High-Resolution Detection of Quantitative Trait Loci for Seven Important Yield Components in Wheat (Triticum aestivum L.) using a High-Density SALF-Seq Genetic Map

Background: Yield-related traits including thousand grain weight (TGW), grain number per spike (GNS), grain width (GW), grain length (GL), plant height (PH), spike length (SL), and spikelet number per spike (SNS) are greatly associated with wheat (Triticum aestivum L.) grain yield. To detect quantitative trait loci (QTL) associated with them, 193 recombinant inbred lines derived from two elite winter wheat varieties Chuanmai42 and Chuanmai39 were employed to perform QTL mapping in six or eight environments. Results: A total of 30 QTLs on chromosomes 1A, 1B, 1D, 2A, 2B, 2D, 3A, 4A, 5A, 5B, 6A, 6D, 7A, 7B and 7D were identied. Among them, six major QTLs QTgw.cib-6A.1, QTgw.cib-6A.2, QGw.cib-6A, QGl.cib-3A, QGl.cib-6A, and QSl.cib-2D explaining 5.96-23.75% of the phenotypic variance were detected in multiple environments and showed strong and stable effects on corresponding traits. Three QTL clusters on chromosomes 2D and 6A containing 10 QTLs were also detected, which showed signicant pleiotropic effects on multiple traits. Additionally, three Kompetitive Allele Specic PCR (KASP) markers linked to ve of these major QTLs were developed. Candidate genes of QTgw.cib-6A.1/QGl.cib-6A and QGl.cib-3A were analyzed based on the spatiotemporal expression patterns, gene annotation, and orthologous search. Conclusions: Six major QTLs for TGW, GL, GW and SL were detected. Three KASP markers linked with ve of these major QTLs were developed. These QTLs and KASP markers will be useful for elucidating the genetic architecture of grian yield and developing new wheat varieties with high and stable yield in wheat. QTgw.cib-6A.2, QGw.cib-6A, QGl.cib-3A, QGl.cib-6A, and QSl.cib-2D showed strong and stable effects on corresponding traits in different environments and the BLUP datasets. Three KASP markers linked with ve of these major QTLs were developed. These QTLs and KASP markers will be useful for elucidating the genetic architecture of grian yield and developing new wheat varieties with high and stable yield in wheat. Additonally, candidate genes of QTgw.cib-6A.1/QGl.cib-6A and QGl.cib-3A were preliminary analyzed.


Background
Common wheat (Triticum aestivum L.) is one of the three major crops worldwide and provides approximately 30% of global grain production and 20% of the calories consumed for humans [1]. Due to ongoing decrease of the global arable cultivated land area and increase of the population, the current rate of wheat yield increase will be insu cient to meet the future demand. The development of high-yield varieties, one of the important targets of modern wheat breeding programs worldwide, thus must be accelerated to ful ll future global food and nutrition security [2].
Wheat yield is a complex quantitative trait controlled by multiple genes and signi cantly in uenced by interacting genetic and environmental factors [3,4]. By contrast, yield components including thousand grain weight (TGW), grain number per spike (GNS), grain width (GW), grain length (GL), plant height (PH), spike length (SL) and spikelet number per spike (SNS) typically show higher heritability than that of the yield [5][6][7]. Therefore, targeting these traits and identifying the related genes or quantitative trait loci (QTL) is an important approach to improve grain yield potential in wheat.
To date, several genes associated with wheat yield have been mapped and cloned in wheat. For example, the application of semi-dwarfng genes Rht-B1b and Rht-D1b not only effectively improved the lodging resistance but also improved the harvest index, resulting in increased yields since the 1970s [8 -10]. The vernalization insensitive alleles of Vrn-1 (Vrn-A1, Vrn-B1, and Vrn-D1) shorten both the vegetative and the reproductive stages and have considerable impact on spike morphological traits [11,12]. TaGW2 homologous to rice GW2 was located on chromosome 6A and could alter grain width and weight and to lesser extend grain length owing to changes in the width of the spikelet hull [13,14]. The grain-shape gene TasgD1 encoding a Ser/Thr protein kinase glycogen synthase kinase3 and independently control semispherical grain trait [15]. A jasmonic acid synthetic gene keto-acyl thiolase 2B was cloned in a TGW mutant and showed signi cant effect on TGW and GW [16].
Before the gene cloning, quantitative trait loci (QTL) mapping provides an effective approach to depict the genetic architecture of complex quantitative traits.
Over the past decades, numerous QTLs associated with yield or yield components have been identi ed on all wheat chromosomes [3,4,11,[17][18][19][20][21][22][23]. For example, Rht8 located on chromosome 2DS was closely linked with marker xfdc53 and reduced plant height by 10% [24]; Rht25 on wheat chromosome arm 6AS showed pleiotropic effects on coleoptile length, heading date, SL, SNS and grain weight [25]. Two major QTLs for grain size and weight were detected on chromosome 4B, which together explained 46.3% of the phenotypic variance [26,27]. Five stable QTLs for PH, SL and HD on chromosomes 1A, 2A, 2D and 6A were detected in an introgression line population [28]. Twelve major genomic regions with stable QTL controlling yield-related traits were detected on chromosomes 1B, 2A, 2B, 2D, 3A, 4A, 4B, 4D, 5A, 6A, and 7A [1]. However, among those QTLs reported previously, few of them were stably detected in multienvironments, which greatly restricts their potential utilization in marker-assisted selection (MAS) in breeding programs. Additionally, the lower density of genetic maps also limits the resolution and accurancy of the QTL intervals reported previously.
In the present study, a high-resolution genetic map constructed through speci c-locus amplifed fragment (SALF) sequencing was adopted for detection of yield componant QTLs in a recombinant inbred line (RIL) population derived from two elite winter wheat varieties Chuanmai42 (CM42) and Chuanmai39 (CM39) [29]. Seven traits including TGW, GW, GL, PH, GNS, SL and SNS were assessed in multiple environments to detected potential major and stable QTL, which will lay out a foundation for further study on ne mapping and cloning of the underlying key genes for yield.

