http://orcid.org/0000-0001-7358-4690
Figures
Abstract
Genome wide association studies (GWAS) are important in discerning the genetic architecture of complex traits such as biomass allocation for improving drought tolerance and carbon sequestration potential of wheat. The objectives of this study were to deduce the population structure and marker-trait association for biomass traits in wheat under drought-stressed and non-stressed conditions. A 100-wheat (Triticum aestivum L.) genotype panel was phenotyped for days to heading (DTH), days to maturity (DTM), shoot biomass (SB), root biomass (RB), root to shoot ratio (RS) and grain yield (GY). The panel was sequenced using 15,600 single nucleotide polymorphism (SNPs) markers and subjected to genetic analysis using the compressed mixed linear model (CMLM) at false discovery rate (FDR < 0.05). Population structure analysis revealed six sub-clusters with high membership ancestry coefficient of ≤0.65 to their assigned sub-clusters. A total of 75 significant marker-trait associations (MTAs) were identified with a linkage disequilibrium threshold of 0.38 at 5cM. Thirty-seven of the MTAs were detected under drought-stressed condition and 48% were on the B genome, where most quantitative trait loci (QTLs) for RB, SB and GY were previously identified. There were seven pleiotropic markers for RB and SB that may facilitate simultaneous selection. Thirty-seven putative candidate genes were mined by gene annotation on the IWGSC RefSeq 1.1. The significant MTAs observed in this study will be useful in devising strategies for marker-assisted breeding for simultaneous improvement of drought tolerance and to enhance C sequestration capacity of wheat.
Citation: Mathew I, Shimelis H, Shayanowako AIT, Laing M, Chaplot V (2019) Genome-wide association study of drought tolerance and biomass allocation in wheat. PLoS ONE 14(12): e0225383. https://doi.org/10.1371/journal.pone.0225383
Editor: Pawan L. Kulwal, Mahatma Phule Krishi Vidyapeeth College of Agriculture, INDIA
Received: June 19, 2019; Accepted: November 4, 2019; Published: December 4, 2019
Copyright: © 2019 Mathew et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files.
Funding: IM received funding from South Africa Water Research Commission, Grant/Award Number: Project No. K5-2721-4; Nation Research Foundation of South Africa. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Bread wheat (Triticum aestivum L., 2n = 6x = 42, AABBDD) is a commodity crop with a global harvested area of over 210 million hectares [1]. It is a source of food for over 2.5 billion people worldwide [2]. Wheat production and productivity is challenged by numerous biotic and abiotic stresses. Among the major abiotic constraints is recurrent drought driven by climate change. In addition, the inherently low-fertile soils, notably in sub-Sahara Africa, exacerbate the impact of drought stress resulting in higher wheat yield losses compared to other regions [3].
One mechanism by which plants respond to environmental stresses is to adjust their biomass allocation [4]. Therefore, exploiting the phenotypic plasticity present in biomass allocation encompassing the root system of modern wheat cultivars has been proposed as a method to improve drought resilience and yield potential [5]. Enhancing biomass allocation to roots will improve drought tolerance by increasing the moisture extraction capability, while promoting soil C input via root exudation and turnover [6]. However, simultaneous improvement for drought tolerance and C sequestration has not been pursued in crop breeding programs [7], particularly in cereals such as wheat where breeding for high grain yield is the primary objective. In addition, there has been few studies on genetic analysis of root traits because they are difficult to phenotype and require routine destructive sampling [8]. Most importantly, progress in breeding for drought tolerance is slow because of its low heritability and polygenic nature. Identifying the underlying genetic loci for root, shoot and grain biomass under contrasting environments will enable marker-assisted selection to improve selection efficiency [9] and to accelerate development of cultivars with optimal biomass allocation for drought tolerance and C sequestration.
The advent of next generation sequencing (NGS) and genotyping by sequencing (GBS) technologies has provided a means for examining genetic diversity and discovering novel markers [10]. Marker systems such as simple sequence repeats (SSR) and SNPs have been used successfully to elucidate the genetic attributes of complex traits in wheat [11; 12; 13]. Micro-array-based diversity array technology sequencing (DArTseq)-derived single nucleotide polymorphisms (SNPs) have become increasingly important in genome-wide association studies (GWAS) [14]. The DArTseq-derived SNPs have been used extensively on genetic studies of wheat [15; 16; 17]. These markers are reproducible and provide a powerful means to identify genetic variation and genetic makeup at large number of analogous genomic loci such as present in hexaploid wheat. This enables breeders to deduce genetic diversity and genomic loci controlling economic traits through association mapping [18] and to identify QTLs responsible for traits such as drought tolerance and high C sequestration.
It is important to deduce associations between markers and traits to improve efficiency of conventional breeding methods. Genetic markers for several agronomic traits have been identified [17] but genetic control of biomass allocation to yield, shoot and root components remains less investigated despite its important implications for drought tolerance. The genomic loci associated with improved drought tolerance due to high rooting capacity have been elucidated in other crop species such as soya [19], rice [20] and chickpea [21]. The identification of genomic loci for root traits in wheat is limited by the difficulties associated with root phenotyping and the relatively large size and complexity of the wheat genome [22; 23]. However, the scarcity of genetic markers and limited studies on marker-trait associations for biomass allocation and related traits impede the use of marker-assisted selection (MAS) in developing breeding populations for drought tolerance and C sequestration in wheat. Hence, studies on biomass allocation to roots, shoots and grains are required to identify reliable and stable markers. Therefore, the objective of this study was to deduce marker-trait associations for biomass allocation and yield-related traits in a diverse population of wheat genotypes for future marker-assisted breeding to improve drought tolerance and enhanced C sequestration capacity of wheat.
Material and methods
The germplasm
A panel of 100 wheat genotypes were evaluated. The bread wheat genotypes included 95 drought and heat tolerant genotypes initially acquired from the International Maize and Wheat Improvement Center (CIMMYT). These genotypes were purposefully selected for their genetic divergence and breeding history for drought tolerance. The remainder of the bread wheat genotypes were two local checks and two commercial cultivars adapted to temperate climates. The temperate commercial cultivars were included in the study to widen the genetic diversity for rooting ability. The commercial varieties adapted to temperate climates have twice the rooting capacity of wheat grown in warmer winters [24]. The details of the germplasm are presented in S1 Table.
