Novel genomic approaches unravel genetic architecture of complex traits in apple

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.

Figure 1

Principal component analysis plot of the first two components of 1,120 individuals derived from their SNP genotypes. Pedigree-based grouping (i.e. full-sib families) is also depicted in different colors.

Figure 2

Proportion of phenotypic variation explained (${\mathbf{R}}_{\mathit{LR}}^{\mathbf{2}})$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).

Figure 3

Genome-wide average LD decay estimated from first generation (n=1,120) and second generation (n=1,600) individuals.

Figure 4

Manhattan plots of the –log₁₀(p) values for various apple fruit traits (FF: fruit firmness; WCI: weighted cortical intensity; IB: internal browning; TA: titratable acidity; CR: fruit splitting; BP: bitter pit) from a genome-wide scan are plotted against position on each of 17 linkage groups (represented by different colours). Grey horizontal line indicates the genome-wide significance threshold.

Figure 5

Quantile-quantile plot of the observed and expected –log₁₀(p) values for various traits (FF: fruit firmness; WCI: weighted cortical intensity; BR: internal browning; TA: titratable acidity; CR: fruit splitting; BP: bitter pit) from a genome-wide scan. The values exceeding the genome-wide significance threshold are highlighted in green colour.

Figure 6

Relationship between single nucleotide polymorphism (SNP) allele substitution effects obtained from single-SNP (y-axis) and all-SNP (x-axis) analysis. Correlation coefficient (r) is also shown for each trait.

Figure 7

Linkage group-level and whole genome-level (the dotted horizontal red line) estimated genetic correlation (r_g) between two pairs of traits. A: weighted cortical intensity (WCI) and internal browning (IB); B: fruit splitting (CR) and bitter pit (BP).

Figure 8

Power of detecting marker-trait association for various parameters: sample size =1,120; Linkage disequilibrium (r²) = 0.25; QTL size = 2% of phenotypic variation; Marker (p) and QTL (q) allele frequency = 0.50 or 0.20; Narrow-sense heritability (h²) = 0.15 and 0.40.