Dissection and validation of a novel QTL controlling spikelet number on 5B in bread wheat

doi:10.21203/rs.3.rs-2340320/v1

Spikelet number per spike (SNS)is an important target trait for improving wheat yield. Thus, the identification and verification of novel quantitative trait loci (QTL)/genes controlling SNS isessential for dissecting the underlying molecular basisand hence for grain yield improvement. In thisstudy, we constructed a high-density genetic map for the recombinant inbred line (RIL)population derived from Zhengnong 17 (ZN17) and Yangbaimai (YBM) using 99,031 single-nucleotide polymorphism (SNP) markers from the Wheat660K SNP array. Based on this genetic map, a total of 19 QTL associated with SNS were detected in nine environments. Among them, four QTL were identifiedin more than six environments, explaining the phenotypic variance ranging from 2.91% to 37.71%. Particularly, QSns.ucas-5B isprobably a novellocus, which islocated on chromosome arm 5BL flanking by the markers AX-110648717 and AX-109557107, withexplained the phenotypic variation range from 5.09% to 7.26% in sixenvironments. A residual heterozygous line was developed for QSns.ucas-5B, and the SNSshowed 6.74~13.17% higher in lines of elite haplotype from ZN17 than that from YBM at four diverse environments, which implied the promising potential to improve SNS using this QTL. Simultaneously, the ZN17haplotype in QSns.ucas-5B has a consistent positive effect on grain number per spike and grain weight per spike. These results lay the foundation for the precise mappingand cloning of gene(s) underlying QSns.ucas-5B in the future. The developed and validated InDel marker of QSns.ucas-5Bcould be appliedin molecular breeding aiming to improve wheat grain yield.

spikelet number per spike

QTL

Wheat660K array

wheat

Four environmentally stable QTL for spikelet number per spike were identified using a high-genetic map containing 99,031 SNPs, among which QSns.ucas-5B was validated using residual heterozygous line at multiple environments.

Wheat is one of the major grain crops in the world and provides approximately 20% of the calories in the human diet (Simmonds et al. 2016). Shrinking arable land, climate change and a rapidly increase of the global population have created an urgent need for breeders to breed high-yield wheat varieties to secure global wheat production. Wheat yield can be largely partitioned into three components, including spike number per hectare, grain number per spike (GNS) and thousand kernel weight (TKW) (Isham et al. 2021). Generally, GNS can be further subdivided into spikelet number per spike (SNS) and grains per spikelet (Quarrie et al. 2006). Numerous reports have demonstrated that an increased SNS could increase the yield potential of wheat (Sakuma et al. 2019; Zhang et al. 2022). Thereby, modifying SNS could be a strategy for improving yield potential of wheat as it is associated with higher GNS (Lewis et al. 2008; Wang et al. 2022).

SNS is a complex quantitative trait controlled by multiple genes as well as environmental factors (Corsi et al. 2021; Muqaddasi et al. 2019). The application of molecular markers associated with SNS would increase the efficiency for improving yield potential attained via modern wheat breeding, such as marker-assisted selection (MAS) (Chhetri et al. 2017; Wurschum et al. 2018). Thus, determination of the chromosomal localization, number, and genetic effects of the corresponding genes is desirable for obtaining optimal genotype in wheat breeding. However, due to the complexity of wheat genome and the compensatory effects of homoeologous genes, only a few genes involved in spike morphology or SNS have been characterized through homologous cloning method or map-based cloning (Huang et al. 2009; Zhang et al. 2015). For examples, the photoperiod-sensitive gene Photoperiod1 (Ppd1), the wheat FRIZZY PANICLE (WFZP) gene, the well-known maize (Zea mays L.) domestication gene TEOSINTE BRANCHED1, and DUO-B1 have been found to regulate the formation of “paired spikelet” (the supernumerary spikelet) in wheat (Boden et al. 2015; Dixon et al. 2018; Du et al. 2021; Li et al. 2021b; Wang et al. 2022). In addition, Ppd1 significantly affects spike length, spikelet number, and heading date (Okada et al. 2019). The domestication gene Q, which encodes a member of AP2 transcription factor family, not only affects threshability and spike architecture, but also changes flowering time and plant height (He et al. 2021; Liu et al. 2020; Simons et al. 2006; Song et al. 2019; Zhao et al. 2018). The transgenic wheat plants carrying extra copies of WAPO-A1 driven by its native promotor had more spikelet, a more compact spike apical region, and smaller terminal spikelet than the wild type (Kuzay et al. 2022). Null mutants of FLOWERING LOCUS T2 (FT2) and FT-D1 showed increased SNS accompanied with a longer spike development period in common wheat (Chen et al. 2022; Shaw et al. 2018). Constitutive overexpression of the dominant TaCol-B5 allele (without the region encoding B-boxes), a rare allele in a global collection of modern wheat cultivars, in the common wheat cultivar Yangmai 18 increased SNS as well as tillers and spikes, thereby enhancing grain yield averaging an 11.9% compared to wild-type under field conditions (Zhang et al. 2022).

