Single- and multi-locus genome-wide association study reveals genomic regions of thirteen yield-related traits in common wheat

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In this study, we genotyped 245 wheat varieties and measured 13 yield-related plant height-, grain- and spike-related traits, in seven environments, and identified 778 loci for these traits by genome-wide association study (GWAS) using single- and multi-locus models. Among them, nine were major loci, of which seven were novel, including Qph/lph.ahau-7A for plant height (PH) and leaf pillow height (LPH), Qngps/sps.ahau-1A for number of grains per spike (NGPS) and spikelet number per spike (SPS), Qsd.ahau-2B.1 and Qsd.ahau-5A.2 for spikelet density (SD), Qlph.ahau-7B.2 for LPH, Qgl.ahau-7B.3 for grain length (GL), and Qsl.ahau-3A.3 for spike length (SL). Through marker development, re-GWAS, gene annotation and cloning, and sequence variation, haplotype, and expression analyses, we confirmed two novel major loci and identified potential candidate genes, TraesCS7A02G118000 (named TaF-box-7A ) and TraesCS1A02G190200 (named TaBSK2-1A ) underlying Qph/lph.ahau-7A for PH-related traits and Qngps/sps.ahau-1A for spike-related traits, respectively. Furthermore, we reported two favorable haplotypes, including TaF-box-Hap1 associated with low PH and LPH and TaBSK2-Hap3 associated with high NGPS and SPS. In summary, these findings are valuable for improving wheat yield and enriching our understanding of the complex genetic mechanisms of yield-related traits. wheat yield genome-wide association study marker-trait association single nucleotide polymorphism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Wheat ( Triticum aestivum L.), a food staple for approximately 35% of the world’s population, is an important crop for global food security. However, at existing wheat production levels it will become increasingly difficult to meet food requirements because of climate change, rapid population expansion, and decreases in available arable land. It is estimated that wheat production must be increased by 1–1.6% annually to meet predicted global food requirements of 9 billion people by 2050 [1]. Accordingly, it is imperative that the genetic basis of grain yield is better understood to facilitate the breeding of high-yield wheat varieties. Grain yield is controlled by major and minor genes. Many quantitative trait loci (QTLs) related to yield-related traits have been identified across wheat’s 21 chromosomes [2, 3]. Many genes controlling these traits have also been isolated, such as TaGA2ox-A9 [4], TaGW2-6A , TaCKX6-D1 , TaCwi-A1 , TaGS1a , TaSus2-2A , TaSus1-7A/7B , 6-SFT-A2 , TaTGW6 , TaTGW-7A , TaFlo2-A1 , TaSnRK2.3 , TaSnRK2.10 , TaSPL20 / 21 , TaTPP-6AL1 , TaSUTs , TaSAP1-A1 , TaGS-D1 , TaMOC1-7A , TaGS5-3A , TaGL3‑5A , TaCYP78A5 , TaAPO-A1 / WAPO-A1 [2], TaCol-B5 [5], TaSPL17 [6], TaARF12 [7], TaGSNE [8], TaGL1-B1 [9], DRG1/TaACT7 [10], TaGSK3 [11], TaAGP-S1-7A , TaAGP-L-1B [12], KAT-2B [13], TaIAA21 [14], FT-D1 [15], and TaMADS-GS [16]. Although many yield-related genes have been cloned, the genetic mechanisms controlling yield-related traits in wheat remain unclear. Genome-wide association studies (GWAS) analyze associations between nucleotide polymorphisms and phenotypic variations. These studies represent powerful tools to identify QTLs associated with agronomic traits. The GWAS approach enables the simultaneous detection of numerous natural allelic variations within a single study. However, a drawback of this approach is that false positives may result from population structure and relatedness. To overcome this problem, the mixed linear model (MLM) was developed, incorporating kinship and population structure as covariates [17]. However, this model can elevate false negatives, potentially eliminating significant QTL. Therefore, other multi-locus GWAS methods such as mrMLM [18], FASTmrEMMA [19], FASTmrMLM [20], and IIIVmrMLM [21] have been developed. The introduction of multi-locus GWAS methodologies has enhanced precision of minor-effect QTL detection, thereby enriching our understanding of the complex genetic basis of yield-related traits. In the present study, we measured 13 yield-related traits, including those related to plant height (plant height [PH], leaf pillow height [LPH], subspike internode length [SIL], and spike neck length [SNL]), grain size and weight (thousand grain weight [TGW], grain width [GW], and grain length [GL]), and spike (spikelet density [SD], grain weight per spike [GWPS], number of grains per spike [NGPS], spike length [SL], spikelet number per spike [SPS], and spikelet set percentage [SSP]), in 245 wheat varieties in seven environments). Using these phenotypic data and genotypes from wheat Illumina iSelect 90K Infinium single nucleotide polymorphism (SNP) array, we performed GWAS to detect loci for yield-related traits using single- and multi-locus models. We then identified potential candidate genes underlying major loci for yield-related traits through transcriptome database, gene cloning, and sequence variation, haplotype and expression analyses, and marker development. In doing so, we identified valuable genetic resources that can be used to improve wheat yield. 2. Materials and methods 2.1. Plant materials and field experiments A total of 245 wheat varieties collected from four wheat regions in China were used for association mapping (Table S1 ) [22]. Yield-related traits of these varieties were evaluated in seven environments; from 2020–2021, E1 (Huaibei, Anhui, 116°757′E, 33°760′N), E2 (Hefei, Anhui, 117°213′E, 31°919′N), and E4 (Jiyuan, Henan, 112°590′E, 35°090′N); and from 2021–2022, E3 (Hefei, Anhui, 117°213′E, 31°919′N), E5 (Jiyuan, Henan, 112°590′E, 35°090′N), E6 (Bengbu, Anhui, 116°873′E, 33°102′N), and E7 (Suzhou, Anhui, 117°119′E, 33°883′N). We applied a complete block design that was randomized with two replications for all 245 materials. Each experimental unit was represented by a 2.0-m row plot and a 20-cm inter-row gap. To ensure appropriate wheat growth and development, regional conventional cultivation techniques were used in field experiments. 2.2. Phenotypic evaluation and statistical analyses After heading, PH-related traits of five representative plants from each genotype were assessed. PH was measured from the soil surface to the spike tip, excluding awns. LPH was measured from the soil surface to the base of the flag leaf. SIL was calculated as the distance from the spike’s base to its first internode. SNL was calculated as the distance between the base of the spike and the base of the flag leaf (Fig. S1 ). At the grain-maturity stage, five representative spikes from each genotype were sampled to determine spike-related traits. SL was measured with a ruler (in cm) from the base of the first spikelet to the apex of the last spikelet, excluding awns. SPS were counted and averaged across five spikes. NGPS was counted and then averaged for five spikes. GWPS was determined by multiplying TGW by NGPS and dividing the result by 1000. SD was determined by dividing SPS by SL. SSP was calculated by dividing NGPS by SPS. In phenotypic analysis, deletions, damage, or line mixing at harvest were excluded. In addition, 15 representative spikes were threshed, and their GL (mm), GW (mm), and TGW (g) were measured using a TGW meter and WanShen SC-G automatic seed analyzer (Hangzhou WanShen Testing Technology Co., Ltd.). Experiments were replicated three times, and average values were determined. To mitigate environmental effects, we calculated the best linear unbiased predictions (BLUPs) for all traits across the seven environments. Analysis of variance (ANOVA) and descriptive statistics were performed using IBM SPSS Statistics version 25.0 software (IBM Corporation, Armonk, NY, USA). The broad-sense heritability ( H 2 ) of a trait in a combined environment analysis was evaluated based on variance estimates by using the linear mixed model: H 2 = VG / (VG + VE), where VG and VE represent genetic and environmental variance estimates, respectively [23]. Correlation matrices between investigated features were determined using estimated BLUPs. 2.3. SNP genotyping and genome-wide association study In total, 245 varieties were genotyped using a wheat 90K SNP array. We excluded SNP markers with > 10% missing data, and those with minor allele frequencies < 5%. Finally, we retained 32,368 SNP markers for association analysis [22]. For single-locus analysis, we used the MLM in TASSEL to identify significant marker-trait associations (MTAs), incorporating the Q matrix for population structure and the K matrix for relatedness [24]. A Bonferroni correction threshold with an estimated P value < 0.05, corresponding to − log10( P ) = 5.81, was used to identify significant loci. However, this criterion is highly stringent because it considers every SNP (rather than independent tests) in the dataset. Consequently, a corrected Bonferroni threshold based on independent testing or an exploratory threshold is often used [25]. To identify an MTA as significant within specific contexts, we used − log10( P ) = 4.00 as an exploratory threshold. For multi-locus analysis, the 3V multi-locus stochastic SNP effect mixed linear model (IIIMmrMLM) in the R package “IIIMrMLM” was used to identify significant MTAs using the Q and K matrices in MLM [21]. We determined the significance of MTAs based on Bonferroni correction or a logarithm of odds > 3. We considered MTAs within linkage disequilibrium (LD) decay distance as a single QTL, and determined the major loci using two methods: (i) single- and multi-locus models in three or more environments; and (ii) three or more MTAs in the major loci using single- and multi-locus models. 2.4. Marker development We used 720 resequencing data of hexaploid wheat to obtain annotation information on gene variation sites through the online website WheatUnion ( http://wheat.cau.edu.cn/WheatUnion/ ) [26–28]. The franking sequence of each variation site was downloaded from the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v1.0. Based on preliminary GWAS results, SNPs located within half LD decay distance of the target interval of the major loci were converted into cleaved amplified polymorphic sequence (CAPS) or derived CAPS (dCAPS) markers, which were then integrated into the GWAS panel and used for re-GWAS. Within the interval of Qph/lph.ahau-7A and Qngps/sps.ahau-1A , 39 SNPs were converted to CAPS and dCAPS markers (Table S5). CAPS and dCAPS markers were developed using Primer Premier v. 5.0. 2.5. Candidate gene prediction and expression analysis The wheat genome and gene annotation information (IWGSC RefSeq v1.1) were downloaded from WheatOmics 1.0 ( http://202.194.139.32/ ) and used to screen candidate genes underlying major loci. Expression patterns of candidate genes were investigated using transcriptome data for wheat “Chinese Spring Development (single and pair).” Expression levels of candidate genes were analyzed using transcripts per million (TPM) values and normalized using the ZeroToOne method. Gene expression heatmaps were created using ChiPlot ( https://www.chiplot.online/ ). 2.6. Candidate gene cloning and haplotype analysis According to phenotypic differences and sequence variations queried on the WheatUnion website, we selected three wheat varieties to clone candidate genes TraesCS7A02G118000 and TraesCS1A02G190200 : Yangmai 158 (PH, 84.1 cm; LPH, 59.1 cm; NGPS, 63.6; SPS, 18.1), Jimai 22 (PH, 69.5 cm; LPH, 55.3 cm; NGPS, 52.8; SPS, 17.2), and Henong 825 (PH, 78.3 cm; LPH, 57.9 cm; NGPS, 51.8; SPS, 18.0). PlantCARE ( http://bioinformatics.psb.ugent.be/webtools/plantcare/html/ ) was used to analyze changes in promoter cis-elements. We used ProteinTools ( https://proteintools.uni-bayreuth.de/ ) to investigate the effects of non-synonymous mutations in the coding region on protein folding and stability. Key variations in promoter and coding regions were converted to gene-specific markers (Table S5). BLUP values were analyzed using a Mann-Whitney’s U-test to identify significant differences between haplotypes. 2.7 Expression analysis Nine wheat varieties, three each carrying TaF-box-7A-Hap1 (Henong 825, Annong 9267, and Bainong 207), TaF-box-7A-Hap2 (Jimai 22, Jinan 17, and Yangmai 16), and TaF-box-7A-Hap3 (Annong 1124, Jimai 20, and Yangmai 158), were used to investigate expression patterns of TaF-box-7A in wheat stems. Nine wheat varieties, three each carrying TaBSK2-1A-Hap1 (Lankao 298, Yannong 19, and Annong 9267), TaBSK2-1A-Hap2 (Yanzhan 4110, Haoyou 2018, and Zhongmai 895), and TaBSK2-1A-Hap3 (Shimai 12, Zhoumai 16, and Annong 8455), were used to investigate expression patterns of TaBSK2-1A in wheat spikes. Total RNA was extracted using a total RNA kit (Takara). Reverse transcription and quantitative real time PCR (qRT-PCR) was performed on an Accurate Biology system using Evo M-MLV and SYBR Green Pro Taq HS. All reactions were performed in triplicate for each sample. The qRT-PCR primers for candidate genes are presented in Table S5. The actin gene ( TraesCS1D02G020000 ) was used as the endogenous control. 