Abstract Although large-scale populations are used to detect genes for polygenic traits, few studies integrate genes and gene-by-environment interactions (GEIs) into breeding by design. Here, we present Fast3VmrMLM, which uses eight big-data techniques to analyze climatic, phenomic, and genomic data together to detect GEIs, decipher plasticity, and guide breeding. In multi-environment joint analyses (MEJA) of maize, rice and soybean datasets, a total of 396 known genes and 84 known GEIs validated Fast3VmrMLM. In twelve-environment maize dataset, six GEIs interacting with five meteorological factors and two MEJA-detected GEIs helped to explain flowering time plasticity. Thirteen known genes, eight known GEIs and seven plasticity genes advanced flowering by 1.10 ~ 6.61 days, whereas nine known genes, one known GEIs and three plasticity genes increased yield by 0.51 ~ 3.56 Mg ⋅ ha -1, identifying fifteen high breeding potential hybrids and 29 genes. By incorporating SNPs, haplotypes and structural variations, Fast3VmrMLM offers a big-data platform for identifying GEIs and developing climate-adaptive cultivars.
Wang et al. (Tue,) studied this question.