Phenotyping
Test materials were phenotyped involving two experiments under drought-stressed and non-stressed conditions. The two experiments corresponded to four test environments that included two experiments each under drought-stressed and non-stressed conditions in the greenhouse and field. The greenhouse experiments were conducted at controlled environment facility of the University of KwaZulu-Natal (UKZN), while the field trial was conducted at Ukulinga Farm of the UKZN (LAT: 29.667 LON: 30.406 and ALT: 811m) between 2016 and 2018. In the greenhouse experiment, 100 genotypes were sown in October in 2016, while the second experiment was established in May 2017. Both trials were conducted using a 10×10 alpha lattice design with 2 replications. The greenhouse provided shelter against rainfall and irrigation was provided via an automated drip irrigation system inserted directly into individual pots. Fertilizer was also applied through automated drip irrigation at a rate of 300 kg N ha-1 and 200 kg P2O5 ha-1. The different water regimes were initiated 6 weeks after planting to ensure good establishment but also to ensure early exposure of all growth stages to drought. In the non-stress condition, the plants were watered to field capacity (FC) whenever average soil water content fell to 80% of FC, while in the drought stress conditions volumetric soil water content was allowed to drop to 30% of FC before watering to FC. The soil water content was monitored by a soil moisture probe and random weighing of the pots. The two watering treatments were maintained until maturity (~120 days).
The field experiments was established in May 2017 following a similar design. The soil surface was covered by a custom made plastic, which acted as a mulch to prevent rain water from entering into the soil. Basal fertilizer composed of nitrogen (N), phosphorous (P) and potassium (K) was applied at a rate of 120:30:30 kg ha-1 (N:P:K). Other agronomic practices were as per normal wheat production practice in South Africa [25]. Irrigation was applied through a drip irrigation system with the aim to maintain soil water content at FC in the well-watered regime. Under the drought stress treatment, irrigation was withheld 5 weeks after crop emergence until just before signs of permanent wilting were observed upon which irrigation was reinstated. This differs from the 80 and 30% FC soil water regimes maintained in the greenhouse because it is more difficult to determine field capacity and regulate soil water content appropriately under field conditions compared to a controlled greenhouse environment. During the field experiment, irrigation was withheld before anthesis to induce drought stress in a way that simulated in situ field wheat production. Amount of water applied and prevailing temperatures were recorded for the period to determine the extent of drought stress [26]. The following phenotypic traits were assessed: the number of days to 50% heading (DTH) and number of days to 50% maturity (DTM) were counted from date of planting, plant height (PH) expressed in centimeter and spike length (SL, cm) were measured with a calibrated ruler, shoot biomass (SB, grams per m2), root biomass (RB, grams per m2), thousand kernel weight (TKW, g 1000−1 seeds) and grain yield (GY grams per m2) were weighed on a laboratory precision digital scale at maturity while root to shoot ratio (RS) were derived from RB and SB accordingly and the number of kernels per spike (KPS) were counted after shelling.
Phenotypic data were subjected to the Shapiro-Wilk test for normality before analysis of variance in Genstat 18th edition [27]. Variance components were calculated in Genstat 18th edition using the general linear model [27] where the environments and water regimes were considered to have fixed effects while genotype effects were treated as random following [28]. General statistics including means, standard error, and coefficient of variation for the phenotypic data were computed in Genstat 18th [27]. The associations among the phenotypic traits were tested using Pearson correlations. Broad sense heritability (H2) estimates were calculated from phenotypic variance (σ2p) and the genotypic variance (σ2g) according to [29] as follows:
Where δ2p = δ2g+ δ2ge/e + δ2e/re
Where δ2p = phenotypic variance, δ2g = genotypic variance, δ2ge = genotype × environment interaction variance, δ2e = residual variance while r = number of replications and e = number of environments. The inclusion of three environments allows for an effective evaluation of quantitative traits and ensures precision in estimating heritability values [30; 31].
Genotyping
The 100 genotypes were planted in the greenhouse in seedling trays. Genomic DNA was extracted from leaves of 3-week old seedlings. The DNA was extracted using CTAB method [32]. After extraction, the nucleic acid concentration and purity of the DNA was checked using a NanoDrop 2000 spectrophotometer (ND- 2000 V3.5, NanoDrop Technologies, Inc.) before being shipped to Diversity Arrays Technology (DArT) Pty Ltd, Australia for whole genome sequencing on the DArTseq platform. Whole-genome genotyping for the 100 wheat genotypes was carried out on the platform developed by [33] using 28,356 DArT markers. The markers were integrated into a linkage map by inferring marker order and position from the consensus DArT map. The mean polymorphic information of the markers was 0.16 and ranged between 0.0 and 0.50 with a reproducibility index of 0.93.
DArTseq SNP filtering.
All the individuals were genotyped using 28,356 silico DArT markers assigned to 21 chromosomes. A total of 15,600 informative DArTseq-derived SNP markers and 99 genotypes were used after data imputation where SNP loci and individuals with >20% missing data and rare SNPs with <5% minor allele frequencies (MAF) were pruned from the data before analysis as previously described by [16].
Population structure.
The population structure of the 99 genotypes was assessed using the Bayesian clustering method in STRUCTURE version 2.3.4 [34]. A 10,000 burn-in period and 10,000 Markov Chain Monte Carlo (MCMC) iterations were used to derive the population structure based on 15,600 DArTseq-derived SNP markers distributed across the wheat genome. The K-value was set between 1 and 10 to generate the number of subpopulations in the accessions. The best K-value for estimating a suitable population size for the dataset was determined by the K-value with the highest likelihood to reduce the risk of false positive associations [35]. The optimal number of clusters and sub-clusters in the population were determined by the Evanno method based on ΔK and the highest median values of Ln(Pr Data) in CLUMPAK [36].
Association mapping.
