Identification of stable QTLs and candidate genes associated with plant height and spike length in common wheat

doi:10.21203/rs.3.rs-2964934/v1

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Identification of stable QTLs and candidate genes associated with plant height and spike length in common wheat

https://doi.org/10.21203/rs.3.rs-2964934/v1

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Three RIL mapping populations derived from bi-parental crosses between a common parental line Roshan (Triticum aestivum L.) and three cultivars: Sabalan, Falat and Superhead. RILs were grown over three years at five separate locations under water stress and well-watered condition. Plant height and spike length were collected at each experiment. Genetic map distances among markers were carried out using DArT markers. Single-environment and multi-environment analyses were performed using inclusive composite interval mapping (ICIM). Epistasis and epistasis × environment interactions were also performed through an epistatic QTL analysis in Ici-Mapping 4. Single locus analysis allowed the detection of 32 main effect QTL (M-QTL) in the three populations. Three novel stable and non-epistatic QTL, one QTL for plant height (qPH1D, in Sabalan background) and two QTLs for spike length (qSL1D and qSL1B.3, in Falat and Superhead backgrounds, respectively) were identified. 191 candidate genes were predicted in the physical interval between the flanking markers on chromosomes 1B, 1D and 4B. 20 genes were highlighted as putative candidate genes with probable role in plant height and spike-related traits in wheat. These results would lay a good foundation for fine mapping of QTLs/genes for molecular marker-assisted breeding in wheat in the future.

Bread wheat

Candidate genes

DArT marker

Epistatic QTL

Multi-environments

QTL mapping

Plant height (PH) and spike length (SL) are important traits that have a significant impact on plant architecture and yield potential in wheat (Deng et al. 2017; Zhang et al. 2017; Hassan et al. 2019; Li et al. 2021; Xu et al. 2022). In bread wheat, dwarfing has been achieved through the introduction of reduced height (Rht) genes (Evans 1996). It has been demonstrated that all 21 wheat chromosomes carry factors affecting PH (Marza et al. 2006; Cui et al. 2011). To date, twenty-four Rht genes (Rht1–Rht24) have been reported, including Rht1 (Rht-B1), Rht3 (Rht-B1c), Rht11, and Rht17 located on chromosome 4BS, and Rht2 (Rht-D1) and Rht10 (Rht-D1c) located on chromosome 4DS (Peng et al. 2011; McIntosh et al. 2013; Chen et al. 2015; Lu et al. 2015b; Zhang et al. 2021). Rht4, Rht5, Rht6, Rht7 and Rht8 have been assigned to chromosomes 2BL, 3BS, 4D, 2AS and 2DS, respectively (Ellis et al. 2005; Chen et al. 2015). Rht9 and Rht13 were mapped on chromosome 7BS, while Rht12, Rht22, Rht23, and Rht24 have been mapped to chromosomes 5AL, 7AS, 5DL, and 6AL, respectively. Rht14, Rht16, Rht18, and Rht25 were mapped to chromosome 6AS (Peng et al. 1999, 2011; Ellis et al. 2005; McIntosh et al. 2013; Chen et al. 2015; Zhang et al. 2021). Of these 24 major genes, Rht1, Rht2, Rht8, and Rht9 are extensively used in wheat breeding (Peng et al. 1999; Ellis et al. 2002; Asplund et al. 2012). In addition to these major genes, several quantitative trait loci (QTL) for plant height have also been identified in previous reports (Börner et al. 2002; Quarrie et al. 2005; Griffiths et al. 2012; Würschum et al. 2015; Jahani et al. 2019; Wang et al. 2020). Xiong et al. (2021) reported a QTL cluster on chromosome 4B for plant height (PH) explaining 12.2% of the phenotypic variation. Luo et al. (2021) detected five stable QTLs for PH, located on chromosomes 2B, 2D, 3D, 4B, and 6A, respectively. Many previous studies have reported QTLs associated with spike length (SL) on almost all 21 chromosomes (Börner et al. 2002; Cui et al. 2012; Cao et al. 2019; Li et al. 2021; Xu et al. 2022). Xiaofeng et al. (2005) found that 10 chromosomes (4A, 5A, 6A, 7A, 1B, 3B, 4B, 5B, 6B, and 7D) significantly affect SL. Cao et al. (2019) identified nine QTLs for SL on chromosomes 2D, 3A, 4A, 5B, 5D, and 6B. Eleven SL-QTLs were detected on chromosomes 2A, 2D, 3A, 4B, 4D, 5D, 6B, and 7B under four different treatments in both locations and years, accounting for 5.19–24.2% of the phenotypic variation (Deng et al. 2017). Despite significant progress in QTL mapping and gene cloning, improving yield under field conditions remains challenging because quantitative traits are influenced by the environment (Meng et al. 2015). QTL × environmental interaction and QTL × genetic background are factors limiting QTL use in molecular breeding (Bernier et al. 2008; Lafitte et al. 2012). QTLs with consistent effects across environments are the main target for breeding programs because they can improve crop performance in all regions where the crop can be grown (El-Soda et al. 2014). In this study, we used three RIL mapping populations derived from bi-parental crosses between a common parental line Roshan and three cultivars: Sabalan, Falat, and Superhead. Our objectives were to: (i) evaluate genetic background effects on the expression of QTLs, (ii) identify genomic regions with stable and major QTLs associated with PH and SL, (iii) identify candidate genes suitable for marker-assisted breeding strategies in wheat breeding, and (iv) provide diagnostic markers for use in marker-assisted selection (MAS) breeding.

Plant materials and field trials

Three RIL mapping populations comprising a total of 703 RILs were created by crossing a common parental line, Roshan, which is a tall, high-yielding and drought-tolerant Iranian landrace, with three different cultivars: Falat, Sabalan, and Superhead. Falat is an Iranian cultivar known for its early maturity, high grain yield, dwarf stature, and sensitivity to drought stress (Sardouei-Nasab et al. 2019). Sabalan is a tall Iranian cultivar with moderate tolerance to drought and high grain yield. Superhead, on the other hand, is a dwarf cultivar with a high grain yield but susceptible to drought. It was developed at the Seed and Plant Improvement Institute (SPII), Karaj, Iran. The F2 seeds of the crosses were advanced by single seed descent at the Agricultural Biotechnology Research Institute of Iran in 2010, resulting in the development of 303 RILs in Falat background (RF-RILs), 260 RILs in Sabalan background (RS-RILs), and 140 RILs in Superhead background (RSH-RILs).

