Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice

Developing high yielding rice varieties that are tolerant to drought stress is crucial for the sustainable livelihood of rice farmers in rainfed rice cropping ecosystems. Genomic selection (GS) promises to be an effective breeding option for these complex traits. We evaluated the effectiveness of two rather new options in the implementation of GS: trait and environment-specific marker selection and the use of multi-environment prediction models. A reference population of 280 rainfed lowland accessions endowed with 215k SNP markers data was phenotyped under a favorable and two managed drought environments. Trait-specific SNP subsets (28k) were selected for each trait under each environment, using results of GWAS performed with the complete genotype dataset. Performances of single-environment and multi-environment genomic prediction models were compared using kernel regression based methods (GBLUP and RKHS) under two cross validation scenarios: availability (CV2) or not (CV1) of phenotypic data for the validation set, in one of the environments. Trait-specific marker selection strategy achieved predictive ability (PA) of genomic prediction up to 22% higher than markers selected on the bases of neutral linkage disequilibrium (LD). Tolerance to drought stress was up to 32% better predicted by multi-environment models (especially RKHS based models) under CV2 strategy. Under the less favorable CV1 strategy, the multi-environment models achieved similar PA than the single-environment predictions. We also showed that reasonable PA could be obtained with as few as 3,000 SNP markers, even in a population of low LD extent, provided marker selection is based on pairwise LD. The implications of these findings for breeding for drought tolerance are discussed. The most resource sparing option would be accurate phenotyping of the reference population in a favorable environment and under a managed drought, while the candidate population would be phenotyped only under one of those environments.

培育抗旱高产水稻品种，对雨养稻作生态系统中稻农的可持续生计至关重要。基因组选择（Genomic selection, GS）有望成为应对这类复杂性状的高效育种手段。本研究评估了基因组选择实施过程中两种较新策略的有效性：性状与环境特异性标记选择，以及多环境预测模型的应用。本研究针对280份雨养低地水稻种质资源开展表型鉴定，这些资源携带21.5万单核苷酸多态性（Single Nucleotide Polymorphism, SNP）标记数据，鉴定环境涵盖1个适宜生长环境与2个受控干旱环境。本研究借助全基因组关联分析（Genome-Wide Association Study, GWAS）对完整基因型数据集的分析结果，为各环境下的每个目标性状筛选出2.8万个性状特异性SNP标记子集。本研究基于核回归的两种方法：基因组最佳线性无偏预测（Genomic Best Linear Unbiased Prediction, GBLUP）与再生核希尔伯特空间（Reproducing Kernel Hilbert Space, RKHS），在两种交叉验证场景下对比单环境与多环境基因组预测模型的性能：场景一为验证集在某一环境下拥有表型数据（CV2），场景二为验证集无对应环境的表型数据（CV1）。性状特异性标记选择策略的基因组预测预测能力（Predictive Ability, PA）较基于中性连锁不平衡（Linkage Disequilibrium, LD）筛选的标记最高提升22%。在CV2场景下，多环境模型（尤其是基于RKHS的模型）对干旱胁迫耐性的预测能力最高提升32%。而在更具挑战性的CV1场景下，多环境模型的预测能力与单环境模型相当。本研究同时证实，即便在连锁不平衡程度较低的群体中，只要标记选择基于成对连锁不平衡分析，仅需3000个SNP标记即可获得较为可观的预测能力。本研究最后讨论了上述发现对水稻耐旱性育种的启示。最节约资源的育种方案为：对参考群体在适宜生长环境与受控干旱环境下开展精准表型鉴定，而候选群体仅需在其中一类环境下进行表型鉴定即可。