3. Results 3.1. Phenotypic data analysis In the 245 wheat varieties, significant differences in phenotypic data of 13 yield-related traits were observed between genotype and environment ( P 0.90 (Table S2 ). The 13 yield-related traits exhibited continuous distributions across environments, showing typical quantitative characteristics controlled by multiple genes (Fig. 1 ). Based on BLUP values, the variation in the range of these 13 yield-related traits was analyzed. Variation in PH-, and spike-related traits was large, but variation in grain-related traits was small. For example, the variation coefficients of PH, LPH, SIL, and SNL were 11.6%, 11.89%, 11.19%, and 26.55%, respectively, while those for GL and GW were 4.02% and 2.73%, respectively (Table S3). 3.2. Correlations among phenotypic data We further analyzed correlations among 13 yield-related traits based on BLUP datasets (Fig. 2 ). All PH-related traits correlated positively with each other, but negatively with SD ( P < 0.05). Both GL and GW were positively correlated with TGW ( P < 0.05). GWPS was positively correlated with grain- (GW and TGW) and spike-related traits (NGPS, SD, SPS, and SSP) ( P < 0.01). NGPS was correlated negatively with PH- (PH and LPH) and grain-related traits (GL and TGW) ( P < 0.05), but positively with GWPS, SPS, and SSP ( P < 0.01). SD correlated negatively with SL but positively with SPS ( P < 0.01). SL correlated positively with SPS but negatively with SSP ( P < 0.01). SPS was negatively correlated with SSP ( P < 0.01). These results indicate that cross-links exist among PH-, grain- and spike-related traits, which together affect wheat yield. 3.3. Genetic loci for yield-related traits Both single-locus MLM and multi-locus IIIMrMLM models were used to detect MTAs for 13 yield-related traits, including plant height-, grain- and spike-related traits. GWAS revealed 414 MTAs based on single-locus models, and 528 MTAs based on multi-locus models (Table S4). According to the environmental repeatability of the significant MTAs and the LD decay of the subgenome [22], we classified these MTAs into 778 QTLs (or genetic loci) for yield-related traits. Among them, nine detected by both single-locus and multi-locus models were considered to be major loci, including three for PH and LPH (designated Qph/lph.ahau-1A , Qph/lph.ahau-1D , and Qph/lph.ahau-7A ), one each for LPH ( Qlph.ahau-7B.2 ), GL ( Qgl.ahau-7B.3 ), NGPS and SPS ( Qngps/sps.ahau-1A ), and SL ( Qsl.ahau-3A.3 ), and two for SD ( Qsd.ahau-2B.1 and Qsd.ahau-5A.2 ) (Table 1 ). Compared with reported loci, seven of these nine major QTLs were considered novel; two new loci, Qph/lph.ahau-7A and Qngps/sps.ahau-1A , were selected for further analysis. Table 1 Nine major QTLs identified based on single-locus MLM and multi-locus IIIVmrMLM models. Major QTL Trait Marker Position (bp) MLM 3VmrMLM Previously reported Gene .-Log10( P ) Marker R 2 Env .-Log10( P ) Marker R 2 Env Qph/lph.ahau-1A PH wsnp_Ex_c23598_32826926 567,979,312 4.38 8.9 BLUP 5.96–13.48 1.94–8.99 E1/E2/E3/E4/E6/E7/BLUP TaAPP1-A1 LPH wsnp_Ex_c23598_32826926 567,979,312 4.41 8.88 BLUP 5.93–14.13 3.32–7.90 E1/E2/E3/E6/E7/BLUP LPH RAC875_rep_c71093_1070 568,012,820 21.43 9.73 E4 LPH BS00079088_51 568,013,354 4.35–4.54 9.12–9.51 E4/BLUP Qph/lph.ahau-1D PH Ku_c111306_399 7,902,509 4.05 8.28 E7 10.03 4.01 E5 DRG1/TaACT7 LPH Ku_c111306_399 7,902,509 5.92 2.55 E5 PH wsnp_Ex_c1358_2600929 8,605,331 4.26 8.7 E1 LPH wsnp_Ex_c1358_2600929 8,605,331 4.35 8.89 E1 PH wsnp_Ex_c1358_2601510 8,605,912 4.9 10.15 E1 11.61–17.66 4.39–10.37 E1/BLUP LPH wsnp_Ex_c1358_2601510 8,605,912 5.37 11.12 E1 8.21–20.11 3.19–12.82 E1/E7/BLUP PH wsnp_Ex_c1358_2602235 8,606,637 4.45 9.12 E1 LPH wsnp_Ex_c1358_2602235 8,606,637 4.43 9.03 E1 PH Kukri_c837_436 8,609,839 4.04 8.27 E1 LPH Kukri_c837_436 8,609,839 4.06 8.27 E1 Qph/lph.ahau-7A PH BS00023225_51 76,124,550 4.01–4.46 8.67–9.53 E1/E2/E3/E4/E6/BLUP 12.78–13.13 8.04–9.38 E3/BLUP LPH BS00023225_51 76,124,550 4.27–5.23 9.08–11.33 E2/E3/E4/BLUP 11.33 8.10 E3 Qlph.ahau-7B.2 LPH RAC875_c5646_440 594,413,510 4.07–4.8 6.64–8.07 E2/E4/E7 16.32–20.54 7.65–8.53 E2/E4/E7/BLUP Qgl.ahau-7B.3 GL Tdurum_contig41998_1213 620,599,975 4.09 7.96 E5 5.35–11.18 4.98–9.36 E4/E5/E7/BLUP Qngps/sps.ahau-1A NGPS Excalibur_c24041_794 343,682,053 4.8–4.95 7.95–8.27 E1/BLUP 7.34–9.01 6.49–7.60 E5/BLUP SPS Excalibur_c24041_794 343,682,053 5.11 8.54 E4 10.09 9.02 E4 NGPS BS00062715_51 344,250,176 4.08 6.59 BLUP SPS BS00062715_51 344,250,176 4 6.4 E4 NGPS Ex_c5759_628 345,108,312 4.21 6.85 BLUP NGPS wsnp_Ex_c33831_42253707 345,308,693 4.46–4.48 7.32–7.35 E1/BLUP SPS wsnp_Ex_c33831_42253707 345,308,693 4 6.4 E4 SPS Excalibur_c102582_360 345,786,652 4.28 6.94 E4 6.27 3.80 E2 Qsd.ahau-2B.1 SD BobWhite_c9843_117 26,101,612 4.66–5.35 8.41–9.76 E5/E6 4.29–6.09 3.07–5.41 E5/E6/E7/BLUP SD wsnp_Ku_c2486_4751761 26,314,294 12.31 4.53 E6 Qsd.ahau-5A.2 SD RFL_Contig2251_1070 37,619,944 4.09–5.75 6.66–9.92 E1/E3/E4/BLUP 3.85–7.75 2.50–5.81 E1/E3/E7/BLUP Qsl.ahau-3A.3 SL Excalibur_c98205_83 25,493,701 4.61 9.96 E2 22.60 16.13 E4 SL Excalibur_c24990_1482 27,247,151 4.01 8.87 E4 SL Kukri_c7508_805 27,428,730 13.94 32.37 E5 Chr: Chromosome; Env: Environments; Physical position is based on IWGSC RefSeq v1.1. 3.4. Validation of major loci Qph/lph.ahau-7A and Qngps/sps.ahau-1A and prediction of candidate genes To validate Qph/lph.ahau-7A and Qngps/sps.ahau-1A , we developed 27 and 12 molecular markers in these two regions, respectively, and then integrated them into the wheat Illumina iSelect 90K Infinium SNP array for re-GWAS. The loci Qph/lph.ahau-7A was mapped to 76.12–77.40 Mb on chromosome 7A, and Qngps/sps.ahau-1A was mapped to 343.68–345.79 Mb on chromosome 1A (Fig. 3 , 4 ; Table S6). The results support associations of Qph/lph.ahau-7A with PH-related traits, and Qngps/sps.ahau-1A with spike-related traits. Within the intervals of Qph/lph.ahau-7A (~ 1.28 Mb), 24 high-confidence genes were annotated (Table S7). Based on the transcriptome database of “Chinese Spring Development (single and pair),” we found that nine of these genes were expressed in wheat stems (Fig. S2 A, 3D). Of these nine genes, four were highly expressed at the early stage of stem development and downregulated with stem development (Fig. S2 B, 3E). F-box protein is a key element in gibberellin (GA) signaling responsible for proteasome-dependent degradation of DELLA, which can regulate plant height [30–32]. Therefore, among the four genes expressed in wheat stems, TraesCS7A02G118000 (named TaF-box-7A ), which encodes a F-box family protein, was preferentially considered as a candidate gene underlying Qph/lph.ahau-7A . In the Qngps/sps.ahau-1A (~ 2.11 Mb) region, 12 high-confidence genes were annotated (Table S7). Based on the transcriptome database of “Chinese Spring Development (single and pair),” we found that five of these genes were expressed in wheat spikes (Fig. S2 C, 4D). Among these, two were highly expressed at the early stage of spike development and downregulated with spike development (Fig. S2 D, 4E). Serine/threonine protein kinases play important roles in regulating wheat grains per spike and rice grain number per panicle [33, 34]. Therefore, TraesCS1A02G190200 (named TaBSK2-1A ), encoding the serine/threonine-protein kinase BSK2, was preferentially considered to be a candidate gene underlying Qngps/sps.ahau-1A . 3.5. Sequence analysis of candidate genes TaF-box-7A and TaBSK2-1A To explore the roles of TaF-box-7A and TaBSK2-1A in PH- and spike-related traits, respectively, we cloned the promoter and CDS regions of TaF-box-7A and TaBSK2-1A in three wheat varieties with different PH- and spike-related phenotypes: Yangmai 158, Jimai 22, and Henong 825. TaF-box-7A was 1431 bp in length and contained only one exon, encoding 476 amino acids containing unknown function DUF295 domain (Fig. 5 A). Sequence alignment analysis revealed 11 variants, including 6 in the promoter and 5 in the coding region (4 missense mutations and 1 synonymous mutation) (Table S8). One mutation (− 744 bp, C/T) in the promoter led to changes in the GARE-motif and the Myb-binding site. Because there were four missense mutations in the coding region, we predicted the protein’s three-dimensional structure and found that the mutation (G/T) at + 304 bp in the coding region changed leucine (G) to methionine (T), thus affecting the folding and stability of the protein (Fig. 5 B). TaBSK2-1A was 5138 bp in length and contained a 232 bp 5’UTR, 10 exons, 9 introns, and one 643 bp 3’UTR. Its CDS was 1512 bp in length, encoding 503 amino acids containing tetratricopeptide-like helical and serine-threonine/tyrosine-protein kinase domain (Fig. 6 A). Sequence alignment revealed 17 variants, including 13 in the promoter, 2 in the 5’UTR, and 2 in the coding region (2 synonymous mutations). Of the 13 mutations in the promoter, one at − 1421 bp (G/A) led to changes in the ABRE and G-box elements, and one at − 937 bp (T/C) led to changes in the TGA-element (Table S8). 3.6. Associations of TaF-box-7A and TaBSK2-1A with PH- and spike-related traits Based on two mutations (C/T, − 744 bp; T/G, + 304 bp) in the promoter and coding regions of TaF-box-7A , we developed two molecular markers (named TaF-box-7A-Q-744 and TaF-box-C + 304, respectively) (Fig. S3). These two markers were then used to genotype 245 wheat varieties (Table S10). A total of three haplotypes were formed, named TaF-box-Hap1 (65.5%), TaF-box-Hap2 (18.8%), and TaF-box-Hap3 (15.7%) (Fig. 5 C). Haplotype analysis revealed that wheat varieties carrying TaF-box-Hap1 had significantly lower PH and LPH compared with those carrying TaF-box-Hap2 and TaF-box-Hap3 , and the PH values of wheat varieties carrying TaF-box-Hap2 were significantly lower than those carrying TaF-box-Hap3 (Fig. 5 D, E). To investigate the association of TaF-box-7A with PH-related traits at the transcriptional level, we detected the expression level of TaF-box-7A in stems in varieties with three haplotypes. Expression levels of TaF-box-7A in varieties with TaF-box-Hap3 were significantly higher than those with TaF-box-Hap1 (Fig. 5 F), similar to trends in corresponding PH and LPH phenotypes. These results confirm the association of TaF-box-7A with PH-related traits at both DNA and transcriptional levels. Based on two mutations (G/A, − 1421 bp; T/C, − 937 bp) in the promoter region of TaBSK2-1A , we developed two molecular markers (named TaBSK2-Q-1421 and TaBSK2-Q-937, respectively) (Fig. S4). These two markers were then used to genotype 245 wheat varieties (Table S10). A total of three haplotypes were formed, named TaBSK2-Hap1 (23.2%), TaBSK2-Hap2 (12.0%), and TaBSK2-Hap3 (64.8%) (Fig. 6 B). Haplotype analysis revealed that wheat varieties with TaBSK2-Hap3 had significantly higher NGPS compared with those with TaBSK2-Hap1 and TaBSK2-Hap2 (Fig. 6 C), and the SPS values of varieties carrying TaBSK2-Hap3 were significantly higher than those carrying TaBSK2-Hap1 (Fig. 6 D). To investigate the association of TaBSK2-1A with spike-related traits at the transcriptional level, we detected expression levels of TaBSK2-1A in spikes in varieties with three haplotypes. Expression levels of TaBSK2-1A in varieties with TaBSK2-Hap3 were significantly higher than those with TaBSK2-Hap1 , similar to trends in corresponding NGPS and SPS phenotypes (Fig. 6 E). These results confirm the association of TaBSK2-1A with spike-related traits at both DNA and transcriptional levels. 4. Discussion Enhancing the yield potential of wheat has become a priority in breeding programs. Exploring genes associated with high yield is therefore important to engineer wheat varieties with superior traits. We used genotype and phenotype data from 245 wheat varieties to perform GWAS, and found numerous QTLs for yield-related traits based on single- and multi-locus models. Among QTLs associated with PH-related traits, 16 were consistent with previous reports (Table S9). The two major loci Qph/lph.ahau-1A and Qph/lph.ahau-1D were close to genes that control PH-related traits, including TaAPP1-A1 [29] and DRG1/TaACT7 [10]. Additionally, 20 QTLs for grain-related traits were co-localized with or close to previously reported loci, with eight of them located in the genes reported to control grain-related traits ( TaSUT4-6D , 6-SFT-D , TaGS-D1 , TaTKW-7A , TaSnRK2.3-1B ) [2, 3]; TaGSNE-5A [8]; and TaAGP-L-1B and TaAGP-S1-7A [12]) (Table S9). By comparing the loci for spike-related traits detected in this study with known genes or loci, we found that 17 QTLs were close to or co-localized with previously reported loci (Table S9). We identified a new major QTL ( Qph/lph.ahau-7A ) associated with PH and LPH. Based on the transcriptome database, gene annotation and functional analysis of reported gene family members, we considered TraesCS7A02G118000 (named TaF-box-7A ) encoding a F-box family protein containing unknown function DUF295 domain to be a candidate gene underlying Qph/lph.ahau-7A . The association of TaF-box-7A with PH-related traits was further confirmed through sequence variation, haplotype, and expression analyses, and marker development. The F-box family protein acts as a key component of GA signaling, which is a component of the SCF (SKP1-cullin-F-box) E3 ubiquitin ligase complex, mediating the ubiquitination and subsequent proteasomal degradation of target proteins [30]. Several studies have confirmed the role of the F-box family members in PH. For example, GID2 , encoding a F-box protein, is a positive regulator of GA signaling and regulates PH in rice [31]; OsFBK4 , encoding a F-box protein, positively regulates PH by promoting internode cell size and participates in GA signaling and biosynthesis pathways [32]. We speculate that TaF-box-7A may influence PH and LPH in wheat. We identified one further new major QTL ( Qngps/sps.ahau-1A ) associated with NGPS and SPS. Based on the transcriptome database, gene annotation and functional analysis of reported gene family members, we considered TraesCS1A02G190200 (named TaBSK2-1A ), encoding the serine/threonine-protein kinase BSK2, to be a candidate gene underlying Qngps/sps.ahau-1A . We confirmed the association of TaBSK2-1A with NGPS and SPS through sequence variation, haplotype, and expression analyses, and marker development. Serine/threonine protein kinases play important roles in mediating spike development. For example, GRAIN SIZE AND NUMBER1 (GSN1), a mitogen-activated protein kinase phosphatase OsMKP1 , negatively regulates the OsMKKK10–OsMKK4–OsMPK6 cascade (belonging to the serine/threonine protein kinase family) to coordinate the trade-off between grain number per panicle and grain size in rice [33]. The haplotype Hap -5A-4 of the TaSnRK2.9-5A gene which encodes a serine/threonine protein kinase is significantly associated with high grains per spike in wheat [34]. KERNEL NUMBER PER ROW6 ( KNR6 ), a serine/threonine protein kinase gene, controls maize yield by influencing the number of female panicle florets, panicle length, and row number [35]. TaCol-B5 is differentially phosphorylated by the serine/threonine protein kinase TaK4 , thereby modifying spike architecture and enhancing wheat grain yield [5]. Based on these findings, we hypothesize that TaBSK2-1A may regulate spike-related traits (such as NGPS and SPS) in wheat. Further experimentation is required to confirm this hypothesis. 