Phenotypic variation
The measured traits of the CM42×CM39 RILs as well as the two parents are listed in Table 1. CM42 had higher trait values for TGW, GW, GL, GNS, PH and SL than those of CM39 in each of environments and the best linear unbiased prediction (BLUP) datasets. In the RIL population, all traits showed wide and signi cant variations in all environments and the BLUP datasets (Table 1). Of them, the TGW ranges from 20.81 to 72.7 gram (g), the GW ranged from 2.6 to 4.21 millimeter (mm), the GL ranged from 5.88 to 8.81 mm, the PH ranged from 65.08 to 148.3 centimeter (cm), the GNS ranged from 24 to 81.2, the SL ranged from 6.65 to 18.17 cm, and the SNS ranged from 15.83 to 27 (Table 1), respectively. The BLUP datasets of all traits showed normal distributions in the RIL lines, which suggests polygenic inheritance of these traits (Fig. 1A). Additionally, the TGW, GL, PH, GNS and SL showed high broad-sense heritability of Table 1 Phenotypic variation of seven yield components, including thousand grain weight (TGW), grain number per spike (GNS), grain width (GW), grain length (GL), plant height (PH), spike length (SL) and spikelet number per spike (SNS), for the parents and the CM42×CM39 RIL lines in different environments.

Correlation analyses among different traits
The BLUP datasets of each trait was employed to assess their correlations in the CM42×CM39 RIL population. TGW had signi cantly positive correlation with GW, GL, PH and SL, and signi cantly negative correlation with GNS and SNS (P < 0.001) (Fig. 1). GW was signi cantly and positively correlated with GL (P < 0.001), weakly and positively correlated with SL (P < 0.05), signi cantly and negatively correlated with GNS and SNS (P < 0.001), and not correlated with PH, respectively ( Fig. 1). GL had signi cantly positive correlation with PH and SL (P < 0.001), signi cantly negative correlation with GNS (P < 0.001), and weakly negative correlation with SNS (P < 0.05) (Fig. 1). Moreover, signi cantly positive correlations between PH and SL, GNS and SNS, and SL and SNS (P < 0.001), weakly positive correlations between PH and SNS (P < 0.05), signi cantly negative correlations between PH and GNS (P < 0.001), and no correlations between GNS and SL were detected, respectively ( Fig. 1).

