Characterization of Sugarcane QTLs for Tillering and stalks Quality through a Two-Way Pseudo-Testcross

doi:10.21203/rs.3.rs-3575441/v1

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Characterization of Sugarcane QTLs for Tillering and stalks Quality through a Two-Way Pseudo-Testcross

https://doi.org/10.21203/rs.3.rs-3575441/v1

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Sugarcane is grown worldwide from cultivars with complex genetic constitutions. Tillers and effective stalks are important agronomic traits of sugarcane that play important roles in yield. Using a two-way pseudo-testcross mapping strategy, we obtained a total of 172 F1 population of Yuenong73-204 (low tillering sugarcane variety) × CP72-1210 (ultra-high tillering germplasm), then, a total length of 1578.04 cM genetic map was constructed using 572 polymorphism SNPs genotyped by chip array. In addition, sprouts, tillers, and effective stalks of the F1 population were collected from four different environments. The total number of sprouts and tillers and the number of effective stalks were significantly positively correlated. The broad-sense heritability of tiller rate and effective stalk ranged 0.44, from 0.74. Quantitative trait loci (QTL) mapping showed 5 QTLs associated with tillers and 5 QTLs associated with effective stalks, one of which was identified in at least two environments. The phenotypic contribution rates of these (QTLs) ranged from 7.7–11.1% for tillers and 5.6–10.7% for effective stalks. A total number of 23 candidate genes, including 5 transcription factors and 5 proteins, were further identified in these QTLs intervals, among which the C2H2 transcription factor and E3 ubiquitin-protein ligase were the most significantly expressed in tillers and the effective stalk. These findings provide a reference for the genetic improvement of tillers and effective stalks of sugarcane.

Biological sciences/Biotechnology

Biological sciences/Plant sciences

quantitative trait loci

sugarcane

tiller

effective stalk

candidate genes

Sugarcane (Saccharum spp.) is a major industrial crop grown in the tropical and subtropical regions worldwide. It is cultivated mainly for sugar production and supplies > 70% of the world’s sugar(Meena et al., 2022). Modern sugarcane cultivars originate from an interspecific hybridization between S. officinarum (2n = 8x = 80; x = 10) and S. spontaneum (2n = 5x–16x = 40–128; x = 8). Approximately 70–80% of the genome is from S. officinarum, 10–20% is from S. spontaneum, and the remaining 10% of the genome has arisen from interspecific recombination (Garsmeur et al., 2018; Piperidis & D’Hont, 2020). Many desirable cane-yielding traits, such as single cane weight, number of tillers, and number of millable canes, and cane quality traits, such as sucrose and sugar yield, have been explored using these recent biotechnological tools (Meena et al., 2022). Tiller and effective stalk numbers are important agronomic traits for sugarcane yield. One of the most effective ways to improve sugarcane yield is to promote tillering and accelerate effective stalk development from tillers. Many researchers have found a positive relationship between yield and effective stalks or tillering, which is the main component of yield; that is, the more tillers and effective stalks, the greater the yield. The number of tillers and effective stalks increases and decreases synchronously, especially during ratooning years (Ming et al., 2002; Xue et al., 2020). However, research on tillering in sugarcane remains limited, particularly with respect to understanding the molecular regulation of these mechanisms (Qiu et al., 2018). Therefore, the genetic basis of these two traits and explored the associated genes will help to improve the tillers and effective stalks (Wang et al., 2023)

Thus, quantitative trait loci (QTL) mapping is an important tool to identify and locate QTLs along the genome, in order to understand the genetic architecture of these traits. This would be useful in sugarcane breeding programs (Brandão et al., 2010). Due to the complex polyploid genome, it is difficult to construct genetic linkage maps and (QTLs) mapping for sugarcane. With rapid molecular technology development, the Axiom Sugarcane100 K SNP array was used to construct a genetic map and identify QTLs related to sugarcane yellow leaf virus (SCYLV) resistance (You et al., 2019a). Based on the above SNP array, two high-density genetic maps of sugarcane varieties YT93-159 and ROC22 were constructed, six (QTLs) related to leaf blight (SLB) were identified, and nine transcription factors (TFS) and seven plant regulation growth (PRGS) genes in the main effect QTLs (qSLB-1) interval were highly expressed in YT93-159 (Wang et al., 2022). Using the above F1 population and a similar strategy, 26 tiller QTLs (PVE ranged from 4.27–25.70%) and a high PVE (25.70%)major QTL associated with ubiquitin-conjugating enzymes were identified. The tiller QTL is located on chromosome LG8 of the linked population (Wang et al., 2023).

