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. 2021 Jan 12;41(1):4. doi: 10.1007/s11032-020-01196-8

Characterization of near-isogenic lines confirmed QTL and revealed candidate genes for plant height and yield-related traits in common wheat

Yunxiao Zhang 1, Hui Liu 1,✉, Guijun Yan 1,✉
PMCID: PMC10231565  PMID: 37309530

Abstract

Plant height (PH) is closely associated with yield-related traits and environmental adaptation. Seven pairs of near-isogenic lines (NILs) targeting four QTL on 3AL, 4BL, 4AS, and 7AL wheat chromosome arms were assessed for PH and four yield-related traits including yield per plant (Y/P), grain number per spike (G/S), thousand kernel weight (TKW), and biomass per plant (B/P). Significant differences were observed in the NIL pairs for the measured traits. NIL pairs targeting the 3AL QTL differed significantly in PH, G/S, and TKW; NILs targeting the 4BL QTL differed significantly in PH, Y/P, and B/P; NIL pairs targeting the 4AS QTL differed significantly in all the traits; and NIL pairs targeting the 7AL QTL differed significantly in PH. A 90 K SNP genotyping assay of the NILs detected nineteen SNPs associated with fourteen functional genes. Among them, eight candidate genes are related to Rht proteins, four genes are related to hormone pathways and two genes are related to carbohydrate synthesis and transport. By searching the interval marker physical positions, it was found that the four targeted QTL in this study overlapped with eight previously reported QTL for PH, TKW, biomass, and yield. Correlation analysis revealed that PH significantly and positively correlated with B/P and G/S. The SNP and candidate gene information is potentially useful for marker-assisted selection in breeding programs, and the four targeted QTL are proved to be critical genomic regions controlling the investigated agronomic traits, which can be further fine mapped to identify the underlying genes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-020-01196-8.

Keywords: Plant height, Yield, Candidate genes, Rht genes, QTL, NIL

Introduction

Green revolution successfully tackled human food demand by cultivating dwarf high-yielding crops (Hazell 2009). In common wheat (Triticum aestivum L.), however, there is still ample room for production improvement (Iizumi and Ramankutty 2016). Wheat production is determined by yield and yield components including grain number per spike (G/S), thousand kernel weight (TKW), and biomass. Plant height (PH), which is the final height at maturity, shows a close relationship to yield and abiotic stress tolerance in wheat (Zhang et al. 2017). Reduced PH minimizes the risk of lodging which can damage canopy structure to decrease photosynthetic capacity and impede water transportation by affecting grain filling (Zhang et al. 2016). As introduction of dwarf or reduced height (Rht) genes has dramatically increased wheat yields during the Green Revolution, exploration of novel Rht genes and revelation of their genetic controls are crucial for breeding high-yield wheat cultivars (Wang et al. 2010).

PH and yield-related traits are determined by many genes and environment factors (Wu et al. 2010; Liu et al. 2020). In wheat, twenty-four genes relating to PH have been reported and designated as Rht genes, distributed on chromosome 2AS, 2BL, 2DS, 3BS, 4BS, 4DS, 5AL, 5DL, 6AS, 7AS, and 7BS (GrainGenes, https://wheat.pw.usda.gov/cgi- bin/GG3/browse.cgi?query=%2ARht%2A;class=gene;begin=1). Rht genes reduce PH by diverse mechanisms that can be divided into two categories: gibberellic acid (GA)-insensitive (GAI) genes and GA-responsive/sensitive genes. GAI-Rht genes, such as Rht-B1 and Rht-D1, encode DELLA proteins to inhibit the sensitivity of GA, which can reduce PH and increase yield in various crops (Peng et al. 1999; Cao et al. 2020). GA-sensitive genes are related to a deficiency in endogenous GA due to blocked biosynthesis and signaling pathway (Milach and Federizzi 2001). Apart from Rht genes, mechanisms controlling PH and yield-related traits are also related to other phytohormone pathways, and protein and carbohydrate metabolism and transport (Cao et al. 2020; Liu et al. 2020).

Various quantitative trait loci (QTL) controlling PH have been identified across all the 21 chromosomes in common wheat. In this study, we used seven pairs of NILs targeting four QTL, namely QPhs.ccsu.3A.1 (Wang et al. 2018), Qphs.ocs-4B.1 (Wang et al. 2019), one unnamed QTL on 4AS (Pinto et al. 2010), and QHtscc.ksu-7A (Talukder et al. 2014; Lu et al. 2020). These genomic regions were chosen because they have overlaps with previously reported QTL for PH and yield-related traits (Huang et al. 2004; Maccaferri et al. 2010; Rebetzke et al. 2008; McCartney et al. 2005; Huang et al. 2004; Araki et al. 1999; Kuchel et al. 2007). In addition, Wang et al. (2018) reported that one NIL pair targeting the pre-harvest sprouting resistance locus QPhs.ccsu.3A.1 had a significant difference in PH. Later, Wang et al. (2019) reported that one NIL pair targeting another pre-harvest sprouting resistance locus Qphs.ocs-4B.1 had significant differences in PH and G/S. Kirigwi et al. (2007) reported an unnamed 4AS QTL controlling drought tolerance which may also play a role in PH. For QHtscc.ksu-7A, Lu et al. (2020) revealed that the isolines of one NIL pair targeting the heat tolerance locus were significantly different in PH, grain numbers, and final yield. The NILs targeting these four loci were, therefore, used to further investigate PH and yield-related traits.

