Genomic Prediction and Selection for Fruit Traits in Winter Squash.

Genomic selection: the option for new robustness traits?

Self-fertilization (selfing) is commonly used for population development in plant breeding, and it is well established that selfing increases genetic variance between lines, thus increasing response to phenotypic selection. Furthermore, numerous studies have explored how selfing can be deployed to maximal benefit in the context of traditional plant breeding programs (Cornish in Heredity 65:201-211,1990a, Heredity 65:213-220,1990b; Liu etal. in Theor Appl Genet 109:370-376, 2004; Pooni and Jinks in Heredity 54:255-260, 1985). However, the impact of selfing on response to genomic selection has not been explored. In the current study we examined how selfing impacts the two key aspects of genomic selection-GEBV prediction (training) and selection response. We reach the following conclusions: (1) On average, selfing increases genomic selection gains by more than 70 %. (2) The gains in genomic selection response attributable to selfing hold over a wide range population sizes (100-500), heritabilities (0.2-0.8), and selection intensities (0.01-0.1). However, the benefits of selfing are dramatically reduced as the number of QTLs drops below 20. (3) The major cause of the improved response to genomic selection with selfing is through an increase in the occurrence of superior genotypes and not through improved GEBV predictions. While performance of the training population improves with selfing (especially with low heritability and small population sizes), the magnitude of these improvements is relatively small compared with improvements observed in the selection population. To illustrate the value of these insights, we propose a practical genomic selection scheme that substantially shortens the number of generations required to fully capture the benefits of selfing. Specifically, we provide simulation evidence that indicates the proposed scheme matches or exceeds the selection gains observed in advanced populations (i.e. F 8 and doubled haploid) across a broad range of heritability and QTL models. Without sacrificing selection gains, we also predict that fully inbred candidates for potential commercialization can be identified as early as the F 4 generation.

Phytophthora blight, caused by the oomycete pathogen, Phytophthora capsici, is a devastating disease on bell pepper and cucurbit crops in the United States and worldwide (29,40). P. capsici causes a root and crown rot, as well as an aerial blight of leaves, fruit, and stems, on bell pepper (Capsicum annuum), tomatoes, cucumber, watermelon, squash, and pumpkin (29,35, 40,57,73). The disease was first described on bell pepper in New Mexico in 1922 (40). In recent years, epidemics have been severe in areas of North Carolina, Florida, Georgia, Michigan, and New Jersey. Oospores are believed to provide the initial source of inoculum in the field, and the disease is polycyclic within seasons (1,7,59,60,67). In this article, we discuss the biology and epidemiology of Phytophthora blight on bell pepper and also describe management strategies that can be implemented based on existing knowledge of the ecology of this devastating pathogen. The objectives of ecologically based pest management (EBPM) are the safe, profitable, and durable management of pests that includes a total systems approach (25). EBPM relies primarily on biological input of knowledge concerning a pathogen life cycle, and secondarily, when necessary, on physical, chemical, and biological supplements for disease management. An understanding of the ecological processes that are suppressive to plant diseases is emphasized rather than secondary inputs (25). Fortunately, we have a considerable amount of information available on the biology and ecology of P. capsici, which can now be integrated to improve our ability to manage the disease using ecologically based approaches. Strategies recommended for management of Phytophthora blight involve integrated approaches that focus first on cultural practices that reduce high soil moisture conditions, but also include monitoring and reduction of propagules of P. capsici that persist in the soil, utilization of cultivars with resistance to the disease, and when necessary, judicious fungicide applications. Symptoms and Life Cycle P. capsici can infect virtually every part of the pepper plant. The pathogen causes a root and crown rot on pepper (Fig. 1) and also forms distinctive black lesions on the stem (Fig. 2). P. capsici can also infect the leaves and causes lesions that are circular, grayish brown, and water-soaked (Fig. 3). Leaf lesions and stem lesions are common when inoculum is splash dispersed from the soil to lower portions of the plant. The pathogen can also infect fruit and causes lesions that are typically covered with white sporangia, a sign of the pathogen (Fig. 4). P. capsici typically causes a fruit rot or stem rot on cucumbers and squash (Fig. 5). P. capsici reproduces by both sexual and asexual means (Fig. 6). The pathogen produces two mating types, known as the A1 and A2. These are actually compatibility types and do not correspond to dimorphic forms. Each mating type produces hormones that are responsible for gametangia differentiation in the opposite mating type. Both A1 and A2 mating types are common in fields in North Carolina and have also been identified within the same plant (59). P. capsici produces a male gametangium, called the antheridium, and a female gametangium, called the oogonium. The antheridium is amphigynous in this species. Meiosis occurs within the gametangia, and plasmogamy and karyogamy result

