Integrating a growth degree-days based reaction norm methodology and multi-trait modeling for genomic prediction in wheat

Abstract

Multi-trait and multi-environment analyses can improve genomic prediction by exploiting between-trait correlations and genotype-by-environment interactions. In the context of reaction norm models, genotype-by-environment interactions can be described as functions of high-dimensional sets of markers and environmental covariates. However, comprehensive multi-trait reaction norm models accounting for marker × environmental covariates interactions are lacking. In this article, we propose to extend a reaction norm model incorporating genotype-by-environment interactions through (co)variance structures of markers and environmental covariates to a multi-trait reaction norm case. To do that, we propose a novel methodology for characterizing the environment at different growth stages based on growth degree-days (GDD). The proposed models were evaluated by variance components estimation and predictive performance for winter wheat grain yield and protein content in a set of 2,015 F6-lines. Cross-validation analyses were performed using leave-one-year-location-out (CV1) and leave-one-breeding-cycle-out (CV2) strategies. The modeling of genomic [SNPs] × environmental covariates interactions significantly improved predictive ability and reduced the variance inflation of predicted genetic values for grain yield and protein content in both cross-validation schemes. Trait-assisted genomic prediction was carried out for multi-trait models, and it significantly enhanced predictive ability and reduced variance inflation in all scenarios. The genotype by environment interaction modeling via genomic [SNPs] × environmental covariates interactions, combined with trait-assisted genomic prediction, boosted the benefits in predictive performance. The proposed multi-trait reaction norm methodology is a comprehensive approach that allows capitalizing on the benefits of multi-trait models accounting for between-trait correlations and reaction norm models exploiting high-dimensional genomic and environmental information.

Genomic prediction for grain yield and micro-environmental sensitivity in winter wheat

“…The , and estimated with the LMM-HET and the DHGLM where in general similar across environments, except for environments #2 and #8 where the DHGLM had a considerable higher estimate of and . Despite the differences across environments, the and were in the range of previous studies for grain yield using Nordic Seed A/S data ( Guo et al., 2020 ; Raffo et al., 2022a ; Raffo et al., 2022b ).…”

“…six generations of selfing, F 6 ), so that individuals originating from the same line can be considered genetically homogeneous. Another reason that makes wheat a valuable species for this study is that there has been broad literature reporting a considerable variation due to macro-environmental sensitivity ( Bhatt and Derera, 1975 ; Cooper et al., 1995 ; Sial et al., 2000 ; Roozeboom et al., 2008 ; Lopez-Cruz et al., 2015 ; Crossa et al., 2017 ; Ly et al., 2017 ; Sukumaran et al., 2017 ; Ly et al., 2018 ; Crossa et al., 2021 ; Raffo et al., 2022a ), and therefore, it encourages hypothesizing on a relevant genetic variation for micro-environmental sensitivity. In this study, we used a winter wheat breeding population phenotyped for grain yield, and we had two specific objectives:…”

Partitioning the forms of genotype-by-environment interaction in the reaction norm analysis of stability

“…It is increasingly common for METs to collect environmental information to understand the causal factors underlying G × E (Cooper et al 2021 ). A common way to incorporate the large number of resulting ECs is through (co)variance structures which describe the relationships between the environments based on the ECs (Jarquín et al 2014 ; Raffo et al 2022 ). Because these models implicitly regress phenotypes on ECs, the have also been interpreted as reaction norm models.…”

Methods to account for heterogenous genetic variance in the analysis of stability of genotype performance across an environmental covariable

“…It is increasingly common for METs to collect environmental information to understand the causal factors underlying G×E (Cooper et al, 2021). A common way to incorporate the large number of resulting ECs is through (co)variance structures which describe the relationships between the environments based on the ECs (Jarquín et al, 2014;Raffo et al, 2022). Because these models implicitly regress phenotypes on ECs, the have also been interpreted as reaction norm models.…”