QTL mapping is a traditional genetic tool for dissecting genetic basis of key traits related to grain yield. It provides molecular markers closely related to yield component traits and increased the genetic gains in breeding process by MAS. Numerous QTL for SNS have been identified on almost all the 21 chromosomes in wheat by bi-parental QTL mapping or genome-wide association studies (GWAS) (Cui et al. 2012; Echeverry-Solarte et al. 2015; Li et al. 2021a; Xu et al. 2022; Yao et al. 2019). For examples, Ma et al. (2007) reported a QTL (QSpn.nau-5A) for SNS on 5A chromosome using RIL and immortalized F₂ populations, which simultaneously affected spike length and spike density. Xu et al. (2022) identified two QTL (QTsn.cau-5D.3 and QTsn.cau-6D) for SNS derived from the D genome of the natural and resynthetic allohexaploid wheat. Furthermore, with the advent of high-density genotyping assays, such as the wheat 55K (Liu et al. 2018), 90K (Chen et al. 2020) and 660K (Cui et al. 2017; Sun et al. 2020) SNP arrays, significant progress has been reported for QTL associated with yield component traits. Such as, Sun et al. (2017) revealed new allelic variation distribution for SNS by GWAS method with the wheat 90K assay, and found 116 significant SNPs were associated with SNS, the phenotypic variation explained (PVE) by each SNP ranged from 15.5–33.7%. Chen et al. (2020) found a QTL for SNS on the short arm of chromosome 7A, which had the pleiotropic effects of FT-A1, a gene controlling flowering time. Li et al. (2021a) identified a novel QTL QTsn/Fsn.cib-3D associated with both SNS and fertile spikelet number per spike located in a 4.5 cM interval on chromosome arm 3DL. Nevertheless, only a few of QTL have been genetically verified, which greatly restrict the dissection of the molecular basis underlying spikelet number. Thus, the identification and verification of novel QTL/genes for SNS is desirable for obtaining optimal genotype during breeding practices.

In this study, we constructed a RIL population derived from a cross between two winter wheat varieties, built a high-density genetic map with SNPs from the Wheat660K SNP array, evaluated SNS of this population in nine environments, explored the QTL for SNS, and identified a novel QTL on chromosome 5BL. We further validated the QTL by residual heterogeneous line in the field trials. Collectively, these data provide further insight for the genetic basis of spikelet development in wheat, and the related linkage marker will be useful for marker-assisted selection in wheat improvement.

Mapping populations

QTL mapping was performed using a F₈ RIL population with 188 lines derived from a cross between the winter common wheat lines Zhengnong17 (ZN17) and Yangbaimai (YBM) (denoted as ZY-RILs) by the single seed descent method described by Si et al. (2021). ZN17 is an elite parent carrying the 1BL.1RS translocation, while YBM is a Chinese landrace characterized by high tiller number. For phenotyping, the ZY-RILs population along with two parents were planted at the following two experimental locations: Zhaoxian county, Hebei province (37° 50′ N, 114° 49′ E) and Beijing (40° 16′ N, 116° 24′ E), China. The field trials were carried out in four growth seasons (2016-17, 2017-18, 2018-19, and 2020-21) at Zhaoxian with multiple fertilizer treatments, and one crop season (2016-17) at Beijing. The detailed information of nine trials was described in Supplementary Table S1 (referred to as E1-9). These field trials were arranged in an alpha-lattice block design with two replications, and each plot consisted of two one-meter-long rows with the row-space of 25 cm. All trials were sown by hand with the aid of a homemade hole digger, which punched 11 seeding holes with 10 cm space between adjacent holes each time.

A heterozygous F₉ line (RIL-55) was identified from ZY-RILs population using a QTL-specific marker InDel-SNS5B (F:5’ CGAAACTGAGAAACCAAGAAAC and R:5’ CATACCACCAAATCTCAATCG) and self-pollinated to produce near isogenic line (NIL) NIL-5B^ZN17 (genotype same with ZN17) and NIL-5B^YBM (genotype same with YBM) with contrasting alleles at QSns.ucas-5B region. The NIL pairs were sown in four environments to evaluate the genetic effect of QSns.ucas-5B at Zhaoxian county (E6-8) and Beijing (2018BJ) during the 2018-19 wheat growing seasons.

