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Rice is staple food in many countries of Africa and a major part of the diet in many others. However, Africa’s demand for rice exceeds production with the deficit of 40% being imported. One way to improve Africa’s rice production is through breeding high yielding varieties suitable for the different environment conditions. This study was conducted to assess the genetic variability and stability performance of 48 lowland rice genotypes including 37 interspecific (Oryza glaberrima × Oryza sativa ssp. indica) and 11 intraspecific (O. sativa ssp. indica × O. sativa ssp. indica) in 12 environments in Nigeria, Benin Republic and Togo using Additive Main Effect and Multiplicative Interaction (AMMI) and Genotype+ Genotype x Environment (GGE) biplot models. The combined analysis of variance revealed significant differences (P<0.01) among the genotypes, environments, and genotypes x environment interaction. Both the AMMI and GGE models identified NERICA-L8 and NERICA-LI2 as the best genotypes for cultivation across environments. Ouedeme environments in Benin Republic were the most stable and ideal for rice cultivation, while Ibadan sites were the most unstable. TOG 5681 had the least yield and was the most unstable across seasons.
Genetic diversity was analyzed using 22 important morpho-agronomic traits and 50 simple sequence repeat (SSR) markers and the results were subjected to principal components analysis (PCA). The results revealed that the first eight PC axes (PC1–8) accounted for 75.13% of the total variation, while PC1–4 accounted for 50.39% of the total variation among rice genotypes. However, 10 of the 50 SSR markers were polymorphic and generated 49 alleles (average = 4.9 alleles per locus), suggesting moderate to substantial genetic diversity among the rice genotypes. The polymorphic information content (PIC) ranged from 0.24 to 0.65, with an average PIC value of 0.45. Two structured populations were observed which clustered into five heterotic groups and an outgroup, respectively. This suggests that heterosis could be exploited in the next hybridization program by crossing one of the genotypes in any SSR marker-defined cluster, with the rice accession TOG 5681 in cluster I. The results of this study suggest that morpho-agronomic traits should be used to compliment SSR data in rice diversity studies, especially if a few polymorphic SSR markers are to be used.
Keywords
rice, morpho-agronomic data, principal component analysis, SSR markers, polymorphic information content, genetic diversity
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This preprint is under consideration at Rice. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Original article
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 26 Oct, 2021
No community comments so far
Reviews received
Received 08 Nov, 2021
Reviewer #1 agreed
On 07 Nov, 2021
Reviewers invited
Invitations sent on 31 Oct, 2021
Submission checks complete
On 20 Oct, 2021
Editor invited
On 20 Oct, 2021
Editor assigned
On 19 Oct, 2021
First submitted
On 15 Aug, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Health Economics & Outcomes Research
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Rice is staple food in many countries of Africa and a major part of the diet in many others. However, Africa’s demand for rice exceeds production with the deficit of 40% being imported. One way to improve Africa’s rice production is through breeding high yielding varieties suitable for the different environment conditions. This study was conducted to assess the genetic variability and stability performance of 48 lowland rice genotypes including 37 interspecific (Oryza glaberrima × Oryza sativa ssp. indica) and 11 intraspecific (O. sativa ssp. indica × O. sativa ssp. indica) in 12 environments in Nigeria, Benin Republic and Togo using Additive Main Effect and Multiplicative Interaction (AMMI) and Genotype+ Genotype x Environment (GGE) biplot models. The combined analysis of variance revealed significant differences (P<0.01) among the genotypes, environments, and genotypes x environment interaction. Both the AMMI and GGE models identified NERICA-L8 and NERICA-LI2 as the best genotypes for cultivation across environments. Ouedeme environments in Benin Republic were the most stable and ideal for rice cultivation, while Ibadan sites were the most unstable. TOG 5681 had the least yield and was the most unstable across seasons.
Genetic diversity was analyzed using 22 important morpho-agronomic traits and 50 simple sequence repeat (SSR) markers and the results were subjected to principal components analysis (PCA). The results revealed that the first eight PC axes (PC1–8) accounted for 75.13% of the total variation, while PC1–4 accounted for 50.39% of the total variation among rice genotypes. However, 10 of the 50 SSR markers were polymorphic and generated 49 alleles (average = 4.9 alleles per locus), suggesting moderate to substantial genetic diversity among the rice genotypes. The polymorphic information content (PIC) ranged from 0.24 to 0.65, with an average PIC value of 0.45. Two structured populations were observed which clustered into five heterotic groups and an outgroup, respectively. This suggests that heterosis could be exploited in the next hybridization program by crossing one of the genotypes in any SSR marker-defined cluster, with the rice accession TOG 5681 in cluster I. The results of this study suggest that morpho-agronomic traits should be used to compliment SSR data in rice diversity studies, especially if a few polymorphic SSR markers are to be used.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
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