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Analysis of main effect QTL for thousand grain weight in European winter wheat (Triticum aestivum L.) by genome-wide association mapping.

Zanke CD, Ling J, Plieske J, Kollers S, Ebmeyer E, Korzun V, Argillier O, Stiewe G, Hinze M, Neumann F, Eichhorn A, Polley A, Jaenecke C, Ganal MW, Röder MS - Front Plant Sci (2015)

Bottom Line: The highest number of significant SNP-markers was found on chromosomes 3B and 1B, while for the SSRs most markers were significant on chromosomes 6D and 3D.Overall, TGW was determined by many markers with small effects.Only three SNP-markers had R(2) values above 6%.

View Article: PubMed Central - PubMed

Affiliation: Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Germany.

ABSTRACT
Grain weight, an essential yield component, is under strong genetic control and at the same time markedly influenced by the environment. Genetic analysis of the thousand grain weight (TGW) by genome-wide association study (GWAS) was performed with a panel of 358 European winter wheat (Triticum aestivum L.) varieties and 14 spring wheat varieties using phenotypic data of field tests in eight environments. Wide phenotypic variations were indicated for the TGW with BLUEs (best linear unbiased estimations) values ranging from 35.9 to 58.2 g with a mean value of 45.4 g and a heritability of H(2) = 0.89. A total of 12 candidate genes for plant height, photoperiodism and grain weight were genotyped on all varieties. Only three candidates, the photoperiodism gene Ppd-D1, dwarfing gene Rht-B1and the TaGW-6A gene were significant explaining up to 14.4, 2.3, and 3.4% of phenotypic variation, respectively. For a comprehensive genome-wide analysis of TGW-QTL genotyping data from 732 microsatellite markers and a set of 7769 mapped SNP-markers genotyped with the 90k iSELECT array were analyzed. In total, 342 significant (-log10 (P-value) ≥ 3.0) marker trait associations (MTAs) were detected for SSR-markers and 1195 MTAs (-log10(P-value) ≥ 3.0) for SNP-markers in all single environments plus the BLUEs. After Bonferroni correction, 28 MTAs remained significant for SSR-markers (-log10 (P-value) ≥ 4.82) and 58 MTAs for SNP-markers (-log10 (P-value) ≥ 5.89). Apart from chromosomes 4B and 6B for SSR-markers and chromosomes 4D and 5D for SNP-markers, MTAs were detected on all chromosomes. The highest number of significant SNP-markers was found on chromosomes 3B and 1B, while for the SSRs most markers were significant on chromosomes 6D and 3D. Overall, TGW was determined by many markers with small effects. Only three SNP-markers had R(2) values above 6%.

No MeSH data available.


Related in: MedlinePlus

Linear regressions of the most TGW promoting (“best”) and the most TGW reducing (“worst”) SSR-alleles with TGW-BLUEs. Linear regression resulted in a relationship between TGW-BLUEs and the 15 “best” or “worst” SSR-alleles in 372 varieties.
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Figure 5: Linear regressions of the most TGW promoting (“best”) and the most TGW reducing (“worst”) SSR-alleles with TGW-BLUEs. Linear regression resulted in a relationship between TGW-BLUEs and the 15 “best” or “worst” SSR-alleles in 372 varieties.

Mentions: We tried to extract the 15 “best” and 15 “worst” markers for SSRs and SNPs, respectively, by choosing the markers with the largest positive or negative additive effects based on the BLUEs, that means the markers with on average having the biggest phenotypic effects in increasing or decreasing grain size. Co-locating or very closely linked markers were omitted (Tables 3, 4). The wheat varieties carried between zero and eight of the 15 “best” TGW enhancing alleles and between zero and six of the 15 “worst” TGW-reducing SSR alleles. The Spearman Rank correlations for the number of “best” or “worst” SSR-alleles per variety with TGW-BLUEs were 0.460 (P = 0.001) or −0.393 (P = 0.001), respectively. The fit for linear regression with TGW-BLUEs was Y = 43.4 +1.2X with R2 = 0.218 for the 15 “best” alleles and Y = 46.3 −1.2X with R2 = 0.178 for the 15 “worst” alleles (Figure 5). This means that varieties with many positive alleles and few negative alleles have the highest TGW and that the effects of alleles are at least partially additive.