Experimental sites

RS and RF populations together with their parents were evaluated in five environments under water stress (WS) and well-watered condition (WW) in Yazd and Kerman Province, Iran: WW condition in Yazd in 2012 (WW-Y12), WW condition in Kerman in 2013 (WW-K13); WW condition in Kerman in 2014 (WW-K14), WS condition in Kerman in 2013 (WS-K13) and WS condition in Kerman in 2014 (WS-K14). RSH population and its parents were evaluated in four environments: WW-Y12, WW-K13, WW-K14, and WS-K14. The climate at both locations (Yazd and Kerman) is hot and dry, and the soil type at both sites is a sandy loam with a pH of 7.8. Agro-climatic characteristics of the environments are given in Supplemental Table S1. The seeds were sown in mid-November at WW and WS experiments. RF, RS, and RSH RILs in WW-Y12, were seeded in an incomplete block design (lattice) with two replications (Three populations were evaluated separately). In Kerman-2013, both experiments (WW and WS) were laid out in an augmented design (sets of 20 lines were randomly assigned to a block with 4 check cultivars). In Kerman-2014, three populations were evaluated in the same trial under WS and WW conditions based on an incomplete block design (LATTICE) with two replications. Each experimental plot consisted of four 1.5 m long rows with 25cm spacing between the rows. In WS trials, the drought stress was imposed by stopping irrigation at the flowering stage. The Plant height (PH), measured from the base of stem to the tip of the ear in cm and Spike length (SL), measured from the base of the spike to the tip of spike in cm, were collected at each experiment.

Statistical analysis

The experimental error was calculated based on replicated checks in an augmented design, which was used to calculate adjusted genotype means for blocks. For lattice design experiments, the PROC LATTICE procedure in SAS (SAS Institute, version 9.1.3) was utilized to calculate adjusted means. The adjusted phenotypic values for each line in each experiment were used for subsequent phenotypic summarization and QTL mapping.

Genotyping and construction of genetic map

The DNA of each RIL line and their parents were submitted to Triticarte Pty Ltd (Canberra, Australia; http://www.triticarte.com.au/) for genotyping. The loci were scored as present (1) or absent (0), with the locus designations as used by Triticarte Pty Ltd. Genotyping was performed on RF, RS, and RSH populations using 2605, 2717, and 868 DArT markers, respectively. To construct the genetic map, redundant or monomorphic markers were excluded from the dataset using the BIN function in IciMapping v4 (Meng et al. 2015). Grouping was performed based on anchored marker information, with a threshold LOD score of 3. The ordering algorithm of 'nnTwoOpt' and the rippling criteria SARF (sum of adjacent recombination frequencies) were used, with a window size of 5 markers. Only one marker was randomly selected from each bin to construct the genetic map using the MAP function of IciMapping v4. The Kosambi mapping function (Kosambi 1994) was used to calculate map distances. In the RS population, a genetic map was constructed using 260 genotypes and 459 polymorphic DArT markers. In the RF population, a map was constructed using 805 markers and 303 RILs. Finally, in the RSH population, a linkage map was constructed using 140 lines and 573 markers.

QTL mapping

Linkage analysis was performed using the inclusive composite interval mapping (ICIM) function of QTL IciMapping version 4.1 (Wang et al. 2016a), with the default mapping function. QTL detection for plant height (PH) and spike length (SL) in each environment and the average values across all environments were performed with LOD score values ≥ 2.5. Epistasis and epistasis × environment interactions (E-QTL) were identified using ICIM epistatic interaction analysis, with a scanning step of 5 cM. P values for entering variables (PIN) and removing variables (POUT) were set at 0.0001 and 0.0002, respectively. A LOD score of 5 was used as the threshold to declare the presence of a QTL. Stable QTL were identified with absolute additive effects greater than the additive by environment effects (Li et al. 2015).

Candidate gene identification

To obtain the sequence of flanking DArT markers tightly linked to the QTL, we accessed the Diversity Arrays database (https://www.diversityarrays.com/seq2download/DArT_Wheat.fasta). The physical positions of these markers were determined by aligning them with the Chinese Spring genome (IWGSC RefSeq v2.1.) using the BLAST database on the URGI server (https://urgi.versailles.inrae.fr/blast/). The sequences of the significant DArT markers and their corresponding physical positions on the genome are presented in Supplemental Table S2. We extracted genes located between the flanking markers from IWGSC RefSeq v2.1 and annotated them using wheat genome databases such as PersephoneSoft (https://persephonesoft.com/) and EnsemblPlants (http://plants.ensembl.org/Triticum_aestivum). To identify genes, gene networks and traits associated with the candidate genes, we used the KnetMiner website (https://knetminer.com/Triticum_aestivum/). Our search included candidate genes to identify if any of them had a link with a known plant height gene.

Phenotypic summary

Phenotypic evaluation revealed a wide range of variation in the studied traits. Significant differences were observed among the recombinant inbred lines (RILs) for PH and SL in each environment (data not shown). A summary of the phenotypic traits for the three RIL populations across the five environments is presented in Table 1. In the majority of environments, PH and SL exhibited significant variation, indicating their strong environmental influence. For example, the spike length in the RF population ranged from 2.31 cm in WW-K13 to 24.35 cm in WW-K14, while the RS population spike length ranged from 3.52 to 14.9 cm in WW-K13 and WW-K14, respectively. The shortest genotype was observed in the RF population in WW-K14 (26.56 cm) and the tallest in the RF population in WW-K13 (156.2 cm). Drought stress resulted in a reduction in plant height and spike length in all three populations.