Heidaritabar, M. (2016). Genomic selection in egg-laying chickens. PhD thesis, Wageningen University, the Netherlands In recent years, prediction of genetic values with DNA markers, or genomic selection (GS), has become a very intense field of research. Many initial studies on GS have focused on the accuracy of predicting the genetic values with different genomic prediction methods. In this thesis, I assessed several aspects of GS. I started with evaluating results of GS against results of traditional pedigree-based selection (BLUP) in data from a selection experiment that applied both methods side by side. The impact of traditional selection and GS on the overall genome variation as well as the overlap between regions selected by GS and the genomic regions predicted to affect the traits were assessed. The impact of selection on genome variation was assessed by measuring changes in allele frequencies that allowed the identification of regions in the genome where changes must be due to selection. These frequency changes were shown to be larger than what could be expected from random fluctuations, indicating that selection is really affecting the allele frequencies and that this effect is stronger in GS compared with BLUP. Next, concordance was tested between the selected regions and regions that affect the traits, as detected by a genome-wide association study. Results showed a low concordance overall between the associated regions and the selected regions. However, markers in associated regions did show larger changes in allele frequencies compared with the average changes across the genome. The selection experiment was performed using a medium density of DNA markers (60K). I subsequently explored the potential benefits of whole-genome sequence data for GS by comparing prediction accuracy from imputed sequence data with the accuracy obtained from the 60K genotypes. Before sequencing, the selection of key animals that should be sequenced to maximize imputation accuracy was assessed with the original 60K genotypes. The accuracy of genotype imputation from lower density panels using a small number of selected key animals as reference was compared with a scenario where random animals were used as the reference population. Even with a very small number of animals as reference, reasonable imputation accuracy could be obtained. Moreover, selecting key animals as reference considerably improved imputation accuracy of rare alleles compared with a set of random reference animals. While imputation from a small reference set was successful, imputation to whole-genome sequence data hardly improved genomic prediction accuracy compared with the predictions based on 60K genotypes. Using only those markers from the whole-genome sequence that are more likely to affect the phenotype was expected to remove noise from the data, but resulted in slightly lower prediction accuracy compared with the complete genome sequence. Finally, I evaluated the inclusion of dominance effects besides additive effects in GS models. The proportion of variance due to additive and dominance effects were estimated for egg production and egg quality traits of a purebred line of layers. The proportion of dominance variance to the total phenotypic variance ranged from 0 to 0.05 across traits. Also, the impact of fitting dominance besides additive effects on prediction accuracy was investigated, but was not found to improve accuracy of genomic prediction of breeding values.

