Mapping Quantitative Trait Loci for Pre-Harvest Sprouting Resistance in Wheat Using Berkut × Worrakatta Recombinant Inbred Lines

Mapping Quantitative Trait Loci for Pre-Harvest Sprouting Resistance in Wheat Using Berkut × Worrakatta Recombinant Inbred Lines | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mapping Quantitative Trait Loci for Pre-Harvest Sprouting Resistance in Wheat Using Berkut × Worrakatta Recombinant Inbred Lines Yunkun Cheng, Yiling Xing, Lei Xie, Wanlong He, Jinjin Ding, Haiyan Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7708950/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Pre-harvest sprouting (PHS) in wheat is a significant global challenge influenced by climate. This study aimed to decipher the genetic underpinnings of PHS and identify resistance genes using 309 recombinant inbred lines (RILs) derived from the "Berkut × Worrakatta" cross. Methods:Phenotypic assessment of PHS traits was performed using the whole-spike sprouting method across various environments, complemented by quantitative trait loci (QTL) analysis employing a wheat 50K SNP chip. Results Results showed high PHS rates in both parental lines across multiple environments. Progeny exhibited substantial variation in PHS rates, with coefficients of variation ranging from 0.16 to 0.19 and a phenotypic variation ranging from 23.92% to 100%, suggesting pronounced transgressive segregation. Nine QTLs associated with PHS were identified on chromosomes 1AL, 1DL, 2AL, 2AS, 2BS, 3DS, 4BL, and 7BL. These loci accounted for 2.67% to 6.39% of the phenotypic variation. Notably, the enhancer alleles at four loci—1DL, 2BS, 4BL, and 7BL—originated from 'Worrakatta', and 'Berkut' contributed the enhancer alleles at the remaining five loci. Two QTLs, QPHS.xjau-1AL.1 and QPHS.xjau-1AL.2 , were stable across multiple environments. Specifically, QPHS.xjau-1AL.1 was present in three environments and explained 3.86% to 6.39% of the phenotypic variation, while QPHS.xjau-1AL.2 appeared in one environment under average conditions, explaining 2.67% to 4.87% of the variation. Conclusions Our study also identified eight candidate genes associated with wheat PHS, including those encoding MYB transcription factors that influence flavonoid biosynthesis and grain color, as well as genes involved in stress response and gibberellin biosynthesis, which are crucial for plant growth and development. These genes represent vital targets for enhancing wheat PHS resistance. Wheat Pre-harvest sprouting QTL Candidate genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Background PHS in wheat is a significant global climatic challenge caused by climatic conditions, occuring during ripening following sustained rainfall or hingh humidity (Derera et al. 1977 ; Ogbonnaya et al. 2008 ). In China, PHS frequently affects major wheat-growing regions including the Huang-Huai Valley, the southwestern winter wheat zone, and the middle and lower reaches of the Yangtze River, which collectively account for approximately 83% of the nation's total wheat cultivation area. Severe PHS events in 2016, 2018, and 2023 significantly reduced yields and degraded grain quality in provinces including Jiangsu, Anhui, Sichuan, Hubei, and Henan. PHS onset is influenced by multiple factors, including intrinsic wheat variety characteristics—such as the water-holding capacity of spikes, glume traits, grain moisture content, endogenous hormones in seeds, and seed dormancy levels—as well as environmental conditions. However, breeding for PHS resistance is difficult for its quantitatively inheritance affected by genetic and environmental factors (Zhou et al. 2017 ). Therefore, understanding the PHS characteristics of existing wheat germplasm and the functional genes controlling these traits is crucial for strategically utilizing these resistance resources to breed new, high-quality, and high-yield varieties with PHS-resistant. Substantial research indicates that wheat PHS is a quantitative trait influenced by multiple QTLs or genes. For example, Munkvold et al. ( 2009 ) identified a significant QTL, QPhs.cnl-2B.1 , on chromosome 2B using a doubled haploid (DH) population, which accounted for 5%-31% of the phenotypic variation. Torada et al. ( 2016 ) identified mitogen-activated protein kinase kinase 3 (MKK3), designated TaMKK3-A , as the candidate gene for the seed dormancy locus Phs1 on