QTL Mapping for Growth-Related Traits by Constructing the First Genetic Linkage Map in Simao pine

doi:10.21203/rs.3.rs-49639/v1

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QTL Mapping for Growth-Related Traits by Constructing the First Genetic Linkage Map in Simao pine

https://doi.org/10.21203/rs.3.rs-49639/v1

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Simao pine is one of primary economic tree species for resin and timber production in southwest China, the exploitation and utilization are constrained by relatively lacking the genetic information. Construction a fine genetic linkage map and detecting QTLs for growth-related traits is an important section in molecular breeding program for Simao Pine. In our study, a high-resolution Simao pine genetic map employing specific locus amplified fragment sequencing (SLAF-seq) with an F₁ population has been constructed. There were 11,544 SNPs assigned to 12 linkage groups in the map, and the total length of the map was 2,062.85 cM with a mean distance of 0.37 cM between markers. According to the phenotypic variation analysis during three consecutive years, seventeen QTLs for four traits which including six for plant height (Dh.16.1, Dh16.2, Dh17.1, Dh18.1-3), five for basal diameter (Dbd.17.1-5), four for needle length (Dnl17.1-3, Dnl18.1) and two for needle diameter (Dnd17.1 and Dnd18.1) were identified. These QTLs individually explained phenotypic variance from 11.0-16.3 %, and the LOD value ranged from 2.52 to 3.87. The result in this study provide helpful information for genomic studies and marker-assisted selection (MAS) in Simao pine.

Plant Physiology and Morphology

Plant Molecular Biology and Genetics

QTL mapping

growth-related traits

genetic linkage map

Simao pine

SLAF-seq

Pinus kesiya Royle ex Gordon var. langbianensis (A. Chev.) Gaussen (2n = 24) (Wu & Zhao, 1999), also called Simao/Szemao pine in China, is a geographic variant of Pinus kesiya (Wang et al., 2012). It is mainly distributed in the mountainous areas where altitudes ranging from 600 to 1800 m of Pu’er, Xishuangbanna and Lincang city in Yunnan Province (Zhu et al., 2017; Li et al., 2018). Compared with other conifer tree species, it has unique rapid growth characteristics that its branches can grow 2 or more rounds a year (Xu et al., 2012; Ou et al., 2016), and its timber is widely used in fiber, furniture, and the building industry (Cai et al., 2017). In addition, Simao pine is not only a high economical plant for its high turpentine output (Dong et al., 2009; Wang et al., 2018), but also an important species in forest ecosystem for the function of carbon sequestration biological and water conservation (Wen et al., 2010; Zhu et al., 2016; Cao & Zhang, 2017; Li et al., 2017). However, it’s exploitation and utilization are constrained by relatively lacking the genetic information (Cai et al., 2017).

Highly saturated genetic linkage maps are effective tool for QTL mapping, and MAS (Zhao et al., 2016; Gao et al., 2018). During the last two decades, a large number of genetic maps for perennial woody plants had been constructed (Freeman et al., 2006; Jiang et al., 2011; Wang et al., 2014), various QTLs associated with essential traits had been identified based on these maps (Devey et al., 2004; Rönnberg et al., 2005; Lowry et al., 2015). However, great majority of them were low saturated frame-work maps which lowered the degree of accuracy of QTL mapping (Li et al., 2014). Single nucleotide polymorphism (SNP) is a sort of molecular marker technique developed by high-throughput sequencing. It is convenient, abundant, highly polymorphic and commonly used in genetic map construction (Troggio et al., 2007; Liu et al., 2012; Sun et al., 2013). In pace with the rapid development technology of next-generation sequencing (NGS), the technology of specific-locus amplified fragment sequencing (SLAF-seq) becomes one of popular methods for SNP markers development and high-resolution genetic map construction (Zhang et al., 2013; Shang et al., 2016; Chang et al., 2018). Until now, various plants genetic linkage maps had been established by SLAF-seq, and it greatly heighten the efficiency and the degree of QTL mapping accuracy (Zhang et al., 2015; Luo et al., 2016; Zhang et al., 2016; Conson et al., 2018).

