Characterization of a major QTL for tiller number at the seedling stage in wheat landrace Yanda 1817

doi:10.21203/rs.3.rs-2063007/v1

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Characterization of a major QTL for tiller number at the seedling stage in wheat landrace Yanda 1817

https://doi.org/10.21203/rs.3.rs-2063007/v1

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Effective tillers contribute to yield through adjusting population structure and affect 1000-kernel weight and kernel number per spike, which are 3favorable for wheat (Triticum aestivum) improvement. Wheat landraces show strong tillering ability and thus considerable potential for utilization in breeding. In this study, tiller number under nutrient solution condition was evaluated for 269 wheat recombinant inbred lines and their parents: the modern cultivar Beinong 6 and the landrace Yanda 1817, which exhibit strong tillering ability. A major QTL, qTN-7B.1, significant in three independent experiments, was detected by linkage analysis and confirmed by sequenced-based bulked segregant analysis. Its positive allele was from Yanda 1817. Pathway enrichment among tiller-bud transcriptome data for the two parents suggested that tillering ability was associated with lignin biosynthesis and energy metabolism. Further combination of differentially gene expression analysis and variant calling identified 33 nonsynonymous SNVs on 20 DGEs. Three differentially expressed genes were identified in the qTN-7B.1region. In particular, TraesCS7B02G282100 encoded cinnamyl alcohol dehydrogenase crucially in lignin biosynthesis was considered candidate gene. Then four genetic markers were developed and the peak of qTN-7B.1 was narrowed between KASP513 and KASP516, and explained 11.04%–19.01% of the phenotypic variation and positively affected kernel number per spike but negatively affected 1000-kernel weight. An allelic frequency test of a natural population revealed that Hap2 (the Yanda 1817 haplotype) and a third haplotype (Hap3) were selected during wheat improvement. This study lays a foundation for map-based cloning of qTN-7B.1 and developed genetic markers showed potentially application in improving plant architecture though marker assistant selection.

tiller number

landrace

QTL mapping

RNA-seq

genetic markers

Common wheat (Triticum aestivum) is a valuable crop grown worldwide and contributes 40% of the dietary energy intake of the global population. Although remarkable progress in yield improvement has been achieved, it is estimated that yield must increase by more than 60% to feed the world’s population by 2050. Therefore, continued improvement and selection of high-yielding cultivars is an ongoing goal for wheat breeders.

Grain yield is a complex trait that is determined by 1000-kernel weight (TKW), kernel number per spike (KPS), and spike number per unit. Spike number per unit is strongly positively correlated with grain yield through adjusting the population structure and affects the other two yield components (Bulman and Hunt 1988; Frederick et al. 1999; Ishag and Taha 1974; Moragues et al. 2006a). Tillers refer to the branches in wheat that arise belowground or near the soil surface, and which are derived from the swollen nodes. Early-tillering shows great potential to ultimately form spikes. In contrast, the plasticity of spike formation from later tillers reflects long-term adaptation to the external environment and inter-plant interaction (Ishag and Taha 1974; McMaster et al. 1994; Sharma 1995).

Variation in functional genes that contribute to variability in tiller number has been extensively characterized in rice. MOC1 is a crucial regulator that controls tillering and branching in rice (Li et al. 2003). MOC1 encodes a GRAS family nuclear protein, which is an ortholog of tomato Lateral suppressor (Ls) (Schumacher et al. 1999) and Arabidopsis LATERAL SUPPRESSOR (LAS) (Greb et al. 2003). Plants of the moc1 mutant develop only a single culm without tillers owing to defective formation of tiller buds. In addition, the degradation of MOC1 is inhibited by SLR, whose degradation is triggered by gibberellin signaling (Liao et al. 2019). MOC3 exhibits phenotypes similar to those of the moc1 mutant but less severe. MOC3 directly binds to the promoter of FON1 and subsequently activates FON1 expression to control tiller bud formation and outgrowth (Shao et al. 2019). An additional two genes, LAX1 and LAX2, physically interact to control initiation of axillary meristems (Komatsu et al. 2003). In addition, genes that regulate tillering and branching through activation of plant hormone pathways have been reported. TB1 encodes a putative transcription factor carrying a helix–loop–helix type of DNA-binding motif that functions as a negative regulator for lateral branching in rice (Choi et al. 2012; Takeda et al. 2003). Its homolog BRC1/TBL1 in Arabidopsis is involved in auxin signaling (Aguilar-Martínez et al. 2007). The gene HTD2 encodes an esterase/lipase/thioesterase that negatively regulates tiller bud outgrowth in rice and htd2 results in significantly increased expression of HTD1, D10, and D3 (Liu et al. 2009). Furthermore, a suite of dwarfing genes, such as D10 (Arite et al. 2007; Zhang et al. 2010), D3 (Ishikawa et al. 2005), and D14 (Arite et al. 2009), involved in strigolactone biosynthesis and affecting tillering has also been reported.