chromosome 4A in bread wheat. Additionally, Liu and Bai ( 2010 ) located another significant PHS-resistant QTL tightly linked to the marker Xbarc57 on chromosome 3AS, and successfully cloned Triticum aestivum Pre-Harvest Sprouting 1 ( TaPHS1 ) from the white-grained, PHS-resistant wheat Rio Blanco. Osa et al. ( 2003 ) used a RIL population from a Zenkoujikomugi (Zen) and Chinese Spring (CS) to identify a major resistance locus, QPhs.ocs-3A.1 , located near the molecular marker Xfbb370 on chromosome 3AS. Zhou et al. ( 2017 ) performed a genome-wide association study on 717 Chinese wheat landraces and detected a major QTL for PHS resistance on chromosome 3D, which co-localizes with the grain color transcription factor TaMyb10 . Yang et al. ( 2019 ) identified another major QTL for PHS resistance, QPHS.sicau-3D , on chromosome 3D in the synthetic wheat SHW-1, which was derived from the highly dormant tetraploid AS60 and moderately PHS-resistant diploid AS2255, and this QTL accounted for 42.47% of the phenotypic variation in PHS resistance across various environments. Furthermore, Zhang et al. ( 2014 ) discovered TaSdr-B1 , a homolog of the rice seed dormancy gene Oryza sativa Seed Dormancy 4 ( OsSdr4 ), located on chromosome 2BS, and they also developed a functional marker Sdr2B . TaSdr-B1 is considered a candidate gene for a previously reported major PHS resistant QTL on chromosome 2B. Despite significant advances in QTL mapping and wheat PHS research (Osa et al. 2003 ; Ogbonnaya et al. 2008 ; Munkvold et al. 2009 ; Rasul et al. 2009 ; Tyagi and Gupta, 2012 ; Zhu et al. 2014 ; Zuo et al. 2019 ), the efficacy of PHS resistance in wheat varies significantly under different environmental conditions. Aggregating multiple PHS-resistant genes can effectively enhance the resistance levels of wheat varieties. Currently, the primary method for improving PHS resistance is the aggregation of effective PHS-resistant genes through marker-assisted selection (MAS) breeding (Chang et al. 2023 ). To identify stable PHS resistance loci and key genes, this study focused on a recombinant inbred line (RIL) population consisting of 309 families derived from the cross between the "Berkut" and "Worrakatta" wheat varieties. QTL mapping analysis for PHS was conducted Using a wheat 50K SNP chip on this population under various environmental conditions. Our goal is to facilitate MAS breeding and the aggregation of multiple resistance genes to develop new wheat varieties with enhanced PHS resistance. 2 Materials and methods 2.1 Plant materials The parental lines and the RIL population were generously provided by the Wheat Research Institute of the Chinese Academy of Agricultural Sciences. The experimental materials consisted of 309 RILs (F 6 generation) derived from the wheat varieties "Berkut" and "Worrakatta". These varieties were originally sourced from the International Maize and Wheat Improvement Center (CIMMYT) and donated by the Wheat Research Institute. The experimental materials were cultivated over three consecutive years (2018 to 2020) at the Manas Experimental Station of the Xinjiang Academy of Agricultural Sciences, referred to as 2018M, 2019M, and 2020M, respectively. Additionally, in 2019, a trial was also conducted at the Sanping Farm Experimental Base of Xinjiang Agricultural University, denoted as 2019S. The average data from these three consecutive years, covering a total of four environments, was used to represent the mean environment (abbreviated as A). The experimental materials were systematically planted in single rows, each measuring 2 m in length with a spacing of 25 cm between rows. The management practices, including fertilization, drip irrigation, pest control, and weed management, conformed to the standard local field protocols. 