QTL detection for three tiller-related traits and their effects on yield in wheat (Triticum aestivum L.)

doi:10.21203/rs.3.rs-2852669/v1

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QTL detection for three tiller-related traits and their effects on yield in wheat (Triticum aestivum L.)

https://doi.org/10.21203/rs.3.rs-2852669/v1

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Tiller related traits play important roles in determining yielding potential in wheat. It is of great value to elucidate the genetic basis of tiller number for the genetic improvement of wheat yield. In the present study, a recombinant inbred lines (RILs) mapping population derived from the cross between Kenong 9204 (KN9204) × Jing 411 (J411) (denoted by KJ-RILs) was used for quantitative trait locus (QTL) analysis of three tiller related traits. A total of 78 putative additive QTLs for spike number per plant (SNPP), the maximum tiller number (MTN) and ear-bearing tiller rate (EBTR) were detected in eight different environments. Among them, 12 repeatable QTL might be novel QTL that were firstly documented in the present study. The genetic effect analysis of two co-located major stable QTL, i.e., qSnpp-KJ-5D.1 and qMtn-KJ-5D.1, on yield-related traits were characterized. Moreover, the favored alleles of qSnpp-KJ-5D.1 and qMtn-KJ-5D.1 have been undergone selection in wheat breeding programs; its future application potential in the genetic improvement of yield based on molecular breeding technology was evaluated. The candidate genes underlying qMtn-KJ-5D.1 were predicated based on multi-omics data information, and TraesKN5D01HG00080 is likely to be its candidate gene. Overall, our findings will be useful for elucidating the genetic architecture of tiller-related traits and developing novel wheat varieties with high yield.

wheat

tiller

yield

QTL

molecular breeding

candidate gene

A total of 78 putative additive QTLs for tiller-related traits were reported and the candidate genes underlying qMtn-KJ-5D.1, a novel major stable QTL for the maximum tiller number, were characterized.

Common wheat (Triticum aestivum L.) is one of the most important cereal crops worldwide, and it serves as the staple diet for approximately 40% of the human population (Tadesse et al. 2016). The world population is projected to reach 9 billion by 2050 and it poses challenges to global food security taking into account current rate of genetic gain of crops (Ray et al. 2013). Therefore, it is urgent to improve wheat grain yield to meet the increasing food requirements of the increasing human population.

The three yield components of productive tiller number (PTN), kernel number per spike (KNPS) and thousand kernel weight (TKW) together determine the yielding potential in wheat. Maintaining a harmonious balance among these components should be of great vital to ensure high grain yield in specific environments. Tillering is a crucial factor for wheat as it directly or indirectly influences other yield components such as KNPS, TKW, and ultimately, the grain yielding potential (Nasseer et al. 2016; Tilley et al. 2019; Bilgrami et al. 2020). Tillering determines shoot architecture, thus affecting the synchrony of flowering, illumination, seed set and grain uniformity, and ultimately affects the grain commodity value (Kuraparthy et al. 2007; Xu et al. 2017). The maximum tiller number per unit area (MTN) determines spike number per unit area, also known as PTN, a key component of grain yield (Naruoka et al. 2011; Ren et al. 2018). Among the three yield components, PTN is the most easily influenced by environmental factors with the lowest heritability controlled by multiple minor or moderate genes (Isham et al. 2021). Conventional wheat breeding predominately based on phenotypic direct selection, therefore, resulted in limited genetic gain for PTN. It would be of great value to elucidate the genetic basis of tiller-related traits for the wheat molecular breeding programs designed to improve yielding potential.

Quantitative trait loci (QTL) mapping that utilizes molecular markers is an effective approach to dissect complex traits including wheat tiller-related traits. To date, numerous QTLs for tiller-related traits have been identified distributing on all the 21 wheat chromosomes (Araki et al. 1999; Shah et al. 1999; Kato et al. 2000; Li et al. 2002; Huang et al. 2003; Campbell et al. 2004; An et al. 2006; Kumar et al. 2007; Li et al. 2010; Naruoka et al. 2011; Nasseer et al. 2016; Hu et al. 2017; Ren et al. 2018; Liu et al. 2018, 2020; Wang et al. 2018; Bilgrami et al. 2020; Isham et al. 2021). Of these, 19 meta-QTLs distributing on chromosomes 1A, 2A, 2B, 2D, 5D, 6A, 6B and 7A have been verified in no less than three different experiments under multiple genetic backgrounds (Bilgrami et al. 2020); recently, QPTN.uia2-6A was repeatedly identified in all five datasets using an individual bi-parental mapping population (Isham et al. 2021). Besides, five single genes (tin1, tin2, tin3, tin4 and ftin) inhibiting tillering have been identified and characterized in wheat (Peng et al. 1998; Spielmeyer and Richards 2004; Kuraparthy et al. 2007; Zhang et al. 2013; Wang et al. 2022). More recently, tin4 was narrowed down to a 0.35 cM interval spanning 311 kb genomic region with twelve predicted candidate genes (Wang et al. 2022). It would be urgent to identify and clone more stable QTL for tiller-related traits in the genetic improvement of yield.

In this study, QTL analysis for three tiller-related traits in eight environments was conducted. The objectives of this study are to: (1) identify major and stable QTLs for tiller-related traits among multiple environments, (2) specify their genetic effects on yield-related traits, and (3) evaluate future application potential of the repeatable QTL for tiller-related traits in wheat molecular breeding programs.

Plant materials

The mapping population used in this study comprised 187 F_8:9 recombinant inbred lines (RIL) derived from the cross between Kenong 9204 (KN9204) × Jing 411 (J411) (denoted by KJ-RILs) (Fan et al. 2015; Cui et al. 2016; Cui et al. 2017; Zhang et al. 2017). Eight phenotyping experiments were conducted. Experiments 1 (E1) and 2 (E2) were conducted during 2011 − 2012 cropping season in Shijiazhuang (37°53’N, 114°41’E) under low nitrogen (LN) and high nitrogen (HN) conditions, respectively. E3 and E4 were conducted during 2012 − 2013 cropping season in Shijiazhuang under LN and HN, respectively. E5 and E6 referred to phenotyping experiments during 2012 − 2013 cropping season in Beijing (40°06’N, 116°24’E) under LN and HN, respectively. E7 and E8 referred to phenotyping experiments during 2012 − 2013 cropping season in Xinxiang (35°27’N, 113°48’E) under LN and HN, respectively. The field setting as well as the corresponding soil nitrogen contents in eight environments were described in our previous study (Cui et al. 2014). The natural mapping population consisting of 314 cultivars/advanced lines was planted in four different environments, i.e, 2019 − 2020 in Yantai; 2019 − 2020 in Weifang; 2019 − 2020 in Shijiazhuang; 2020 − 2021 in Yantai, with an aim to evaluate their yield-related traits (Cao et al. 2022).