Analysis of near-isogenic lines (NILs) is an essential approach in the characterization of target loci in quantitative genetic studies (Tuinstra et al. 1997). NILs are a pair of plants possessing almost the same genetic background except at one single gene or locus. Hence, any phenotypic differences shown in this pair are supposed to be contributed by the target genes harbored by the locus, which can convert the quantitative QTL to a Mendelian factor with predictable segregation and inheritance. In this study, NILs targeting QTL located on chromosome 3AL, 4AS, 4BL, and 7AL were analyzed by genotype-phenotype association analysis to identify their putative candidate genes responsible for PH and yield-related traits. The study aimed to (1) characterize the NILs targeting the four loci for PH and yield-related traits; (2) identify putative candidate genes controlling the traits through genotype-phenotype association analysis of the contrasting NILs; and (3) test whether any candidate genes identified in this study overlap with previously identified QTL and genes.

Materials and methods

Plant materials

Seven pairs of NILs were developed by a heterogeneous inbred family method with a fast generation cycling system (Yan et al. 2017) with one pair targeting a 3AL QTL Qphs.ccsu-3A.1 derived from Chara/ DM5637B*8 (Wang et al. 2018), one targeting a 4BL QTL Qphs.csc-4B.1 derived from Chara/ DM5637B*8 (Wang et al. 2019), four targeting a 4AS QTL for drought tolerance (Pinto et al. 2010) derived from Babax/Dhwar Dry, and one targeting a 7AL QTL for heat tolerance derived from Cascade/W156 (Talukder et al. 2014; Lu et al. 2020), as listed in Table 1. F8 seeds were grown to characterize the seven pairs of NILs for PH and yield-related traits including Y/P, TKW, G/S, and B/P.

Table 1.

The details of NILs used in this study

Pair no. NIL name Parents Target QTL name Chromosome location Marker interval of target QTL Physical position of target QTL (Mb) Screening marker References
1 NIL-PHS3AL-3R Chara × DM5637B*8 Qph.ccsu-3A.1 3AL Xwmc153 and Xgwm155 701.7–703.0 Xgwm155 Wang et al. 2018
NIL-PHS3AL-3S
2 NIL-PHS4BL-6R Chara × DM5637B*8 Qphs.ocs-4B.1 4BL Xgwm495 and Xgwm375 482.8–568.6 Xgwm495 Wang et al. 2019
NIL-PHS4BL-6S
3 NIL-DT4ABD-1B Babax × Dharwar Dry QTL in linkage group 4A-a 4AS Xgwm397 and Xwmc491 0.14–42.0 Xwmc491 Pinto et al. 2010
NIL-DT4ABD-1D
4 NIL-DT4ABD-3B Babax × Dharwar Dry QTL in linkage group 4A-a 4AS Xgwm397 and Xwmc491 0.14–42.0 Xwmc491 Pinto et al. 2010
NIL-DT4ABD-3D
5 NIL-DT4ABD-4B Babax × Dharwar Dry QTL in linkage group 4A-a 4AS Xgwm397 and Xwmc491 0.14–42.0 Xwmc491 Pinto et al. 2010
NIL-DT4ABD-4D
6 NIL-DT4ABD-5B Babax × Dharwar Dry QTL in linkage group 4A-a 4AS Xgwm397 and Xwmc491 0.14–42.0 Xwmc491 Pinto et al. 2010
NIL-DT4ABD-5D
7 NIL-HT3-13R Cascade × W156 QHtmd.ksu-7A 7AL Xgwm397 and Xwmc491 165.1–611.8 Xbarc49 Talukder et al. 2014; Lu et al. 2020
NIL-HT3-13S
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Growth conditions

The NILs were grown in a temperature-controlled glasshouse at The University of Western Australia (UWA) (31° 57′, 115° 47′), in which the temperature was set at 15/22 °C (night/day). All the seeds were sown in 20-cm pots to a depth of 2 cm with sterilized soil mixture (fine composted pine barks: coco peat: brown river sand = 5:2:3, pH = 6). Six replicates were used for each line. The pot positions were randomly re-arranged every Friday. Irrigation was applied every 2 days. Plants were fertilized once at 60 days after sowing with a water-soluble NPK fertilizer (poly feed greenhouse grade). The set temperature in the glasshouse was maintained, and all the plants grew in the same well-watered conditions during the whole experiment. All plants were harvested at the physiological mature stage when the loss of green color from the stem was shown in more than 50% of the spikes. After harvesting, all the plants were stored for drying in the temperature-controlled dark room set at a temperature of 35 °C for 1 week before measurements.

Measurements

PH refers to the shortest vertical distance between the upper boundary of the main photosynthetic tissues on a plant and the ground level, which was measured at the maturity stage. A 100 cm ruler was used for measuring PH. Yield-related traits were investigated. Dried plants were put on the balance, accurate to two decimal digitals, for measuring the B/P. All the spikes were gently hand threshed to get the grains, and after threshing, the seeds were counted and weighed using a balance, accurate to three decimal digitals. Yield per plant measured was equivalent to the total weight of grains (g). TKW was calculated by the formula of TKW=yieldgrain numbers×1000 (g).

Statistics

A two-sample t test was used for detecting a significant difference between the isolines. Correlation analysis of the measured traits (PH, B/P, G/S, TKW, and Y/P) was done using RStudio v1.0.153.

90 K SNP genotyping and candidate gene identification

The SNP genotyping was done at UWA. Specifically, Wheat 90K Illumina iSelect Array was used for genotyping genomic DNA samples of the NILs. SNP clustering and genotype calling were done by GenomeStudio 2.0 software (Illumina). SNPs with a call frequency < 0.8 (i.e., missing data points > 20%) or minor allele frequency (MAF) < 0.05 and SNPs with > 0.25 heterozygous calls were removed (Wang et al. 2014).

