Integrating statistical genetics, geographical information systems and envirotyping: a novel approach for predictive breeding and decision-making - Araújo, Mauricio dos Santos (2024)
The crossover genotype-by-environments (G × E) interaction is responsible for the variation in genotype performance across diﬀerent environments. Disregarding the eﬀect of this inter- action means neglecting the speciﬁc adaptations of genotypes in the target population of environments. Environmental characterization enables an understanding of the speciﬁcities and similarities among diﬀerent environments. In this context, enviromics has emerged as a new area that integrates information from data analysis, quantitative genetics, geographic information systems (GIS), and principles of ecophysiology. Incorporating environmental features into Statistical Genetics models contributes to enhancing the predictive capability of these models and a better understanding of the cultivation environment. Thus, this study aimed to propose a new predictive breeding method (GIS-FA) that integrates GIS information, factor analytic (FA) models, partial least squares regression (PLS), and enviromics to predict purelines of rice and soybean in untested environments. Two databases were used: one for rice, with 80 purelines cultivated in the years 2009/10 and 2010/11 in 21 environments in eight Brazilian states; and the second composed of 195 soybean purelines evaluated in 49 environments (in years 2019/20, 2020/21, and 2021/22) in the state of Mato Grosso do Sul. The term “environment” refers to the location-year combination. For both datasets, FA models were adjusted, with FA4 being selected based on the average semivariance ratio. A total of 32 environmental features (EF) were collected, including three geographical, 16 climatic, and 13 soil-related features. To make predictions, 50 points were randomly chosen within each municipality in the evaluated states, and, for each point, EFs data were obtained from a historical series (2000-2021). Leveraging the FA model outcomes, we used the PLS method to predict the overall performance and stability of both crops in untested environments. Cross-validation was performed using the leave-one-out method, and subsequently, the GIS-FA method was compared with the GGE-GIS approach, which uses directly the within-environment eBLUPs to perform the prediction. After the spatial prediction, performance and stability parameters were represented in thematic maps. For predicting eBLUEs, GIS-FA was 10% and 1% superior to GIS-GGE in the rice and soybean datasets, respectively. For predicting eBLUPs, GIS-FA was 9% and 5% more eﬀective than GIS-GGE. Three types of maps were created: (i) zones of genotype adaptation; (ii) pairwise comparison between pureline vs. check and pureline vs. pureline; (iii) which-won-where. The GIS-FA approach proved to be eﬃcient in predicting genotypes for untested environments, allowing the evaluation of the G × E interaction throughout the experimental network. Keywords: Environmental features. Factor analytic. Predictive models. Partial Least Squares.