Phenotypic evaluation and statistical analysis

Tiller-related traits including spike number per plant (SNPP) or PTN, the maximum tiller number (MTN) and ear-bearing tiller rate (EBTR) together with other yield-related traits such as spikelet number per spike (SNPS), thousand kernel weight (TKW), kernel number per spike (KNPS), kernel weight per spike (KWPS), and yield per plant (YPP) were evaluated in this study. Fifteen representative plants from the middle of inner rows of each replicate were selected to measure the MTN at approximately 80 days after seeding according to previous reports (Zadoks et al. 1974; Ren et al. 2018). The main shoots of five plants in the center of each plot were randomly selected for counting the productive spikes per plant, i.e, SNPP. EBTR was calculated as EBTR = SNPP/MTN. The measurement methods of the remaining yield-related traits referred to Cui et al. (2014, 2016) and Fan et al. (2015). The best linear unbiased estimators (BLUEs) of tiller-related traits and other yield-related traits of the 187 KJ-RILs among the eight environments were estimated by QGAStation 2.0 (Chen et al., 2017). Similarly, the BLUEs of each yield-related traits across five environments in the natural mapping population were also estimated by QGAStation 2.0. Descriptive statistics as well as correlation analysis between SNPS and tiller-related traits in KJ-RIL population was analyzed by SPSS 21.0. The genetic effects were parsed based on the association analysis between genotype and phenotype data. Tukey’s test was used to determine statistical differences by one-way analysis of variance (ANOVA).

Linkage map construction and QTL detection

The genotypes of KN9204, J411 and 187 KJ-RILs were scored by wheat 660K SNP array, and the genotypes of the 314 accessions in the natural mapping population were identified by Wheat 55K SNP array (Cui et al. 2017; Cao et al. 2022). A high-density genetic linkage map with 119566 loci has been documented in our previous study (Cui et al. 2017). In this study, physical map was used to perform QTL analysis (Cao et al. 2022). QTL detection was conducted using inclusive composite interval mapping by IciMapping 4.1 based on stepwise regression of simultaneous consideration of all marker information (http://www.isbreeding.net/). For QTL with additive effects, the walking speed chosen for all QTL was 0.01 Mb, and the P value inclusion threshold was 0.001. The threshold of log-of-odds (LOD) scores was evaluated using 1,000 permutations with a type I Error of 0.05.

Phenotypic performance for tiller-related traits in the KJ-RIL population

The phenotypic performance results of all the three tiller-related traits of the 187 KJ-RILs as well as the two parents in eight environments were shown in Table 1. KN9204 has significantly less MTN than J411 in almost all the eight environments, whereas it has significantly higher EBTR than J411. SNPP showed inconsistent difference tendency between KN9204 and J411 among the eight environments, indicating a significant influence by environmental factors. The variation of SNPP in KJ-RILs ranged from 3.10 to 18.00, with an average of 7.42; MTN ranged from 2.73 to 24.05, with an average of 12.16; EBTR ranged from 0.25 to 0.99, with an average of 61.83% among the eight environments. SNPP, MTN and EBTR in the 187 KJ-RILs showed continuous variation with the absolute values of skewness and kurtosis less than 1.0 in most cases and therefore all of the traits fitted or approximately fitted normal distribution, indicating that they were typical quantitative traits and suitable for QTL analysis (Table 1). The broad-sense heritability of the three traits was 18.97 − 36.50%, 19.69 − 48.25% and 11.69 − 27.56%, respectively, indicating that these traits were more easily affected by environmental factors.

Table 1

Phenotypic values for the three tiller-related traits of the two parents and the KJ-RIL population in eight growing environments in wheat
Traits ^a	Env ^b	Parents ^c		KJ-RILs
Traits ^a	Env ^b	KN9204	J411	Min	Max	Mean	Std	Variance	Skewness	Kurtosis	Heritability (%)
SNPP	E1	4.40 ± 0.57	5.47 ± 0.94	3.40	8.10	4.84	0.70	0.49	0.81	1.95	21.00
	E2	9.00 ± 0.00	7.87 ± 0.00	5.80	12.30	8.55	1.25	1.55	0.52	0.11	28.42
	E3	4.14 ± 0.89	3.93 ± 0.72	3.10	7.40	4.78	0.86	0.73	0.46	0.12	18.97
	E4	8.74 ± 1.16	11.16 ± 1.31	4.90	18.00	10.94	1.92	3.68	0.34	0.71	20.34
	E5	5.90 ± 0.84	8.04 ± 1.66	3.90	10.00	6.81	1.06	1.13	0.20	−0.09	29.92
	E6	6.69 ± 0.88	8.05 ± 1.53	4.40	11.60	7.58	1.13	1.28	0.40	0.87	36.50
	E7	7.65 ± 1.04	7.40 ± 1.51	4.20	11.00	7.79	1.24	1.53	−0.07	−0.39	29.95
	E8	8.38 ± 1.79	8.10 ± 1.17	5.00	12.40	8.10	1.19	1.42	0.61	0.88	21.22
	BLUE	−	−	5.14	8.92	7.37	0.54	0.29	−0.10	1.33	−
MTN	E1	5.99 ± 2.07	8.79 ± 1.32	2.91	13.24	8.81	1.52	2.32	−0.27	0.91	35.75
	E2	9.75 ± 0.19	15.20 ± 0.95	5.07	21.06	14.05	2.31	5.33	0.14	1.02	48.25
	E3	6.19 ± 1.11	7.44 ± 1.74	2.73	11.22	7.65	1.34	1.78	−0.02	0.45	31.69
	E4	9.91 ± 1.06	14.66 ± 2.60	4.75	24.05	15.23	2.70	7.28	0.07	1.14	46.84
	E5	10.56 ± 1.06	15.49 ± 2.20	5.20	20.50	13.77	2.28	5.18	0.17	1.07	47.30
	E6	9.65 ± 1.20	13.49 ± 2.81	4.75	20.48	13.66	2.41	5.81	0.26	0.91	19.69
	E7	11.16 ± 1.32	12.53 ± 0.68	8.20	17.00	12.36	1.89	3.57	0.19	−0.36	24.63
	E8	12.26 ± 1.77	13.81 ± 1.51	7.10	17.30	11.70	1.88	3.54	0.24	0.08	27.39
	BLUE	−	−	5.70	15.36	12.15	1.44	2.07	−0.34	1.45	−
EBTR	E1	77.21 ± 8.08	63.75 ± 11.34	0.36	0.82	0.56	0.10	0.01	0.68	0.38	19.72
	E2	93.00 ± 1.41	51.87 ± 3.25	0.34	0.89	0.62	0.10	0.01	0.09	−0.38	27.56
	E3	60.91 ± 9.71	55.57 ± 5.84	0.39	0.98	0.63	0.12	0.01	0.50	0.36	11.69
	E4	85.15 ± 7.58	78.48 ± 9.05	0.42	1.00	0.72	0.13	0.02	0.09	−0.73	13.69
	E5	56.12 ± 8.07	52.62 ± 12.75	0.25	0.81	0.50	0.09	0.01	0.57	0.77	24.99
	E6	69.91 ± 10.06	60.25 ± 7.79	0.33	0.93	0.57	0.11	0.01	0.50	0.30	14.39
	E7	69.99 ± 6.59	65.16 ± 12.28	0.40	0.99	0.65	0.11	0.01	0.33	−0.04	18.54
	E8	77.84 ± 11.86	74.93 ± 12.84	0.43	1.31	0.71	0.12	0.01	0.77	2.60	13.30
	BLUE	−	−	0.51	0.77	0.62	0.05	0.00	0.17	0.43	−

