A fast method for breeding by design via G × E interactions detected in large-scale climatic, phenomic and genomic data

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 analysis (MEJA) of maize, rice and soybean datasets, a total of 396 known genes and 84 known GEIs validated Fast3VmrMLM. In a 12-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 GEI and three plasticity genes increased yield by 0.51–3.56 Mg·ha−1, identifying 15 high breeding potential hybrids and 29 genes. By incorporating single nucleotide polymorphisms, haplotypes and structural variations, Fast3VmrMLM offers a big-data platform for identifying GEIs and developing climate-adaptive cultivars.