Multi-trait selection in multi-environments for performance and stability in cassava genotypes
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ABSTRACT:
SUBMITTER: Sampaio Filho J
PROVIDER: S-EPMC10642803 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:Under global climate changes, understanding climate variables that are most associated with environmental kinships can contribute to improving the success of hybrid selection, mainly in environments with high climate variations. The main goal of this study is to integrate envirotyping techniques and multi-trait selection for mean performance and the stability of maize genotypes growing in the Huanghuaihai plain in China. A panel of 26 maize hybrids growing in 10 locations in two crop seasons was evaluated for 9 traits. Considering 20 years of climate information and 19 environmental covariables, we identified four mega-environments (ME) in the Huanghuaihai plain which grouped locations that share similar long-term weather patterns. All the studied traits were significantly affected by the genotype × mega-environment × year interaction, suggesting that evaluating maize stability using single-year, multi-environment trials may provide misleading recommendations. Counterintuitively, the highest yields were not observed in the locations with higher accumulated rainfall, leading to the hypothesis that lower vapor pressure deficit, minimum temperatures, and high relative humidity are climate variables that -under no water restriction- reduce plant transpiration and consequently the yield. Utilizing the multi-trait mean performance and stability index (MTMPS) prominent hybrids with satisfactory mean performance and stability across cultivation years were identified. G23 and G25 were selected within three out of the four mega-environments, being considered the most stable and widely adapted hybrids from the panel. The G5 showed satisfactory yield and stability across contrasting years in the drier, warmer, and with higher vapor pressure deficit mega-environment, which included locations in the Hubei province. Overall, this study opens the door to a more systematic and dynamic characterization of the environment to better understand the genotype-by-environment interaction in multi-environment trials.
| S-EPMC9702090 | biostudies-literature
Project description:Water is a scarce, strategic resource and the most important input for economic development, especially in agricultural countries such as Brazil. Cocoa production is directly related to water availability, and, as climate changes, selecting drought-tolerant genotypes is vital to keep cacao crops sustainable. Here, we evaluated cacao genotypes under irrigated and water-stressed conditions and selected drought-tolerant ones based on nutritional and physiological traits. Thirty-nine genotypes were monitored for three years for agronomic traits and higher fruit yield. After this evaluation, the 18 most promising genotypes were evaluated in a randomized block design, under a 2 (with and without irrigation) × 18 (genotypes) factorial arrangement, with three replicates and five plants per plot. We evaluated seven physiological and 11 nutritional traits, selecting genotypes based on the Genotype-by-Trait Biplot approach. Significant effects (p < 0.05) were observed for the nutritional traits N, P, Mg, S, Zn, Cu, Mn and for the physiological traits CO2 assimilation rate (A), stomatal conductance (gs), transpiration (E), intercellular and atmospheric CO2 concentrations (Ci/Ca), intrinsic water use efficiency (A/gs), instantaneous water use efficiency (A/E), and instantaneous carboxylation efficiency (A/Ci), as determined by analysis of variance. The genotype × irrigation treatment interaction was significant (p < 0.05) for the traits A, gs, and E. Genotypes CP 41, CP 43, and CCN 51 exhibited superior performance for both nutritional and physiological traits (A, gs, and E). In the irrigated environment, CP 41 showed superiority in traits such as P, A/E, A/gs, Mn, S, and Zn. Conversely, under non-irrigated conditions, CP 43 exhibited better performance in nutritional properties, specifically Mn, Mg, and Zn. Notably, in both irrigated and non-irrigated environments, CCN 51 excelled in key physiological traits, including A/Ci, A/E, and A/gs. This robust performance across diverse conditions suggests that these three genotypes possess physiological mechanisms to endure water-stressed conditions. Our research can generate valuable insights into these genotypes informing suitable choices for cocoa cultivation, especially in the context of global climate change.