^a SNPP, spike number per plant; MTN, the maximum tiller number; EBTR, ear-bearing tiller rate (EBTR) .

^b E1 and E2 represent the environments of 2011–2012 in Shijiazhuang (37°53’N, 114°41’E) under LN and HN, respectively; E3 and E4 represent the environments of 2012–2013 in Shijiazhuang under LN and HN, respectively; E5 and E6 represent the environments of 2012–2013 in Beijing (40°06’N, 116°24’E) under LN and HN, respectively; E7 and E8 represent the environments of 2012–2013 in Xinxiang (35°27’N, 113°48’E) under LN and HN, respectively.

^c KN9204, Kenong 9204; J411, Jing 411.

Correlation analysis between tiller- and yield-related traits in the KJ-RIL population

The correlation coefficients between tiller- and yield-related traits in the KJ-RIL population were listed in Table 2. SNPP was significantly positively correlated with MTN, EBTR and YPP under both LN and HN conditions, but significantly negatively correlated with TKW and KWPS under both LN and HN conditions. MTN was significantly positively correlated with YPP under both LN and HN conditions, but significantly negatively correlated with EBTR, TKW and KWPS under both nitrogen treatments. EBTR was significantly positively correlated with YPP and KWPS simultaneously under HN condition, but it was significantly negatively correlated with SNPS under LN. The above findings indicated that whether under HN condition or LN condition, SNPP, MTN and EBTR might affect yield potential to some extent.

Table 2

Phenotypic correlation analysis between tiller- and yield-related traits in KJ-RIL population
Environments (LN/HN)	SNPP	MTN	EBTR	YPP	SNPS	TKW	KNPS	KWPS
SNPP	1	0.61/0.54	0.19*/0.17	0.59/0.46	−0.11/0.068	−0.18/−0.17	−0.049/0.065	−0.25*/−0.14
MTN	0.61/0.54	1	−0.63/−0.71	0.33*/0.15	0.063/0.11	−0.21/−0.21	0.014/−0.031	−0.20/−0.28
EBTR	0.19*/0.17	−0.63/−0.71	1	0.13/0.17*	−0.17*/−0.087	0.078/0.074	−0.061/0.72	−0.0010/0.19*
Notes: The BLUEs of all the tiller- and yield-related traits under low nitrogen (LN) and high nitrogen (HN) conditions were used for correlation analysis, respectively. For each cells, the first number indicates the correlation coefficient under LN, and that of the second indicates the correlation coefficient under HN. * and ** represent the significance levels of P < 0.05 and P < 0.01, respectively.

Putative additive effect QTL for the three tiller-related traits

Up to 78 putative additive QTLs for SNPP, MTN and EBTR were detected in the KJ-RIL population in eight different environments. They together covered all the wheat chromosomes with the exception of 6D (Fig. 1; Table 3). Of these, 24, 31 and 23 QTLs were mapped to the A, B, and D sub-genomes, respectively. These QTL individually explained 0.96 − 20.59% of the phenotypic variance, with LOD values ranging from 2.00 to 25.65 (Fig. 1; Table 3).

QTL for SNPP

A total of 25 putative additive QTLs for SNPP were identified in the eight environments and in the BLUE datasets (Fig. 2; Table 3). They individually explained 0.96 − 18.29% of the phenotypic variance with LOD values ranging from 2.02 to 25.65. Five (20.0%) of the 25 QTL were major QTLs with LOD values > 3.0 and PVE value > 7.0%. Of these, qSnpp-KJ-5D.1 was a major stable QTL for SNPP with LOD values of 2.31 − 11.91 and phenotypic variation explained (PVE) from 1.79–18.29%; J411 contributed to its positive alleles that increase SNPP. qSnpp-KJ-4B could be reproducibly identified in E5 and E6 with moderate additive effects. qSnpp-KJ-3A.1, qSnpp-KJ-6B.2 and qSnpp-KJ-6B.3 were environment-dependent QTL that could be detected only in one environment. Twelve and thirteen favorable alleles increasing SNPP were contributed by KN9204 and J411, respectively.

QTL for MTN

Twenty-five putative additive QTLs for MTN were detected across the eight environments (Fig. 2; Table 3). These QTLs each individually explained 3.28 − 20.59% of the phenotypic variance with LOD values ranging from 2.08 to 13.1 (Table 3). Seven (28.0%) of the 25 QTLs were major QTLs with LOD values > 3.0 and PVE > 7.0%. Of these, there were two major stable QTLs for MTN, i.e., qMtn-KJ-5B.2 with LOD values of 2.28 − 5.4 and PVE of 5.10 − 8.90%, and qMtn-KJ-5D.1 with LOD values of 2.32 − 13.10 and PVE of 5.29 − 20.59%. Additive effects of qMtn-KJ-5B.2 were verified in four environments as well as in the BLUE datasets; qMtn-KJ-5D.1 was repeatedly identified in seven environments as well as in BLUE datasets. The remaining five major QTLs, i.e., qMtn-KJ-3B.1, qMtn-KJ-4A.1, qMtn-KJ-4B.2, qMtn-KJ-4B.3 and qMtn-KJ-7A.1, could be detected only in one or two environments. In addition, qMtn-KJ-3A could be repeatedly detected in four environments but only explained less than 7% of the phenotypic variance with LOD values of 3.15 − 4.12. qMtn-KJ-2D.2, qMtn-KJ-6A.2 and qMtn-KJ-7B.2 were three moderate effect QTL that could be verified in two different environments. Sixteen and nine favorable alleles increasing MTN were donated by KN9204 and J411, respectively.