The construction of the first genetic linkage map will provide facilitate to understand the genetic information of genome in Simao pine. Growth-related traits were important economic traits for woody tree breeding, and detecting QTLs for growth-related traits is an important section in molecular breeding program for Simao Pine. Therefore, we employed the technology of SLAF-seq to actualize fast development of SNPs, based on these SNP markers a high-density linkage map will be constructed and the QTLs which linked to growth-related traits will be identified. It will provide a powerful tool for future detection of other important economic traits and MAS in Simao pine breeding.

2.1 Mapping population and DNA extraction

According to factorial mating design, eleven superior clones were selected for hybrid parents (5 for male parents and 6 for female parents). Controlled pollination for 30 hybridized combinations were conducted in spring of 2014. Two years later, totally 9 full-sib families were obtained and grown at the farm of Pu’er city institute of forestry sciences (N 22◦ 47′/E 100◦ 59′) by harvesting, sowing and culturing the seedlings. Family 9 was selected as the mapping population for Simao pine genetic linkage map construction by analysis of phenotypic variation for populations and genetic similarity coefficient among parents (Wang et al., 2018). The parents for mapping population were superior clone SM11 (maternal, with characteristic of high resin content) and superior clone JG1 (paternal, with characteristic of rapid growth) respectively. Fresh and young healthy needles from parents and mapping population (100 hybrid individuals) were collected and frozen in liquid nitrogen at once. The genomic DNA was isolated using the improved cetyl trimethyl ammonium bromide (CTAB) method (Wang et al., 2019).

2.2 SLAF library establishment and sequencing

The similar experiment procedure of high-throughput sequencing and establishment of SLAF library for the mapping population was applied according to previously study by Zhang et al. (2015) with minor modified. Briefly, two different steps were applied. First, all of genomic DNA for SLAF libraries construction was digested by a single enzyme Hae III (New England Biolabs, NEB, USA). Second, only the SLAF fragments which length ranging 414 to 464 bp fragments will be excised and diluted for pair-end sequenced by Illumina HiSeq 2500 platform (Illumina, Inc; San Diego, CA, U.S.).

2.3 Analysing and genotyping for sequence data

The SLAF-seq data grouping and SNP genotyping was same to Wang et al. (2017). After discarding the low-quality reads, the remaining reads with more than 90% similarity will be gathered in the same SLAF locus. As Simao pine is a diploid plant, so only the SLAF which has 2 to 4 alleles will be identified as potential and polymorphic marker. Since the F₁ mapping population of Simao pine was obtained by a cross between two heterozygote parents, that the markers with aa × bb segregation pattern can’t use for the construction of genetic map (Jansen 2005; He et al., 2017).

2.4 Linkage map construction and evaluation

The HighMap software (He et al., 2017) with the cross-pollination (CP) option was utilized for Simao pine genetic linkage map construction. The parameters of estimation were set for a minimum logarithm of odds (LOD) threshold of 5.0 for linkage groups, and the map distance were estimated with the Kosambi mapping algorithm (Vinod 2011). After linkage grouping, the maximum likelihood method was used to order the SLAFs markers in all LGs (Van Ooijen 2011; Luo et al., 2016), and SMOOTH algorithm was utilized to be put right the errors of genotyping (Van et al., 2005). To evaluate the quality of constructed Simao pine genetic map, the analysis of mapped markers integrity, and construction of haplotype maps and heat maps for each LG were carried out (West et al., 2006; Liu et al., 2017; Wu et al., 2018).

2.5 Growth-related traits Assessment and QTL analysis

Growth-related traits (including plant height, basal diameter, needle length, needle diameter) of the progenies were determined during 3 consecutive years (2016, 2017 and 2018). The analysis of phenotypic variation including coefficients of variation (CV) and the correlation coefficients between all investigated traits were performed with software SPSS 20.0. The QTLs underlying the growth-related traits were identified by MapQTL 6.0 software and the interval mapping method (Van Ooijen 2009), and the method of 95% Bayesian credible interval was used to calculate the confidence intervals for all QTLs (Sen & Churchill, 2001). The threshold value was decided by 1000 permutations. According to the permutations, the minimum LOD score of 2.5 was conducted in our study. The percentage of phenotypic variance explained of each detected QTL was achieved based on the phenotypic variance in the population (Xia et al., 2018).

