Skip to main content

Advertisement

Inheritance of pre-emergent metribuzin tolerance and putative gene discovery through high-throughput SNP array in wheat (Triticum aestivum L.)

Article metrics

Abstract

Background

Herbicide tolerance is an important trait that allows effective weed management in wheat crops in dryland farming. Genetic knowledge of metribuzin tolerance in wheat is needed to develop new cultivars for the industry. Here, we investigated gene effects for metribuzin tolerance in nine crosses of wheat by partitioning the means and variances of six basic generations from each cross into their genetic components to assess the gene action governing the inheritance of this trait. Metribuzin tolerance was measured by a visual senescence score 21 days after treatment. The wheat 90 K iSelect SNP genotyping assay was used to identify the distribution of alleles at SNP sites in tolerant and susceptible groups.

Results

The scaling and joint-scaling tests indicated that the inheritance of metribuzin tolerance in wheat was adequately described by the additive-dominance model, with additive gene action the most significant factor for tolerance. The potence ratio for all the crosses ranged between − 1 and + 1 for senescence under metribuzin-treated conditions indicating a semi-dominant gene action in the inheritance of metribuzin tolerance in wheat. The number of segregating genes governing metribuzin tolerance was estimated between 3 and 15. The consistent high heritability range (0.82 to 0.92) in F5–7 generations of Chuan Mai 25 (tolerant) × Ritchie (susceptible) cross indicated a significant contribution of additive genetic effects to metribuzin tolerance in wheat. Several genes related to photosynthesis (e.g. photosynthesis system II assembly factor YCF48), metabolic detoxification of xenobiotics and cell growth and development (cytochrome P450, glutathione S-transferase, glycosyltransferase, ATP-binding cassette transporters and glutathione peroxidase) were identified on different chromosomes (2A, 2D, 3B, 4A, 4B, 7A, 7B, 7D) governing metribuzin tolerance.

Conclusions

The simple additive–dominance gene effects for metribuzin tolerance will help breeders to select tolerant lines in early generations and the identified genes may guide the development of functional markers for metribuzin tolerance.

Background

Wheat (Triticum aestivum L.) is a major global cereal crop in terms of production and area coverage (FAO 2018) [1]. Wheat is Australia’s largest grain crop and contributes around 12% of world trade. Western Australia (WA) has the highest reported occurrence of herbicide-resistant weeds in Australia, which is the key agronomic issue for WA farmers. There are instances where weed infestations have caused serious reductions (up to 50%) in wheat yields [2]. Higher tolerance for metribuzin is advantageous for WA wheat industry to protect crops against herbicide damage and maximize crop yields. Therefore, breeding wheat cultivars for higher herbicide tolerance through improvement programs is paramount, particularly in Mediterranean-type climatic regions.

Inheritance of metribuzin tolerance has a different modes of genetic control in crop plants. A monogenic recessive inheritance was reported in soyabean (Glycine max L.) [3, 4] and potato (Solanum tuberosum L.) [5]. Si et al. [6] reported two independent semi-dominant genes having additive effects in narrow-leafed lupin (Lupinus angustifolius L.). The inheritance of tolerance to metribuzin in durum wheat (T. turgidum L.) is a complex character controlled by both nuclear and cytoplasmic genes in wheat [7, 8]. This was supported by the observation that physiological processes, such as uptake, translocation and metabolism/detoxification, modified the amount of herbicide reaching the target site. Investigations into the genetic control and heritability of metribuzin tolerance will guide breeders to formulate the appropriate selection program for the breeding of herbicide tolerant cultivars.

Variation in metribuzin tolerance in wheat from six continents, reported in our previous investigation [9], provides a valuable source to breeders for estimating gene effects and formulating advantageous breeding procedures to improve herbicide tolerance. The natural variability observed between genotypes for metribuzin tolerance indicates that selection may be an effective method for improving yields. However, selection efficiency is related to the magnitude of heritability and genetic advances. Heritability estimates along with genetic advances are important selection parameters, and usually more helpful for predicting genetic gain under selection [10]. Therefore, a detailed understanding of the nature of gene action, heritability and predicted genetic gain is helpful for selecting superior wheat germplasm in breeding programs to improve herbicide tolerance and yield.

DNA markers have enormous potential for improving the efficiency and precision of conventional plant breeding via marker-assisted selection (MAS). The molecular mechanism of metribuzin tolerance in wheat is poorly understood. Advances in next-generation sequencing have facilitated the discovery of SNPs in the whole genome [11, 12] to provide a large amount of genome-wide polymorphism, as they potentially represent all the mutations that have occurred in the genome [13, 14]. The recent wheat 90 K SNP iSelect assay developed by Illumina is a useful genetic resource for tagging agronomically important traits. The closed-end assay incorporates existing sequence knowledge onto a microarray platform enabling high-throughput SNP discovery in diverse pools.

