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Novel genomic approaches unravel genetic architecture of complex traits in apple.

Kumar S, Garrick DJ, Bink MC, Whitworth C, Chagné D, Volz RK - BMC Genomics (2013)

Bottom Line: Genomic regions were identified, some of which coincided with known candidate genes, with significant effects on various traits.Correlations between allele substitution effects obtained from single-marker and all-marker analyses were about 0.90 for all traits.Genomic regions with probable pleiotropic effects were supported by the corresponding higher linkage group (LG) level estimated genetic correlations.

View Article: PubMed Central - HTML - PubMed

Affiliation: The New Zealand Institute for Plant & Food Research Limited, Private Bag 1401, Havelock North 4157, New Zealand. Satish.Kumar@plantandfood.co.nz

ABSTRACT

Background: Understanding the genetic architecture of quantitative traits is important for developing genome-based crop improvement methods. Genome-wide association study (GWAS) is a powerful technique for mining novel functional variants. Using a family-based design involving 1,200 apple (Malus × domestica Borkh.) seedlings genotyped for an 8K SNP array, we report the first systematic evaluation of the relative contributions of different genomic regions to various traits related to eating quality and susceptibility to some physiological disorders. Single-SNP analyses models that accounted for population structure, or not, were compared with models fitting all markers simultaneously. The patterns of linkage disequilibrium (LD) were also investigated.

Results: A high degree of LD even at longer distances between markers was observed, and the patterns of LD decay were similar across successive generations. Genomic regions were identified, some of which coincided with known candidate genes, with significant effects on various traits. Phenotypic variation explained by the loci identified through a whole-genome scan ranged from 3% to 25% across different traits, while fitting all markers simultaneously generally provided heritability estimates close to those from pedigree-based analysis. Results from 'Q+K' and 'K' models were very similar, suggesting that the SNP-based kinship matrix captures most of the underlying population structure. Correlations between allele substitution effects obtained from single-marker and all-marker analyses were about 0.90 for all traits. Use of SNP-derived realized relationships in linear mixed models provided a better goodness-of-fit than pedigree-based expected relationships. Genomic regions with probable pleiotropic effects were supported by the corresponding higher linkage group (LG) level estimated genetic correlations.

Conclusions: The accuracy of artificial selection in plants species can be increased by using more precise marker-derived estimates of realized coefficients of relationships. All-marker analyses that indirectly account for population- and pedigree structure will be a credible alternative to single-SNP analyses in GWAS. This study revealed large differences in the genetic architecture of apple fruit traits, and the marker-trait associations identified here will help develop genome-based breeding methods for apple cultivar development.

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Proportion of phenotypic variation explained (by using SNP-based (green color) and pedigree-based (blue color) coefficient of relationships (in Equation 1) for various apple fruit traits (FF: fruit firmness; WCI: weighted cortical intensity; IB: internal browning; TA: titratable acidity; CR: fruit splitting; BP: bitter pit).
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Figure 2: Proportion of phenotypic variation explained (by using SNP-based (green color) and pedigree-based (blue color) coefficient of relationships (in Equation 1) for various apple fruit traits (FF: fruit firmness; WCI: weighted cortical intensity; IB: internal browning; TA: titratable acidity; CR: fruit splitting; BP: bitter pit).

Mentions: A plot of the first two principal components of the SNP genotypes data matrix grouped seedlings largely according to their familial relationships (Figure 1). Some individuals did not cluster within their pedigree-assigned full-sib family groupings. For example, individuals in two families, namely A402 and A406, which have the same maternal parent, were clustered less tightly than the other five families. A break-away group of individuals from families A401 and A405, having the same maternal parent, apparently formed a separate cluster away from their respective full-sibs (Figure 1). These patterns of clustering suggested some pollen contamination, so the actual number of pollen parents should be higher than that suggested by the mating design. Overall, a product–moment correlation of 0.65 was observed between pedigree-based (A matrix) and SNP-based estimates of pair-wise coefficient of relationships. The average pair-wise coefficient of relationships among all study individuals, obtained from the A and G matrices, were 0.36 and 0.50 respectively, reflecting that there are many more relationships not captured by the known pedigree records. The proportion of phenotypic variation explained using the G matrix (in Equation 1) was higher than that using the A matrix for all traits (Figure 2). Results obtained after removing apparent contaminant seedlings, identified from PCA analysis (Figure 1) and also by using PLINK software (http://pngu.mgh.harvard.edu/~purcell/plink), suggested that the magnitude of differences in values were almost identical (not shown) to those in Figure 2. Information presented in Figures 1 and 2 would suggest that using G would better account for population stratification than A, so only GWAS results (Equation 3) using G are presented here.