2.2 Method Phenotypic identification of pre-harvest sprouting (PHS) was conducted on the RIL population across four environments from 2018 to 2020. At the late dough stage of wheat development, 10 main stem spikes, including the peduncle, were harvested from each line. These spikes were air-dried indoors for one day and subsequently stored at -20°C to preserve dormancy. After harvesting all materials, a unified PHS assessment was carried out. The process began with soaking the whole spikes in distilled water for 10–12 hours. Subsequently, they were disinfected using a 0.1% sodium hypochlorite solution for 15 minutes, rinsed thoroughly with sterile water, wrapped in germination paper, placed in ventilated plastic bags (with 3–5 micropunctures of 0.5 mm diameter) to maintain humidity while allowing gas exchange. These preparations were then incubated in a controlled environment chamber set at 20°C for seven days, with a photoperiod of 16 h of light and 8 h of darkness and relative humidity of 80%. Following this incubation period, the spikes were quickly dried in an electric constant temperature oven set at 150°C to halt further germination. The grains were then manually threshed, using embryo rupture as the criterion for successful germination (Groos et al. 2002 ). The PHS percentage for each line was calculated using the average results from two replicates. The whole spike germination percentage (GP) was determined by dividing the number of germinated grains in five spikes by the total number of grains, and then multiplying by 100%. 2.3 Statistical analysis of phenotypic data Basic statistical analyses were performed using Microsoft Excel 2016 and SPSS software version 21.0. Descriptive statistics and analysis of variance were conducted using QTL IciMapping V4.1 software(Lin et al. 2015 ). Broad-sense heritability ( H ²) was calculated using the formula: H ² = σ ² g /[( σ ² g + σ ² gt )/r + σ ² e /r], where σ ² g is the genotypic variance, σ ² e is the error variance, σ ² gt is the genotype-by-environment interaction variance, and r is the number of replicates. Variance components ( σ ² g , σ ² gt , and σ ² e ) were estimated from ANOVA using the following equations: σ² g = (MS_G – MS_GxE)/(rE), σ ² gt = (MS_GxE - MS_e)/r, and σ ² e = MS_e, where MS_G is mean square for genotypes, MS_GxE is mean square for genotype × environment interaction, MS_e is mean square for residual error, r is number of replicates, E is number of environments, and rE is the product of the number of replicates ( r ) and the number of environments (E) (Lynch and Walsh, 1998 ). 2.4 Linkage map construction and QTL analysis The wheat 50K SNP chip used in the study was processed and genotyped by Beijing CapitalBio Technology Co. Ltd. After the screening of polymorphic markers, genotype data of the segregating population were imported into QTL IciMapping version 4.1 software. Redundant markers were removed using the BIN program, and the genetic linkage map for the population was constructed using the MAP program. A total of 28 linkage groups were established, covering all 21 chromosomes of common wheat. The Inclusive Composite Interval Mapping (ICIM-ADD) method was employed to identify major QTLs, with the LOD threshold set to 2.0 and default settings for other parameters. QTL loci detected on the same chromosome and sharing overlapping genetic positions of peak values were classified as the same locus, and QTLs identified in two or more environmental conditions were considered to be stably inherited. QTLs were named according to the format "Q" followed by the trait abbreviation, the institution abbreviation (xjau), and the chromosome where the QTL locus is located. 2.5 Prediction of candidate genes For QTL identified as stably inherited or having significant phenotypic contributions, candidate genes were predicted. This involved mapping population markers to the wheat genome based on their physical positions and utilizing significant SNP marker sequences identified across multiple environments. BLAST comparisons were performed using the Chinese Spring wheat reference genome database ( http://www.wheatgenome.org/ ) and the National Center for Biotechnology Information (NCBI) website ( http://www.ncbi.nlm.nih.gov/ ). Functional annotations of the candidate genes were conducted to delineate their potential roles. 2.6 QTL meta-analysis The collected QTL data, including QTL name, QTL position, LOD score, phenotypic variation rate, closely linked markers, confidence interval, and population size, were analyzed using BioMercator V4.2.3 (Cheng et al. 2023 ). Among these parameters, the QTL position (confidence interval and peak position) and the genetic contribution rate are critical for effectively conducting QTL meta-analysis. 