Linkage map construction

In the RF population, a total of 805 polymorphic markers were mapped, resulting in 21 linkage groups covering 5169.27 cM with an average marker density of one per 6.42 cM. Among these markers, 253 (31.42%) were mapped to the A genome, 399 (49.56%) to the B genome, and 153 (19%) to the D genome. The RS population linkage map covered 3988.44 cM using 459 markers, with an average interval of 8.70 cM between loci. QTL mapping analysis excluded chromosomes 4D, 5D, and 6D. Of the 459 markers, 253 (55.11%) were localized to the A genome. In the RSH population, a total of 573 polymorphic markers were mapped, resulting in a linkage map of 4238.02 cM with an average marker density of one per 7.40 cM. Chromosome 4D was excluded from QTL mapping analysis. Of the 573 markers, 39.79% were mapped to the D genome, while 28.97% and 31.24% were localized to the A and B genomes, respectively. Additional information about the linkage maps is presented in Table 2.

Table 1

Performance of recombinant inbred lines of Roshan\(\times\)Falat (RF), Roshan \(\times\) Sabalan (RF) and Roshan \(\times\) Superhead (RSH) for plant height and spike length across the five environments
		RF		RS		RSH
Env.		PH (cm)	SL(cm)	PH (cm)	SL(cm)	PH (cm)	SL(cm)
WW-K13	Mean	125.95	12.3	92.03	8.94	91.05	8.43
	Min.	36.5	2.31	56.87	3.52	54	5.5
	Max.	161.25	18.56	129.2	14.08	116	12.5
	SD	15.99	2.118	12.44	1.245	10.89	1.42
WW-K14	Mean	93.63	10.31	100.23	10.55	90.34	11.09
	Min.	26.56	6.60	46.54	6.71	54.84	6.81
	Max.	144.59	24.85	130.31	26.25	123.3	27.87
	SD	12.79	1.616	10.3	1.18	12.45	2.3
WW-Y12	Mean	115.4	9.33	124.12	9.4	121.6	8.76
	Min.	87.83	6.97	86.83	7.8	93.83	7.9
	Max.	141.83	13.13	145.2	12.27	145.2	11.17
	SD	11.1	1.01	9.7	0.75	9.66	0.71
WS-K13	Mean	121.97	12.21	88.9	8.49	-	-
	Min.	75.99	8.5	54.93	5.35	-	-
	Max.	166.24	18	120	12.19	-	-
	SD	15.24	1.632	11.64	1.221	-	-
WS-K14	Mean	82.67	10.07	89.43	10.45	80.91	10.53
	Min.	48.49	6.48	60.13	7.61	50.25	5.87
	Max.	105.88	14.92	109.8	13.48	116.1	16
	SD	10.28	1.533	7.82	1.107	13.66	1.96

WW-Y12: Well-watered condition in Yazd in 2012. WW-K13: Well-watered condition in Kerman in 2013; WW-K14: Well-watered condition in Kerman in 2014. WS-K13: Water stress condition in Kerman in 2013; WS-K14: Water stress condition in Kerman in 2014.

Table 2

Characteristics of the genetic maps employed in this study
	RF			RS			RSH
Chr. ^a	N^b	Length^c	Ave^d	N^b	Length^c	Ave^d	N^b	Length^c	Ave^d
1A	31	319.3	10.30	46	307.3	6.68	44	366.5	8.33
2A	34	237.13	6.97	10	163.72	16.37	13	101.55	7.81
3A	41	275.55	6.72	14	223.18	15.94	22	234.97	10.68
4A	41	147.77	3.60	35	207.56	5.93	36	120.7	3.35
5A	12	193.37	16.11	10	232.31	23.23	10	89.27	8.93
6A	45	180.01	4.00	27	164.35	6.09	38	353.7	9.31
7A	49	347.47	7.09	16	360.21	22.51	3	60.2	20.07
1B	86	295.14	3.43	29	198.62	6.85	28	261.05	9.32
2B	37	229.52	6.20	25	256.06	10.24	32	268.24	8.38
3B	127	613.09	4.83	52	276.41	5.32	19	180.94	9.52
4B	19	186.93	9.84	13	130.56	10.04	12	219.46	18.29
5B	32	342.91	10.72	34	401.2	11.80	35	430.38	12.30
6B	53	469.33	8.86	22	199.44	9.07	28	369.49	13.20
7B	45	386.89	8.60	29	304.54	10.50	25	180.87	7.23
1D	46	134.67	2.93	15	160.28	10.69	25	162.02	6.48
2D	31	173.07	5.58	12	136.51	11.38	33	314.64	9.53
3D	25	246.72	9.87	31	197.34	6.37	17	142.81	8.40
4D	3	14.77	4.92	-	-	-	-	-	-
5D	8	79.52	9.94	-	-	-	4	108.92	27.23
6D	10	207.15	20.72	-	-	-	15	116.51	7.77
7D	30	88.96	2.97	39	68.85	1.77	134	155.8	1.16
Total	805	5169.27	6.42	459	3988.44	8.70	573	4238.02	7.40
A	253	1700.6	6.72	253	1371.81	5.42	166	1326.89	7.99
B	399	2523.81	6.33	399	1386.21	3.47	179	1910.43	10.67
D	153	944.86	6.18	153	1230.42	8.04	228	1000.7	4.39

^a Chromosome

^b Number of DArT markers

^c Length of map

^d Average interval

Single environment analysis (M-QTL)

In single environment analyses across environments, 32 distinct main-effect QTLs (M-QTL) were detected for PH and SL (Table 3). Falat background had the most M-QTLs with 21, while Sabalan and Superhead backgrounds had 5 and 6 M-QTLs, respectively. For PH, 16 M-QTLs were identified in RF-RILs (10), RSH-RILs (4), and RS-RILs (2), which were specific to individual populations/environments. Two QTLs were mapped on chromosomes 4B (qPH4B: R² = 6.21%, increasing allele PH was contributed by Roshan) and 5B (qPH5B: R² = 4.63%, the Roshan allele increased the trait) in RS-RILs. Of the ten QTLs mapped in RF-RILs, two major effect QTLs were identified on chromosomes 7A (qPH7A: R² = 10.19% in WW-K14) and 5A (qPH5A: R² = 10.59% in WS-K13). In RSH-RILs, three major QTLs were mapped on chromosomes 6A and 4B (qPH6A, qPH4B.1 and qPH4B.2), explaining a phenotypic variance of 19.51%, 14.54%, and 20.37%, respectively, and specific to WW-Y12. Two major QTLs were located on chromosomes 1B (qSL1B: R² = 61.59%) and 3A (qSL3A: R² = 13.87%) in RS-RILs, which were specific to WW-K14 and WS-K14, respectively. Eleven M-QTLs were identified under Falat background, which were found in more than one environment. All of these QTLs were minor-effect QTLs except for qSL4B on chromosome 4B, which explained a phenotypic variance of 12.8% and was specific to WS-K13. The allele of Falat at this locus would reduce SL by 0.58 cm based on population mean. In RSH-RILs, two minor SL-QTLs were mapped on chromosomes 7B (qSL7B) and 1B (qSL1B).