SNP data analysis was conducted to detect the differences within or close to the QTL intervals between the isolines. Brodie et al. (2016) found that SNPs may be up to 2 Mb away from their associated genes. Therefore, SNPs closely located towards the screening markers, especially those within the targeted QTL region (as they overlapped with QTL for the traits in other studies), were scrutinized. Specifically, the SNPs located on the 3AL, 4AS, 4BL, and 7AL chromosome arms were searched, especially those located closely to the markers used for developing the NILs, namely marker Xgwm155 for Qphs.ccsu.3A.1, Xgwm495 for Qphs.ocs-4B.1, Xwmc491 for the 4AS QTL, and Xbarc49 for QHtscc.ksu-7A. SNPs that differed between the NIL pairs were blasted against the wheat reference genome RefV1.0 (IWGSC et al. 2018). JBrowse (http://www.wheatgenome.org/Tools-and-Resources/Sequences) was used for candidate gene(s) discovery. Genes having key functions related to the investigated traits encoding carbohydrate, hormone, and Rht associated proteins and located within a 2-Mb distance from the SNPs were considered as candidate genes.

Results

Variations of PH and other yield-related traits

The seven NIL pairs showed variations in different traits. PH ranged from 54.1 cm to 80.6 cm, with four pairs including Pair 1, Pair 2, Pair 3, and Pair 7 showed significant differences between the isolines (Table 2). Average PH for the Pair 1 isoline with DM5637B*8 allele was 60.1 cm, significantly taller than the isoline with Chara allele at 54.1 cm; the Pair 2 isoline with DM5637B*8 allele was 59.2 cm, significantly taller than the isoline with Chara allele at 54.2 cm; the Pair 3 isoline with Dhawar Dry allele measured at 80.6 cm, significantly taller than the isoline with Babax allele at 73.6 cm; and the Pair7 isoline with Cascades allele was 70.5 cm, significantly taller than the isolines with W156 allele at 61.7 cm (Table 2; Fig. 1; Fig. 2). No significance in PH was detected for NIL pairs 4, 5, and 6.

Table 2.

The traits where the NIL pairs showing significant differences between their isolines

Traits Pair Mean value of isolines with allele 1 (allele name) Mean value of isolines with allele 2 (allele name) p value
Plant height 1 60.1 cm (DM5637B*8 allele) 54.1 cm (Chara allele) 0.032*
2 59.2 cm (DM5637B*8 allele) 54.2 cm (Chara allele) 0.049*
3 73.6 cm (Babax allele) 80.6 cm (Dhwar Dry allele) 0.024*
7 61.7 cm (W156 allele) 70.5 cm (Cascades allele) 0.034*
Biomass per plant 2 12.9 g (DM5637B*8 allele) 9.5 g (Chara allele) 0.0017**
4 23.2 g (Babax allele) 10.8 g (Dhwar Dry allele) 6.84E−12***
5 11.5 g (Babax allele) 20.0 g (Dhwar Dry allele) 8.03E−05***
6 10.5 g (Babax allele) 15.9 g (Dhwar Dry allele) 6.18E−05***
Yield per plant 2 5.8 g (DM5637B*8 allele) 4.6 g (Chara allele) 0.019*
4 7.4 g (Babax allele) 4.6 g (Dhwar Dry allele) 1.19E−08***
5 3.3 g (Babax allele) 6.5 g (Dhwar Dry allele) 0.0044**
6 5.2 g (Babax allele) 2.3 g (Dhwar Dry allele) 5.53E−05***
Thousand kernel weight 1 38.9 g (DM5637B*8 allele) 43.5 g (Chara allele) 0.012*
6 40.1 g (Babax allele) 17.2 g (Dhwar Dry allele) 5.26E−05***
Grain number per spike 1 29.3 (DM5637B*8 allele) 23.4 (Chara allele) 0.039*
3 34.7 (Babax allele) 54.6 (Dhwar Dry allele) 0.00097***
4 48.8 (Babax allele) 29.3 (Dhwar Dry allele) 5.2E-06***
6 53.4 (Babax allele) 22.7 (Dhwar Dry allele) 8.76E-06***
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*Indicates significant difference at p < 0.05, **indicates highly significant difference at p < 0.01, and ***indicates highly significant difference at p < 0.001

Fig. 1.

Fig. 1

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Average plant height of the NIL isolines at maturity. Pair 1: NIL-PHS3AL-3R and NIL-PHS3AL-3S; Pair 2: NIL-PHS4BL-6R and NIL-PHS4BL-6S; Pair 3: NIL-DT4ABD-1B and NIL-DT4ABD-1D; Pair 4: NIL-DT4ABD-3B and NIL-DT4ABD-3D; Pair 5: NIL-DT4ABD-4B and NIL-DT4ABD-4D; Pair 6: NIL-DT4ABD-5B and NIL-DT4ABD-5D; Pair 7: NIL-HT3-13R and NIL-HT3-13S. AA: allele 1; aa: allele 2 (the allele information is as listed in Table 2). *Denotes significant level at p<0.05

Fig. 2.

Fig. 2

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Four pairs of NILs showing significant differences in plant height. a Pair 1: NIL-PHS3AL-3R (left) and NIL-PHS3AL-3S (right) (Wang et al. 2018); b Pair 2: NIL-PHS4BL-6R (left) and NIL-PHS4BL-6S (right) (Wang et al. 2019); c Pair 3: NIL-DT4ABD-1D (left) and NIL-DT4ABD-1B (right) (Pinto et al. 2010); d Pair 7: NIL-HT3-13S (left) and NIL-HT3-13R (right) (Talukder et al. 2014; Lu et al. 2020)

Evaluations of yield-related traits also showed variations. In addition to showing significant difference in PH, Pair 1 showed a significant difference in TKW and G/S, Pair 2 showed a highly significant difference in B/P and a significant difference in Y/P, and Pair 3 showed a highly significant difference in G/S, whereas Pair 7 showed no significant differences in yield-related traits. Although exhibiting no significant differences in PH, Pair 4 showed highly significant differences in B/P, G/S, and Y/P, Pair 5 showed highly significant differences in B/P and Y/P, and Pair 6 showed highly significant differences in all the yield-related traits including Y/P, B/P, G/S and TKW (Table 2; Fig. 3).