In wheat, the large size and complexity of the genome has hindered progress in functional genomic research. To date, most research on yield-related traits has rested on genetic mapping. Several monofactorial inheritance genes (tin1, tin2, tin3, ftin, and tin5) underlying tiller inhibition have been mapped on wheat chromosomes 1A, 2A, and 3A using specific genetic resources, such as mutants (Kuraparthy et al. 2007a; Si et al. 2022; Spielmeyer and Richards 2004; Zhang et al. 2013). However, a majority of studies have shown that tiller number is controlled by quantitative trait loci (QTLs) mapped on numerous chromosomes, such as 1B (Liu et al. 2020), 2B (Wang et al. 2016), 2D (Xu et al. 2017), 3A (Kuraparthy et al. 2007b), 4A (Wang et al. 2018), 4B (Li et al. 2020; Liu et al. 2018a; Liu et al. 2018b), 4D (Hu et al. 2017), 5A (Snape et al. 1985), 6A (Wang et al. 2018), 6B (Naruoka et al. 2011), and 7D (Wang et al. 2016). Furthermore, certain orthologous genes associated with tiller number in wheat have been isolated with a comparative genomics method. TaD27 on wheat group 7 homologous chromosomes plays a negative role in tillering by participating in strigolactone biosynthesis (Zhao et al. 2020). TaMOC1 has been mapped on wheat group 7 homologous chromosomes and is highly expressed in ears. Association analysis indicates that TaMOC1-7A HapH is a favorable haplotype for increased spikelet number per spike (Zhang et al. 2015).

Wheat landraces harbor rich genetic diversity that offers enhanced environmental adaptation for breeding applications (Jaradat 2013; Zhou et al. 2018). The landraces can display a strong capability for abiotic stress tolerance (Jafari-Shabestari et al. 1995; Reynolds et al. 2006; Zhou et al. 2017), disease resistance (Lu et al. 2020; Yao et al. 2020), and grain quality (Li et al. 2009b). Moreover, agronomic traits in landraces, such as number of tillers per plant, plant height, and root traits, may be superior to those of modern wheat cultivars (Bektas et al. 2016; Casañas et al. 2017; Zeven 1998). Mapping genetic regions for desirable traits (such as tiller number) in landraces and their application in marker-assisted selection is an effective strategy for wheat improvement (Belay et al. 1993).

The wheat landrace Yanda 1817 (YD) was a founder parent in the Yellow and Huai Wheat Production Zone (YHWPZ) that shows strong tillering ability. Genetic dissection of loci contributing to tiller number in YD and identification of distinct haplotypes is not only beneficial for wheat breeding, but also provides a foundation for map-based gene cloning. In the quest to attain this objective, the present study evaluates tiller number of 269 recombinant inbred lines (RILs) derived from a cross between YD and Beinong 6 (BN). Genetic mapping of major loci associated with tiller number by linkage mapping and bulk segregation analysis (BSA), development of genetic markers to delimit the genetic interval of major QTLs, and identification of different haplotypes of major QTLs by screening natural wheat populations was performed.

Plant material

The RIL population comprising 269 lines, derived from the cross of YD and BN, was used for tiller number evaluation and QTL mapping. BN is a breeding line in the northern Chinese winter wheat production region with a semi-dwarf habit and large grains. A mini-core collection (MCC) of 224 wheat accessions, consisting of both landraces and cultivars grown in China, was used to determine haplotypes of the major QTL qTN-7B.1. This population captures the genetic diversity of Chinese wheat grown before and around 2000 (Hao et al. 2020). An additional natural population was constructed using 419 wheat cultivars recently released (from 2016 to 2021) from the YHWPZ that are cultivated in the same ecological region as YD.

Phenotyping And Selection Of Bulks

The tiller number (TN) was evaluated at the seedling stage under nutrient-solution culture. Good-quality seeds of uniform size of each RIL were placed on filter paper in a petri dish that was kept moist with sterilized distilled water at 24°C for 2 days for germination. Each seedling was wrapped in gauze and secured in a hole drilled into a foam board. The foam board was floated on Hoagland’s nutrient solution (Hoagland and Arnon 1950) for 4 weeks, during which the root system was submerged in the solution. The nutrient solution was replaced every week to ensure stability in the nutrient supply. All plants were cultivated under a cycle of 24°C for 16 h (with light) and 16°C for 8 h (without light).

Tiller number was recorded for six plants on one foam board, which formed one replication. The mean of three replications was calculated for each line in one trial. Three independent trials (T1, T2, and T3) were established and the mean for each line across the three trials was also calculated.

