Construction of prediction models for growth traits of soybean cultivars based on phenotyping in diverse genotype and environment combinations. (2nd August 2022)

• Record Type: Journal Article
• Authors: Manggabarani, Andi Madihah; Hashiguchi, Takuyu; Hashiguchi, Masatsugu; Hayashi, Atsushi; Kikuchi, Masataka; Mustamin, Yusdar; Bamba, Masaru; Kodama, Kunihiro; Tanabata, Takanari; Isobe, Sachiko; Tanaka, Hidenori; Akashi, Ryo; Nakaya, Akihiro; Sato, Shusei
• Is Part Of: DNA research. Volume 29:Number 4(2022)
• Publication Date: 2022-08-02
• Subjects: soybean -- field phenotyping -- multiple sowing times -- multiple regression models; Genes -- Periodicals; Genomes -- Periodicals; Genes -- Research -- Periodicals; Genomics -- Periodicals; DNA -- Research -- Periodicals; 572.86072
• DOI: 10.1093/dnares/dsac024
• Languages: English
• ISSNs: 1340-2838

Abstract

Abstract: As soybean cultivars are adapted to a relatively narrow range of latitude, the effects of climate changes are estimated to be severe. To address this issue, it is important to improve our understanding of the effects of climate change by applying the simulation model including both genetic and environmental factors with their interactions (G×E). To achieve this goal, we conducted the field experiments for soybean core collections using multiple sowing times in multi-latitudinal fields. Sowing time shifts altered the flowering time (FT) and growth phenotypes, and resulted in increasing the combinations of genotypes and environments. Genome-wide association studies for the obtained phenotypes revealed the effects of field and sowing time to the significance of detected alleles, indicating the presence of G×E. By using accumulated phenotypic and environmental data in 2018 and 2019, we constructed multiple regression models for FT and growth pattern. Applicability of the constructed models was evaluated by the field experiments in 2020 including a novel field, and high correlation between the predicted and measured values was observed, suggesting the robustness of the models. The models presented here would allow us to predict the phenotype of the core collections in a given environment.