This study aimed to (1) characterize the inheritance of tolerance to metribuzin in nine wheat crosses, (2) investigate heritability in F5–7 RILs of the most diverse cross (Chuan Mai 25 × Ritchie) (3) conduct 90 K iSelect SNP genotyping assay in diverse cultivars to discover allelic variants in SNP markers in tolerant and susceptible groups, and (4) determine the likely chromosomal locations and candidate genes responsible for metribuzin tolerance in wheat.

Results

Phenotypic variation

The average senescence (SS) for the tolerant and susceptible parents used in this study are in Table 1. The susceptible parents had significantly (P < 0.05) higher SS than the tolerant parents. Average SS for F1, F2, BCT, and BCS populations are in Table 2. The ANOVA indicated a highly significant difference between generations, indicating genetic variability for metribuzin tolerance in wheat. F2 means had a comparable range to F1 means. The mean SS of the backcrosses varied depending on the crossed parents. The abbreviations representing crosses are in Table 1. Backcrossing F1 lines (BCT) to tolerant parents had lower SS than the mid-parent (mp) value, except for the K × D cross, indicating positive additive gene action and higher expression of metribuzin tolerance. In contrast, BCS had higher SS than the mid-parent value. The crosses of F1 with susceptible Dagger differed the most from the mp value, by 31.3, 38.6 and 29% for the crosses CM × D, F × D and K × D, respectively. The comparisons of reciprocal crosses revealed significant differences (P ≤ 0.05) in average SS except for three reciprocal cross combinations (Table 3). Therefore the reciprocal crosses were not pooled for generation mean analysis.

Table 1 Origin and average senescence score of seven wheat genotypes used in crosses
Table 2 Generation means of senescence score (standard error in parenthesis) and potence ratio showing relationship between alleles for reaction to metribuzin in wheat crosses
Table 3 Observed metribuzin tolerance measured as senescence score in reciprocal crosses following application of metribuzin (400 g a.i. ha− 1)

Genetic effects

Genetic model and gene action of metribuzin tolerance in wheat

The results of the scaling tests (A, B, C and D) of nine hybrids (Table 4) were not significant, which indicated the absence of epistatic gene interaction and adequacy of the simple additive–dominance model. The genetic parameters for mp, additive gene effects (d) and dominance gene effects (h) and their standard deviations estimated by the joint–scaling test are presented in Table 4. The mp, which reflects the contribution of the locus effects and interaction of fixed loci, were significant for all nine crosses. The additive gene effects were significant (P = 0.05) for all nine crosses, and dominance gene effects were significant (P = 0.05) for four crosses (CM × R, CM × S, F × D and ER × D). The additive-dominance model fitted well for all crosses. The model significance was checked using χ2 statistic, which showed insignificant difference between the expected and observed generation mean values, confirming a significant additive–dominance model for metribuzin tolerance in wheat (Table 4).

Table 4 Genetic model testing based on A, B, C and D scales and estimates of additive and dominance effects (standard error in parenthesis) for metribuzin tolerance in wheat

Metribuzin tolerance in wheat is either partially dominant or recessive dominant (Fig. 1). The potence ratio presented in Table 2 ranged from − 0.75 to 0.51 for SS under metribuzin– treated conditions, thereby falling between − 1 and + 1, indicating a semi-dominant gene action for the inheritance of metribuzin tolerance in wheat. The crosses with a negative potence ratio (CM × R, F × S, ER × R, K × S and K × D) had lower F1 means (lower phytotoxic effect) and were more similar to the tolerant parents, indicating the presence of partial dominance gene effects. The crosses with a positive potence ratio (CM × S, CM × D, F × R, F × D, ER × S, ER × D) had higher F1 means (higher phytotoxic effect), indicating recessive dominance (Fig. 1).