Based on the mean of three phenotyping results, an equimolar concentration of DNA (100 ng) from each of 50 RIL individuals with the highest and lowest tiller number was pooled to create a high tiller number pool (BSA_H) and a low tiller number pool (BSA_L), respectively.

Qtl Mapping

The QTL analysis was performed using the inclusive composite interval mapping of additive and dominant QTLs (ICIM-ADD) method (Li et al. 2007) with QTL IciMapping v4.1 (Meng et al. 2015) using a previously constructed genetic map and the measured phenotype of each RIL in this study (Wu et al. 2015a). The step size was set to 1.0 cM. A reliable QTL was claimed to be significant at a logarithm of the odds (LOD) value of 2.5.

Resequencing And Exome Capture

Twenty seeds were mixed for each parent for DNA extraction and sequencing. High-quality DNA (2 µg) was used for library construction. The Illumina DNA library for each sample was digested, then subjected to end repairing, adapter ligation, PCR amplification, and 200–300 bp fragments were selected by 2% agarose gel electrophoresis. These libraries were used to generate 200 bp paired-end reads by sequencing on the Illumina HiSeq 2000 platform (BGI, Shenzhen, China).

A total of 4.5 mg genomic DNA from each pool was fragmented into 200 bp for library construction. The fragments were end-repaired, adapter-ligated, and hybridized to exome capture probes. These qualified hybrids were sequenced using the Illumina HiSeq Nova platform to generate 150 bp paired-end reads (Tcuni, Chengdu, China).

Bsa Data Analysis

Raw sequencing reads were processed using fastp (v0.12.4) (Chen et al. 2018) to remove low-quality reads and adapters. BWA (v0.7.16) (Heng et al. 2009) was applied to align the high-quality reads to the wheat ‘Chinese Spring’ genome assembly (IWGSC RefSeq V1.0) with the default parameters. SAMtools (v1.9) (Li et al. 2009a) and GATK (v4.0.10.1) (Mccormick et al. 2015) were used to call variants, and bcftools (v1.9) was applied to filter high-quality variants with the criteria QUAL > 30 and DP > 5.

The variants between the two pools were filtered based on an allele frequency > 0.7 or < 0.3. The statistical model varBScore (Dong et al. 2020b) was applied to calculate the varBScore in sliding windows, which comprised ten adjacent single-nucleotide polymorphisms (SNPs) and five SNPs were used as one step. The top 5% genomic regions with the highest varBScore were selected as candidate regions associated with the phenotypic variation. Finally, the genomic regions significantly associated with tiller number were evaluated by the QTL-seq method (Takagi et al. 2013) for confirmation.

Molecular marker development and refining qTN-7B.1

To refine the qTN-7B.1 genetic region, molecular markers within the physical region of qTN-7B.1 were developed based on SNP mining derived from resequencing data of the parents. Kompetitive allele-specific PCR (KASP) markers (Table 1) were designed based on the sequences flanking the SNPs (from IWGSC RefSeq v1.0) that differed between the two parents. For each 10 µl PCR mixture for the KASP genotyping procedure, PCR thermal cycling was performed in accordance with the protocol of a previous study (Rasheed et al. 2016). The primers were synthesized by Thermo Fisher Scientific (Shanghai, China).

Table 1

Primers for developed KASP markers at *qTN-7B.1* interval. The Tail is the unique nucleotide oligos that are essential for fluorescence signal stimulation.
Marker ID	Primer for detection of YD allele	Primer for detection of BN allele	Common primer
KASP487	Tail1-CCTGTACAGCGCTGCAGGCGCCC	Tail2-CCTGTACAGCGCTGCAGGCGCCT	TACGTCTAGCATTACTCGCCAAC
KASP509	Tail1-AGGAGGGCCTCCACCCCGGTCGT	Tail2-AGGAGGGCCTCCACCCCGGTCGC	TATTTTATCAAGAATATTGCACG
KASP513	Tail1-TGTAACTAGTACTAGGGTGTTCA	Tail2-TGTAACTAGTACTAGGGTGTTCG	CACCTCCTTAAGGGAACACGGAC
KASP516	Tail1-TTTCTTTTCTTTGTCAGCTGCAA	Tail2-TTTCTTTTCTTTGTCAGCTGCAC	TCTCTTCCTCTTGAGACACTGTA

All 269 RILs were genotyped with the developed markers. A new genetic map of chromosome 7B was constructed by anchoring the genotypes of the developed markers in the previous genotypes using Joinmap v4.0 (Ooijen 2006) based on Kosambi’s mapping method (Kosambi 1943). The QTL was reanalyzed using the ICIM-ADD method with QTL IciMapping 4.1 software.

