tested positive for endophyte infection (Epichloё 126 festucae var lolli) by immunoblotting (HAHN et al. 2003), were polycrossed in isolation during spring 127 2012 in Palmerston North, New Zealand (FAVILLE et al. 2018). Polycrosses were performed separately 128 for each population, without admixing, and seeds from the maternal parents were harvested and 129 cleaned. In total 543 half-sib families were harvested for seed, however only 517 families had sufficient 130 seed (≥ 3.6g) for sowing field trials. 131 A total of six trials were sown (FAVILLE et al. 2018), of which two were used for the current study. 132 These were trials established at Lincoln (Canterbury region, southern New Zealand, 43.38°S 172.62°E; 133 Wakanui silt loam) and Aorangi (Manawatu region, central New Zealand, 40.34°S 175.46°E; Kairanga 134 sandy loam), during the autumn of 2013. The experimental design at each site was row-column with 135 three replicates. Within each replicate, populations were blocked, and families randomized within 136 blocks. Multiple repeated checks (clonal replicates) were also randomly allocated within and across 137 the replicated blocks. Half-sib families were evaluated as a 1m row of plants (referred to from now as 138 plots), by sowing 0.2 g of seed (which is equivalent to 14 kg ha -1 , if a sward was sown at 7 rows m -1 ). 139 Nitrogen and phosphate fertilizer was applied at the rate of 15-30 kg N ha -1 and 8.8 kg P ha -1 , in late 140 autumn each year (FAVILLE et al. 2018). 141 2.2 Phenotypic measurements 142

White clover (Trifolium repens L.) is an important pasture legume in temperate regions, but growth is often strongly reduced under summer drought. Cloned individuals from a full-sib progeny of a pair cross between two phenotypically distinct white clover populations were exposed to water deficit in pots under outdoor conditions for 9 weeks, while control pots were maintained at field capacity. Water deficit decreased leaf water potential by more than 50% overall, but increased the levels of the flavonol glycosides of quercetin (Q) and the ratio of quercetin and kaempferol glycosides (QKR) by 111% and by 90%, respectively. Water deficit reduced dry matter (DM) by 21%, with the most productive genotypes in the controls showing the greatest proportional reduction. The full-sib progeny displayed a significant increase in the root : shoot ratio by 53% under water deficit. Drought-induced changes in plant morphology were associated with changes in Q, but not kaempferol (K) glycosides. The genotypes with high QKR levels reduced their DM production least under water deficit and increased their Q glycoside levels and QKR most. These data show, at the individual genotype level, that increased Q glycoside accumulation in response to water deficit stress can be positively associated with retaining higher levels of DM production.