Fig. 1
figure1

Dominance relationships between a pair of alleles A and B. Phenotypes corresponding to the different genotypes AA, AB and BB. -ve PR, negative potence ratio; F1 mean phenotypic value is similar to tolerant phenotypic value +ve PR, positive potence ratio; F1 mean phenotypic value is similar to susceptible phenotypic value

Heritability and the number of resistance genes

The frequency distribution of the metribuzin reaction of F5–7 RILs of the Chuan Mai 25 × Ritchie appeared to be normal, indicating metribuzin tolerance as a quantitative trait (Fig. 2). The population means remained higher than those of the parents, indicating transgressive segregations in both directions of parents. Heritability was high and comparable in F5 (0.82), F6 (0.95) and F7 (0.92) RILs of the cross Chuan Mai 25/Ritchie (Table 5). There were minimum of eight major peaks representing major genes and some minor modifier genes in the F5, F6 and F7 RIL populations. Gene number, n1, estimated based on variances of parents and F2 and gene number n2, estimated based on variances of parents, F1 and F2 varied for most of the crosses. Wright’s formula estimated a minimum of three genes and a maximum of 15 genes controlling metribuzin tolerance in wheat (Table 6).

Fig. 2
figure2

Phenotypic distribution of senescence score in Chuan Mai 25/Ritchie F5–-7 RIL population. P1 indicates the means of Chuan Mai 25 P2 indicates the means of Ritchie

Table 5 Genotypic and phenotypic coefficients of variation, and broad sense heritability of metribuzin tolerance in wheat
Table 6 Estimates of the minimum number of genes for metribuzin tolerance measured by senescence score

SNP discovery and potential candidate genes

The 90 K iSelect SNP genotyping assay contained 81,587 SNPs. A total of 60,635 monomorphic alleles (74%) with no clustering patterns for all genotypes were removed. A total of 12,294 loci had no call and were removed. The remaining 8,661 loci (12.9%) had ≥2 clusters and were used for principal component analysis (PCA) analysis; the results for allelic variation in seven genotypes are presented in Fig. 3. The PCA analysis revealed significant variation between tolerant and susceptible groups. A clear separation of tolerant and susceptible groups, according to PCA component 1, indicated high genetic diversity between the two groups. A total of 296 SNPs were polymorphic/biallelic markers between the two groups (Additional file 1: Table S1).

Fig. 3
figure3

Principal component analysis showing genetic diversity based on 8,661 SNPs. Each point represents one individual. Principal component 1 (PC1) explains 28% of the variation and principal component 2 (PC2) explains 22.7% of the variation in the data

Putative genes related to the identified SNPs with differences between tolerant and susceptible groups were investigated by a blastN search of markers on Triticum aestivum IWGSC_refseqv1.0. The results suggested that metribuzin tolerance was a quantitative trait governed by several loci on different chromosomes (2A, 2D, 3B, 4A, 4B, 7A, 7B, 7D) (Table 7). Only genes related to photosynthesis and metabolic detoxification pathways were considered for candidate gene analysis. Multiple SNPs and candidate genes identified on chromosome 7B (photosynthesis system II assembly factor YCF48 and ABC transporter), chromosome 4A (cytochrome P450 family), chromosome 7A (glutathione S-transferase), chromosome 2A and 3B (glycosyltransferase), and chromosome 2D (glutathione peroxidase) represented the possible genes/gene families with significant association with metribuzin tolerance in wheat.

Table 7 List of 12 candidate genes with a known function related to photosynthesis and metabolic detoxification

Discussion

The mode of inheritance and gene action of pre-emergent herbicide tolerance will help breeders to choose appropriate breeding methods to develop more tolerant cultivars and combat early weed competition to enhance wheat yields. The efficiency of selection and plant breeding programs depend on the existence of genetic variability [15]. Genetic variation for metribuzin tolerance in wheat was evident in our previous research [9, 16]. Metribuzin tolerance/sensitivity is controlled by both cytoplasmic and nuclear genes because reciprocal differences in expression of metribuzin tolerance existed in most F1 hybrids. Previously, Ratliff et al. [7] reported the role of both nuclear and cytoplasmic genes in metribuzin tolerance in wheat. Metribuzin tolerance is a polygenic trait and the present investigation revealed a maximum of 15 genes responsible for the trait. Villarroya et al. [8] reported metribuzin tolerance as a quantitative trait controlled by many genes in wheat, which supports the present findings. The Transgressive phenotypes observed in segregated populations (Fig. 2) compared to parental phenotypes were due to recombination of additive alleles both on positive and negative direction. Recombination results in new pairs of alleles at two or more loci. The changed/enhanced gene expression at these loci give rise to new phenotypes [17].

Metribuzin tolerance is explained by the simple additive–dominance model, indicating absence of epistasis or non-allelic interaction. The absence of epistasis and significant additive effect efficiently responds to selection [18]. The alleles of such traits are fixed in early generations. These facts can guide breeders in the selection of lines in early generations. The results of the scaling and joint-scaling tests and chi-square statistic can be used as evidence that the additive gene effect is higher than the dominance gene effect, indicating the former as a decisive type of gene action for metribuzin tolerance. Highly significant additive gene effects (d) for all crosses indicated the preponderance of additive gene effects for metribuzin tolerance and the potential for improving the performance of chlorophyll traits using early a pedigree selection program in wheat.