Statistical analysis

The tightly linked markers were used for screening natural accessions to identify the haplotype frequencies of qTN-7B.1 in natural populations and to calculate the effects of qTN-7B.1 on TKW and KPS. Significance tests were conducted using one-way analysis of variance (ANOVA) with IBM SPSS Statistics 22.0 at the 5% significance level (p < 0.05). The phenotype means under multiple environments for the two traits of RILs were obtained from previous studies (Wu et al. 2015a; Wu et al. 2015b).

Tissue Collection, Rna Extraction, And Rna-sequencing

A tiller bud of each parent was sampled from the nutrient solution-cultured seedlings after 1 month for RNA-sequencing analysis. Tiller buds were collected from seedlings from all three biological replications used for phenotype evaluation and were mixed as one replicate. Samples were collected from each of the three trials as three independent biological replications (designate BNtiller_1, BNtiller_2, BNtiller_3, YDtiller_1, YDtiller_2 and YDtiller_3). The samples were immediately frozen in liquid nitrogen and stored in an ultra-low-temperature refrigerator (− 86 ℃) until use.

Total RNA was extracted using an RNA extraction kit (TIANGEN Biotech, Beijing, China) in accordance with the manufacturer’s instructions. The total RNA was electrophoresed in 0.7% agarose gel to check for genomic DNA contamination. The RNA concentration was measured with a NanoDrop spectrophotometer (Thermo Scientific). The RNA integrity (RIN value > 7, and 28S/18S ratio = 1.8–2.0) were measured with an Agilent 2100 Bioanalyzer. Total extracted RNA samples were labeled and stored at − 80°C for downstream experiments.

The cDNA libraries were constructed using the Illumina seq RNA Sample Preparation Kit (NEB, Ipswich, MA, USA) following the manufacturer’s instructions. The cDNA libraries were sequenced (BGI, Shenzhen, China) using the Illumina HiSeq platform.

Screening of candidate genes for qTN-7B.1

Clean reads were obtained after performing filtering and quality control on the raw sequencing data with fastp software (Chen et al. 2018). The clean data were aligned with the wheat reference genome (IWGSC RefSeq v1.0) using the HISAT2 package (Kim et al. 2015) with the default parameters. The fragments per kilobase per million mapped fragments (FPKM) was utilized as an indicator of gene expression (Garber et al. 2011).

Annotated genes (from IWGSC RefSeq v1.0) in the qTN-7B.1 confidence interval that showed significant differential expression between the two parents were selected as candidates. However, the flanked genetic markers wsnp_Ex_c52527_56097039 and 7ABD_wsnp_be518436B_Ta_2_3 was mapped on chromosome 7D and not in order in CSRef v1.0, respectively. Therefore, an enlarger region between genetic markers wsnp_Ex_c5830_10233776 and wsnp_Ex_c5270_9324025 were used for search candidate genes. Differential gene expression (DGE) was determined from the read counts for each gene based on empirical Bayesian estimation and a negative binomial model (Robinson et al. 2010). False discovery rate values less than 0.05 and concomitant log₂ (absolute fold change) values higher than 1.0 were adopted as credible screening thresholds for DGE. Then, information about sequence variations such as nonsynonymous SNV on DGEs was further applied for filtering candidate genes.

Phenotype evaluation

The tillering ability of YD was stronger than that of BN at several developmental stages or under different growth environments. In the field, the mean tiller number of YD and BN was 34.17 (range 27–39) and 23.38 (range 15–29) at the seedling stage (Feekes’ scale 29). In the greenhouse, the mean tiller number of YD and BN seedlings were 5.7 (range 2–7) and 2.46 (range 1–3), respectively (Fig. 1).

The distribution of tiller number of the RILs in the three trials exhibited an asymmetric normal distribution (Fig. 2), with most lines producing a low tiller number. The pairwise comparisons of tiller numbers of the RILs among the three trials were significantly correlated and exhibited a moderate correlation coefficient, ranging from 0.222 (T1 vs. T3) to 0.365 (T2 vs. T3) (p < 0.05; Table 2).