Table 3

QTL with main effects for plant height and spike length in RILs population detected by the single-environment analysis approach under well-watered (WW) and water stress (WS) conditions.
Pop.name	Trait	Env.	QTL	Chr.	Genetic interval (cM)	Physical distance (Mb)	Marker interval	LOD	R²(%)^a	Add ^b	+Allele ^e
RS	PH	WW-K14	qPH4B	4B	98	548.25-589.97	wPt-1101-wPt-2214	2.83	6.21	3.93	Roshan
	PH	WS-K13	qPH6A	6A	157	614.43	wPt-4229-wPt-731936	2.58	4.63	2.55	Roshan
RF	PH	WW-K14	qPH1A	1A	145	487.95-780.68	wPt-8172-wPt-5776	3.78	5.20	-3.08	Falat
	PH	WW-K13	qPH4A	4A	95	666.11-730.65	wPt-0610-wPt-7354	5.24	8.09	5.22	Roshan
	PH	WS-K13	qPH5A	5A	170	452.2-537.63	wPt-9094-wPt-4778	5.48	10.59	5.29	Roshan
	PH	WW-K13	qPH6A	6A	42	319.98-620.23	wPt-8124-wPt-5597	2.64	6.32	4.02	Roshan
	PH	WS-K13	qPH6A	6A	45	619.63	wPt-8252-wPt-6661	4.20	6.18	3.79	Roshan
	PH	WW-K14	qPH7A	7A	103	230.78	wPt-742244-wPt-4199	6.51	10.19	4.83	Roshan
	PH	WS-K13	qPH7A	7A	200	119.2-231.97	wPt-2525-wPt-0961	4.09	5.95	3.87	Roshan
	PH	WW-K14	qPH4B.1	4B	42	170.02-324.32	wPt-1046-wPt-667593	4.52	7.27	3.93	Roshan
	PH	WS-K14	qPH4B.2	4B	78	536.13-575.56	wPt-1708-wPt-9067	4.17	6.70	3.15	Roshan
	PH	WS-K13	qPH2D	2D	81	330.85-654.56	wPt-6775-wPt-2644	3.67	4.71	-4.28	Falat
RSH	PH	WW-Y12	qPH6A	6A	263	135.4-500.4	wPt-7938-wPt-9342	4.86	19.51	-5.05	SuperHead
	PH	WW-Y12	qPH3B	3B	7	802.79	wPt-4933-wPt-666964	3.00	7.57	3.16	Roshan
	PH	WW-Y12	qPH4B.1	4B	170	134.43-664.68	wPt-2525-wPt-8292	4.55	14.54	-4.19	SuperHead
	PH	WW-Y12	qPH4B.2	4B	216	183.04	wPt-0391-wPt-4931	6.51	20.37	4.92	Roshan
RS	SL	WW-K14	qSL1B	1B	113	977.28	wPt-3465-wPt-7259	36.60	61.59	-8.56	Sabalan
	SL	WS-K14	qSL3A	3A	123	126.53-135.82	wPt-3620-wPt-7890	2.75	13.87	-0.41	Sabalan
	SL	WW-Y12	qSL4A	4A	140	651.9-558.24	wPt-7924-wPt-1155	2.70	5.00	-3.39	Sabalan
RF	SL	WS-K14	qSL2A.1	2A	218	612.03	wPt-1142-wPt-734106	2.95	4.73	0.336	Roshan
	SL	WS-K13	qSL2A.2	2A	202	518.74-765.11	wPt-9277-wPt-1657	3.63	4.90	0.372	Roshan
	SL	WS-K14	qSL4A	4A	60	283.06–648.5	wPt-734345-wPt-5730	3.11	6.96	-0.41	Falat
	SL	WW-K14	qSL4A	4A	60	283.06–648.5	wPt-734345-wPt-5730	3.19	7.08	-0.43	Falat
	SL	WW-K13	qSL7A	7A	0	448.81-584.15	wPt-6221-wPt-4515	2.93	4.06	-0.43	Falat
	SL	WS-K13	qSL1B	1B	294	659.63	wPt-2526-wPt-4532	6.04	7.72	0.461	Roshan
	SL	WW-K13	qSL3B	3B	159	427.73–635.3	wPt-1191-wPt-6216	3.32	4.97	-0.47	Falat
	SL	WS-K13	qSL3B	3B	440	700.23	wPt-3107-wPt-11278	5.70	7.51	-0.45	Falat
	SL	WS-K13	qSL4B	4B	105	141.93-433.01	wPt-1505-wPt-5390	4.19	12.08	-0.58	Falat
	SL	WW-K14	qSL6B	6B	149	121.01	wPt-744632-wPt-7662	2.82	6.07	-0.47	Falat
	SL	WW-K14	qSL7B	7B	247	715.54-759.62	wPt-733669-wPt-4300	2.87	4.25	-0.34	Falat
RSH	SL	WS-K14	qSL7B	7B	154	-	wPt-0530-wPt-4297	2.93	9.71	0.654	Roshan
	SL	WW-Y12	qSL1B	1B	261	689.22-680.29	wPt-1770-wPt-4721	3.14	9.93	-0.45	SuperHead

WW-Y12: Well-watered condition in Yazd in 2012. WW-K13: Well-watered condition in Kerman in 2013; WW-K14: Well-watered condition in Kerman in 2014. WS-K13: Water stress condition in Kerman in 2013; WS-K14: Water stress condition in Kerman in 2014.

