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

Abstract

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.

Impact of early genomic prediction for recurrent selection in an upland rice synthetic population

“…We are aware that the optimized scheme we suggest, based on random sampling of the training set, genome-wide markers considered as random effects, and random allocation of genotypes to sparse testing could be improved further still by considering other criteria known to increase the performance of GP. It remains to be seen whether PA can be improved by optimized assembly of the training set as performed in various studies ( Rincent et al 2012 , 2017 ; Bustos-Korts et al 2016 ; Akdemir and Isidro-Sánchez 2019; Mangin et al 2019 ), by inclusion of particular weights for some specific loci ( Spindel et al 2016 ; Bhandari et al 2019 ; Frouin et al 2019 ) or by use of an efficient method to proceed to sparse testing in the context of GxE models ( Ahmadi et al 2020 ).…”

Multiple Small-Effect Alleles of Indica Origin Enhance High Iron-Associated Stress Tolerance in Rice Under Field Conditions in West Africa

“…These results have immediate implications for rice breeding programs in West Africa. The abundance of small-effect loci, predominantly from the indica subpopulation, contributing to HIA stress tolerance suggests that a recurrent selection program focusing on an expanded indica genepool could be used in combination with genomic selection (GS) to increase the efficiency of the breeding process ( Begum et al, 2015 ; Grenier et al, 2015 ; Cobb et al, 2019 ; Frouin et al, 2019 ). The accuracy of GS models can often be improved by differentially weighting SNPs known to be associated with favorable GWA-QTL alleles, such as those reported here ( Spindel et al, 2016 ; Bhandari et al, 2019 ; Li et al, 2019 ; Liu et al, 2019 ; Rice and Lipka, 2019 ).…”

Genome-wide Association Studies of Agronomic Traits Consisting of Field- and Molecular-based Phenotypes

“…However, GWAS further identified loci, including new genes associated with Cd accumulation and tolerance (Arabidopsis: Chao et al, 2012;Aegilops tauschii: Qin et al, 2015;barley: Wu et al, 2015;rapeseed: Chen et al, 2018;rice: Zhao et al, 2018a, maize: Zhao et al, 2018b. Similarly, GWAS for As accumulation was conducted, by which some associated genes were identified that were functionally distinct from the previously characterized genes (Chao et al, 2014;Zhao et al, 2018c;Frouin et al, 2019). The information of loci revealed by GWAS can be exploited for improving the efficiency of breeding of not only significantly low accumulation in the edible parts but also for enhancing the tolerance for toxic minerals, in addition to using major responsible genes and the orthologue genes in various plants Plant-derived food provides essential micronutrients for human health.…”