Table 2

QTL mapping of tiller number in Beinong 6/YD 1817 RIL population
Trait	QTL	Chromosome	Position of QTL peak	Left marker of QTL peak	Right marker of QTL peak	LOD	PVE(%)	Add
T1	qTN-3B	3B	24	wsnp_Ra_rep_c69820_67401482	gwm533.1	3.62	8.97	0.4266
T1	qTN-7B.2	7B	90	wsnp_Ex_rep_c66351_64532511	wsnp_CD454314B_Ta_2_1	5.51	14.02	0.5339
T1	qTN-7B.1	7B	102	Xgwm333	2ABD7ABD_wsnp_BE443010B_Ta_2_1	6.55	12.91	0.4393
T2	qTN-7B.1	7B	105	2ABD7ABD_wsnp_BE443010B_Ta_2_1	7ABD_wsnp_be518436B_Ta_2_1	4.37	10.21	0.3144
T2	qTN-6B	6D	138	wsnp_Ex_c13188_20825019	wsnp_Ex_c14691_22765150	2.77	6.13	0.2431
T3	qTN-4A	4A	21	wsnp_Ex_c41313_48161689	wsnp_Ex_c26740_35969367	2.66	0.73	0.1678
T3	qTN-2B	2B	25	wsnp_Ku_c23305_33210628	wsnp_Ku_c34759_44069854	4.09	1.16	0.2115
T3	qTN-7B.1	7B	104	Xgwm333	2ABD7ABD_wsnp_BE443010B_Ta_2_1	45.01	18.89	0.8521
Mean value	qTN-7B.1	7B	104	Xgwm333	2ABD7ABD_wsnp_BE443010B_Ta_2_1	27.08	11.87	0.6

Qtl Mapping

A total of six QTLs on chromosomes 2B, 3B, 4A, 6D, and 7B (with two QTLs) were detected in the three trials and from the mean value, and were designated qTN-2B, qTN-3B, qTN-4A, qTN-6D, qTN-7B.1, and qTN-7B.2 (Table 2).

Among these QTLs, The positive allele of “qTN-7B.2” was from BN, whereas the other five positive alleles of identified QTLs were from YD. Among these QTLs, qTN-7B.1 was stably detected in T1, T2, T3, and Mean value, and explained 12.91%, 10.21%, 18.89%, and 11.87% of the phenotypic variation, respectively. The QTL peak was located between the genetic markers Xgwm333 and wsnp_BE443010B_Ta_2_1 in T1, T3, and Mean value, which corresponded to the physical region of 475646000–518919079 bp in the IWGSC RefSeq 1.0 genome assembly. In addition, qTN-7B.1 identified in T2 was a subtle shift, which was located between 2ABD7ABD_wsnp_BE443010B_Ta_2_1 and 7ABD_wsnp_be518436B_Ta_2_1 and corresponded to the physical region of 518919079–525133262 bp. Therefore, “qTN-7B.1” was considered to be a major QTL owing to its stability and significance in all three trials and Mean value. An additional five QTLs (qTN-3B and qTN-7B.2, qTN-6B, and qTN-2B and qTN-4A) were detected in T1, T2, and T3, respectively.

Bsa Analysis

Genomic DNA of the two parents and two DNA pools were sequenced by whole-genome resequencing and exome capture, respectively. A total of 260 Gb sequencing reads were generated, with an average of 77 Gb data (average 7.28×) per parent and 33 Gb data per pool (Table S1).

All clean reads of the parents and DNA pools were properly paired and successfully mapped to the wheat reference genome (IWGSC RefSeq v1.0). In total, 4577 variants were detected between the two pools (Fig. S1). In particular, one core region with clustered variants was located at 516,000,000–545,000,000 bp of chromosome 7B, which corresponded to qTN-7B.1.

The genomic regions with the top 5% highest varBScore were determined to be candidate regions associated with tiller number variation (Table S2). A total of 45 sliding windows on chromosomes 1B, 2A, 2B, 3A, 3B, 4B, 5A, 5B, 6A, 7A, and 7B that contained 438 variants were detected (Fig. 3). The highest varBScore (538527382) was located in the genomic region 673248463–673248508 on chromosome 4B. Two genomic regions with a varBScore in the top 5% were located near identified QTLs: 524229915–525135465 on chromosome 7B with a varBScore of 31280858 was within qTN-7B.1; and 49084553–49515985 on chromosome 2B with a varBScore of 44861399 was within qTN-2B.

The QTL-seq method was used to validate the results with the varBScore algorithm. In total, 8131 variants with the top 0.05 allele frequency difference on chromosomes 1A, 1D, 2B, 2D, 3A, 3B, 4A, 4B, 4D, 5A, 5D, 6A, 6D, 7A, 7B, and 7D were identified (Fig S2). The significant regions around qTN-2B and qTN-7B.1 identified by varBScore were also covered by the QTL-seq method, which ranged from 42137236 to 58298327 bp on chromosome 2B and 220297741 to 612305075 bp on chromosome 7B.

