Abstract
In conjunction with big data analysis methods, plant omics technologies have provided scientists with cost-effective and promising tools for discovering genetic architectures of complex agronomic traits using large breeding populations. In recent years, there has been significant progress in plant phenomics and genomics approaches for generating reliable large datasets. However, selecting an appropriate data integration and analysis method to improve the efficiency of phenome-phenome and phenome-genome association studies is still a bottleneck. This study proposes a hyperspectral wide association study (HypWAS) approach as a phenome-phenome association analysis through a hierarchical data integration strategy to estimate the prediction power of hyperspectral reflectance bands in predicting soybean seed yield. Using HypWAS, five important hyperspectral reflectance bands in visible, red-edge, and near-infrared regions were identified significantly associated with seed yield. The phenome-genome association analysis of each tested hyperspectral reflectance band was performed using two conventional genome-wide association studies (GWAS) methods and a machine learning mediated GWAS based on the support vector regression (SVR) method. Using SVR-mediated GWAS, more relevant QTL with the physiological background of the tested hyperspectral reflectance bands were detected, supported by the functional annotation of candidate gene analyses. The results of this study have indicated the advantages of using hierarchical data integration strategy and advanced mathematical methods coupled with phenome-phenome and phenome-genome association analyses for a better understanding of the biology and genetic backgrounds of hyperspectral reflectance bands affecting soybean yield formation. The identified yield-related hyperspectral reflectance bands using HypWAS can be used as indirect selection criteria for selecting superior genotypes with improved yield genetic gains in large breeding populations.
Highlights
Soybean (Glycine max [L.] Merr.) can be considered one of the super crops that is substantially used for food and feed, green manure, biodiesel, and fiber (Seck et al, 2020)
With respect to the genome-wide association study, we proposed the term of hyperspectral wide association study (HypWAS) for detecting hyperspectral reflectance bands associated with the trait of interest
We found that support vector regression (SVR)-mediated genome-wide association studies (GWAS) had the same performance in detecting numbers of quantitative trait loci (QTL) when compared to conventional GWAS methods
Summary
Soybean (Glycine max [L.] Merr.) can be considered one of the super crops that is substantially used for food and feed, green manure, biodiesel, and fiber (Seck et al, 2020). Soybean breeders continually breed soybean genotypes with improved desired traits of interest, such as yield (Yoosefzadeh-Najafabadi et al, 2021a). Yield is a complex trait affected by intrinsic and extrinsic factors as well as their interactions (Anuarbek et al, 2020; Yoosefzadeh-Najafabadi et al, 2021a). A sophisticated understanding of the biological aspects of plant genomes is required for sustainable improvements of yield potential in major crops (Somegowda et al, 2021), such as soybean. Having a phenotypic profile of a large plant population with highdensity genetic markers are two of the most important factors for better understanding the phenotype and genotype of complex quantitative traits that are usually controlled by various genes with minor and major effects (Wang et al, 2020)
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