5. Conclusions We detected many QTLs associated with yield-related traits using single- and multi-locus models; of these, seven were new major loci. We then identified two potential candidate genes ( TaF-box-7A and TaBSK2-1A ) underlying Qph/lph.ahau-7A for PH-related traits and Qngps/sps.ahau-1A for spike-related traits, respectively. In doing so, we identified useful gene resources and molecular markers for breeding high-yield wheat varieties. Further functional characterizations of the two candidate genes are necessary to elucidate their applications in wheat high-yield breeding. Declarations Acknowledgements Not applicable. Funding This work was supported by grants from the breeding of new wheat varieties with super-high yield and wide suitability in Southern region of Yellow and Huai River of China (2023ZD040230307), the Joint Key Project of Improved Wheat Variety of Anhui Province (22805001), the Agriculture Research System of Anhui Province (AHCYTX-02), and Jiangsu Collaborative Innovation Center for Modern Crop Production (JCIC-MCP). Author information Authors and Affiliations College of Agronomy, Anhui Agricultural University, Key Laboratory of Wheat Biology and Genetic Improvement on Southern Yellow & Huai River Valley, Ministry of Agriculture, Hefei, 230036, Anhui, China Yuxia Lv, Liansheng Dong, Xiatong Wang, Linhong Shen, Wenbo Lu, Fan Si, Yaoyao Zhao, Guanju Zhu, Yiting Ding, Shujun CAO, Jiajia Cao, Jie Lu, Chuanxi Ma, Cheng Chang & Haiping Zhang Contributions YXL, LSD conceived and designed the study, as well as outlined and wrote the manuscript. XTW, LHS, WBL, FS, YYZ, GJZ, YTD, SJC, JJC performed the experiments. JL, CXM helped to write the manuscript. CC, HPcompleted the writing, review and editing. Corresponding author Correspondence to Haiping Zhang and Chang Cheng. Ethics approval and consent to participate This article does not contain any studies with human participants or animals performed by the authors. These methods were carried out in accordance with relevant guidelines and regulations. We confirm that all experimental protocols were approved by Anhui Agricultural University. Consent for publication Not applicable. Availability of data and materials All data related to this manuscript can be found within this paper and its supplementary data. Competing interests The authors declare that they have no competing interests. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Li A, Hao C, Wang Z, Geng S, Jia M, Wang F, Mao, L. Wheat breeding history reveals synergistic selection of pleiotropic genomic sites for plant architecture and grain yield. Mol Plant, 2022; 15(3), 504–519. Cao S, Xu D, Hanif M, Xia X, He Z. Genetic architecture underpinning yield component traits in wheat. Theor Appl Genet, 2020; 133, 1811–1823. Saini DK, Srivastava P, Pal N, Gupta PK. Meta-QTLs, ortho-meta-QTLs and candidate genes for grain yield and associated traits in wheat ( Triticum aestivum L.). Theor Appl Genet, 2022; 135(3), 1049–1081. Tian X, Xia X, Xu D, Liu Y, Xie L, Hassan MA, Cao S. Rht24b , an ancient variation of TaGA2ox-A9 , reduces plant height without yield penalty in wheat. New Phytol, 2022; 233(2), 738–750. Zhang X, Jia H, Li T, Wu J, Nagarajan R, Lei L, Yan L. TaCol-B5 modifies spike architecture and enhances grain yield in wheat. Science, 2022; 376(6589), 180–183. Liu Y, Chen J, Yin C, Wang Z, Wu H, Shen K, Guo Z. A high-resolution genotype–phenotype map identifies the TaSPL17 controlling grain number and size in wheat. Genome Biol, 2023; 24(1), 196. Kong X, Wang F, Wang Z, Gao X, Geng S, Deng Z, Li A. Grain yield improvement by genome editing of TaARF12 that decoupled peduncle and rachis development trajectories via differential regulation of gibberellin signaling in wheat. Plant Biotechnol J, 2023; 21(10), 1990–2001. Khan N, Zhang Y, Wang J, Li Y, Chen X, Yang L, Jing R. TaGSNE , a WRKY transcription factor, overcomes the trade-off between grain size and grain number in common wheat and is associated with root development. J Exp Bot, 2022; 73(19), 6678–6696. Niaz M, Zhang L, Lv G, Hu H, Yang X, Cheng Y, Chen F. Identification of TaGL1-B1 gene controlling grain length through regulation of jasmonic acid in common wheat. Plant Biotechnol J, 2023; 21(5), 979–989. Xie Z, Zhang L, Zhang Q, Lu Y, Dong C, Li D, Kong X. A Glu209Lys substitution in DRG1/TaACT7 , which disturbs F-actin organization, reduces plant height and grain length in bread wheat. New Phytol, 2023; 240(5), 1913–1929. Dong H, Li D, Yang R, Zhang L, Zhang Y, Liu X, Sun J. GSK3 phosphorylates and regulates the Green Revolution protein Rht-B1b to reduce plant height in wheat. Plant Cell, 2023; 35(6), 1970–1983. Hou J, Li T, Wang Y, Hao C, Liu H, Zhang X. ADP-glucose pyrophosphorylase genes, associated with kernel weight, underwent selection during wheat domestication and breeding. Plant Biotechnol J, 2017; 15(12), 1533–1543. Chen Y, Yan Y, Wu TT, Zhang GL, Yin H, Chen W, Gou JY. Cloning of wheat keto-acyl thiolase 2B reveals a role of jasmonic acid in grain weight determination. Nat Commun, 2020; 11(1), 6266. Jia M, Li Y, Wang Z, Tao S, Sun G, Kong X, Li A. TaIAA21 represses TaARF25 -mediated expression of TaERFs required for grain size and weight development in wheat. Plant J, 2021; 108(6), 1754–1767. Chen Z, Ke W, He F, Chai L, Cheng X, Xu H, Ni Z. A single nucleotide deletion in the third exon of FT-D1 increases the spikelet number and delays heading date in wheat ( Triticum aestivum L.). Plant Biotechnol J, 2022; 20(5), 920–933. Zhang J, Zhang Z, Zhang R, Yang C, Zhang X, Chang S, Yao Y. Type I MADS-box transcription factor TaMADS‐GS regulates grain size by stabilizing cytokinin signaling during endosperm cellularization in wheat. Plant Biotechnol J, 2024; 22(1), 200–215. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet, 2006; 38(8), 904–909. Wang SB, Feng JY, Ren WL, Huang B, Zhou L, Wen YJ, Zhang YM. Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology. Sci Rep, 2016; 6(1), 19444. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wu R. Methodological implementation of mixed linear models in multi-locus genome-wide association studies. Brief Bioinform, 2018; 19(4), 700–712. Zhang YM, Jia Z, Dunwell JM. The applications of new multi-locus GWAS methodologies in the genetic dissection of complex traits. Front Plant Sci, 2019; 10, 100. Li M, Zhang YW, Zhang ZC, Xiang Y, Liu MH, Zhou YH, Zhang YM. A compressed variance component mixed model for detecting QTNs and QTN-by-environment and QTN-by-QTN interactions in genome-wide association studies. Mol Plant, 2022; 15(4), 630–650. Pan X, Nie XL, Gao W, Yan SN, Feng HS, Cao JJ, Zhang HP. Identification of genetic loci and candidate genes underlying freezing tolerance in wheat seedlings. Theor Appl Genet, 2024; 137(3), 57. Smith SE, Kuehl RO, Ray IM, Hui R, Soleri D. Evaluation of simple methods for estimating broad-sense heritability in stands of randomly planted genotypes. Crop Sci, 1998; 38(5), 1125–1129. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics, 2007; 23(19), 2633–2635. Bland JM, Altman DG. Multiple significance tests: the Bonferroni method. Bmj, 1995; 310(6973), 170. Guo W, Xin M, Wang Z, Yao Y, Hu Z, Song W, Sun Q. Origin and adaptation to high altitude of Tibetan semi-wild wheat. Nat Commun, 2020; 11(1), 5085. Hao C, Jiao C, Hou J, Li T, Liu H, Wang Y, Zhang X. Resequencing of 145 landmark cultivars reveals asymmetric sub-genome selection and strong founder genotype effects on wheat breeding in China. Mol Plant, 2020; 13(12), 1733–1751. Niu J, Ma S, Zheng S, Zhang C, Lu Y, Si Y, Ling H Q. Whole-genome sequencing of diverse wheat accessions uncovers genetic changes during modern breeding in China and the United States. Plant Cell, 2023; 35(12), 4199–4216. Niu KX., Chang CY, Zhang MQ, Guo YT, Yan Y, Sun HJ, Gou JY. Suppressing ASPARTIC PROTEASE 1 prolongs photosynthesis and increases wheat grain weight. Nat Plants, 2023; 9(6), 965–977. Hernández-García J, Briones-Moreno A, Blázquez MA. Origin and evolution of gibberellin signaling and metabolism in plants. Semin Cell Dev Biol, 2021; 109, 46–54. Sasaki A, Itoh H, Gomi K, Ueguchi-Tanaka M, Ishiyama K, Kobayashi M, Matsuoka M. Accumulation of phosphorylated repressor for gibberellin signaling in an F-box mutant. Science, 2003; 299(5614), 1896–1898. Zegeye WA, Chen D, Islam M, Wang H, Riaz A, Rani MH, Zhang Y. OsFBK4 , a novel GA insensitive gene positively regulates plant height in rice ( Oryza Sativa L.). E Genet Genomics, 2022; 23, 100115. Guo T, Chen K, Dong, NQ, Shi C L, Ye WW, Gao JP, Lin HX. GRAIN SIZE AND NUMBER1 negatively regulates the OsMKKK10-OsMKK4-OsMPK6 cascade to coordinate the trade-off between grain number per panicle and grain size in rice. Plant Cell, 2018; 30(4), 871–888. Ur Rehman S, Wang J, Chang X, Zhang X, Mao X, Jing R. A wheat protein kinase gene TaSnRK2.9-5A associated with yield contributing traits. Theor Appl Genet, 2019; 132, 907–919. Jia H, Li M, Li W, Liu L, Jian Y, Yang Z, Zhang Z. A serine/threonine protein kinase encoding gene KERNEL NUMBER PER ROW6 regulates maize grain yield. Nat Commun, 2020; 11(1), 988. Additional Declarations No competing interests reported. Supplementary Files FigS17.docx TableS110.xls Cite Share Download PDF Status: Published Journal Publication published 21 Dec, 2024 Read the published version in BMC Plant Biology → Version 1 posted Editorial decision: Revision requested 18 Nov, 2024 Reviews received at journal 17 Nov, 2024 Reviews received at journal 13 Nov, 2024 Reviewers agreed at journal 12 Nov, 2024 Reviewers agreed at journal 11 Nov, 2024 Reviewers agreed at journal 10 Nov, 2024 Reviewers agreed at journal 10 Nov, 2024 Reviewers invited by journal 10 Nov, 2024 Editor invited by journal 08 Nov, 2024 Editor assigned by journal 07 Nov, 2024 Submission checks completed at journal 07 Nov, 2024 First submitted to journal 04 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5391583","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":380235197,"identity":"0d673585-0b41-45e1-b330-0b0dfe4b10c7","order_by":0,"name":"Yuxia 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Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Chang","suffix":""},{"id":380235211,"identity":"11cffc47-a3fe-4d57-ae97-479d0a71863f","order_by":14,"name":"Haiping Zhang","email":"","orcid":"","institution":"Anhui Agricultural University, Ministry of Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Haiping","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-11-05 02:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5391583/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5391583/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-024-05956-y","type":"published","date":"2024-12-21T15:57:32+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69832320,"identity":"c87fc938-cb5e-42f0-82b7-0132aa158299","added_by":"auto","created_at":"2024-11-25 15:42:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":283634,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic distribution of yield-related traits evaluated in seven environments (E1–7) and BLUP values in 245 wheat varieties. GL, grain length; GW, grain width; GWPS, grain weight per spike; LPH, leaf pillow height; NGPS, number of grains per spike; PH, plant height; SD, spikelet density; SIL, subspike internode length; SL, spike length; SNL, spike neck length; SPS, spikelet number per spike; SSP, spikelet set percentage; TGW, thousand grain weight.