Sorghum Pan-Genome Explores the Functional Utility for Genomic-Assisted Breeding to Accelerate the Genetic Gain.
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ABSTRACT:
SUBMITTER: Ruperao P
PROVIDER: S-EPMC8204017 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Young breeding programs in developing countries, like the Chibas sorghum breeding program in Haiti, face the challenge of increasing genetic gain with limited resources. Implementing genomic selection (GS) could increase genetic gain, but optimization of GS is needed to account for these programs' unique challenges and advantages. Here, we used simulations to identify conditions under which genomic-assisted recurrent selection (GARS) would be more effective than phenotypic recurrent selection (PRS) in small new breeding programs. We compared genetic gain, cost per unit gain, genetic variance, and prediction accuracy of GARS (two or three cycles per year) vs. PRS (one cycle per year) assuming various breeding population sizes and trait genetic architectures. For oligogenic architecture, the maximum relative genetic gain advantage of GARS over PRS was 12-88%, which was observed only during the first few cycles. For the polygenic architecture, GARS provided maximum relative genetic gain advantage of 26-165%, and was always superior to PRS. Average prediction accuracy declines substantially after several cycles of selection, suggesting the prediction models should be updated regularly. Updating prediction models every year increased the genetic gain by up to 33-39% compared to no-update scenarios. For small populations and oligogenic traits, cost per unit gain was lower in PRS than GARS. However, with larger populations and polygenic traits cost per unit gain was up to 67% lower in GARS than PRS. Collectively, the simulations suggest that GARS could increase the genetic gain in small young breeding programs by accelerating the breeding cycles and enabling evaluation of larger populations.
| S-EPMC6385988 | biostudies-other
Project description:Genome-enabled biotechnologies have the potential to accelerate breeding efforts in long-lived perennial crop species. Despite the transformative potential of molecular tools in pecan and other outcrossing tree species, highly heterozygous genomes, significant presence-absence gene content variation, and histories of interspecific hybridization have constrained breeding efforts. To overcome these challenges, here, we present diploid genome assemblies and annotations of four outbred pecan genotypes, including a PacBio HiFi chromosome-scale assembly of both haplotypes of the 'Pawnee' cultivar. Comparative analysis and pan-genome integration reveal substantial and likely adaptive interspecific genomic introgressions, including an over-retained haplotype introgressed from bitternut hickory into pecan breeding pedigrees. Further, by leveraging our pan-genome presence-absence and functional annotation database among genomes and within the two outbred haplotypes of the 'Lakota' genome, we identify candidate genes for pest and pathogen resistance. Combined, these analyses and resources highlight significant progress towards functional and quantitative genomics in highly diverse and outbred crops.
| S-EPMC8257795 | biostudies-literature
Project description:BackgroundCrop biofortification is a successful strategy to ameliorate Vitamin A deficiency. Sorghum is a good candidate for vitamin A biofortification, as it is a staple food in regions with high prevalence of vitamin A deficiency. β-carotene-the main provitamin A carotenoid-is below the target concentration in sorghum grain, therefore biofortification breeding is required. Previous studies found evidence that sorghum carotenoid variation is oligogenic, suggesting that marker-assisted selection can be an appropriate biofortification method. However, we hypothesize that sorghum carotenoids have both oligogenic and polygenic components of variation. Genomics-assisted breeding could accelerate breeding efforts, but there exists knowledge gaps in the genetics underlying carotenoid variation, as well as appropriate germplasm to serve as donors.ResultsIn this study, we characterized carotenoids in 446 accessions from the sorghum association panel and carotenoid panel using high-performance liquid chromatography, finding high carotenoid accessions not previously identified. Genome-wide association studies conducted with 345 accessions, confirmed that zeaxanthin epoxidase is a major gene underlying variation for not only zeaxanthin, but also lutein and β-carotene. High carotenoid lines were found to have limited genetic diversity, and originated predominantly from only one country. Potential novel genetic diversity for carotenoid content was identified through genomic predictions in 2,495 accessions of unexplored germplasm. Oligogenic variation of carotenoids was confirmed, as well as evidence for polygenic variation, suggesting both marker-assisted selection and genomic selection can facilitate breeding efforts.ConclusionsSorghum vitamin A biofortification could be beneficial for millions of people who rely on it as a dietary staple. Carotenoid content in sorghum is low, but high heritability suggests that increasing concentrations through breeding is possible. Low genetic diversity among high carotenoid lines might be the main limitation for breeding efforts, therefore further germplasm characterization is needed to assess the feasibility of biofortification breeding. Based on germplasm here evaluated, most countries' germplasm lacks high carotenoid alleles, thus pre-breeding will be needed. A SNP marker within the zeaxanthin epoxidase gene was identified as a good candidate for use in marker-assisted selection. Due to the oligogenic and polygenic variation of sorghum grain carotenoids, both marker-assisted selection and genomic selection can be employed to accelerate breeding efforts.