A total of 15,600 DArTseq derived SNP markers and best linear unbiased predictors (BLUPs) for the phenotypic traits measured under different environments were used to determine marker-trait associations among the 99 accessions in the population. This panel formed a core set of new wheat introductions for drought tolerance breeding in South Africa. Usually large number of accessions are used but population sizes between 60 and 150 have been used successfully in previous studies [37; 38; 39; 40]. The information obtained from this GWAS would provide useful baseline information since there are very few studies elucidating genetic control of biomass allocation and its impact on drought tolerance. Prior to conducting GWAS analysis, the phenotypic data collected from each of the experiments were analyzed using the nlme package in R to generate best linear unbiased predictors (BLUPs). Genotype was fitted as a fixed effect and environment were fitted as random effect. The BLUPs for each genotype were used as input in GWAS analysis to handle the variations among the environments. The BLUPs would allow to make unbiased adjustment for fixed effects thus eliminating the need to consider marker trait associations (MTAs) for individual environments. The association mapping was conducted on biomass allocation traits (RB, SB, GY and RS) using a compressed mixed linear model (CMLM) method that factors in both population structure and kinship using the Q + K model where Q = population structure determined by principal component analysis and K = kinship matrix generated in TASSEL 5. The marker-trait association analysis was conducted in the GAPIT program of the R software [41]. The population structure matrix (Q) was fitted as a fixed factor while the kinship matrix (K) was treated as a random factor. The markers were considered as significant for each trait individually at a critical p-value of 1% and false discovery rate of 5%, which was deemed to be highly stringent to reduce the risk of false marker-trait associations (MTAs) [35; 42].
Determination of Linkage disequilibrium.
The GAPIT program in R software was used to conduct linkage disequilibrium analysis following [41]. Linkage disequilibrium (LD) was based on trait specific genome-wide markers whose positions were specific out of the 15,600 polymorphic markers. The squared allele frequency correlations (R2) at p-values <0.001 for each pair of loci were considered to estimate significant linkage disequilibrium. The LD was presented graphically as a heat map constructed using the LDHeatmap package [43] in R [44] based on pairwise R2 of SNPs that were significantly associated with each of the traits by plotting the R2 values against the genetic distance, in centiMorgans (cM).
Putative candidate gene analysis and expression data.
Candidate genes overlapping the significant markers were blasted on the IWGSC RefSeq v1.1 using BLASTn function [45]. Genes adjacent to the significant MTAs were identified by the RefSeq v1.0.Gene Ontology (GO) annotation of the potential candidate genes was carried out using Blast2GO pro tool v.3.1.3 [46] and the physical map was visualized on KnetMiner [47; 48]. Subsequently, their molecular function and associated traits were mined from Ensembl plant for T. aestivum.
Results
Phenotyping variation across genotypes and water regimes
The 3-way interaction involving the levels of the following three factors: genotypes, water regime and test environment was significant (p<0.05) for NPT, PH, DTM and RS as revealed by the analysis of variance (ANOVA) (Table 1). The number of days to maturity (DTM) was significantly (p<0.05) affected by the interaction between genotype and water regime. The genotype × test environment interaction effects were significant (p<0.05) on all traits except GY. Individually, the genotype, water regime and test environment effects significantly (p<0.05) affected all traits except TKW. Only six traits, DTH, DTM, RB, SB, RS and GY were considered for further analysis in accordance with the objective of elucidating genetic control of biomass allocation. The DTH and DTM were considered as they affect the phenological development of biomass accumulation and partitioning between vegetative and reproductive organs.
Days to heading ranged between 40 and 138 with and a mean of 40 (Table 2). Under drought stress, the genotypes matured earlier by an average of 10 days compared to non-stress conditions. Root biomass ranged between 131.3 and 1622.3 g m-2 under non-stressed, while it ranged from 64.6 to 735.8 g m-2 under drought-stressed conditions. A 32% reduction in mean RB due to drought stress was observed. The lowest shoot biomass was 109 g m-2 obtained under stressed condition, while the highest was 1,244 g m-2 with improved water availability. The root to shoot ratios varied from a minimum of 0.14 in wheat genotype LM39 and a maximum of 1.45 recorded in triticale. On average, grain yield declined by 48% under drought-stressed condition.
Population structure analysis
The results of STRUCTURE based on markers with MAF>0.05 for the wheat genotypes showed that ΔK was highest at K = 2, showing the presence of two main clusters in the population while the highest median values of Ln (Pr Data) occurred at K = 6, showing that the main clustered could further be divided into six sub-clusters (Fig 1A). The kinship matrix shows a clear stratification of the genotypes into two main clusters and different sub-clusters (Fig 1B). The highest median values of Ln(Pr Data) determined that the population could further be sub-divided into six minor clusters shown by k = 6 (Fig 1C). The principal component analysis (PCA) based on the first three principal components accounted for 47% of the total variation (Fig 2A) and revealed two distinct clusters in the population (Fig 2B). The six minor clusters deduced by the highest median values of Ln(Pr Data) were of different sizes and composition. Membership of all genotypes to a particular sub-cluster was based on at least 65% ancestry. Cluster 1 had the largest membership with 34% of the population, while the smallest was Cluster 4 with only 6% (Table 3). Cluster 1 was comprised entirely of the genotypes from the heat tolerant nursery with code BW, except 2 genotypes LM23 and LM47. Clusters 3 and 6 exhibited the highest level of heterozygosity with an average of 0.23, while the rest of the clusters averaged below 0.15. The mean fixation index (Fst) ranged between 0.45 and 0.85 among the clusters. The genetic distance among the different populations showed that clusters 1 and 5 were the most divergent with a genetic distance of 0.40 while the shortest distance was between clusters 3 and 6 (S2 Table).
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.
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.
Marker-trait associations under different water regimes
Phenological traits (DTH and DTM) and biomass allocation traits (RB, SB, RS and GY) were subjected to GWAS using the 15,600 SNP markers. A total of seventy-six marker traits associations (MTAs) were identified at a stringent FDR-adjusted p value <0.001 revealing candidate loci for each trait across different water regimes (Tables 4 and 5). There were 38 MTAs identified under each water regime (Tables 4 and 5, Fig 3). Quantile-quantile (QQ) plots (S1 Fig and S2 Fig) showed that the -log10 (p-values) for the different traits evaluated under each water regime conformed to normal distribution and were constricted enough towards expected values to account for population stratification. A total of eight, four and four significant (P<0.001) MTAs were detected for DTH, DTM and RS, respectively, under non-stressed condition (Table 4, Fig 3A, 3B and 3C), while there were nine and five MTAs detected for the respective traits under stressed condition (Table 5, Fig 3D and 3E). There were four markers associated with RS under drought-stress conditions (Fig 3F). A similar number of significant markers were identified for RS under non-stress and drought-stressed conditions. There were two pleiotropic markers for RB and SB detected on chromosome 1B under non-stressed conditions (Table 4, Fig 4A and 4B). A total of nine markers were observed to have significant association with grain yield, with seven occurring under non-stress conditions. Only markers on chromosome 4D were identified to have significant association with GY under non-stressed conditions (Fig 4C). Under drought-stressed conditions, three pleiotropic markers for RB and SB were identified, two were on chromosome 2B and one on chromosome 3B (Table 5, Fig 4D and 4E). There were only two markers identified for GY under drought stress (Fig 4F).