Genomic selection can increase the rate of genetic gain in crops through accumulation of positive alleles and reduce phenotyping costs by shortening the breeding cycle time. We performed genomic prediction for resistance to wheat rusts in tetraploid wheat accessions using three cross-validation with the objective of predicting: (1) rust resistance when individuals are not tested in all environments/locations, (2) the performance of lines across years, and (3) adult plant resistance (APR) of lines with bivariate models. The rationale for the latter is that seedling assays are faster and could increase prediction accuracy for APR. Predictions were derived from adult plant and seedling responses for leaf rust (Lr), stem rust (Sr) and stripe rust (Yr) in a panel of 391 accessions grown across multiple years and locations and genotyped using 16,483 single nucleotide polymorphisms. Different Bayesian models and genomic best linear unbiased prediction yielded similar accuracies for all traits. Site and year prediction accuracies for Lr and Yr ranged between 0.56–0.71 for Lr and 0.51–0.56 for Yr. While prediction accuracy for Sr was variable across different sites, accuracies for Yr were similar across different years and sites. The changes in accuracies can reflect higher genotype × environment (G × E) interactions due to climate or pathogenic variation. The use of seedling assays in genomic prediction was underscored by significant positive genetic correlations between all stage resistance (ASR) and APR (Lr: 0.45, Sr: 0.65, Yr: 0.50). Incorporating seedling phenotypes in the bivariate genomic approach increased prediction accuracy for all three rust diseases. Our work suggests that the underlying plant-host response to pathogens in the field and greenhouse screens is genetically correlated, but likely highly polygenic and therefore difficult to detect at the individual gene level. Overall, genomic prediction accuracies were in the range suitable for selection in early generations of the breeding cycle.

Genomic selection (GS), as a key technology in modern breeding programs, has significantly advanced crop and livestock breeding. By integrating quantitative genetics and genome prediction models, GS has improved the accuracy of predicting complex traits and accelerated the cultivation of high-yield and stress resistant varieties. This study explores the historical evolution, technological innovation, and practical applications of genome selection in breeding. It analyzes the advantages brought by innovative technologies such as high-density genotyping and whole genome prediction, especially their widespread application in multi trait and multi environment models. Although GS has great potential in modern breeding, it still faces challenges such as genotype environment interaction, prediction accuracy, and data complexity. I hope to summarize the latest progress of GS through case analysis and provide direction for future research, in order to promote the application of quantitative genetics and genome selection in a wider range of fields, and provide support for global food security and sustainable agricultural development.

Genomic selection (GS) can substantially reduce breeding cycle times in forest trees compared to traditional breeding cycles. Practical implementation of GS in tree breeding requires an assessment of significant drivers of genetic gains over time, which may differ among species and breeding objectives. We present results of a GS study of growth and wood quality traits in an operational Eucalyptus grandis breeding program in South Africa. The training population consisted of 1575 full and half-sib individuals, genotyped with the Eucalyptus (EUChip60K) SNP chip resulting in 15,040 informative SNP markers. The accuracy of the GS models ranged from 0.47 (diameter) to 0.67 (fibre width). We compared a 4-year GS breeding cycle equivalent to half of a traditional 8-year E. grandis breeding cycle and obtained GS efficiencies ranging from 1.20 (wood density) to 1.62 (fibre length). Simulated over 17 years, the ratio of the accumulated genetic gains between three GS cycles and two traditional breeding cycles ranged from 1.53 (diameter) to 3.35 (wood density). To realise these genetic gains per unit time in E. grandis breeding, we show that significant adjustments have to be made to integrate GS into operational breeding steps.

We developed a simulation study to test the efficiency of genomic selection (GS) in the case of Eucalyptus breeding. We simulated a recurrent selection scheme for clone production over four breeding cycles. Scenarios crossing broad sense heritabilities (H 2 = 0.6 and 0.1) and dominance to additive variance ratios (R = 0.1; 0.5; and 1) were compared. GS was performed with 1,000 SNPs and 22 QTLs per Morgan and tested against phenotypic selection (PS) based on best linear unbiased prediction of parents and clones. When the training population was made up of the first cycle progeny tests and the candidate populations were the progeny tests of three successive cycles, GS accuracy decreased with breeding cycles (e.g., from 0.9 to 0.4 with H 2 = 0.6 and R = 0.1), whereas PS presented constant performances (accuracy of 0.8 with H 2 = 0.6 and R = 0.1). When the training population set was updated by associating data of previous cycles, GS accuracy was improved from 25 % to 418 %, especially with H 2 = 0.1. The GS model including dominance effects performed better in clone selection (genotypic value) when dominance effects were preponderant (R = 1), heritability was high (H 2 = 0.6 and with an updated training set), but no improvement was detected for parent selection (breeding value). The genetic gains over cycles were lower with the GS method without updating the data set but, with an updated training set, were similar to PS. However, the genetic gain per unit time with GS was 1.5 to 3 times higher than with PS for breeding and clone populations. These results highlight the value of GS in Eucalyptus breeding.