Fig. 3.

Fig. 3

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Four agronomic traits including biomass per plant (a), thousand kernel weight (b), yield per plant (c), and grains per spike (d) measured in the seven NIL pairs. *Denotes significant level at p < 0.05, **denotes significant level at p < 0.01, and ***denotes significant level at p < 0.001. The NIL pair information is as same as in Fig. 1

Correlation of the investigated traits

Correlation analysis revealed the relationship between PH and the four yield-related traits (Y/P, G/S, TKW, and B/P) (Fig. 4). PH showed significantly positive but weak correlation with B/P (p < 0.05, r = 0.26) and highly significantly positive but weak correlation with G/S (p < 0.01, r = 0.28), but no significant correlation with TKW (r = 0.00). With no statistical significance, PH negatively correlated with Y/P (r = − 0.16). Y/P showed highly significantly positive but weak correlation with B/P (p < 0.001, r = 0.37) and G/S (p < 0.001, r = 0.36), and significantly positive but weak correlation with TKW (p < 0.01, r = 0.24). B/P showed significantly positive but weak correlation with G/S (p < 0.05, r = 0.26), but no correlation with TKW (r = 0.00).

Fig. 4.

Fig. 4

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Correlation between plant height (PH) and four other agronomic traits in the seven NIL pairs. Data are from the results of Pearson’s product-moment correlation. *, **, and ***Denote the significant levels at 0.05, 0.01, and 0.001, respectively. Each plot shows the relationship between the variables in horizontal and vertical axis of the grid. The diagonal shows a histogram of each variable. The numbers represent the ratio of correlation (r) between the variables in horizontal and vertical axis

SNP genotyping analysis

Nineteen SNPs were identified between the isolines and they are associated with fourteen functional genes (Table 3 and Table S1).

Table 3.

SNPs and candidate genes identified in the targeted QTL genomic region

Targeted QTL Marker interval Physical position (Mb) Traits SNPs Genes Functions
Qph.ccsu.3A.1 Xwmc153-Xgwm155 703 PH, TKW, G/S BS00024548_51 TraesCS3A01G469000 Hydroxyethylthiazole kinase
Qphs.ocs-4B.1 Xgwm495-Xgwm375 482.8 PH, Y/P, B/P tplb0043l14_1189 TraesCS4B01G235400 Branched-chain-amino-acid aminotransferase
Jagger_c1432_289 TraesCS4B02G230000 Purple acid phosphatase 3-like
Tdurum_contig37811_134 TraesCS4B02G229900 Prolyl 3-hydroxylase 2
Unnamed 4A QTL Xgwm397-Xwmc491 0.14 PH. Y/P, TKW, G/S, B/P RAC875_c1606_644 TraesCS4A01G029300LC Protein FAR1-RELATED SEQUENCE 5
Excalibur_c85853_521 TraesCS4A01G118900LC Same to E4
BobWhite_c13975_80 TraesCS4A01G035700 Cell division cycle protein 48-like protein
BS00066851_51 TraesCS4A01G049600 F-box protein
D_GBUVHFX01B5RWO_166 TraesCS4A01G071300 26S proteasome non-ATPase regulatory subunit 4
Ku_c10539_2031 TraesCS4A01G083600 Protein FAR1-RELATED SEQUENCE 6
GENE-3126_21.2 TraesCS4A01G001600 Sn1-specific diacylglycerol lipase alpha
QHtscc.ksu-7A Xbarc121-Xbarc49 165.1 PH RAC875_c1227_1098 TraesCS7A01G402900 Starch synthase 3
RAC875_c1227_1471 “ “
wsnp_Ex_c4883_8705816 “ “
IAAV3084 TraesCS7A01G416400 Auxin response factor
BS00022747_51 TraesCS7A01G419300 UDP-sugar pyrophosphorylase
BS00092631_51 “ “
wsnp_CAP11_c78_114341 “ “
BobWhite_c17095_237 TraesCS7A01G597300LC F-box/LRR-repeat protein 13
BS00075525_51 TraesCS7A01G412800 NBS-LRR disease resistance protein-like
RAC875_c12665_1272 TraesCS7A01G408900 Zinc knuckle family protein
wsnp_Ra_c40910_48252997 “ “
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Indicates the value is the same as above. The markers in bold are the screening markers for the NIL development

For NIL Pair 1 targeting the 3AL QTL, 379 polymorphic SNPs out of a total of 53,052 filtered markers were located on the chromosome 3A, and 112 of them were on the long arm of the 3A chromosome. Only one SNP BS00024548_51 was located within the targeted interval and close to the screening markers Xgwm155. The functions of genes within a 2-Mb physical distance from the SNP were examined. A gene TraesCS3A01G469000 near marker BS00024548_51 was detected, which is about 1.1 Mb away from the screening marker, functioning as hydroxyethylthiazole kinase.

For NIL Pair 2 targeting the 4BL QTL, 13 SNPs were identified and assigned to the region of the targeted QTL Qphs.ocs-4B.1. Two SNPs Jagger_c1432_289 and Tdurum_contig37811_134 close to the screening marker Xgwm495 (482.8 Mb) were linked to two functional genes including TraesCS4B01G230000 and TraesCS4B01G22990 that were involved in purple acid phosphatase 3-like and prolyl 3-hydroxylase 2, respectively. In addition, within the QTL Qphs.ocs-4B.1, there was one candidate gene TraesCS4B01G235400 associated with SNP tplb0043l14_1189 having the key function of branched-chain-amino-acid aminotransferase.