^a Variation explained by each putative QTL

^b Additive effect of detected QTL

^e Source of positive allele

Multi-environment QTL analysis (ME-QTL)

A total of 49 QTLs with QTL-by-environment interaction (QEI) effects were mapped for PH (Supplemental Table S3), including 30 QTLs in the RF population, 10 QTLs in the RS population, and 9 QTLs in the RSH population. In RS-RILs, one QTL was detected in both single and multiple environment mapping, while nine QTLs were only detected in multiple environment mapping analysis. Among the 30 QTLs identified in RF-RILs, ten QTLs were detected in both single and multiple environment mapping. In RSH-RILs, three QTLs were detected in both single and multiple environment mapping. In RF-RILs, seven stable QTLs were identified with absolute additive effects greater than the additive by environment effects (Li et al. 2015). Roshan contributed the allele for PH in these QTLs. In RS-RILs, the QTLs located on chromosomes 1D (qPH1D) and 5B (qPH5B) were found to be stable across environments. From the nine putative QTLs for PH, only the QTL located on chromosome 4B (qPH4B.1: R² = 14.54%) at wPt-2525-wPt-8292 in the Superhead background was found to be stable across environments, with the alleles increasing PH coming from Superhead. For these QTLs, replacing a Superhead allele with a Roshan allele is expected to decrease PH by 1.95 and 1.35 cm, respectively, in all four environments. The major QTLs (qPH4B.2: R² = 20.37%) in RSH-RILs had a significant QEI, where the environment effect made a higher phenotypic contribution than the additive effect. In single environment analysis, this QTL was detected in WW-Y12, and in multiple environments mapping, it had a larger effect in this environment than in other environments. A total of 39 QEIs were identified for SL in the three RIL populations (Supplemental Table S3). They were more or less environment-specific. From the 11 SL-QTLs in RF-RILs, four QTLs (qSL1D, qSL4B, qSL4A and qSL7B) showed a consistent effect across environments. Except for qSL1D, Falat provided the favorable allele for these QTLs. The phenotypic variation explained by each QTL ranged from 0.09 to 1.82, and the phenotypic variation explained by each QTL × environment interaction ranged from 0.02 to 1.24%. Ten loci with significant additive ×environment effects were mapped in RS-RILs. qSL3As was also detected in single-environment analysis, which was specific to WS-K14. In RSH-RILs, seven QEIs were identified for SL, among them, five QTLs (qSL1B.2, qSL1B.3, qSL2D, qSL3A, and qSL6B) had a consistent effect across environments. These loci possessed a favorable allele from Superhead.

Table 4

Stable QTL identified for plant height and spike length in RILs population detected by the multi-environment analysis approach under well-watered (WW) and water stress (WS) conditions
Pop. name	QTL	Chr.	Position	Marker interval	Physical distance (Mb)	LOD	PVE	Add	AbyE1	AbyE2	AbyE3	AbyE4	AbyE5	M-QTL
RF	qPH2B	2B	91	wPt-9767-wPt-8776	80.27-498.360	2.62	0.61	0.97	0.47	0.35	-0.23	-0.73	0.13
	qPH4B.1	4B	3	wPt-2525-wPt-6869	3.20–13.4	7.97	2.15	1.79	0.18	0.28	-0.91	1.42	-0.97
	qPH4B.2	4B	77	wPt-1708-wPt-9067	536.13-575.56	8.24	1.84	2.07	-0.44	0.79	-0.12	-0.85	0.62	qPH4B.2
	qPH4D	4D	0	wPt-4572-wPt-672143	-	4.94	1.09	1.59	0.68	-1.10	0.05	0.15	0.22
	qPH5D	5D	66	wPt-2256-wPt-1400	403.80-448.53	5.06	1.30	1.48	0.48	0.21	-0.65	0.19	-0.23
	qPH6B	6B	9	wPt-1761-wPt-732061		3.66	0.91	1.21	0.27	0.37	0.45	-0.46	-0.63
	qPH7A	7A	198	wPt-2525-wPt-0961	119.2-231.97	3.06	0.83	1.025	0.77	0.82	-0.13	-1.03	-0.42	qPH7A
RS
	qPH1D	1D	67	wPt-2206-wPt-9664	444.49-722.52	4.07	1.40	-1.25	-0.67	0.21	-0.82	0.82	0.47
	qPH6A	6A	157	wPt-4229-wPt-731936	614.43	4.32	1.40	1.27	0.09	-0.02	-0.43	1.12	-0.76	qPH5B
RSH
	qPH4B.1	4BL	173	wPt-2525-wPt-8292	13.4-664.68	5.83	3.30	-1.95	0.33	-1.54	0.89	-	0.33	qPH4B.1
RF	qSL1D	1D	93	wPt-2968-wPt-7437	477.08	3.95	0.15	0.20	-0.12	0.11	-0.01	0.00	0.03
	qSL4B	4B	97	wPt-1505-wPt-5390	141.93-433.01	3.10	0.18	-0.18	-0.01	0.12	-0.17	-0.07	0.12	qSL4B
	qSL4A	4A	61	wPt-5730-wPt-743435	283.06–648.5	7.12	0.37	-0.27	-0.02	0.06	-0.15	0.14	-0.03	qSL4A
	qSL7B	7B	247	wPt-733669-wPt-4300	715.54-759.62	4.11	0.22	-0.21	-0.12	0.08	-0.12	0.06	0.09	qSL7B
RSH
	qSL1B.2	1B	160	wPt-9032-wPt-2786	639.21-150.71	4.48	3.94	-0.31	-0.20	0.00	0.22	-	-0.02
	qSL1B.3	1B	261	wPt-1770-wPt-4721	680.29-689.22	3.79	2.60	-0.21	-0.11	-0.24	0.19	-	0.16
	qSL2D	2D	93	wPt-667843-wPt-0330	626.55-659.98	3.71	1.87	-0.20	-0.09	-0.14	0.07	-	0.17
	qSL3A	3A	196	wPt-6422-wPt-9422	708.87	2.62	1.88	-0.20	-0.10	-0.09	0.18	-	0.00
	qSL6B	6B	225	wPt-2786-wPt-8336	95.33	4.04	3.15	-0.28	-0.17	-0.01	0.19	-	0.00
E1 = Water stress condition in Kerman in 2013; E2 = Water stress condition in Kerman in 2014; E3 = Well-watered condition in Yazd in 2012; E4 = Well-watered condition in Kerman in 2013; E5 = Well-watered condition in Kerman in 2014

^a QTL marked by bold typeface indicated intervals which co-located with detected M-QTLs.

