Genomic selection (GS) can accelerate crop breeding and enhance selection efficiency. However, accurately predicting genomic estimated breeding values (GEBVs) for complex traits and applying GS in diverse environments remains challenging. To address these issues, we developed a novel hybrid method capable of modelling gene-gene and gene-environment interactions. This method offers precise predictions of phenotypic performance for complex traits, identifies haplotypes associated with desirable phenotypes, and enables prediction of optimal haplotypes tailored to specific environments. We evaluated the approach using a dataset of 855 barley lines, with phenotypic data for grain yield and flowering time collected across multiple environments. The model incorporated 30,543 SNPs, nine soil parameters, and six daily environmental variables, achieving high prediction accuracies, with correlation coefficients of 0.93 for flowering time and 0.82 for grain yield. Our method identified 10 haplotype blocks significantly associated with flowering time and 13 blocks with grain yield, collectively accounting for over 90% of the total genetic variance. Additionally, we predicted the phenotypic effects of each haplotype and identified elite varieties carrying the most favourable haplotypes for crossing design and selection. The method also allows prediction of untested genotype × environment combinations, enabling selection of optimal genotypes for targeted environments. To facilitate its application, we developed a web-based interface (accessible at [https://penghaowang.shinyapps.io/shinygui/]), which enables breeders to identify optimal haplotypes and the varieties that carry them, streamlining the process of haplotype-based, environment-informed breeding. We note that the reverse prediction framework is currently applied on a single-trait basis and does not resolve multi-trait trade-offs such as between flowering time and yield, which remains a topic for future extensions.

Improved yield potential is the goal of barley domestication and cultivation. During this process, two- and six-rowed barley types emerged and have been utilised in breeding and production. The six-rowed type could produce three times as many grains as its ancestral two-rowed forms, thus dominating barley cultivation for thousands of years. The deficiens form of the two-rowed type, characterised by extremely suppressed lateral spikelets, has gained dominance over the past few decades in barley-growing regions worldwide. We hypothesised that the absence of lateral spikelets in deficiens barley affects spike architecture and spike-related traits, contributing to its superior yield potential of deficiens barley cultivation. Currently, a deficiens barley variety, RGT Planet, is the most popular barley variety in the world. In this study, we used two F₂ populations derived from crossing RGT Planet with two canonical two-rowed barley and identified the functional allele Vrs1.t1 associated with deficiens morphology. We observed that the Vrs1.t1 allele may contribute to high yield potential by optimising spike architecture through increased spikelet length, grain number, and grain size. Phylogenetic analysis suggests that the deficiens mutation was likely present from the early stages of barley cultivation in the Fertile Crescent and spread to Ethiopia and beyond with agricultural expansion. We conclude that the ancient deficiens allele Vrs1.t1 has been a critical driver for the recent success of modern barley improvement by optimising spike architecture.