Novel genomic approaches unravel genetic architecture of complex traits in apple.

Kumar S, Garrick DJ, Bink MC, Whitworth C, Chagné D, Volz RK - BMC Genomics (2013)

Proportion of phenotypic variation explained (by using SNP-based (green color) and pedigree-based (blue color) coefficient of relationships (in Equation 1) for various apple fruit traits (FF: fruit firmness; WCI: weighted cortical intensity; IB: internal browning; TA: titratable acidity; CR: fruit splitting; BP: bitter pit).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3686700&req=5

Figure 2: Proportion of phenotypic variation explained (by using SNP-based (green color) and pedigree-based (blue color) coefficient of relationships (in Equation 1) for various apple fruit traits (FF: fruit firmness; WCI: weighted cortical intensity; IB: internal browning; TA: titratable acidity; CR: fruit splitting; BP: bitter pit).
Mentions: A plot of the first two principal components of the SNP genotypes data matrix grouped seedlings largely according to their familial relationships (Figure 1). Some individuals did not cluster within their pedigree-assigned full-sib family groupings. For example, individuals in two families, namely A402 and A406, which have the same maternal parent, were clustered less tightly than the other five families. A break-away group of individuals from families A401 and A405, having the same maternal parent, apparently formed a separate cluster away from their respective full-sibs (Figure 1). These patterns of clustering suggested some pollen contamination, so the actual number of pollen parents should be higher than that suggested by the mating design. Overall, a product–moment correlation of 0.65 was observed between pedigree-based (A matrix) and SNP-based estimates of pair-wise coefficient of relationships. The average pair-wise coefficient of relationships among all study individuals, obtained from the A and G matrices, were 0.36 and 0.50 respectively, reflecting that there are many more relationships not captured by the known pedigree records. The proportion of phenotypic variation explained using the G matrix (in Equation 1) was higher than that using the A matrix for all traits (Figure 2). Results obtained after removing apparent contaminant seedlings, identified from PCA analysis (Figure 1) and also by using PLINK software (http://pngu.mgh.harvard.edu/~purcell/plink), suggested that the magnitude of differences in values were almost identical (not shown) to those in Figure 2. Information presented in Figures 1 and 2 would suggest that using G would better account for population stratification than A, so only GWAS results (Equation 3) using G are presented here.

Bottom Line: Genomic regions were identified, some of which coincided with known candidate genes, with significant effects on various traits.Correlations between allele substitution effects obtained from single-marker and all-marker analyses were about 0.90 for all traits.Genomic regions with probable pleiotropic effects were supported by the corresponding higher linkage group (LG) level estimated genetic correlations.

View Article: PubMed Central - HTML - PubMed

Affiliation: The New Zealand Institute for Plant & Food Research Limited, Private Bag 1401, Havelock North 4157, New Zealand. Satish.Kumar@plantandfood.co.nz

ABSTRACT

Background: Understanding the genetic architecture of quantitative traits is important for developing genome-based crop improvement methods. Genome-wide association study (GWAS) is a powerful technique for mining novel functional variants. Using a family-based design involving 1,200 apple (Malus × domestica Borkh.) seedlings genotyped for an 8K SNP array, we report the first systematic evaluation of the relative contributions of different genomic regions to various traits related to eating quality and susceptibility to some physiological disorders. Single-SNP analyses models that accounted for population structure, or not, were compared with models fitting all markers simultaneously. The patterns of linkage disequilibrium (LD) were also investigated.

Results: A high degree of LD even at longer distances between markers was observed, and the patterns of LD decay were similar across successive generations. Genomic regions were identified, some of which coincided with known candidate genes, with significant effects on various traits. Phenotypic variation explained by the loci identified through a whole-genome scan ranged from 3% to 25% across different traits, while fitting all markers simultaneously generally provided heritability estimates close to those from pedigree-based analysis. Results from 'Q+K' and 'K' models were very similar, suggesting that the SNP-based kinship matrix captures most of the underlying population structure. Correlations between allele substitution effects obtained from single-marker and all-marker analyses were about 0.90 for all traits. Use of SNP-derived realized relationships in linear mixed models provided a better goodness-of-fit than pedigree-based expected relationships. Genomic regions with probable pleiotropic effects were supported by the corresponding higher linkage group (LG) level estimated genetic correlations.

Conclusions: The accuracy of artificial selection in plants species can be increased by using more precise marker-derived estimates of realized coefficients of relationships. All-marker analyses that indirectly account for population- and pedigree structure will be a credible alternative to single-SNP analyses in GWAS. This study revealed large differences in the genetic architecture of apple fruit traits, and the marker-trait associations identified here will help develop genome-based breeding methods for apple cultivar development.

Show MeSH
Related in: MedlinePlus