Traits In Plant Breeding Research Articles

Intellectual property rights and plants made by new genomic techniques: Access to technology and gene-edited traits in plant breeding

Gene editing has the potential to make new crop varieties faster and more efficiently. New and more suitable crop varieties can increase sustainable agriculture, for instance, in the form of disease-resistant varieties that facilitate integrated pest management. The European Commission's proposal on the regulation of gene-edited and cisgenic plants produced with New Genomic Techniques (including CRISPR/Cas) has re-opened the discussion on Intellectual Property Rights on plants in Europe. We provide an overview of the possible impact of patent rights and Plant Variety Rights on the availability of technology and gene-edited alleles to breeders on an European level. We highlight potential problems with the two Intellectual Property Right systems and indicate potential avenues to solutions.

Ability of Genomic Prediction to Bi-Parent-Derived Breeding Population Using Public Data for Soybean Oil and Protein Content.

Genomic selection (GS) is a marker-based selection method used to improve the genetic gain of quantitative traits in plant breeding. A large number of breeding datasets are available in the soybean database, and the application of these public datasets in GS will improve breeding efficiency and reduce time and cost. However, the most important problem to be solved is how to improve the ability of across-population prediction. The objectives of this study were to perform genomic prediction (GP) and estimate the prediction ability (PA) for seed oil and protein contents in soybean using available public datasets to predict breeding populations in current, ongoing breeding programs. In this study, six public datasets of USDA GRIN soybean germplasm accessions with available phenotypic data of seed oil and protein contents from different experimental populations and their genotypic data of single-nucleotide polymorphisms (SNPs) were used to perform GP and to predict a bi-parent-derived breeding population in our experiment. The average PA was 0.55 and 0.50 for seed oil and protein contents within the bi-parents population according to the within-population prediction; and 0.45 for oil and 0.39 for protein content when the six USDA populations were combined and employed as training sets to predict the bi-parent-derived population. The results showed that four USDA-cultivated populations can be used as a training set individually or combined to predict oil and protein contents in GS when using 800 or more USDA germplasm accessions as a training set. The smaller the genetic distance between training population and testing population, the higher the PA. The PA increased as the population size increased. In across-population prediction, no significant difference was observed in PA for oil and protein content among different models. The PA increased as the SNP number increased until a marker set consisted of 10,000 SNPs. This study provides reasonable suggestions and methods for breeders to utilize public datasets for GS. It will aid breeders in developing GS-assisted breeding strategies to develop elite soybean cultivars with high oil and protein contents.

Agro-Morphological Traits and Molecular Diversity of Proso Millet (Panicum miliaceum L.) Affected by Various Colchicine Treatments

Colchicine is a substance used to induce mutations in order to regulate important agronomic traits. The genotypes Pavlodarskoe 4, Quartet, and PI 289324, originating from Kazakhstan, the Russian Federation, and Hungary, respectively, were used as materials. The objective of this study was to investigate the effects of different colchicine concentrations (0.0, 0.04, 0.06, 0.08, and 0.1%) and treatment times (6, 12, and 24 h) on the agronomic traits of proso millet (Panicum miliaceum L.) and to assess the genetic diversity of the M2 generation using inter simple sequence repeat (ISSR) markers. The experiment was conducted in 2021 for the M1 generation and in 2022 for the M2 generation, from May to September. The percentage of field germination decreased with increasing colchicine concentrations and exposure durations. The mean field germination percentages were 48.57% in Pavlodarskoe 4, 43.28% in Quartet, and 53.14% in PI 289324 under colchicine treatment. Chlorophyll-defective M1-M2 plants were obtained using various colchicine concentrations and exposure periods. The highest number of mutational modifications was attained with the 0.08–0.1% concentrations of colchicine. Based on the research results, a total of 248 plants with chlorophyll-defective mutations were selected from 2214 plants. The growing seasons of M1 and M2 plants were shortened by higher colchicine concentrations (0.08–0.1%) combined with soaking times of 12 and 24 h. Thus, the longest growing season (84 days) was observed with a 6 h treatment time for PI 289324, while the shortest (78 days) was recorded for 12 and 24 h treatments. The possibility of obtaining morphological mutations using colchicine has been confirmed. The ISSR primers amplified a total of 1333 fragments; 1281 bands were found to be polymorphic, and 52 bands were monomorphic. The percentage of polymorphism varied from 80 to 100%, with an average of 96.11%. Most of the different allelic bands were detected when applying the 0.08% colchicine concentration. These positive variations are a great opportunity to use colchicine as a tool for improving agronomic traits in plant breeding.