The main goal of this study was to clarify the genetic basis of important sugarcane agronomic features, such as tillers and effective stalks, through a genetic map of the low-tiller sugarcane variety Yuenong73-204 by crossing it with an ultra-high-tiller germplasm, CP72-1210, using a two-way pseudo-testcross mapping approach. The entire genetic linkage map was created using array genotyping techniques, with an estimated length of 1578.04 cM. To assess trait exhibition/performance and variability, the phenotypes of sprouts, tillers, and effective stalks were investigated in four different environments. This information can be used to calculate board-sense heritability for particular features and to evaluate correlations between the number of seeds, which includes the entire value of sprouts and tillers, and the number of effective stalks. The next step of this research is to pinpoint the (QTLs) that have an impact on how these qualities appear. Examining the potential genes around these significant (QTLs), focusing on the TFs and proteins that might be involved in trait expression, is an important objective. The ultimate goal is to apply these discoveries to sugarcane genetic engineering, with the aim of improving the quantity and caliber of productive stalks, ultimately contributing to the overall objective of increasing sugarcane output.

In this study, genetic maps of Yuenong73-204 (low tillers) and CP72-1210 (high-tillering germplasm) were constructed using the Axiom Sugarcane 100 K SNP. The phenotypes of sprouts, tillers, and effective stalks of 172 F1 populations and their parents were investigated, and 5 (QTLs) for tillers and five for effective stalks were detected. and related candidate genes were explored. The goal was to apply these discoveries to sugarcane genetic engineering with the aim of improving the quantity and caliber of productive stalks, ultimately contributing to the overall objective of increasing sugarcane output.

2.1. Construction of F1 population

F1 population of Yuenong73-204 (low tillers) and CP72-1210 (high tillering germplasm) plant in March 2020 at Zhanjiang Academy of Agricultural Sciences experimental base (21^。27^, N, 110^。28^, E) and in March 2021 at Hongnan Village, Suixi, Zhanjiang (21^。24^, N, 109^。98^, E) were used in the field experiments. A random block design was used for the field experiments, irrigation equipment was used, and artificial fertilization was applied.

2.2 Determination of phenotypic heritability and correlation

Two agronomic traits, the tiller ratio and effective stalk (Grover & Sharma, 2016). were collected from 172 F1 populations of Yuenong73-204 (low tillers) and CP72-1210 (high tillering germplasm) in four environments.

$$\text{T}\text{i}\text{l}\text{l}\text{e}\text{r} \text{r}\text{a}\text{t}\text{i}\text{o} \left(\text{%}\right)=\frac{ total number of tiller}{number of sprouts}$$

The total number of sprouts of different varieties was measured, and at the end of tillering, the total number of tillers was measured. The effective stalk was accessed during the maturity stage of sugarcane, and a stalk height of more than 1m was counted. Phenotypic data for tillers and effective stalks were analyzed and visualized using Microsoft Excel 2016 and (IDM SPSS V25 software). The w-test value was used to test the normal distribution of phenotypic data using (GACD V1.2 software) (Vetter, 2017). Origin software was used to analyze and plot the correlation between the number of seeds and effective stalks of sugarcane. Board-sense heritability (H²) was calculated as follows:

H² = V_G/(V_G +$\frac{1}{\text{r}}$V_ε)

where VG and Vε are the estimates of genotype and environmental variance, respectively, and r is the number of environments.

2.3 SNP array genotyping and simplex marker selection

An SNP array containing 100 K probes was used (You et al., 2019a). SNP arrays specifically designed for sugarcane cultivars were used for the genotyping. These arrays contain probes that target single-nucleotide polymorphism (SNP) positions in the sugarcane genome. DNA samples from sugarcane plants or individuals were processed and hybridized to arrays. The (TaKaRa MiniBEST) Plant Genomic DNA Extraction Kit (Code No.9768) was used to extract DNA from the hybrid F1 population and its parents, and the concentration was set to 15 ng µL-1 for array genotyping by Thermo Fisher Scientific (Guangzhou, China) using the GeneTitan Multi-Channel Instrument Systalk (Affymetrix, Santa Clara, CA, USA) (Table 1).

