Table 1.
Mean squares after combined analysis of variance for phenotypic traits of 99 wheat genotypes and a triticale accession evaluated across three test environments under drought-stressed and non-stressed conditions.
Table 2.
Summary statistics of biomass and agronomic traits measured in 100 genotypes evaluated in three environments under drought-stressed and non-stressed conditions.
Fig 1.
Population structure of 97 wheat genotypes based on 15,600 SNP markers.
A. The ΔK determined by the Evanno method showing the stratification of the population into two main clusters. B. The kinship matrix shows the relationship among genotypes. Fig 2. Principal component analysis of 97 wheat genotypes based on 15,600 high quality SNPs with MAF > 0.05 using the first three principal components. A. The first three principal components accounted for about 47% of variation as indicated on the scree plot. B. The genotypes were stratified into two distinct clusters. The six sub-clusters as determined by the highest median values of Ln(Pr Data) based on STRUCTURE. The different colored segment estimate proportion of membership of each genotype to the respective clusters.
Fig 2.
Principal component analysis of 97 wheat genotypes based on 15,600 high quality SNPs with MAF > 0.05 using the first three principal components.
A. The first three principal components accounted for about 47% of variation as indicated on the scree plot. B. The genotypes were stratified into two distinct clusters.
Table 3.
Genetic clusters and their member genotypes, proportion of membership, expected heterozygosity and the mean values of Fst observed from structure analysis of 97 wheat genotypes and a triticale accession.
Fig 3.
Manhattan plots showing SNP markers associated with different traits using CMLM at p-value <0.001.
A. DTH, B. DTM C. RS under non-stress conditions, and D. DTH, E. DTM and F. RS under drought-stress conditions. The horizontal red line represents FDR adjusted p< 0.001.
Fig 4.
Manhattan plots showing SNP markers associated with different traits using CMLM at p-value <0.001.
A. RB, B. SB and C. GY under non-stress conditions, and D. RB, E. SB and F. GY under drought-stress conditions. The horizontal red line represents FDR adjusted p< 0.001.
Table 4.
SNPs significantly associated with agro-morphological traits and putative candidate genes identified in the study under non-stressed conditions.
Table 5.
SNPs significantly associated with agro-morphological traits and putative candidate genes identified in the study under drought stress conditions.
Fig 5.
Physical map of the wheat genome showing the positions of the identified genes localized with the some of the SNP markers.
TRAESCS2B02G321800 = IDM1, TRAESCS2B02G114000 = CIPK3, TRAESCS2B02G398200 = PAL4, TRAESCS3B02G154000 = CYP73A5, TRAESCS2D02G370400 = ABCG11, TRAESCS4D02G238900 = WAKL21, TRAESCS5B02G236600 = AMY1, TRAESCS7B02G377800 = RPS15AE, TRAESCS1D02G276600 = CYP94C1, TRAESCS7D02G150700 = NPY1, TRAESCS4D02G272500 = PSAT2, TRAESCS1B02G340800 = FH6, TRAESCS3B02G045600 = RXF12, TRAESCS3B02G337500 = TIN1.
Fig 6.
Linkage disequilibrium (R2) plot of all the 15,600 SNP markers across genomes in 97 wheat genotypes used in the mapping study.
Fig 7.
Summary of the local LD among markers with significant MTAs for different traits.
A. DTH, B. DTM and C. RS under non-stress conditions and D. DTH, E. DTM and F. RS under drought-stress conditions. The R2 color key indicates the degree of significant association.
Fig 8.
Summary of the local LD among markers with significant MTAs for different traits.
A. RB, B. SB and C. GY under non-stress conditions and D. RB, E. SB and F. GY under drought-stress conditions. The R2 color key indicates the degree of significant association.