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Genome-wide association mapping of salinity tolerance in rice (Oryza sativa).

Kumar V, Singh A, Mithra SV, Krishnamurthy SL, Parida SK, Jain S, Tiwari KK, Kumar P, Rao AR, Sharma SK, Khurana JP, Singh NK, Mohapatra T - DNA Res. (2015)

Bottom Line: It is a complex quantitative trait having different components, which can be dissected effectively by genome-wide association study (GWAS).In addition to Saltol, we also found GWAS peaks representing new QTLs on chromosomes 4, 6 and 7.The gene-based SNP array used in this study was found cost-effective and efficient in unveiling genomic regions/candidate genes regulating salinity stress tolerance in rice.

View Article: PubMed Central - PubMed

Affiliation: National Research Centre on Plant Biotechnology, New Delhi 110012, India.

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Summary of percentage of variance explained by significant loci in the study. The x-axis represents the trait, and the y-axis shows the contribution (%) of significant loci. The label on the top of average bar is the total number of significant SNPs for respective trait.
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DSU046F5: Summary of percentage of variance explained by significant loci in the study. The x-axis represents the trait, and the y-axis shows the contribution (%) of significant loci. The label on the top of average bar is the total number of significant SNPs for respective trait.

Mentions: For plant height under control condition, seven SNPs in a cluster on chromosome 4 (8559661–12215278) were found significant and each of the seven significant SNPs explained almost 15% of the phenotypic variation. SNPs found significant for spikelet fertility under control condition explained on an average 5% of the phenotypic variation where both the SNPs on chromosome 7 contributed maximum 6% variation. A graph summarizing the QTL regions associated with all traits, as well as the percent of the phenotypic variation explained by significant SNPs for each trait, is provided in Fig. 5. For Na+/K+ ratio under stress condition, the minimum effect locus contributed 6% while its maximum effect locus contributed 12% to the phenotypic variation with an average of 9%. Among the traits tagged under stress condition, the maximum average phenotypic variation was explained for productive tillers (15%), followed by yield/plant (12%). For SFSSI, 37 significant SNPs explained on an average 11% of the phenotypic variation.Figure 5.


Genome-wide association mapping of salinity tolerance in rice (Oryza sativa).

Kumar V, Singh A, Mithra SV, Krishnamurthy SL, Parida SK, Jain S, Tiwari KK, Kumar P, Rao AR, Sharma SK, Khurana JP, Singh NK, Mohapatra T - DNA Res. (2015)

Summary of percentage of variance explained by significant loci in the study. The x-axis represents the trait, and the y-axis shows the contribution (%) of significant loci. The label on the top of average bar is the total number of significant SNPs for respective trait.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4401324&req=5

DSU046F5: Summary of percentage of variance explained by significant loci in the study. The x-axis represents the trait, and the y-axis shows the contribution (%) of significant loci. The label on the top of average bar is the total number of significant SNPs for respective trait.
Mentions: For plant height under control condition, seven SNPs in a cluster on chromosome 4 (8559661–12215278) were found significant and each of the seven significant SNPs explained almost 15% of the phenotypic variation. SNPs found significant for spikelet fertility under control condition explained on an average 5% of the phenotypic variation where both the SNPs on chromosome 7 contributed maximum 6% variation. A graph summarizing the QTL regions associated with all traits, as well as the percent of the phenotypic variation explained by significant SNPs for each trait, is provided in Fig. 5. For Na+/K+ ratio under stress condition, the minimum effect locus contributed 6% while its maximum effect locus contributed 12% to the phenotypic variation with an average of 9%. Among the traits tagged under stress condition, the maximum average phenotypic variation was explained for productive tillers (15%), followed by yield/plant (12%). For SFSSI, 37 significant SNPs explained on an average 11% of the phenotypic variation.Figure 5.

Bottom Line: It is a complex quantitative trait having different components, which can be dissected effectively by genome-wide association study (GWAS).In addition to Saltol, we also found GWAS peaks representing new QTLs on chromosomes 4, 6 and 7.The gene-based SNP array used in this study was found cost-effective and efficient in unveiling genomic regions/candidate genes regulating salinity stress tolerance in rice.

View Article: PubMed Central - PubMed

Affiliation: National Research Centre on Plant Biotechnology, New Delhi 110012, India.

Show MeSH
Related in: MedlinePlus