Table 1

Array genotyping
Conversion Type	Annotation
Poly high resolution(PHR)	Three alleles in the genotypes
No minor homozygote(NMH)	One cluster is homozygous, and one is heterozygous for biallelic SNPs
Mono-high resolution(MHR)	Monomorphic across all samples
Call rate below threshold(CRBT)	A very low call rate
Off target variant(OTV)	Low-intensity cluster
Other	More than one problematic issue

Axiom™ Analysis Suite v5.2 made the genotyping clustering diagram and screened the samples with a call rate (CR) threshold of ≥ 90 in the NMH category. Based on the coding standards, GACD V1.2 (Zhang et al., 2015). In the 172 F1 population, male- (category A = B) and female-polymorphic (C = D) markers with a deletion rate of less than 20% were selected. In this study, a two-way pseudo-test cross strategy was used to construct mapping populations (the parental genotypes were ab×aa or aa×ab) (Grattapaglia & Sederoff, 1994). As single-dose markers only existed in one parent, the p values of the chi-square tests were p < 0.01. Markers that deviated significantly from the separation ratio of 1:1 in the population were excluded.

2.4 Linkage map construction

A two-way pseudo-test cross-mapping strategy is adopted. The male- (category: A = B) and female-polymorphic (C = D) high-quality single-dose markers were used to construct the genetic maps of Yuenong73-204 and CP72-1210 using the Kosambi mapping function with the following parameters: GACD V1.2 (Zhang et al., 2015).

The odd (LOD) value is 10.0 for grouping.

The nnTwoOpt algorithm was used for the sorting.

Sum of adjacent recombination frequencies (SARF) for rippling.

To assign LGs to HGs, Blast 2.11 was used to assign SNP markers to the chromosomes of S. Officinarum (LA-purple) according to the probe sequences of the markers (Wang et al., 2022). The genome sequence downloaded from website of http://sugarcane.zhangjisenlab.cn/sgd/html/index.html.

Finally, linkage groups (LGs) containing less than two markers were dropped, and the remaining LGs were used for QTL mapping. LGs were displayed using the R package Linkage Map View 2.1.2 (Ouellette et al., 2018).

2.5 QTL analysis and gene annotations

Markers located in the linkage group (LG) were used as genotype data, and the tiller rate and effective stalk of the hybrid F1 populations in the four environments were used as phenotypic values. The ICIM function of GACD V1.2 was used for QTL mapping, and the default LOD value was set to 3. After QTL mapping, an in-house Perl script was used to extract all the genes in the interval of the left and right markers of the main QTL with each extension of 1 Mb, and their structures and functions were searched on PlantTFDB 5.0, (http://planttfdb.gao-lab.org/), and NCBI (https://www.ncbi.nlm.nih.gov) (Balsalobre et al., 2017).

3.1 Correlation Evaluation and Phenotypic Heritability

Correlation evaluation involves assessing the degree of association between different traits or characteristics within a population of sugarcane plants. These results showed a mechanical link in which effective stalk efficiency was significantly influenced by the number of tillers and sprouts ( Fig. 1). Furthermore, the phenotypic data of F1 populations and w-test of tillering rate and effective stalk of F1 population in four ecological environments were 0.64–0.96, which was supported by the frequency distributions of the tiller rate and effective stalk. These results provide a strong basis for understanding the relationships between these important plant characteristics. ( Fig. 2, Table 2). From a genetic perspective, the first-year tiller rate, ratoon tiller rate, and effective stalk had heritability values of 0.62, 0.44, and 0.74, respectively ( Fig. 2, Table 2). Certain scores indicated that these features were largely determined by genetic factors, indicating a moderate-to-high level of heritability. A characteristic desired in plant breeding aimed at creating more robust strains is that these traits have moderate to high heritability, indicating that they are less vulnerable to environmental variability. The heritability results provided strong evidence that selective breeding interventions may be beneficial for maximizing these features.

During 2021 and 2022, the dataset recorded annual fluctuations across two distinct regions, HN and NKY. Table 2 shows that the ratoon tiller rate in NKY for 2020 (mean of 244.84) was substantially greater than that in HN 2021, HN 2022, and NKY 2021. This implies that either the NKY region in 2020 was more favorable for higher rates of ratoon tillering or that the year's particular farming or breeding techniques played a role in the increase. Comparing the effective stalk numbers, it was also found that HN 2021 had a mean value of 92,927, which was considerably greater than the means for NKY 2020, HN 2022, and NKY 2021. This notable disparity highlights the distinct features (genetic, environmental, and agronomic) displayed by the HN 2021 data points and has contributed to a notable increase in effective stalk numbers. When combined, these findings offer a sophisticated picture of how plant characteristics differ between seasons and regions, highlighting crucial factors to consider when it comes to genetic selection and agricultural practices. Additionally, they add to the larger body of knowledge regarding the ways in which multiyear and multilocation data can enhance and deepen our understanding of plant biology.