Genomic prediction of quantitative resistance toward Phytophthora sojae indicated that genomic selection may increase breeding efficiency. Statistical model and marker set had minimal effect on genomic prediction with > 1000 markers. Quantitative disease resistance (QDR) toward Phytophthora sojae in soybean is a complex trait controlled by many small-effect loci throughout the genome. Along with the technical and rate-limiting challenges of phenotyping resistance to a root pathogen, the trait complexity can limit breeding efficiency. However, the application of genomic prediction to traits with complex genetic architecture, such as QDR toward P. sojae, is likely to improve breeding efficiency. We provide a novel example of genomic prediction by measuring QDR to P. sojae in two diverse panels of more than 450 plant introductions (PIs) that had previously been genotyped with the SoySNP50K chip. This research was completed in a collection of diverse germplasm and contributes to both an initial assessment of genomic prediction performance and characterization of the soybean germplasm collection. We tested six statistical models used for genomic prediction including Bayesian Ridge Regression; Bayesian LASSO; Bayes A, B, C; and reproducing kernel Hilbert spaces. We also tested how the number and distribution of SNPs included in genomic prediction altered predictive ability by varying the number of markers from less than 50 to more than 34,000 SNPs, including SNPs based on sequential sampling, random sampling, or selections from association analyses. Predictive ability was relatively independent of statistical model and marker distribution, with a diminishing return when more than 1000 SNPs were included in genomic prediction. This work estimated relative efficiency per breeding cycle between 0.57 and 0.83, which may improve the genetic gain for P. sojae QDR in soybean breeding programs.

BackgroundRecurrent selection is a foundational breeding method for quantitative trait improvement. It typically features rapid breeding cycles that can lead to high rates of genetic gain. Usually, generations are discrete in recurrent selection, which means that breeding candidates are evaluated and considered for selection for only one cycle. Alternately, generations can overlap, with breeding candidates considered for selection as parents for multiple cycles. With recurrent genomic selection but not phenotypic selection, candidates can be re-evaluated by using genomic estimated breeding values without additional phenotyping of the candidates themselves. Therefore, it may be that candidates with true high breeding values discarded in one cycle due to underestimation of breeding value could be identified and selected in subsequent cycles. The consequences of allowing generations to overlap in recurrent selection are unknown. We assessed whether maintaining overlapping and discrete generations led to differences in genetic gain for phenotypic, genomic truncation, and genomic optimum contribution recurrent selection by stochastic simulation.ResultsWith phenotypic selection, overlapping generations led to decreased genetic gain compared to discrete generations due to increased selection error bias. Selected individuals, which were in the upper tail of the distribution of phenotypic values, tended to also have high absolute error relative to their true breeding value compared to the overall population. Without repeated phenotyping, these individuals erroneously believed to have high value were repeatedly selected across cycles, leading to decreased genetic gain. With genomic truncation selection, overlapping and discrete generations performed similarly as updating breeding values precluded repeatedly selecting individuals with inaccurately high estimates of breeding values in subsequent cycles. Overlapping generations did not outperform discrete generations in the presence of a positive genetic trend with genomic truncation selection, as individuals from previous breeding cycles typically had truly lower breeding values than candidates from the current generation. With genomic optimum contribution selection, overlapping and discrete generations performed similarly, but overlapping generations slightly outperformed discrete generations in the long term if the targeted inbreeding rate was extremely low.ConclusionMaintaining discrete generations in recurrent phenotypic selection leads to increased genetic gain, especially at low heritabilities, by preventing selection error bias. With genomic truncation selection and genomic optimum contribution selection, genetic gain does not differ between discrete and overlapping generations assuming non-genetic effects are not present. Overlapping generations may increase genetic gain in the long term with very low targeted rates of inbreeding in genomic optimum contribution selection.