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Project description:Red sorghum, a versatile crop with high nutritional and biochemical value, was evaluated for agronomic and grain quality traits using 27 genotypes across diverse environmental zones of Tamil Nadu. The data collected were subjected to Additive Main Effects and Multiplicative Interaction (AMMI) and Genotype-by-Environment Interaction (GGE) biplot models to recommend a specific genotype for the particular locality. The analysis of variation of the pooled data revealed a significant interaction effect between genotype and environment for the majority of the examined traits. Correlation analysis revealed a significant positive association of panicle weight and a negative association of micronutrient content with grain yield. The G × E was smaller than the genetic variation of grain yield as it portrayed the maximum contribution of genotypic effects (50.2%). GGE biplot showed E2 as a highly discriminating environment for grain yield and also identified environment-specific genotypes viz., G11 for E1, E3 and G6 for E2 environments. The GGE biplots recommended G9 and G14 as high-yielding stable genotypes, outperforming the check variety PAIYUR 2 (G15) by 15.83% and 36.16% respectively, across all environments, particularly in E1 and E3. The genotypes G14, G18, G21 and G26 shown wide adaptation and excelled as biofortified genotypes with significant Fe and Zn contents. Multi-trait stability evaluation approaches such as multi trait stability index (MTSI), multitrait genotype‒ideotype distance index (MGIDI), multi-trait index based on factor analysis and genotype-ideotype distance (FAI-BLUP), multitrait mean performance and stability index (MTMPS) and Smith-Hazel index assuming 15% selection intensity were adopted. G14 consistently ranked as the top and steady performer in all evaluation methods. The identified genotype demonstrated exceptional reliability, high yield potential, and early maturity, making them suitable candidates for variety and hybrid development, as well as ideotype breeding programs aimed at ensuring food and nutritional security.
| S-EPMC11877764 | biostudies-literature
Project description:Regularly consuming orange-fleshed sweet potatoes (OFSP) is widely recognised as an effective way to treat vitamin A deficiency (VAD), particularly in low-income countries. Unfortunately, cultivars of OFSP are poorly disseminated in most countries in sub-Saharan Africa, where VAD is a major cause of blindness. This study was conducted to evaluate the effect of the genotype-environment interaction (GEI) on the performance and stability of the yield components of OFSP cultivars to trigger their adoption by farmers. Nine OFSP genotypes were evaluated through a multi-environment trial (MET) carried out in 14 environments and in a complete randomised block (RCB) design to select the best genotypes based on the multi-trait mean performance and stability index (MTMPS) and participatory variety selection (PVS). The across-environment likelihood ratio test (LRT) showed significant differences across environments, genotypes, and GEIs for all traits studied, except for the number of marketable roots (NMR). Our findings revealed that genotypes ACAB, Apomuden, and BF59xCIP had satisfactory mean performances and stabilities across all evaluation environments. The PVS revealed that farmers assigned high importance to yield performance for field evaluation and root dry matter content. In addition, they prioritized many different traits, such as the attractiveness of orange flesh colour and the lower fibrousness of boiled roots. The genotypes preferred by farmers were ACAB and Apomuden, generally followed by BF59xCIP, indicating excellent concordance between the genotypes selected with the MTPMPS and those selected by farmers during the PVS. We concluded that farmers' participation in the OFSP genotype evaluation process for future dissemination is necessary to select the most suitable genotypes for production and increase the chances of adopting of these genotypes in rural areas. ACAB and Apomuden will be submitted for registration in the national catalogue.
| S-EPMC11770543 | biostudies-literature
Project description:Key messageThe inclusion of multiple traits and multiple environments within a partially separable factor analytic approach for genomic selection provides breeders with an informative framework to utilise genotype by environment by trait interaction for efficient selection. This paper develops a single-stage genomic selection (GS) approach which incorporates information on multiple traits and multiple environments within a partially separable factor analytic framework. The factor analytic linear mixed model is an effective method for analysing multi-environment trial (MET) datasets, but has not been extended to GS for multiple traits and multiple environments. The advantage of using all information is that breeders can utilise genotype by environment by trait interaction (GETI) to obtain more accurate predictions across correlated traits and environments. The partially separable factor analytic linear mixed model (SFA-LMM) developed in this paper is based on a three-way separable structure, which includes a factor analytic matrix between traits, a factor analytic matrix between environments and a genomic relationship matrix between genotypes. A diagonal matrix is then added to enable a different genotype by environment interaction (GEI) pattern for each trait and a different genotype by trait interaction (GTI) pattern for each environment. The results show that the SFA-LMM provides a better fit than separable approaches and a comparable fit to non-separable and partially separable approaches. The distinguishing feature of the SFA-LMM is that it will include fewer parameters than all other approaches as the number of genotypes, traits and environments increases. Lastly, a selection index is used to demonstrate simultaneous selection for overall performance and stability. This research represents an important continuation in the advancement of plant breeding analyses, particularly with the advent of high-throughput datasets involving a very large number of genotypes, traits and environments.
| S-EPMC10082131 | biostudies-literature
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.