Phenotype Evaluations And Statistical Analyses

The phenotype investigation of ZY-RILs population and their parental lines was evaluated at nine different environments (E1-9). The SNS values were determined as the average SNS from ten randomly selected main spikes in the middle region of each plot right before harvest. For each genotype, the average values of SNS across all replicates were used to represent the data in each environment. The best linear unbiased predictions (BLUP) value for SNS was calculated using the ANOVA function in IciMapping v4.2 (https://isbreeding.caas.cn/) (Meng et al. 2015), and the corresponding normal distribution was tested by the Shapiro-Wilk test (p = 0.05) with IBM SPSS Statistics 19 (SPSS, Chicago, USA). The broad-sense heritability (H2 B) of SNS was calculated as described by Xu et al. (2022).

Genetic Map Construction And Qtl Detection

The ZY-RILs population and their parents were genotyped using the wheat660K Illumina iSelect array in the company of CapitalBio Technology (Beijing) as described in Si et al. (2021). The SNP was remained if it pass all of the following criterions: 1) Polymorphic between the two parents; 2) Missing rate < 5% (the heterozygous SNP was regarded as missing); 3) Unique mapped against the reference genome of IWGSC RefSeq v2.1. Redundant SNP markers were removed using the “BIN” function of IciMapping v4.2 as described by Li et al. (2021a). Specifically, the best marker (with the lowest missing rate) for each BIN marker was selected for the genetic map construction. Then, the BIN markers were sorted into groups and ordered using the “MAP” tool with the Kosambi mapping function of IciMapping

v4.2. The parameters of “logarithm of the odds (LOD)” and “recombination fraction” were set as 3.5 and 0.3, respectively. The physical locations and chromosomal arms of the genetic maps were determined based on the blast results of the flanking sequences of BIN markers on the IWGSC RefSeq v2.1 genome assembly. The genetic maps were visualized by R package “LinkageMapView” (https://github.com/louellette/LinkageMapView).

The observed phenotypic values of SNS obtained from each environment (E1-9) were used for QTL detection using the inclusive composite interval mapping (ICIM) method by the software IciMapping v4.2. In QTL analysis, the missing phenotype was deleted and the walking speed was 1.00 cM, with a p value inclusion threshold of 0.001. Furthermore, a LOD score of 3.5 was selected as a minimum to determine the presence of a QTL in a genomic region. Only QTL identified in at least three individual environments were defined as stable QTL, and these with mean PVE ≥ 10% were defined as major QTL. All QTL were named as suggested by McIntosh et al. (2017).

Linkage map construction

In our previous study, the ZY-RILs population and their parental lines were genotyped by the Wheat660K SNP array (Si et al. 2021). Of the 630,517 SNPs called from the 660K SNP array, 201,792 were polymorphic between ZN17 and YBM. After filtering these polymorphic SNPs, 108,987 high quality SNP_S were retained and used for linkage analysis and map construction. These robust SNPs were divided into 6,454 bin markers, while only 6,291 bins were mapped to the wheat genetic map by linkage analysis. Of them, 3,615 bins contain only one SNP, the rest 42.5% bins contain multiple SNPs, and the largest bin contains 4,631 SNPs on chromosome 1A. Then, based on the genetic information of bins, all bin markers as well as the redundant SNPs were integrated onto the genetic map. Finally, a high-density map consisted with 99,031 SNP markers spanning a total length of 6,280.62 cM was constructed (Fig. 1, Supplementary Table S2). Most mapped SNPs were located on the A (53.53%) and B (38.66%) genomes, and only 7.81% markers were placed on the D genome with a total length of 2,575.65, 1,969.62, and 1,735.35 cM, respectively (Table 1). In addition, the length of constructed linkage maps range from 67.14 cM (7B) to 493.38 cM (5B), with a mean of 299.08 cM per chromosome. Chromosome 7B had the least mapped SNP markers (300), while 5B had the most SNP markers (11,822), averaging 4,715.76 loci per chromosome. Correspondingly, 7B and 5B have the least (55) and most (627) bin markers, respectively (Table 1). The average interval distance between adjacent bin markers ranges from 0.79 cM for 5B to 1.73 cM for 5D, with an average distance 1.00 cM per bin. In this study, about 1.94% of genetic gaps were more than 5 cM, and only four gaps present in chromosomes 3A (30.59 cM), 4D (42.27 cM), and 6D, (24.29 cM, 27.20 cM) were more than 20.0 cM but less than 50.0 cM. Furthermore, there were 20 gaps greater than 10 cM but less than 20.0 cM were located on chromosomes 1B (11.92 cM), 1D (11.61 cM), 2A (11.46 cM, 15.93 cM), 2D (10.20 cM, 12.68 cM, 12.86 cM), 3B (12.23 cM), 3D (19.01 cM, 19.28 cM), 4A (14.75 cM, 15.48 cM), 4B (13.78 cM), 5A (17.32 cM), 5B (15.01 cM), 5D (12.77 cM, 12.95 cM), 6A (15.63 cM), 6B (16.40 cM), and 7A (10.88 cM) (Supplementary Table S2).