For NIL Pair 3 targeting the 4AS QTL, 106 SNPs were detected on the 4A chromosome. For NIL Pair 4 and 5, eighty-three SNPs and 130 SNPs were detected on chromosome 4A respectively. In terms of Pair 6, 145 SNPs were shown on chromosome 4A. Six of these SNPs were located within the QTL interval, which overlapped with the coding sequence (CDS) regions of six candidate genes: gene TraesCS4A01G029300LC with SNP RAC875_c1606_644 and gene TraesCS4A01G118900LC with SNP Excalibur_c85853_521 encode protein FAR1-RELATED SEQUENCE 5; gene TraesCS4A01G035700 with SNP BobWhite_c13975_80 functions as cell division cycle protein 48-like protein; gene TraesCS4A01G049600 with BS00066851_51 encodes F-box protein; gene TraesCS4A01G071300 with SNP D_GBUVHFX01B5RWO_166 functions in production of 26S proteasome non-ATPase regulatory subunit 4; and gene TraesCS4A01G083600 with SNP Ku_c10539_2031 encodes protein FAR1-RELATED SEQUENCE 6. SNP GENE-3126_21.2, located outside of the 4AS QTL but 1.1 Mb away from the screening marker, were overlapping with the CDS of another candidate gene TraesCS4A01G001600, which functions as Sn1-specific diacylglycerol lipase alpha.

For Pair 7 targeting the 7AL QTL, there were a total of 116 SNPs located on chromosome 7A, with eleven of them assigned to the targeted QTL region, which linked to six candidate genes. Three SNPs RAC875_c1227_1098, RAC875_c1227_1471 and wsnp_Ex_c4883_8705816 overlap with gene TraesCS7A01G402900 functioning as starch synthase 3. IAAV3084 is closely located to TraesCS7A01G416400 which functions as an auxin response factor. BS00022747_51, wsnp_CAP11_c78_114341 and BS00092631_51 are located within the CDS region of TraesCS7A01G419300 that functions as UDP-sugar pyrophosphorylase. Gene TraesCS7A01G597300LC is closely linked to BobWhite_c17095_237 functions like NBS-LRR disease resistance protein. RAC875_c12665_1272 and wsnp_Ra_c40910_48252997 overlap the CDS of gene TraesCS7A01G408900 with a function of Zinc knuckle family protein.

Discussion

Candidate genes are identified for the targeted QTL which overlap with previously reported QTL for PH and yield-related traits

`The targeted QTL in this study overlap with eight QTL reported from previous research (Table 4). Specifically, QTgw.igk.3A.1 (Huang et al. 2004) shares the same marker Xgwm155 with QPhs.ccsu.3A.1, and QHt.IDuWUE-4B.2 (Maccaferri et al. 2010) shares the same marker Xgwm495 with Qphs.ocs-4B.1. They both play a role in PH. In addition to PH, QTgw.igk-3A also affects TKW. The 4AS QTL targeted in this study overlapped with two 4AS QTL reported by Rebetzke et al. (2008) that affects plant height, yield, and biomass. Additionally, three QTL Qyld.crc-4A, QGyld.agt-4A.1 and Qyld.ocs-4A.1 overlapping with the targeted 4AS QTL have effects in yield (McCartney et al. 2005; Kuchel et al. 2007; Araki et al. 1999). Meanwhile, QHtscc.ksu-7A is located on chromosome 7AL overlapping with QHt.crc-7A for PH (McCartney et al. 2005).

Table 4.

Summary of previously identified QTL on 3AL, 4BL, 4AS, and 7AL chromosomes for plant height and yield-related traits in wheat

Trait Chromosome Identified QTL Parents Genomic region Phenotypic variance (%) Physical position (Mb)
Plant height 3AL QTgw.igk-3A (Huang et al. 2004) Flair × XX86 Xgwm155-Xgwm751 8.40 701.7–703.0
4BL QHt.IDuWUE-4B.2 (Maccaferri et al. 2010) Cvs. × advanced lines IWB34975-gwm495 482–497
4AS An unnamed QTL in Rebetzke et al. (2008) Cranbrook × Hallberd gwm192 10.8–59.7
An unnamed QTL in Rebetzke et al. (2008) Cd87× Katepwa Abg484 17.9–65.9
7AL QHt.crc-7A (McCartney et al. 2005) Wmc603-wmc139 488.7–530.2
TKW 3AL QTgw.igk-3A (Huang et al. 2004) Flair × XX86 Xgwm155-Xgwm751 8.40 701.7–703.0
Yield 4AS Qyld.crc-4A (McCartney et al. 2005) RL4452 × AC Domain Xgwm397-Xwmc680 6.30 151.7–514.7
An unnamed QTL in Rebetzke et al. (2008) Cranbrook × Hallberd gwm192 10.8–59.7
An unnamed QTL in Rebetzke et al. (2008) Cd87× Katepwa Abg484 17.9–65.9
Qyld.ocs-4A.1(Araki et al.1999) Chinese Spring × Chinese Spring (Kanto 107 4A) Xbcd1738 27–17 90.2
QGyld.agt-4A (Kuchel et al. 2007) Trident × Molineux Xgwm397 151.7
Biomass 4AS An unnamed QTL in Rebetzke et al. (2008) Cd87× Katepwa Abg484 17.9–65.9
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A total of 14 candidate genes responsible for PH and yield-related traits were identified for the four targeted QTL. These genes distributed on chromosome 3AL, 4BL, 4AS and 7AL can be categorized on the basis of distinct mechanisms: two genes related to auxin metabolism pathway, two genes functioning in a similar way as Rht8, one gene related to DELLA protein action, six genes related to ABA pathway, two genes related to starch synthesis, and one gene related to both DELLA protein and ABA.

