Genomic prediction offers the most effective marker assisted breeding approach for ability to prevent arsenic accumulation in rice grains

The high concentration of arsenic (As) in rice grains, in a large proportion of the rice growing areas, is a critical issue. This study explores the feasibility of conventional (QTL-based) marker-assisted selection and genomic selection to improve the ability of rice to prevent As uptake and accumulation in the edible grains. A japonica diversity panel (RP) of 228 accessions phenotyped for As concentration in the flag leaf (FL-As) and in the dehulled grain (CG-As), and genotyped at 22,370 SNP loci, was used to map QTLs by association analysis (GWAS) and to train genomic prediction models. Similar phenotypic and genotypic data from 95 advanced breeding lines (VP) with japonica genetic backgrounds, was used to validate related QTLs mapped in the RP through GWAS and to evaluate the predictive ability of across populations (RP-VP) genomic estimate of breeding value (GEBV) for As exclusion. Several QTLs for FL-As and CG-As with a low-medium individual effect were detected in the RP, of which some colocalized with known QTLs and candidate genes. However, less than 10% of those QTLs could be validated in the VP without loosening colocalization parameters. Conversely, the average predictive ability of across populations GEBV was rather high, 0.43 for FL-As and 0.48 for CG-As, ensuring genetic gains per time unit close to phenotypic selection. The implications of the limited robustness of the GWAS results and the rather high predictive ability of genomic prediction are discussed for breeding rice for significantly low arsenic uptake and accumulation in the edible grains.

在绝大多数水稻种植区域，稻米籽粒中砷（Arsenic）的高浓度积累是一项亟待解决的关键问题。本研究探讨了基于传统数量性状基因座（Quantitative Trait Locus，QTL）的标记辅助选择以及基因组选择技术，用于提升水稻阻隔砷在可食用籽粒中吸收与积累的可行性。本研究使用了包含228份粳稻种质的多样性群体（RP），对其剑叶砷浓度（Flag Leaf Arsenic，FL-As）与脱壳糙米砷浓度（Dehulled Grain Arsenic，CG-As）进行表型鉴定，并利用22370个单核苷酸多态性（Single Nucleotide Polymorphism，SNP）位点进行基因分型，通过全基因组关联分析（Genome-Wide Association Study，GWAS）定位QTL并训练基因组预测模型。研究同时利用95份具有粳稻遗传背景的优良育种品系群体（VP）的相似表型与基因型数据，通过GWAS验证RP群体中定位到的相关QTL，并评估跨群体基因组估计育种值（Genomic Estimated Breeding Value，GEBV）对砷阻隔性状的预测能力。在RP群体中，共检测到数个对FL-As与CG-As具有中低个体效应的QTL，其中部分位点与已报道的QTL及候选基因存在共定位现象。然而，在不放宽共定位判定参数的前提下，仅有不到10%的上述QTL可在VP群体中得到验证。与之相反，跨群体GEBV的平均预测能力较高，FL-As为0.43，CG-As为0.48，其单位时间内的遗传增益可接近表型选择的水平。本文针对GWAS结果稳健性不足、基因组预测能力较高这两点，探讨了其对培育可食用籽粒砷吸收与积累量显著降低的水稻品种的育种实践的指导意义。