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Genome-Wide Association Mapping A Case Study In Bread Wheat

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Phenotypic traits evaluation

Analysis of variance indicated significant differences (P < 0.05) due to the genotype, site, water regime and their interaction effects for all the studied traits. These and the Pearson’s correlations (r) were reported by Mwadzingeni et al. [29]. High and positive correlations and high heritability estimates were detected for most of the traits considered in the current study. Spike length, number of spikelets per spike, plant height, number of kennels per spike, number of days-to-heading and thousand kernel weight had higher levels of genotypic variance (σ2g), hence high heritability values of > 50% (Table 1). The number of days-to-maturity and grain yield had moderate heritability estimates (20% ≤ H2 < 50%).

Population structure

Population structure was constructed to reveal the genetic relationships and to aid genotype selection. Nine distinct populations were recognised (Fig 1) after the LnP (D) kept increasing from -766,307 at K = 1 to -627,026 (with a mean value of ln likelihood of -590,791) at K = 9. Fig 1 presents the population structure for K = 9 where each colour represents a different genetic cluster. The list of genotypes and the overall representation of membership of the sample in each of the 9 clusters are presented in Table 2. The expected heterozygosity of genes among individuals varied from 0.07 to 0.29 with fixation index (Fst) varying from 0.31 to 0.89 among clusters.

Table 2. Nine genetic clusters with their respective list of wheat genotypes, proportion of membership, expected heterozygosity and the mean values of Fst observed from the study population.

https://doi.org/10.1371/journal.pone.0171692.t002

In the structure, Cluster 1 consisted of six and four genotypes from the heat and drought tolerance nurseries, respectively (Table 2). Cluster 2 consisted of only four genotypes from the heat tolerance nursery. This was followed by the largest group (Cluster 3) which comprised of 29 genotypes of which 21 were from the heat tolerance nursery while the remaining eight were from the drought tolerance nursery. Cluster 4 had only genotypes from the heat tolerance nurseries, while Clusters 5, 6 and 7 had mixtures of genotypes. All the local checks (LM61, LM64, LM66, LM67 and LM70) were grouped in Cluster 8, together with ten other genotypes including LM12 from the heat tolerance nursery and nine genotypes from the drought tolerance nursery (Table 2). Likewise, the last cluster contained the genotypes LM78 and LM94 from the drought tolerance nursery.

Linkage disequilibrium

Linkage disequilibrium analysis revealed the presence of 597,871 loci pairs within a physical distance extending up to 16,356 bp. About 45,835 (7.67%) of loci pairs were in significant LD (P < 0.05). Further, 5,188 (0.87%) of the pairs were in complete LD (R2 = 1). Marker pairs in LD were observed over long distances, however, a clear and rapid decline in LD with distance was observed. Pearson’s correlation coefficients revealed negative correlation (r = -0.0813 between the linkage disequilibrium (R2) and the physical distance (bp); as well as between the P-value and R2 (r = -0.59), revealing the existence of linkage decay.

Marker-trait association

A total of 334 significant (P < 0.05) marker-trait associations (MTAs) were observed. S1 Table provides the list of significant (0.05 > P > 0.001) MTAs that could also have influence on respective traits. Only the MTAs that had P values < 0.001 (Table 3) were considered as significant for all traits except for grain yield, thousand seed weight and number of days-to-maturity where significant (P < 0.05) marker-trait associations were considered because the three traits are highly complex, often with moderate to low heritability [36]. These markers explained > 20% of the total phenotypic variation observed on all respective traits. Of the MTAs that were considered significant, four loci were identified to be highly associated with the number of days-to-heading, explaining 24.96% to 37.77% of the total phenotypic variation. Two of these makers were located on chromosome 5A, while the other two were found on chromosomes 5B and 6B (Table 3). The number of days-to-heading were recorded immediately before imposing drought stress but the means from the stressed and non-stressed experiments were used separately for GWAS to check for repeatability.