A statistical package for evaluation of hybrid performance in plant breeding via genomic selection

Hybrid breeding employs heterosis, which could potentially improve the yield and quality of a crop. Genomic selection (GS) is a promising approach for the selection of quantitative traits in plant breeding. The main objectives of this study are to (i) propose a GS-based approach to identify potential parental lines and superior hybrid combinations from a breeding population, which is composed of hybrids produced by a half diallel mating design; (ii) develop a software package for users to carry out the proposed approach. An R package, designated EHPGS, was generated to facilitate the employment of the genomic best linear unbiased model considering additive plus dominance marker effects for the hybrid performance evaluation. The R package contains a Bayesian statistical algorithm for calculating genomic estimated breeding value (GEBVs), GEBV-based specific combining ability, general combining ability, mid-parent heterosis, and better-parent heterosis. Three datasets that have been published in literature, including pumpkin (Cucurbita maxima), maize (Zea mays), and wheat (Triticum aestivum L.), were reanalyzed to illustrate the use of EHPGS.

Morphometric frequency and spectrum of gamma-ray-induced chlorophyll mutants identified by phenotype and development of novel variants in lentil (Lens culinaris Medik.).

Genetic variations are a crucial source of germplasm heterogeneity, as they contribute to the development of new traits for plant breeding by offering an allele resource. Gamma rays have been widely used as a physical agent to produce mutations in plants, and their mutagenic effect has attracted much attention. Nonetheless, few studies have examined the whole mutation spectrum in large-scale phenotypic evaluations. To comprehensively investigate the mutagenic effects of gamma irradiation on lentils, biological consequences on the M1 generation and substantial phenotypic screening on the M2 generation were undertaken. Additionally, the study followed the selected mutants into the M3 generation to evaluate the agronomic traits of interest for crop improvement. Seeds of lentil variety Moitree were irradiated with a range of acute gamma irradiation doses (0, 100, 150, 200, 250, 300, and 350 Gy) to induce unique genetic variability. This research focused on determining the GR50 value while considering seedling parameters and examining the status of pollen fertility while comparing the effects of the gamma irradiation dosages. The GR50 value was determined to be 217.2 Gy using the seedling parameters. Pollens from untreated seed-grown plants were approximately 85% fertile, but those treated with the maximum dosage (350 Gy) were approximately 28% fertile. Numerous chlorophyll and morphological mutants were produced in the M2 generation, with the 300 Gy -treated seeds being the most abundant, followed by the 250 Gy -treated seeds. This demonstrated that an appropriate dosage of gamma rays was advantageous when seeking to generate elite germplasm resources for one or multiple traits. Selected mutants in the M3 generation showed improved agronomic traits, including plant height, root length, number of pods per plant, and yield per plant. These investigations will contribute to a comprehensive understanding of the mutagenic effects and actions of gamma rays, providing a basis for the selection and design of suitable mutagens. This will facilitate the development of more controlled mutagenesis protocols for plant breeding and help guide future research directions for crop improvement using radiation-induced mutation breeding techniques.

Proleptic and Sylleptic Shoot Formation Is Affected by Rootstock Genotype in Two-Year-Old Branches of Almond Trees

The implementation of new planting systems in almond (Prunus amygdalus (L.) Batsch, syn P. dulcis (Mill.)) orchards for more sustainable practices has made tree architecture increasingly relevant as an important selection trait in plant breeding. Multiple features define the three-dimensional structure of the tree, with shoot production being the most important. Shoots can develop after a period of rest (proleptic shoots) or immediately (sylleptic shoots), at the same time as the extension of the parent shoot. Scion/rootstock interactions may change the proportion of proleptic and sylleptic shoots and thus alter tree architecture. To study this effect, we analyzed shoot formation on three almond scion cultivars grafted onto five interspecific hybrid rootstocks. The type of shoot (proleptic or sylleptic) and internode number per shoot were collected for two-year-old branches on three-year-old unpruned trees. We found that rootstock genotypes can alter the pattern of shoot production, especially the number of sylleptic shoots formed. More information on the molecular response is needed to comprehend the biological processes behind these differential phenotypes, which appear to be modulated by both apical dominance and apical control.