Table 1

General information of the high-density genetic map
Chromosome	Number of SNP markers	Number of bin markers	Length (cM)	Density (cM/bin)
1A	11,186	417	350.67	0.84
1B	2,484	213	169.23	0.79
1D	1,680	191	223.38	1.17
2A	10,366	428	388.84	0.91
2B	7,567	426	366.95	0.86
2D	1,162	259	318.09	1.23
3A	6,637	389	366.11	0.94
3B	8,627	411	402.82	0.98
3D	1,011	171	263.72	1.54
4A	5,577	382	353.28	0.92
4B	1,543	173	181.68	1.05
4D	301	100	141.27	1.41
5A	6,693	418	394.50	0.94
5B	11,822	627	493.38	0.79
5D	994	147	253.69	1.73
6A	2,610	242	233.59	0.97
6B	5,938	328	288.43	0.88
6D	825	143	219.68	1.54
7A	9,945	531	488.65	0.92
7B	300	55	67.14	1.22
7D	1,763	240	315.52	1.31
A genome	53,014	2,807	2,575.65	0.92
B genome	38,281	2,233	1,969.62	0.88
D genome	7,736	1,251	1,735.35	1.39
Total	99,031	6,291	6,280.62	1.00

As ZN17 carries the 1BL.1RS translocation, although SNPs on the 1RS/1BS chromosome were polymorphic between the two parents, they showed co- and distorted segregation in the ZY-RILs population. For this reason, these SNPs were deleted during map construction resulting in only 1BL genetic map was retained (Fig. S1). Coincidentally, SNPs on almost the whole chromosome of 7B (~ 710 of 760 Mb) showed severe distorted separation for some unknown reason, and only the markers located in the ~ 50 Mb interval of the end of 7B was retained (Supplementary Table S3), resulting in the constructed genetic map only released a part of 7BL only (Fig. S1). Based on the physical locations of these mapped SNPs on the IWGSC RefSeq v2.1 genome, the marker order was relatively consistent with that in the wheat genome assembly on most of the chromosomes, except for part of chromosome 1A and 6B (Fig. S1).

Phenotypic Performance

Descriptive statistics of the two parental and ZY-RILs population for SNS across nine environments were listed in Table 2. Compared to YBM, ZN17 had more spikelet number. For the ZY-RILs population, the SNS ranged from 11.9 to 27.5, showing wide and significant variation (Table 2). The frequency distribution and Shapiro-Wilk test indicated that SNS exhibited normal distribution with the BLUP values of nine environments (P > 0.05) (Fig. S2). In addition, the SNS values across all environments were significantly positively correlated (P < 0.001) (Supplementary Table S4), showing high broad-sense heritability (H² = 0.95), indicating a strong genetic contribution to SNS in the population (Table 2).

Table 2

Phenotypic variation and heritability (H²) of SNS for the parents and the ZY-RILs population in different environments
Environment	Parents		The ZY-RILs population		H²
Environment	ZN17	YBM	Mean	Range	H²
E1	21.2 ± 1.6	20.0 ± 1.2^*	21.5	17.2–25.4	0.95
E2	21.1 ± 1.3	20.1 ± 1.0^**	21.4	16.9–25.2
E3	16.8 ± 1.0	15.8 ± 0.5^**	19.1	14.0-24.9
E4	19.0 ± 1.0	16.4 ± 1.4^**	18.2	15.2–21.6
E5	18.2 ± 1.2	17.1 ± 1.1^*	16.5	11.9–20.6
E6	20.1 ± 1.0	19.3 ± 0.7^*	21.0	15.8–27.5
E7	19.8 ± 1.1	18.9 ± 0.7^*	20.5	16.2–25.1
E8	20.1 ± 2.1	17.3 ± 0.9^**	19.7	16.2–24.9
E9	22.0 ± 0.6	20.4 ± 1.0^**	22.0	17.4–27.2
Note. SNS, spikelet number per spike; H², broad-sense heritability. Student’s t-test was used for statistical significance analysis. ^* and ^** represent significance at P < 0.05 and < 0.01, respectively.