Dominance in genetics is a relationship between the alleles of one gene, where the effect on phenotype of one allele masks the contribution of a second allele at the same locus. It is a key concept in Mendelian inheritance and classical genetics. Often the dominant allele codes for a functional protein whereas the recessive allele does not [19]. In quantitative genetics, phenotypes are measured and treated numerically. In the present investigation, F1 hybrids with lower SS exhibited a partial dominant gene action. Therefore the F1 hybrids with a negative potence ratio had mid- to low- metribuzin phytotoxic effects and expressed a phenotype similar to the tolerant parent (Fig. 1). However, F1 hybrids with higher SS had recessive, dominant gene action. Therefore, the F1 hybrids with a positive potence ratio had mid- to high- metribuzin phytotoxic effects and expressed a phenotype similar to the susceptible parent.

Heritability was consistent and above 80% in the F5–7 RIL population of Chuan Mai 25 × Ritchie, which indicated stability of the metribuzin tolerance trait. These traits could be easily transferred through generations in breeding programs to generate more tolerant cultivar. The absence of epistasis increased the accuracy of the gene number estimate in the present study because it complied with Wright’s assumption of no epistasis [20]. The crosses had unidirectional distribution of genes based on the degree of susceptibility in susceptible parents. The crosses involving Ritchie as the susceptible parent segregated the most genes, followed by Dagger and Spear.

The candidate genes identified for SNPs having homozygous allele in the tolerant group encodes for the network of xenobiotic detoxification proteins protecting cells from oxidative damage and keeping the photosynthesis process intact by PSII complex repair under stress. The identified gene superfamilies or domains, notably cytochrome P450 (CYPs) and glutathione S-transferase (GSTs) glycosyltransferase (GT), ATP-binding cassette transporters and glutathione peroxidase (GPX) are essentially xenobiotic detoxifying enzymes involved in vacuolar sequestration of conjugated pesticide metabolites [21,22,23]. Plants can metabolize a diverse range of xenobiotics, such as organic pollutants and pesticides, and herbicides using enzymes [22]. The most commonly observed route for the detoxification of herbicides in wheat involves an initial hydroxylation, typically mediated by a cytochrome P450 mixed function oxidases (CYPs) and glutathione conjugation mediated by glutathione S-transferases (GSTs). CYPs and GSTs are implicated in metabolism-based resistance to multiple herbicides in grass weeds such as black-grass [24].

The identified glycosyltransferase and oxidoreductase mediate different biological processes. They are involved in sucrose metabolism and metabolic detoxification of xenobiotic detoxification. The candidate genes detected from our previous investigation [16] of QTL mapping suggested glycosyltransferase and oxidoreductase involved in metabolic detoxification, partially imparts metribuzin tolerance in wheat. The microarray analysis conducted by Pilcher et al. [25] revealed that sucrose metabolism was highly responsive to metribuzin stress in wheat. The identified photosystem (PS) II assembly factor YCF48 is the thylakoid-embedded large pigment-protein complexes of photosynthetic electron transfer chain, i.e. PSII, PSI, the cytochrome b6f complex, and the ATP synthase. These multiportion complexes harness solar energy and, together with ATP synthase, produce reducing power (NADPH) and chemical energy (ATP) for the production of carbohydrates in the Calvin cycle [26,27,28,29].. The ubiquitination pathway is involved in nitrogen recycling and prevents senescence during herbicide stress [30]. In conclusion, the proteins encoded by the identified genes are involved in the metabolic detoxification, carbon metabolism, and repair of the PSII complex.

Understanding the genetics of herbicide tolerance in wheat will guide breeders in the development of herbicide-tolerant cultivars with wider safety margins. Metribuzin tolerance in wheat has high heritability and significant additive gene action with no epistasis. Therefore, MAS may be a feasible routine solution for selecting herbicide-tolerant lines in crop improvement programs. Metribuzin tolerance in wheat is most likely a non-target-based mechanism where metribuzin is detoxicated by a series of metabolic enzymes. However, transcriptome-wide gene expression profiling is needed to reveal genes and pathways endowing metabolic herbicide resistance in wheat.