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5391583/v1/55d6988fd8096b50164a53a8.png"},{"id":69832321,"identity":"028ba147-24a9-4cef-8baa-67196c0a9d78","added_by":"auto","created_at":"2024-11-25 15:42:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":941914,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation coefficients among yield-related traits calculated from BLUP values. The pair plot shows bivariate scatter plots below the diagonal, histograms on the diagonal, and Pearson’s correlations between given traits above the diagonal. GL, grain length; GW, grain width; GWPS, grain weight per spike; LPH, leaf pillow height; NGPS, number of grains per spike; PH, plant height; SD, spikelet density; SIL, subspike internode length; SL, spike length; SNL, spike neck length; SPS, spikelet number per spike; SSP, spikelet set percentage; TGW, thousand grain weight. Statistically significant correlations are denoted by asterisks, where * \u003cem\u003eP\u003c/em\u003e ≤ 0.05, ** \u003cem\u003eP\u003c/em\u003e ≤ 0.01, and ***\u003cem\u003e P\u003c/em\u003e ≤ 0.001.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5391583/v1/f9cfd77ffea354e3ff51d871.png"},{"id":69832325,"identity":"07726c5f-7f3b-40b2-a4ab-b86ec0179334","added_by":"auto","created_at":"2024-11-25 15:42:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":201992,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of the major locus \u003cem\u003eQph/lph.ahau-7A\u003c/em\u003eassociated with PH and LPH, and expression analysis of the candidate gene \u003cem\u003eTraesCS7A02G118000\u003c/em\u003e. \u003cstrong\u003eA\u003c/strong\u003e PH and \u003cstrong\u003eB\u003c/strong\u003e LPH Manhattan plot of the locus \u003cem\u003eQph/lph.ahau-7A\u003c/em\u003e. \u003cstrong\u003eC\u003c/strong\u003e Linkage disequilibrium heatmap of the locus \u003cem\u003eQph/lph.ahau-7A\u003c/em\u003e. Expression patterns of \u003cstrong\u003eD \u003c/strong\u003e\u003cem\u003eTraesCS7A02G118000\u003c/em\u003e in different wheat tissues and \u003cstrong\u003eE \u003c/strong\u003e\u003cem\u003eTraesCS7A02G118000\u003c/em\u003e at different stem developmental stages. LPH, leaf pillow height; PH, plant height; Stem_Z30, _Z32, and _Z65, stems at 1 cm spike, two-node and anthesis stages, respectively.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5391583/v1/e1e7fd78c4c4e20c95a16de7.png"},{"id":69833441,"identity":"518ebeb2-27b7-4d99-aa0f-b16d5a810a0c","added_by":"auto","created_at":"2024-11-25 15:50:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":209049,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of the major locus \u003cem\u003eQngps/sps.ahau-1A\u003c/em\u003eassociated with NGPS and SPS, and expression analysis of the candidate gene \u003cem\u003eTraesCS1A02G190200\u003c/em\u003e. \u003cstrong\u003eA\u003c/strong\u003e NGPS and \u003cstrong\u003eB\u003c/strong\u003e SPS Manhattan plot of the locus \u003cem\u003eQngps/sps.ahau-1A\u003c/em\u003e. \u003cstrong\u003eC\u003c/strong\u003e Linkage disequilibrium heatmap of the locus \u003cem\u003eQngps/sps.ahau-1A\u003c/em\u003e. Expression patterns of \u003cstrong\u003eD\u003c/strong\u003e \u003cem\u003eTraesCS1A02G190200\u003c/em\u003e in different wheat tissues and \u003cstrong\u003eE \u003c/strong\u003e\u003cem\u003eTraesCS1A02G190200 \u003c/em\u003eat different spike developmental stages. NGPS, number of grains per spike; SPS, spikelet number per spike; Spike_Z32, _Z39, and _Z65, spikes at two-node, flag leaf and anthesis stages, respectively.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5391583/v1/d3d2c35134d676b7501f1be2.png"},{"id":69832322,"identity":"0347df04-cb22-49d8-98f5-b957f5d69b63","added_by":"auto","created_at":"2024-11-25 15:42:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":481712,"visible":true,"origin":"","legend":"\u003cp\u003eHaplotype and expression analysis of candidate gene\u003cem\u003eTaF-box-7A\u003c/em\u003e underlying \u003cem\u003eQph/lph.ahau-7A\u003c/em\u003e. \u003cstrong\u003eA\u003c/strong\u003e Gene structure of \u003cem\u003eTaF-box-7A.\u003c/em\u003e \u003cstrong\u003eB\u003c/strong\u003eProtein 3D structure diagram of \u003cem\u003eTaF-box-7A\u003c/em\u003e. \u003cstrong\u003eC\u003c/strong\u003e Three haplotypes of \u003cem\u003eTaF-box-7A\u003c/em\u003e (\u003cem\u003eTaF-box-Hap1\u003c/em\u003e, \u003cem\u003eTaF-box-Hap2\u003c/em\u003e, and \u003cem\u003eTaF-box-Hap3\u003c/em\u003e). \u003cstrong\u003eD, E\u003c/strong\u003e Phenotypic differences in PH and LPH between wheat varieties with three haplotypes\u003cem\u003e \u003c/em\u003eof \u003cem\u003eTaF-box-7A\u003c/em\u003e. \u003cstrong\u003eF\u003c/strong\u003e Expression analysis of \u003cem\u003eTaF-box-7A\u003c/em\u003ein stems in wheat varieties with three haplotypes \u003cem\u003eTaF-box-Hap1\u003c/em\u003e, \u003cem\u003eTaF-box-Hap2\u003c/em\u003e, and \u003cem\u003eTaF-box-Hap3\u003c/em\u003e. LPH, leaf pillow height; PH, plant height. Statistically significant differences are denoted by asterisks, where * \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05; ** \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01; *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; **** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001; “ns” indicates no significant difference.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5391583/v1/4daa746ea7475ba4a8a4b5f2.png"},{"id":69833442,"identity":"db1d93ff-6074-4c24-b956-083036abd0df","added_by":"auto","created_at":"2024-11-25 15:50:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":270154,"visible":true,"origin":"","legend":"\u003cp\u003eHaplotype and expression analysis of candidate gene \u003cem\u003eTaBSK2-1A\u003c/em\u003e underlying \u003cem\u003eQngps/sps.ahau-1A\u003c/em\u003e. \u003cstrong\u003eA\u003c/strong\u003e Gene structure of \u003cem\u003eTaBSK2-1A.\u003c/em\u003e \u003cstrong\u003eB\u003c/strong\u003e Three haplotypes of \u003cem\u003eTaBSK2-1A\u003c/em\u003e (\u003cem\u003eTaBSK2-Hap1\u003c/em\u003e, \u003cem\u003eTaBSK2-Hap2\u003c/em\u003e, and \u003cem\u003eTaBSK2-Hap3\u003c/em\u003e). \u003cstrong\u003eC, D\u003c/strong\u003e Phenotypic differences in NGPS and SPS between wheat varieties with three haplotypes\u003cem\u003e \u003c/em\u003eof \u003cem\u003eTaBSK2-1A\u003c/em\u003e. \u003cstrong\u003eE \u003c/strong\u003eExpression analysis of \u003cem\u003eTaBSK2-1A\u003c/em\u003e in spikes in wheat varieties with three haplotypes \u003cem\u003eTaBSK2-Hap1\u003c/em\u003e, \u003cem\u003eTaBSK2-Hap2\u003c/em\u003e, and \u003cem\u003eTaBSK2-Hap3\u003c/em\u003e. NGPS, number of grains per spike; SPS, spikelet number per spike. Statistically significant differences are denoted by asterisks, where * \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05; ** \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01; *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; **** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001; “ns” indicates no significant difference.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5391583/v1/e4c4953c31cdc6914b49926d.png"},{"id":72201721,"identity":"3f0080f1-520b-489b-8bcd-41aec788a7d4","added_by":"auto","created_at":"2024-12-23 16:10:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3323015,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5391583/v1/e0a8b017-62be-4e37-8735-1f873dc47d71.pdf"},{"id":69832326,"identity":"9df6c65b-31fd-40f4-8d6d-48e49bc9c970","added_by":"auto","created_at":"2024-11-25 15:42:56","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3646295,"visible":true,"origin":"","legend":"","description":"","filename":"FigS17.docx","url":"https://assets-eu.researchsquare.com/files/rs-5391583/v1/80e3f7365d0084e9823bd8c0.docx"},{"id":69832328,"identity":"e0d98a13-2d1d-450a-9a77-5036252b53a9","added_by":"auto","created_at":"2024-11-25 15:42:56","extension":"xls","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":140614,"visible":true,"origin":"","legend":"","description":"","filename":"TableS110.xls","url":"https://assets-eu.researchsquare.com/files/rs-5391583/v1/7a5f45d2c3be0e785ae7ef90.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Single- and multi-locus genome-wide association study reveals genomic regions of thirteen yield-related traits in common wheat","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.), a food staple for approximately 35% of the world\u0026rsquo;s population, is an important crop for global food security. However, at existing wheat production levels it will become increasingly difficult to meet food requirements because of climate change, rapid population expansion, and decreases in available arable land. It is estimated that wheat production must be increased by 1\u0026ndash;1.6% annually to meet predicted global food requirements of 9\u0026nbsp;billion people by 2050 [1]. Accordingly, it is imperative that the genetic basis of grain yield is better understood to facilitate the breeding of high-yield wheat varieties.\u003c/p\u003e \u003cp\u003eGrain yield is controlled by major and minor genes. Many quantitative trait loci (QTLs) related to yield-related traits have been identified across wheat\u0026rsquo;s 21 chromosomes [2, 3]. Many genes controlling these traits have also been isolated, such as \u003cem\u003eTaGA2ox-A9\u003c/em\u003e [4], \u003cem\u003eTaGW2-6A\u003c/em\u003e, \u003cem\u003eTaCKX6-D1\u003c/em\u003e, \u003cem\u003eTaCwi-A1\u003c/em\u003e, \u003cem\u003eTaGS1a\u003c/em\u003e, \u003cem\u003eTaSus2-2A\u003c/em\u003e, \u003cem\u003eTaSus1-7A/7B\u003c/em\u003e, \u003cem\u003e6-SFT-A2\u003c/em\u003e, \u003cem\u003eTaTGW6\u003c/em\u003e, \u003cem\u003eTaTGW-7A\u003c/em\u003e, \u003cem\u003eTaFlo2-A1\u003c/em\u003e, \u003cem\u003eTaSnRK2.3\u003c/em\u003e, \u003cem\u003eTaSnRK2.10\u003c/em\u003e, \u003cem\u003eTaSPL20\u003c/em\u003e/\u003cem\u003e21\u003c/em\u003e, \u003cem\u003eTaTPP-6AL1\u003c/em\u003e, \u003cem\u003eTaSUTs\u003c/em\u003e, \u003cem\u003eTaSAP1-A1\u003c/em\u003e, \u003cem\u003eTaGS-D1\u003c/em\u003e, \u003cem\u003eTaMOC1-7A\u003c/em\u003e, \u003cem\u003eTaGS5-3A\u003c/em\u003e, \u003cem\u003eTaGL3‑5A\u003c/em\u003e, \u003cem\u003eTaCYP78A5\u003c/em\u003e, \u003cem\u003eTaAPO-A1\u003c/em\u003e/\u003cem\u003eWAPO-A1\u003c/em\u003e [2], \u003cem\u003eTaCol-B5\u003c/em\u003e [5], \u003cem\u003eTaSPL17\u003c/em\u003e [6], \u003cem\u003eTaARF12\u003c/em\u003e [7], \u003cem\u003eTaGSNE\u003c/em\u003e [8], \u003cem\u003eTaGL1-B1\u003c/em\u003e [9], \u003cem\u003eDRG1/TaACT7\u003c/em\u003e [10], \u003cem\u003eTaGSK3\u003c/em\u003e [11], \u003cem\u003eTaAGP-S1-7A\u003c/em\u003e, \u003cem\u003eTaAGP-L-1B\u003c/em\u003e [12], \u003cem\u003eKAT-2B\u003c/em\u003e [13], \u003cem\u003eTaIAA21\u003c/em\u003e [14], \u003cem\u003eFT-D1\u003c/em\u003e [15], and \u003cem\u003eTaMADS-GS\u003c/em\u003e [16]. Although many yield-related genes have been cloned, the genetic mechanisms controlling yield-related traits in wheat remain unclear.\u003c/p\u003e \u003cp\u003eGenome-wide association studies (GWAS) analyze associations between nucleotide polymorphisms and phenotypic variations. These studies represent powerful tools to identify QTLs associated with agronomic traits. The GWAS approach enables the simultaneous detection of numerous natural allelic variations within a single study. However, a drawback of this approach is that false positives may result from population structure and relatedness. To overcome this problem, the mixed linear model (MLM) was developed, incorporating kinship and population structure as covariates [17]. However, this model can elevate false negatives, potentially eliminating significant QTL. Therefore, other multi-locus GWAS methods such as mrMLM [18], FASTmrEMMA [19], FASTmrMLM [20], and IIIVmrMLM [21] have been developed. The introduction of multi-locus GWAS methodologies has enhanced precision of minor-effect QTL detection, thereby enriching our understanding of the complex genetic basis of yield-related traits.\u003c/p\u003e \u003cp\u003eIn the present study, we measured 13 yield-related traits, including those related to plant height (plant height [PH], leaf pillow height [LPH], subspike internode length [SIL], and spike neck length [SNL]), grain size and weight (thousand grain weight [TGW], grain width [GW], and grain length [GL]), and spike (spikelet density [SD], grain weight per spike [GWPS], number of grains per spike [NGPS], spike length [SL], spikelet number per spike [SPS], and spikelet set percentage [SSP]), in 245 wheat varieties in seven environments). Using these phenotypic data and genotypes from wheat Illumina iSelect 90K Infinium single nucleotide polymorphism (SNP) array, we performed GWAS to detect loci for yield-related traits using single- and multi-locus models. We then identified potential candidate genes underlying major loci for yield-related traits through transcriptome database, gene cloning, and sequence variation, haplotype and expression analyses, and marker development. In doing so, we identified valuable genetic resources that can be used to improve wheat yield.