Qtl Analysis

In the study, phenotypic data of SNS evaluated in nine environments were used for QTL mapping by IciMapping v4.2. A total of 19 QTL for SNS were detected, which were located on chromosomes 1B, 2A, 2B, 2D, 3A, 3D, 5B, 6B, and 7A, respectively. Among them, four QTL (QSns.ucas-1B, QSns.ucas-2D.1, QSns.ucas-3D.1 and QSns.ucas-5B) defined as stable were identified in more than three environments, explaining the phenotypic variance ranging from 2.91–37.71% (Table 3). Of them, QSns.ucas-1B was detected on the end of 1B in seven of nine environments, explaining 3.30–13.03% of the phenotypic variance with LOD values ranging from 3.84 to 10.60 (Table 3). Notably, the presence of 1BL.1RS translocation in the parent ZN17 resulted in only 1BL remained in the genetic map (Fig. S1). Thus, the peak value of this QTL may be located on the “missing” 1BS/1RS translocation chromosome. The major stable QTL on 2D, QSns.ucas-2D.1, was repeatedly identified in all environments, which had LOD score of 5.26–29.95 and explained the largest phenotypic variance (2.91%-37.71%) for SNS (Table 3). Remarkably, the peak marker of QSns.ucas-2D.1 was mapped in the flanking region of Ppd1, a well-known gene significantly affecting spikelet number, spike length, and heading date in wheat (Okada et al. 2019). As expected, the positive allele of QSns.ucas-2D.1 was contributed by YBM. Another stable QTL was observed on chromosome 3D (QSns.ucas-3D.1) with average phenotypic variations of as much as 7.01%, and YBM contributed the increasing allele (Table 3). QSns.ucas-5B was significant in six environments evaluated and elucidating 4.23–5.47% of the total phenotypic variance. The allele for increased SNS in QSns.ucas-5B was provided by ZN17. In addition, the remaining 15 were putative QTL detected in less than three environments, explaining 2.02–24.06% of the phenotypic variation (Supplementary Table S5).

Table 3

Summary of environmentally stable QTL detected in the ZY-RILs population
QTL	Env.	Pos. (cM)	Interval (cM)	Left Marker	Right Marker	LOD	PVE (%)	Add
QSns.ucas-1B	E1	0	0-2.5	AX-109320638	AX-111731227	10.60	13.03	-0.43
	E2	0	0-3.5	AX-109320638	AX-111731227	6.97	3.30	-0.40
	E3	1	0-4.5	AX-109320638	AX-111731227	6.11	5.92	-0.51
	E4	4	0-5.5	AX-109320638	AX-111731227	4.72	6.26	-0.33
	E5	1	0-4.5	AX-109320638	AX-111731227	6.39	7.75	-0.41
	E6	1	0-3.5	AX-109320638	AX-111731227	8.46	9.65	-0.59
	E7	3	0-10.5	AX-109320638	AX-111731227	3.84	7.20	-0.38
QSns.ucas-2D.1	E1	74	73.5–75.5	AX-86163393	AX-110744761	11.74	15.38	0.46
	E2	74	70.5–74.5	AX-86163393	AX-110744761	5.78	2.91	0.37
	E3	72	70.5–72.5	AX-109836946	AX-86163393	29.95	37.71	1.26
	E4	74	73.5–74.5	AX-86163393	AX-110744761	20.46	33.33	0.76
	E5	74	72.5–74.5	AX-86163393	AX-110744761	23.10	32.56	0.83
	E6	74	73.5–74.5	AX-86163393	AX-110744761	18.71	23.72	0.91
	E7	74	69.5–74.5	AX-86163393	AX-110744761	5.26	10.40	0.45
	E8	73	70.5–74.5	AX-86163393	AX-110744761	6.28	10.36	0.43
	E9	74	71.5–74.5	AX-86163393	AX-110744761	9.24	14.94	0.52
QSns.ucas-3D.1	E1	89	87.5–89.5	AX-111026977	AX-109914019	3.94	4.56	0.25
	E3	89	87.5–89.5	AX-111026977	AX-109914019	4.51	4.20	0.42
	E5	89	87.5–89.5	AX-111026977	AX-109914019	6.51	7.55	0.40
	E6	89	87.5–89.5	AX-111026977	AX-109914019	7.14	7.69	0.51
	E7	89	87.5–89.5	AX-111026977	AX-109914019	4.99	9.01	0.42
	E9	88	87.5–89.5	AX-109499958	AX-111026977	6.05	9.06	0.40
QSns.ucas-5B	E1	259	254.5-261.5	AX-110648717	AX-108764357	4.97	5.71	-0.28
	E2	259	254.5-261.5	AX-110648717	AX-108764357	5.47	5.55	-0.35
	E3	254	252.5-257.5	AX-110648717	AX-108764357	5.09	5.09	-0.46
	E4	257	252.5-261.5	AX-110648717	AX-108764357	4.23	6.13	-0.33
	E5	262	261.5-262.5	AX-109557107	AX-109930199	4.69	5.37	-0.34
	E9	260	255.5-261.5	AX-108764357	AX-109557107	4.81	7.26	-0.36
Note. Env., Environment; Pos., position; Chr., chromosome; LOD, limit of detection; PVE, phenotypic variation explained; Add, additive effect (positive values indicate that alleles from YBM are increasing the trait scores, otherwise vice versa).

