Fradgley, N. | Search bioRxiv

bioRxiv2022

ENVIROME-WIDE ASSOCIATIONS ENHANCE MULTI-YEAR GENOME-BASED PREDICTION OF HISTORICAL WHEAT BREEDING DATA

Linking high-throughput environmental data (enviromics) into genomic prediction (GP) is a cost-effective strategy for increasing selection intensity under genotype-by-environment interactions (GxE). This study developed a data-driven approach based on Environment-Phenotype Associations (EPA) aimed at recycling important GxE information from historical breeding data. EPA was developed in two applications: (1) scanning a secondary source of genetic variation, weighted from the shared reaction-norms of past-evaluated genotypes; (2) pinpointing weights of the similarity among trial-sites (locations), given the historical impact of each envirotyping data variable for a given site. Then, the EPA outcomes were integrated into multi-environment GP models through a new single-step GBLUP. The wheat trial data used included 36 locations, 8 years and 3 target populations of environments (TPE) in India. Four prediction scenarios and 6 kernel-models within/across TPEs were tested. Our results suggest that the conventional GBLUP, without enviromic data or when omitting EPA, is inefficient in predicting the performance of wheat lines in future years. However, when EPA was introduced as an intermediary learning step to reduce the dimensionality of the GxE kernels while connecting phenotypic and environmental-wide variation, a significant enhancement of GxE prediction accuracy was evident. EPA revealed that the effect of seasonality makes strategies such as "covariable selection" unfeasible because GxE is year-germplasm specific. We propose that the EPA effectively serves as a "reinforcement learner" algorithm capable of uncovering the effect of seasonality over the reaction-norms, with the benefits of better forecasting the similarities between past and future trialing sites. EPA combines the benefits of dimensionality reduction while reducing the uncertainty of genotype-by-year predictions and increasing the resolution of GP for the genotype-specific level.

genetics↗

bioRxiv2020

Effects of breeding history and crop management on the root architecture of wheat

AimsSelection for optimal root system architecture (RSA) is important to ensure genetic gains in the sustainable production of wheat (Triticum aestivum L.). Here we examine the idea that past wheat breeding has led to changes in RSA and that future breeding efforts can focus directly on root traits to improve adaptation to a target environment. MethodsWe conducted three field trials using diverse wheat varieties, including modern and historic UK varieties and non-UK landraces, tested under contrasting tillage regimes (non-inversion tillage versus conventional ploughing) or different seeding rates (standard rate versus high rate). We used field excavation, washing and measurement of root crowns ( shovelomics) to characterise RSA traits, including: numbers of seminal, crown and nodal roots per plant, and crown root growth angle. ResultsWe found large differences among genotypes for all root traits. Modern varieties generally had fewer roots per plant than historic varieties. There were fewer crown roots and root angles were wider, on average, under shallow non-inversion tillage compared with conventional ploughing. Crown root numbers per plant also tended to be smaller at a high seeding rate compared with the standard rate. There were significant genotype-by-year, genotype-by-tillage and genotype-by-seeding-rate interactions for many root traits. ConclusionsSmaller root systems is likely to be a result of past selection and may have facilitated historical yield increases by reducing below-ground competition within the crop. The effects of crop management practices on RSA depend on genotype, suggesting that future breeding could select for improved RSA traits in resource-efficient farming systems.

plant biology↗