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Genomic prediction offers the most effective marker assisted breeding approach for ability to prevent arsenic accumulation in rice grains.
PLoS One 2019; 14(6):e0217516Plos

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.

Authors+Show Affiliations

CIRAD, UMR AGAP, Montpellier, France. AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France.CIRAD, UMR AGAP, Montpellier, France. AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France.Centre Français du Riz, Mas du Sonnailler, Arles, France.Università degli Studi di Milano, Milano, Italy.CIRAD, UMR AGAP, Montpellier, France. AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31194746

Citation

Frouin, Julien, et al. "Genomic Prediction Offers the Most Effective Marker Assisted Breeding Approach for Ability to Prevent Arsenic Accumulation in Rice Grains." PloS One, vol. 14, no. 6, 2019, pp. e0217516.
Frouin J, Labeyrie A, Boisnard A, et al. Genomic prediction offers the most effective marker assisted breeding approach for ability to prevent arsenic accumulation in rice grains. PLoS ONE. 2019;14(6):e0217516.
Frouin, J., Labeyrie, A., Boisnard, A., Sacchi, G. A., & Ahmadi, N. (2019). Genomic prediction offers the most effective marker assisted breeding approach for ability to prevent arsenic accumulation in rice grains. PloS One, 14(6), pp. e0217516. doi:10.1371/journal.pone.0217516.
Frouin J, et al. Genomic Prediction Offers the Most Effective Marker Assisted Breeding Approach for Ability to Prevent Arsenic Accumulation in Rice Grains. PLoS ONE. 2019;14(6):e0217516. PubMed PMID: 31194746.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Genomic prediction offers the most effective marker assisted breeding approach for ability to prevent arsenic accumulation in rice grains. AU - Frouin,Julien, AU - Labeyrie,Axel, AU - Boisnard,Arnaud, AU - Sacchi,Gian Attilio, AU - Ahmadi,Nourollah, Y1 - 2019/06/13/ PY - 2019/02/07/received PY - 2019/05/13/accepted PY - 2019/6/14/entrez PY - 2019/6/14/pubmed PY - 2019/6/14/medline SP - e0217516 EP - e0217516 JF - PloS one JO - PLoS ONE VL - 14 IS - 6 N2 - 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. SN - 1932-6203 UR - https://www.unboundmedicine.com/medline/citation/31194746/Genomic_prediction_offers_the_most_effective_marker_assisted_breeding_approach_for_ability_to_prevent_arsenic_accumulation_in_rice_grains L2 - http://dx.plos.org/10.1371/journal.pone.0217516 DB - PRIME DP - Unbound Medicine ER -