QTL detection
Phenotypic data of all traits evaluated in each environment and the BLUP datasets were used for QTL detection, in which the BLUP datasets were treated as an additional environment. A total of 30 QTLs were identi ed in multiple environments and located on all chromosomes excepting 3B, 3D, 4B, 4D, 5D and 6B (Table 2).
For TGW, two QTLs were detected on chromosomes 6A. QTgw.cib-6A.1 was detected in two environments and the BLUP datasets, explaining 9.89-16.38% of the phenotypic variance. QTgw.cib-6A.2 was a major QTL detected in four environments and the BLUP datases and explained 15.31-23.75% of the phenotypic variance. Alleles of CM42 for the two QTLs contributed to higher TGW (Table 2).
Among the six QTLs for GL, two major QTL QGl.cib-3A and QGl.cib-6A were identi ed in ve environments and the BLUP datasets, explaining 6.55-11.86% and 5.96-13.11% of the GL variation, respectively. The positive additive effects of the two QTLs on GL were contributed by CM42. The rest four minor QTLs were identi ed in two or three environments on chromosoems 5A, 6D and 7D, explaining 5.17-11.34% of the GL variation. The positive alleles of QGl.cib-5A.1, QGl.cib-5A.2 and QGl.cib-7D were derived from CM42, and that of QGl.cib-6D was from CM39 (Table 2).
Among the six QTLs for PH, QPh.cib-2D on chromosome 2D was a stable QTL and detected in ve environments and the BLUP datasets, explaining 4.54-9.38% of the PH variation. The allele of CM39 contributed to higher PH. The rest ve minor QTLs on chromosomes 1A, 4A, 5A, 5B and 6A were detected in two or three environments, explaining 3.8-11.37% of the PH variation. The positive alleles of QPh.cib-1A and QPh.cib-5B were from CM39, and that of QPh.cib-4A, QPh.cib-5A and QPh.cib-6A were from CM42 ( Table 2).
Two minor QTLs for GNS on chromosomes 2D and 6A were detected in two environments and the BLUP datasets and explained 4.97-6.46% and 6.56-7.73% of the GNS variation, respectively. Alleles from CM42 and CM39 at QGns.cib-2D and QGns.cib-6A, respectively, contributed to positive effects on GNS (Table 2).
For SL, four QTLs were detected on chromosomes 2D, 5A, 5B and 6A. A major QTL QSl.cib-2D was detected in eight environments and the BLUP datasets, explaining 6.18-14.89% of the SL variation. QSl.cib-5B was a stable QTL and detected in three environments and the BLUP datasets, explaining 3.79-5.96% of the SL variation. Alleles of CM39 for the two QTLs contributed to increase of SL. Two minor QTLs QSl.cib-5A and QSl.cib-6A were detected in two or three environments, explaining 3.47-7.8% and 5.63-5.9% of the SL variation, respectively. The positive alleles of the two QTLs were contributed by CM42 (Table 2).