Frequency and Spectrum of Mutations Induced by Gamma Rays Revealed by Phenotype Screening and Whole-Genome Re-Sequencing in Arabidopsis thaliana.

Genetic variations are an important source of germplasm diversity, as it provides an allele resource that contributes to the development of new traits for plant breeding. Gamma rays have been widely used as a physical agent for mutation creation in plants, and their mutagenic effect has attracted extensive attention. However, few studies are available on the comprehensive mutation profile at both the large-scale phenotype mutation screening and whole-genome mutation scanning. In this study, biological effects on M1 generation, large-scale phenotype screening in M2 generation, as well as whole-genome re-sequencing of seven M3 phenotype-visible lines were carried out to comprehensively evaluate the mutagenic effects of gamma rays on Arabidopsis thaliana. A total of 417 plants with visible mutated phenotypes were isolated from 20,502 M2 plants, and the phenotypic mutation frequency of gamma rays was 2.03% in Arabidopsis thaliana. On average, there were 21.57 single-base substitutions (SBSs) and 11.57 small insertions and deletions (InDels) in each line. Single-base InDels accounts for 66.7% of the small InDels. The genomic mutation frequency was 2.78 × 10−10/bp/Gy. The ratio of transition/transversion was 1.60, and 64.28% of the C > T events exhibited the pyrimidine dinucleotide sequence; 69.14% of the small InDels were located in the sequence with 1 to 4 bp terminal microhomology that was used for DNA end rejoining, while SBSs were less dependent on terminal microhomology. Nine genes, on average, were predicted to suffer from functional alteration in each re-sequenced line. This indicated that a suitable mutation gene density was an advantage of gamma rays when trying to improve elite materials for one certain or a few traits. These results will aid the full understanding of the mutagenic effects and mechanisms of gamma rays and provide a basis for suitable mutagen selection and parameter design, which can further facilitate the development of more controlled mutagenesis methods for plant mutation breeding.

The performance of phenomic selection depends on the genetic architecture of the target trait

Key messageThe phenomic predictive ability depends on the genetic architecture of the target trait, being high for complex traits and low for traits with major QTL.Genomic selection is a powerful tool to assist breeding of complex traits, but a limitation is the costs required for genotyping. Recently, phenomic selection has been suggested, which uses spectral data instead of molecular markers as predictors. It was shown to be competitive with genomic prediction, as it achieved predictive abilities as high or even higher than its genomic counterpart. The objective of this study was to evaluate the performance of phenomic prediction for triticale and the dependency of the predictive ability on the genetic architecture of the target trait. We found that for traits with a complex genetic architecture, like grain yield, phenomic prediction with NIRS data as predictors achieved high predictive abilities and performed better than genomic prediction. By contrast, for mono- or oligogenic traits, for example, yellow rust, marker-based approaches achieved high predictive abilities, while those of phenomic prediction were very low. Compared with molecular markers, the predictive ability obtained using NIRS data was more robust to varying degrees of genetic relatedness between the training and prediction set. Moreover, for grain yield, smaller training sets were required to achieve a similar predictive ability for phenomic prediction than for genomic prediction. In addition, our results illustrate the potential of using field-based spectral data for phenomic prediction. Overall, our result confirmed phenomic prediction as an efficient approach to improve the selection gain for complex traits in plant breeding.