Conclusions

The simple additive-dominance mode of gene action suggests that a simple selection procedure could be successfully exploited in an early segregating generation to select lines for metribuzin tolerance breeding in wheat. The present investigation emphasized the degree of gene expression in the PSII assembly factor, antioxidants and detoxifying systems (CYPs, GSTs, GT, GPX) as the responsible factors for determining metribuzin tolerance in wheat. The identified markers could be used in marker-assisted selection of lines for breeding tolerant cultivars. Alternatively, tolerant genes could be introduced into elite wheat cultivars by natural introgression to enhance metribuzin tolerance.

Methods

Herbicide

Metribuzin (C8H14N4OS), a triazinone herbicide was purchased from Syngenta Crop Protection. Metribuzin binds its target site D1 protein in PSII and inhibits electron flow between the primary electron acceptor to plastoquinone, arresting photosynthesis. The metribuzin dose of 400 g a.i. ha− 1 was used to assess tolerance status in parents, F1, F2, BCT and BCS populations and F5–7 RILs of the cross, CM × R (for all abbreviations refer to Table 1).

Plant material

Seven wheat genotypes with differential tolerance to metribuzin (Table 1) were obtained from Australian winter wheat collection. The tolerant and susceptible parents selected for this study were from previous tolerance screening [9] and local WA cultivars identified by Kleemann and Gill [31]. Plants of metribuzin T (tolerant) and S (susceptible) parental type were grown in 1 L pots containing potting mix (50% peat moss: 50% river sand) and maintained in a glasshouse at The University of Western Australia during a normal winter growing season. Single T and S plants growing individually in pots were paired according to floral synchronicity to produce F1 maternal R and paternal S (F1 RS) and F1 maternal S and paternal R (F1 SR) hybrids. Reciprocal crosses were used to check maternal effects of herbicide resistance. Subsequently, RS F1s were selfed and backcrossed to their R and S plants to produce F2 and backcross (BCT and BCS) generations, respectively. Additionally, the Chuan Mai 25 (T) × Ritchie (S) cross was selected to develop recombinant inbred lines (RILs) in the growth chamber using rapid generation single seed-descent in-vitro embryo culture technique (Fig.4) [32]. A total of 73 F5–7 RILs were screened for metribuzin tolerance in the glasshouse to calculate heritability.

Fig. 4
figure4

A Rapid generation single seed-descent method used to produce recombinant inbred lines of Chuan Mai 25 × Ritchie cross - (a) plants growing under a controlled environment room; and (b) wheat embryos (left) germinating in-vitro (right) in a culturing medium

Herbicide screening and phytotoxic assessment

The parents, F1, F2, BCT and BCS populations and F5–7 RILs of the cross, CM × R were evaluated for metribuzin tolerance in a sand-tray system [9]. The trays were sprayed with 400 g a.i. ha− 1 of metribuzin via a twin flat-fan nozzle, perpendicular to the tray surface in two passes at a flow rate of 118 L ha− 1 and 200 kPa pressure in a cabinet spray chamber. The amount of herbicide required for 400 g a.i. ha− 1 in L/ha was calculated using the ratio of herbicide rate by flowrate of twin flat-fan nozzle. The trays were maintained in a phytotron, where the temperature was set to 25/15 °C day/night and watered regularly every 48 h.

Senescence score (SS)/visual damage was measured 21 days after treatment (DAT) (Fig. 5). Plants with no visual symptoms were scored as 0, increasing levels of yellowing and stunting were scored from 1 to 4, increasing levels of leaf abnormalities (leaves wrinkling) and leaf necrosis were scored from 5 to 8, and dead plants with total leaf browning and necrosis of the apex were scored as 9. Lines with an average SS ≤ 3 recorded tolerant (T), 4 to 5 moderately tolerant (MT), and 6 to 9 as susceptible (S). For parents and F1 hybrids, SS was averaged over the three repeats.

Fig. 5
figure5

Leaf senescence rating from 0 to 9; plants with an average SS ≤ 3 recorded tolerant (T), 4 to 5 moderately tolerant (MT), and 6 to 9 as susceptible (S)

Identification of SNP and potential candidate genes

The distribution of alleles at the SNP sites was assessed using the wheat 90 K iSelect SNP genotyping assay, containing 81,587 genome-wide distributed SNPs following the procedure described by Wang et al. [33]. Allele calls were generated for the seven parents used in this study (Table 1), with the four tolerant genotypes as group 1 and three susceptible genotypes as group 2 for comparison. SNP clustering and genotype calling were performed using Genome Studio 2.0 software (Illumina). The monomorphic and poor-quality SNP markers, which had more than 20% missing values, ambiguous SNP calling, or minor allele frequencies below 5%, were excluded from further analyses. The polymorphic SNP loci between the two groups were used for candidate gene analysis.