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.
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.
Putative candidate gene analysis and expression data
The physical genetic map shows that a number of the markers were co-localized, especially on chromosomes 2B, 2D, 3B, 4A and 4D (Fig 5). Co-localization of genes was observed on chromosome 2D and 3B at positions 591.6 and 785Mb, respectively. DTH had two genes TraesCS2D02G462600 and TraesCS2D02G514100 in close proximity on the 2D chromosome and TraesCS2D02G370400 overlapping the significant MTAs for RB. The two pleiotropic markers for RB and SB detected on chromosome 1B flanked a region overlapping the TraesCS1B02G340800 (Table 5). One of the identified marker on chromosome 2B covered a region overlapping the gene TraesCS2B02G398200 while the markers on chromosome 3B overlapped gene TraesCS3B02G154000 and TraesCS3B02G061700. On chromosome 3B, there was co-localization of genes overlapping significant MTAs for DTH, RB and RS. Interestingly, RB and SB shared common loci and pleiotropic markers showing that they are genetically highly correlated. The common loci for RB and SB on chromosome 1B was associated with gene FH6 (position 568.75Mb). The other pleiotropic loci on chromosomes 2B overlapping genes PAL4 at positions 565.06Mb, and on chromosome 3B in the region coding for TraesCS3B02G061700 (position 33.95Mb) and CYP73A5 (at 146.55Mb).
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.
Linkage disequilibrium among the markers
The markers exhibited a linkage disequilibrium decay across the whole genome with an estimated threshold value of R2 = 0.38 at about 50Mbp based on the 95th percentile of the distribution (Fig 6). The linkage disequilibrium (LD) analysis was conducted on SNP markers with significant association with a particular trait under each water regime. The LD ranged from very weak correlation (r<0.20, p<0.001) for SNP markers associated with DTM under non-stress conditions (Fig 7A–7F) to strong correlations (r>0.80, p<0.001) for SB under well-watered conditions (Fig 8A–8F). For RB, there were two markers M1576 and M6665, which exhibited linkage disequilibrium (r>0.080, p<0.001) under non-stress conditions (Fig 8A). Three markers M1576, M6660 and M9150 were in disequilibria for SB under non-stress and they were within 47cM (Fig 8B). For GY, there were two markers in linkage disequilibria (r>0.80, p<0.001) identified under non-stress conditions spanning a 33cM length (Fig 8C). None of the significant MTAs showed high level of LD for RB under drought stress conditions (Fig 8D). There were suggestions of distinct haplotypes for SB under drought stress (Fig 8E). For SB under drought stress, the markers exhibited low to high LD over 57cM (Fig 8E) while the significant markers for GY had low LD (Fig 8F).
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.
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.
Discussion
Phenotypic variability of germplasm and environmental response
Understanding biomass allocation in wheat could provide an opportunity and an alternative approach to developing drought tolerant cultivars that can also sequester relatively more C for soil remediation. The wheat genotypes evaluated in this study exhibited wide genetic variation for agronomic and biomass accumulation traits (roots, shoot and grain) (Tables 1 and 2). The wide genetic variation was expected since the population included genotypes from CIMMYT, local accessions and temperate adapted cultivars. Biomass accumulation in roots, shoot and grains was significantly reduced by 32, 30 and 48%, respectively, under drought stress confirming that biomass accumulation has phenotypic plasticity. This plasticity could be exploited in drought tolerance breeding of wheat to mitigate water scarcity [49]. The variance components, heritability and genetic correlations of the traits were reported in [50]. The traits exhibited different levels of heritability, with RB (H = 78%) and SB (H = 64%) having higher heritability estimates than RS (H = 28%) and GY (H = 17%). However, the lower heritability estimates observed under stressed condition could reduce selection efficiency [49] and may impact negatively on QTL detection [51].
Population structure and linkage disequilibrium
The population structure and principal component analyses revealed that the genotypes could be divided into two distinct major clusters (Figs 1 and 2). Following the method of [52], the K value with the highest ΔK-value confirms the number of appropriate clusters for that population [16; 35; 53]. The two clusters identified separated the genotypes into one cluster composed mainly of the genotypes from the CIMMYT heat tolerant nursery while the other clusters consisted of drought tolerant and local checks. However, the structure analysis also identified the presence of admixtures and the two clusters could be further delineated into six sub-clusters. The genotypes form part of a training population that can be used to develop and optimize a model for predicting genomic estimated breeding value (GEBV) since it has now been phenotyped and genotyped [54]. The mean fixation indices (Fst) associated with the six clusters ranged between 0.45 and 0.85, indicating a potentially high level of differentiation among the clusters although within cluster variation was low as shown by the heterozygosity values ranging between 0.09 and 0.24. Based on the mean fixation indices and the genetic distances, the genotypes form part of a training population that can be used to develop and optimize a model for predicting genomic estimated breeding value since it has now been phenotyped and genotyped [54]. However, these Fst values should be used cautiously in analyzing diversity or differentiation as these statistics are often misconstrued [55]. The resultant population structure and genetic distances between pairs of clusters observed in this study also confirmed the existence of admixtures and kinship. The admixtures and kinship patterns observed were attributed to sharing of common parentage among some of the genotypes. For instance, 17 out of the 32 genotypes including genotypes BW124, BW147, BW151 and BW159 in sub-cluster 1 shared a common parent CGSS05B00258T-099TOPY. Parent CROC_1/AE.SQUARROSA was common for 4 genotypes LM79, LM81, LM81 and LM90 in sub-cluster 2 while WBLL1 and PASTOR were common parents a considerable number of genotypes. The use of a small number of elite varieties exhibiting desirable traits and routinely crossed to fix the desirable alleles is a standard practice in developing modern wheat cultivars [56], which contributes to narrowing of genetic diversity.