Using UAV-based hyperspectral imaging and functional regression to assist in predicting grain yield and related traits in wheat under heat-related stress environments for the purpose of stable yielding genotypes

Quantifying certain physiological traits under heat-stress is crucial for maximizing genetic gain for wheat yield and yield-related components. In-season estimation of different physiological traits related to heat stress tolerance can ensure the finding of germplasm, which could help in making effective genetic gains in yield. However, estimation of those complex traits is time- and labor-intensive. Unmanned aerial vehicle (UAV) based hyperspectral imaging could be a powerful tool to estimate indirectly in-season genetic variation for different complex physiological traits in plant breeding that could improve genetic gains for different important economic traits, like grain yield. This study aims to predict in-season genetic variations for cellular membrane thermostability (CMT), yield and yield related traits based on spectral data collected from UAVs; particularly, in cases where there is a small sample size to collect data from and a large range of features collected per sample. In these cases, traditional methods of yield-prediction modeling become less robust. To handle this, a functional regression approach was employed that addresses limitations of previous techniques to create a model for predicting CMT, grain yield and other traits in wheat under heat stress environmental conditions and when data availability is constrained. The results preliminarily indicate that the overall models of each trait studied presented a good accuracy compared to their data’s standard deviation. The yield prediction model presented an average error of 13.42%, showing the function-on-function algorithm chosen for the model as reliable for small datasets with high dimensionality.

Training set design in genomic prediction with multiple biparental families.

Genomic selection is a powerful tool to reduce the cycle length and enhance the genetic gain of complex traits in plant breeding. However, questions remain about the optimum design and composition of the training set. In this study, we used 944 soybean [Glycine max (L.) Merr.] recombinant inbred lines from eight families derived through a partial-diallel mating design among five parental lines. The cross-validated prediction accuracies for the six traits seed yield, 1,000-seed weight, protein yield, plant height, protein content, and oil content were high, ranging from 0.79 to 0.87. We investigated among-family predictions, making use of the special mating design with different degrees of relatedness among families. Generally, the prediction accuracy decreased from full-sibs to half-sib families to unrelated families. However, half-sib and unrelated families also showed substantial variation in their prediction accuracy for a given family, which appeared to be caused at least in part by the shared segregation of quantitative trait loci in both the training and prediction sets. Combining several half-sib families in composite training sets generally led to an increase in the prediction accuracy compared with the best family alone. The prediction accuracy increased with the size of the training set, but for comparable prediction accuracy, substantially more half-sibs were required than full-sibs. Collectively, our results highlight the potential of genomic selection for soybean breeding and, in a broader context, illustrate the importance of the targeted design of the training set.

Genomic prediction for grain yield in a barley breeding program using genotype × environment interaction clusters

AbstractGenotype × environment interaction (GEI) is one of the key factors affecting breeding value estimation accuracy for agronomic traits in plant breeding. Measures of GEI include fitting prediction models with various kernels to capture the variance resulting from GEI, and characterizing trials into megaenvironment (ME) clusters within which breeding values can be estimated to remove the main GEI effects. However, many of the current approaches require observations of common genotypes across all trials, which is unavailable in most breeding programs. Our study introduces two methods that can be implemented on unbalanced data to categorize trials into clusters, where both need a correlation matrix between trials: one estimated via a factor analytic (FA) model and another estimated via weather variables. The methods were tested using empirical barley (Hordeum vulgare L.) yield data in a commercial breeding program from 102 trials over 5 yr spread across multiple locations in Australia. Leave‐one‐year‐out cross‐validation achieved comparable predictive accuracies using either trials or clusters as the observed variable in GEI FA models (max. 0.45), which was higher than the accuracy achieved using the non‐GEI model (0.37). In the random cross‐validations, accuracies achieved within clusters (0.42–0.64) were mostly comparable with those achieved in the full population (0.62). In the within‐cluster validations, higher predictive accuracies were achieved when the training population was from the same cluster (mean 0.22) than outside of the cluster (mean 0.16). Our proposed methods of characterizing multienvironment trials into clusters provides a novel way to define training populations by reducing the variance resulting from GEI and could be implemented in any plant breeding program.

Perspectives on Applications of Hierarchical Gene-To-Phenotype (G2P) Maps to Capture Non-stationary Effects of Alleles in Genomic Prediction.