QTL for EBTR

Twenty-eight QTLs associated with EBTR were detected in the eight environments and in BLUE datasets. These QTL individually accounted for 2.37 − 13.06% of the phenotypic variation, with a LOD value of 2.00 − 9.57 (Fig. 2; Table 3). Seven (25.0%) of the 28 were major QTL with LOD values > 3.0 and PVE value > 7.0%. Of these, qEbtr-KJ-5D.1 was a major stable QTL with LOD values of 3.68 − 5.52 and PVE of 6.13 − 9.57%; KN9204 contributed to its excellent alleles that increase EBTR in all the four datasets. qEbtr-KJ-2D.3, qEbtr-KJ-4B.1, qEbtr-KJ-4B.3, qEbtr-KJ-4B.4, qEbtr-KJ-4D and qEbtr-KJ-7B.2 were detected only in one or two environments. In addition, qEBTR-KJ-7A.2 was a relative stable QTL with moderate effects, which could be repeatedly identified in E1, E4 and E7. qEBTR-KJ-3D was another moderate effect QTL that were verified in E1 and E3. KN9204 and J411 contributed to excellent alleles of 15 and 13 QTL, respectively (Table 3).

Table 3

Putative additive QTLs for the three tiller-related traits identified in eight individual environments as well as in the BLUE datasets
Trait	QTL^a	Environments	Position (Mb)^b	Flanking markers	LOD^c	PVE(%)^d	Add^e
SNPP	qSnpp-KJ-1A.1	E4/ BLUE	8.13/8.13	AX-109822513 − AX-109983096	2.15/2.78	5.10/3.89	0.424/0.110
	qSnpp-KJ-1A.2	E8	267.13	AX-94646578 − AX-111480833	2.79	5.69	0.288
	qSnpp-KJ-1B	BLUE	0.16	AX-94940584 − AX-108746525	2.04	2.62	−0.092
	qSnpp-KJ-1D	E3	418.13	AX-110692154 − AX-111564830	2.09	0.96	−0.165
	qSnpp-KJ-2A	E5/ BLUE	20.93/26.93	AX-94992901 − AX-109432422	2.79/3.55	6.28/ 4.94	0.266/0.124
	qSnpp-KJ-2D	BLUE	65.75	AX-112286080 − AX-94556384	2.74	3.74	−0.108
	qSnpp-KJ-3A.1	E7	25.76	AX-109010030 − AX-111083535	3.13	7.37	0.329
	qSnpp-KJ-3A.2	BLUE	69.76	AX-109607322 − AX-111661409	2.19	7.48	0.152
	qSnpp-KJ-3A.3	E2	242.76	AX-109930806 − AX-94495161	2.46	3.87	0.261
	qSnpp-KJ-4A.1	E1	573.91	AX-110526171 − AX-111830901	2.18	4.15	0.191
	qSnpp-KJ-4A.2	E1	658.91	AX-111711476 − AX-108738265	2.36	3.18	0.167
	qSnpp-KJ-4B	E5/ E6	641.70/657.70	AX-108827702 − AX-111483368	3.24/2.15	7.35/ 4.85	−0.288/−0.250
	qSnpp-KJ-4D	E2/ BLUE	479.53/469.53	AX-110427792 − AX-108728919	2.58/3.35	3.86/ 4.64	−0.261/−0.120
	qSnpp-KJ-5A.1	E2/ BLUE	12.67	AX-110925606 − AX-110044897	2.15/2.12	3.09/ 2.78	−0.234/−0.093
	qSnpp-KJ-5A.2	E3	502.67	AX-111785424 − AX-111236830	2.79	1.36	−0.194
	qSnpp-KJ-5B	E7	651.32	AX-110671838 − AX-109924304	2.02	4.47	−0.259
	qSnpp-KJ-5D.1	E6/ BLUE/ E2/ E1/ E8/ E3	0.62/0.62/1.62/3.62/ 5.62/6.62	AX-94938800 − AX-89502620	2.83/11.91/10.36/ 2.31/6.06/3.39	6.71/18.29/17.09/ 3.09/12.91/1.79	−0.296/−0.240/−0.553/ −0.165/−0.435/−0.225
	qSnpp-KJ-5D.2	E2	413.62	AX-110570148 − AX-111681832	4.00	6.02	0.326
	qSnpp-KJ-6B.1	E2	124.76	AX-110068322 − AX-110007311	2.14	6.82	0.347
	qSnpp-KJ-6B.2	E3	438.76	AX-111051521 − AX-89596871	25.65	16.82	0.685
	qSnpp-KJ-6B.3	E3	506.76	AX-109457311 − AX-111680997	17.10	10.00	−0.528
	qSnpp-KJ-7A	E4	96.95	AX-108801346 − AX-110428693	2.44	6.11	−0.463
	qSnpp-KJ-7B	E7	139.06	AX-109992913 − AX-110922781	2.64	6.09	−0.302
	qSnpp-KJ-7D.1	E5	23.79	AX-111680983 − AX-110451562	2.10	4.52	0.226
	qSnpp-KJ-7D.2	BLUE	270.79	AX-110403163 − AX-109694700	3.20	4.40	−0.117
MTN	qMtn-KJ-1B	E1	447.16	AX-111583311 − AX-111701470	2.49	5.34	−0.384
	qMtn-KJ-2A	E5/ BLUE	25.93	AX-111524997 − AX-110597573	2.08/3.41	3.74/4.78	0.423/ 0.310
	qMtn-KJ-2B	E2	780.06	AX-94509892 − AX-89535756	3.44	5.33	−0.519
	qMtn-KJ-2D.1	E8	10.75	AX-111132985 − AX-108926113	2.51	5.14	0.412
	qMtn-KJ-2D.2	E7/ E6	536.75/585.75	AX-109972625 − AX-94732299	3.18/3.04	5.19/ 5.59	−0.458/−0.546
	qMtn-KJ-3A	E2/ E5/ E1/ BLUE	0.76/0.76/9.76/0.76	AX-95181209 − AX-111011679	3.15/3.35/4.12/3.37	4.76/6.22/5.72/4.58	0.549/0.612/0.382/0.340
	qMtn-KJ-3B.1	E6	5.17	AX-110949053 − AX-111641568	4.25	7.96	0.653
	qMtn-KJ-3B.2	BLUE	180.17	AX-111672319 − AX-109913603	2.94	4.15	−0.288