Table 2

Descriptive statistics of phenotype of the tillering rate and effective stalk
Phenotype	Environment	Min(%)	Max(%)	Mean(%)	CV(%)	W-test
Tillering rate	HN 2021	28.33	501.90	227.02	40.47	0.96
	HN 2022	14.91	565.13	90.14	83.78	0.64
	NKY 2020	66.67	566.67	244.84	36.7	0.67
	NKY 2021	20.00	275.00	102.73	61.09	0.90
Effective stalk	HN 2021	20000	183703	92927	32.30	0.98
	HN 2022	5926	351111	90538	53.51	0.92
	NKY 2020	7273	301819	80896	56.63	0.91
	NKY 2021	0	214545	73700	62.82	0.94

3.2 SNP array genotyping and simplex marker selection

The sugarcane genome study used highly effective and technologically advanced technology (SNP) array genotyping. A multi-category framework was used in this study to evaluate the quality of the genotyping results.

Our results show that the three high-quality categories (PHR (4.16%), NMH (25.27%), and MHR), addition to unqualified CRBT (0.40%), OTV (0.07%), and Other (19.62 percent) (50.47%). SNP in MHR have no polymorphism and belong to the same genotype; therefore, they cannot be used for genetic map construction. There were three genotypes (AA, AB, and BB) in the PHR category that did not conform to the genetic rule of simplex marker in the sugarcane F1 population. The remaining three categories, OTV, CRBT, and Other, were of poor quality and could be directly ignored as markers for constructing genetic maps. As shown in Fig. 3, the NMH category had only two clusters, which meets the requirements of single-dose markers. These included two genotypes: AA and AB or BB and AB. Notably, for the male CP72-1210 and female Yuenong73-204 sugarcane lines, high-quality single-dose markers with a call rate (CR) of > = 90% were identified. Through rigorous statistical analysis, these markers were further filtered to hold only single-dose markers with a p-value of less than 0.01, as determined by chi-square (χ2) testing.

3.3 Linkage map construction

Geneticists and breeders can gain an understanding of gene localization, inheritance patterns, and trait connections within the sugarcane genome by producing linkage maps. Genetic maps for the two sugarcane parent lines in this study, male CP72-1210 and female Yuenong73-204, were created using simple markers. A total of 459 markers were identified. As shown in Fig. 4, 364 of these were successfully mapped onto 76 linkage groups (LGs), spanning a considerable genetic distance of 1578.04 centimorgans (cM) at a marker density of roughly 3.0 cM per marker.

As modern sugarcane cultivars are aneuploid, and the ploidy of each group of homologous chromosomes is unknown, we cannot accurately genotype multiplex markers to construct HGs (Aitken et al., 2005). Using the homology between LGs and chromosomes of sugarcane relatives, we can theoretically divide LGs into 10 HGs, and the LGs belonging to one HG may be distributed on different chromosomes. Using BLAST, 459 (67.6%) of the 572 simplex markers from the two parents were anchored to the S. officinarum monoploid genome. According to the BLAST results, LGs belonging to the same chromosome were classified as a single HG (Fig. 4). According to previous studies, the homologous or allopolyploid sugarcane genome adds a significant layer of complexity to linkage map building and genotyping (Aitken et al. 2005, 2014). Because of the similarities between the 76 linkage groups (LGs) and the chromosomes of sugarcane genetic cousins, a clear workaround was used in the current study to theoretically classify the LGs into 10 homology groups (HGs) using BLAST analysis.

The diverse distribution of LGs among the various HGs is a startling aspect of this classification. In particular, the distribution of LGs was richer for HG01 and HG04 and somewhat sparser for HG05 and HG08. Likewise, the parental sugarcane lines under investigation, male CP72-1210 and female Yuenong73-204, showed evidence of unequal distributions. Both lines included 38 LGs, although they were unevenly distributed among HGs. The marker density and genetic distance covered in the data highlighted the extremely thorough mapping of the sugarcane genome. Significantly, no two neighbor markers on the LGs had a genetic distance greater than 35.0 cM, indicating that the markers were distributed evenly throughout the linkage groups. Because it allows for more thorough and precise trait association studies and improves the effectiveness of marker-assisted selection in breeding programs, evenness is a desirable feature in a genetic map.