Genomic prediction of complex traits across environments, breeding cycles, and populations remains a challenge for plant breeding. A potential explanation for this is that underlying non-additive genetic (GxG) and genotype-by-environment (GxE) interactions generate allele substitution effects that are non-stationary across different contexts. Such non-stationary effects of alleles are either ignored or assumed to be implicitly captured by most gene-to-phenotype (G2P) maps used in genomic prediction. The implicit capture of non-stationary effects of alleles requires the G2P map to be re-estimated across different contexts. We discuss the development and application of hierarchical G2P maps that explicitly capture non-stationary effects of alleles and have successfully increased short-term prediction accuracy in plant breeding. These hierarchical G2P maps achieve increases in prediction accuracy by allowing intermediate processes such as other traits and environmental factors and their interactions to contribute to complex trait variation. However, long-term prediction remains a challenge. The plant breeding community should undertake complementary simulation and empirical experiments to interrogate various hierarchical G2P maps that connect GxG and GxE interactions simultaneously. The existing genetic correlation framework can be used to assess the magnitude of non-stationary effects of alleles and the predictive ability of these hierarchical G2P maps in long-term, multi-context genomic predictions of complex traits in plant breeding.

Genomic selection of forage agronomic traits in winter wheat

AbstractGenomic selection (GS) can improve genetic gain of complex traits in plant breeding. Phenotyping agronomic traits of winter wheat (Triticum aestivum L.) for dual‐purpose use is expensive and time‐consuming. In this study, we compared the prediction accuracies of four GS models (RR‐BLUP, GBLUP, GAUSS, and BL) for forage yield (FY), plant height (PH) and heading date (HD) of the hard winter wheat diversity panel (n = 298) using random and stratified sampling methods. In addition, we determined the appropriate training population (TP) size and marker density for GS of the traits. Moderate to high prediction accuracies ranging from 0.66 to 0.69 for FY, 0.46 to 0.49 for PH, and 0.71 to 0.74 for HD were observed for the GS models. However, the sampling method had little or no impact on prediction accuracy. The RR‐BLUP, GBLUP, and GAUSS models produced slightly greater prediction accuracies than BL for all traits studied. Prediction accuracies increased with increasing TP size and marker density in all the GS models tested. However, increase of prediction accuracy started to plateau at nTP = 180 lines and 1,000; 1,500; or 3,000 SNPs suggesting that the minimum TP size and marker density were about 180 lines and 1,000 or more SNPs, depending on the model and trait. The impact of TP size on prediction accuracy was greater for RR‐BLUP, GAUSS, and GBLUP than for BL model. This study suggests that RR‐BLUP, GBLUP, and GAUSS are viable models for selecting the forage agronomic traits during dual‐purpose wheat breeding.

Canopy architecture and leaf type as traits of heat resistance in pea

Pea (Pisum sativum L.) is sensitive to heat stress, and thus traits of heat resistance are desirable to minimize crop damage and maintain yield stability in improved cultivars. Crop research has focused on flowering and pollination success for heat resistant traits and vegetative structure is usually overlooked. The plant canopy, through leaf shape, plant habit and resistance to lodging, may offer additional sources of heat resistance in many crops. In pea, commercial cultivars have upright or vining plant habits, and either normal leaves comprised of stipules, leaflets and tendrils or semileafless leaves lacking leaflets. To determine the best canopy architecture and leaf traits associated with heat resistance, 24 diverse pea cultivars were tested in field trials across six environments in western Canada during 2014 to 2016. Compared to control conditions, heat stress increased canopy temperature from 24.9 °C to 27.8 °C and reduced reproductive stem length by 37% (17 cm), flowering duration by 21% (4.7 days), pod number by 30% (3 pods plant−1), and seed yield by 16% (54 g m-2). Pea cultivars had differential responses for measured traits; under heat stress conditions, semileafless cultivars with the upright habit had cooler canopies and greater seed yield, pod number and pod to node ratio than normal leafed vining cultivars. Lodging was associated with increased canopy temperature, a novel finding, implying greater heat stress. In contrast, growth and yield-related traits were greater in control environments for normal leaf, indeterminate and vining cultivars. Overall, the semileafless leaf and upright plant habit were superior traits that not only reduced lodging but maintained lower canopy temperatures in heat and drought stress environments. Therefore, vegetative heat stress can be avoided by management options that resist lodging and by selecting traits in plant breeding that can keep a cultivar upright.

Heritability of Plant Architecture in Diploid Roses (Rosa spp.)

