Multi-objective optimized genomic breeding strategies for sustainable food improvement.
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ABSTRACT:
SUBMITTER: Akdemir D
PROVIDER: S-EPMC6461918 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Key messageA breeding strategy combining genomic with one-stage phenotypic selection maximizes annual selection gain for net merit. Choice of the selection index strongly affects the selection gain expected in individual traits. Selection indices using genomic information have been proposed in crop-specific scenarios. Routine use of genomic selection (GS) for simultaneous improvement of multiple traits requires information about the impact of the available economic and logistic resources and genetic properties (variances, trait correlations, and prediction accuracies) of the breeding population on the expected selection gain. We extended the R package "selectiongain" from single trait to index selection to optimize and compare breeding strategies for simultaneous improvement of two traits. We focused on the expected annual selection gain (ΔGa) for traits differing in their genetic correlation, economic weights, variance components, and prediction accuracies of GS. For all scenarios considered, breeding strategy GSrapid (one-stage GS followed by one-stage phenotypic selection) achieved higher ΔGa than classical two-stage phenotypic selection, regardless of the index chosen to combine the two traits and the prediction accuracy of GS. The Smith-Hazel or base index delivered higher ΔGa for net merit and individual traits compared to selection by independent culling levels, whereas the restricted index led to lower ΔGa in net merit and divergent results for selection gain of individual traits. The differences among the indices depended strongly on the correlation of traits, their variance components, and economic weights, underpinning the importance of choosing the selection indices according to the goal of the breeding program. We demonstrate our theoretical derivations and extensions of the R package "selectiongain" with an example from hybrid wheat by designing indices to simultaneously improve grain yield and grain protein content or sedimentation volume.
| S-EPMC8580912 | biostudies-literature
Project description:We consider the verification of multiple expected reward objectives at once on Markov decision processes (MDPs). This enables a trade-off analysis among multiple objectives by obtaining a Pareto front. We focus on strategies that are easy to employ and implement. That is, strategies that are pure (no randomization) and have bounded memory. We show that checking whether a point is achievable by a pure stationary strategy is NP-complete, even for two objectives, and we provide an MILP encoding to solve the corresponding problem. The bounded memory case is treated by a product construction. Experimental results using Storm and Gurobi show the feasibility of our algorithms.
| S-EPMC7439746 | biostudies-literature
Project description:Climate change during the last 40 years has had a serious impact on agriculture and threatens global food and nutritional security. From over half a million plant species, cereals and legumes are the most important for food and nutritional security. Although systematic plant breeding has a relatively short history, conventional breeding coupled with advances in technology and crop management strategies has increased crop yields by 56 % globally between 1965-85, referred to as the Green Revolution. Nevertheless, increased demand for food, feed, fiber, and fuel necessitates the need to break existing yield barriers in many crop plants. In the first decade of the 21st century we witnessed rapid discovery, transformative technological development and declining costs of genomics technologies. In the second decade, the field turned towards making sense of the vast amount of genomic information and subsequently moved towards accurately predicting gene-to-phenotype associations and tailoring plants for climate resilience and global food security. In this review we focus on genomic resources, genome and germplasm sequencing, sequencing-based trait mapping, and genomics-assisted breeding approaches aimed at developing biotic stress resistant, abiotic stress tolerant and high nutrition varieties in six major cereals (rice, maize, wheat, barley, sorghum and pearl millet), and six major legumes (soybean, groundnut, cowpea, common bean, chickpea and pigeonpea). We further provide a perspective and way forward to use genomic breeding approaches including marker-assisted selection, marker-assisted backcrossing, haplotype based breeding and genomic prediction approaches coupled with machine learning and artificial intelligence, to speed breeding approaches. The overall goal is to accelerate genetic gains and deliver climate resilient and high nutrition crop varieties for sustainable agriculture.
| S-EPMC7903322 | biostudies-literature
Project description:BackgroundMiscanthus has potential as a biomass crop but the development of varieties that are consistently superior to the natural hybrid M. × giganteus has been challenging, presumably because of strong G × E interactions and poor knowledge of the complex genetic architectures of traits underlying biomass productivity and climatic adaptation. While linkage and association mapping studies are starting to generate long lists of candidate regions and even individual genes, it seems unlikely that this information can be translated into effective marker-assisted selection for the needs of breeding programmes. Genomic selection has emerged as a viable alternative, and prediction accuracies are moderate across a range of phenological and morphometric traits in Miscanthus, though relatively low for biomass yield per se.MethodsWe have previously proposed a combination of index selection and genomic prediction as a way of overcoming the limitations imposed by the inherent complexity of biomass yield. Here we extend this approach and illustrate its potential to achieve multiple breeding targets simultaneously, in the absence of a priori knowledge about their relative economic importance, while also monitoring correlated selection responses for non-target traits. We evaluate two hypothetical scenarios of increasing biomass yield by 20 % within a single round of selection. In the first scenario, this is achieved in combination with delaying flowering by 44 d (roughly 20 %), whereas, in the second, increased yield is targeted jointly with reduced lignin (-5 %) and increased cellulose (+5 %) content, relative to current average levels in the breeding population.Key resultsIn both scenarios, the objectives were achieved efficiently (selection intensities corresponding to keeping the best 20 and 4 % of genotypes, respectively). However, the outcomes were strikingly different in terms of correlated responses, and the relative economic values (i.e. value per unit of change in each trait compared with that for biomass yield) of secondary traits included in selection indices varied considerably.ConclusionsAlthough these calculations rely on multiple assumptions, they highlight the need to evaluate breeding objectives and explicitly consider correlated responses in silico, prior to committing extensive resources. The proposed approach is broadly applicable for this purpose and can readily incorporate high-throughput phenotyping data as part of integrated breeding platforms.