3.1 Analysis of sequencing data and SLAF markers

The Simao pine SLAF libraries were successfully constructed. There were 461.41 M reads obtained by the sequencing which with guanine-cytosine of 40.23 % and Q30 of 92.25 %, the number of reads for the maternal and paternal parents were 22,947,158 and 18,534,664, and the mean for the F₁ individual was 4,659,126 (Table 1). After filtering out the low-quality reads, the number of SLAFs for the two parents and average in F₁ progeny were 535,598, 482,851 and 375,315, respectively. The average depth of the SLAFs for the maternal and paternal parent was 10.48-fold and 9.77-fold, and average for each F₁ individual was 3.47-fold (Table 1). A total of 633,086 high quality SLAFs were obtained, among these markers 239,790 were polymorphic markers (37.88 %), 385,976 were non-polymorphic markers (60.97 %), and 7,320 were repetitive markers (1.15 %). Finally, 140,485 polymorphic SLAFs of 8 segregation patterns were achieved (Figure 1). After removing the markers which showed aa × bb segregation pattern, 97,891 (15.46 %) polymorphic SLAFs will using for further Simao pine genetic map construction.

3.2 Construction and evaluation of genetic linkage map

After discarding the unsuitable markers, 5,643 SLAFs were successfully used for the linkage map construction. Among them, a total of 11,544 SNP markers were detected, and SNP types were summarized (Table 2). Based on these markers a high saturated genetic linkage map which covering 2062.85 cM, comprising 12 linkage groups (LGs), and with a mean distance of 0.37 cM was constructed (Figure 2, Table 2). The genetic length of individual LGs varied from 147.38 (LG4) to 194.85 cM (LG9) with a mean of 171.90 cM. Among 12 LGs, LG8 was the largest linkage group contained 523 SLAFs, while the smallest group (LG7) contained 367 SLAFs. The average number of markers for each LG was 470. For the density, LG5 was the densest linkage group with the minimum marker distance (0.32 cM), whereas LG1, LG3, and LG7 were the lowest density linkage groups, with the marker distance of 0.40 cM. The max gap in the map was 11.17 cM located in LG2 and LG3.

Three approaches were performed for evaluated the quality of Simao pine genetic map. The result of markers integrity analysis showed that the complete degree of each individual mapped marker was 99.99% (Figure 3), and the average depth for parents was more than five times of the offspring (Table 3). It suggested that genotyping was accurate and the mapping population was suitable for further analysis. According to the Haplotype maps (Supplementary Figure 1), that most of the recombination blocks were distinctly defined, suggested that the constructed high saturated genetic map was adaptive for subsequent genetic analysis. The map quality was also assessed by Heat maps (Supplementary Figure 2), it indicated that the markers were well ordered in most linkage groups and the constructed Simao pine genetic map with high accuracy.

3.3 Phenotypic variation analysis

The 3 years phenotypic data and statistical values for growth-related traits were summarized (Table 4). The results showed that the four traits were normal distribution for three years (Figure 4). And a relative higher degree of genetic variation was found. The CV of plant height was 21.74 %, 14.80 % and 12.07 % during 2016, 2017 and 2018 respectively. The CV of basal diameter was 25.21 % (2016), 23.42 % (2017), and 22.33 % (2018) respectively. The CV of needle length was 21.69 % (2017) and 18.89 % (2018). The CV of needle diameter was 25.96 % (2017) and 21.44% (2018). The Pearson correlations analyses showed significant correlation among four traits (Table 5).

3.4 QTL mapping

By using the constructed map and analysis the data of phenotypic characteristics in the mapping population, totally 17 QTLs linked to 4 traits were identified (Table 6, Supplementary Figure 3). The individual QTL explained the phenotypic variation varied from 11.0-16.3 %, and the LOD value ranged from 2.52 to 3.87. There were 6 plant height QTLs detected in the map. In 2016, two plant height QTLs were located on LG3 (Dh16.1) and LG10 (Dh16.2) explained 12.7 % and 12.4 % of the phenotypic variance, respectively. In 2017, only one plant height QTL which located on LG9 (Dh17.1) explained 15.5 % of the phenotypic variation were detected. In 2018, the other three plant height QTLs were identified which located on LG11 (Dh18.1, Dh18.2), and LG12 (Dh18.3), and explained 16.3 %, 16.1 % and 13.8 % of the phenotypic variation, respectively. A total of 5 QTLs associated with basal diameter were detected on LG3 (Ddb17.1, 11.6 %), LG4 (Ddb17.2, 12.7%) and LG6 (Ddb17.3, 11.6 %; Ddb17.4, 13.1 %; Ddb17.5, 11.4 %), respectively. Four needle length QTLs were identified which located on LG1 (Dnl17.1, 12.1 %; Dnl17.2, 11.0 %; Dnl17.3, 12.1 %) and LG12 (Dnl18.1, 13.5%), respectively. There were two needle diameter QTLs which both located on LG4 (Dnb17.1 and Dnb18.1) explained 12.9 % and 13.1 % of the phenotypic variation were detected.