3.4 QTL mapping of tillers and effective stalks in sugarcane

In our results significant discovery were the identified 5 QTLs associated with tillers, primarily in Yuenong73-204 (four QTLs) and CP72-1210 (one QTL). The phenotypic contribution rates (PVE) for these QTLs ranged from 7.7–11.1%, indicating moderate but significant contribution rates. Notably, significant phenotypic contributions of (qST-1),11.1%,(qST-2) 10.0%, and (qST-3) 10.5%, respectively (Fig. 5 (A,B, and C), Table 3), were made by the QTLs. These results indicated that these QTLs may be important manipulators of tillering characteristics in sugarcane breeding. Furthermore, the investigation revealed four potent stalk-related QTLs with PVE values varying from 5.6–10.7%. Two of these showed by QTLs, noteworthy PVEs of (qSES-1 ) 10.7% and (qSES-2) 10.3%, respectively (Fig. 5 (D), Table 3), were particularly startling. These QTLs were found in homology group 01 linkage group 57, where they were found at the same genomic locations in various environments (LG57 of HG01, Fig. 5D and 5E). The qSES-1/2 measure of environmental resilience indicated that these QTLs are consistent and stable across a range of circumstances.

Table 3

QTLs for tiller and effective stalk of F₁ population derived from Yuenong73-204 × CP72-1210
Environment	Parent	QTL Name	LGs	LeftMarker/RightMarker	LOD	PVE(%)	EffectF	EffectM
2022 HN	Yuenong73-204	qST-1	2	AX-171366753/AX-171249347	4.4	11.1	0.2269	-0.0872
2020 NKY	CP72-1210	qST-2	91	AX-171238167/AX-171247946	3.4	10.0	0.2767	0.0267
2021 NKY	Yuenong73-204	qST-3	68	AX-171328098/AX-171258628	3.2	10.5	-0.1396	-0.1095
2021 HN	Yuenong73-204	qST-4	65	AX-171263169/AX-171263169	5.1	9.7	0.2891	0.0363
2021 HN	Yuenong73-204	qST-5	55	AX-171253898/AX-171291058	4.1	7.7	-0.05	0.1379
2021 NKY	Yuenong73-204	qSES-1	57	AX-171307910/AX-171260872	4.8	10.7	-6167.7	4665.0
2020 NKY	Yuenong73-204	qSES-2	57	AX-171307910/AX-171260872	3.2	10.3	-4843.3	8429.1
2022 HN	Yuenong73-204	qSES-4	3	AX-171310731/AX-171242708	3.1	7.5	8258.9	10976.5
2022 HN	Yuenong73-204	qSES-3	3	AX-171293335/AX-171293885	3.2	5.6	11501.0	-4706.1
2021 NKY	CP72-1210	qSES-5	114	AX-171360015/AX-171360015	3.1	6.8	-12415.0	276.8

3.5 Screening Candidate Genes Related to Tillering and Effective Stalks.

In these results indicated the sugarcane such as qST-1 to qST-3 and qSES-1/2, by (QTLs), significant traits. However, 23 candidate genes that are potentially involved in the regulation of tillers and effective stalks, including 12 transcription factor (TF) genes (Table 4) and 13 protein genes (Table 5), were identified in the preliminary stages. 5 transcription factors have been identified. C2H2, NAC, Trihelix, bZIP, and C3H. C2H2 was found to be crucial in controlling the tillers and effective stalk features (Table 4), revealing the intricately coordinated genetic regulation that underlies these traits. The key involvement of the C2H2 transcription factor as a regulatory hub was demonstrated by its overlapping role in both tillering and effective stalk QTLs. Considering that C2H2 is highly expressed in the primary QTLs controlling effective stalks, it is plausible that it functions in a regulatory network that is essential for coordinating the interaction between tiller and stalk development, as demonstrated in (Tables 4). Its inclusion among several zinc finger protein types, which is recognized for its involvement in an extensive array of biological processes, supports this master regulatory role.

By comparing and annotating 1253 genes in the main QTLs region using the NCBI database of BLAST, 15 proteins related to tillering or effective stalks were identified. These 15 proteins belong to the ubiquitin-protein ligase CCCH domain-containing protein and Transducin/WD40 repeat-like superfamily proteins. It has been speculated that they may play an important role in the tillering and effective stalks of sugarcane. (Table 5). Our results on the genetic architecture of sugarcane were significantly enhanced by the discovery that ubiquitin-protein ligase is present in both tillering QTLs (qST-1-qST-3) and effective stalk QTLs (qSES-1/2). The significant function of ubiquitin-protein ligase as a protein element in the genetic networks governing sugarcane shape is highlighted by the high expression levels of this gene in the major tiller and effective stalk QTLs. It uses the ubiquitin-proteasome pathway to maintain the stability of different regulatory proteins to survive in an ever-changing environment, we consider that ubiquitin-protein ligase it can either repress or activate protein involved in tiller and effective stalk development.