‘*’ indicates an increasing effect from a common parental line, Roshan

Epistatic and epistatic × environment interactions (E-QTL) (dup: abstract ?)

All epistatic interactions among the M-QTLs are listed in Supplemental Table S4. In the case of PH, 16 M-QTLs were identified in the three populations. Among them, only one M-QTL (qPH4B) in RS-RILs displayed a significant interaction with a locus on chromosome 7A (qPH4B/qPH7A in WW-K14). No epistatic interactions were found for M-QTLs related to SL in RS-RILs and RSH-RILs. In RF-RILs, three M-QTLs (qSL4A, qSL6B, and qSL7B) exhibited significant epistatic interactions in WW-K14. In RF-RILs, 89 QTLs for PH and 62 QTLs for SL were involved in epistatic × environment interactions (Supplemental Table S5). Among these, only five pairs of E-QTLs for PH (qPH1A/qPH3A, qPH4A/qPH2D, qPH6A/qPH3D, qPH4B.2/qPH6B and qPH2D/qPH4A) and two pairs for SL (qSL4B/qSL1A, qSL7B/qSL1B) co-localized with QEIs identified through ME-QTL analysis. In RS-RILs, 37 and 29 digenic interactions were detected for SL and PH, respectively. No E-QTL was detected for the two stable PH-QTLs identified in RS-RILs. In RSH-RILs, 18 and 25 E-QTLs were identified for SL and PH, respectively. Among these, no regions were found to be co-localized with stable QTLs identified through ME-QTL analysis, except for qSL3A that displayed a significant epistatic interaction with qSL5B at 210 cM (wPt-3922-wPt-7665).3

Candidate genes for stable and novel QTLs

Markers that showed significant associations with traits were aligned to the wheat reference genome sequence (IWGSC Ref Seq v.2). All genes physically located in the QTL interval were considered as candidate genes. The three novel stable QTLs (qPH1D, qSL1D, qSL1B.3), and a major-effect but specific-environment QTL (qPH4B.2) were selected for candidate gene mining. A total of 191 genes were identified from these four QTL regions (Supplemental Table S6). The qSL1D was mapped between positions 477081665 and 477082456 (792 bp) on chromosome arm 1DL using its flanking markers wPt-2968 and wPt-7437. There were 24 predicted genes in the corresponding chromosomal interval. Several gene models had similar function descriptions, including eight gene models for F-box protein, five for transmembrane protein, and two for DUF1645 family protein. A candidate gene search within the 1B interval (qSL1B.3 :680.29-689.22 Mb) identified 146 gene models. Within 713.3 Kb of qPH1D, 17 candidate genes were identified, with the Zinc finger family protein being the most abundant. We also identified four putative genes for qPH4B.2 at 183.04 Mb on 4B. To dissect the traits associated with these genes, we searched for candidate genes in the WheatIS (KnetMiner) database. The search results revealed 20 genes that were reportedly associated with plant height and spike-related traits in wheat (Supplemental Table S6). These genes are predicted to encode F-box protein (TraesCS1D02G415400, TraesCS1D02G415600, TraesCS1D02G415800, TraesCS1D02G416128) on 1D, Proline-, glutamic acid- and leucine-rich protein 1 (TraesCS4B02G136000) and Clathrin heavy chain (TraesCS4B02G136100) on 4B. Histidine ammonia-lyase (TraesCS1B02G457400), Cytochrome c biogenesis (TraesCS1B02G457900), ROP guanine nucleotide exchange factor 5 (TraesCS1B02G459100), Indole-3-acetic acid-amido synthetase GH3.3 (TraesCS1B02G459500), and Cardiolipin synthase B (TraesCS1B02G461000) are also shown in Supplemental Table S6. Gene networks were generated using the KnetMiner tool to identify known genes associated with plant height and spike-related traits. The results of the gene networks are illustrated in Supplemental Figure S1. The search results revealed several important known genes and transcription factors (TFs), including VRN1, AP1, RGL3, WRKY14, SNL3, TOE1, WRI1, MYB, SNL3, and SIGA, which have previously been reported to play crucial roles in plant height and spike-related traits in wheat and other crops (Jung et al., 2007; Zhu et al., 2009; Chen et al., 2017; Li et al., 2019; Liu et al., 2020; Cao et al., 2021; Zhang et al., 2021; Waseem et al., 2022).

