Identification of the QTL-allele System Underlying Two High-Throughput Physiological Traits in the Chinese Soybean Germplasm Population

Abstract

The QTL-allele system underlying two spectral reflectance physiological traits, NDVI (normalized difference vegetation index) and CHL (chlorophyll index), related to plant growth and yield was studied in the Chinese soybean germplasm population (CSGP), which consisted of 341 wild accessions (WA), farmer landraces (LR), and released cultivars (RC). Samples were evaluated in the Photosynthetic System II imaging platform at Nanjing Agricultural University. The NDVI and CHL data were obtained from hyperspectral reflectance images in a randomized incomplete block design experiment with two replicates. The NDVI and CHL ranged from 0.05–0.18 and 1.20–4.78, had averages of 0.11 and 3.57, and had heritabilities of 78.3% and 69.2%, respectively; the values of NDVI and CHL were both significantly higher in LR and RC than in WA. Using the RTM-GWAS (restricted two-stage multi-locus genome-wide association study) method, 38 and 32 QTLs with 89 and 82 alleles and 2–4 and 2–6 alleles per locus were identified for NDVI and CHL, respectively, which explained 48.36% and 51.35% of the phenotypic variation for NDVI and CHL, respectively. The QTL-allele matrices were established and separated into WA, LR, and RC submatrices. From WA to LR + RC, 4 alleles and 2 new loci emerged, and 1 allele was excluded for NDVI, whereas 6 alleles emerged, and no alleles were excluded, in LR + RC for CHL. Recombination was the major motivation of evolutionary differences. For NDVI and CHL, 39 and 32 candidate genes were annotated and assigned to GO groups, respectively, indicating a complex gene network. The NDVI and CHL were upstream traits that were relatively conservative in their genetic changes compared with those of downstream agronomic traits. High-throughput phenotyping integrated with RTM-GWAS provides an efficient procedure for studying the population genetics of traits.

An Improved Genome-Wide Association Procedure Explores Gene–Allele Constitutions and Evolutionary Drives of Growth Period Traits in the Global Soybean Germplasm Population

“…The relative completeness and accuracy in identifying genes with their alleles from GASM-RTM-GWAS made possible the identification of further results on the population. These include the establishment of the gene-allele matrix to demonstrate the gene-allele structure of each variety and the whole population [37,55], population evolutionary genetic study through direct comparisons among the matrices of derived subpopulations, optimal cross prediction of the population and subpopulations [56], gene network exploration of the population, and the identification of major genes with their major alleles. Without the relatively thorough identification of the gene-allele system, these extended results are not possible.…”

Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods

“…The application of GWAS was reported in different plant species such as soybean ( Brown et al, 2021 ), maize ( Xu et al, 2018 ), wheat ( Tsai et al, 2020 ), rice ( Zhong et al, 2021 ), and sorghum ( Somegowda et al, 2021 ). While there is no report on the genetic dissection of soybean yield-related hyperspectral reflectance bands, genetic dissection of vegetation index was previously reported in wheat ( Wang, 2019 ; Galán et al, 2020 ; Wang et al, 2021 ). Several detected candidate genes related to NDVI, SPAD, and LR in durum wheat ( Wang et al, 2021 ) overlapped with dry biomass, grain yield, and chlorophyll contents.…”

“…While there is no report on the genetic dissection of soybean yield-related hyperspectral reflectance bands, genetic dissection of vegetation index was previously reported in wheat ( Wang, 2019 ; Galán et al, 2020 ; Wang et al, 2021 ). Several detected candidate genes related to NDVI, SPAD, and LR in durum wheat ( Wang et al, 2021 ) overlapped with dry biomass, grain yield, and chlorophyll contents. Although GWAS can be considered as a powerful tool to detect the associated genomic regions with major effects, there are several barriers in applying conventional statistical methods in GWAS for identifying genomic regions associated with complex traits ( Szymczak et al, 2009 ).…”