ArticleG3 (Bethesda, Md.)2018
Genome-Wide Analysis of Grain Yield Stability and Environmental Interactions in a Multiparental Soybean Population.
Article in G3 (Bethesda, Md.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed, 91 citations in OpenAlex.
- Photosynthesis-informed breeding to enhance Soybean (Glycine max) yield potential: a framework integrating high-throughput phenomics, genomic prediction and G×E×M.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Non-linear kernel methods for genomic prediction of soybean yield and quality in multi-environment trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Yield Stability and Adaptability of Black Soybean Line BSCM41 Support Bi-Directional Seed Rotation System in Northeast Thailand.Plants (Basel, Switzerland) · 2026Article
- Korean Soybean Nested Association Mapping (K-SoyNAM) population: genetic linkage map, genetic mapping, and genotype imputation.BMC genomics · 2026Article
- MAGIC populations: a next-generation framework for dissecting complex quantitative traits and accelerating molecular breeding in crops.Frontiers in plant science · 2026Review
- Genomewide association and prediction of phenotypic stability in barley.The plant genome · 2025Article
- Genetic mapping of novel QTL for seed protein stability in food-grade soybean (Glycine max).TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Phenological variation associates with the stability of fruit quality traits in cultivated tetraploid blueberry.G3 (Bethesda, Md.) · 2025Article
- Article
- Embracing plant plasticity or robustness as a means of ensuring food security.Nature communications · 2025Review
- Combining phenotypic and genomic data to improve prediction of binary traits.Journal of applied statistics · 2024Article
- The elite variations in germplasms for soybean breeding.Molecular breeding : new strategies in plant improvement · 2023Article
- Genetic architecture of protein and oil content in soybean seed and meal.The plant genome · 2023Article
- Gene-by-environment interactions in plants: Molecular mechanisms, environmental drivers, and adaptive plasticity.The Plant cell · 2023Review
- A multi-locus linear mixed model methodology for detecting small-effect QTLs for quantitative traits in MAGIC, NAM, and ROAM populations.Computational and structural biotechnology journal · 2023Article
- Genome-wide association analysis identified consistent QTL for seed yield in a soybean diversity panel tested across multiple environments.The plant genome · 2022Article
- statgenMPP: an R package implementing an IBD-based mixed model approach for QTL mapping in a wide range of multi-parent populations.Bioinformatics (Oxford, England) · 2022Article
- A new approach fits multivariate genomic prediction models efficiently.Genetics, selection, evolution : GSE · 2022Article
- Article
- Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components.International journal of molecular sciences · 2022Article
Corrections and comments
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Authors and funding
20 authors at 12 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genetic improvement toward optimized and stable agronomic performance of soybean genotypes is desirable for food security. Understanding how genotypes perform in different environmental conditions helps breeders develop sustainable cultivars adapted to target regions. Complex traits of importance are known to be controlled by a large number of genomic regions with small effects whose magnitude and direction are modulated by environmental factors. Knowledge of the constraints and undesirable effects resulting from genotype by environmental interactions is a key objective in improving selection procedures in soybean breeding programs. In this study, the genetic basis of soybean grain yield responsiveness to environmental factors was examined in a large soybean nested association population. For this, a genome-wide association to performance stability estimates generated from a Finlay-Wilkinson analysis and the inclusion of the interaction between marker genotypes and environmental factors was implemented. Genomic footprints were investigated by analysis and meta-analysis using a recently published multiparent model. Results indicated that specific soybean genomic regions were associated with stability, and that multiplicative interactions were present between environments and genetic background. Seven genomic regions in six chromosomes were identified as being associated with genotype-by-environment interactions. This study provides insight into genomic assisted breeding aimed at achieving a more stable agronomic performance of soybean, and documented opportunities to exploit genomic regions that were specifically associated with interactions involving environments and subpopulations.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.