Ribeiro Pedro C O, Howard Reka, Jarquin Diego, Oliveira Isadora C M, Chaves Saulo, Carneiro Pedro C S, Souza Vander F, Schaffert Robert E, Damasceno Cynthia M B, Parrella Rafael A C, Dias Kaio Olimpio G, Pastina Maria M
Department of General Biology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil.
Department of Statistics, University of Nebraska - Lincoln (UNL), Lincoln, NE, USA.
Theor Appl Genet. 2025 May 9;138(6):113. doi: 10.1007/s00122-025-04895-y.
Incorporating environmental features improved the predictive ability of genomic prediction models under multi-environment trials in tropical conditions. Gathering environmental and genomic information can benefit the breeding of sorghum hybrids by overcoming complications imposed by the genotype-by-environment interaction (GEI). In this study, we explored the value of combining environmental features (EFs) and genomic data to enhance predictions for biomass sorghum hybrid breeding, addressing GEI complexities. We also investigated if considering specific time windows for EFs improves the prediction. We used a historical dataset from a tropical biomass sorghum breeding program featuring 253 genotypes across 64 trials. Initially, a first-stage analysis was performed to obtain the adjusted means (EBLUEs) and scrutinize the impact of 29 EFs (geographic, climatic, and soil-related EFs) on GEI. Subsequently, in the second-stage analysis, we used data from 221 hybrids that had both parents genotyped to evaluate the predictive ability and assertiveness of 12 models with different effects. The most relevant EFs included soil organic carbon, insolation on a horizontal surface, longitude, temperature at dew point, and nitrogen content. Across three cross-validation scenarios (CV1, CV0, and CV00), the most effective model encompassed main combining ability effects, GEI, and G I (genotype-by-specific environmental effects interaction), utilizing an environmental kinship matrix ( ) derived from mean EF values. Only in CV2, a model with a similar structure but utilizing from specific time windows outperformed others. Our findings highlight the potential of integrating environmental and genomic data to refine predictive models for optimizing biomass sorghum hybrid breeding strategies.
纳入环境特征提高了基因组预测模型在热带条件下多环境试验中的预测能力。收集环境和基因组信息可以通过克服基因型与环境互作（GEI）带来的复杂性，从而有利于高粱杂交种的育种。在本研究中，我们探索了结合环境特征（EFs）和基因组数据的价值，以增强对生物量高粱杂交种育种的预测，解决GEI的复杂性。我们还研究了考虑EFs的特定时间窗口是否能改善预测。我们使用了一个来自热带生物量高粱育种项目的历史数据集，该数据集包含64个试验中的253个基因型。最初，进行了第一阶段分析以获得调整均值（EBLUEs），并仔细研究29个EFs（地理、气候和土壤相关的EFs）对GEI的影响。随后，在第二阶段分析中，我们使用了221个双亲均已基因分型的杂交种的数据，以评估12个具有不同效应的模型的预测能力和确定性。最相关的EFs包括土壤有机碳、水平面上的日照、经度、露点温度和氮含量。在三种交叉验证方案（CV1、CV0和CV00）中，最有效的模型包括主要配合力效应、GEI和G I（基因型与特定环境效应互作），利用从平均EF值导出的环境亲缘关系矩阵（ ）。仅在CV2中，一个结构相似但利用特定时间窗口的 的模型表现优于其他模型。我们的研究结果突出了整合环境和基因组数据以改进预测模型以优化生物量高粱杂交种育种策略的潜力。