Marker-trait- association analyses revealed association between specific phenotypes and genetic variants within a genome, which could lead to the discovery of genes controlling the traits. Two markers located on chromosomes 1A and 2D were associated with plant height under drought-stress. Under non-stressed condition, six markers were associated with plant height of which two were located on chromosome 2B and the rest were on chromosomes 5A, 5B, 6B, and 7B. These markers explained 23.75% to 28.8% of the variation in plant height. Spike length was associated with thirteen markers under drought-stressed condition explaining 22.17% to 31.96% of the total phenotypic variation; and eight markers under non-stressed condition; explaining 21.20% to 30.45% of the variation in spike length. The markers observed for this trait under drought-stress were from chromosomes 1B, 2B, 2D, 3A, 4B, 5B, 6A, 6B and 7A. Eight DArT markers were associated with spike length under non-stressed condition of which seven markers were consistent under both drought-stressed and non-stressed conditions from chromosomes 2B, 2D, 5B, 5B and 7A (Table 3). Under drought-stress, SPS was highly associated with eight markers located on chromosomes 6B, 2D, 2B, 5D, 1B and 4B; while under the same stress level, seven significant MTAs were recorded that were located on chromosomes 1B, 2D, 4B, 5A, 5B and 6B. Six of the markers, except for one located on chromosome 2B, one on 5A and one on 5B were consistent with the ones obtained under drought-stressed condition (Table 3).

The B genome had most of the significant MTAs observed for this trait. The marker 6B|079.586479380|3949288|3949288 explained the highest proportion of the phenotypic variation (41%) under drought-stressed condition while a marker on chromosome 2B explained the least proportion (28.06%) of the phenotypic variation observed under the non-stressed condition. Under drought-stressed condition, the number of kernels per spike was associated with two markers located on chromosomes 2D and 4A, explaining 30.04% and 30.24% of the observed phenotypic variation, in that order. Eight significant MTAs were detected under non-stressed condition on chromosomes 2D, 6B and 7A explaining 28.06% to 36.46% of the variation of the number of spikelets per spike, respectively. Three MTAs on chromosomes 6B, 7B and 5D were considered significant (0.05 > P > 0.001) for the number of days-to-maturity, thousand seed weight and grain yield accounting for 24.03%, 23.94% and 22.57% of the phenotypic variation, respectively. Table 4 summarises the number of DArTseq markers observed for each of the nine agronomic traits evaluated under drought stressed and non-stressed conditions. Subject to further validation, these markers will be useful for marker-assisted selection for respective traits under target growing conditions.

A pleiotropic locus is associated and affects the expression of more than one phenotypic trait. In this study, several pleiotropic loci were identified including the marker 5A|084.411633690|3534155|3534155 that was associated with DTH, PHT and SPS under non-stressed condition (Table 3). Days-to-heading, PHT and DTM under non-stressed condition; SPL under drought-stressed condition; and SPS under drought-stressed condition were associated with the marker 6B|079.586479380|3949288|3949288 located on chromosome 6B. On chromosome 2D, the locus 2D|128.146584600|4021827|4021827 was associated with PHT under drought-stress condition as well as SPL, SPS, and KPS under both drought-stressed and non-stressed conditions. Plant height and SPL under drought-stressed condition were associated with the marker 1B|063.445873190|3937163|3937163 on chromosome 1B. The marker 7A|065.934336980|1118335|1118335 was associated with SPL under both drought-stressed and non-stressed conditions as well as with KPS under non-stressed condition. Additionally, 5B|000.000000000|3023157|3023157 was associated with SPL under drought-stressed and non-stressed conditions as well as with SPS under drought-stressed condition only. Spike length and SPS under drought-stressed condition were associated with the marker 2B|108.086871100|1132117|1132117, while the marker 4B|042.180040830|1081624|1081624 was associated with SPL under drought-stressed condition only and SPS under both drought-stressed and non-stressed conditions. Further, the locus 5B|000.649324338|1209883|1209883 was associated with DTH and PHT under non-stressed condition as well as SPS under drought-stressed condition. Finally, 6B|031.043100140|1237876|1237876 was associated with SPL and KPS under drought-stressed condition. Blast searches of the marker 6B|031.043100140|1237876|1237876 on the National Center for Biotechnology Information (NCBI) (http://blast.ncbi.nlm.nih.gov/Blast.cgi) and GrainGenes (http://wheat.pw.usda.gov/GG2/blast.shtml) databases indicated that this marker has a sequence alignment that is 97% identical to the TaMFT gene that regulates seed dormancy on chromosome 3A (Nakamura et al. [37]; http://www.uniprot.org/uniprot/A0A0K2RW47).

Out of the 65 significant marker-trait associations observed, 25 trait-specific MTAs were differentiated. Chromosome 2B had four trait specific MTAs of which one was associated with spike length under drought-stress, two with plant height under non-stressed condition and one with spike length under non-stressed condition. Traits that were represented by at least one significant trait-specific marker-trait association under either of the two water conditions were days-to-heading, plant height, spike length, number of spikelet per spike, number of kernels per spike, days-to-maturity, and grain yield (Table 3).

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