| S-EPMC10045421 | biostudies-literature
Project description:Under-utilised orphan crops hold the key to diversified and climate-resilient food systems. Here, we report on orphan crop genomics using the case of Lablab purpureus (L.) Sweet (lablab) - a legume native to Africa and cultivated throughout the tropics for food and forage. Our Africa-led plant genome collaboration produces a high-quality chromosome-scale assembly of the lablab genome. Our assembly highlights the genome organisation of the trypsin inhibitor genes - an important anti-nutritional factor in lablab. We also re-sequence cultivated and wild lablab accessions from Africa confirming two domestication events. Finally, we examine the genetic and phenotypic diversity in a comprehensive lablab germplasm collection and identify genomic loci underlying variation of important agronomic traits in lablab. The genomic data generated here provide a valuable resource for lablab improvement. Our inclusive collaborative approach also presents an example that can be explored by other researchers sequencing indigenous crops, particularly from low and middle-income countries (LMIC).
| S-EPMC10110558 | biostudies-literature
Project description:Dwarfing and the selection of optimal plant types constitute the primary focus of sorghum breeding. However, the lack of clarity regarding the gene types associated with plant height genes Dw1-Dw4 in the primary breeding materials has led to increased plant heights in improved offspring of the same plant height type, resulting in unsatisfactory morphological traits. This study aimed to elucidate the gene types related to plant height in breeding materials, validate the regulatory mechanisms, and establish a material improvement system. The goal was to achieve molecular-marker-assisted dwarf breeding through the detection of plant height genes and the test cross verification of main Chinese sorghum materials. Using 38 main male sterile lines and 57 main restorer lines of grain sorghum as materials, three plant height genes were detected and classified. Ninety-five F1 generation hybrids of these materials, along with typical materials, were measured at the wax maturity stage. Test cross results demonstrated that the variation in dw1-dw3 genes in the breeding materials significantly influenced the plant height of hybrid offspring. The main male sterile lines in Chinese sorghum predominantly exhibited the "three-dwarf" type of Kafir and its improved lines, characterized by the genotype (Dw1-Dw2-dw3-dw4). On the other hand, restorer lines mainly showcased the improved "two-dwarf" (Dw1-Dw2-dw3-dw4) genotype of the Kaoliang/Caudatum subspecies, along with the "three-dwarf" type of some Kafir and its improved lines. The test materials predominantly contained dw3 genes, with relatively fewer dw1 genes in the restorer lines. The primary restorer materials lacked the dw2 gene, and dw2 significantly influenced plant type. The increased plant height in improved offspring of the same plant height type material was attributed to differences in gene types. Therefore, the enhancement of plant height in breeding materials should prioritize the use of different methods in conjunction with Dw1 and Dw2 classification.