QTL with additive effects and additive × environment (AE) interactions

Plant height (PH) and spike length are important traits for improving wheat grain yield and environmental adaptation. Therefore, identifying genetic loci that control these traits is essential for yield improvement (Li et al. 2021; Zhang et al. 2021). In this study, we identified 16 QTLs with additive effects that were associated with plant height in different genetic backgrounds. These QTLs explained between 4.63% and 20.37% of the phenotypic variance, with additive effects ranging from − 5.05 cm to 5.29 cm. Among them, qPH5A and qPH7A in RF-RILs, and qPH6A, qPH4B.1, and qPH4B.2 in RSH-RILs had the most significant effects and accounted for 10.20–20.37% of the phenotypic variation. These QTLs spanned a region from 134.43 Mb to 664.68 Mb on chromosome 4BL, which did not overlap with the physical position of the dwarfing gene Rht1. The Rht-B1 gene TraesCS4B03G0093100 is located on the 33.617–33.641 Mb region of Chinese Spring Refv2.1 chr4B (Zhang et al. 2022). Wang et al. (2019) reported a QTL for plant height (PH) located on chromosome 4B between 482.8–568.6 Mb. In a separate study, Semagn et al. (2021) identified four QTLs for PH on chromosome 4B at 31.4 Mb (QPht.dms-4B.1), 474.9 Mb (QPht.dms-4B.2), 569.9 Mb (QPht.dms-4B.3), and 611.1 Mb (QPht.dms-4B.4). The discovery of consistent yield-associated QTLs under different genetic backgrounds and environmental conditions is valuable for marker-assisted breeding (MAB) to develop high-yielding wheat cultivars. The present study identified 49 quantitative trait loci (QEIs) associated with plant height across various genetic backgrounds and environments. Among these QTLs, 10 were found to be environmentally stable, indicating a significant influence of genotype × environment interaction on the expression of these QTLs in different environments. Seven consistent PH-QTLs were identified in the RF population and were located on different chromosomes including 2B (qPH2B), 4B (qPH4B.1, qPH4B.2), 4D (qPH4D), 5D (qPH5D), 6B (qPH6B), and 7A (qPH7A). QTLs qPH4B.2 and qPH7A were also identified as major QTLs (M-QTLs) with the beneficial allele derived from Roshan. Notably, qPH4B.1 was physically located approximately 20.2 Mb away from the Rht-B1 locus, between 3.20 and 13.4 Mb of the wheat reference genome (IWGSC RefSeqv2.1). In the RSH population, a stable locus (qPH4B.1) affecting PH was identified on chromosome 4BL at 173 cM. This QTL was also found to be a M-QTL, explaining 14.54% of the phenotypic variance. The RhtD1 genes (TraesCS4D03G0067100) have been reported to be located between 19.18 Mb and 19.2 Mb (Semagn et al. 2022). Although we identified a stable QTL for PH on chromosome 4D (flanked by wPt-4572-wPt-672143), we were unable to locate this QTL on the physical map as there was no sequence available for the linked markers on the diversity arrays database. However, a previous study reported an association between wPT-4572 (23 cM) and Rht-D1b (Rht2) (Rosewarne et al. 2012). A stable PH QTL (qPH5D) was identified on chromosome 5D at 403.80-448.53 Mb. The dwarfing gene Rht23 has been previously located within a 10.16 Mb region between positions 187.98 and 194.14 Mb on chromosome 5DL (Chen et al. 2015). Wei et al. (2010) also identified a stable PH QTL on chromosome 5D. In addition, Guan et al. (2018) reported 17 stable QTLs for PH located on chromosomes 1B, 2D, 3A, 4B, 4D, 5A, 6A, 6D, 7A, and 7B. Across environments in the RS population, 10 QTLs were identified in this study, of which two QTLs (qPH1D and qPH6A) showed stable patterns. A minor but stable PH QTL (qPH6A) was detected at 157 cM. Cui et al. (2011) identified three PH QTLs on chromosome 7A, two on 7B, and one each on 1A, 2B, 2D, 4D, and 5D using the unconditional mapping method, all of which were significant in more than three trials. qPH6A has been reported by a few QTL mapping studies (e.g. Spielmeyer et al. 2007; Griffiths et al. 2012; Mao et al. 2010; Cui et al. 2011). Our comparison with previous studies suggests that qPH1D (corresponding to the 21.43–22.14 Mbp physical region in the Chinese Spring RefSeq v2.0) is likely a novel QTL and should be considered in wheat breeding worldwide. Although numerous QTLs for spike length on various wheat chromosomes, including 2D, 4A, 4B, 5A, 5B, 6A, and 7B, have been reported in previous studies (Chai et al. 2018; Cao et al. 2019; Xu et al. 2022), only a limited number of stable QTLs have been identified that can be effectively used for marker-assisted selection (MAS) in wheat breeding. Liu et al. (2018) detected the main stable QTLs for SL on chromosomes 4B, 5A, and 6B. Jahani et al. (2019) and Ji et al. (2021) reported stable QTLs associated with SL on chromosome arms 3AS and 5AL, respectively. Li et al. (2021) detected two major and stable QTLs associated with SL on chromosomes 5A and 6A. In this study, we identified 39 QTLs for SL in the RF (22), RS (10), and RSH (7) populations using ME QTL analysis. Among them, four QTLs in the RF population and five QTLs in the RSH population were environmentally stable and located on chromosomes 1D, 4A, 4B, 7B, 1B, 2D, 3A, and 6B. To the best of our knowledge, two stable QTLs for SL (qSL1D: at 477.08 Mbp on chromosome 1D and qSL1B.3: corresponding to a 0.07 Mb (680.29-689.22 Mb) physical region in the Chinese Spring RefSeq v2.0) have not been previously reported and are likely new QTLs. These novel QTLs were identified in the RF and RSH populations, respectively.

Epistatic and epistatic × environment interactions (E-QTL)

Understanding the genetic basis of complex traits requires determining the contribution of epistasis (Zhang et al. 2008; Reif et al. 2011). In this study, most interacting loci did not show significant main effects at the single-locus level, and only three interactions co-localized with M-QTL. Our findings were consistent with previous reports in wheat (Reif et al. 2011; Jahani et al. 2019). We identified 151 epistasis x environment (AAE) interactions for PH and SL in the three populations, with a focus on stable QTLs that were not influenced by environmental factors. We found no significant E-QTL co-localized with stable PH-QTL in the interacting loci, except for qPH4B.2 (qPH4B.2/qPH2B) and qPH4D (qPH4D/qPH3B) in RF population, and a pair of AAE effects for qSL3A (qSL3A/qSL5B) in RSH population. Our results indicate that additive effects are crucial genetic factors underlying wheat plant height and spike length. Most QTL mapping studies have suggested that additive genes are important for SL in wheat (Deng et al. 2017).