3 Results 3.1 Phenotypic analysis of PHS in parents and RILs population Across four distinct environments, whole-spike pre-harvest sprouting (WSPS) rates showed generally consistent trends within the RIL population. Both parental lines exhibited high WSPS rates, and the RIL population displaying a high average rate, including weak overall PHS resistance (Table 1 and Fig. 1 ). Within the RIL population, the coefficient of variation for WSPS rates ranged from 0.16 to 0.19, with observed values varying widely from 24% to 100%. This substantial variation highlights significant differences in PHS rates among the RIL families, suggesting transgressive segregation. Correlation analysis revealed strong positive relationships between WSPS rates measured across the four environments ranging from 0.51 to 0.84 ( p < 0.01) (Table 1 ). Variance analysis identified extremely significant differences in WSPS rates among RILs across environments ( P = 1.0e-05) (Table 2 ), suggesting that genotype, environmental, and their interaction all significantly influence PHS resistance. The broad-sense heritability for PHS traits across environments was 0.86, underscoring that while genetic factors are predominantly responsible for variation in resistance, environmental factors also substantially affect these traits. These results suggest that genetic factors are the primary drivers of phenotypic variation, and that selection in early generations is an effective strategy. Table 1 RIL population wheat sprouting trait statistical analysis Environment B W Min(%) Max(%) Mean SD(%) CV 2018M 2019M 2019S 2020M 2018M 84.65 99.07 28.04 100.00 82.24 14.32 0.17 1 2019M 89.44 98.78 46.75 99.59 82.91 15.05 0.18 0.84** 1 2019S 83.72 95.61 33.93 98.54 81.49 13.28 0.16 0.63** 0.51** 1 2020M 84.28 96.82 23.92 98.71 78.44 14.96 0.19 0.77** 0.67** 0.76** 1 Note: 2018M、2019M and 2020M: The experimental materials were cultivated over three consecutive years (2018 to 2020) at the Manas Experimental Station of the Xinjiang Academy of Agricultural Sciences, referred to as 2018M, 2019M, and 2020M; 2019S: In 2019, a trial was also conducted at the Sanping Farm Experimental Base of Xinjiang Agricultural University, denoted as 2019S. Table 2 ANOVA and generalized heritability of wheat sprouting in RIL population Source of variance df SS MS F-value P-value h 2 Genotype 308 274475.97 894.06 52.15 1.0e-05 0.86 Environment 3 4529.44 1509.81 88.07 1.0e-05 G×E 551 77467.71 140.59 8.20 1.0e-05 Error 858.00 14709.25 17.14 Total variation 1720 371351.47 3.2 QTL loci of wheat PHS traits By integrating PHS rates data from 309 RIL families with genotype data from the wheat 50K SNP chip, additive effect QTL mapping was performed using the inclusive composite interval mapping (ICIM) method. This analysis identified nine QTLs associated with PHS traits, distributed across seven chromosomes (Table 3 ). Fve QTLs exhibited positive additive effects, while four displayed negative additive effects. Individual QTLs accounted for 2.67% − 6.39% of the observed phenotypic variation, depending on the environmental conditions. Alleles associated with increasing effects located on chromosomes 1A, 2A, and 3D originated from the 'Berkut' parent, while those on chromosomes 1D, 2B, 4B, and 7B were derived from the 'Worrakatta' parent. QPHS.xjau-1AL.1 , detected under three different environmental conditions (2018M, 2019M, and 2019S), was mapped within the interval AX-110067057 - AX-179561683 at a physical position of 549.08Mb-552.85Mb. This QTL explains 3.86%-6.39% of the phenotypic variation. Another QTL, QPHS.xjau-1AL.2 , was identified in the 2019S environment and under average environmental conditions; it was located within the interval AX-109326239 - AX-94417718 at a physical position of 535.72Mb-545.65Mb and accounted for 2.67%-4.87% of the phenotypic variation. Upon comparison with previously reported results, three of the detected QTLs were found to be proximal to markers or QTL intervals identified in previous studies, while the other six were located in chromosomal regions not previously associated with these traits (Table 3 and Fig. 2). Comparison with previously reported results indicated that three of detected QTLs were located near markers/QTL intervals identified in previous studies, while the rother six QTLs were in chromosomal regions not previously reported (Table 3 and Fig. 2). In the 2018M, 2020M, and the average environment (A), one PHS-related QTL was detected per environment: QPHS.xjau-1AL.1 , QPHS.xjau-2BS , and QPHS.xjau-1AL.2 . These QTLs exhibited LOD scores between 2.4–3.1and collectively explained 