Amoebic gill disease (AGD) is one of the largest threats to salmon aquaculture, causing serious economic and animal welfare burden. Treatments can be expensive and environmentally damaging, hence the need for alternative strategies. Breeding for disease resistance can contribute to prevention and control of AGD, providing long-term cumulative benefits in selected stocks. The use of genomic selection can expedite selection for disease resistance due to improved accuracy compared to pedigree-based approaches. The aim of this work was to quantify and characterize genetic variation in AGD resistance in salmon, the genetic architecture of the trait, and the potential of genomic selection to contribute to disease control. An AGD challenge was performed in ∼1,500 Atlantic salmon, using gill damage and amoebic load as indicator traits for host resistance. Both traits are heritable (h2 ∼0.25-0.30) and show high positive correlation, indicating they may be good measurements of host resistance to AGD. While the genetic architecture of resistance appeared to be largely polygenic in nature, two regions on chromosome 18 showed suggestive association with both AGD resistance traits. Using a cross-validation approach, genomic prediction accuracy was up to 18% higher than that obtained using pedigree, and a reduction in marker density to ∼2,000 SNPs was sufficient to obtain accuracies similar to those obtained using the whole dataset. This study indicates that resistance to AGD is a suitable trait for genomic selection, and the addition of this trait to Atlantic salmon breeding programs can lead to more resistant stocks.

Next-generation molecular tools with AI integration can accelerate genetic gain in sugarcane by enhancing variation, accuracy, and efficiency, enabling rapid development of high-yielding, high-quality, and climate-resilient varieties. Enhancing genetic gain is essential for sustainable sugar and bioenergy production, especially amid growing global reliance on renewable energy sources. Sugarcane and its byproducts serve as important feedstocks for both first and second-generation biofuels, and face several breeding challenges due to its genetic complexity, extended breeding cycles, and strong environmental interactions. The breeder's equation offers a quantitative framework to accelerate genetic improvement by optimizing four key components: additive genetic variation (σa), heritability (h2), selection intensity (i), and breeding cycle length (L). Additive genetic variation can be enhanced through genome-wide exploration, including genome, pan-genome, and super pangenome analyses, gene discovery, characterization, and the induction of novel variations. Precise estimation of heritability in sugarcane can be achieved through the large-scale characterization of germplasm, high-throughput phenotyping, and detailed genotype × environment interaction (G × E) studies. Selection intensity can be increased by expanding population sizes via genotypic, genomic, and invitro selection, leveraging the law of large numbers, and adopting technologies that provide greater throughput, precision, and cost efficiency. Breeding cycle time can be significantly reduced using tools, such as marker-assisted selection, genomic selection, emerging doubled haploid strategies (though still challenging in polyploid crops like sugarcane), speed breeding, transgenic approaches, and genome editing technologies like CRISPR/Cas9 (including base and prime editing), TALENs. This review provides a comprehensive overview of each component of the breeder's equation in sugarcane breeding and highlights next-generation molecular strategies and tools aligned to these components. The integration of these advanced tools with artificial intelligence holds immense potential to enhance genetic gain and accelerate the development of high-yielding, high-quality, and climate-resilient sugarcane varieties.

Genomic selection empirically appeared valuable for reciprocal recurrent selection in oil palm as it could account for family effects and Mendelian sampling terms, despite small populations and low marker density. Genomic selection (GS) can increase the genetic gain in plants. In perennial crops, this is expected mainly through shortened breeding cycles and increased selection intensity, which requires sufficient GS accuracy in selection candidates, despite often small training populations. Our objective was to obtain the first empirical estimate of GS accuracy in oil palm (Elaeis guineensis), the major world oil crop. We used two parental populations involved in conventional reciprocal recurrent selection (Deli and Group B) with 131 individuals each, genotyped with 265 SSR. We estimated within-population GS accuracies when predicting breeding values of non-progeny-tested individuals for eight yield traits. We used three methods to sample training sets and five statistical methods to estimate genomic breeding values. The results showed that GS could account for family effects and Mendelian sampling terms in Group B but only for family effects in Deli. Presumably, this difference between populations originated from their contrasting breeding history. The GS accuracy ranged from -0.41 to 0.94 and was positively correlated with the relationship between training and test sets. Training sets optimized with the so-called CDmean criterion gave the highest accuracies, ranging from 0.49 (pulp to fruit ratio in Group B) to 0.94 (fruit weight in Group B). The statistical methods did not affect the accuracy. Finally, Group B could be preselected for progeny tests by applying GS to key yield traits, therefore increasing the selection intensity. Our results should be valuable for breeding programs with small populations, long breeding cycles, or reduced effective size.