Enriched Potential Pathways Associated With Tillering Ability

The DGE analysis identified a set of 2806 and 2853 up-regulated genes for BN and YD, respectively. To evaluate the potential functions of genes associated with tillering ability, gene ontology (GO) enrichment analyses were conducted. A total of 431, 2469, and 1068 GO terms were enriched among 3514, 3537, and 3161 DGEs for the cellular component, molecular function, and biological process categories, respectively. The top five enriched categories were nutrient reservoir activity, manganese ion binding, ubiquitin-protein ligase binding, ubiquitin-like protein ligase binding, and nuclear chromatin (Fig. S3).

The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to identify the metabolic pathways that may be fully or partially represented by annotated DGEs. A total of 1856 DGEs were assigned to 137 KEGG pathways (Fig. S4). Among these pathways, metabolic pathways, biosynthesis of secondary metabolites, and plant–pathogen interaction were the three most highly enriched KEGG pathways.

Among metabolism pathways, the phenylpropanoid biosynthesis pathway was the most highly enriched among the DGEs, followed by starch and sucrose metabolism, and carbon metabolism.

DGEs in qTN-7B.1

The confidence interval cross 133Mb (between 435789094-bp to 566605694-bp) of qTN-7B.1 was used to search candidate genes. A differential expression analysis of the RNA-sequencing data from the tiller bud for both parents was performed (Table S3). Thirty-eight showed significantly differential expression between BN and YD. Among these DGEs, 9 and 29 DGEs were up-regulated in YD and BN, respectively.

Next, sequence variation among the DGEs was surveyed to identify the most likely candidate gene. Moreover, 305 nonsynonymous SNV were identified in qTN-7B.1 region, where 44 were distributed on 20 DGEs (Table S4).

Marker Development And Anchoring In Genetic Map

Four KASP markers (KASP487, KASP509, KASP513, and KASP516) were developed based on SNPs mined from whole-genome resequencing of the parents. Based on these genotypes of the RILs, a new genetic frame map with 110.078 cM of chromosome 7B was constructed by anchoring the four KASP markers to the prior 71 90K SNP markers. All four new genetic markers were mapped between Xgwm333 and 2ABD7ABD_wsnp_BE443010B_Ta_2_1, and the genetic positions were consistent with their physical positions. The QTLs for tiller number were recalculated and the QTL peak for qTN-7B.1 was remapped between KASP513 and KASP516. The genetic distances of qTN-7B.1 to KASP513 and KASP516 were 0.207 cM and 0.403 cM, respectively. This newly detected QTL explained 11.04%, 19.01%, and 13.34% of the phenotypic variation in T2, T3, and Mean value, respectively (Fig. 4).

Genetic effects of qTN-7B.1 on TKW and KPS

One-way ANOVA was used to examine the genetic effects of qTN-7B.1 on TKW and KPS based on the genotypes of the marker KASP513 in the RILs. Significant differences in TKW and KPS were observed between two alleles for qTN-7B.1. However, the genetic effects of the two traits were opposite. The QTL qTN-7B.1 increased KPS by an average of 1.24, whereas TKW decreased by 1.24 g on average (Table 3).

Table 3

The genetic effects of *qTN-7B.1* on KPS and TKW.
Trait	qTN-7B.1 allele	N	Mean value	Sd	Minimum	Maximum	F value	p-value
KPS	qTN-7B+	127	47.02	5.2	36	64	4	0
KPS	qTN-7B-	128	45.78	4.7	34	60
TKW	qTN-7B+	127	33.51	4.2	24	45	5.5	0
TKW	qTN-7B-	128	34.75	4.3	26	46

Haplotype frequencies of qTN-7B.1 during wheat improvement

The qTN-7B.1 haplotypes in two wheat natural populations were determined using genotypes of the co-segregating markers KASP513 and KASP516. Three haplotypes were detected in the MCC, which were designated Hap1 (YD haplotype, KASP513^YD/KASP516^YD), Hap2 (BN haplotype, KASP513^BN/KASP516^BN), and Hap3 (third haplotype, KASP513^BN/KASP516^YD). In all 232 wheat accessions, 62 (26.72%), 128 (55.17%), and 42 (18.10%) accessions harbored Hap1, Hap2, and Hap3, respectively. Among these accessions, 53, 71, and 13 landraces harbored Hap1, Hap2, and Hap3, comprising 38.69%, 51.82%, and 9.49%, respectively. Accordingly, 9, 57, and 29 cultivars harbored Hap1, Hap2, and Hap3, comprising 9.47%, 60.00%, and 30.53%, respectively (Fig. 5a Table S5).

Among newly released cultivars from the YHWPZ, no accessions were identified as Hap1, whereas 402 and 17 accessions were classified as Hap2 and Hap3, comprising 95.94% and 4.06%, respectively (Fig. 5b, Table S6).