4.20%-4.87% of the phenotypic variation. The alleles contributing to increased effects for QPHS.xjau-1AL.1 and QPHS.xjau-1AL.2 are derived from Berkut, while the allele contributing to increased effects for QPHS.xjau-2BS is derived from Worrakatta. In the 2019M environment, two PHS related QTLs were identified: QPHS.xjau-1AL.1 and QPHS.xjau-3DS , located on chromosomes 1AL and 3DS, respectively. QPHS.xjau-1AL.1 is situated within the interval AX-110067057 - AX-179561683 and has a LOD value of 2.6, explaining 6.39% of the phenotypic variation. QPHS.xjau-3DS is located within the interval AX-108907550 - AX-94479963 and has a LOD value of 2.2, explaining 4.03% of the phenotypic variation. The alleles contributing to increased effects for both QTLs originate from the parent Berkut. In the 2019S environment, seven PHS-related QTLs were identifiedon chromosomes 1AL (two loci), 2AL, 2AS, 4BL, 7BL, and 1DL. Specifically, the two QTLs on chromosome 1AL, QPHS.xjau-1AL.1 and QPHS.xjau-1AL.2 , are located within the intervals AX-110067057 - AX-179561683 and AX-109326239 - AX-94417718 , respectively, with LOD values of 2.2 and 2.1, explaining 3.86% and 2.67% of the phenotypic variation. The alleles with increasing effects for both QTLs originate from the parent Berkut. Additionally, two QTLs detected on chromosome 2A, QPHS.xjau-2AL and QPHS.xjau-2AS , are located within the intervals AX-89471863 -A X-158572584 and AX-111037158 - AX-94566723 , respectively, with LOD values of 3.6 and 2.2, explaining 4.75% and 4.02% of the phenotypic variation. The alleles with increasing effects for these QTLs also originate from the parent Berkut. One QTL each was detected on chromosomes 4BL, 7BL, and 1DL, namely QPHS.xjau-4BL , QPHS.xjau-7BL , and QPHS.xjau-1DL , respectively. These QTLs have LOD values ranging from 2.6 to 3.1, collectively explaining 3.42%-4.00% of the phenotypic variation. Unlike other QTLs detected, the alleles with increasing effects for these QTLs all originate from the parent Worrakatta. Table 3 QTL locus information of wheat sprouting QTL Environment Marker interval Physical position (Mb) Genetic position (cM) LOD peak R2(%) Add effect Known loci QPHS.xjau-1AL.1 2018M AX-110067057-AX-179561683 549.08-552.85 55 3.1 4.62 3.44 2019M 2.6 6.39 4.19 2019S 2.2 3.86 2.88 QPHS.xjau-1AL.2 2019S AX-109326239-AX-94417718 535.72-545.65 49 2.1 2.67 2.40 Singh A 2.6 4.87 2.55 QPHS.xjau-2AS 2019S AX-111037158-AX-94566723 31.83–33.78 6 2.2 4.02 3.38 Mohan QPHS.xjau-2AL 2019S AX-89471863-AX-158572584 651.60-679.54 56 3.6 4.75 3.22 QPHS.xjau-2BS 2020M AX-94849048-AX-94632942 38.44–50.99 32 2.4 4.20 -3.11 Lin QPHS.xjau-4BL 2019S AX-111489623-AX-109455736 602.90-605.79 64 2.9 3.76 -2.84 QPHS.xjau-7BL 2019S AX-110292317-AX-179558612 684.86-685.78 85 3.1 4.00 -2.96 QPHS.xjau-1DL 2019S AX-89314186-AX-111558345 365.35-368.07 28 2.6 3.42 -2.71 QPHS.xjau-3DS 2019M AX-108907550-AX-94479963 77.01–99.98 67 2.2 4.03 3.33 Note: 2018M、2019M and 2020M: The experimental materials were cultivated over three consecutive years (2018 to 2020) at the Manas Experimental Station of the Xinjiang Academy of Agricultural Sciences, referred to as 2018M, 2019M, and 2020M; 2019S: In 2019, a trial was also conducted at the Sanping Farm Experimental Base of Xinjiang Agricultural University, denoted as 2019S. Note the right side of each chromosome is the molecular marker and the QTL mapped, and the left side is the genetic location corresponding to the QTL. The red is 2018M, green is 2019M, blue is 2019S, yellow is 2020M and black is A. Figure 2 Locations of QTL for wheat sprouting trait 3.3 Prediction of candidate genes related to wheat PHS Based on the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq Annotations database, gene mining was conducted for QTLs identified as stably inherited or having significant phenotypic contributions, leading to the identification of eight candidate genes potentially linked with PHS (Table 4 ). These candidate genes are primarily involved in pathways related to seed dormancy, plant hormone biosynthesis, and signal transduction. The annotated candidate genes include: TraesCS1A01G378100 encodes a protein belonging to the zinc finger family; TraesCS2A01G407800 and TraesCS7B01G701400LC encode F-box proteins; TraesCS1A01G365900 and TraesCS2B01G082400 are identified as encoding Myb transcription factors; TraesCS2A01G075000 and TraesCS2B01G083400 