Genome-Wide Analysis of Grain Yield Stability and Environmental Interactions in a Multiparental Soybean Population

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.

Quantitative Genomic Dissection of Soybean Yield Components

“…The significant markers found from this study do not overlap with the signals found for grain yield (Diers et al 2018) and yield stability (Xavier et al 2018) in this population. However, markers Gm02_639640, Gm07_7868756 and Gm12_2838455 are near seed size QTLs reported by Diers et al (2018).…”

Genetic Improvement of US Soybean in Maturity Groups V, VI, and VII

“…Herein, the Finlay and Wilkinson method was used for calculating yield stability coefficients for each genotype, which is a measure of the Type 2 dynamic stability concept, where the environments are expected to influence cultivar yields (Finlay and Wilkinson, 1963; Tollenaar and Lee, 2002). Recently, Xavier et al (2018) conducted a genome‐wide association study and used the same Finlay and Wilkinson approach to provide performance stability estimates for genotypes in a large soybean nested association mapping population, revealing a genomic region associated with yield stability on chromosome 18.…”

Quantitative Disease Resistance Loci towards Phytophthora sojae and Three Species of Pythium in Six Soybean Nested Association Mapping Populations

“…The Soybean Nested Association Mapping (SoyNAM) Project developed and made available to the soybean research community a large nested association mapping (NAM) population composed of 40 RIL populations derived from crosses between one common, high-yielding hub parent (IA3023) and exotic germplasm, plant introductions, and high-yielding breeding lines with the goal of identifying QTL associated with yield and other desirable traits (Grant et al, 2009;Diers, 2014;Xavier et al, 2017aXavier et al, , 2017bXavier et al, , 2018Diers et al, 2018;www.soybase.org/ SoyNAM). The parents and the 40 related F 5 RIL populations were previously genotyped with the SoySNP6K Illumina Infinium BeadChip genotyping array and the linkage maps developed for each NAM RIL population (Song et al, 2017), which are publicly available at SoyBase (http://soybase.org, accessed April 2017).…”