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Plant materials and field experiments\u003c/h2\u003e \u003cp\u003eA total of 245 wheat varieties collected from four wheat regions in China were used for association mapping (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) [22]. Yield-related traits of these varieties were evaluated in seven environments; from 2020\u0026ndash;2021, E1 (Huaibei, Anhui, 116\u0026deg;757\u0026prime;E, 33\u0026deg;760\u0026prime;N), E2 (Hefei, Anhui, 117\u0026deg;213\u0026prime;E, 31\u0026deg;919\u0026prime;N), and E4 (Jiyuan, Henan, 112\u0026deg;590\u0026prime;E, 35\u0026deg;090\u0026prime;N); and from 2021\u0026ndash;2022, E3 (Hefei, Anhui, 117\u0026deg;213\u0026prime;E, 31\u0026deg;919\u0026prime;N), E5 (Jiyuan, Henan, 112\u0026deg;590\u0026prime;E, 35\u0026deg;090\u0026prime;N), E6 (Bengbu, Anhui, 116\u0026deg;873\u0026prime;E, 33\u0026deg;102\u0026prime;N), and E7 (Suzhou, Anhui, 117\u0026deg;119\u0026prime;E, 33\u0026deg;883\u0026prime;N). We applied a complete block design that was randomized with two replications for all 245 materials. Each experimental unit was represented by a 2.0-m row plot and a 20-cm inter-row gap. To ensure appropriate wheat growth and development, regional conventional cultivation techniques were used in field experiments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Phenotypic evaluation and statistical analyses\u003c/h2\u003e \u003cp\u003eAfter heading, PH-related traits of five representative plants from each genotype were assessed. PH was measured from the soil surface to the spike tip, excluding awns. LPH was measured from the soil surface to the base of the flag leaf. SIL was calculated as the distance from the spike\u0026rsquo;s base to its first internode. SNL was calculated as the distance between the base of the spike and the base of the flag leaf (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the grain-maturity stage, five representative spikes from each genotype were sampled to determine spike-related traits. SL was measured with a ruler (in cm) from the base of the first spikelet to the apex of the last spikelet, excluding awns. SPS were counted and averaged across five spikes. NGPS was counted and then averaged for five spikes. GWPS was determined by multiplying TGW by NGPS and dividing the result by 1000. SD was determined by dividing SPS by SL. SSP was calculated by dividing NGPS by SPS. In phenotypic analysis, deletions, damage, or line mixing at harvest were excluded. In addition, 15 representative spikes were threshed, and their GL (mm), GW (mm), and TGW (g) were measured using a TGW meter and WanShen SC-G automatic seed analyzer (Hangzhou WanShen Testing Technology Co., Ltd.). Experiments were replicated three times, and average values were determined.\u003c/p\u003e \u003cp\u003eTo mitigate environmental effects, we calculated the best linear unbiased predictions (BLUPs) for all traits across the seven environments. Analysis of variance (ANOVA) and descriptive statistics were performed using IBM SPSS Statistics version 25.0 software (IBM Corporation, Armonk, NY, USA). The broad-sense heritability (\u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) of a trait in a combined environment analysis was evaluated based on variance estimates by using the linear mixed model: \u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;VG / (VG\u0026thinsp;+\u0026thinsp;VE), where VG and VE represent genetic and environmental variance estimates, respectively [23]. Correlation matrices between investigated features were determined using estimated BLUPs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. SNP genotyping and genome-wide association study\u003c/h2\u003e \u003cp\u003eIn total, 245 varieties were genotyped using a wheat 90K SNP array. We excluded SNP markers with \u0026gt;\u0026thinsp;10% missing data, and those with minor allele frequencies\u0026thinsp;\u0026lt;\u0026thinsp;5%. Finally, we retained 32,368 SNP markers for association analysis [22].\u003c/p\u003e \u003cp\u003eFor single-locus analysis, we used the MLM in TASSEL to identify significant marker-trait associations (MTAs), incorporating the Q matrix for population structure and the K matrix for relatedness [24]. A Bonferroni correction threshold with an estimated \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, corresponding to \u0026minus;\u0026thinsp;log10(\u003cem\u003eP\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;5.81, was used to identify significant loci. However, this criterion is highly stringent because it considers every SNP (rather than independent tests) in the dataset. Consequently, a corrected Bonferroni threshold based on independent testing or an exploratory threshold is often used [25]. To identify an MTA as significant within specific contexts, we used\u0026thinsp;\u0026minus;\u0026thinsp;log10(\u003cem\u003eP\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;4.00 as an exploratory threshold.\u003c/p\u003e \u003cp\u003eFor multi-locus analysis, the 3V multi-locus stochastic SNP effect mixed linear model (IIIMmrMLM) in the R package \u0026ldquo;IIIMrMLM\u0026rdquo; was used to identify significant MTAs using the Q and K matrices in MLM [21]. We determined the significance of MTAs based on Bonferroni correction or a logarithm of odds\u0026thinsp;\u0026gt;\u0026thinsp;3.\u003c/p\u003e \u003cp\u003eWe considered MTAs within linkage disequilibrium (LD) decay distance as a single QTL, and determined the major loci using two methods: (i) single- and multi-locus models in three or more environments; and (ii) three or more MTAs in the major loci using single- and multi-locus models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Marker development\u003c/h2\u003e \u003cp\u003eWe used 720 resequencing data of hexaploid wheat to obtain annotation information on gene variation sites through the online website WheatUnion (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://wheat.cau.edu.cn/WheatUnion/\u003c/span\u003e\u003cspan address=\"http://wheat.cau.edu.cn/WheatUnion/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [26\u0026ndash;28]. The franking sequence of each variation site was downloaded from the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v1.0. Based on preliminary GWAS results, SNPs located within half LD decay distance of the target interval of the major loci were converted into cleaved amplified polymorphic sequence (CAPS) or derived CAPS (dCAPS) markers, which were then integrated into the GWAS panel and used for re-GWAS. Within the interval of \u003cem\u003eQph/lph.ahau-7A\u003c/em\u003e and \u003cem\u003eQngps/sps.ahau-1A\u003c/em\u003e, 39 SNPs were converted to CAPS and dCAPS markers (Table S5). CAPS and dCAPS markers were developed using Primer Premier v. 5.0.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Candidate gene prediction and expression analysis\u003c/h2\u003e \u003cp\u003eThe wheat genome and gene annotation information (IWGSC RefSeq v1.1) were downloaded from WheatOmics 1.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://202.194.139.32/\u003c/span\u003e\u003cspan address=\"http://202.194.139.32/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and used to screen candidate genes underlying major loci. Expression patterns of candidate genes were investigated using transcriptome data for wheat \u0026ldquo;Chinese Spring Development (single and pair).\u0026rdquo; Expression levels of candidate genes were analyzed using transcripts per million (TPM) values and normalized using the ZeroToOne method. Gene expression heatmaps were created using ChiPlot (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.chiplot.online/\u003c/span\u003e\u003cspan address=\"https://www.chiplot.online/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Candidate gene cloning and haplotype analysis\u003c/h2\u003e \u003cp\u003eAccording to phenotypic differences and sequence variations queried on the WheatUnion website, we selected three wheat varieties to clone candidate genes \u003cem\u003eTraesCS7A02G118000\u003c/em\u003e and \u003cem\u003eTraesCS1A02G190200\u003c/em\u003e: Yangmai 158 (PH, 84.1 cm; LPH, 59.1 cm; NGPS, 63.6; SPS, 18.1), Jimai 22 (PH, 69.5 cm; LPH, 55.3 cm; NGPS, 52.8; SPS, 17.2), and Henong 825 (PH, 78.3 cm; LPH, 57.9 cm; NGPS, 51.8; SPS, 18.0). PlantCARE (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.psb.ugent.be/webtools/plantcare/html/\u003c/span\u003e\u003cspan address=\"http://bioinformatics.psb.ugent.be/webtools/plantcare/html/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to analyze changes in promoter cis-elements. We used ProteinTools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://proteintools.uni-bayreuth.de/\u003c/span\u003e\u003cspan address=\"https://proteintools.uni-bayreuth.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to investigate the effects of non-synonymous mutations in the coding region on protein folding and stability. Key variations in promoter and coding regions were converted to gene-specific markers (Table S5). BLUP values were analyzed using a Mann-Whitney\u0026rsquo;s U-test to identify significant differences between haplotypes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Expression analysis\u003c/h2\u003e \u003cp\u003eNine wheat varieties, three each carrying \u003cem\u003eTaF-box-7A-Hap1\u003c/em\u003e (Henong 825, Annong 9267, and Bainong 207), \u003cem\u003eTaF-box-7A-Hap2\u003c/em\u003e (Jimai 22, Jinan 17, and Yangmai 16), and \u003cem\u003eTaF-box-7A-Hap3\u003c/em\u003e (Annong 1124, Jimai 20, and Yangmai 158), were used to investigate expression patterns of \u003cem\u003eTaF-box-7A\u003c/em\u003e in wheat stems. Nine wheat varieties, three each carrying \u003cem\u003eTaBSK2-1A-Hap1\u003c/em\u003e (Lankao 298, Yannong 19, and Annong 9267), \u003cem\u003eTaBSK2-1A-Hap2\u003c/em\u003e (Yanzhan 4110, Haoyou 2018, and Zhongmai 895), and \u003cem\u003eTaBSK2-1A-Hap3\u003c/em\u003e (Shimai 12, Zhoumai 16, and Annong 8455), were used to investigate expression patterns of \u003cem\u003eTaBSK2-1A\u003c/em\u003e in wheat spikes. Total RNA was extracted using a total RNA kit (Takara). Reverse transcription and quantitative real time PCR (qRT-PCR) was performed on an Accurate Biology system using Evo M-MLV and SYBR Green Pro Taq HS. All reactions were performed in triplicate for each sample. The qRT-PCR primers for candidate genes are presented in Table S5. The actin gene (\u003cem\u003eTraesCS1D02G020000\u003c/em\u003e) was used as the endogenous control.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Phenotypic data analysis\u003c/h2\u003e \u003cp\u003eIn the 245 wheat varieties, significant differences in phenotypic data of 13 yield-related traits were observed between genotype and environment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The \u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e of these 13 yield-related traits ranged from 0.57\u0026ndash;0.95, with GWPS having the lowest heritability (0.57), and PH, LPH, SIL, GL, and SL having heritability\u0026thinsp;\u0026gt;\u0026thinsp;0.90 (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The 13 yield-related traits exhibited continuous distributions across environments, showing typical quantitative characteristics controlled by multiple genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Based on BLUP values, the variation in the range of these 13 yield-related traits was analyzed. Variation in PH-, and spike-related traits was large, but variation in grain-related traits was small. For example, the variation coefficients of PH, LPH, SIL, and SNL were 11.6%, 11.89%, 11.19%, and 26.55%, respectively, while those for GL and GW were 4.02% and 2.73%, respectively (Table S3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Correlations among phenotypic data\u003c/h2\u003e \u003cp\u003eWe further analyzed correlations among 13 yield-related traits based on BLUP datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All PH-related traits correlated positively with each other, but negatively with SD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Both GL and GW were positively correlated with TGW (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). GWPS was positively correlated with grain- (GW and TGW) and spike-related traits (NGPS, SD, SPS, and SSP) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). NGPS was correlated negatively with PH- (PH and LPH) and grain-related traits (GL and TGW) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but positively with GWPS, SPS, and SSP (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). SD correlated negatively with SL but positively with SPS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). SL correlated positively with SPS but negatively with SSP (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). SPS was negatively correlated with SSP (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These results indicate that cross-links exist among PH-, grain- and spike-related traits, which together affect wheat yield.