encode germin-like proteins; and TraesCS3D01G124500 is annotated as a gibberellin 3-β-hydroxylase, a key enzyme in gibberellin biosynthesis. Table 4 Screening for candidate gene information Chr QTL Marker interval Position(Mb) Gene Gene annotation or coding protein 1AL QPHS.xjau-1AL.1 AX-110067057-AX-179561683 550.04 TraesCS1A01G378100 zinc finger superfamily protein QPHS.xjau-1AL.2 AX-109326239-AX-94417718 544.74 TraesCS1A01G365900 MYB transcription factor 2AL QPHS.xjau-2AL AX-89471863-AX-158572584 664.47 TraesCS2A01G407800 F-box family protein 2AS QPHS.xjau-2AS AX-111037158-AX-94566723 33.27 TraesCS2A01G075000 Germin-like protein 2BS QPHS.xjau-2BS AX-94849048-AX-94632942 45.73 TraesCS2B01G082400 MYB-related transcription factor 46.59 TraesCS2B01G083400 Germin-like protein 7BL QPHS.xjau-7BL AX-110292317-AX-179558612 685.38 TraesCS7B01G701400LC F-box family protein 3DS QPHS.xjau-3DS AX-108907550-AX-94479963 77.35 TraesCS3D01G124500 Gibberellin 3-beta-hydroxylase 3.4 Analysis of Relative Expression Levels of Candidate Genes Utilizing gene annotation data from the wheat genome database, we analyzed the expression levels of these eight candidate genes across various wheat tissues and generated a heatmap (Fig. 3 ). The results indicated that two genes consistently show high expression in both stem and leaf tissues (Fig. 4 ). TraesCS7B02G701400LC exhibits negligible expression, with faint activity observed only in the spike tissue (spike_Z39). TraesCS3D02G124500 demonstrates relatively high expression levels in stem tissues (stem_Z30, stem_Z32) and spike tissues (spike_Z39, spike_Z65), moderate expression in roots (root_Z10, root_Z13, root_Z39), and lower expression in leaves (leaf_Z23, leaf_Z71) and grains (grain_Z71, grain_Z75). TraesCS2A02G075000 shows extremely high expression in the stem (stem_Z65) and leaves (leaf_Z10, leaf_Z23), especially during leaf development stages, while exhibiting lower expression levels in other tissues such as roots, spikes, and grains. TraesCS2B02G083400 displays elevated expression in the spike (spike_Z32, spike_Z39, spike_Z65) and grain (grain_Z71), with minimal expression detected in tissues like roots, stems, and leaves. TraesCS1A02G365900 is notably expressed during early root development phases (root_Z10, root_Z13), and also shows high expression in leaves (leaf_Z23, leaf_Z71) and stems (stem_Z30, stem_Z32, stem_Z65), but displays lower levels in spike and grain tissues. TraesCS1A02G378100 exhibits relatively high expression levels in roots (root_Z13, root_Z39) and stems (stem_Z30, stem_Z65), with moderate expression observed in spikes (spike_Z32, spike_Z39, spike_Z65). Its expression in grains and leaves remains comparatively low. TraesCS2B02G082400 shows significant expression in roots (root_Z10, root_Z13, root_Z39) and spikes (spike_Z32, spike_Z39), while its activity in other tissues is minimal. TraesCS2A02G407800 displays elevated expression during the late stage of spike development (spike_Z65), with negligible expression detected in roots, stems, and leaves. 4 Discussion 4.1 Phenotypic analysis of PHS in RIL population of wheat Wheat resistance to PHS is an extremely complex, influenced by both genotype and environmental factors. (Detje 1992 ; Groos et al. 2002 ; Himi and Noda 2005 ; Wang et al. 2016 ; Zhou et al. 2017 ; Zhu et al. 2019 ). Wang et al. ( 2016 ) have highlighted that the dormancy characteristics of seeds are a major genetic determinant of PHS resistance. Zhou et al. ( 2017 ) have identified temperature and moisture as the predominant environmental factors affecting PHS, with higher risks associated with conditions of elevated temperature and humidity. Additionally, Pu et al. ( 2015 ) emphasized that the coefficient of variation (CV) for population quantitative traits serves as a critical measure of trait variability, while heritability is indicative of the differences in trait expression and development within a population. In this study, both parental lines exhibited high PHS rates across four different environments. Similarly, most RIL families displaying high average PHS rates, indicating generally weak PHS resistance within the population. This suggests a generally weak PHS resistance within the RIL population. Specifically, PHS rates within the RIL population ranged from 23.92%to 100%, with coefficients of variation (CV) between 0.16 and 0.19. This wide range