Dataset Information

The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment.

ABSTRACT: High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.

SUBMITTER: Rogers AR

PROVIDER: S-EPMC8022981 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment.

Rogers Anna R AR Dunne Jeffrey C JC Romay Cinta C Bohn Martin M Buckler Edward S ES Ciampitti Ignacio A IA Edwards Jode J Ertl David D Flint-Garcia Sherry S Gore Michael A MA Graham Christopher C Hirsch Candice N CN Hood Elizabeth E Hooker David C DC Knoll Joseph J Lee Elizabeth C EC Lorenz Aaron A Lynch Jonathan P JP McKay John J Moose Stephen P SP Murray Seth C SC Nelson Rebecca R Rocheford Torbert T Schnable James C JC Schnable Patrick S PS Sekhon Rajandeep R Singh Maninder M Smith Margaret M Springer Nathan N Thelen Kurt K Thomison Peter P Thompson Addie A Tuinstra Mitch M Wallace Jason J Wisser Randall J RJ Xu Wenwei W Gilmour A R AR Kaeppler Shawn M SM De Leon Natalia N Holland James B JB

G3 (Bethesda, Md.) 20210201 2

High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to ...[more]

PMID: 33585867

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Project description:BackgroundThe development of superior summer maize hybrids with high-yield potential and essential agronomic traits, such as resistance to lodging, is crucial for ensuring the sustainability of maize cultivation. However, the task of identifying and breeding genotypes that exhibit exceptional performance and stability across multiple environment conditions, while considering a wide range of traits, is challenging. Given the backdrop of global climate change, understanding which climate variables and soil properties most significantly impact environmental similarity is essential for selecting hybrids with improved adaptability to regions with diverse climatic and soil conditions. This study aimed to integrate envirotyping techniques (ETs) with a multi-trait selection approach to carry out a comprehensive evaluation of maize genotypes for performance and stability.ResultsThe grain yields of 13 maize hybrids, along with their four critical agronomic parameters, were assessed in the Huang-Huai-Hai Plain of China across 40 locations in eight provinces. By considering 20 years of climatic factors and soil covariates, these 40 locations were divided into six mega-environments (MEs) based on similar long-term weather patterns and soil characteristics. Additive main effects and multiplicative interaction (AMMI) analyses revealed that genotype (G), environment (E), and the GxE interaction had significant effects on all agronomic parameters (P < 0.001). The mean performance and stability of the genotypes in each mega-environment were assessed, allowing for the identification of superior hybrids using the multi-trait stability index (MTSI). In two of the MEs (ME2 and ME3), only two hybrids, HY321 and HY9112, were selected concurrently.ConclusionOverall, this study provides valuable insights into the effects of ETs on maize hybrids and enhances our understanding of GxE interactions in multi-environment trials. This understanding is essential for improving maize cultivation practices and breeding program in diverse environments.

Project description:Multi-parent Advanced Generation Inter-crosses (MAGIC) lines have mosaic genomes that are generated shuffling the genetic material of the founder parents following pre-defined crossing schemes. In cereal crops, these experimental populations have been extensively used to investigate the genetic bases of several traits and dissect the genetic bases of epistasis. In plants, genomic prediction models are usually fitted using either diverse panels of mostly unrelated accessions or individuals of biparental families and several empirical analyses have been conducted to evaluate the predictive ability of models fitted to these populations using different traits. In this paper, we constructed, genotyped and evaluated a barley MAGIC population of 352 individuals developed with a diverse set of eight founder parents showing contrasting phenotypes for grain yield. We combined phenotypic and genotypic information of this MAGIC population to fit several genomic prediction models which were cross-validated to conduct empirical analyses aimed at examining the predictive ability of these models varying the sizes of training populations. Moreover, several methods to optimize the composition of the training population were also applied to this MAGIC population and cross-validated to estimate the resulting predictive ability. Finally, extensive phenotypic data generated in field trials organized across an ample range of water regimes and climatic conditions in the Mediterranean were used to fit and cross-validate multi-environment genomic prediction models including G×E interaction, using both genomic best linear unbiased prediction and reproducing kernel Hilbert space along with a non-linear Gaussian Kernel. Overall, our empirical analyses showed that genomic prediction models trained with a limited number of MAGIC lines can be used to predict grain yield with values of predictive ability that vary from 0.25 to 0.60 and that beyond QTL mapping and analysis of epistatic effects, MAGIC population might be used to successfully fit genomic prediction models. We concluded that for grain yield, the single-environment genomic prediction models examined in this study are equivalent in terms of predictive ability while, in general, multi-environment models that explicitly split marker effects in main and environmental-specific effects outperform simpler multi-environment models.

Project description:Genotype-by-environment interaction (GEI) is among the greatest challenges for maize breeding programs. Strong GEI limits both the prediction of genotype performance across variable environmental conditions and the identification of genomic regions associated with grain yield. Incorporating GEI into yield prediction models has been shown to improve prediction accuracy of yield; nevertheless, more work is needed to further understand this complex interaction across populations and environments. The main objectives of this study were to: 1) assess GEI in maize grain yield based on reaction norm models and predict hybrid performance across a gradient of environmental (EG) conditions and 2) perform a genome-wide association study (GWAS) and post-GWAS analyses for maize grain yield using data from 2014 to 2017 of the Genomes to Fields initiative hybrid trial. After quality control, 2,126 hybrids with genotypic and phenotypic data were assessed across 86 environments representing combinations of locations and years, although not all hybrids were evaluated in all environments. Heritability was greater in higher-yielding environments due to an increase in genetic variability in these environments in comparison to the low-yielding environments. GWAS was carried out for yield and five single nucleotide polymorphisms (SNPs) with the highest magnitude of effect were selected in each environment for follow-up analyses. Many candidate genes in proximity of selected SNPs have been previously reported with roles in stress response. Genomic prediction was performed to assess prediction accuracy of previously tested or untested hybrids in environments from a new growing season. Prediction accuracy was 0.34 for cross validation across years (CV0-Predicted EG) and 0.21 for cross validation across years with only untested hybrids (CV00-Predicted EG) when compared to Best Linear Unbiased Prediction (BLUPs) that did not utilize genotypic or environmental relationships. Prediction accuracy improved to 0.80 (CV0-Predicted EG) and 0.60 (CV00-Predicted EG) when compared to the whole-dataset model that used the genomic relationships and the environmental gradient of all environments in the study. These results identify regions of the genome for future selection to improve yield and a methodology to increase the number of hybrids evaluated across locations of a multi-environment trial through genomic prediction.

Dataset Information

The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment.

Publications

The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment.

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