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Genetic loci for yield-related traits\u003c/h2\u003e \u003cp\u003eBoth single-locus MLM and multi-locus IIIMrMLM models were used to detect MTAs for 13 yield-related traits, including plant height-, grain- and spike-related traits. GWAS revealed 414 MTAs based on single-locus models, and 528 MTAs based on multi-locus models (Table S4). According to the environmental repeatability of the significant MTAs and the LD decay of the subgenome [22], we classified these MTAs into 778 QTLs (or genetic loci) for yield-related traits. Among them, nine detected by both single-locus and multi-locus models were considered to be major loci, including three for PH and LPH (designated \u003cem\u003eQph/lph.ahau-1A\u003c/em\u003e, \u003cem\u003eQph/lph.ahau-1D\u003c/em\u003e, and \u003cem\u003eQph/lph.ahau-7A\u003c/em\u003e), one each for LPH (\u003cem\u003eQlph.ahau-7B.2\u003c/em\u003e), GL (\u003cem\u003eQgl.ahau-7B.3\u003c/em\u003e), NGPS and SPS (\u003cem\u003eQngps/sps.ahau-1A\u003c/em\u003e), and SL (\u003cem\u003eQsl.ahau-3A.3\u003c/em\u003e), and two for SD (\u003cem\u003eQsd.ahau-2B.1\u003c/em\u003e and \u003cem\u003eQsd.ahau-5A.2\u003c/em\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Compared with reported loci, seven of these nine major QTLs were considered novel; two new loci, \u003cem\u003eQph/lph.ahau-7A\u003c/em\u003e and \u003cem\u003eQngps/sps.ahau-1A\u003c/em\u003e, were selected for further analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNine major QTLs identified based on single-locus MLM and multi-locus IIIVmrMLM models.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMajor QTL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePosition (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMLM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e3VmrMLM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePreviously reported Gene\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.-Log10(\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMarker R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEnv\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.-Log10(\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMarker R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEnv\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eQph/lph.ahau-1A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ewsnp_Ex_c23598_32826926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e567,979,312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.96\u0026ndash;13.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.94\u0026ndash;8.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eE1/E2/E3/E4/E6/E7/BLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eTaAPP1-A1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ewsnp_Ex_c23598_32826926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e567,979,312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.93\u0026ndash;14.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.32\u0026ndash;7.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eE1/E2/E3/E6/E7/BLUP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRAC875_rep_c71093_1070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e568,012,820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBS00079088_51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e568,013,354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.35\u0026ndash;4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.12\u0026ndash;9.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE4/BLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cem\u003eQph/lph.ahau-1D\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKu_c111306_399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,902,509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eE5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cem\u003eDRG1/TaACT7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKu_c111306_399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,902,509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eE5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ewsnp_Ex_c1358_2600929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,605,331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ewsnp_Ex_c1358_2600929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,605,331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ewsnp_Ex_c1358_2601510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,605,912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.61\u0026ndash;17.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.39\u0026ndash;10.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eE1/BLUP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ewsnp_Ex_c1358_2601510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,605,912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.21\u0026ndash;20.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.19\u0026ndash;12.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eE1/E7/BLUP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ewsnp_Ex_c1358_2602235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,606,637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ewsnp_Ex_c1358_2602235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,606,637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKukri_c837_436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,609,839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKukri_c837_436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,609,839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eQph/lph.ahau-7A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBS00023225_51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76,124,550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.01\u0026ndash;4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.67\u0026ndash;9.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE1/E2/E3/E4/E6/BLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.78\u0026ndash;13.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.04\u0026ndash;9.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eE3/BLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBS00023225_51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76,124,550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.27\u0026ndash;5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.08\u0026ndash;11.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE2/E3/E4/BLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQlph.ahau-7B.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRAC875_c5646_440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e594,413,510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.07\u0026ndash;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.64\u0026ndash;8.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE2/E4/E7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16.32\u0026ndash;20.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.65\u0026ndash;8.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eE2/E4/E7/BLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQgl.ahau-7B.3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTdurum_contig41998_1213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e620,599,975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.35\u0026ndash;11.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.98\u0026ndash;9.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eE4/E5/E7/BLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cem\u003eQngps/sps.ahau-1A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNGPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcalibur_c24041_794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e343,682,053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.8\u0026ndash;4.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.95\u0026ndash;8.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE1/BLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.34\u0026ndash;9.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.49\u0026ndash;7.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eE5/BLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"7\" rowspan=\"8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcalibur_c24041_794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e343,682,053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNGPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBS00062715_51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e344,250,176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBS00062715_51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e344,250,176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNGPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEx_c5759_628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e345,108,312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNGPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ewsnp_Ex_c33831_42253707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e345,308,693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.46\u0026ndash;4.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.32\u0026ndash;7.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE1/BLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ewsnp_Ex_c33831_42253707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e345,308,693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcalibur_c102582_360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e345,786,652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eQsd.ahau-2B.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBobWhite_c9843_117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26,101,612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.66\u0026ndash;5.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.41\u0026ndash;9.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE5/E6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.29\u0026ndash;6.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.07\u0026ndash;5.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eE5/E6/E7/BLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ewsnp_Ku_c2486_4751761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26,314,294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eE6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQsd.ahau-5A.2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRFL_Contig2251_1070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37,619,944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.09\u0026ndash;5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.66\u0026ndash;9.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE1/E3/E4/BLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.85\u0026ndash;7.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.50\u0026ndash;5.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eE1/E3/E7/BLUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eQsl.ahau-3A.3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcalibur_c98205_83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25,493,701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcalibur_c24990_1482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27,247,151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKukri_c7508_805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27,428,730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eChr: Chromosome; Env: Environments; Physical position is based on IWGSC RefSeq v1.1.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Validation of major loci \u003cem\u003eQph/lph.ahau-7A\u003c/em\u003e and \u003cem\u003eQngps/sps.ahau-1A\u003c/em\u003e and prediction of candidate genes\u003c/h2\u003e \u003cp\u003eTo validate \u003cem\u003eQph/lph.ahau-7A\u003c/em\u003e and \u003cem\u003eQngps/sps.ahau-1A\u003c/em\u003e, we developed 27 and 12 molecular markers in these two regions, respectively, and then integrated them into the wheat Illumina iSelect 90K Infinium SNP array for re-GWAS. The loci \u003cem\u003eQph/lph.ahau-7A\u003c/em\u003e was mapped to 76.12\u0026ndash;77.40 Mb on chromosome 7A, and \u003cem\u003eQngps/sps.ahau-1A\u003c/em\u003e was mapped to 343.68\u0026ndash;345.79 Mb on chromosome 1A (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Table S6). The results support associations of \u003cem\u003eQph/lph.ahau-7A\u003c/em\u003e with PH-related traits, and \u003cem\u003eQngps/sps.ahau-1A\u003c/em\u003e with spike-related traits.\u003c/p\u003e \u003cp\u003eWithin the intervals of \u003cem\u003eQph/lph.ahau-7A\u003c/em\u003e (~\u0026thinsp;1.28 Mb), 24 high-confidence genes were annotated (Table S7). Based on the transcriptome database of \u0026ldquo;Chinese Spring Development (single and pair),\u0026rdquo; we found that nine of these genes were expressed in wheat stems (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA, 3D). Of these nine genes, four were highly expressed at the early stage of stem development and downregulated with stem development (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB, 3E). F-box protein is a key element in gibberellin (GA) signaling responsible for proteasome-dependent degradation of DELLA, which can regulate plant height [30\u0026ndash;32]. Therefore, among the four genes expressed in wheat stems, \u003cem\u003eTraesCS7A02G118000\u003c/em\u003e (named \u003cem\u003eTaF-box-7A\u003c/em\u003e), which encodes a F-box family protein, was preferentially considered as a candidate gene underlying \u003cem\u003eQph/lph.ahau-7A\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eIn the \u003cem\u003eQngps/sps.ahau-1A\u003c/em\u003e (~\u0026thinsp;2.11 Mb) region, 12 high-confidence genes were annotated (Table S7). Based on the transcriptome database of \u0026ldquo;Chinese Spring Development (single and pair),\u0026rdquo; we found that five of these genes were expressed in wheat spikes (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC, 4D). Among these, two were highly expressed at the early stage of spike development and downregulated with spike development (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eD, 4E). Serine/threonine protein kinases play important roles in regulating wheat grains per spike and rice grain number per panicle [33, 34]. Therefore, \u003cem\u003eTraesCS1A02G190200\u003c/em\u003e (named \u003cem\u003eTaBSK2-1A\u003c/em\u003e), encoding the serine/threonine-protein kinase BSK2, was preferentially considered to be a candidate gene underlying \u003cem\u003eQngps/sps.ahau-1A\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Sequence analysis of candidate genes \u003cem\u003eTaF-box-7A\u003c/em\u003e and \u003cem\u003eTaBSK2-1A\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eTo explore the roles of \u003cem\u003eTaF-box-7A\u003c/em\u003e and \u003cem\u003eTaBSK2-1A\u003c/em\u003e in PH- and spike-related traits, respectively, we cloned the promoter and CDS regions of \u003cem\u003eTaF-box-7A\u003c/em\u003e and \u003cem\u003eTaBSK2-1A\u003c/em\u003e in three wheat varieties with different PH- and spike-related phenotypes: Yangmai 158, Jimai 22, and Henong 825.