Verification of QSns.ucas-5B using NILs in different environments

The residual heterozygous lines provides an effective method for QTL verification without extensive backcross (Zhai et al. 2018). Based on the initial mapping result of QSns.ucas-5B, we developed an InDel marker, InDel-SNS5B. Then, RIL-55 was selected from the ZY-RILs population with InDel-SNS5B, because it carried residual heterozygosity across the mapping interval of QSns.ucas-5B. Heterozygous F₉ individuals from RIL-55 were identified and self-pollinated to produce near-isogenic lines (NIL-5B^ZN17 and NIL-5B^YBM). To evaluate the independently genetic effect of QSns.ucas-5B, the NIL pairs were genotyped to identify the consistency of genetic background using the wheat660K SNP array. The results showed that the similarity between them was 99.73%, and only 1,153 SNPs were different (Fig. 2a). Of these polymorphic SNPs, 523 (45.36%) were located on chromosome 5B, mainly (457, 91.6%) spanning the physical intervals 545.43-559.46 Mb in the Chinese Spring RefSeq v2.1 sequence (Fig. 2b).

To further validate and investigate the effect of QSns.ucas-5B for SNS, the NIL-5B^ZN17 and NIL-5B^YBM pairs were evaluated in four field trials. Compared with NIL-5B^YBM, NIL-5B^ZN17 showed a significant increase in SNS ranging from 1.31 (6.74%) to 2.54 (13.17%) (Fig. 2c). Collectively, these results demonstrated that the ZN17 haplotype in QSns.ucas-5B has a consistent positive effect on SNS. Additionally, we further analyzed the effects of QSns.ucas-5B on other yield-related traits including plant height (PH), spike number (SN), seed length (SL), seed width (SW), TKW, GNS and GWS in the ZY-RILs populations based on the genotype detected by marker InDel-SNS5B. The result showed that the positive alleles contributed by ZN17 were identified with a significant influence on SNS, GNS and GWS in ZY-RILs population (Fig. 2i-j), while the differences for PH, SN, SL, SW and TKW between the NIL pairs was not significant (Fig. 2d-h), suggesting that QSns.ucas-5B combined with developed InDel marker had potential application value in wheat breeding.

As one of three major components affecting wheat yield, grain number per spike is ultimately determined by spikelet number per spike. In the present study, by assessing SNS in a RIL population among nine environments, we found and validated a stable QTL, QSns.ucas-5B, which was also significantly correlated with GNS and GWS. Therefore, increasing spikelet number could be considered as an effective strategy for increasing GNS and GWS, and hence the grain yield.

Comparison Of Constructed Genetic Linkage Maps With Previous Reports

QTL analysis is an effective and widely used strategy in the genetic dissection of complex traits in wheat, and high-density genetic maps play a crucial role in the accuracy of QTL mapping. In the present study, we constructed a high-density genetic map containing 99,031 SNPs with a total length of 6,280.62 cM using the wheat660 SNP array (Fig. 1). Compared with another genetic map constructed with SNPs of the wheat660K array, which included 119,566 markers, 4,959 bin markers and a total length of 4,424.4 cM (Cui et al, 2017), the genetic map constructed in this study contains fewer SNPs but more bin markers (6,291 bin markers), resulting in a higher total genetic distance (Table 1). The average genetic distance per bin markers in the two studies did not differ much (1.0 cM vs 0.9 cM). The much longer map length in this study may be due to the higher genetic polymorphism between the parents: ZN17 (a modern cultivar) and YBM (a landrace). The genetic positions of mapped markers were relatively consistent with that in the wheat genome assembly on most of the chromosomes (Fig. S1), indicating that the genetic map constructed in the study was accurate and credible, and could be effectively used for QTL analysis.

It is worth mentioning that similar to heavily distorted distribution of markers in the 1BS/1RS region because of the linkage disequilibrium of 1RS, the markers on the short arm of chromosome 7B also showed distorted separation and were deleted when the construction of the genetic map. To exam the reasons for the distorted segregation of markers on chromosome 7B in the ZY-RILs population, we analyzed the pedigree of both parents and found that one of the ancestral parents of ZN17 is Zhoumai 11, which carries the 1BL.1RS translocation and the stripe rust resistance gene YrZH84 (located on chromosome 7BL), both inherited from ‘Zhou8425B’, a widely used elite parent of Chinese wheat (Li, 2006). Previous studies showed that all the markers (more than 40 markers) closely linked to YrZH84 were dominant in the F₂ population from the cross of Zhou8425B × ‘Chinese Spring’ (Yin et al. 2009). We hypothesized that the chromosome 7B of Zhou8425B and Chinese Spring were quite different. Moreover, an ancestral parent of Zhou8425B is ‘guangmai 74’, a hexaploid Triticale (AABBRR). Therefore, in combination with the results of this study, we speculated that Zhou8425B may also have an ectopic 7BL chromosome like 1BL.1RS translocation. Of course, more experiments are needed to test this surmise.