Genetic background (GB) effect of QTL

The identification of QTLs that are independent of genetic background is crucial for effective molecular breeding of complex traits like drought tolerance. However, the consistency of drought-related QTLs across different genetic backgrounds is relatively low (17.9%), as reported in previous studies (Wang et al., 2014; Meng et al., 2015). It has been shown that genotype-by-environment interactions can influence QTL detection and limit the utility of MAS in breeding (Wei et al. 2009; Han et al. 2012; Yao et al. 2016). In our study, we found that most of the detected QTLs were specific to particular genetic backgrounds. Among the 32 M-QTLs identified, only one QTL for plant height (qPH4B.2) was consistently detected in both Sabalan and Falat backgrounds. It is worth noting that the intervals of qPH4B.2 detected in the RF population showed different genetic positions from qPH4B in the RS population, but their physical locations were approximately the same. This common QTL, with increasing effects from the common parent Roshan, could be a promising target for wheat breeding programs aimed at improving yield. Our findings are consistent with previous studies such as Wang et al. (2013), who reported that the majority of QTLs were detected in only one genetic background, with only 18% being detected in both backgrounds. Wang et al. (2014) reported that 20.8% of QTLs showed genetic background-independence in both backgrounds and environments. In another study, Cui et al. (2014) found that 7–36% of QTLs were consistently detected in at least two of three RIL populations. Kumar et al. (2007) detected only one common QTL (on 4AL) in both populations. Notably, our study found that QEI was more sensitive to genetic backgrounds than M-QTL, as no QTL was commonly detected in all three genetic backgrounds.

Candidate genes for stable QTLs

We selected four QTLs, including three stable and novel QTLs for plant height and spike length (qPH1D, qSL1D, qSL1B.3) and a major effect M-QTL for plant height (qPH4B.2), for gene mining. The sequences of markers flanking the QTLs were used to identify 191 candidate genes on the Chinese Spring reference genome (IWGSC Ref Seq v.2.1). For qPH1D, which was flanked by markers wPt-2206 and wPt-9664, 17 high confidence annotated genes were identified at 713.3 Kb. The qSL1D corresponded to 24 gene models, with F-box proteins being particularly abundant among the candidate genes for SL. The stable QTL for SL, qSL1B.3, was located on the 1D chromosome between markers wPt-1770 and wPt-4721, with 146 putative genes identified in the marker interval. We used gene networks available on the KnetMiner website to shorten the candidate gene list to a few genes for each QTL. Out of the 191 annotated genes we identified, only 20 have been previously reported to be associated with plant height and spike-related traits in wheat. We selected these candidate genes and presented them to the KnetMiner website to predict their gene networks. We found several candidate genes and transcription factors (TFs) that are known to be involved in plant height and spike-related traits in wheat. These included VRN1, AP1, RGL3, WRKY14, SNL3, TOE1, WRI1, and MYB. Among these, VRN1 was particularly interesting as it plays a crucial role in spikelet and spike development, and also influences flowering time and plant height (Li et al. 2019). Additionally, we identified APETALA1 (AP1) as a MIKC-type MADS-box gene in Arabidopsis. MADS-box genes have been found to be involved in numerous aspects of plant growth and development in a range of species, including Arabidopsis, soybean, maize, barley, and rice (Zhang et al. 2021). In addition to AP1, other members of the AP2/ERF family have also been reported to play important roles in wheat development. Cao et al. (2021) reported that AP2-2 and AP2-4 are involved in the regulation of flower and spike development in wheat. Similarly, in soybean, GmAP1 has been shown to regulate flowering time and alter plant height (Chen et al. 2020). The DELLA proteins, including RGA and RGLs, act as negative regulators of GA signaling (Waseem et al. 2022) and are involved in various plant growth and developmental processes, such as seed germination, stem elongation, and floral development (Hedden and Phillips 2000). In wheat, the role of DELLA proteins in regulating plant height has been well established, and mutations in the Rht-1 and Rht-2 genes encoding DELLA proteins have been used to develop semi-dwarf wheat cultivars (Peng et al. 1999). WRKY transcription factors also function as a critical component of the GA-mediated signaling pathway, and have been shown to participate in various biotic and abiotic stress responses, as well as several developmental and physiological processes (Chen et al. 2017). Genes in the AP2-like family, such as TARGET OF EAT1 (TOE1), TOE2, and TOE3, have been demonstrated to act as flowering repressors (Jung et al. 2007). Loss-of-function mutants of the AP2-like family in maize, rice, and wheat have resulted in altered spike architecture (Zhu et al. 2009). Furthermore, AP2-like family genes have been shown to influence a regulatory network that controls plant height, spike shape, and floral organs in wheat (Liu et al. 2020). Overexpression of the transcription factor AsWRI1 in the wheat endosperm resulted in an increased spike density and a clear reduction in plant height (Liu et al. 2020).

In conclusion, 32 main-effect QTLs associated with plant height (PH) and spike length (SL) were detected in various environments, with 19 QTLs being stably identified across environments. Three of these QTLs (qSL1D, qSL1B, and qPH1D) are potentially newly discovered genes, exhibiting consistent expression across environments and no epistatic effects. The absence of epistatic and genotype by environment interactions in these loci suggests that the flanking markers of these QTLs on chromosomes 1D and 1B are useful for marker-assisted selection to improve the target traits. By combining QTL mapping and gene annotation, we predicted 191 candidate genes and their putative functions for PH and SL, highlighting 20 genes as putative candidates with a probable role in plant height and spike-related traits in wheat. This information provides a strong foundation for future studies aimed at fine mapping QTLs/genes for molecular marker-assisted breeding in wheat.

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis, validation and visualization were performed by Somayeh Sardouei-Nasab. The first draft of the manuscript was written by Somayeh Sardouei-Nasab and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Data Availability

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

Acknowledgments

The authors would like to acknowledge contributions made by the Iranian Center of Excellence for Abiotic Stress in Cereal Crops and Research and Technology Institute of Plant Production (RTIPP) located in the Shahid-Bahonar University of Kerman.

Conflict of interest

All of authors have no competing interests.

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Identification of stable QTLs and candidate genes associated with plant height and spike length in common wheat

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Abstract

Introduction

Materials and methods

Plant materials and field trials

Experimental sites

Statistical analysis

Genotyping and construction of genetic map

QTL mapping

Candidate gene identification

Results

Phenotypic summary

Linkage map construction

Single environment analysis (M-QTL)

Multi-environment QTL analysis (ME-QTL)

Epistatic and epistatic × environment interactions (E-QTL) (dup: abstract ?)

Candidate genes for stable and novel QTLs

Discussion

QTL with additive effects and additive × environment (AE) interactions

Epistatic and epistatic × environment interactions (E-QTL)

Genetic background (GB) effect of QTL

Candidate genes for stable QTLs

Conclusion

Statements and Declarations

Conflict of interest

References

Additional Declarations

Supplementary Files

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