\u003c/p\u003e \u003cp\u003e \u003cem\u003eTaF-box-7A\u003c/em\u003e was 1431 bp in length and contained only one exon, encoding 476 amino acids containing unknown function DUF295 domain (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Sequence alignment analysis revealed 11 variants, including 6 in the promoter and 5 in the coding region (4 missense mutations and 1 synonymous mutation) (Table S8). One mutation (\u0026minus;\u0026thinsp;744 bp, C/T) in the promoter led to changes in the GARE-motif and the Myb-binding site. Because there were four missense mutations in the coding region, we predicted the protein\u0026rsquo;s three-dimensional structure and found that the mutation (G/T) at +\u0026thinsp;304 bp in the coding region changed leucine (G) to methionine (T), thus affecting the folding and stability of the protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003cem\u003eTaBSK2-1A\u003c/em\u003e was 5138 bp in length and contained a 232 bp 5\u0026rsquo;UTR, 10 exons, 9 introns, and one 643 bp 3\u0026rsquo;UTR. Its CDS was 1512 bp in length, encoding 503 amino acids containing tetratricopeptide-like helical and serine-threonine/tyrosine-protein kinase domain (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Sequence alignment revealed 17 variants, including 13 in the promoter, 2 in the 5\u0026rsquo;UTR, and 2 in the coding region (2 synonymous mutations). Of the 13 mutations in the promoter, one at \u0026minus;\u0026thinsp;1421 bp (G/A) led to changes in the ABRE and G-box elements, and one at \u0026minus;\u0026thinsp;937 bp (T/C) led to changes in the TGA-element (Table S8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Associations of \u003cem\u003eTaF-box-7A\u003c/em\u003e and \u003cem\u003eTaBSK2-1A\u003c/em\u003e with PH- and spike-related traits\u003c/h2\u003e \u003cp\u003eBased on two mutations (C/T, \u0026minus;\u0026thinsp;744 bp; T/G, +\u0026thinsp;304 bp) in the promoter and coding regions of \u003cem\u003eTaF-box-7A\u003c/em\u003e, we developed two molecular markers (named TaF-box-7A-Q-744 and TaF-box-C\u0026thinsp;+\u0026thinsp;304, respectively) (Fig. S3). These two markers were then used to genotype 245 wheat varieties (Table S10). A total of three haplotypes were formed, named \u003cem\u003eTaF-box-Hap1\u003c/em\u003e (65.5%), \u003cem\u003eTaF-box-Hap2\u003c/em\u003e (18.8%), and \u003cem\u003eTaF-box-Hap3\u003c/em\u003e (15.7%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Haplotype analysis revealed that wheat varieties carrying \u003cem\u003eTaF-box-Hap1\u003c/em\u003e had significantly lower PH and LPH compared with those carrying \u003cem\u003eTaF-box-Hap2\u003c/em\u003e and \u003cem\u003eTaF-box-Hap3\u003c/em\u003e, and the PH values of wheat varieties carrying \u003cem\u003eTaF-box-Hap2\u003c/em\u003e were significantly lower than those carrying \u003cem\u003eTaF-box-Hap3\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, E). To investigate the association of \u003cem\u003eTaF-box-7A\u003c/em\u003e with PH-related traits at the transcriptional level, we detected the expression level of \u003cem\u003eTaF-box-7A\u003c/em\u003e in stems in varieties with three haplotypes. Expression levels of \u003cem\u003eTaF-box-7A\u003c/em\u003e in varieties with \u003cem\u003eTaF-box-Hap3\u003c/em\u003e were significantly higher than those with \u003cem\u003eTaF-box-Hap1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e5\u003c/span\u003eF), similar to trends in corresponding PH and LPH phenotypes. These results confirm the association of \u003cem\u003eTaF-box-7A\u003c/em\u003e with PH-related traits at both DNA and transcriptional levels.\u003c/p\u003e \u003cp\u003eBased on two mutations (G/A, \u0026minus;\u0026thinsp;1421 bp; T/C, \u0026minus;\u0026thinsp;937 bp) in the promoter region of \u003cem\u003eTaBSK2-1A\u003c/em\u003e, we developed two molecular markers (named TaBSK2-Q-1421 and TaBSK2-Q-937, respectively) (Fig. S4). These two markers were then used to genotype 245 wheat varieties (Table S10). A total of three haplotypes were formed, named \u003cem\u003eTaBSK2-Hap1\u003c/em\u003e (23.2%), \u003cem\u003eTaBSK2-Hap2\u003c/em\u003e (12.0%), and \u003cem\u003eTaBSK2-Hap3\u003c/em\u003e (64.8%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Haplotype analysis revealed that wheat varieties with \u003cem\u003eTaBSK2-Hap3\u003c/em\u003e had significantly higher NGPS compared with those with \u003cem\u003eTaBSK2-Hap1\u003c/em\u003e and \u003cem\u003eTaBSK2-Hap2\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), and the SPS values of varieties carrying \u003cem\u003eTaBSK2-Hap3\u003c/em\u003e were significantly higher than those carrying \u003cem\u003eTaBSK2-Hap1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). To investigate the association of \u003cem\u003eTaBSK2-1A\u003c/em\u003e with spike-related traits at the transcriptional level, we detected expression levels of \u003cem\u003eTaBSK2-1A\u003c/em\u003e in spikes in varieties with three haplotypes. Expression levels of \u003cem\u003eTaBSK2-1A\u003c/em\u003e in varieties with \u003cem\u003eTaBSK2-Hap3\u003c/em\u003e were significantly higher than those with \u003cem\u003eTaBSK2-Hap1\u003c/em\u003e, similar to trends in corresponding NGPS and SPS phenotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). These results confirm the association of \u003cem\u003eTaBSK2-1A\u003c/em\u003e with spike-related traits at both DNA and transcriptional levels.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eEnhancing the yield potential of wheat has become a priority in breeding programs. Exploring genes associated with high yield is therefore important to engineer wheat varieties with superior traits. We used genotype and phenotype data from 245 wheat varieties to perform GWAS, and found numerous QTLs for yield-related traits based on single- and multi-locus models. Among QTLs associated with PH-related traits, 16 were consistent with previous reports (Table S9). The two major loci \u003cem\u003eQph/lph.ahau-1A\u003c/em\u003e and \u003cem\u003eQph/lph.ahau-1D\u003c/em\u003e were close to genes that control PH-related traits, including \u003cem\u003eTaAPP1-A1\u003c/em\u003e [29] and \u003cem\u003eDRG1/TaACT7\u003c/em\u003e [10]. Additionally, 20 QTLs for grain-related traits were co-localized with or close to previously reported loci, with eight of them located in the genes reported to control grain-related traits (\u003cem\u003eTaSUT4-6D\u003c/em\u003e, \u003cem\u003e6-SFT-D\u003c/em\u003e, \u003cem\u003eTaGS-D1\u003c/em\u003e, \u003cem\u003eTaTKW-7A\u003c/em\u003e, \u003cem\u003eTaSnRK2.3-1B\u003c/em\u003e) [2, 3]; \u003cem\u003eTaGSNE-5A\u003c/em\u003e [8]; and \u003cem\u003eTaAGP-L-1B\u003c/em\u003e and \u003cem\u003eTaAGP-S1-7A\u003c/em\u003e [12]) (Table S9). By comparing the loci for spike-related traits detected in this study with known genes or loci, we found that 17 QTLs were close to or co-localized with previously reported loci (Table S9).\u003c/p\u003e \u003cp\u003eWe identified a new major QTL (\u003cem\u003eQph/lph.ahau-7A\u003c/em\u003e) associated with PH and LPH. Based on the transcriptome database, gene annotation and functional analysis of reported gene family members, we considered \u003cem\u003eTraesCS7A02G118000\u003c/em\u003e (named \u003cem\u003eTaF-box-7A\u003c/em\u003e) encoding a F-box family protein containing unknown function DUF295 domain to be a candidate gene underlying \u003cem\u003eQph/lph.ahau-7A\u003c/em\u003e. The association of \u003cem\u003eTaF-box-7A\u003c/em\u003e with PH-related traits was further confirmed through sequence variation, haplotype, and expression analyses, and marker development. The F-box family protein acts as a key component of GA signaling, which is a component of the SCF (SKP1-cullin-F-box) E3 ubiquitin ligase complex, mediating the ubiquitination and subsequent proteasomal degradation of target proteins [30]. Several studies have confirmed the role of the F-box family members in PH. For example, \u003cem\u003eGID2\u003c/em\u003e, encoding a F-box protein, is a positive regulator of GA signaling and regulates PH in rice [31]; \u003cem\u003eOsFBK4\u003c/em\u003e, encoding a F-box protein, positively regulates PH by promoting internode cell size and participates in GA signaling and biosynthesis pathways [32]. We speculate that \u003cem\u003eTaF-box-7A\u003c/em\u003e may influence PH and LPH in wheat.\u003c/p\u003e \u003cp\u003eWe identified one further new major QTL (\u003cem\u003eQngps/sps.ahau-1A\u003c/em\u003e) associated with NGPS and SPS. Based on the transcriptome database, gene annotation and functional analysis of reported gene family members, we considered \u003cem\u003eTraesCS1A02G190200\u003c/em\u003e (named \u003cem\u003eTaBSK2-1A\u003c/em\u003e), encoding the serine/threonine-protein kinase BSK2, to be a candidate gene underlying \u003cem\u003eQngps/sps.ahau-1A\u003c/em\u003e. We confirmed the association of \u003cem\u003eTaBSK2-1A\u003c/em\u003e with NGPS and SPS through sequence variation, haplotype, and expression analyses, and marker development. Serine/threonine protein kinases play important roles in mediating spike development. For example, GRAIN SIZE AND NUMBER1 (GSN1), a mitogen-activated protein kinase phosphatase \u003cem\u003eOsMKP1\u003c/em\u003e, negatively regulates the OsMKKK10\u0026ndash;OsMKK4\u0026ndash;OsMPK6 cascade (belonging to the serine/threonine protein kinase family) to coordinate the trade-off between grain number per panicle and grain size in rice [33]. The haplotype \u003cem\u003eHap\u003c/em\u003e-5A-4 of the \u003cem\u003eTaSnRK2.9-5A\u003c/em\u003e gene which encodes a serine/threonine protein kinase is significantly associated with high grains per spike in wheat [34]. \u003cem\u003eKERNEL NUMBER PER ROW6\u003c/em\u003e (\u003cem\u003eKNR6\u003c/em\u003e), a serine/threonine protein kinase gene, controls maize yield by influencing the number of female panicle florets, panicle length, and row number [35]. \u003cem\u003eTaCol-B5\u003c/em\u003e is differentially phosphorylated by the serine/threonine protein kinase \u003cem\u003eTaK4\u003c/em\u003e, thereby modifying spike architecture and enhancing wheat grain yield [5]. Based on these findings, we hypothesize that \u003cem\u003eTaBSK2-1A\u003c/em\u003e may regulate spike-related traits (such as NGPS and SPS) in wheat. Further experimentation is required to confirm this hypothesis.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eWe detected many QTLs associated with yield-related traits using single- and multi-locus models; of these, seven were new major loci. We then identified two potential candidate genes (\u003cem\u003eTaF-box-7A\u003c/em\u003e and \u003cem\u003eTaBSK2-1A\u003c/em\u003e) underlying \u003cem\u003eQph/lph.ahau-7A\u003c/em\u003e for PH-related traits and \u003cem\u003eQngps/sps.ahau-1A\u003c/em\u003e for spike-related traits, respectively. In doing so, we identified useful gene resources and molecular markers for breeding high-yield wheat varieties. Further functional characterizations of the two candidate genes are necessary to elucidate their applications in wheat high-yield breeding.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the breeding of new wheat varieties with super-high yield and wide suitability in Southern region of Yellow and Huai River of China (2023ZD040230307), the Joint Key Project of Improved Wheat Variety of Anhui Province (22805001), the Agriculture Research System of Anhui Province (AHCYTX-02), and Jiangsu Collaborative Innovation Center for Modern Crop Production (JCIC-MCP).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCollege of Agronomy, Anhui Agricultural University, Key Laboratory of Wheat Biology and Genetic Improvement on Southern Yellow \u0026amp; Huai River Valley, Ministry of Agriculture, Hefei, 230036, Anhui, China\u003c/p\u003e\n\u003cp\u003eYuxia Lv, Liansheng Dong, Xiatong Wang, Linhong Shen, Wenbo Lu, Fan Si, Yaoyao Zhao, Guanju Zhu, Yiting Ding, Shujun CAO, Jiajia Cao, Jie Lu, Chuanxi Ma, Cheng Chang \u0026amp; Haiping Zhang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYXL, LSD conceived and designed the study, as well as outlined and wrote the manuscript. XTW, LHS, WBL, FS, YYZ, GJZ, YTD, SJC, JJC performed the experiments. JL, CXM helped to write the manuscript. CC, HPcompleted the writing, review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Haiping Zhang and Chang Cheng.