Construction of the high-resolution map for Simao pine will provide helpful information for genomic studies and facilitate the breeding applications. In our study, a fine genetic map of Simao pine applied the technology of SLAF-seq has been constructed. It contained 12 LGs and 11,544 SNPs spanned 2,062.85 cM with a mean marker distance of 0.37 cM, representing a significant improvement over the previous linkage maps in coniferous plants (Eckert et al., 2009; Chen et al., 2010; Chancerel et al., 2011; Echt et al., 2011; Neves et al., 2014; Westbrook et al., 2015). As we know, this was one of the highest saturated genetic maps to date in coniferous tree species. A total of 17 QTLs for 4 growth-related traits were identified based on the constructed genetic map, these QTLs were valuable genetic resources for MAS in Simao pine.

An appropriate mapping population laid solid foundation for the genetic map construction (Zhu et al., 2015). For perennial woody trees, it’s hard to get Backcross (BC), Recombination Inbred Lines (RILs) and F2 populations in a short term, because of the long generation constraints. The pseudo-testcross strategy has been put forward that the F1 population was created replaced the others populations (Grattapaglia & Sederoff 1994). This strategy has been successfully applied to various forestry trees, especially non-model and un-sequenced species (Ukrainetz et al., 2008; Wu et al., 2014; Liu et al., 2016; Xia et al., 2018). In this report, nine F₁populations were obtained by artificial hybridization, based on the analysis of field phenotypic characteristics variation among populations and genetic similarity coefficient among parents, superior clones SM11 (high resin content) and JG1 (fast growth) were chosen for maternal and paternal parents, and the F₁ hybrid population was applied as the mapping population for map construction for Simao pine. The obvious variation will present in the segregation population due to significant difference in the resin content and the growth speed between the parents, which could facilitate QTL mapping for these traits.

Molecular markers were powerful tools for genetic map construction (Alsaleh et al., 2015). The mainstream molecular markers for genetic linkage map construction of heterozygous perennial forest tree species included SNP, simple sequence repeat (SSR), inter-simple sequence repeat (ISSR), amplified fragment length polymorphism (AFLP) and random amplified polymorphic DNA (RAPD) et al. (Casasoli et al., 2001; Gulsen et al., 2010; Li et al., 2014; Wang et al., 2017; Mortaza et al., 2018). Among these markers, SNP was thought of one of the most ideal markers for genetic map construction for the merits of abundance, fast and covering the whole genome (Baird et al., 2008; Elshire et al., 2011). Great changes have taken place in genetic map construction with the development of low-cost and high-throughput sequencing technology (Eckert et al., 2009). Recently, SLAF-seq technique has become one of the most popular methods for SNP marker development (Sun et al., 2013). By using this approach, a high-density genetic linkage map had been successful constructed by SNP markers, it indicated that SNP markers can be efficiently applied in the construction of genetic linkage map of Simao pine.

High quality genetic maps can increase the accuracy of QTL mapping (Li et al., 2014; Liu et al., 2016). The number of markers in the genetic map is one of important indicator to evaluate its quality, a genetic map with a large number of markers has the characteristics of suitable distance and high-resolution (Zhang et al., 2016). This constructed genetic map was the first map which contained over ten thousand SNP markers in coniferous tree species. It supported that we have constructed a high-quality genetic map for Simao Pine. Moreover, additional three approaches were performed for the map quality evaluation. All results indicated that the current high-accuracy map will provide the effective information for QTL mapping.

