A dataset on multi-trait selection approaches for screening desirable wild relatives of wheat
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ABSTRACT:
SUBMITTER: Pour-Aboughadareh A
PROVIDER: S-EPMC8605208 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Wheat crop is very sensitive to osmotic stress conditions. As an abiotic stress, drought may exert a considerable effect on the levels of specialized metabolites in plants. These metabolites may exert beneficial biological activities in the prevention or treatment of disorders linked to oxidative stress in plants and humans. Furthermore, osmoprotector accumulation helps wheat to increase the maintenance of osmotic balance. Therefore, identifying wheat genotypes with better drought tolerance is extremely important. In this sense, this research aimed to understand agronomic, physiological and biochemical responses of spring wheat strains and cultivars to drought stress, under field conditions, and jointly select strains via multi-trait index. We evaluated agronomic, physiological and biochemical variables in 18 genotypes under field condition. The results demonstrated that all variables were affected by the drought. Most genotypes were significantly reduced in grain yield, except VI_14774, VI_14668, VI_9007 and TBIO_ATON. The variables related to photosynthesis were also affected. An increase above 800% was observed in proline contents in genotypes under drought. Sodium and potassium also increased, mainly for VI_131313 (Na), while VI_130758 and VI_14774 presented increased K. We evaluated the antioxidant potential of the different strains and the total content of phenolic compounds. The most drought-responsive genotypes were BRS_264, VI_14050 and VI_14426. Reduced grain yield and photosynthetic variables, and increased specialized metabolism compounds are due to plant defense mechanisms against drought conditions. Furthermore, variation in genotypes can be explained by the fact that each plant presents a different defense and tolerance mechanism, which may also occur between genotypes of the same species. Four strains were selected by the multivariate index: VI_14055, VI_14001, VI_14426 and VI_1466. Such results allow us to predict which genotype(s) performed best in semi-arid environments and under climatic fluctuations.
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Project description:We studied variation in adaptive traits and genetic association to understand the low P responses, including the symbiotic association of arbuscular mycorrhizal (AM) fungal colonization in Oryza species (O. sativa, O. nivara, and O. rufipogon). In the present experiment, we performed the phenotypic variability of the morphometric and geometric traits for P deficiency tolerance and conducted the association studies in GLM and MLM methods. A positive association between the geometric trait of the top-view area and root traits suggested the possibility of exploring a non-destructive approach in screening genotypes under low P. The AMOVA revealed a higher proportion of variation among the individuals as they belonged to different species of Oryza and the NM value was 2.0, indicating possible gene flow between populations. A sub-cluster with superior-performing accessions had a higher proportion of landraces (42.85%), and O. rufipogon (33.3%) was differentiated by four Pup1-specific markers. Association mapping identified seven notable markers (RM259, RM297, RM30, RM6966, RM242, RM184, and PAP1) and six potential genotypes (IC459373, Chakhao Aumbi, AC100219, AC100062, Sekri, and Kumbhi Phou), which will be helpful in the marker-assisted breeding to improve rice for P-deprived condition. In addition, total root surface area becomes a single major trait that helps in P uptake under deficit P up to 33% than mycorrhizal colonization. Further, the phenotypic analysis of the morphometric and geometric trait variations and their interactions provides excellent potential for selecting donors for improving P-use efficiency. The identified potential candidate genes and markers offered new insights into our understanding of the molecular and physiological mechanisms driving PUE and improving grain yield under low-P conditions.
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Project description:KEY MESSAGE:Simultaneous genomic selection for grain yield, protein content and dough rheological traits enables the development of resource-use efficient varieties that combine superior yield potential with comparably high end-use quality. Selecting simultaneously for grain yield and baking quality is a major challenge in wheat breeding, and several concepts like grain protein deviations have been developed for shifting the undesirable negative correlation between both traits. The protein quality is, however, not considered in these concepts, although it is an important aspect and might facilitate the selection of genotypes that use available resources more efficiently with respect to the quantity and quality of the final end products. A population of 480 lines from an applied wheat breeding programme that was phenotyped for grain yield, protein content, protein yield and dough rheological traits was thus used to assess the potential of using integrated genomic selection indices to ease selection decisions with regard to the plethora of quality traits. Additionally, the feasibility of achieving a simultaneous genetic improvement in grain yield, protein content and protein quality was investigated to develop more resource-use efficient varieties. Dough rheological traits related to either gluten strength or viscosity were combined in two separate indices, both of which showed a substantially smaller negative trade-off with grain yield than the protein content. Genomic selection indices based on regression deviations for the two latter traits were subsequently extended by the gluten strength or viscosity indices. They revealed a large merit for identifying resource-use efficient genotypes that combine both superior yield potential with comparably high end-use quality. Hence, genomic selection opens up the opportunity for multi-trait selection in early generations, which will most likely increase the efficiency when developing new and improved varieties.