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by the authors. These methods were carried out in accordance with relevant guidelines and regulations. We confirm that all experimental protocols were approved by Anhui Agricultural University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data related to this manuscript can be found within this paper and its supplementary data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLi A, Hao C, Wang Z, Geng S, Jia M, Wang F, Mao, L. Wheat breeding history reveals synergistic selection of pleiotropic genomic sites for plant architecture and grain yield. Mol Plant, 2022; 15(3), 504\u0026ndash;519.\u003c/li\u003e\n\u003cli\u003eCao S, Xu D, Hanif M, Xia X, He Z. Genetic architecture underpinning yield component traits in wheat. Theor Appl Genet, 2020; 133, 1811\u0026ndash;1823.\u003c/li\u003e\n\u003cli\u003eSaini DK, Srivastava P, Pal N, Gupta PK. Meta-QTLs, ortho-meta-QTLs and candidate genes for grain yield and associated traits in wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.). Theor Appl Genet, 2022; 135(3), 1049\u0026ndash;1081.\u003c/li\u003e\n\u003cli\u003eTian X, Xia X, Xu D, Liu Y, Xie L, Hassan MA, Cao S. \u003cem\u003eRht24b\u003c/em\u003e, an ancient variation of \u003cem\u003eTaGA2ox-A9\u003c/em\u003e, reduces plant height without yield penalty in wheat. New Phytol, 2022; 233(2), 738\u0026ndash;750.\u003c/li\u003e\n\u003cli\u003eZhang X, Jia H, Li T, Wu J, Nagarajan R, Lei L, Yan L. \u003cem\u003eTaCol-B5\u003c/em\u003e modifies spike architecture and enhances grain yield in wheat. Science, 2022; 376(6589), 180\u0026ndash;183.\u003c/li\u003e\n\u003cli\u003eLiu Y, Chen J, Yin C, Wang Z, Wu H, Shen K, Guo Z. A high-resolution genotype\u0026ndash;phenotype map identifies the \u003cem\u003eTaSPL17\u003c/em\u003e controlling grain number and size in wheat. Genome Biol, 2023; 24(1), 196.\u003c/li\u003e\n\u003cli\u003eKong X, Wang F, Wang Z, Gao X, Geng S, Deng Z, Li A. Grain yield improvement by genome editing of \u003cem\u003eTaARF12\u003c/em\u003e that decoupled peduncle and rachis development trajectories via differential regulation of gibberellin signaling in wheat. Plant Biotechnol J, 2023; 21(10), 1990\u0026ndash;2001.\u003c/li\u003e\n\u003cli\u003eKhan N, Zhang Y, Wang J, Li Y, Chen X, Yang L, Jing R. \u003cem\u003eTaGSNE\u003c/em\u003e, a WRKY transcription factor, overcomes the trade-off between grain size and grain number in common wheat and is associated with root development. J Exp Bot, 2022; 73(19), 6678\u0026ndash;6696.\u003c/li\u003e\n\u003cli\u003eNiaz M, Zhang L, Lv G, Hu H, Yang X, Cheng Y, Chen F. Identification of \u003cem\u003eTaGL1-B1\u003c/em\u003e gene controlling grain length through regulation of jasmonic acid in common wheat. Plant Biotechnol J, 2023; 21(5), 979\u0026ndash;989.\u003c/li\u003e\n\u003cli\u003eXie Z, Zhang L, Zhang Q, Lu Y, Dong C, Li D, Kong X. A Glu209Lys substitution in \u003cem\u003eDRG1/TaACT7\u003c/em\u003e, which disturbs F-actin organization, reduces plant height and grain length in bread wheat. New Phytol, 2023; 240(5), 1913\u0026ndash;1929.\u003c/li\u003e\n\u003cli\u003eDong H, Li D, Yang R, Zhang L, Zhang Y, Liu X, Sun J. \u003cem\u003eGSK3\u003c/em\u003e phosphorylates and regulates the Green Revolution protein \u003cem\u003eRht-B1b\u003c/em\u003e to reduce plant height in wheat. Plant Cell, 2023; 35(6), 1970\u0026ndash;1983.\u003c/li\u003e\n\u003cli\u003eHou J, Li T, Wang Y, Hao C, Liu H, Zhang X. ADP-glucose pyrophosphorylase genes, associated with kernel weight, underwent selection during wheat domestication and breeding. Plant Biotechnol J, 2017; 15(12), 1533\u0026ndash;1543.\u003c/li\u003e\n\u003cli\u003eChen Y, Yan Y, Wu TT, Zhang GL, Yin H, Chen W, Gou JY. Cloning of wheat keto-acyl thiolase 2B reveals a role of jasmonic acid in grain weight determination. Nat Commun, 2020; 11(1), 6266.\u003c/li\u003e\n\u003cli\u003eJia M, Li Y, Wang Z, Tao S, Sun G, Kong X, Li A. \u003cem\u003eTaIAA21\u003c/em\u003e represses \u003cem\u003eTaARF25\u003c/em\u003e-mediated expression of \u003cem\u003eTaERFs\u003c/em\u003e required for grain size and weight development in wheat. Plant J, 2021; 108(6), 1754\u0026ndash;1767.\u003c/li\u003e\n\u003cli\u003eChen Z, Ke W, He F, Chai L, Cheng X, Xu H, Ni Z. A single nucleotide deletion in the third exon of \u003cem\u003eFT-D1\u003c/em\u003e increases the spikelet number and delays heading date in wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.). Plant Biotechnol J, 2022; 20(5), 920\u0026ndash;933.\u003c/li\u003e\n\u003cli\u003eZhang J, Zhang Z, Zhang R, Yang C, Zhang X, Chang S, Yao Y. Type I MADS-box transcription factor \u003cem\u003eTaMADS‐GS\u003c/em\u003e regulates grain size by stabilizing cytokinin signaling during endosperm cellularization in wheat. Plant Biotechnol J, 2024; 22(1), 200\u0026ndash;215.\u003c/li\u003e\n\u003cli\u003ePrice AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet, 2006; 38(8), 904\u0026ndash;909.\u003c/li\u003e\n\u003cli\u003eWang SB, Feng JY, Ren WL, Huang B, Zhou L, Wen YJ, Zhang YM. Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology. Sci Rep, 2016; 6(1), 19444.\u003c/li\u003e\n\u003cli\u003eWen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wu R. Methodological implementation of mixed linear models in multi-locus genome-wide association studies. Brief Bioinform, 2018; 19(4), 700\u0026ndash;712.\u003c/li\u003e\n\u003cli\u003eZhang YM, Jia Z, Dunwell JM. The applications of new multi-locus GWAS methodologies in the genetic dissection of complex traits. Front Plant Sci, 2019; 10, 100.\u003c/li\u003e\n\u003cli\u003eLi M, Zhang YW, Zhang ZC, Xiang Y, Liu MH, Zhou YH, Zhang YM. A compressed variance component mixed model for detecting QTNs and QTN-by-environment and QTN-by-QTN interactions in genome-wide association studies. Mol Plant, 2022; 15(4), 630\u0026ndash;650.\u003c/li\u003e\n\u003cli\u003ePan X, Nie XL, Gao W, Yan SN, Feng HS, Cao JJ, Zhang HP. Identification of genetic loci and candidate genes underlying freezing tolerance in wheat seedlings. Theor Appl Genet, 2024; 137(3), 57.\u003c/li\u003e\n\u003cli\u003eSmith SE, Kuehl RO, Ray IM, Hui R, Soleri D. Evaluation of simple methods for estimating broad-sense heritability in stands of randomly planted genotypes. Crop Sci, 1998; 38(5), 1125\u0026ndash;1129.\u003c/li\u003e\n\u003cli\u003eBradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics, 2007; 23(19), 2633\u0026ndash;2635.\u003c/li\u003e\n\u003cli\u003eBland JM, Altman DG. Multiple significance tests: the Bonferroni method. Bmj, 1995; 310(6973), 170.\u003c/li\u003e\n\u003cli\u003eGuo W, Xin M, Wang Z, Yao Y, Hu Z, Song W, Sun Q. Origin and adaptation to high altitude of Tibetan semi-wild wheat. Nat Commun, 2020; 11(1), 5085.\u003c/li\u003e\n\u003cli\u003eHao C, Jiao C, Hou J, Li T, Liu H, Wang Y, Zhang X. Resequencing of 145 landmark cultivars reveals asymmetric sub-genome selection and strong founder genotype effects on wheat breeding in China. Mol Plant, 2020; 13(12), 1733\u0026ndash;1751.\u003c/li\u003e\n\u003cli\u003eNiu J, Ma S, Zheng S, Zhang C, Lu Y, Si Y, Ling H Q. Whole-genome sequencing of diverse wheat accessions uncovers genetic changes during modern breeding in China and the United States. Plant Cell, 2023; 35(12), 4199\u0026ndash;4216.\u003c/li\u003e\n\u003cli\u003eNiu KX., Chang CY, Zhang MQ, Guo YT, Yan Y, Sun HJ, Gou JY. Suppressing ASPARTIC PROTEASE 1 prolongs photosynthesis and increases wheat grain weight. Nat Plants, 2023; 9(6), 965\u0026ndash;977.\u003c/li\u003e\n\u003cli\u003eHern\u0026aacute;ndez-Garc\u0026iacute;a J, Briones-Moreno A, Bl\u0026aacute;zquez MA. Origin and evolution of gibberellin signaling and metabolism in plants. Semin Cell Dev Biol, 2021; 109, 46\u0026ndash;54.\u003c/li\u003e\n\u003cli\u003eSasaki A, Itoh H, Gomi K, Ueguchi-Tanaka M, Ishiyama K, Kobayashi M, Matsuoka M. Accumulation of phosphorylated repressor for gibberellin signaling in an F-box mutant. Science, 2003; 299(5614), 1896\u0026ndash;1898.\u003c/li\u003e\n\u003cli\u003eZegeye WA, Chen D, Islam M, Wang H, Riaz A, Rani MH, Zhang Y. \u003cem\u003eOsFBK4\u003c/em\u003e, a novel GA insensitive gene positively regulates plant height in rice (\u003cem\u003eOryza Sativa\u003c/em\u003e L.). E Genet Genomics, 2022; 23, 100115.\u003c/li\u003e\n\u003cli\u003eGuo T, Chen K, Dong, NQ, Shi C L, Ye WW, Gao JP, Lin HX. GRAIN SIZE AND NUMBER1 negatively regulates the OsMKKK10-OsMKK4-OsMPK6 cascade to coordinate the trade-off between grain number per panicle and grain size in rice. Plant Cell, 2018; 30(4), 871\u0026ndash;888.\u003c/li\u003e\n\u003cli\u003eUr Rehman S, Wang J, Chang X, Zhang X, Mao X, Jing R. A wheat protein kinase gene \u003cem\u003eTaSnRK2.9-5A\u003c/em\u003e associated with yield contributing traits. Theor Appl Genet, 2019; 132, 907\u0026ndash;919.\u003c/li\u003e\n\u003cli\u003eJia H, Li M, Li W, Liu L, Jian Y, Yang Z, Zhang Z. A serine/threonine protein kinase encoding gene KERNEL NUMBER PER ROW6 regulates maize grain yield. Nat Commun, 2020; 11(1), 988.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"wheat, yield, genome-wide association study, marker-trait association, single nucleotide polymorphism","lastPublishedDoi":"10.21203/rs.3.rs-5391583/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5391583/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGenetic dissection of yield-related traits can be used to improve wheat yield through molecular design breeding. In this study, we genotyped 245 wheat varieties and measured 13 yield-related plant height-, grain- and spike-related traits, in seven environments, and identified 778 loci for these traits by genome-wide association study (GWAS) using single- and multi-locus models. Among them, nine were major loci, of which seven were novel, including \u003cem\u003eQph/lph.ahau-7A\u003c/em\u003e for plant height (PH) and leaf pillow height (LPH), \u003cem\u003eQngps/sps.ahau-1A\u003c/em\u003e for number of grains per spike (NGPS) and spikelet number per spike (SPS), \u003cem\u003eQsd.ahau-2B.1\u003c/em\u003e and \u003cem\u003eQsd.ahau-5A.2\u003c/em\u003e for spikelet density (SD), \u003cem\u003eQlph.ahau-7B.2\u003c/em\u003e for LPH, \u003cem\u003eQgl.ahau-7B.3\u003c/em\u003e for grain length (GL), and \u003cem\u003eQsl.ahau-3A.3\u003c/em\u003e for spike length (SL). Through marker development, re-GWAS, gene annotation and cloning, and sequence variation, haplotype, and expression analyses, we confirmed two novel major loci and identified potential candidate genes, \u003cem\u003eTraesCS7A02G118000\u003c/em\u003e (named \u003cem\u003eTaF-box-7A\u003c/em\u003e) and \u003cem\u003eTraesCS1A02G190200\u003c/em\u003e (named \u003cem\u003eTaBSK2-1A\u003c/em\u003e) underlying \u003cem\u003eQph/lph.ahau-7A\u003c/em\u003e for PH-related traits and \u003cem\u003eQngps/sps.ahau-1A\u003c/em\u003e for spike-related traits, respectively. Furthermore, we reported two favorable haplotypes, including \u003cem\u003eTaF-box-Hap1\u003c/em\u003e associated with low PH and LPH and \u003cem\u003eTaBSK2-Hap3\u003c/em\u003e associated with high NGPS and SPS. In summary, these findings are valuable for improving wheat yield and enriching our understanding of the complex genetic mechanisms of yield-related traits.\u003c/p\u003e","manuscriptTitle":"Single- and multi-locus genome-wide association study reveals genomic regions of thirteen yield-related traits in common wheat","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-25 15:42:51","doi":"10.21203/rs.3.rs-5391583/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-18T19:56:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-17T14:00:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-13T06:06:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87810411719528380890283908520890123729","date":"2024-11-12T09:15:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11823406520188890515691711051871182348","date":"2024-11-11T06:08:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105692434107062968994212070269821119862","date":"2024-11-11T03:32:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309021751640539350368403886421442035634","date":"2024-11-10T15:28:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-10T06:03:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-08T13:52:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-07T11:41:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-07T11:39:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2024-11-05T02:43:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fca595c6-87bb-498d-9401-8c5c257f9fee","owner":[],"postedDate":"November 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T16:01:37+00:00","versionOfRecord":{"articleIdentity":"rs-5391583","link":"https://doi.org/10.1186/s12870-024-05956-y","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2024-12-21 15:57:32","publishedOnDateReadable":"December 21st, 2024"},"versionCreatedAt":"2024-11-25 15:42:51","video":"","vorDoi":"10.1186/s12870-024-05956-y","vorDoiUrl":"https://doi.org/10.1186/s12870-024-05956-y","workflowStages":[]},"version":"v1","identity":"rs-5391583","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5391583","identity":"rs-5391583","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}