Table 4

Functional annotation of transcription factors of candidate genes related to tillering and effective stalk.
QTL name	Gene name	Annotation
qST-1	Soffic.03G0018550-1A	C2H2 zinc-finger potein(C2H2-ZFP) transcription factors(C2H2) gfffffbgactorfactors(C2H2_)
	Soffic.03G0032060-1A	C2H2 zinc-finger potein(C2H2-ZFP) transcription factors(C2H2)
	Soffic.03G0030730-1A	Cystenine3Histidine(C3H) gene family (C3H)
	Soffic.03G0025570-1A	NAC transcription factor (NAC)
	Soffic.03G0031540-1A	Trihelix transcription factors(Trihelix)
	Soffic.03G0034390-1P	basic region leucine zipper (bZIP)
	Soffic.03G0034390-1T	basic region leucine zipper (bZIP)
qST-2	Soffic.04G0001180-1A	C2H2 zinc-finger potein(C2H2-ZFP) transcription factors(C2H2)
qST-3	Soffic.02G0013650-1A	C2H2 zinc-finger potein(C2H2-ZFP) transcription factors(C2H2)
qSES-1/2	Soffic.01G0055400-1A	C2H2 zinc-finger potein(C2H2-ZFP) transcription factors(C2H2)
	Soffic.01G0055400-1P	C2H2 zinc-finger potein(C2H2-ZFP) transcription factors(C2H2)
	Soffic.01G0055410-1A	C2H2 zinc-finger potein(C2H2-ZFP) transcription factors(C2H2)

Table 5

Functional annotation of the candidate genes related to sugarcane tillering and effective stalk.
QTL name	Gene name	Gene ID in NCBI	EVALUE	Gene Description
qST-1	Soffic.03G0033970-1A	ONM34247.1	0	ubiquitin-protein ligase
	Soffic.03G0033750-1A	PWZ11692.1	0	E3 ubiquitin-protein ligase HERC2/
	Soffic.03G0033750-1A	PWZ11692.1	0	E3 ubiquitin-protein ligase HERC2 [Sorghum bicolor]
	Soffic.03G0032900-1A	PWZ09532.1	0	putative E3 ubiquitin-protein ligase HERC1 [Zea mays]
	Soffic.03G0022270-1P	ONM38016.1	0	Zinc finger CCCH domain-containing protein 19 [Zea mays]
	Soffic.03G0018510-1A	XP_008675266.1	0	zinc finger CCCH domain-containing protein 13 isoform X1 [Zea mays]
	Soffic.03G0031250-1A	AQK98519.1	0	GYF domain-containing protein
	Soffic.03G0018500-1A	XP_002456070.1	0	Histone-lysine N-methyltransferase ATX4 [Sorghum bicolor]
qST-2	Soffic.04G0005780-1A	XP_002451611.2	0	putative E3 ubiquitin-protein ligase LIN-1 [Sorghum bicolor]
	Soffic.04G0005490-1A	NP_001352444.1	1×10–¹⁹⁹	Zinc finger CCCH domain-containing protein 13 [Zea mays]
qST-3	Soffic.02G0013310-1A	NP_001147953.1	0	ubiquitin-protein ligase
qSES-1/2	Soffic.01G0055150-1A	XP_023157429.1	1×10^− 220	Transducin/WD40 repeat-like superfamily protein [Zea mays]
	Soffic.01G0055400-1P	XP_002465203.1	6×10^− 57	zinc finger protein 2
	Soffic.01G0055410-1A	XP_002465204.1	2×10^− 65	zinc finger protein STAMENLESS 1 [Sorghum bicolor]

Overall, the identification of major QTLs and candidate genes associated with tillering and effective stalk development in sugarcane provides valuable insights into the genetic improvement of these traits and development of high-yield sugarcane varieties. These findings contribute to our understanding of the genetic mechanisms underlying these agronomic traits and can serve as a reference for future breeding efforts aimed at enhancing tillering and stalk development in sugarcane crops. The tillering ability determines the number of effective stalks in sugarcane, which is the main component of yield, especially for its perennial root characteristics (Qiu et al., 2018). A variety with good perennial roots must have a strong tillering ability and sufficiently effective stalks for many years to ensure yield.