Plant architecture is a crucial trait in plant breeding because it is linked to crop yield. For ornamental crops such as roses, plant architecture is key for their aesthetic and economic value. In 2015, six rose plant architectural traits were evaluated on 2- to 3-year-old plants of F1 rose populations in May and December in College Station, TX, to estimate variability and heritability. The traits included plant height, the number of primary shoots, the length of primary shoots, the number of nodes on the primary shoot, the number of secondary shoots per primary shoot, and the number of tertiary shoots per primary shoot. Among these traits, plant height, the number of primary shoots, and the length of primary shoots showed a substantial amount of variability, whereas the number of secondary and tertiary shoots per primary shoot were skewed toward zero. Using a random effects model restricted maximum likelihood (REML) analysis, the architectural traits demonstrated low to moderate narrow-sense heritability (0.12–0.50) and low to high broad-sense heritability (0.34–0.92). Plant height and the number of primary shoots changed little after the first growth phase, whereas the other four traits were affected greatly by the genotype-by-environment (growth phase) interaction.

A Benchmarking Between Deep Learning, Support Vector Machine and Bayesian Threshold Best Linear Unbiased Prediction for Predicting Ordinal Traits in Plant Breeding.

Genomic selection is revolutionizing plant breeding. However, still lacking are better statistical models for ordinal phenotypes to improve the accuracy of the selection of candidate genotypes. For this reason, in this paper we explore the genomic based prediction performance of two popular machine learning methods: the Multi Layer Perceptron (MLP) and support vector machine (SVM) methods vs. the Bayesian threshold genomic best linear unbiased prediction (TGBLUP) model. We used the percentage of cases correctly classified (PCCC) as a metric to measure the prediction performance, and seven real data sets to evaluate the prediction accuracy, and found that the best predictions (in four out of the seven data sets) in terms of PCCC occurred under the TGLBUP model, while the worst occurred under the SVM method. Also, in general we found no statistical differences between using 1, 2 and 3 layers under the MLP models, which means that many times the conventional neuronal network model with only one layer is enough. However, although even that the TGBLUP model was better, we found that the predictions of MLP and SVM were very competitive with the advantage that the SVM was the most efficient in terms of the computational time required.

ЭФФЕКТИВНОСТЬ ОТБОРА В РАННИХ ПОКОЛЕНИЯХ ГИБРИДОВ CЕЛЬСКОХОЗЯЙСТВЕННЫХ КУЛЬТУР ПО УРОЖАЙНОСТИ И ПРИЗНАКАМ ПРОДУКТИВНОСТИ (обзор)

Despite the success of molecular genetics, overwhelming majority of Russian breeders still rely on selection based on plant phenotype. The most common way of identifying traits for such selection is calculation of their correlation with yield. However, the world science has accumulated extensive experience in direct selection among early generations of hybrid populations for different traits with the purpose to increase yield. The aim of this study was to inform Russian scientific community about the results of such experiments and analyze them in order to find the most efficient selection criteria. Sixty-seven scientific papers on direct or indirect selection in early generations have been analyzed. Thirty-three papers have reported positive results. The highest ratio of positive research results to negative ones refers to 1000 kernel weight and biological yield (aboveground biomass), and the lowest ratio of positive research results to negative ones is found in harvest index and yield. Taking into account high heritability of 100 kernel weight and simplicity of measuring, this trait is recommended for use in breeding practice. This result contradicts some reports on the evolution of productivity traits in plant breeding. Five efficiency-reducing factors have been identified for early generation selection: genotype-environment interaction, heterozygosity, low heritability of quantitative traits, intergenotype competition, and polygenic nature of productivity traits. The main conclusion of the study is that promising plants often emerge in the process of breeding, but many of them cannot be identified due to imperfect selection techniques. A still unresolved fundamental problem is making prognoses for hybrid populations where selection according to a definite selection criterion may lead to the appearance of a promising line.

An investigation of genotype-phenotype association in a festulolium forage grass population containing genome-spanning Festuca pratensis chromosome segments in a Lolium perenne background.