	qMtn-KJ-4A.1	E1	48.91	AX-111163489 − AX-111591351	5.19	8.58	0.467
	qMtn-KJ-4A.2	E7	604.91	AX-110475444 − AX-111122958	2.12	3.28	0.363
	qMtn-KJ-4B.1	E2	91.70	AX-108732071 − AX-109434530	2.57	3.86	0.443
	qMtn-KJ-4B.2	BLUE	182.70	AX-109915467 − AX-94416376	5.60	8.67	0.417
	qMtn-KJ-4B.3	E8/ E7	600.70/626.70	AX-89755491 − AX-110596054	5.80/ 8.83	12.77/15.82	0.650/ 0.797
	qMtn-KJ-4D.1	E8	8.53	AX-111567155 − AX-110984743	2.41	5.91	0.441
	qMtn-KJ-4D.2	E1	105.53	AX-89743887 − AX-109750243	3.46	4.63	0.343
	qMtn-KJ-5B.1	E2	544.32	AX-108866467 − AX-109830907	2.11	3.87	0.446
	qMtn-KJ-5B.2	E5/ E2/ E7/ E3/ BLUE	607.32/646.32/ 651.32/656.32/ 646.32	AX-86178161 − AX-109438924	2.70/4.84/5.27/2.28/ 5.40	5.10/ 7.46/ 8.90/ 5.28/ 7.52	−0.494/−0.617/−0.604/ −0.311/−0.391
	qMtn-KJ-5D.1	E1/ E2/ E3/ E4/ E5/ E6/ E8/ BLUE	0.62/1.62/2.62/2.62/ 2.62/2.62/2.62/2.62	AX-94938800 − AX-111475778	8.12/11.47/2.32/ 3.91/8.44/8.37/4.03/ 13.10	11.48/19.22/5.29/ 8.22/17.11/16.76/ 8.99/20.59	−0.546/−0.994/−0.312/ −0.779/−0.911/ −0.953/−0.548/−0.648
	qMtn-KJ-5D.2	E6/ E3	449.62/499.62	AX-111471260 − AX-110160610	2.09/2.17	3.62/4.82	−0.444/−0.296
	qMtn-KJ-6A.1	E6/ E3	16.01/19.01	AX-108857230 − AX-110951904	2.65/2.78	4.87/6.51	0.517/0.348
	qMtn-KJ-6A.2	E4/ E6	578.01/578.01	AX-109467855 − AX-108836281	2.79/2.21	5.76/3.86	0.647/0.454
	qMtn-KJ-7A.1	E1	329.95	AX-109838679 − AX-111265016	4.97	7.24	−0.429
	qMtn-KJ-7A.2	E5	420.95	AX-109404538 − AX-110957870	3.05	5.64	−0.520
	qMtn-KJ-7B.1	E4	478.06	AX-111534601 − AX-108738377	3.12	6.39	0.702
	qMtn-KJ-7B.2	E1/ E2	595.06/595.06	AX-111712945 − AX-94535335	2.81/2.72	3.71/ 4.07	0.311/0.458
EBTR	qEbtr-KJ-1D	E7	354.13	AX-110163017 − AX-111463875	2.34	5.83	0.025
	qEbtr-KJ-2B.1	E2	47.06	AX-111052506 − AX-94904074	2.00	2.37	0.020
	qEbtr-KJ-2B.2	E2	111.06	AX-111540502 − AX-109900116	2.05	2.42	0.020
	qEbtr-KJ-2B.3	E2	786.06	AX-111491698-AX-110956795	2.74	3.43	0.024
	qEbtr-KJ-2D.1	E8	11.75	AX-108926113 − AX-94418886	2.50	5.96	−0.030
	qEbtr-KJ-2D.2	E5	422.75	AX-111608425 − AX-111711411	2.91	5.72	0.020
	qEbtr-KJ-2D.3	BLUE	562.75	AX-108888527 − AX-109670830	5.82	7.64	0.012
	qEbtr-KJ-2D.4	E4	626.75	AX-109277962 − AX-109898408	3.73	6.75	0.034
	qEbtr-KJ-3A	E7	25.76	AX-109010030 − AX-111083535	2.09	5.26	0.024
	qEbtr-KJ-3B.1	BLUE	5.17	AX-110949053 − AX-111641568	4.21	5.23	−0.010
	qEbtr-KJ-3B.2	E4	770.17	AX-111028201 − AX-110918207	2.03	3.81	−0.026
	qEbtr-KJ-3D	E1/ E3	568.08/582.08	AX-111543252 − AX-109405917	2.22/2.29	4.20/5.55	0.022/0.028
	qEbtr-KJ-4A	E6	641.91	AX-108889285 − AX-112286181	2.05	4.53	−0.024
	qEbtr-KJ-4B.1	E1/ E6	94.70/134.70	AX-109434530 − AX-109389019	2.85/4.99	5.43/11.37	−0.025/−0.038
	qEbtr-KJ-4B.2	E7	491.70	AX-109473770 − AX-110933764	2.39	5.94	−0.026
	qEbtr-KJ-4B.3	BLUE	522.70	AX-110190163 − AX-108770233	7.92	10.12	−0.014
	qEbtr-KJ-4B.4	E5/ E2	613.70/ 671.70	AX-109841712 − AX-110025178	6.03/2.13	12.25/2.58	−0.030/−0.020
	qEbtr-KJ-4D	E5/ BLUE	122.53/ 122.53	AX-110014164 − AX-110428080	2.86/5.99	5.62/7.52	−0.020/−0.012
	qEbtr-KJ-5B.1	E4	576.32	AX-89376056 − AX-110046042	3.11	5.80	0.031
	qEbtr-KJ-5B.2	E1/ BLUE	662.32/646.32	AX-109921974 − AX-111114294	2.17/3.13	6.82/ 3.76	0.028/ 0.009
	qEbtr-KJ-5D.1	E4/ E5/ E2/ BLUE	402.62/403.62/ 413.62/402.62	AX-111902972 − AX-111681832	4.99/3.68/4.78/5.52	9.57/7.30/6.13/7.24	0.041/0.023/0.032/0.012
	qEbtr-KJ-5D.2	E1	533.62	AX-108800523 − AX-111028270	2.10	3.92	−0.021
	qEbtr-KJ-5D.3	BLUE	535.62	AX-108800523 − AX-111028270	3.10	3.79	−0.009
	qEbtr-KJ-6A	BLUE	585.01	AX-94475556 − AX-111119670	4.22	5.22	−0.010
	qEbtr-KJ-7A.1	BLUE	440.95	AX-110552361 − AX-94491266	2.85	4.89	0.010
	qEbtr-KJ-7A.2	E1/E7/E4	664.95/665.95/677.95	AX-110487560 − AX-111829116	2.07/2.22/3.02	3.60/5.56/5.47	0.021/ 0.025/ -0.031
	qEbtr-KJ-7B.1	E2	74.06	AX-110002576 − AX-109277778	4.76	6.13	0.031
	qEbtr-KJ-7B.2	E7/ E2	435.06/492.06	AX-108786710 − AX-109977913	2.60/9.57	6.91/13.06	−0.028/−0.047