Capacity of nutrient solution culture on distinction of early tiller number difference

Tillering is the foundation for spike formation and is important for determination of wheat population structure. Inter-spike and inter-plant competition occurs within wheat populations, and can be intense under various field conditions, such as soil fertility, sowing density, and light quality (Evers and Vos 2013; LAFARGE and HAMMER 2002; Rodríguez et al. 1999; Sparkes et al. 2006). Tillering ability at an early developmental stage is advantageous for growth and development, and results in less mortality (Klepper et al. 1982). In particular, early-appearing tillers may be responsible for more than 87% of the grain yield (Tilley et al. 2019). Therefore, stable genetic improvement of number of early tillers is important to explore the genetic basis of plant spike formation and yield. In the present study, the tiller number under nutrient-solution culture was less than that in a field environment, which is probably because insufficient energy was available for the growth of later-appearing tillers. As a result, the tillers counted in this study were only the main culms for spike formation. However, a significant difference in tiller number between the two parents was observed and the phenotypic variation exhibited among the RILs was favorable for genetic study.

Bsa Is Satisfactory For Qtl Mapping But Is Sensitive To Background

Conventional QTL mapping relies on genetic populations that require multiple generations of selfing and high-density genetic linkage maps. Recently, mapping of genomic regions associated with phenotypic variation in a segregating population through BSA has been extensively studied in plants (Tiwari et al. 2016; Vogel et al. 2021). Previous studies have demonstrated that BSA is extremely powerful for rapidly mapping traits controlled by a single gene or major genes (Abe et al. 2012; Cao et al. 2021; Ji et al. 2021; Lu et al. 2020; Takagi et al. 2015). In the present study, qTN-7B.1 was identified by linkage analysis and BSA to be a credible (high LOD) and stable (detected in multiple environments) QTL. However, much more candidate regions were detected by BSA analysis than linkage mapping, possibly because of the smaller effect of these regions, or these regions were false positives. Simultaneously, VarScore of qTN-7B.1 identified by BSA was slightly lower than other two genomic region on 2B and 6A that not mapped by linkage analysis, indicated that the estimation of significance through values of peaks was not accuracy. Overall, these results indicated that BSA was less powerful for mapping complex traits because it was much more sensitive to the genetic background. Moreover, we observed that qTN-7B.1 showed medium genetic effects and no other stable QTL was detected, which is probably because the phenotypic variation of tiller number under the nutrient-solution culture system was more limited than that in the field environment.

In this study, fewer candidate SNPs were identified by the varBScore algorithm than by the QTL-seq method. A distinct genomic region around qTN-7B.1 was identified by the varBScore statistical method but not by the SNP index, indicating that varBScore may simplify the background noise to a certain extent, even for complex traits (Dong et al. 2020a).

qTN-7B.1 was novel and uncharacterized

Previous studies have reported that QTLs for tiller number on wheat chromosome 7B are associated with the plant response to heading date. Genetic mapping of progenies of Chinese Spring with a 7B substitution line identified a genomic region around genetic marker e1 that was significantly associated with tiller number and that also affected ear emergence date under continuous light (Law 1967). An ortholog of the FLOWERING LOCUS T (FT) gene is located in a similar region of chromosome 7BS, which has been reported to be associated with heading date (Bonnin et al. 2008; Chen et al. 2020). Therefore, the e1 marker may be tightly linked with FT, which is located ~ 400 Mb from qTN-7B.1. A tiller inhibition gene, tin4, was mapped on the short arm of chromosome 7 in Triticum urartu (Si et al. 2022), which differs from the present study. It was notable that TraesCS7B02G285500, which is homologous to rice MOC1, was located in the qTN-7B.1 region. However, this gene showed extremely low expression (based on RNA-sequencing data) in the tiller bud of both parents. Accordingly, the variants between the two parents showed a low impact on gene function (data not shown). Therefore, TaMoc-1B was excluded as a potential candidate gene for qTN-7B.1. Hence, we conclude that qTN-7B.1 is a novel QTL for tiller number that encodes an uncharacterized gene.

Lignin biosynthesis-related genes might be the candidate underlying qTN-7B.1

The tiller node is a storage tissue that produces branches. In wheat, lignin biosynthesis and cellulose metabolism were identified as the main pathways associated with tillering ability (Wang et al. 2019). In the current study, the phenylpropanoid biosynthesis, starch and sucrose metabolism, and carbon metabolism pathways were most highly enriched among DGEs, indicating that lignin biosynthesis and energy metabolism were important for tiller development.