The candidate genes controlling metribuzin tolerance were identified using BLASTN program, against the Ensembl Plants (Triticum aestivum IWGSC_refseqv1.0) to find the Traes numbers of genes. BLAST hits were filtered with an e-value threshold of 10− 5 and sequence similarity higher than 95%. The Traes numbers were searched in UniProt in TrEMBL (http://www.uniprot.org) and UniParc (https://www.uniprot.org/uniparc/) to obtain more information including protein domain, family, molecular and biological functions of the potential candidate genes. Further, the key features of the domain and InterPro annotation were searched in pfam and Prosite to check the characteristics of the protein. Only those genes with known function and/or related to photosynthesis and metabolic detoxification were considered as potential candidate genes for metribuzin tolerance in wheat.

Principal component analysis (PCA)

PCA was performed on the SNP calls of the seven parents to determine genetic relatedness/diversity. SNP alleles were converted to a 1/0 binary system, followed by PCA performed using the built-in R function ‘prcomp’ and data was visualized using the ‘dudi.pca’ function from the ade4 R package [34] using SNP as variables.

Genetic analyses

The contribution of maternal or cytoplasmic effects on the differences between population means was assessed by comparing the means of reciprocal F1 crosses. The mode of inheritance of metribuzin tolerance was estimated for each cross combination by generation mean analysis. Mean data on SS recorded on different generations, viz. parents (P1 and P2), F1, F2, BCT and BCS for nine cross combinations, were subjected to a scaling (A, B, C and D) and joint-scaling test using the weighted least squares method, which testifies the presence or absence of epistasis [35,36,37]. When the additive–dominance model fitted the data, a generation variance analysis was performed based on the method described by Allard [38]. This provided estimates of additive and dominance components of variance. The estimated gene effects: mean (m), additive (d) and dominance (d) values were tested by t-test at the 0.05 and 0.01 levels of probability. Further, the goodness-of-fit of the model was tested by comparing expected means of the six generations, calculated from the parameter estimates and observed generation means using chi-squared (χ2) statistic, and the significance of each parameter was tested using a t-test [35, 36].

The nature of dominance was determined from the potence ratio according to [38] P \( =\frac{F_1-M.P.}{0.5\ \left({P}_2-{P}_1\right)} \), where P is the relative potence of the gene set, F1 is the first generation mean, P1 is the mean of the lower parent, P2 is the mean of the higher parent, and M.P. is the mid-parent value. Complete dominance was indicated when P was − 1 or + 1, while partial dominance was indicated when ‘P’ was − 1 or + 1, except for zero, which indicates the absence of dominance. Over dominance was indicated when the potence ratio exceeded + 1. The positive and negative signs indicate the direction of the dominance of either parent.

The generalized linear model based on Poisson regression was fitted to the SS data of F5, F6 and F7 RILs from the, Chuan Mai 25 × Ritchie cross using glm() function in R and heritability was calculated based on ANOVA using the formula: h2 = \( {\delta}_g^2/ \) (\( {\delta}_g^2+ \) \( {\delta}_e^2 \)) where \( {\delta}_g^2 \) and \( {\delta}_e^2 \) are the estimated genotypic and error variances, respectively. The estimated genotypic and error variances were calculated as: \( {\delta}_g^2 \) \( =\frac{MSg- MSe}{r} \) and \( {\delta}_e^2 \) \( =\frac{MSe}{r} \) where MSg is the mean square of the RILs, MSe is the residual error and r is the number of replicates. Further, the number of genes controlling metribuzin resistance in each cross was estimated using Wright’s formulae [39, 40].

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

BCS :

Backcross of F1 to susceptible parent

BCT :

Backcross of F1 to tolerant parent

CM:

Chuan Mai 25

CYP:

Cytochrome P450

d:

Additive gene effect

D:

Dagger

ER:

Eagle Rock

F:

Fundulea 490

F1:

First filial generation

F2:

Second filial generation

GPX:

Glutathione peroxidase

GST:

Glutathione S-transferase

GT:

Glycosyltransferase

h:

Dominance gene effect

K:

Kite

MAS:

Marker-assisted selection

mp:

mid-parent value

PCA:

Principlal component analysis

PSII:

Photosystem II

R:

Ritchie

S:

Spear

SNP:

Single nucleotide polymorphism

SS:

Senescence score

References

  1. 1.

    FAO. FAO statistical year book 2018. Food and agriculture Organization of the United Nations. Italy: Rome; 2018.

  2. 2.

    Kleemann SG, Gill GS. Applications of metribuzin for the control of rigid brome (Bromus rigidus) in no-till barley crops of southern Australia. Weed Technol. 2009;22:34–7.