Marker-trait associations and putative genes
The population structure of the panel, the variance components, heritability and genetic correlations for the phenotypic traits confirmed that the panel of genotypes was suitable for use in a genome wide association study involving yield and biomass traits. The use of a diverse panel of genotypes can provide more valuable inference compared to bi-parental populations [57] by taking advantage of maximum allelic diversity [58; 59].
The identified 77 significant markers associated with the phenological and biomass allocation traits included 36 that were detected on the B genome similar to other studies that previously detected significant markers for root and shoot biomass on this genome [57; 60]. [57] found extremely rare haplotype variants that increased root growth on chromosome 5B, while [60] reported significant SNPs on 1A, 2A, 3B, 5B, 6A, and 7B for root dry weight with the major two QTLs being on 1A and 5B. The remaining significant SNPs detected in this study, which have not been reported previously could be novel alleles important for influencing biomass allocation patterns in wheat. The major QTLs reported by [60] were found using seedling data unlike in this study, which used phenotypic data collected on mature plants. This is useful since selection at early stages may not reflect trait performance at later growth stages, particularly, for traits that are relevant for drought tolerance and C sequestration.
Significant pleiotropic loci were detected on the B genome for root and shoot biomass, showing that root and shoot biomass have common and distinct genomic loci. Root and shoot biomass shared an association region on chromosomes 1B, 2B and 3B which suggest that this could be the basis for their high genetic correlation as reported by [50]. The common loci for RB and SB on chromosome 1B was associated with gene FH6 (TraesCS1B02G340800 gene), which is known for signaling pathways in root lateral meristem and shoot apex development [61]. The identified marker on chromosome 2B covered a region overlapping the gene PAL4, known for upregulating protein for stress response and stem elongation [62]. The identification of a putative gene TraesCS3B02G061700 on chromosome 3B for RB and SB and its co-localization with gene RXF12 for DTH is an indication of a strong physical linkage among these traits. The putative gene TraesCS3B02G061700 is known to be actively involved in the photosystem I [63], which could explain its influence on biomass accumulation. The RXF12 gene has been implicated in the defense mechanism against drought and heat stress in Arabidopis [64] and their suggested strong linkage could assist in simultaneous selection for high root and shoot biomass and drought tolerance in wheat. The detection of common SNPs for root and shoot biomass on the B genome under drought stress suggests that it carries the critical loci controlling for root biomass and possibly drought tolerance mechanisms [58] and provides an opportunity for effective simultaneous improvement using the overlapping markers. In reality, many complex traits exhibit linkage and selection of pleiotropic genes has potential to cause major simultaneous changes in the traits [65; 66]. However, there is a concern that increasing below ground biomass might negatively affect other economic traits due to undesirable linkage drag associated with unfavorable pleiotropy. [67] asserted that simultaneous improvement of root and above ground traits will only be possible if they have common and distinct genomic loci that can be manipulated independently or simultaneously. The marker was found to be in LD with other markers associated with DTH, SB and RB indicating tight linkage, which could provide opportunities for biomass allocation improvement in wheat. [57] suggested that unfavorable linkage drag between negatively correlated traits can be overcome by identifying rare recombinant genes.
The current association study identified two markers significant for GY under drought stressed conditions, of which TRAESCS4A02G124400 was reported to prolonged seed dormancy, caused male sterility, and dwarfism in rice [68]. Under non-stress conditions, two putative genes were identified. The gene TRAESCS4D02G238900 possibly affected grain yield accumulation through indirect effects on 100-seed weight, seed length and the regulation of cytokins [69] while TRAESCS4D02G238900 regulated leaf senescence [70]. The indirect effects on grain yield via up- or down-regulation of cytokins or control of leaf senescence has been established in wheat [71]. The low number of observed MTAs for GY under drought-stressed condition was consistent with [16] and [72] who also found reduced number of MTAs for GY under drought stress in wheat. Grain yield is highly influenced by genotype by environment interaction, which could have negatively impacted the ability to detect the associated markers under stressed conditions. The identified MTAs under non-stressed conditions are useful for future marker-assisted selection. The genomic information obtained here would be useful to improve accuracy in estimating the breeding value of related genotypes.
Overall, across all genomes and markers, an LD of 0.38 occurring at 5cM indicated that the LD decay occurred at relatively shorter distances, which can be attributed to narrow genetic variation due to repeated backcrossing to a limited number of elite breeding lines. A substantial number of SNPs that were significantly associated with RB, SB and GY occurred on genetic regions spanning between 9 and 60cM at an average LD of 0.40, showing the possibility of tight to moderate linkage. Similarly, [73] found an average LD of 0.2 extending over 2-3cM while there were some loci extending between 25 and 41cM with LD >0.7. [74] found moderate (<20cM) and loose (>50cM) inter-chromosomal linkage in closely related durum (Triticum durum Desf.) using microsatellite markers. The observed markers with non-significant LD is not unique given that other studies reported them as a result of possible admixtures of the genotypes [75; 76].
Conclusions
The use of a diverse population of wheat genotypes with different pedigrees allowed for detection of 77 MTAs for days to heading and maturity and, biomass allocation to roots, shoots and grain yield. The identified markers such as M788, M1576 and M7199 for root biomass can be used in marker-assisted selection to improve the root system of wheat. These markers are useful in breeding for drought tolerance and C sequestration. The seven pleiotropic markers for root and shoot biomass will enable simultaneous selection for above and below ground biomass suggesting that drought tolerance and C sequestration are tightly linked. The identified MTAs on chromosomes 1B, 2B, 3A, 4D and 7A that have not been previously reported could provide novel genes for wheat breeding. This study provides a foundation for marker-assisted breeding for biomass allocation drought tolerance and C sequestration in wheat. Validation of the identified markers using a diverse and large size or bi-parental population, and using tissue and stage specific gene expression data from RNASeq would be required before embarking on a large scale breeding program.