| S-EPMC10815169 | biostudies-literature
Project description:Breeding schemes that utilize modern breeding methods like genomic selection (GS) and speed breeding (SB) have the potential to accelerate genetic gain for different crops. We investigated through stochastic computer simulation the advantages and disadvantages of adopting both GS and SB (SpeedGS) into commercial breeding programs for allogamous crops. In addition, we studied the effect of omitting one or two selection stages from the conventional phenotypic scheme on GS accuracy, genetic gain, and inbreeding. As an example, we simulated GS and SB for five traits (heading date, forage yield, seed yield, persistency, and quality) with different genetic architectures and heritabilities (0.7, 0.3, 0.4, 0.1, and 0.3; respectively) for a tall fescue breeding program. We developed a new method to simulate correlated traits with complex architectures of which effects can be sampled from multiple distributions, e.g. to simulate the presence of both minor and major genes. The phenotypic selection scheme required 11 years, while the proposed SpeedGS schemes required four to nine years per cycle. Generally, SpeedGS schemes resulted in higher genetic gain per year for all traits especially for traits with low heritability such as persistency. Our results showed that running more SB rounds resulted in higher genetic gain per cycle when compared to phenotypic or GS only schemes and this increase was more pronounced per year when cycle time was shortened by omitting cycle stages. While GS accuracy declined with additional SB rounds, the decline was less in round three than in round two, and it stabilized after the fourth SB round. However, more SB rounds resulted in higher inbreeding rate, which could limit long-term genetic gain. The inbreeding rate was reduced by approximately 30% when generating the initial population for each cycle through random crosses instead of generating half-sib families. Our study demonstrated a large potential for additional genetic gain from combining GS and SB. Nevertheless, methods to mitigate inbreeding should be considered for optimal utilization of these highly accelerated breeding programs.
| S-EPMC6873660 | biostudies-literature
Project description:Key messageEarly generation genomic selection is superior to conventional phenotypic selection in line breeding and can be strongly improved by including additional information from preliminary yield trials. The selection of lines that enter resource-demanding multi-environment trials is a crucial decision in every line breeding program as a large amount of resources are allocated for thoroughly testing these potential varietal candidates. We compared conventional phenotypic selection with various genomic selection approaches across multiple years as well as the merit of integrating phenotypic information from preliminary yield trials into the genomic selection framework. The prediction accuracy using only phenotypic data was rather low (r = 0.21) for grain yield but could be improved by modeling genetic relationships in unreplicated preliminary yield trials (r = 0.33). Genomic selection models were nevertheless found to be superior to conventional phenotypic selection for predicting grain yield performance of lines across years (r = 0.39). We subsequently simplified the problem of predicting untested lines in untested years to predicting tested lines in untested years by combining breeding values from preliminary yield trials and predictions from genomic selection models by a heritability index. This genomic assisted selection led to a 20% increase in prediction accuracy, which could be further enhanced by an appropriate marker selection for both grain yield (r = 0.48) and protein content (r = 0.63). The easy to implement and robust genomic assisted selection gave thus a higher prediction accuracy than either conventional phenotypic or genomic selection alone. The proposed method took the complex inheritance of both low and high heritable traits into account and appears capable to support breeders in their selection decisions to develop enhanced varieties more efficiently.
| S-EPMC5263211 | biostudies-literature
Project description:Fusarium head blight (FHB) is a fungal disease of wheat (Triticum aestivum.L) that causes yield losses and produces mycotoxins which could easily exceed the limits of the EU regulations. Resistance to FHB has a complex genetic architecture and accurate evaluation in breeding programs is key to selecting resistant varieties. The Area Under the Disease Progress Curve (AUDPC) is one of the commonly metric used as a standard methodology to score FHB. Although efficient, AUDPC requires significant costs in phenotyping to cover the entire disease development pattern. Here, we show that there are more efficient alternatives to AUDPC (angle, growing degree days to reach 50% FHB severity, and FHB maximum variance) that reduce the number of field assessments required and allow for fair comparisons between unbalanced evaluations across trials. Furthermore, we found that the evaluation method that captures the maximum variance in FHB severity across plots is the most optimal approach for scoring FHB. In addition, results obtained on experimental data were validated on a simulated experiment where the disease progress curve was modeled as a sigmoid curve with known parameters and assessment protocols were fully controlled. Results show that alternative metrics tested in this study captured key components of quantitative plant resistance. Moreover, the new metrics could be a starting point for more accurate methods for measuring FHB in the field. For example, the optimal interval for FHB evaluation could be predicted using prior knowledge from historical weather data and FHB scores from previous trials. Finally, the evaluation methods presented in this study can reduce the FHB phenotyping burden in plant breeding with minimal losses on signal detection, resulting in a response variable available to use in data-driven analysis such as genome-wide association studies or genomic selection.