^a A putative major QTL was marked in bold typeface and was characterized by a mean LOD > 3.0 and a mean PVE > 7.0% (the average over environments), and a putative stable QTL was underlined indicating the QTL was detected in at least four of the eight environments and BLUE. A major and stable QTL was with the both characteristics of major QTL and stable QTL.

^b The physical positions of markers on KN9204 genome. ^c The LOD values > 2.0 of detected QTLs.

^d The positive values indicated the alleles from KN9204 increased the trait values, and the negative values indicated the alleles from J411 increased the corresponding trait values.

^e The phenotypic variance effect (PVE) that one QTL could explained.

QTL mapping for the three tiller related traits and comparison with previous studies

With the advancement of molecular marker technology, numerous reports regarding QTL analysis for tiller-related traits have been documented (http://wheatqtldb.net/yield_new.php; Araki et al. 1999; Shah et al. 1999; Kato et al. 2000; Li et al. 2002; Huang et al. 2003; Campbell et al. 2004; An et al. 2006; Li et al. 2007; Kumar et al. 2007; Li et al. 2010; Naruoka et al. 2011; Tang et al. 2011; Zhang et al. 2013; Liu et al. 2014; Nasseer et al. 2016; Chen et al. 2017a; Hu et al. 2017; Ren et al. 2018; Liu et al. 2018, 2020; Wang et al. 2018; Zhang et al. 2019; Bilgrami et al. 2020; Isham et al. 2021; Kang et al. 2021; Wang et al. 2022). Due to the great influence of environmental factors on tiller-related traits, stable QTL should be of particular value for their further utilization in molecular breeding programs designed to improve plant architecture and thus to improve the yielding potential. Li et al. (2007) reported a stable QTL for SNPP in three different environments that was designated as QSn.sdau-7D. QSsm.saas-4B and QSsm.saas-5B were proved to be significant in three and four datasets by Tang et al. (2011), respectively. QSnp.cd-1A.1 was proved to be a stable QTL for SNPP that was verified in all the five datasets (Chen et al. 2017a). qSpp.cau-4B.3 and qSpp.cau-4B.4, two adjacent QTL on chromosome 4BS, were proved to be significant in five and six datasets by Guan et al. (2018). Six QTL for the MTN and/or PTN have been verified in two different environments by Ren et al. (2018), distributing on chromosomes 2D, 4A, 4D, 5D and 7D. QPtn.sau-4B was proved to be significant in all the five datasets by Liu et al. (2018) using a RIL mapping population; Qetn-sau-1B.1 was proved to be significant in eight of the nine environments by Liu et al. (2020) using another RIL mapping population. QSn-4B.3, a stable QTL for SNPP, showed significance in five environments (Zhang et al. 2019). QPTN.uia2-6A was repeatedly identified in five datasets by Isham et al. (2021) using a doubled haploid line mapping population; in addition, QPTN.uia2-4A was verified in three of the nine datasets. One QTL for tiller number, i.e., QTN-1 on chromosome 5B, has been proved to be significant in all the three environments by Kang et al. (2021). Recently, 29 meta-QTL controlling total tiller and fertile tiller number, i.e., PTN, in wheat has been documented by Bilgrami et al. (2020) based on QTL information on ten chromosomes of 1A, 2A, 2B, 2D, 5D, 6A, 6B, 6D, 7A, and 7D in previous reports.