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Project description:Key messageWe evaluated the potential of wheat wild relatives for the improvement in grain quality characteristics including micronutrients (Fe, Zn) and gluten and identified diploid wheats and the timopheevii lineage as the most promising resources. Domestication enabled the advancement of civilization through modification of plants according to human requirements. Continuous selection and cultivation of domesticated plants induced genetic bottlenecks. However, ancient diversity has been conserved in crop wild relatives. Wheat (Triticum aestivum L.; Triticum durum Desf.) is one of the most important staple foods and was among the first domesticated crop species. Its evolutionary diversity includes diploid, tetraploid and hexaploid species from the Triticum and Aegilops taxa and different genomes, generating an AA, BBAA/GGAA and BBAADD/GGAAAmAm genepool, respectively. Breeding and improvement in wheat altered its grain quality. In this review, we identified evolutionary patterns and the potential of wheat wild relatives for quality improvement regarding the micronutrients Iron (Fe) and Zinc (Zn), the gluten storage proteins α-gliadins and high molecular weight glutenin subunits (HMW-GS), and the secondary metabolite phenolics. Generally, the timopheevii lineage has been neglected to date regarding grain quality studies. Thus, the timopheevii lineage should be subject to grain quality research to explore the full diversity of the wheat gene pool.
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Project description:Since the first Genome-Wide Association Studies (GWAS), thousands of variant-trait associations have been discovered. However, the sample size required to detect additional variants using standard univariate association screening is increasingly prohibitive. Multi-trait GWAS offers a relevant alternative: it can improve statistical power and lead to new insights about gene function and the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been discussed, the strategy to select trait, among overwhelming possibilities, has been overlooked. In this study, we conducted extensive multi-trait tests using JASS (Joint Analysis of Summary Statistics) and assessed which genetic features of the analysed sets were associated with an increased detection of variants as compared to univariate screening. Our analyses identified multiple factors associated with the gain in the association detection in multi-trait tests. Together, these factors of the analysed sets are predictive of the gain of the multi-trait test (Pearson's ρ equal to 0.43 between the observed and predicted gain, P < 1.6 × 10-60). Applying an alternative multi-trait approach (MTAG, multi-trait analysis of GWAS), we found that in most scenarios but particularly those with larger numbers of traits, JASS outperformed MTAG. Finally, we benchmark several strategies to select set of traits including the prevalent strategy of selecting clinically similar traits, which systematically underperformed selecting clinically heterogenous traits or selecting sets that issued from our data-driven models. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outline practical strategies for multi-trait testing.
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Project description:The TaHOX-1 gene of common wheat Triticum aestivum L. (BAD-genome) encodes transcription factor (HD-Zip I) which is characterized by the presence of a DNA-binding homeodomain (HD) with an adjacent Leucine zipper (LZ) motif. This gene can play a role in adapting plant to a variety of abiotic stresses, such as drought, cold, salinity etc., which strongly affect wheat production. However, it's both functional role in stress resistance and divergence during wheat evolution has not yet been elucidated. This data in brief article is associated with the research paper "Structural and functional divergence of homoeologous copies of the TaHOX-1 gene in polyploid wheats and their diploid ancestors". The data set represents a recent survey of the primary HOX-1 gene sequences isolated from the first wheat allotetraploids (BA-genome) and their corresponding Triticum and Aegilops diploid relatives. Specifically, we provide detailed information about the HOX-1 nucleotide sequences of the promoter region and both nucleotide and amino acid sequences of the gene. The sequencing data used here is available at DDBJ/EMBL/GenBank under the accession numbers MG000630-MG000698.