Alien chromosome introgression is used for the transfer of beneficial traits in plant breeding. For temperate forage grasses, much of the work in this context has focused on species within the ryegrasses (Lolium spp.) and the closely related fescues (Festuca spp.) particularly with a view to combining high forage quality with reliability and enhanced environmental services. We have analysed a L. perenne (perennial ryegrass) population containing the majority of a F. pratensis (meadow fescue) genome as introgressed chromosome segments to identify a) marker-trait associations for nutrient use and abiotic stress response across the family, and b) to assess the effects of introgression of F. pratensis genomic regions on phenotype. Using container-based assays and a system of flowing solution culture, we looked at phenotype responses, including root growth, to nitrogen and phosphorus status in the growing medium and abiotic stresses within this festulolium family. A number of significant marker/trait associations were identified across the family for root biomass on chromosomes 2, 3 and 5 and for heading date on chromosome 2. Of particular interest was a region on chromosome 2 associated with increased root biomass in phosphorus-limited conditions derived from one of the L. perenne parents. A genotype containing F. pratensis chromosome 4 as a monosomic introgression showed increased tiller number, shoot and root growth and genotypes with F. pratensis chromosome segment introgressions at different ends of chromosome 4 exhibited differential phenotypes across a variety of test conditions. There was also a general negative correlation between the extent of the F. pratensis genome that had been introgressed and root-related trait performances. We conclude that 1) the identification of alleles affecting root growth has potential application in forage grass breeding and, 2) F. pratensis introgressions can enhance quantitative traits, however, introgression can also have more general negative effects.

Water Deficit and Salinity Stress Reveal Many Specific QTL for Plant Growth and Fruit Quality Traits in Tomato.

Quality is a key trait in plant breeding, especially for fruit and vegetables. Quality involves several polygenic components, often influenced by environmental conditions with variable levels of genotype × environment interaction that must be considered in breeding strategies aiming to improve quality. In order to assess the impact of water deficit and salinity on tomato fruit quality, we evaluated a multi-parent advanced generation intercross (MAGIC) tomato population in contrasted environmental conditions over 2 years, one year in control vs. drought condition and the other in control vs. salt condition. Overall 250 individual lines from the MAGIC population—derived from eight parental lines covering a large diversity in cultivated tomato—were used to identify QTL in both experiments for fruit quality and yield component traits (fruit weight, number of fruit, Soluble Solid Content, firmness), phenology traits (time to flower and ripe) and a vegetative trait, leaf length. All the traits showed a large genotype variation (33–86% of total phenotypic variation) in both experiments and high heritability whatever the year or treatment. Significant genotype × treatment interactions were detected for five of the seven traits over the 2 years of experiments. QTL were mapped using 1,345 SNP markers. A total of 54 QTL were found among which 15 revealed genotype × environment interactions and 65% (35 QTL) were treatment specific. Confidence intervals of the QTL were projected on the genome physical map and allowed identifying regions carrying QTL co-localizations, suggesting pleiotropic regulation. We then applied a strategy for candidate gene detection based on the high resolution mapping offered by the MAGIC population, the allelic effect of each parental line at the QTL and the sequence information of the eight parental lines.

Simultaneous selection for multiple traits in sugarcane breeding populations using logistic regression models

Simultaneous selection for multiple traits in plant breeding increases the value of cultivars to growers. Visual selection is inefficient while multivariate statistical analysis is too complicated for early stages. The objective of this study was to demonstrate logistic regression models as a statistical tool for simultaneous selection of several traits in sugarcane breeding. Data for stalk number, height, diameter, sucrose and fibre contents, and number of Eldana saccharina Walker (Lepidoptera) bored stalks were collected from BSL13 and SSL13 populations grown in the Midlands region of KwaZulu-Natal, South Africa. Data were analysed using the logistic procedure of SAS. Coefficients for stalk number, height, diameter and number of eldana bored stalks were highly significant (p < 0.0001), which indicated their influence in selecting for high cane yield. Selected genotypes in BSL13 produced 20% more stalks that were 23% taller and 7% thicker than those discarded. For SSL13, selected genotypes produced 22% more stalks that were 21% taller and 10% thicker. The selected genotypes produced 66% higher cane yield for BSL13 and 74% higher cane yield for SSL13. Eldana damage in selected genotypes was 37% lower for SSL13 and 14% lower for BSL13. The logistic regression models can be applied for simultaneous selection in several crops.