Sugarcane is an important sugar crop that is mainly harvested from the aboveground stalks. Tillering is the key to the formation of sugarcane effective stalks, and the number of tillers is very important for sugarcane yield. Studies have shown that sugarcane has high heritability of tillering ability and effective stalks. (Ming et al., 2002) There was identified a QTL for effective stalk number (an agronomic trait positively correlated with stalk formation in sugarcane tillers). However, the marker density of the linkage map was very low. Later, some genes or loci that might be related to tillering were identified in sugarcane using linkage analysis and homologous gene cloning (Ming et al., 2002; Qiu et al., 2018). However, studies on the genetic mechanisms of sugarcane tillering are limited and relevant genes or loci that can be applied in breeding have not been reported.

Sugarcane is a highly heterozygous homologous or allopolyploid crop with a relatively complex genetic structure and genome, which is dominated by multiple quantitative trait loci (QTLS) and genomic regions, leading to slow progress in sugarcane-related gene mapping and excellent gene discovery. In response to the genomic analysis of crop polyploids, (K.K.W et al.) proposed the use of single-dose restriction fragments for relevant studies. The simplex marker represented a simplex allele in a homologous polyploid, and the hybrid parent F1 generation single-dose restriction fragments (an allele) were separated in a 1:1 ratio (presence/absence) in gametes (Wu et al., 1992). Therefore, simple markers (gametes) can be used to construct a genetic map of a parent.

Axiom 345

K SNPS (da Silva et al., 1995) and Affymetrix Axion 100 K SNPS (You et al., 2019a) are now widely used in sugarcane, Using SNP array genotyping and advanced high-throughput genotyping platforms to accelerate the detection of simplex markers (Balsalobre et al., 2017) and construct a density genetic map to locate QTLs. Several disease resistance and agronomic traits have been explored, including red rot resistance, SCYLV, leaf blight, mosaic disease resistance, and chlorophyll content (Banerjee et al., 2023; Lu et al., 2021; Wang et al., 2022; You et al., 2019b). In this study, QTLs were discovered, and candidate genes were screened for important agronomic traits of tillers and effective stalks in hybrid populations of Yuenong73-204 (low tillering) and CP72-1210 (high tillering). Sugarcane cross population, in order to ensure the high quality and accuracy of markers, the false hybrid F1 population ware removed. Later integration with the BIN function of GACD software led to the selection of fewer simplex markers (459) than existing sugarcane markers (da Silva et al., 1995; LIU et al., 2010; Wang et al., 2022),and the total length of the genetic map established using high-quality simplex markers was 1578.04cM.

The growth and development processes of sugarcane are affected by genotype, ecological environment, and other factors. To obtain phenotypic data as accurately as possible, field management measures such as fertilization (Castro et al., 2023) and irrigation (Brauman & Viart, 2016) should be planned and implemented, and multi-environment planting is necessary for the experiment. On this basis, data on the growth of plants, tillers, and effective stalk phenotypes at the two test sites were collected over two years. In a study of QTLs related to sugarcane traits, the population number was 100–200 individuals, and SP80-3280 (female parent) × RB835486 (male parent) sugarcane cultivars gained 151 F1 hybrid offspring. SSR and TRAP markers were used to construct genetic maps and to obtain seven QTLs related to yield traits (Balsalobre et al., 2017). Xiping Yang et al identified the loci associated with Orange Rust Resistance by using 172 sugarcane clones CP95-1039 × CP88-1762 and revealed three QTLs. They explained 12%, 8%, and 1% of the phenotypic variation, respectively (Yang et al., 2018).

In this study, the phenotypic contribution rate (PVE) of sugarcane tillering and effective stalk QTL were 7.7 ~ 11.1% and 5.6 ~ 10.7%, respectively. Due to the Beavis effect, QTL may be overestimated when the population size is less than 500 (Xu, 2003). Wang et al showed a PVE of 25.70% for a tiller dominant QTL in YT93-159 × ROC22 hybrid population (T. Wang et al., 2023). All the main- effective stalk QTLs were located at the same loci (PVE = 10.7% and 10.3%, respectively) at the Zhanjiang Academy of Agricultural Sciences (NKY 2020/NKY 2021). In QTLs mapping studies based on sugarcane-related traits, the main QTLS were generally based on 10%-20% (Lu et al., 2023; Z. Wang et al., 2022; Yang et al., 2018); therefore, it was speculated that the effective stalks might also be regulated by multiple genes.