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Project description:The dataset herein indicated the novelty of the article entitled "Dataset on the use of MGIDI in screening drought-tolerant wild wheat accessions at the early growth stage". Data were gathered during 2018-2019 on a set of wild wheat germplasm under two control and water deficit stress conditions. One hundred and forty-six accessions belonging to Ae. tauschii, Ae. cylindrica, and Ae. crassa were assessed under optimal glasshouse conditions to screen the drought-tolerant samples at the early growth stage. Nine drought tolerance and susceptibility indices along with the multi-trait genotype-ideotype distance index (MGIDI) were used to visualize the dataset. The obtained data can highlight the potential of the MGIDI index in accelerating screening of a large number of plant materials using multiple traits or selection indices in crop breeding programs, especially at the early growth stage.
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Project description:Plant breeders make selection decisions based on multiple traits, such as yield, plant height, flowering time, and disease resistance. A commonly used approach in multi-trait genomic selection is index selection, which assigns weights to different traits relative to their economic importance. However, classical index selection only optimizes genetic gain in the next generation, requires some experimentation to find weights that lead to desired outcomes, and has difficulty optimizing nonlinear breeding objectives. Multi-objective optimization has also been used to identify the Pareto frontier of selection decisions, which represents different trade-offs across multiple traits. We propose a new approach, which maximizes certain traits while keeping others within desirable ranges. Optimal selection decisions are made using a new version of the look-ahead selection (LAS) algorithm, which was recently proposed for single-trait genomic selection, and achieved superior performance with respect to other state-of-the-art selection methods. To demonstrate the effectiveness of the new method, a case study is developed using a realistic data set where our method is compared with conventional index selection. Results suggest that the multi-trait LAS is more effective at balancing multiple traits compared with index selection.
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Project description:A genome-wide association study (GWAS) for 10 yield and yield component traits was conducted using an association panel comprising 225 diverse spring wheat genotypes. The panel was genotyped using 10,904 SNPs and evaluated for three years (2016-2019), which constituted three environments (E1, E2 and E3). Heritability for different traits ranged from 29.21 to 97.69%. Marker-trait associations (MTAs) were identified for each trait using data from each environment separately and also using BLUP values. Four different models were used, which included three single trait models (CMLM, FarmCPU, SUPER) and one multi-trait model (mvLMM). Hundreds of MTAs were obtained using each model, but after Bonferroni correction, only 6 MTAs for 3 traits were available using CMLM, and 21 MTAs for 4 traits were available using FarmCPU; none of the 525 MTAs obtained using SUPER could qualify after Bonferroni correction. Using BLUP, 20 MTAs were available, five of which also figured among MTAs identified for individual environments. Using mvLMM model, after Bonferroni correction, 38 multi-trait MTAs, for 15 different trait combinations were available. Epistatic interactions involving 28 pairs of MTAs were also available for seven of the 10 traits; no epistatic interactions were available for GNPS, PH, and BYPP. As many as 164 putative candidate genes (CGs) were identified using all the 50 MTAs (CMLM, 3; FarmCPU, 9; mvLMM, 6, epistasis, 21 and BLUP, 11 MTAs), which ranged from 20 (CMLM) to 66 (epistasis) CGs. In-silico expression analysis of CGs was also conducted in different tissues at different developmental stages. The information generated through the present study proved useful for developing a better understanding of the genetics of each of the 10 traits; the study also provided novel markers for marker-assisted selection (MAS) to be utilized for the development of wheat cultivars with improved agronomic traits.Supplementary informationThe online version contains supplementary material available at 10.1007/s11032-021-01240-1.
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Project description:In agriculture, diversifying production implies picking up, in the wild biodiversity, species or populations that can be domesticated and fruitfully produced. Two alternative approaches are available to highlight wild candidate(s) with high suitability for aquaculture: the single-trait (i.e. considering a single phenotypic trait and, thus, a single biological function) and multi-trait (i.e. considering multiple phenotypic traits involved in several biological functions) approaches. Although the former is the traditional and the simplest method, the latter could be theoretically more efficient. However, an explicit comparison of advantages and pitfalls between these approaches is lacking to date in aquaculture. Here, we compared the two approaches to identify best candidate(s) between four wild allopatric populations of Perca fluviatilis in standardised aquaculture conditions. Our results showed that the single-trait approach can (1) miss key divergences between populations and (2) highlight different best candidate(s) depending on the trait considered. In contrast, the multi-trait approach allowed identifying the population with the highest domestication potential thanks to several congruent lines of evidence. Nevertheless, such an integrative assessment is achieved with a far more time-consuming and expensive study. Therefore, improvements and rationalisations will be needed to make the multi-trait approach a promising way in the aquaculture development.
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