Supporting information
S1 Table. List of genotypes and their pedigree used in the study.
https://doi.org/10.1371/journal.pone.0225383.s001
(DOCX)
S2 Table. Genetic distances between different clusters obtained from structure analysis of 99 wheat genotypes and a triticale accession.
https://doi.org/10.1371/journal.pone.0225383.s002
(DOCX)
S3 Table. GAPIT Results for DTH under non stress.
https://doi.org/10.1371/journal.pone.0225383.s003
(CSV)
S4 Table. GAPIT Results for DTH under drought.
https://doi.org/10.1371/journal.pone.0225383.s004
(CSV)
S5 Table. GAPIT Results for DTM under non stress.
https://doi.org/10.1371/journal.pone.0225383.s005
(CSV)
S6 Table. GAPIT Results for DTM under drought.
https://doi.org/10.1371/journal.pone.0225383.s006
(CSV)
S7 Table. GAPIT Results for RS under non stress.
https://doi.org/10.1371/journal.pone.0225383.s007
(CSV)
S9 Table. GAPIT Results for RB under non stress.
https://doi.org/10.1371/journal.pone.0225383.s009
(CSV)
S10 Table. GAPIT Results for RB under drought.
https://doi.org/10.1371/journal.pone.0225383.s010
(CSV)
S11 Table. GAPIT Results for SB under non stress.
https://doi.org/10.1371/journal.pone.0225383.s011
(CSV)
S12 Table. GAPIT Results for SB under drought.
https://doi.org/10.1371/journal.pone.0225383.s012
(CSV)
S13 Table. GAPIT Results for GY under non stress.
https://doi.org/10.1371/journal.pone.0225383.s013
(CSV)
S14 Table. GAPIT Results for GY under drought.
https://doi.org/10.1371/journal.pone.0225383.s014
(CSV)
S1 Fig.
Quantile-Quantile plots indicating the normality of data 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.
https://doi.org/10.1371/journal.pone.0225383.s015
(TIF)
S2 Fig.
Quantile-Quantile plots indicating the normality of data 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.
https://doi.org/10.1371/journal.pone.0225383.s016
(TIF)
References
- 1. Portmann FT, Siebert S, Döll P. MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: A new high‐resolution data set for agricultural and hydrological modeling. Global Biogeochem Cy. 2010; 24(1): 1–18.
- 2. Pfeifer M, Kugler KG, Sandve SR, Zhan B, Rudi H, Hvidsten TR, et al. Genome interplay in the grain transcriptome of hexaploid bread wheat. Science. 2014; 345(6194): 1250091–101. pmid:25035498
- 3. Mapfumo P, Onyango M, Honkponou SK, El Mzouri EH, Githeko A, Rabeharisoa L, et al. Pathways to transformational change in the face of climate impacts: an analytical framework. Climate and Development. 2017; 9(5): 439–51.
- 4. Poorter H, Nagel O. The role of biomass allocation in the growth response of plants to different levels of light, CO2, nutrients and water: a quantitative review. Func Plant Bio. 2000; 27(12): 1191–126.
- 5. White CA, Sylvester-Bradley R, Berry PM. Root length densities of UK wheat and oilseed rape crops with implications for water capture and yield. J Exp Bot. 2015; 66(8): 2293–303. pmid:25750427
- 6. Baldock JA, Skjemstad JO. Role of the soil matrix and minerals in protecting natural organic materials against biological attack. Org Geochem. 2000; 31(7–8): 697–710.
- 7. Paustian K, Lehmann J, Ogle S, Reay D, Robertson GP, Smith P. Climate-smart soils. Nature. 2016; 532(7597): 49–57. pmid:27078564
- 8. Osmont KS, Sibout R, Hardtke CS. Hidden branches: developments in root system architecture. Ann Rev Plant Bio. 2007; 58: 93–113.
- 9. Collard BC, Mackill DJ. Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Philos Trans R Soc Lond B Biol Sci. 2008; 363(1491): 557–72. pmid:17715053
- 10. Korte A, Farlow A. The advantages and limitations of trait analysis with GWAS: a review. Plant Methods. 2013; 9(1): 29–39.
- 11. Breseghello F, Sorrells ME. Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics. 2006; (2): 1165–77. pmid:16079235
- 12. Crossa J, Burgueno J, Dreisigacker S, Vargas M, Herrera-Foessel SA, Lillemo M, et al. Association analysis of historical bread wheat germplasm using additive genetic covariance of relatives and population structure. Genetics. 2007; 177(3): 1889–1913. pmid:17947425
- 13. Charmet G, Masood-Quraishi U, Ravel C, Romeuf I, Balfourier F, Perretant MR, et al. Genetics of dietary fibre in bread wheat. Euph. 2009; 170(1–2): 155–168.
- 14. Qiu X, Gong R, Tan Y, Yu S. Mapping and characterization of the major quantitative trait locus qSS7 associated with increased length and decreased width of rice seeds. Theor App Gen. 2012; 125(8): 1717–26.
- 15. Liu Y, Lin Y, Gao S, Li Z, Ma J, Deng M, et al. A genome‐wide association study of 23 agronomic traits in Chinese wheat landraces. The Plant Journal. 2017; 91(5): 861–73. pmid:28628238
- 16. Mwadzingeni L, Shimelis H, Rees DJ, Tsilo TJ. Genome-wide association analysis of agronomic traits in wheat under drought-stressed and non-stressed conditions. PloS one. 2017; 12(2): e0171692. pmid:28234945
- 17. Sukumaran S, Reynolds MP, Sansaloni C. Genome-wide association analyses identify QTL hotspots for yield and component traits in durum wheat grown under yield potential, drought, and heat stress environments. Front Plant Sci. 2018; 9: 81–96. pmid:29467776
- 18. Maccaferri M, Ricci A, Salvi S, Milner SG, Noli E, Martelli PL, et al. A high‐density, SNP‐based consensus map of tetraploid wheat as a bridge to integrate durum and bread wheat genomics and breeding. Plant Biotechnol J. 2015; 13(5): 648–63. pmid:25424506
- 19. Ye H, Song L, Chen H, Valliyodan B, Cheng P, Ali L, et al. A major natural genetic variation associated with root system architecture and plasticity improves waterlogging tolerance and yield in soybean. Plant Cell Environ. 2018; 41(9): 2169–82. pmid:29520811
- 20. Uga Y, Sugimoto K, Ogawa S, Rane J, Ishitani M, Hara N, et al. Control of root system architecture by DEEPER ROOTING 1 increases rice yield under drought conditions. Nat Genet. 2013; 45(9): 1097. pmid:23913002
- 21. Varshney RK, Thudi M, Nayak SN, Gaur PM, Kashiwagi J, Krishnamurthy L, et al. Genetic dissection of drought tolerance in chickpea (Cicer arietinum L.). Theor App Gen. 2014; 127(2): 445–462.