QTL analysis and comparison with previous studies
Wheat yield components are signi cantly associated with yield and typically show higher heritability than the yield itself, and thus, mining the genes or QTLs related to yield components will be help for elucidating the genetic basis of wheat yield and facilitating the genetic improvement of varieties with high yield [5][6][7]. In the present study, a RIL population derived from two elite winter wheat varieties were used to dissect the genetic basis of variation for seven yield components, including TGW, GNS, GW, GL, PH, SL and SNS. A total of 30 QTLs were identi ed in multiple environments, explaining 2.34-23.75% of the phenotypic variance.
PH and SL are important traits related to plant architecture and yield potential in wheat [12,38]. In the present study, six and four QTLs for PH and SL were identi ed, respectively. Among them, QPh.cib-2D and QSl.cib-2D were co-located in the same interval on chromosome arm 2DS, which was overlapped with the dwar ng gene Rht8 [24,39]. QPh.cib-4A and QPh.cib-5A were located near to two loci for PH reported by Luján Basile et al [40]. QPh.cib-6A on chromosome 6A was overlapped with the dwar ng gene Rht18 [41]. QSl.cib-5A on chromosome 5A was located near to QSL5A.3 detected by Liu et al [42]. For the rest four QTLs QPh.cib-1A, QPh.cib-5B, QSl.cib-5B and QSl.cib-6A, no stable QTL for PH and SL reported previously was overlapped with them, indicating they were likely novel (Table. 3).
Additionally, two QTLs for GNS and four QTLs for SNS were identi ed in the present study. Of them, QGns.cib-2D were co-located with QPh.cib-2D and QSl.cib-2D on chromosome 2D and overlapped with the dwar ng gene Rht8 [24,39]. QGns.cib-6A was co-loctaed with QTgw.cib-6A.2 and near to two QTLs for TGW QTKW.caas-6AL and QTKW-6A.1 [31,32]. QSns.cib-1B for SNS on chromosome 1B was overlapped with the QSn.sau-1BL reported recently [5]. QSns.cib-7A for SNS on chromosome 7A was overlapped with QSn-7A.2 detected by Cao et al [43]. For the rest two QTLs QSns.cib-1D and QSns.cib-4A, no stable QTL for SNS reported previously was overlapped with them, indicating they were likely novel (Table. 3). QTL cluster on chromosomes 2D and 6A Numerous co-located QTLs associated with multiple traits have been reported in previous studies [2,5,17,44,45], which are bene cial to improve breeding e ciency for multiple elite traits, and thus is favorable for pyramiding breeding or MAS. In the present study, three QTLs QSl.cib-2D, QPh.cib-2D and QGns.cib-2D were co-located in the interval of 8.4-29.35 Mb on chromosome arm 2DS ( Table 3). The allele of CM42 at the locus decreases SL and PH while increasing GNS. Additionally, the locus was overlapped with the dwar ng gene Rht8, which has been reported to associated with QTLs for PH, SL, SNS, GNS, spikelet compactness, TGW, and grain yield [12,39,[46][47][48]. Interestingly, no QTL for grain size and weight detected in the present study was overlapped with the locus, indicating it had no effect on grain size and weight. Given CM42 was bred by utilizing synthetic wheat germplasm [49], further studies, such as ne-mapping and map-based cloning are needed to future reveal the relationship between the locus and Rht8. However, the results in this study showed that the locus could be utilized in optimization PH with no penalty for grain size and weight in MAS.
Two QTL clusters were detected on chromosome 6A in the present study. One harbored two QTLs for TGW and GL with overlapped intervals on chromosome arm 6AS (Table 2), indicating the locus increases TGW by mainly increasing GL. The other one comprised ve QTLs for TGW, GW, GNS, PH and SL overlapped on chromosome arm 6AL (Table 2), indicating the locus increases TGW by mainly increasing GW. However, interval of the QTL cluster on chromosme 6AL were large and near to TaGW2 and Rht18 [41,50,51]. Therefore, additional populations may be needed to dissect the relationships of them.
Potential applications of these mjaor QTLs for yield improvement To date, numerous QTL for wheat yield and yield components have been identi ed on all wheat chromosomes [3,21,[52][53][54]. However, due to the negative correlation between GNS and TGW, a challenge must being faced during increasing yield is how to optimize the trade-off between GNS and TGW by choosing suitable QTLs in breeding practice [2]. In the present study, three major QTLs, QPh/Sl.cib-2D, QGl.cib-3A and QTgw.cib-6A.2 were selected to explore the balance of TGW and GNS. As showed in the Table 4, lines possessing the allele form CM42 at the three loci have relatively higher TGW and GNS, which might partly explain the high yield of CM42. Additionally, lines possessing the allele from CM42 at QPh/Sl.cib-2D and QTgw.cib-6A.2 and the allele from CM39 at QGl.cib-3A also have relatively higher TGW and GNS. However, for the other combination schemes, either the higher TGW but lower GNS, or higher GNS but lower TGW, or both lower TGW and GNS were harvested. Overall, the QTLs and KASP markers in this study will be useful for elucidating the genetic architecture of grain yield and developing new wheat varieties with high and stable yield in wheat. Potential candidate genes for QTgw.cib-6A.1/QGl.cib-6A and QGl.cib-3A Among these major QTL, QSl.cib-2D was likely allele with Rht8. In the previous study, TraesCS2D01G055700 was reported by Chai et al [55] as a possible candidate gene for Rht8. QTgw.cib-6A.2 and QGw.cib-6A needed additional populations to narrow their intervals. Therefore, we mainly analyzed possible candidates for QTgw.cib-6A.1/QGl.cib-6A and QGl.cib-3A in the present study.
QTgw.cib-6A.1 and QGl.cib-6A were co-loctaed between 73.08 and 82.67 Mb on CS chromosome 6A, and QGl.cib-3A was located between 659.71 and 668.09 Mb on CS chromosme 3A (Table 3). In the interval of QTgw.cib-6A.1/QGl.cib-6A and QGl.cib-3A, there were 81 and 85 predicted genes in the CS genome, respectively. Expression pattern analyses showed that 45 and 57 genes in the interval of QTgw.cib-6A.1/QGl.cib-6A and Gl.cib-3A expressed in various tissue, respectively (Fig. 3, Table S4, Table S5) [56,57]. Among them, several were abundantly expressed in grain, indicating they are likely associated with grain growth and development (Fig. 3). For example, TraesCS6A02G107800 is an ortholog of the rice RGG2 and encodes a guanine nucleotide-binding protein subunit gamma 2 (Table S4). Miao et al [58] previously reported that RGG2 played a negative role in plant growth and yield production and that manipulation of RGG2 can increase the plant biomass, grain weight, length and yield in rice. TraesCS6A02G112400 andTraesCS3A02G424000 encode polyubiquitin and small ubiquitin-related modi er, respectively (Table S4, Table S5). TraesCS3A02G421900 encodes a 26S proteasome regulatory subunit (Table S5), which participates in the ubiquitin/26S proteasome pathway and mediate the degradation of the complex of ubiquitin receptor and polyubiquitinated protein [59,60].
Previous studies reveled that the ubiquitin pathway play a important role in regulation grain size and weight in rice [61, 62]. These results indicated that the four genes were likely closely related to grain size and weight in wheat and they were useful in our following work of fne mapping and cloning of QTgw.cib-6A.