Growth-related traits were important economic traits for woody tree breeding, and detecting QTLs for growth-related traits is an important section in molecular breeding program for Simao Pine. In this study, a total of 17 QTLs for 4 growth-related traits were identified. The individual QTL explained the phenotypic variation varied from 11.0% to16.3 %, it indicated that growth-related traits of Simao pine maybe controlled by several major effective genes (Rönnberg et al., 2005; Li et al., 2014). In the 4 traits, only the QTLs for plant height were consistently detected during the 3 years, but QTLs for the other 3 traits were not consecutive expressed. It suggested that growth-related traits of Simao pine in different season/age might be influenced by different genes/QTLs or that the QTLs stabilization varied by effect of environment change. This is in agreement with previous studies in woody tree that growth-related traits were mainly quantitative traits, which dominated by complex genes and easily affected by environment probably changes as the tree matures (Yang et al., 2013; Kenis & Keulemans, 2007). In other words, the multi-environment QTL analysis is more accurate than single-environment experiment for the heterozygous perennial woody tree growth-related traits QTL mapping (El-Soda et al., 2014). Thus, to eliminate interference brought by environment and improve QTLs accuracy, the multi- environment QTL test in other environments using different backgrounds for Simao pine must be carried out in future (Li et al., 2014; Adhikari et al., 2018).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author Contributions

Dawei Wang, Anan Duan and Hede Gong conceived and designed the study. Dawei Wang, Chen Shi, Siguang Li and Nianhui Cai performed the experiments. Chengzhong He and Hongyan Tang analyzed the data, Dawei Wang and Chen Shi wrote the manuscript. Hede Gong revised the whole manuscript and helped in preparing the Tables and References. All authors reviewed the manuscript.

Funding

The study was funded by National Natural Science Foundation of China [Grants 31500536] and Special Funds for local scientific and Technological Development Guided by the Central Government [Grants 219001].

Supplementary Material

Supplementary Figure 1 were Haplotype maps for 12 LGs. Supplementary Figure 2 were Heat maps for 12 LGs. Supplementary Figure 3 were figures for 17 growth-related traits QTLs.

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Table 1 Statistics for sequenced data

Sample ID	Total Reads	Q30 percentage (%)	GC percentage (%)	SLAF Number	Average Depth
SM11	22,947,158	91.72	40.27	535,598	10.48
JG1	18,534,664	91.57	39.91	482,851	9.77
offspring	4,659,126	92.25	40.23	375,315	3.47

Table 2 Marker information for high-density genetic map

LGs	SLAFs Number	Total Distance (cM)	Average Distance (cM)	Max Gap (cM)	SNPs Number	Trv	Tri	Trv/Tri
LG1	458	184.59	0.40	9.76	918	291	627	0.46
LG2	467	178.60	0.38	11.17	962	302	660	0.46
LG3	452	181.78	0.40	11.17	938	275	663	0.41
LG4	513	178.28	0.35	9.64	1,019	334	685	0.49
LG5	492	155.06	0.32	7.03	1,003	295	708	0.42
LG6	468	174.50	0.37	9.26	934	311	623	0.50
LG7	367	147.38	0.40	7.66	698	207	491	0.42
LG8	523	172.15	0.33	7.53	1,062	333	729	0.46
LG9	508	194.85	0.38	6.71	988	324	664	0.49
LG10	384	151.42	0.40	7.71	882	302	580	0.52
LG11	516	179.28	0.35	9.31	1,063	352	711	0.50
LG12	495	164.96	0.33	4.89	1,077	318	759	0.42
Total	5,643	2,062.85	0.37	11.17	11,544	3,644	7,900	0.46

Note: Trv and Tri indicate the transversion and transition numbers, respectively.