Analysis of main effect QTL for thousand grain weight in European winter wheat (Triticum aestivum L.) by genome-wide association mapping.

Zanke CD, Ling J, Plieske J, Kollers S, Ebmeyer E, Korzun V, Argillier O, Stiewe G, Hinze M, Neumann F, Eichhorn A, Polley A, Jaenecke C, Ganal MW, Röder MS - Front Plant Sci (2015)

Linear regressions of the most TGW promoting (“best”) and the most TGW reducing (“worst”) SSR-alleles with TGW-BLUEs. Linear regression resulted in a relationship between TGW-BLUEs and the 15 “best” or “worst” SSR-alleles in 372 varieties.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4555037&req=5

Figure 5: Linear regressions of the most TGW promoting (“best”) and the most TGW reducing (“worst”) SSR-alleles with TGW-BLUEs. Linear regression resulted in a relationship between TGW-BLUEs and the 15 “best” or “worst” SSR-alleles in 372 varieties.
Mentions: We tried to extract the 15 “best” and 15 “worst” markers for SSRs and SNPs, respectively, by choosing the markers with the largest positive or negative additive effects based on the BLUEs, that means the markers with on average having the biggest phenotypic effects in increasing or decreasing grain size. Co-locating or very closely linked markers were omitted (Tables 3, 4). The wheat varieties carried between zero and eight of the 15 “best” TGW enhancing alleles and between zero and six of the 15 “worst” TGW-reducing SSR alleles. The Spearman Rank correlations for the number of “best” or “worst” SSR-alleles per variety with TGW-BLUEs were 0.460 (P = 0.001) or −0.393 (P = 0.001), respectively. The fit for linear regression with TGW-BLUEs was Y = 43.4 +1.2X with R2 = 0.218 for the 15 “best” alleles and Y = 46.3 −1.2X with R2 = 0.178 for the 15 “worst” alleles (Figure 5). This means that varieties with many positive alleles and few negative alleles have the highest TGW and that the effects of alleles are at least partially additive.

Bottom Line: The highest number of significant SNP-markers was found on chromosomes 3B and 1B, while for the SSRs most markers were significant on chromosomes 6D and 3D.Overall, TGW was determined by many markers with small effects.Only three SNP-markers had R(2) values above 6%.

View Article: PubMed Central - PubMed

Affiliation: Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Germany.

ABSTRACT
Grain weight, an essential yield component, is under strong genetic control and at the same time markedly influenced by the environment. Genetic analysis of the thousand grain weight (TGW) by genome-wide association study (GWAS) was performed with a panel of 358 European winter wheat (Triticum aestivum L.) varieties and 14 spring wheat varieties using phenotypic data of field tests in eight environments. Wide phenotypic variations were indicated for the TGW with BLUEs (best linear unbiased estimations) values ranging from 35.9 to 58.2 g with a mean value of 45.4 g and a heritability of H(2) = 0.89. A total of 12 candidate genes for plant height, photoperiodism and grain weight were genotyped on all varieties. Only three candidates, the photoperiodism gene Ppd-D1, dwarfing gene Rht-B1and the TaGW-6A gene were significant explaining up to 14.4, 2.3, and 3.4% of phenotypic variation, respectively. For a comprehensive genome-wide analysis of TGW-QTL genotyping data from 732 microsatellite markers and a set of 7769 mapped SNP-markers genotyped with the 90k iSELECT array were analyzed. In total, 342 significant (-log10 (P-value) ≥ 3.0) marker trait associations (MTAs) were detected for SSR-markers and 1195 MTAs (-log10(P-value) ≥ 3.0) for SNP-markers in all single environments plus the BLUEs. After Bonferroni correction, 28 MTAs remained significant for SSR-markers (-log10 (P-value) ≥ 4.82) and 58 MTAs for SNP-markers (-log10 (P-value) ≥ 5.89). Apart from chromosomes 4B and 6B for SSR-markers and chromosomes 4D and 5D for SNP-markers, MTAs were detected on all chromosomes. The highest number of significant SNP-markers was found on chromosomes 3B and 1B, while for the SSRs most markers were significant on chromosomes 6D and 3D. Overall, TGW was determined by many markers with small effects. Only three SNP-markers had R(2) values above 6%.

No MeSH data available.


Related in: MedlinePlus