Mechanisms regulating PH, yield-related traits, and corresponding candidate genes

There are twenty-four Rht genes discovered in previous research (Table 5). The best known Rht genes are Rht-B1 (Rht1) and Rht-D1 (Rht 2) on chromosome arm 4BS and 4DS that are GA-insensitive and inhibit plant growth (Zanke et al. 2014). Rht1 and Rht2 encode DELLA proteins to inhibit the sensitivity of GA, reducing PH, and increasing yield in various crops (Peng et al. 1999). DELLA proteins including GA insensitive (GAI), repressor of GAI (RGA), RGA-like1 (RGL1), RGL2, and RGL3 function in a highly similar fashion. Rht3, Rht10, Rht11, and Rht17 are also GA-insensitive genes (Daba et al. 2020). Rht3 and Rht17 located in the same region of Rht1, affect PH without compromising wheat yield (Navaro et al. 2014; Daba et al. 2020). Rht10 is allelic to Rht2, significantly reducing PH (Ellis et al. 2005). Rht11 is allelic to Rht1, having similar reduction effects in wheat height (Divashuk et al. 2012). Except Rht1, Rht2, Rht3, Rht10, Rht11, and Rht17, other Rht genes are GA-sensitive. GA-sensitive mutants are deficient for GA resulting in blocked biosynthesis pathway (Milach and Federizzi 2001). Among GA-responsive Rht genes, Rht4, Rht5, Rht6, Rht7, and Rht23 affect both wheat height and yield, whereas Rht9, Rht14, Rht16, and Rht18 affect PH but have unknown effects on production (Bachir and Hu 2014; Chen et al. 2013; Worland et al. 1980; Chen et al. 2014; Watanabe 2008). GA-responsive Rht8 is closely linked to Ppd-D1 that is insensitive to photoperiod (Pestsova et al. 2002; Chebotar et al. 2013), causing suppression in production obtained from light promotion. It reduces wheat height by producing the insensitivity of brassinosteroids (BRs) rather than GA, displaying a significant effect on grains per spike but not on yield. Moreover, the combination of Rht4 and Rht8 produces greater TKW and more yield, proposing a useful breeding strategy (Liu et al. 2017a, b; Korzun et al. 1998; Du et al. 2018). Rht12 and Rht13 play a negative role in PH, as well as grain numbers and TKW (Bachir and Hu 2014; Grant et al. 2018). According to Peng et al. (2011), Rht15, Rht16, Rht18, and Rht19 were induced by chemicals and radiation. The identification of Rht21 is debatable (Yang et al. 1995; Börner and Worland 2002). Rht22 affects PH and cell length through a differential mechanism to reduce cell numbers (Peng et al. 2011). Rht24 is reported to be valuable for breeding reduced height wheat with an advantageous effect on Fusarium head blight (FHB) resistance (Herter et al. 2018). Rht25 affects not only PH but also yield-related traits under different conditions (Mo et al. 2018). Although many Rht genes have been reported, the positions of four Rht genes including Rht6, Rht15, Rht19, and Rht20 remain uncertain and need further investigation to locate (Daba et al. 2020). Several candidate genes identified in this study are related to Rht protein functions, as discussed below.

Table 5.

Details of twenty-four Rht genes including names, GA-response mechanism, genetic background, and positions

Name (Rht genes) Allelic information GA response Genetic background Position (chromosome) Traits Reference
Rht1 (Rht-B1b) GAI Norin 10-Brevor 14 4BS PH, Y Zanke et al. 2014
Rht2 (Rht-D1b) GAI Norin 10-Brevor 14 4DS PH, Y Zanke et al. 2014
Rht3 (Rht-B1c) Allelic to Rht1 GAI MINISTER DWARF 4BS PH Navaro et al. 2014; Pearce et al. 2011
Rht4 GAR Burt ert 937 2BL PH, Y Bachir and Hu 2014
Rht5 GAR Marfed ert 1 Mutant 1 3BS PH, Y Bachir and Hu 2014
Rht6 Allelic to Rht2 GAR Norin 10-Brevor 14/ BURT 4D PH, Y Bachir and Hu 2014
Rht7 GAR Bersee Mutant A/ Bersee Mutant C 2AS PH, Y Worland et al. 1980
Rht8 GAR Akakomugi 2DS PH, G/S Bachir and Hu 2014, Grant et al. 2018
Rht9 GAR Acciao/Akakomugi/FORLANI/MARA 7BS PH Chen et al. 2013
Rht10 (Rht-D1c) Allelic to Rht2 GAI Aibian 4DS PH, Y Ellis et al. 2005
Rht11(Rht-B1e) Allelic to Rht1 GAI Karlik 1 4BS PH, Y Divashuk et al. 2012
Rht12 GAR Karcagi 522M7K 5AL PH, GN, GS, TKW Grant et al. 2018
Rht13 GAR Matelporignif 41 Mutant 1 7BS PH, GN, TKW Grant et al. 2018
Rht14 GAR CASTELPORIZANO 6AS PH Watanabe 2008
Rht15 GAR Durox ? PH Daba et al. 2020
Rht16 GAR Edmore Mutant 1 6AS PH Watanabe 2008
Rht17(Rht-B1p) Allelic to Rht1 GAI Chris Mutant 4BS PH Daba et al. 2020; Bazhenov et al. 2015
Rht18 GAR Icaro 6AS PH Grant et al. 2018, Watanabe 2008
Rht19 GAR Vic Mutant 1 ? PH Daba et al. 2020
Rht20 GAR Burt Mutant 860 ? PH Daba et al. 2020
Rht21 (debatable) ? XN0004 ? PH Yang et al. 1995; Börner and Worland 2002
Rht22 GAR Aiganfanmai 7AS PH Peng et al. 2011
Rht23 ? NAUH164 5DL PH, Y Chen et al. 2014
Rht24 GAR Aikang 58 6AL PH, FHB resistance Herter et al. 2018
Rht25 GAR UC1110/PI610750 6AS PH, Y (under different conditions) Mo et al. 2018
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GAI: GA insensitive; GAR: GA responsive; PH: plant height, Y: yield, TKW: thousand kernel weight, FHB resistance: Fusarium head blight resistance, GN: grain number per plant, G/S: grain number per spike, GS: grain size