| S-EPMC10634875 | biostudies-literature
Project description:Since the first genome-wide association studies (GWASs), thousands of variant-trait associations have been discovered. However, comprehensively mapping the genetic determinant of complex traits through univariate testing can require prohibitive sample sizes. Multi-trait GWAS can circumvent this issue and improve statistical power by leveraging the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been solved, the strategy to select traits has been overlooked. In this study, we conducted multi-trait GWAS on approximately 20,000 combinations of 72 traits using an omnibus test as implemented in the Joint Analysis of Summary Statistics. We assessed which genetic features of the sets of traits analyzed were associated with an increased detection of variants compared with univariate screening. Several features of the set of traits, including the heritability, the number of traits, and the genetic correlation, drive the multi-trait test gain. Using these features jointly in predictive models captures a large fraction of the power gain of the multi-trait test (Pearson's r between the observed and predicted gain equals 0.43, p < 1.6 × 10-60). Applying an alternative multi-trait approach (Multi-Trait Analysis of GWAS), we identified similar features of interest, but with an overall 70% lower number of new associations. Finally, selecting sets based on our data-driven models systematically outperformed the common strategy of selecting clinically similar traits. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outlines practical strategies for multi-trait testing.
| S-EPMC11260573 | biostudies-literature
Project description:Multi-environment trials (MET) are crucial for selecting genotypes that are well-suited to different environmental conditions. Incorporating multiple traits in the analysis can provide more reliable recommendations for selecting genotypes with desirable traits, including resistance to the Mungbean Yellow Mosaic Virus (MYMV) and high yield potential. The use of a Multi-Trait Stability Index (MTSI) is a good approach for analyzing the stability of genotypes across multiple traits under MYMV stress. In the present investigation, the performance of thirteen green gram genotypes were evaluated for traits such as yield, plant height, number of branches per plant, and resistance to MYMV. The main objective of the study is to identify highly productive and stable mung bean genotypes resistant to MYMV. MTSI can be calculated by combining information on the performance of genotypes across multiple traits and environmental conditions to provide a single index that indicates the overall stability of genotypes across traits and environments. The results helped to identify two green gram genotypes (Yadadri and JNG-18) that were high-yielding with stable resistance to MYMV stress across multiple environmental conditions. This can provide useful information to breeders for the development of suitable genotypes against MYMV in the affected areas.
| S-EPMC11237947 | biostudies-literature
Project description:BackgroundChickpea is a key pulse crop grown in the spring in dryland regions. The cold resistance potential of chickpeas allows for the development of genotypes with varying sowing dates to take advantage of autumn and winter rainfall, particularly in dryland regions. In this study, we assessed grain yield, plant height, 100-seed weight, days to maturity, and days to flowering of 17 chickpea genotypes in five autumn-sown dryland regions from 2019 to 2021. Additionally, the response of selected chickpea genotypes to cold stress was examined at temperatures of -4 °C, 4 °C, and 22 °C by analyzing biochemical enzymes.ResultsMixed linear model of ANOVA revealed a significant genotype × environment interaction for all traits measured, indicating varying reactions of genotypes across test environments. This study reported low estimates of broad-sense heritability for days to flowering (0.34), days to maturity (0.13), and grain yield (0.08). Plant height and seed weight exhibited the highest heritability, with genotypic selection accuracies of 0.73 and 0.92, respectively. Moreover, partial least square regression highlighted the impactful role of rainfall during all months except of October, November, and February on grain yield and its interaction with environments in autumn-planted chickpeas. Among the genotypes studied, G9, G10, and G17 emerged as superior based on stability parameters and grain yield. In particular, genotype G9 stood out as a promising genotype for dryland regions, considering both MTSI and genotype by yield*trait aproaches. The cold assay indicated that - 4 °C is crucial for distinguishing between susceptible and resistant genotypes. The results showed the important role of the enzymes CAT and GPX in contributing to the cold tolerance of genotype G9 in autumn-sown chickpeas.ConclusionsSignificant G×E for agro-morphological traits of chickpea shows prerequisite for multi-trial analysis. Chickpea`s direct root system cause that monthly rainfall during plant establishment has no critical role in its yield interaction with dryland environment. Considering the importance of agro-morphological traits and their direct and indirect effects on grain yield, the utilization of multiple-trait stability approches is propose. Evaluation of chickpea germplasm reaction against cold stress is necessary for autumn-sowing. Finally, autumn sowing of genotype FLIP 10-128 C in dryland conditions can led to significant crop performance.
| S-EPMC11302836 | biostudies-literature
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.
| S-EPMC7404246 | biostudies-literature