Table 1.
List of rice genotypes used in the study.
Fig 1.
Frequency of genotypes with missing data (left), and frequency of DArTseq SNPs (loci) with missing data (right).
Table 2.
Estimation of gene diversity, heterozygosity, PIC and major allele frequency in 59 rice accessions.
Fig 2.
Magnitude of Δ K as a function of Delta K for 59 rice genotypes based on 525 polymorphic DArTseq-derived SNP markers.
Fig 3.
Distribution pattern of 59 rice genotypes based on Bayesian clustering method of DArTseq derived-SNP markers.
Fig 4.
Dendrogram of a Neighbor-Joining (NJ) tree of rice populations constructed for 59 rice genotypes using DArTseq markers based on a mean fixation index (Fst) estimate value of 0.134.
Fig 5.
3D scatter plot of principal component analysis for 59 rice genotypes based on DArTseq-derived SNP markers.
Table 3.
Genetic distances between different populations.
Table 4.
AMOVA of a panel of 59 rice genotypes.
Fig 6.
Phenotypic distribution of GWAS results for grain quality traits (AC, amylose content; ASV, alkali spreading value; GW, grain width; L/W, grain length to width ratio); grain shape (length/width ratio): slender≥3.0; Medium = 2.1–3.0; Bold = 1.1–2.0; Round<1.1; Grain length: Extra-long(≥7.5 mm); Long (6.6–7.5 mm); Medium (5.51–6.6 mm); Short (<5.51mm).
Fig 7.
Manhattan plots of GWAS results for grain quality traits (AC, amylose content; ASV, alkali spreading value; GW, grain width; L_W, grain length to width ratio); Threshold = −log10(p−value) > 3.
Fig 8.
Q-Q plot (left) and patterns of LD blocks (right) of GWAS results indicating the position of candidate genes and/or QTL regions associated with grain quality traits.
Table 5.
Genome wide significant associations (R2) of single nucleotide polymorphisms (SNPs) with amylose content (AC), alkali spreading value (ASV), grain width (GW) and grain length to width ratio (L/W).
Table 6.
Two of the 22 associations previously reported for grain quality traits.