Table 3 Statistics of the mapped marker depth

Sample ID	Marker Number	Total Depth	Average Depth
SM11	5,643	243,344	43.12
JG1	5,643	220,795	39.13
Offspring	5,425	43,439	8.01

Table 4 Statistics of growth traits of mapping population during three consecutive years

Traits	Average	Min	Max	SD	CV (%)
Height 2016 (cm)	43.89	18.4	68.7	9.54	21.74
Height 2017 (cm)	54.49	22.3	76.5	8.07	14.8
Height 2018 (cm)	90.59	57.2	113.5	10.93	12.07
Basal diameter 2016 (mm)	0.42	0.12	0.8	0.11	25.21
Basal diameter 2017 (mm)	7.66	3.26	11.65	1.79	23.42
Basal diameter 2018 (mm)	10.54	6.78	22.43	2.35	22.33
Needle length 2017 (cm)	12.05	5.23	17.91	2.61	21.69
Needle length 2018 (cm)	19	11.61	39.39	3.59	18.89
Needle diameter 2017 (mm)	0.42	0.24	0.92	0.11	25.96
Needle diameter 2018 (mm)	0.54	0.27	0.93	0.11	21.44

Table 5 The correlation coefficients between 4 traits

PH2016	BD2016	PH2017	BD2017	NL2017	ND2017	PH2018	BD2018	NL2018	ND2018
PH2016	1
BD2016	0.695**	1
PH2017	0.755**	0.578**	1
BD2017	0.246*	0.178	0.360**	1
NL2017	0.269**	0.186	0.366**	0.611**	1
ND2017	0.302**	0.323**	0.375**	0.287**	0.320**	1
PH2018	0.595**	0.389**	0.703**	0.582**	0.514**	0.290**	1
BD2018	0.372**	0.223*	0.454**	0.663**	0.447**	0.307**	0.595**	1
NL2018	0.439**	0.358**	0.480**	0.453**	0.412**	0.436**	0.530**	0.603**	1
ND2018	0.308**	0.314**	0.345**	0.288**	0.275**	0.373**	0.414**	0.277**	0.481**	1

Note: *significant at p＜0.05, **significant at p＜0.01

Table 6 Quantitative trait loci (QTLs) for growth-related traits during 3 years in the mapping population

Trait	Year	LG	QTLs	Marker	Peak Position (cM)	Confidence Interval (cM)	LOD	Explained Variance (%)
Plant Height	2016	LG3	Dh16.1	Marker162009	29.068	18.774-45.722	2.94	12.7
	2016	LG10	Dh16.2	Marker186661	49.885	49.885-54.091	2.87	12.4
	2017	LG9	Dh17.1	Marker111742	58.881	53.171-60.966	3.66	15.5
	2018	LG11	Dh18.1	Marker90236	27.819	24.054-38.14	3.87	16.3
		LG11	Dh18.2	Marker120973	56.742	56.237-58.268	3.82	16.1
		LG12	Dh18.3	Marker31588	18.009	16.999-20.409	3.23	13.8
Basal Diameter	2017	LG3	Dbd17.1	Marker110266	170.435	169.425-170.435	2.67	11.6
		LG4	Dbd17.2	Marker69728	137.585	130.262-138.461	2.95	12.7
		LG6	Dbd17.3	Marker171746	82.624	82.624-84.708	2.66	11.6
		LG6	Dbd17.4	Marker92854	89.329	85.214-89.329	3.04	13.1
		LG6	Dbd17.5	Marker145277	104.629	102.175-105.122	2.62	11.4
Needle Length	2017	LG1	Dnl17.1	Marker36587	7.716	7.682-7.822	2.79	12.1
		LG1	Dnl.17.2	Marker170751	27.84	26.017-27.84	2.52	11.0
		LG1	Dnl17.3	Marker129013	95.986	95.481-98.522	2.80	12.1
	2018	LG12	Dnl18.1	Marker52737	16.999	16.689-17.121	3.14	13.5
Needle Diameter	2017	LG4	Dnd17.1	Marker30236	154.595	154.137-154.616	3.00	12.9
Needle Diameter	2018	LG4	Dnd18.1	Marker160818	171.684	171.351-171.752	3.05	13.1

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QTL Mapping for Growth-Related Traits by Constructing the First Genetic Linkage Map in Simao pine

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Abstract

Figures

1. Introduction

2. Materials And Methods

2.1 Mapping population and DNA extraction

2.2 SLAF library establishment and sequencing

2.3 Analysing and genotyping for sequence data

2.4 Linkage map construction and evaluation

2.5 Growth-related traits Assessment and QTL analysis

3. Results

3.1 Analysis of sequencing data and SLAF markers

3.2 Construction and evaluation of genetic linkage map

3.3 Phenotypic variation analysis

3.4 QTL mapping

4. Discussion

Declarations

References

Tables

Supplementary Files

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