indicates substantial segregation of alleles contributing to PHS resistance from the parents to their offspring, resulting in pronounced transgressive segregation within the RIL population. Comparing 2019S and 2019M, PHS rates of both the parents and the RIL population decreased, albeit with a widened range. The correlation coefficient between these two environments was the lowest at 0.51. This discrepancy may be attributed to the frequent precipitation and temperature drops experienced in the Sanping area in 2019, which led to two cold waves and a delayed wheat growth period compared to typical years. The RIL population exhibited some resistance to the cold wave conditions, contributing to an overall enhancement of PHS resistance. However, compared to 2018 and 2020, the RIL population exhibited increased PHS rates and a narrower range of variation in 2019 (including 2019S and 2019M), indicating a weakened overall PHS resistance. According to the "2019 Climate Bulletin and Impact Assessment of Xinjiang Uyghur Autonomous Region" released by the Xinjiang Meteorological Bureau, the average temperature in northern Xinjiang during midsummer (July to August) of 2019 was 24.4°C, which is 1.6°C higher than usual, marking it as the hottest midsummer on record in northern Xinjiang. This increase in temperature likely contributed to the observed decrease in overall PHS resistance in the RIL population for that year. Correlation analysis revealed significantly higher coefficients among the 2018M, 2019M, and 2020M environments compared to 2019S. This indicates a relatively stable PHS resistance performance of the RIL population under different environmental conditions at the same location. Collectively, our findings suggest that the PHS resistance in the RIL population, while influenced by environmental factors, is predominantly determined by genetic makeup. This is supported by the high broad-sense heritability (0.86) observed across four environments, indicating that genetic factors are the primary drivers of phenotypic variation. RIL families exhibiting lower germination rates represent promising candidates for resistant varieties within breeding programs. Furthermore, although some families displayed elevated PHS rates after seven days of germination, their initial lower germination rates during the early days of the whole-spike sprouting test. This suggests an ability to withstand short-term high temperature and high humidity conditions, making them suitable candidates for developing resistant varieties. 4.2 QTL analysis of PHS characters of wheat Advancements in molecular biology techniques have significantly enhanced the identification of QTLs and candidate genes associated with PHS. Previous linkage studies by Munkvold et al. ( 2009 ), Osa et al. ( 2003 ), and Lin et al. ( 2015 ) using different wheat populations combined with gene chip technology have identified major QTLs on chromosomes 2AL, 2BS, 3AS, 3AL, 4AL, 5D, 6B, and 7D. Notably, the loci on 2AL and 2BS identified in these studies align with those found in our study, further validating the reliability of these QTLs. Comparative analysis of the QTLs detected in this research with those reported in previous studies revealed some notable relocations of marker loci/QTLs. For example, QPHS.xjau-1AL.2 (535.72Mb-545.65Mb), located on the long arm of chromosome 1A in our study, is approximately 6Mb away from the major QTL Qphs.ahau-1A (530.2Mb) reported by Singh et al. (Singh et al. 2010 ) on the same chromosome. In addition, both QPHS.xjau-1AL.1 (549.08Mb-552.85Mb) and QPHS.xjau-1AL.2 (535.72Mb-545.65Mb), also located on the long arm of chromosome 1A, were between the QTLs MQTL.PHS-1A.3 (517.48Mb) and MQTL.PHS-1A.4 (581.87Mb) reported by Li et al. ( 2021 ). The location of the two QTLs identified in this research suggest that they may represent new QTLs for PHS. QPHS.xjau-2AS , located on the short arm of chromosome 2A between 31.83-33.78Mb, is approximately 1Mb from the marker IWA1152 (33.3Mb) identified on chromosome 2A by Mohan et al. ( 2009 ). These QTLs could potentially be the same, though variations in their phenotypic contribution rates are observed across different studies. For example, in this study, the QTLs on chromosomes 1A and 2A contributed 2.67%-4.87% and 4.02% to their respective phenotypic effects, whereas the corresponding QTLs identified by Singh et al. ( 