Genome-wide analysis of grain yield stability and environmental interactions in a multiparental soybean population

Alencar Xavier, Diego Jarquin, Reka Howard, Vishnu Ramasubramanian, James E. Specht, George L. Graef, William D. Beavis, Brian W. Diers, Qijian Song, Perry B. Cregan, Randall Nelson, Rouf Mian, J. Grover Shannon, Leah McHale, Dechun Wang, William Schapaugh, Aaron J. Lorenz, Shizhong Xu, William M. Muir, Katy M. Rainey

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)519-529
Number of pages11
JournalG3: Genes, Genomes, Genetics
Issue number2
StatePublished - Feb 1 2018


  • Association mapping
  • Finlay-Wilkinson index
  • GGE
  • MPP
  • Meta-analysis
  • Multiparent Advanced Generation Inter-Cross (MAGIC)
  • Multiparental populations
  • SoyNAM

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics
  • Genetics(clinical)


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