Plant materials and eld trials
A RIL population (F 10 ) comprising 193 lines derived from a cross CM42 and CM39 was used for QTL detection in the present study, which were developed by our laboratory in Chengdu Institute of Biology (CIB). CM42 is the rst wheat elite variety in the world bred by using synthetic hexaploid wheat (Triticum turgidum×Aegilops tauschii) germplasm, and showed high yield potential in Sichuan and the Yangzi River region [49], while CM39 is a elite winter wheat variety with different genetic background to that of CM42. CM42 and CM39 seeds were obtained from Sichuan Academy of Agricultural Sciences (SAAS). The RIL population and their parents were evaluated at two experimental sites in Sichuan province of China, Shuangliu (SHL, 103˚ 52'E, 30˚34'N) and Shifang (SHF, 104˚11'E, 31˚6'N), during four growing seasons from 2015-2016 to 2018-2019. Randomized block design was adopted for all of the trials. Each line was planted in a one-row plot with 50 seeds per row, a row length of 2.0 m, and a row spacing of 0.3 m. Five replicates were performed under each environment. Nitrogen and superphosphate fertilizers were applied at a rate of 80 and 100 kg/ha, respectively, at sowing. Crop management and disease control were performed according to local cultivation practices.

Phenotyping and statistical analysis
At maturity, ten representative plants from middle row of each line were randomly selected to investigate agronomic traits including TGW, GL, GW, GNS, PH, SL and SNS. SL was measured as the length from the base of the rachis to the tip of the terminal spikelet, excluding the awns. SNS was determined by counting the number of spikelets in main spikes; PH was measured from the soil surface to the tip of the spike, excluding the awns. Subsequently, the main spike of all selected plants were harvested and manually threshed for evaluating GNS, TGW, GW and GL using SC-G software (Wseen Co., Ltd, China). PH and SL were evaluated in eight environments, and the rest traits were evaluated in six environments.
Basic phenotypic statistical analyses, frequency distribution, correlation analyses and student's t tests were performed with SPSS version 20.0 (Chicago, IL, USA). The phenotype distribution graph was drawn using the plugin "CorrPlot" in Tbtools [63]. The relationships among measured traits were visualized using the R package "qgraph". The BLUP dataset across evaluated environments was calculated using the "lmer" function implemented in R package "lme4". The broad sense heritability (H2) was estimated using the PROC GLM procedure in SAS (SAS Institute Inc., North Carolina, USA) based on the following equation:, where is the variance of genotypes, is the variance of genotype by environmental effect, is the residual variance, n is the number of environments and r is the number of replicates [64].

Linkage map construction and QTL detection
A whole-genome genetic map constructed previously was adopted for QTL mapping [29]. The genetic map was constructed using the CM42×CM39 RIL population with SALF markers. A total of 4996 Bin SLAFs were distributed in 21 linkage groups and covered a total genetic distance of 2,859.94 cM with an average interval of 0.57 cM between adjacent Bin marker (Table S1) [29].
QTL analysis was conducted using the inclusive composite interval mapping (ICIM) function of IciMapping 4.  All data used in this study was present in the manuscript and supporting materials.
Code availability Not applicable.
Author contributions statement TL undertook the eld trials and subsequent analysis of all available data including the phenotyping and population genotyping, and drafted this manuscript. QL and ZP undertook the genetic map constructed. JW, ZY, YT, YS, JZ, XQ and XP participated in phenotyping. ZL, WY and JLprovided us the CM42 and CM39 seeds. MY, JL and HZ discussed results. HL, GD and YW designed the experiments, guided the entire study, participated in data analysis, discussed results and revised the manuscript. All authors have read and approved the manuscript.  centimorgans; CM42 and CM39 indicate the lines with the alleles from CM42 and CM39, resepectively; ** and *** represent signi cance at P < 0.01 and P < 0.001, respectively.

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