In the present study, two, nine and six QTL for SNPP, MTN and EBTR, respectively, could be repeatedly identified in no less than two different environments. qSnpp-KJ-4B was repeatedly detected in E5 and E6 on chromosome 4B at the physical position of KN4B:641.70 − 657.70 Mb in KN9204 genome, corresponding to Chr4B:641.35 − 657.10 Mb in IWGSC RefSeq v2.0; qMtn-KJ-4B.3 was mapped to KN4B:600.70–626.70 Mb in two different environments; these two adjacent QTL are different from QPtn.sau-4B (Chr4B:31.71 − 34.90 Mb in IWGSC RefSeq v2.0) as reported by Liu et al. (2018), qSpp.cau-4B.3 and qSpp.cau-4B.4 (Chr4B:17.79 − 30.98 Mb in IWGSC RefSeq v2.0) as reported by Guan et al. (2018) and QSn.sdau-4B (Chr4B:442.59 − 482.82 Mb in IWGSC RefSeq v2.0) as reported by Deng et al. (2011). qSnpp-KJ-5D.1 and qMtn-KJ-5D.1 formed a stable QTL cluster for SNPP and MTN; this QTL cluster covered to KN5D:0.62 − 6.62 Mb, corresponding to Chr5D:0.72 − 7.17 Mb in IWGSC RefSeq v2.0; it is different from qTN-5D.1 (Chr5D:39.24 − 238.73 Mb in IWGSC RefSeq v2.0) as reported by Ren et al. (2018) and MQTL5D-1 (Chr5D:302.38 − 396.42 Mb in IWGSC RefSeq v1.0) as reported by Bilgrami et al. (2020). qMtn-KJ-2D.2 was verified in E6 and E7 at the physical position of KN2D:536.75 − 585.75 Mb, corresponding to Chr2D:538.25 − 587.62 Mb in IWGSC RefSeq v2.0; MQTL2D-3 for total tiller and/or fertile tiller number was mapped to Chr2D:553.72–570.41 in IWGSC RefSeq v1.0 (Bilgrami et al. 2020); therefore, qMtn-KJ-2D.2 should be identical to MQTL2D-3. qMtn-KJ-3A was verified in four of the total nine datasets in QTL mapping analysis, with LOD peak position ranging from 0.76 Mb to 9.76 Mb in chromosome 3A in KN9204 genome; no stable QTL at this position has been documented previously. qMtn-KJ-5B.2 was proved to be significant in five of the nine datasets on chromosome 5B at the physical position of KN5B:607.32 − 656.32 Mb in KN9204 genome; QTN-1 on chromosome 5B was mapped to Chr5B:713.06 Mb by Kang et al. (2021); therefore, qMtn-KJ-5B.2 might also be a novel stable QTL for MTN. qMtn-KJ-5D.2 was significant in E3 and E6 with LOD peak positions ranging from 449.62 Mb to 499.62 Mb in KN9204; this QTL is also different from qTN-5D.1 (Chr5D:39.24 − 238.73 Mb in IWGSC RefSeq v2.0); but it shared confidence interval of MQTL5D-2 (Chr5D:472.63 − 481.56 Mb in IWGSC RefSeq v1.0) as reported by Bilgrami et al. (2020). Both qMtn-KJ-6A.1 and qMtn-KJ-6A.2 were repeatedly detected in two different environments, with LOD peak positions at KN6A:16.01 − 19.01Mb and KN6A:578.01 Mb, respectively; QPTN.uia2-6A was mapped to Chr6A:201.35 − 454.65 Mb in IWGSC RefSeq v1.0; MQTL6A-1 and MQTL6A-4 were mapped to Chr6A:16.57 − 18.71 Mb and 520.76 − 581.75 Mb in IWGSC RefSeq v1.0 by Isham et al. (2021), respectively; therefore, qMtn-KJ-6A.1 and qMtn-KJ-6A.2 should be identical to MQTL6A-1 and MQTL6A-4, respectively. No stable QTL for MTN on chromosome 7B has been documented, and therefore qMtn-KJ-7B.2 might be a novel repeatable QTL for MTN. To our knowledge, limited QTL for EBTR, an indirect derivative traits for tiller-related traits, has been reported. Genome wide association analysis showed that wsnp_Ex_rep_c69766_68723140 (Chr1B:557.71 Mb), BS00073989_51 (Chr7A:713.96 Mb) and IAAV3713 (chr7B:716.55 Mb) were significantly associated with EBTR in two of the three different environments (Chen et al. 2017b). In this study, qEBTR-KJ-7A.2 was significant identified in three different environments, with LOD peak positions at KN7A:664.95 − 677.95 Mb; qEBTR-KJ-7B.2 could be identified in two different environments, with LOD peak positions at KN7B:435.06 − 492.06 Mb. Therefore, qEBTR-KJ-7A.2 and qEBTR-KJ-7B.2 should be different from the QTL reported by Chen et al. (2017b). In addition, qEBTR-KJ-5D.1 was significant in four of the nine datasets; qEBTR-KJ-3D, qEBTR-KJ-4B.1 and qEBTR-KJ-4B.4 could be identified in two different environments.

Above all, 12 repeatable QTL for tiller-related traits might be novel QTL that were firstly documented in the present study. These QTL will be of value for marker-assisted selection in breeding novel varieties adapted to various ecological environments.

Genetic effects of the repeatable QTLs on yield-related traits and their utilization potential in wheat breeding programs

The PTN per unit area is a critical yield component of wheat (Tilley et al. 2019). Phenotypic correlation coefficients between spikes per square meter and grain yield were reported to be 0.19 − 0.65 across six different environments by Tang et al. (2011). Genetic correlation coefficients between grain yield and spikes per square meter was observed to be approximately 0.32 by Assanga et al. (2017). Correlation coefficients between grain yield and SNPP were reported to be 0.522 − 0.750 across four different environments by Xu et al. (2017). However, no significant correlation between GY and SNPP was observed by Shi et al. (2017) and Isham et al. (2021). GY and its components are greatly affected by environmental factors, which might account for this in-consistence. Phenotypic correlation analysis in the present study showed that SNPP has great effects on YPP with correlation coefficients up to 0.59 and 0.46 under LN and HN, respectively (Table 2). This results indicated that SNPP has greater effects on YPP under LN than that under HN condition.

We further performed the genetic effect analysis of the major stable QTL of qSnpp-KJ-5D.1 and qMtn-KJ-5D.1 on yield-related traits using two close linkage SNP markers of AX-94938800 (KN5D:0.61 Mb) and AX-110009985 (KN5D:1.62 Mb) based on the KJ-RIL mapping population. The 187 KJ-RILs (excluding the heterozygous lines as well as the lines with missing genotype information) were divided into two groups, with one group carrying homozygous alleles from KN9204 and the other group carrying that from J411. A comparison of the two groups revealed that the excellent alleles of Hap-J411 could significantly increase SNPP and MTN under both LN and HN conditions; Hap-J411 showed significantly negative effects on EBTR and TKW under LN and HN conditions, and on KWPS under HN condition; although the KNPS and SNPS did not change much, YPP increased significantly especially under LN condition (Fig. 2). The above findings indicated that Hap-J411 at qSnpp-KJ-5D.1 and qMtn-KJ-5D.1 could increase SNPP and thus increase the YPP, even at the cost of decreasing TKW to some extent.

Using a natural mapping population as reported by Cao et al. (2022), two SNP markers of AX-110565536 (AA/GG) (KN5D:0.36 Mb) and AX-89390905 (AA/GG) (KN5D:0.92 Mb) that were close linked to qSnpp-KJ-5D.1 and qMtn-KJ-5D.1 were selected for the genetic effect analysis on yield-related traits. Lines with Hap-GG-GG identical to J411 was defined as excellent haplotype, and Hap-AA-AA haplotype identical to KN9204 was defined as non-excellent haplotype; lines with the Hap-GG-AA or Hap-AA-GG haplotype was defined as the recombinants. The results showed that the proportion of excellent haplotype (Hap-GG-GG) was 36.71% among 316 lines, and that of non-excellent haplotype (Hap-AA-AA) was 12.03%. The proportion of recombinants and heterozygous type was 32.28% and 7.28%, respectively. This finding indicated that the favored haplotype of Hap-GG-GG has been undergone artificial selection in wheat breeding programs to some extent.

According to the above genotype classification, the associated analysis of qSnpp-KJ-5D.1 and qMtn-KJ-5D.1 with yield-related traits was performed. The results showed that the excellent haplotype of Hap-GG-GG could significantly increase SNPP and KNPS (Fig. 3). Hap-GG-GG had a significant negative effect on TKW. However, its effects on SNPS and YPP was not significant. This results confirmed the genetic effects of qSnpp-KJ-5D.1 and qMtn-KJ-5D.1 on SNPP, indicating a reliable and stable QTL. However, its effects on yield-related traits showed some differences between the KJ-RIL and the natural mapping population. GY and its components are greatly affected by environmental factors, which might accounted for this difference.