- 22. Fleury D, Jefferies S, Kuchel H, Langridge P. Genetic and genomic tools to improve drought tolerance in wheat. J Exp Bot. 2010; 61(12): 3211–22. pmid:20525798
- 23. Li L, Peng Z, Mao X, Wang J, Chang X, Reynolds M, et al. Genome-wide association study reveals genomic regions controlling root and shoot traits at late growth stages in wheat. Ann Bot. 2019; 1–14.
- 24. Thorup-Kristensen K, Cortasa MS, Loges R. Winter wheat roots grow twice as deep as spring wheat roots, is this important for N uptake and N leaching losses? Plant and Soil. 2009; 322(1–2): 101–14.
- 25. Department of Agriculture, Forestry and Fisheries (DAFF). Wheat Production Guideline. 2010. Pretoria, South Africa
- 26. Mathew I, Shimelis H, Mutema M, Clulow A, Zengeni R, Mbava N, et al. Selection of wheat genotypes for biomass allocation to improve drought tolerance and carbon sequestration into soils. J Agron Crop Sci. 2019; 205(4): 385–400.
- 27.
Payne RW, Murray D A, Harding S A. An introduction to the GenStat command language. 2017. Hemel Hempstead, UK: VSN International.
- 28. Edwards CE, Ewers BE, Weinig C. Genotypic variation in biomass allocation in response to field drought has a greater effect on yield than gas exchange or phenology. BMC Plant Biol. 2016; 16(1): 185. pmid:27558796
- 29.
Allard RW, Allard RW. Principles of plant breeding. John Wiley & Sons. 1999.
- 30. Gillespie JH, Turelli M. Genotype-environment interactions and the maintenance of polygenic variation. Genetics. 1989; 121(1): 129–38. pmid:17246488
- 31. Herzig P, Maurer A, Draba V, Sharma R, Draicchio F, Bull H, et al. Contrasting genetic regulation of plant development in wild barley grown in two European environments revealed by nested association mapping. J Exp Bot. 2018; 69(7): 1517–31. pmid:29361127
- 32. Huang J, Ge X, Sun M. Modified CTAB protocol using a silica matrix for isolation of plant genomic DNA. Biotechniques. 2000; 28(3): 432–4. pmid:10723554
- 33. Cruz VMV, Kilian A, Dierig DA. Development of DArT marker platforms and genetic diversity assessment of the US collection of the new oilseed crop lesquerella and related species. PLoS One. 2013; 8(5): e64062. pmid:23724020
- 34. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000; 155(2): 945–59. pmid:10835412
- 35. Gupta S, Kumari K, Muthamilarasan M, Parida SK, Prasad M. Population structure and association mapping of yield contributing agronomic traits in foxtail millet. Plant Cell Rep. 2014; 33(6): 881–93. pmid:24413764
- 36. Kopelman NM, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I. Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol Eco Res. 2015; 15(5): 1179–1191.
- 37. Valluru R, Reynolds MP, Davies WJ, Sukumaran S. Phenotypic and genome‐wide association analysis of spike ethylene in diverse wheat genotypes under heat stress. New Phyt. 2017; 214(1): 271–83.
- 38. Ma F, Xu Y, Ma Z, Li L, An D. Genome-wide association and validation of key loci for yield-related traits in wheat founder parent Xiaoyan 6. Mol Breed. 2018; 38(7): 91–106.
- 39. Lehnert H, Serfling A, Friedt W, Ordon F. Genome-Wide Association Studies Reveal Genomic Regions Associated With the Response of Wheat (Triticum aestivum L.) to Mycorrhizae Under Drought Stress Conditions. Front Plant Sci. 2018; 9: 1728–1752 pmid:30568663
- 40. Molero G, Joynson R, Pinera‐Chavez FJ, Gardiner LJ, Rivera‐Amado C, Hall A, et al. Elucidating the genetic basis of biomass accumulation and radiation use efficiency in spring wheat and its role in yield potential. Plant Biotech J. 2019; 17(7): 1276–1288.
- 41. Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ, et al. GAPIT: genome association and prediction integrated tool. Bioinformatics. 2012; 28(18): 2397–9. pmid:22796960
- 42. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing J R Stat Soc Series B Stat Methodol. 1995; 57(1): 289–300.
- 43. Shin JH, Blay S, McNeney B, Graham J. LDheatmap: an R function for graphical display of pairwise linkage disequilibria between single nucleotide polymorphisms. J Stat Softw. 2006; 16(3): 1–10.
- 44.
R Core Team. R: A Language and Environment for Statistical Computing. 2017. Vienna: R Core Team.
- 45. The International Wheat Genome Sequencing Consortium (IWGSC), IWGSC RefSeq principal investigators, Appels R, Eversole K, Feuillet C, Keller B, Rogers J, et al. Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science. 2018; 361: eaar7191. P.
- 46. Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005; 21(18): 3674–3646. pmid:16081474
- 47. Hassani-Pak K, Castellote M, Esch M, Hindle M, Lysenko A, Taubert J, et al. Developing integrated crop knowledge networks to advance candidate gene discovery. App Trans Gen. 2016; 11: 18–26.
- 48.
Hassani-Pak, K. KnetMiner-An integrated data platform for gene mining and biological knowledge discovery. PhD Thesis. Bielefeld University, Germany. 2017. Available from: https://pub.uni-bielefeld.de/record/2915227
- 49. Dalal A, Attia Z, Moshelion M. To Produce or to Survive: How Plastic Is Your Crop Stress Physiology? Front Plant Sci. 2017; 8: 2067. pmid:29259613
- 50. Mathew I, Shimelis H, Mwadzingeni L, Zengeni R, Mutema M, Chaplot V. Variance components and heritability of traits related to root: shoot biomass allocation and drought tolerance in wheat. Euph. 2018; 214(12): 225–237.
- 51.