| S-EPMC6821339 | biostudies-literature
Project description:The major drawback to the implementation of genomic selection in a breeding program lies in long-term decrease in additive genetic variance, which is a trade-off for rapid genetic improvement in short term. Balancing increase in genetic gain with retention of additive genetic variance necessitates careful optimization of this trade-off. In this study, we proposed an integrated index selection approach within the genomic inferred cross-selection (GCS) framework to maximize genetic gain across multiple traits. With this method, we identified optimal crosses that simultaneously maximize progeny performance and maintain genetic variance for multiple traits. Using a stochastic simulated recurrent breeding program over a 40-years period, we evaluated different GCS methods along with other factors, such as the number of parents, crosses, and progeny per cross, that influence genetic gain in a pulse crop breeding program. Across all breeding scenarios, the posterior mean variance consistently enhances genetic gain when compared to other methods, such as the usefulness criterion, optimal haploid value, mean genomic estimated breeding value, and mean index selection value of the superior parents. In addition, we provide a detailed strategy to optimize the number of parents, crosses, and progeny per cross that can potentially maximize short- and long-term genetic gain in a public breeding program.
| S-EPMC11367796 | biostudies-literature
Project description:BackgroundThe narrow genetic base of elite germplasm compromises long-term genetic gain and increases the vulnerability to biotic and abiotic stresses in unpredictable environmental conditions. Therefore, an efficient strategy is required to broaden the genetic base of commercial breeding programs while not compromising short-term variety release. Optimal cross selection aims at identifying the optimal set of crosses that balances the expected genetic value and diversity. We propose to consider genomic selection and optimal cross selection to recurrently improve genetic resources (i.e. pre-breeding), to bridge the improved genetic resources with elites (i.e. bridging), and to manage introductions into the elite breeding population. Optimal cross selection is particularly adapted to jointly identify bridging, introduction and elite crosses to ensure an overall consistency of the genetic base broadening strategy.ResultsWe compared simulated breeding programs introducing donors with different performance levels, directly or indirectly after bridging. We also evaluated the effect of the training set composition on the success of introductions. We observed that with recurrent introductions of improved donors, it is possible to maintain the genetic diversity and increase mid- and long-term performances with only limited penalty at short-term. Considering a bridging step yielded significantly higher mid- and long-term genetic gain when introducing low performing donors. The results also suggested to consider marker effects estimated with a broad training population including donor by elite and elite by elite progeny to identify bridging, introduction and elite crosses.ConclusionResults of this study provide guidelines on how to harness polygenic variation present in genetic resources to broaden elite germplasm.
| S-EPMC7216646 | biostudies-literature
Project description:A revival of interest in traditional varieties reflects the change in consumer preferences and the greater awareness of the quality of locally grown products. As ancient cultivars, heirlooms have been selected for decades in specific habitats and represent nowadays potential germplasm sources to consider for breeding high-quality products and cultivation in sustainable agriculture. In this study, 60 heirloom tomato (Solanum lycopersicum L.) accessions, including diverse varietal types (beefsteak, globe, oxheart, plum, and cherry), were profiled over two seasons for the main chemical and biochemical fruit traits. A medium-high level of heritability was found for all traits ranging from 0.52 for soluble solids to 0.99 for fruit weight. The average content of ascorbic acid was ~31 mg 100 g-1 of fw in both seasons, while the greatest variability was found for carotenoids with peaks of 245.65 μg g-1 of fw for total lycopene and 32.29 μg g-1 of fw for β-carotene. Dissection of genotypic (G) and seasonal (Y) factors highlighted genotype as the main source of variation for all traits. No significant effect of Y and G × Y was found for ascorbic acid and fruit weight, respectively, whereas a high influence of Y was found on the variation of lycopene. Molecular fingerprinting was performed using the 10K SolCAP array, yielding a total of 7,591 SNPs. Population structure, phylogenetic relationships, and principal components analysis highlighted a differentiation of plum and cherry genotypes with respect to the beefsteak and globe types. These results were confirmed by multivariate analysis of phenotypic traits, shedding light on how breeding and selection focused on fruit characteristics have influenced the genetic and phenotypic makeup of heirlooms. Marker-trait association showed 11 significantly associated loci for β-carotene and fruit weight. For β-carotene, a single variant on chromosome 8 was found at 12 kb to CCD8, a cleavage dioxygenase playing a key role in the biosynthesis of apocarotenoids. For fruit weight, a single association was located at less than 3 Mbp from SLSUN31 and fw11.3, two candidates involved in the increasing of fruit mass. These results highlight the potentiality of heirlooms for genetic improvement and candidate gene identification.