  3. 3.

    Edwards C, Barrentine W, Kilen T. Inheritance of sensitivity to metribuzin in soybeans. Crop Sci. 1976;16:119–20.

  4. 4.

    Kilen TC, He G. Identification and inheritance of metribuzin tolerance in wild soybean. Crop Sci. 1992;32:684–5.

  5. 5.

    De Jong HJE. Inheritance of sensitivity to the herbicide metribuzin in cultivated diploid potatoes. Euphytica. 1983;32:41–8.

  6. 6.

    Si P, Pan G, Sweetingham M. Semi-dominant genes confer additive tolerance to metribuzin in narrow-leafed lupin (Lupinus angustifolius L.) Mutants. Euphytica. 2011;177:411–8.

  7. 7.

    Ratliff R, Carver B, Peeper T. Expression of metribuzin sensitivity in winter wheat (Triticum aestivum) populations. Weed Sci. 1991;39:130–3.

  8. 8.

    Villarroya M, Escorial M, Garcia-Baudin J, Chueca M. Inheritance of tolerance to metribuzin in durum wheat. Weed Res. 2000;40:293–300.

  9. 9.

    Bhoite RN, Si P, Stefanova KT, Siddique KH, Yan G. Identification of new metribuzin-tolerant wheat (Triticum spp.) genotypes. Crop Pasture Sci. 2017;68:401–8.

  10. 10.

    Sharma S, Sain R, Sharma RJE. Genetics of spike length in durum wheat. Euphytica. 2003;130:155–61.

  11. 11.

    Berkman PJ, Lai K, Lorenc MT, Edwards D. Next-generation sequencing applications for wheat crop improvement. Am J Bot. 2012;99:365–7.

  12. 12.

    Chia J-M, Song C, Bradbury PJ, et al. Maize hapmap2 identifies extant variation from a genome in flux. Nat Genet. 2012;44:803–7.

  13. 13.

    Steemers FJ, Chang W, Lee G, et al. Whole-genome genotyping with the single-base extension assay. Nat Methods. 2006;3:31–3.

  14. 14.

    Elshire RJ, Glaubitz JC, Sun Q, et al. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One. 2011;6:e19379.

  15. 15.

    Said AA. Generation mean analysis in wheat (Triticum aestivum L.) under drought stress conditions. Annals Agri Sci. 2014;59:177–84.

  16. 16.

    Bhoite R, Onyemaobi I, Si P, Siddique KH, Yan G. Identification and validation of QTL and their associated genes for pre-emergent metribuzin tolerance in hexaploid wheat (Triticum aestivum L.). BMC Genet. 2018;19:102.

  17. 17.

    Nolte AW, Sheets HD. Shape based assignment tests suggest transgressive phenotypes in natural sculpin hybrids (Teleostei, Scorpaeniformes, Cottidae). Front Zool. 2005;2:11.

  18. 18.

    Barnard A, Labuschagne M, Van Niekerk HJE. Heritability estimates of bread wheat quality traits in the Western cape province of South Africa. Euphytica. 2002;127:115–22.

  19. 19.

    Bowler PJ: The Mendelian revolution: the emergence of hereditarian concepts in modern science and society: Bloomsbury publishing; 2015.

  20. 20.

    Wright S. The genetics of quantitative variability. In: Evolution and the genetics of populations. Chicago: University of Chicago Press; 1968.

  21. 21.

    Edwards R, Dixon DP, Walbot V. Plant glutathione S-transferases: enzymes with multiple functions in sickness and in health. Trends Plant Sci. 2000;5:193–8.

  22. 22.

    Brazier M, Cole DJ, Edwards R. O-Glucosyltransferase activities toward phenolic natural products and xenobiotics ins wheat and herbicide-resistant and herbicide-susceptible black-grass (Alopecurus myosuroides). Phytochemistry. 2002;59:149–56.

  23. 23.

    Davies J, Caseley JCJPS. Herbicide safeners: a review. Pesticides Sci. 1999;55:1043–58.

  24. 24.

    Hall L, Moss S, Powles SJ. Mechanisms of resistance to aryloxyphenoxypropionate herbicides in two resistant biotypes of Alopecurus myosuroides (blackgrass): herbicide metabolism as a cross-resistance mechanism. Pesticide Biochem Physio. 1997;57:87–98.

  25. 25.

    Pilcher W, Zandkamiri H, Arceneaux K, Harrison S, Baisakh N. Genome-wide microarray analysis leads to identification of genes in response to herbicide, metribuzin in wheat leaves. PLoS One. 2017;12:e0189639.