| S-EPMC9876611 | biostudies-literature
Project description:BackgroundImproving yield prediction and selection efficiency is critical for tree breeding. This is vital for macadamia trees with the time from crossing to production of new cultivars being almost a quarter of a century. Genomic selection (GS) is a useful tool in plant breeding, particularly with perennial trees, contributing to an increased rate of genetic gain and reducing the length of the breeding cycle. We investigated the potential of using GS methods to increase genetic gain and accelerate selection efficiency in the Australian macadamia breeding program with comparison to traditional breeding methods. This study evaluated the prediction accuracy of GS in a macadamia breeding population of 295 full-sib progeny from 32 families (29 parents, reciprocals combined), along with a subset of parents. Historical yield data for tree ages 5 to 8 years were used in the study, along with a set of 4113 SNP markers. The traits of focus were average nut yield from tree ages 5 to 8 years and yield stability, measured as the standard deviation of yield over these 4 years. GBLUP GS models were used to obtain genomic estimated breeding values for each genotype, with a five-fold cross-validation method and two techniques: prediction across related populations and prediction across unrelated populations.ResultsNarrow-sense heritability of yield and yield stability was low (h2 = 0.30 and 0.04, respectively). Prediction accuracy for yield was 0.57 for predictions across related populations and 0.14 when predicted across unrelated populations. Accuracy of prediction of yield stability was high (r = 0.79) for predictions across related populations. Predicted genetic gain of yield using GS in related populations was 474 g/year, more than double that of traditional breeding methods (226 g/year), due to the halving of generation length from 8 to 4 years.ConclusionsThe results of this study indicate that the incorporation of GS for yield into the Australian macadamia breeding program may accelerate genetic gain due to reduction in generation length, though the cost of genotyping appears to be a constraint at present.
| S-EPMC8139092 | biostudies-literature
Project description:We compare genomic selection methods that use correlated traits to help predict biomass yield in sorghum, and find that trait-assisted genomic selection performs best. Genomic selection (GS) is usually performed on a single trait, but correlated traits can also help predict a focal trait through indirect or multi-trait GS. In this study, we use a pre-breeding population of biomass sorghum to compare strategies that use correlated traits to improve prediction of biomass yield, the focal trait. Correlated traits include moisture, plant height measured at monthly intervals between planting and harvest, and the area under the growth progress curve. In addition to single- and multi-trait direct and indirect GS, we test a new strategy called trait-assisted GS, in which correlated traits are used along with marker data in the validation population to predict a focal trait. Single-trait GS for biomass yield had a prediction accuracy of 0.40. Indirect GS performed best using area under the growth progress curve to predict biomass yield, with a prediction accuracy of 0.37, and did not differ from indirect multi-trait GS that also used moisture information. Multi-trait GS and single-trait GS yielded similar results, indicating that correlated traits did not improve prediction of biomass yield in a standard GS scenario. However, trait-assisted GS increased prediction accuracy by up to [Formula: see text] when using plant height in both the training and validation populations to help predict yield in the validation population. Coincidence between selected genotypes in phenotypic and genomic selection was also highest in trait-assisted GS. Overall, these results suggest that trait-assisted GS can be an efficient strategy when correlated traits are obtained earlier or more inexpensively than a focal trait.
| S-EPMC5814553 | biostudies-literature