Information provided by Graingenes: https://graingenes.org/cgi-bin/GG3/browse.cgi?query=%2ARht%2A;class=gene;begin=1

TraesCS4A01G049600 on chromosome 4AS encodes F-box protein. SLEEPY1, also belonging to F-box protein family, was involved in the DELLA protein mechanism in Arabidopsis thaliana (Dill et al. 2004). On chromosome 7A, TraesCS7A01G412800 contributes to biosynthesis of LRR family protein, as a receptor of BRs whose mechanism was also reported in Rht8 in wheat (Gasperini et al. 2012). In rice, one NBS-LRR protein Pik-H4 is active in homeodomain-containing protein that is positively reacted to BRs catabolism to reduce PH by ET pathway (Liu et al. 2017a, b). Additionally, TraesCS7A01G412800 has NBS-LRR disease resistance protein-like function that is not only related to disease resistance but also to abiotic stress such as drought stress, which indirectly promotes growth under harsh environment (DeYoung and Innes 2006). TraesCS7A01G408900, encoding Zinc knuckle family protein and belonging to zinc finger family proteins, covers two SNPs on chromosome 7A. Zinc knuckle family protein regulates plant growth through response to light signaling (Loudet et al. 2008). Moreover, the zinc finger family proteins are assigned to synthetic interval of Rht8 locus and function in plant elongation in wheat (Gasperini et al. 2012). TraesCS7A01G597300LC has a function of F-box/LRR-repeat protein 13. One of the F-box protein subfamilies combined with GID2 can interact with DELLA protein to reduce the PH, which works the same as in GAI-Rht genes (Hirsch and Oldroyd 2009).

Although abscisic acid (ABA) levels have no significant difference between crops with Rht genes and those without them, it also plays a role in plant height (King et al. 1983). ABA, as one of the most important hormones in plant, can be regarded as a plant growth inhibitor. A basic zipper transcription factor ABI5 responding to ABA suppresses cell activity and metabolism. Plant growth benefits from the activities of the plasma membrane (PM) H+-ATPase and nutrient transporters combined with electrochemical proton gradients. ABA can inhibit the production of PM H+-ATPase, which corresponds to restrain plant growth (Planes et al. 2014). Several genes are also detected in this study that associated with the ABA pathway.

On chromosome 3AL, the function of TraesCS3A01G469000 is related to hydroxyethylthiazole kinase participating in the biosynthetic of thiamine (vitamin B1) (Yazdani et al. 2013). Accumulation of thiamine is involved in tolerance to various stresses. When plants confront the stressed environment, thiamine is capable of minimizing the negative effects on plant growth by regulating ABA (Fitzpatrick and Chapman 2020). For example, thiamine enhances the adaptability to salinity, oxidative and osmotic stresses, and boosts plant growth and productivity in oil palm (Subki et al. 2018). As Aminifard et al. (2018) reported that thiamin foliar spraying in coriander and fenugreek increased TKW, because thiamin functions as a H+-ATPase pump to intake more nutrients. Thiamin pyrophosphate, as the active form of thiamin, participates in ATP synthesis and photosynthesis, which supplies energy in central metabolism (photosynthesis and respiration) (Rosado-Souza et al. 2020). The balance and coordination of carbon from central metabolism and nitrogen from α-ketoglutarate (α-KG) pathway are related to yield increase (Fitzpatrick and Chapman 2020). On chromosome 4AS, TraesCS4A01G035700 encodes cell division cycle protein 48-like protein. In rice, cell division cycle protein 48-like protein is vital for expression of AAA-ATPase to trigger ATPase activity, which affects PH and yield in rice (Shi et al. 2019). AAA-ATPase would suit stresses of low temperature and high salinity by regulating ABA levels in Arabidopsis (Baek et al. 2011). TraesCS4A01G071300 originating from Arabidopsis thaliana functions in 26S proteasome non-ATPase regulatory subunit 4. The 26S proteasome–related degradation that occurred in the ABA-signaling protein ABI5 or DPBF1 will affect PH in Arabidopsis (Smalle et al. 2003). TraesCS4A01G049600 encodes F-box protein. FOA1, as one of the F-box proteins, reacts to the signaling of ABA in Arabidopsis (Peng et al. 2012). Protein FAR1-RELATED SEQUENCE 6, encoded by gene TraesCS4A01G083600, and protein FAR1-RELATED SEQUENCE 5, encoded by two genes TraesCS4A01G029300LC and TraesCS4A01G118900LC, derive from the multiple FAR1-RELATED SEQUENCE (FRS) families. The FRS family impacts on the growth and development of Arabidopsis via signaling to ABA and light. The decrease transcript abundance of ABI5 happens in FAR1 mutants, suggesting disruption of FAR1 and FHY3 binding to ABI5 can diminish inhibition of plant growth caused by signaling to ABA. Besides, FAR1 shows insensitivity to ABA-triggered stomatal closure, indicating that FAR1 is necessary for responses to drought stress (Ma and Li 2018).

In addition, hormone auxin promotes plant elongation by improving the extensibility of cell walls. The activity of auxin is tightly linked to TRANSPORT INHIBITOR RESISTANT 1/AUXIN SIGNALING F-BOX (TIR1/AFB) nuclear auxin receptor family, the degradation of the transcriptional regulators AUXIN/INDOLE-3-ACETIC ACID (AUX/IAAs) and the AUXIN RESPONSE FACTORs (ARFs). When the auxin levels increase, the reaction between AUX/IAAs and TIR1/AFB will result in AUX/IAAs degrading, further inducing cell expansion (Majda and Robert 2018). The mechanism of auxin regulating plant yield is complex and undefined, but it is commonly known that the auxin signaling factors can modify the grain size and weight, and the interaction between BRs and auxin signaling pathway also plays a role in grain development (Cao et al. 2020). Two candidate genes identified in this study are related to auxin biosynthesis and signaling.