  26. 26.

    Tikhonov AN. The cytochrome b6f complex at the crossroad of photosynthetic electron transport pathways. Plant Physiol Biochem. 2014;81:163–83.

  27. 27.

    Mulo P, Sicora C, Aro Mari, sciences ml: Cyanobacterial psba gene family: optimization of oxygenic photosynthesis. Cell Mol Life Sci 2009;66:3697.

  28. 28.

    Komenda J, Nickelsen J, Tichý M, Prášil O, et al. The cyanobacterial homologue of HCF136/YCF48 is a component of an early photosystem II assembly complex and is important for both the efficient assembly and repair of photosystem II in Synechocystis sp. PCC 6803. J Biol Chem. 2008;283:22390–9.

  29. 29.

    Rengstl B, Oster U, Stengel A, Nickelsen JJ. An intermediate membrane subfraction in cyanobacteria is involved in an assembly network for photosystem II biogenesis. J Biol Chem. 2011;286:21944–51.

  30. 30.

    Gepstein S, Sabehi G, Carp MJ, Hajouj T, Nesher MFO, Yariv I, Dor C, Bassani M. Large-scale identification of leaf senescence-associated genes. Plant J. 2003;36:629–42.

  31. 31.

    Kleemann SGL, Gill GS. Differential tolerance in wheat (Triticum aestivum L.) genotypes to metribuzin. Crop Pasture Sci. 2007;58:452–6.

  32. 32.

    Zheng Z, Wang H, Chen G, Yan G, Liu CJE. A procedure allowing up to eight generations of wheat and nine generations of barley per annum. Euphytica. 2013;191:311–6.

  33. 33.

    Wang YH, Bible P, Loganantharaj R, Upadhyaya HD. Identification of SSR markers associated with height using pool-based genome-wide association mapping in sorghum. Mol Breed. 2012;30:281–92.

  34. 34.

    Holland SM. Principal components analysis (PCA). Department of Geology, University of Georgia, Athens, GA; 2008. p.30602–32501.

  35. 35.

    Mather K, Jinks JL. Diallels. In: Biometrical Genetics. Springer; 1982. p. 255–291.

  36. 36.

    Kearsey M, Pooni H, Bulmer MJ. The genetical analysis of quantitative traits 1996;68:183.

  37. 37.

    Lynch M, Walsh B. Genetics and analysis of quantitative traits. Sunderland, MA; 1998.

  38. 38.

    Allard RJ, Principal of plant breeding. USA Wiley International Edition, John Wiley and Sons; 1960. p. 85.

  39. 39.

    Smith HH. Fixing transgressive vigor in Nicotiana rustica. In: Heterosis. 1952. p. 161-27.

  40. 40.

    Bai GH, Shaner G, Ohm H. Inheritance of resistance to Fusarium graminearum in wheat. Theor Appl Genet. 2000;100:1–8.

Download references

Acknowledgments

The authors express heartfelt gratitude to Dr. Ifeyinwa Onyemaobi and Ms. Guannan Liu for technical assistance and Robert Creasy and other glasshouse staff for their help with the experimental set-up. The authors would like to thank Professor Jacqueline Batley and Dr. Aneeta Pradhan for assistance in the 90 K SNP array genotyping.

Funding

Roopali acknowledges the Research Training program scholarship from the Australian Government that sponsored her Ph.D. study. Yitpi Research Foundation (Crop Science Research Foundation registered under Plant Breeder’s Rights Act) supported the design of the study and covered the expenses of materials required for crossing, collection of data, data analyses and interpretation of data. Global Innovation Linkage program (GIL53853) from Australian Department of Industry, Innovation and Science funded the SNP work.

Author information

RB, SP, KHMS and GY conceived and designed the experiments; RB conducted the major experiment, and HL and LX assisted in population development and herbicide spraying; RB and HL analyzed the data; RB wrote the manuscript, and HL, SP, GY and KHMS critically reviewed the manuscript. All authors approved the final version of the manuscript.

Correspondence to Guijun Yan.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests for this research.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bhoite, R., Si, P., Liu, H. et al. Inheritance of pre-emergent metribuzin tolerance and putative gene discovery through high-throughput SNP array in wheat (Triticum aestivum L.). BMC Plant Biol 19, 457 (2019) doi:10.1186/s12870-019-2070-x

Download citation

Keywords

  • Metribuzin
  • Gene effects
  • Inheritance
  • Potence ratio
  • Heritability
  • Wheat 90 K iSelect SNP genotyping assay
  • Candidate genes