Comparison With Previous Studies

Like other traits related to yield, spikelet number of wheat is affected by multiple genes and significantly influenced by the environmental factors (Corsi et al. 2021). Only the stable QTL, expressing regardless of differences in environment, is convenient for marker-assisted selection strategy to improve wheat yield. In the study, we verified four stable QTL (QSns.ucas-1B, QSns.ucas-2D.1, QSns.ucas-3D.1, QSns.ucas-5B) for SNS in at least six of nine different environments using this RIL population.

QSns.ucas-1B was mapped at the beginning region of 1B flanked by SNP marker AX-109320638 and AX-111731227 (Fig. S3a) and physically located between 491.04 Mb and 548.75 Mb on chromosome 1BL (IWGSC RefSeq v2.1). Since the parent ZN17 is a 1BL.1RS translocation line and only the genetic map of 1BL was constructed in this study (Fig. S1), we speculated that the QSns.ucas-1B is likely to be located on the 1BS/1RS chromosome arm/fragment. Therefore, we selected 51 polymorphic SNPs uniformly distributed in the first 490 Mb interval of chromosome 1B, and reconstructed the full-length genetic map of 1B combining with the existing 213 bin markers. The result showed that these 51 SNPs were clustered together in the genetic map, and the genetic distance between them and the nearest bin marker was more than 80 cM (Supplementary Table S5). Then, the reconstructed 1B full-length genetic map was used to map SNS again, and the result indicated that a stable QTL was located on the upstream region (1BS) of the QSns.ucas-1B interval (Fig. S3b) and the increasing allele was derived by ZN17. In the previous QTL studies on SNS, several mapping parents were the 1BL.1RS translocation lines, but no QTL controlling spikelet number per spike was identified in the previous studies (Cui et al. 2012; Ma et al. 2019). To the best of our knowledge, QSns.ucas-1B is likely to be the first identified QTL controlling spikelet number per spike on 1BL.1RS translocation. However, the low genetic recombination in the 1BS/1RS translocation region complicated the mapping of interesting loci in this chromosome segment. This indicates care should be taken when genetic populations carrying alien chromosome segments are used for detecting QTL/genes of interest.

QSns.ucas-2D.1 was located in a 3.5 Mb region (35.1–38.6 Mb) flanking by markers AX-109836946 and AX-110744761 on chromosome 2DS. It was detected in all environments and explained the largest phenotypic variance (2.91%-37.71%) for SNS in the study. This QTL was located at the similar genetic region as the QTL reported by Ma et al. (2019) and Zhou et al. (2017). Notably, the region (35.1–36.7) of QSns.sau-2D for SNS (Ma et al. 2019) was overlapped with that of QSns.ucas-2D.1 detected in the study. Likely to QSns.ucas-2D.1, QSns.sau-2D also had higher PVE and could be detected in all environments (Ma et al. 2019). Furthermore, Zhou et al. (2017) found that the QTL for SNS (QTsn.cz-2D.2), which was also related to fertile spikelet number per spike (QFsn.cz-2D.2) and sterile spikelet number per spike (QSsn.cz-2D.1). In the flanking regions of QSns.sau-2D, QTsn.cz-2D.2, and QSns.ucas-2D.1 located a well-known gene Ppd-D1 (~ 36.2 Mb), which significantly affected spike length, spikelet number, and heading date in wheat (Okada et al. 2019). We analyzed the allele types of Ppd1 between the two parents according to the method described by Beales et al. (2007), and found that ZN17 belonged to Ppd-D1a allele, while YBM belonged to Ppd-D1b type. Taken together, we speculated that Ppd1 was the most promising candidate gene for QSns.ucas-2D.1.

QSns.ucas-3D.1, detected in six environments, was mapped to an interval flanked by markers AX-109499958 and AX-109914019 and physically located between 65.9 Mb and 68.1 Mb spanning 2.3 Mb on chromosome 3DS (IWGSC RefSeq v2.1). To the best of our knowledge, few QTL for spike-related traits were found on 3D chromosome. Li et al. (2021a) reported a novel QTL QTsn/Fsn.cib-3D associated with both SNS and fertile spikelet number per spike located in a 4.5-cM interval on chromosome arm 3DL flanked by AX-109429351 (550.3 Mb) and AX-110914105 (555.1 Mb). QSspn.cau-3D (Chen et al. 2020) and QSsn.cau-3D (Zhou et al. 2017) for sterile spikelet number in total were located in nearly the same genomic region (~ 87.6–96.5 Mb) on 3DS. QSc.cau-3D.1 with a LOD score of 6.65 explained 11.12% of spike compactness variation, sharing identical confidence interval with QSl.cau-3D for spike length (~ 30.4–60.0 Mb) reported by Xu et al. (2022). Thus, QSns.ucas-3D.1 identified in the study appeared to be a novel locus located in the downstream and upstream of QSc.cau-3D.1 and QSspn.cau-3D, respectively.