Open-central canopy (OCC) has become the popular tree shape for arboreal apple orchards in full fruit period in the Loess Plateau of China. This study aimed to elucidate the physiological regulation mechanisms related to planting density optimization, photosynthetic performance enhancement, and fruit quality improvement in dense-canopy OCC apple orchards in the Loess Plateau of China through tree thinning practices at different intensities. This study used three planting densities of OCC apple trees as experimental materials. These include no thinning [NT, 3 m (plant) × 4 m (row)], low-intensity thinning [LT, i.e., first thinning based on NT: 4 m (plant) × 6 m (row)], and high-intensity thinning [HT, i.e., second thinning based on LT: 6 m (plant) × 8 m (row)]. In three successive experimental years, changes in spatiotemporal variations of photosynthetically active radiation ( PAR ) intercepted by canopy, leaf gas exchange, gross photosynthetic rate ( P g ), photorespiration rate ( P r ), nonphotochemical quenching ( NPQ ) dark relaxation curves, antioxidant enzyme activity [ascorbate peroxidase, catalase and superoxide dismutase (SOD)], and fruit quality were carefully measured and compared. Results showed that compared with NT, LT increased PAR by 36%, maximum net photosynthetic rate under light saturation ( P nmax,P ) by 30%, carboxylation efficiency ( CE ) by 37%, fruit soluble solid content (SSC) by 4%, and peel anthocyanin content (AC) by 20%. Moreover, LT enhanced the photoprotective capacity through elevated ratio of P r to P g under PAR of 2 000 μmol · m −2 · s −1 (52%), the reversible component in NPQ [r( q E), 9%], and antioxidant enzymes (SOD 10%). Compared with NT, HT enhanced PAR by 71%, P nmax,P by 67%, CE by 65%, SSC by 9%, and AC by 37% and boosted photoprotection through increased P r / P g (116%), r( q E) (11%), and SOD activity (29%). The Pearson Correlation Coefficients between PAR and the fruit quality indices (including single fruit weight, SSC, and AC) were 0.89, 0.73, and 0.96, respectively. Single fruit weight and SSC were significantly and positively correlated with photosynthetic parameters (such as P nmax,P and CE ). In summary, after tree thinning, the light environment in the OCC of apple trees was remarkably improved, and the leaf photosynthetic and photoprotective capacities were enhanced, thus significantly boosting fruit qualities. In terms of the dose responses, high-intensity thinning demonstrated the most significant enhanced effects.

Forests face many threats. While traditional breeding may be too slow to deliver well-adapted trees, genomic selection (GS) can accelerate the process. We describe a comprehensive study of GS from proof of concept to operational application in western redcedar (WRC, Thuja plicata). Using genomic data, we developed models on a training population (TrP) of trees to predict breeding values (BVs) in a target seedling population (TaP) for growth, heartwood chemistry, and foliar chemistry traits. We used cross-validation to assess prediction accuracy (PACC) in the TrP; we also validated models for early-expressed foliar traits in the TaP. Prediction accuracy was high across generations, environments, and ages. PACC was not reduced to zero among unrelated individuals in TrP and was only slightly reduced in the TaP, confirming strong linkage disequilibrium and the ability of the model to generate accurate predictions across breeding generations. Genomic BV predictions were correlated with those from pedigree but displayed a wider range of within-family variation due to the ability of GS to capture the Mendelian sampling term. Using predicted TaP BVs in multi-trait selection, we functionally implemented and integrated GS into an operational tree-breeding program.