| S-EPMC9846345 | biostudies-literature
Project description:The many quantitative traits of interest to plant breeders are often genetically correlated, which can complicate progress from selection. Improving multiple traits may be enhanced by identifying parent combinations - an important breeding step - that will deliver more favorable genetic correlations (rG ). Modeling the segregation of genomewide markers with estimated effects may be one method of predicting rG in a cross, but this approach remains untested. Our objectives were to: (i) use simulations to assess the accuracy of genomewide predictions of rG and the long-term response to selection when selecting crosses on the basis of such predictions; and (ii) empirically measure the ability to predict genetic correlations using data from a barley (Hordeum vulgare L.) breeding program. Using simulations, we found that the accuracy to predict rG was generally moderate and influenced by trait heritability, population size, and genetic correlation architecture (i.e., pleiotropy or linkage disequilibrium). Among 26 barley breeding populations, the empirical prediction accuracy of rG was low (-0.012) to moderate (0.42), depending on trait complexity. Within a simulated plant breeding program employing indirect selection, choosing crosses based on predicted rG increased multi-trait genetic gain by 11-27% compared to selection on the predicted cross mean. Importantly, when the starting genetic correlation was negative, such cross selection mitigated or prevented an unfavorable response in the trait under indirect selection. Prioritizing crosses based on predicted genetic correlation can be a feasible and effective method of improving unfavorably correlated traits in breeding programs.
| S-EPMC6778794 | biostudies-literature
Project description:Instrumental insemination of honeybees allows for two opposing breeding strategies. In single colony insemination (SCI), all drones to inseminate a queen are taken from one colony. In pooled semen insemination (PSI), sperm of many genetically diverse drones is mixed and queens are fertilized from the resulting drone pool. While SCI allows for maximum pedigree control, proponents of PSI claim to reduce inbreeding and maintain genetic variance. Using stochastic simulation studies, we compared genetic progress and inbreeding rates in small honeybee populations under SCI and PSI. Four different selection criteria were covered: estimated breeding values (EBV), phenotypes, true breeding values (TBV) and random selection. Under EBV-based truncation selection, SCI yielded 9.0% to 44.4% higher genetic gain than PSI, but had vastly increased inbreeding rates. Under phenotypical or TBV selection, the gap between SCI and PSI in terms of genetic progress narrowed. Throughout, PSI yielded lower inbreeding rates than SCI, but the differences were only substantial under EBV truncation selection. As a result, PSI did not appear as a viable breeding strategy owing to its incompatibility with modern methods of genetic evaluation. Instead, SCI is to be preferred but instead of strict truncation selection, strategies to avoid inbreeding need to be installed.
| S-EPMC10827426 | biostudies-literature
Project description:Rapid advance in single-cell RNA sequencing (scRNA-seq) allows measurement of the expression of genes at single-cell resolution in complex disease or tissue. While many methods have been developed to detect cell clusters from the scRNA-seq data, this task currently remains a main challenge. We proposed a multi-objective optimization-based fuzzy clustering approach for detecting cell clusters from scRNA-seq data. First, we conducted initial filtering and SCnorm normalization. We considered various case studies by selecting different cluster numbers ( c l = 2 to a user-defined number), and applied fuzzy c-means clustering algorithm individually. From each case, we evaluated the scores of four cluster validity index measures, Partition Entropy ( P E ), Partition Coefficient ( P C ), Modified Partition Coefficient ( M P C ), and Fuzzy Silhouette Index ( F S I ). Next, we set the first measure as minimization objective (?) and the remaining three as maximization objectives (?), and then applied a multi-objective decision-making technique, TOPSIS, to identify the best optimal solution. The best optimal solution (case study) that had the highest TOPSIS score was selected as the final optimal clustering. Finally, we obtained differentially expressed genes (DEGs) using Limma through the comparison of expression of the samples between each resultant cluster and the remaining clusters. We applied our approach to a scRNA-seq dataset for the rare intestinal cell type in mice [GEO ID: GSE62270, 23,630 features (genes) and 288 cells]. The optimal cluster result (TOPSIS optimal score= 0.858) comprised two clusters, one with 115 cells and the other 91 cells. The evaluated scores of the four cluster validity indices, F S I , P E , P C , and M P C for the optimized fuzzy clustering were 0.482, 0.578, 0.607, and 0.215, respectively. The Limma analysis identified 1240 DEGs (cluster 1 vs. cluster 2). The top ten gene markers were Rps21, Slc5a1, Crip1, Rpl15, Rpl3, Rpl27a, Khk, Rps3a1, Aldob and Rps17. In this list, Khk (encoding ketohexokinase) is a novel marker for the rare intestinal cell type. In summary, this method is useful to detect cell clusters from scRNA-seq data.
| S-EPMC6723724 | biostudies-literature