QSns.ucas-5B was identified in the region of 542.4-552.1 Mb flanking by SNP markers AX-110648717 and AX-109930199 on 5BL chromosome. Ma et al. (2007) have mapped a QTL, QSpn.nau-5B, for SNS to an interval (480.5-507.3 Mb, flanked by markers Xgwm499 and Xgwm639) close to that of QSns.ucas-5B. Subsequently, Cui et al. (2012) found that QSpn.WJ.5B.2 confirmed the QTL for SNS detected by Ma et al. (2007). Comparison of physical position of reported QTL for SNS indicated that QSns.ucas-5B identified in the study may be a new locus. To validate the effect of QSns.ucas-5B, we evaluated the phenotypes with the near-isogenic lines at QSns.ucas-5B locus in the field. The results showed that the elite haplotype from ZN17 than that from YBM significantly increased SNS, ranging from 1.31 (6.74%) to 2.54 (13.17%), at multiple environments (Fig. 2c). Moreover, we found that this locus had no influence on PH, SN and seed shape, but moderate positive influence on GNS and GWS (Fig. 2d-k), suggesting that QSns.ucas-5B combined with the linkage marker had potential application value in wheat breeding.

In summary, we constructed a high-density genetic map of ZY-RILs population consisting of 99,031 SNPs with a total length of 6,280.62 cM. A total of 19 QTL for SNS were identified in nine environments. Notably, four QTL were identified in more than six environments with PVE of 2.91–37.71%. Among them, QSns.ucas-1B is in the 1RS/1BS region. QSns.ucas-2D.1 is associated with Ppd1. QSns.ucas-3D.1 is a novel QTL for SNS. QSns.ucas-5B is a new locus for SNS as confirmed in the NIL population, also associated with GNS and GWS. We designed a linkage maker InDel-SNS5B for MAS. This research will enhance the understanding of the genetic basis of SNS, facilitate MAS for SNS and cloning of gene(s) underlying QSns.ucas-5B in the future.

Funding

This work was supported by grants from the Strategic Priority Research Program of Chinese Academy of Sciences (grant no. XDA24010104-1) and China Postdoctoral Science Foundation (2022M710163).

Conflicts of interest/Competing interests

The authors declare that they have no conflicts of interest.

Availability of data and material

All data generated or analyzed during this study are included in the main text article and its supplementary files.

Code availability

Not applicable.

Authors’ Contributions

H-Q L and SZ conceived the project and revised the manuscript; SZ developed the mapping population; YS, ST, JN, SM, QL and HW carried out the experiments; YS analyzed data and wrote the manuscript; all authors assisted in revising the manuscript.

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

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Fig.S1.pptx
Fig. S1 Based on the physical locations of these mapped markers on the IWGSC RefSeq v2.1 genome, the marker order was relatively consistent with that in the wheat genome assembly on most of the chromosomes, except for part of chromosome 1A and 6B. The ordinate and abscissa represent the physical position and the genetic position of markers, respectively.
Fig.S2.pptx
Fig. S2 Frequency distribution and Shapiro-Wilk test indicated that spikelet number per spike exhibited normal distribution with the BLUP value of nine environments (P > 0.05).
Fig.S3.pptx
Fig. S3 The stable QTL on 1B may be located on the 1BS/1RS translocation chromosome. (a) and (b) represent the results of mapping spikelet number per spike using partial 1BL and 1B full-length genetic map, respectively. Colored lines represent environments (E1-9).
TableS16.xlsx
Tables S1-S6 Table S1 Detailed information of the nine planting environments Table S2 The detail information of the high-density map constructed in the study Table S3 The deleted SNPs located on 7B chromosome due to distorted segregation Table S4 Correlation coefficients analysis among the mean values of spikelet number per spike in nine environments Table S5 Effects of putative QTL for spikelet number per spike in individual environments Table S6 The reconstructed full-length 1B chromosome genetic map

Dissection and validation of a novel QTL controlling spikelet number on 5B in bread wheat

Status:

Version 1

Abstract

Figures

Key Message

Introduction

Materials And Methods

Mapping populations

Phenotype Evaluations And Statistical Analyses

Genetic Map Construction And Qtl Detection

Results

Linkage map construction

Phenotypic Performance

Qtl Analysis

Discussion

Comparison Of Constructed Genetic Linkage Maps With Previous Reports

Comparison With Previous Studies

Declarations

References

Supplementary Files

Status:

Version 1