Tian J, Deng Z, Zhang K, Yu H, Jiang X, Li C. Genetic Analysis Methods of Quantitative Traits in Wheat. In: Genetic Analyses of Wheat and Molecular Marker-Assisted Breeding, Volume 1 2015. Springer, Dordrecht. pp. 13–40
- 52. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Eco. 2005; 14(8): 2611–20.
- 53. Mogga M, Sibiya J, Shimelis H, Lamo J, Yao N. Diversity analysis and genome-wide association studies of grain shape and eating quality traits in rice (Oryza sativa L.) using DArT markers. PloS one. 2018; 13(6): e0198012. pmid:29856872
- 54. Kristensen PS, Jahoor A, Andersen JR, Cericola F, Orabi J, Janss LL, et al. Genome-wide association studies and comparison of models and cross-validation strategies for genomic prediction of quality traits in advanced winter wheat breeding lines. Front Plant Sci. 2018; 9:69. pmid:29456546
- 55. Jost LO. GST and its relatives do not measure differentiation. Mol Ecol. 2008; 17(18): 4015–26. pmid:19238703
- 56. Hao C, Wang L, Ge H, Dong Y, Zhang X. Genetic diversity and linkage disequilibrium in Chinese bread wheat (Triticum aestivum L.) revealed by SSR markers. PLOS one. 2011; 6(2): e17279. pmid:21365016
- 57. Vos‐Fels KP, Qian L, Parra‐Londono S, Uptmoor R, Frisch M, Keeble‐Gagnère G, et al. Linkage drag constrains the roots of modern wheat. Plant Cell Environ. 2017; 40(5): 717–25. pmid:28036107
- 58. Ayalew H, Liu H, Börner A, Kobiljski B, Liu C, Yan G. Genome-wide association mapping of major root length QTLs under PEG induced water stress in wheat. Front Plant Sci. 2018; 9: 1759–1763. pmid:30555498
- 59. Onyemaobi I, Ayalew H, Liu H, Siddique KH, Yan G. Identification and validation of a major chromosome region for high grain number per spike under meiotic stage water stress in wheat (Triticum aestivum L.). PloS one. 2018; 13(3): e0194075. pmid:29518125
- 60. Beyer S, Daba S, Tyagi P, Bockelman H, Brown-Guedira G, Mohammadi M. Loci and candidate genes controlling root traits in wheat seedlings—a wheat root GWAS. Funct Integr Genomics. 2018; (1): 91–107. pmid:30151724
- 61. Cvrcková F. Are plant formins integral membrane proteins? Genome Biol. 2000; research001-1
- 62. Huang C., Zhang R., Gui J., Zhong Y., & Li L. The receptor-like kinase AtVRLK1 regulates secondary cell wall thickening. Plant Physiol. 2018; 177(2): 671–683. pmid:29678858
- 63. Seok MS, You YN, Park HJ, Lee SS, Aigen F, Luan S, et al. AtFKBP16‐1, a chloroplast lumenal immunophilin, mediates response to photosynthetic stress by regulating PsaL stability. Physiol Plant. 2014; 150(4): 620–31. pmid:24124981
- 64. Rizhsky L, Liang H, Shuman J, Shulaev V, Davletova S, Mittler R. When defense pathways collide. The response of Arabidopsis to a combination of drought and heat stress. Plant Physiol. 2004; 134(4): 1683–1696. pmid:15047901
- 65. Fang C, Ma Y, Wu S, Liu Z, Wang Z, Yang R, et al. Genome-wide association studies dissect the genetic networks underlying agronomical traits in soybean. Genome Biol. 2017; 18(161): 1–14.
- 66. Guo Z, Yang W, Chang Y, Ma X, Tu H, Xiong F, et al. Genome-wide association studies of image traits reveal genetic architecture of drought resistance in rice. Mol Plant. 2018; 11(6): 789–805. pmid:29614319
- 67. Richards RA, Rebetzke GJ, Watt M, Condon AT, Spielmeyer W, Dolferus R. Breeding for improved water productivity in temperate cereals: phenotyping, quantitative trait loci, markers and the selection environment. Funct Plant Biol. 2010; 37(2): 85–97.
- 68. Kim SR, Yang JI, Moon S, Ryu CH, An K, Kim KM, et al. Rice OGR1 encodes a pentatricopeptide repeat–DYW protein and is essential for RNA editing in mitochondria. Plant J. 2009; 59(5): 738–749. pmid:19453459
- 69. Černý M, Alena K, Wolfgang H, Lena F, Ondřej N, Gabriela R, et al. Proteome and metabolome profiling of cytokinin action in Arabidopsis identifying both distinct and similar responses to cytokinin down-and up-regulation. J Exp Bot. 2013; 64(14): 4193–4206. pmid:24064926
- 70. Dai C, Lee YL, Lee IC, Nam HG, Kwak JM. Calmodulin 1 regulates senescence and ABA response in Arabidopsis. Front Plant Sci. 2018; 9:803. pmid:30013580
- 71. Luo Y, Tang Y, Zhang X, Li W, Chang Y, Pang D, et al. Interactions between cytokinin and nitrogen contribute to grain mass in wheat cultivars by regulating the flag leaf senescence process. Crop J. 2018; 6(5): 538–551.
- 72. Edae EA, Byrne PF, Haley SD, Lopes MS, Reynolds MP. Genome-wide association mapping of yield and yield components of spring wheat under contrasting moisture regimes. Theor App Gen. 2014; 127(4): 791–807.
- 73. Reimer S, Pozniak CJ, Clarke FR, Clarke JM, Somers DJ, Knox RE, et al. Association mapping of yellow pigment in an elite collection of durum wheat cultivars and breeding lines. Genome. 2008; 51(12): 1016–1025. pmid:19088814
- 74. Maccaferri M, Sanguineti MC, Noli E, Tuberosa R. Population structure and long-range linkage disequilibrium in a durum wheat elite collection. Mol Breed. 2005; 15(3): 271–290.
- 75. Neumann K, Kobiljski B, Denčić S, Varshney RK, Börner A. Genome-wide association mapping: a case study in bread wheat (Triticum aestivum L.). Mol Breed. 2011; 27(1): 37–58.
- 76. Nakamura S, Makiko C, Stehno Z, Holubec V, Morishige H, Pourkheirandish M, et al. Diversification of the promoter sequences of wheat Mother of FT and TFL1 on chromosome 3A. Mol Breed. 2015; 35(8): 164–173.