Multiple C2H2 (Table 4) and zinc finger proteins(Table 5) were found in tiller qST-1/qST-2 and effective stalk qSES-1. Zhang X et al found that maize QTL (tin1) (X. Zhang et al., 2019), which encodes C2H2-zinc-finger transcription factor, is highly correlated with tillering. The tin1 transcript level was improved by a splice-site variant (G/GT to C/GT) and the tiller number was significantly increased. After tin1 was explored, map-based cloning and association mapping revealed that Tin8 is downregulated in transcription, which results in decreased tiller number. Further RNA-seq analysis showed that the expression of 13 genes related to tiller development was regulated by Tin8.further RNA-seq analysis showed that the expression of 13 genes related to tiller development was controlled by Tin8. The C2H2-zinc-finger transcription factor is speculated to play an important role in regulating the growth and development of sugarcane tillering and effective stalks.

Research on Plant agronomic traits has found that the ubiquitin-proteasome pathway has the potential to modulate crop productivity by influencing agronomic traits, among which E3 ligases are the major players in this pathway, and agronomic traits are influenced by ubiquitination, especially tillering in rice. Because sugarcane traits are quantitatively inherited traits and polygenic control, we speculate that candidate genes such as Soffic.04G0005780-1A (qST-2), Soffic.02G0013310-1A (qST-3), Soffic.03G0033750-1A, and Soffic.03G0032900-1A (Table 5) plays an important role in the control of sugarcane tillering (Jiang et al., 2023; Varshney & Majee, 2022). In addition, the candidate gene Soffic.01G0055150-1A(qSES-1) associated with the effective stalk encodes Transducin/WD40, which interacts with ribosomal-biogenesis proteins to control seed germination and regulate growth development in Arabidopsis (Gachomo et al., 2014).Therefore, we can conclude that all these candidate genes can be used as priority genes for further research.

Conclusions

This study successfully mapped the genetic framework for tillers and effective stalks in the sugarcane hybrid Yuenong73-204 × CP72-1210, identifying high heritability for these traits. A genetic map was created using a pseudo-test cross-mapping approach, leading to the discovery of five QTLs for each tiller and effective stalk, with moderate phenotypic contributions. The two significant QTLs for effective stalks were stable across the different environments. 23 candidate genes were identified, with C2H2 transcription factors being of particular interest, and E3 ubiquitin-protein ligase being the most significantly expressed in tillers and effective stalks. These findings facilitate targeted breeding for yield improvement and contribute to a wider understanding of plant genetics, providing a substantial but partial explanation for the observed traits, with other genetic elements yet to be explored.

Ethical approval and consent to participate

All methods were performed in accordance with relevant guidelines and regulations.

Ethical approval and consent to participate.

All methods were performed in accordance with relevant guidelines and regulations.

Availability of data and materials

As the data provider has not consented to the public sharing of genotyping data for the inbred sugarcane line, it cannot be shared on this platform. However, interested individuals can obtain data via email. To request access to data, please contact the corresponding author, Dr. Yongwen Qi1, at [email protected].

Consent for publication

Not applicable

Declaration of competing interest

The authors declare that they have no conflicts of publication.

Funding

The project was supported by the by the Laboratory of Lingnan Modern Agriculture Project (NZ2021014), the National Natural Science Foundation of China (32072027), the Guangdong Province special projects in key fields of ordinary colleges and universities, the Guangdong Province key construction discipline research ability enhancement project(2022ZDJS023), and the Special Project for Rural Revitalization Strategy in Guangdong Province (2022-NPY-00-023-5)

Author contribution

J.F and X.C designed the research; Z.C, L.F, W.C, H.L, X.X, Y.L,W.C, Y.S, R.L, , S.C, and J.F analyzed the data; J.F, X.C, and Z.C wrote the paper ；X.L^*and Y.Q^* carried out the experiments. All the authors have read and approved the manuscript.

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Characterization of Sugarcane QTLs for Tillering and stalks Quality through a Two-Way Pseudo-Testcross

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Abstract

Figures

1. Introduction

2. Materials and methods

2.1. Construction of F1 population

2.2 Determination of phenotypic heritability and correlation

2.3 SNP array genotyping and simplex marker selection

2.4 Linkage map construction

2.5 QTL analysis and gene annotations

3. Results

3.1 Correlation Evaluation and Phenotypic Heritability

3.2 SNP array genotyping and simplex marker selection

3.3 Linkage map construction

3.4 QTL mapping of tillers and effective stalks in sugarcane

3.5 Screening Candidate Genes Related to Tillering and Effective Stalks.

4. Discussion

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References

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