To further characterize the utilization potential of qSnpp-KJ-5D.1 and qMtn-KJ-5D.1, its pyramiding effect was analysed using the KJ-RIL mapping population. qMtn-KJ-3A, qMtn-KJ-5B.2 and qMtn-KJ-5D.1 were the three stable QTL for MTN identified herein, which should account for most of the MTN phenotypic variation in the 187 KJ-RILs. Of these, qMtn-KJ-5D.1 has the largest additive effects among the eight tested environments and showed the most stability across environments (Table 3). Thus, qMtn-KJ-3A, qMtn-KJ-5B.2 and qMtn-KJ-5D.1 were used for additive pyramiding effect analysis. The results showed that the MTN of the KJ-RILs increased with the increase of favored QTL number. The combined effects of the three tested QTL with positive alleles could increase the MTN and SNPP by 32.73% and 12.13%, respectively, compared with the lines with no positive alleles at the three QTL locus (Figs. 4 and 5). Moreover, the lines with positive alleles simultaneously at the three QTL could increase YPP by 4.28% on average, compared with these lines with no positive alleles (data not shown). The above finding indicated that the combined application of qMtn-KJ-3A, qMtn-KJ-5B.2 and qMtn-KJ-5D.1 could be of great value in the genetic improvement of yielding potential. It is worthy mentioning that qMtn-KJ-5D.1 showed the largest additive effects on both MTN and SNPP, no matter under the genetic backgrounds of the KJ-RILs with one, two and/or three favored QTLs alleles (Figs. 4 and 5). The favored alleles of qMtn-KJ-5D.1 from J411 could increase the MTN by 17.95%, 14.98% and 13.66% under the genetic backgrounds AB (both qMtn-KJ-3A and qMtn-KJ-5B.2 harbored favored alleles that increase MTN), AA (only qMtn-KJ-3A harbored favored alleles that increase MTN), and BA (neither qMtn-KJ-3A nor qMtn-KJ-5B.2 harbored favored alleles that increase MTN), respectively; however, it could only increase the MTN by 6.84% under the genetic background of BB (only qMtn-KJ-5B.2 harbored favored alleles that increase MTN); this finding indicated that the expression of qMtn-KJ-5D.1 might be influenced by genetic backgrounds (Fig. 6). Over all, the above results confirmed the great utilization potential of qSnpp-KJ-5D.1 and qMtn-KJ-5D.1 in the genetic improvement of tiller-related traits and thus increasing yielding potential.

Candidate gene analysis of qMtn-KJ-5D.1

Previous studies have proved that primary QTL mapping analysis can accurately position the causal genes within 2 cM or less (Price 2006). High-density molecular marker map along with multiple environment phenotypic evaluation makes it possible to precisely predicate candidate genes underlying a stable QTL. qMtn-KJ-5D.1 was repeatedly identified in eight of the nine datasets, with LOD peak position ranging from 0.62 Mb to 2.62 Mb; it’s worthy mentioning that the LOD peak position was identified at 2.62 Mb in six of the eight datasets including in the BLUE datasets (Fig. 7). Therefore, we focus on the candidate genes underlying qMtn-KJ-5D.1 in the region of KN5D:0 − 3.2 Mb. This region harbored 32 high confidence genes in KN9204 genome (Ma et al. 2021; Shi et al. 2022; http://202.194.139.32/).

RNA sequencing data from 16 developmental stages of five tissues of KN9204 and J411 were reported in our recent report (Shi et al. 2022). We analyzed the differential expression of the 32 high confidence genes in the target region. The results showed that nine genes showed differential expression between KN9204 and J411 (Fig. 7). In addition, whole-genome re-sequencing was performed on J411 and the 640 Gb (403) Illumina sequencing reads from J411 were aligned to the KN9204 genome (Shi et al. 2022). Approximately 800 SNP/InDel were identified in the target region of KN5D:0 − 3.2 Mb between KN9204 and J411 (data not shown). Of these, one insertion of GGATGA was identified in KN5D:1044565 in J411, in the second exon of TraesKN5D01HG00080; five SNPs were identified in KN5D:2030387, KN5D:2030402, KN5D:2030405, KN5D:2030420 and KN5D:2030426, in the second exon of TraesKN5D01HG00160. No coding DNA sequence variation was found for the remaining candidate genes. TraesKN5D01HG00160 did not express in all the 26 samples; TraesKN5D01HG00080 showed differential expression in seven of the 26 samples, especially in stem under both HN and LN at shooting and booting stages (Fig. 7). Therefore, TraesKN5D01HG00080 is likely to be the candidate genes underlying qMtn-KJ-5D.1. In addition, the homologous gene of TraesKN5D01HG00080 in Arabidopsi was MUT9-like protein kinase, contributing to phosphorylation of photoexcited CRY2, and it might be related to long day flowering. Further fine mapping and candidate gene complementary function test are needed to be performed to prove this predication.

Acknowledgments This research was supported by the Agricultural Variety Improvement Project of Shandong Province, China (2021LZGC009), Natural Science Foundation of Shandong Province (ZR2022MC119), National Natural Science Foundation of China (32072051, 32101726, 32272119), Major Basic research project of Shandong Provincial Natural Science Foundation (ZR2019ZD16), the Agricultural Variety Improvement Project of Yantai, Shandong, and industry-university cooperative education projects of the Ministry of Education (KGRCYXX2022017).

Data availability statement All data generated or analyzed during this study are included in this published article and further inquiries can be directed to the corresponding authors.

Ethical standards The authors declare that they have no conflict of interest. All authors contributed to the study and approved the final version for submission. The manuscript has not been submitted to any other journal.

Conflict of interest The authors have not disclosed any competing interests.

Author contribution statement YC and XZ wrote the manuscript; CW, AL and ZS conducted the field trials; YC, XZ, SL, XS, SY, YG and JC performed the experiments; YW, RQ and HS analyzed the data, CZ, JL and FC designed the research and all authors read and approved the manuscript.

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QTL detection for three tiller-related traits and their effects on yield in wheat (Triticum aestivum L.)

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Abstract

Figures

Key message

Introduction

Materials and methods

Plant materials

Phenotypic evaluation and statistical analysis

Linkage map construction and QTL detection

Results

Phenotypic performance for tiller-related traits in the KJ-RIL population

Correlation analysis between tiller- and yield-related traits in the KJ-RIL population

Putative additive effect QTL for the three tiller-related traits

QTL for SNPP

QTL for MTN

QTL for EBTR

Discussion

QTL mapping for the three tiller related traits and comparison with previous studies

Declarations

References

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