The expression levels of the causative genes are always altered with mutations, which provides a basic theory that genes that show differential expression and potentially high-impact sequence variation are the most probable candidates for QTLs (Barrero et al. 2015; Huang et al. 2020; Nakamura et al. 2011; Zhang et al. 2022). In the current study, twenty DGEs harboring nonsynonymous SNVs were detected in the qTN-7B.1 region. One gene TraesCS7B02G282100 is orthologous to rice OsCAD5 (Os08g0270400), which encodes a cinnamyl alcohol dehydrogenase and is a crucial gene in lignin biosynthesis (Anterola and Lewis 2002). Relevant studies show that genes affect tiller number by changing lignin content (Hyles et al. 2017). One nonsynonymous SNPs that altered amino acids in their conserved domains with a severe impact on gene function was also found. Hence, it was consistent that the lignin metabolism pathway was associated with tiller number and TraesCS7B02G282100 was the most likely candidate gene.

Low Tiller Haplotype And A Covered Haplotype Were Utilized In Wheat Breeding

Certain previous studies indicate that a high tiller number in a wheat landrace is a less desirable trait for breeders than large spikes because most tillers do not form spikes or form short spikes (Ehdaie and Waines 1989). However, a stronger tillering ability is positively associated with a well-developed root system and biomass, which confers superior stress tolerance (Akman et al. 2017; Bektas et al. 2016; Blum et al. 1989; Li et al. 2019; Moragues et al. 2006b; Narayanan and Vara Prasad 2014). The Hap1 haplotype has almost disappeared in the progression from landraces to modern cultivars. One probably reason was that high tiller number allele qTN-7B.1 was selected off from “green revolution” that plants with more tillers and high plant height were sensitive to lodging especially under high fertilization condition, as well as that qTN-7B.1 is possibly disadvantageous for other traits, such as 1000-kernel weight. Therefore, qTN-7B.1 was a positive allele for improving population structure, in particular under abiotic stress condition. However, further assessment is needed to validate this hypothesis. Importantly, the two strong linked genetic marker developed in this study with more “significance” to tiller number showed potentially application in marker assistant selection.

Author contribution statement

ZL and TC conceived the study. JY and YZ designed the experiments. SZ performed solution culture experiment and collected the tiller number phenotype data. JY and YZ developed genetic markers and analyzed the genotyping data. YZ and WG conducted gene expression analysis. JY and YZ wrote the manuscript. All authors read and approved the manuscript.

Data availability

The phenotypic, genotypic, re-sequence, exon capture and transcriptome data generated and analyzed in current study are available from the corresponding author on reasonable request.

Acknowledgments

This research was supported by The Project of Self-innovation of Henan Agricultural Sciences (2021ZC03, 2022ZC01) and The Special Fund for Henan Agricultural Research System (S2010-01-G03). We also acknowledge Dr. Zhongxu Chen (TCuni, Chengdu, China) for providing exon capture assay and analysis of sequence data.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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FigS1.jpg
Fig S1 Density of SNPs distributed cross wheat 21 chromosomes. (a) SNPs distribution between two pools with frequency ≥ 0.3 and ≤ 0.7; (b) SNPs distribution between two pools that amended by parent’s direction.
FigS2.jpg
Fig S2. Scatter plot for G value cross wheat genome based on QTL-seq statistic.
FigS3..pdf
Fig S3. Annotation of the GO function of the DGEs between two parents.
FigS4.pdf
Fig S4. KEGG enrichment analysis of the DEGs between two parents.
TableS1.xlsx
Table S1. Statistics of re-sequence data for parents and exome capture data for two pools.
TableS2.xlsx
Table S2. The genomic intervals with top 0.05 highest VarScore.
TableS3.xlsx
Table S3. DGEs in two parents within qTN-7B.1 genomic interval.
TableS4.xlsx
Table S4. nonsynonymous SNVs and their annotation in qTN-7B.1 region.
TableS5.xlsx
Table S5. Genotypes results of molecular markers of KASP513 and KASP516in MCC.
TableS6.xlsx
Table S6. Genotypes results of molecular markers of KASP513 and KASP516in newly released cultivars from the YHWPZ.

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Characterization of a major QTL for tiller number at the seedling stage in wheat landrace Yanda 1817

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Abstract

Figures

Introduction

Materials And Methods

Plant material

Phenotyping And Selection Of Bulks

Qtl Mapping

Resequencing And Exome Capture

Bsa Data Analysis

Statistical analysis

Tissue Collection, Rna Extraction, And Rna-sequencing

Results

Phenotype evaluation

Qtl Mapping

Bsa Analysis

Enriched Potential Pathways Associated With Tillering Ability

Marker Development And Anchoring In Genetic Map

Discussion

Capacity of nutrient solution culture on distinction of early tiller number difference

Bsa Is Satisfactory For Qtl Mapping But Is Sensitive To Background

Low Tiller Haplotype And A Covered Haplotype Were Utilized In Wheat Breeding

Declarations

References

Supplementary Files

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