One gene, TraesCS4B01G235400, on chromosome 4BL with the function of producing branched-chain-amino-acid aminotransferase (BCAA) related to l-leucine transaminase activity that can remit the chlorsulfuron-induced growth inhibition. l-leucine transaminase activity is involved in the auxin production through indole-3-pyruvic acid (IPA) pathway as one of most important auxin producing pathways in Neurospora crassa, affecting grain weight and PH (Dastgheib 1993; Sardar and Kempken 2018; Mashiguchi et al. 2011). TraesCS7A01G416400 on chromosome 7AL overlapping with two different SNPs is related to auxin response factors (ARFs), and the regulation of PH by TraesCS7A01G416400 is through auxin signaling in wheat (Qiao et al. 2018).

Moreover, the production and accumulation of starch affect yield. Two main glucose homopolymers, amylose and amylopectin, constitute starch in plant (Dian et al. 2005). Sucrose existing in the cytoplasm of heterotrophic cells can be decomposed to fructose and UDP-glucose (UDPG) recurs to sucrose synthase (SuSy). Then, UDPG was converted to G1P and PPI under the action of UDPG pyrophosphorylase, and G1P is metabolized by cytosolic glucose phosphate mutant enzyme to G6P that can be turned into starch by means of plastidial phosphoglucomutase (pPGM), ADPglucose (AGP), and starch synthase (SS) activities. Two candidate genes located on 7AL are relevant to starch synthesis and transport.

On chromosome 7AL, two genes, TraesCS7A01G402900 and TraesCS7A01G419300, function in the production of carbohydrate, overlapping with two SNPs and having the function of starch synthase 3 (SSIII) and UDP-sugar pyrophosphorylase, respectively. Starch synthase 3 (SSIII) is active in the pathway to starch biosynthesis, and soluble starch synthase (SSS) decreases when wheat plants suffer from heat stress (Mishra et al. 2017). The gene encoding soluble starch synthase can improve heat tolerance in wheat, indirectly boosting wheat yield (Tian et al. 2018). SSIII was proved to participate in the starch synthesis via amylopectin elongation activity in rice (Dian et al. 2005). UDP-glucose (UDPG) pyrophosphorylase, belonging to one of the UDP-sugar pyrophosphorylase family, originates from Arabidopsis thaliana, which is a precursor for biomass producing and plant growing (Decker and Kleczkowski 2019).

By contrast, there are no reports for the functions of 4BL genes TraesCS4B02G230000 and TraesCS4B02G229900 that affect PH and wheat production. Regarding the QTL controlling drought tolerance on chromosome 4AS, one candidate gene TraesCS4A01G001600 was identified for the lipid catabolic process to produce Sn1-specific diacylglycerol lipase alpha. However, there has been no studies showing the relationship between this protein function and yield.

In this study, PH significantly and positively correlated with B/P and G/S. In addition, PH shows a negative correlation with Y/P, but no correlation with TKW. Some of these results are not consistent with previous reports, for instance, Wolde and Eticha (2016) detected the significantly negative correlation between PH and TKW in South Eastern Ethiopia. Sokoto et al. (2012) stated PH in two varieties (Star 11 TR77173/SLM and Kauz/Weaver) significantly and positively correlated with Y/P in the dry conditions. This inconsistency may be caused by differences in plant materials and experimental conditions in individual studies, which may need further investigation.

Conclusion

In this study, seven NIL pairs, originally targeting QTL for pre-harvest sprouting (Qphs.ccsu-3A.1 and Qphs.ocs-4B.1), drought tolerance (the unnamed 4AS QTL), and heat tolerance (QHtscc.ksu-7A), were characterized for PH and yield-related traits. Genotype-phenotype association analysis of the NILs showing contrasting performance between isolines revealed fourteen candidate genes located on 3AL, 4BL, 4AS, 7AL chromosome arms. They have functions related to plant hormone, carbohydrate, and amino-acid metabolism pathways. The four QTL are confirmed to be important in regulating PH and yield-related traits based on findings of this study. The seven pairs of NILs can be used for fine mapping and gene cloning to pinpoint any new genes responsible for PH and productivity, and for the development of useful markers for marker-assisted selection.

Supplementary Information

Supplementary Table 1 (23KB, xlsx)

(XLSX 23 kb)

Acknowledgements

The authors thank Dr. Md Sultan Mia and Dr. Xingyi Wang for providing the NIL seeds for this experiment, Professor Ruihui Wang for technical support, and Professor Jacqueline Batley and Dr. Aneeta Pradhan for assistance in the 90 K SNP array genotyping. The authors also acknowledge the funding body, the Global Innovation Linkage program (GIL53853) from the Australian Department of Industry, Innovation and Science.

Authors’ contributions

YZ, HL and GY conceived and designed the experiments; YZ conducted the experiments; YZ and HL analyzed the data; YZ wrote the manuscript, and HL, GY critically reviewed the manuscript. All authors approved the final version of the manuscript.

Funding

The research was funded by Global Innovation Linkage program (GIL53853) from the Australian Department of Industry, Innovation and Science.

Data availability

The data and materials used in this study from authors are available on reasonable request.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Code availability

RStudio v1.0.153, Excel software v16.32.

Footnotes

Publisher’s note

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

Contributor Information

Hui Liu, Email: hui.liu@uwa.edu.au.

Guijun Yan, Email: guijun.yan@uwa.edu.au.

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Supplementary Table 1 (23KB, xlsx)

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The data and materials used in this study from authors are available on reasonable request.

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