2010 ) and Mohan et al. ( 2009 ) contributed significantly higher rates of 8.7%-13.7% and 6.3%-13.6%, respectively. The discrepancies in phenotypic contribution observed could be attributed to several factors including differences in experimental methods, the genetic makeup of the mapping populations, environmental conditions where the mapping populations were planted, and variations in gene chip throughput. Six QTLs we detected are notably distanced—ranging from 20Mb to 40Mb—away from previously reported loci on the same chromosomes. For instance, QPHS.xjau-2AL located at 651.60Mb-679.54Mb on chromosome 2A and QPHS.xjau-7BL at 684.86Mb-685.78Mb on chromosome 7B are approximately 30Mb and 25Mb away from the Qphs.ahau-2A.2 (709.0Mb–712.7Mb) and barc340 (727.1Mb) identified by Kumar et al. ( 2015 ) and Zhu et al. ( 2014 ), respectively. These are likely new QTLs associated with PHS, contributing novel insights into the genetic basis of PHS resistance. Additionally, QPHS.xjau-1AL.1 was consistently detected across three environments (2018M, 2019M, and 2019S), it is located at 549.08Mb-552.85Mb and explains 3.86% to 6.39% of the phenotypic variation. This suggests that this locus is a critical site influencing wheat PHS resistance. Candidate gene mining within this interval identified: TraesCS1A01G378100 , a gene encoding a zinc finger family protein. This protein regulates seed dormancy by modulating the sensitivity of the plant to the endogenous hormone ABA (Lee et al. 2012 ), highlighting its significance in PHS resistance mechanisms. Moving forward, we plan to conduct map-based cloning and functional validation on this and other candidate genes that show large effect sizes and stable inheritance to further elucidate the genetic mechanisms underpinning wheat PHS. 4.3 Functional analysis of wheat PHS candidate genes Candidate gene mining was performed for QTLs that exhibiting stable inheritance or significant contributions to phenotypic variance,. Using the Chinese Spring wheat genome database, relevant QTL sequences were obtained. Based on gene function annotation information, eight candidate genes potentially related to wheat pre-harvest sprouting traits were identified. Notably, the genes TraesCS1A01G365900 and TraesCS2B01G082400 , located on chromosomes 1AL and 2BS of wheat respectively, encode proteins belonging to the Myb transcription factor family,These proteins are known to regulate the biosynthesis of flavonoids in seeds, significantly impacting grain color(Zhang et al. 2006 ). Himi and Noda༈2005) have shown that an R gene enhances the transcription of flavonoid biosynthesis genes. However, this gene is not expressed in white-grained wheat varieties. Red-grained wheat exhibits enhanced resistance to PHS, which is primarily attributed to the regulatory effects of three R genes that control seed coat color (Himi and Noda, 2005 ). Additionally, R genes are closely associated with dormancy loci, and Myb family transcription factors have been identified within this interval, with corresponding genetic markers developed successfully (Himi and Noda, 2005 ). Furthermore, the genes TraesCS2A01G407800 and TraesCS7B01G701400LC , located on chromosomes 2AL and 7BL respectively, encode proteins from the F-box protein family. These proteins are crucial for several physiological processes, including plant hormone signal transduction, light signal transduction, and floral organ development (Wu et al. 2015 ). Additionally, the genes TraesCS2A01G075000 and TraesCS2B01G083400 , situated on chromosomes 2AS and 2BS respectively, encode germin-like proteins. These proteins are regulated by external environmental signals and play crucial roles throughout the entire growth and development of the plant, particularly in responding to various stress conditions (Li and Liu, 2014 ). The gene TraesCS1A01G378100 , found on chromosome 1AL, encodes a zinc finger protein that regulates seed dormancy by modulating the sensitivity to ABA༈Zhang et al. 2006 ). Another gene, TraesCS3D01G124500 , located on chromosome 3DS, is annotated as encoding gibberellin 3-β-hydroxylase. This enzyme is pivotal in the biosynthesis pathway of Gas. GAs are essential for breaking seed dormancy, controlling internode elongation, managing leaf growth, as well as influencing flowering and fruiting, and gibberellin 3-β-hydroxylase specifically catalyzes the conversion to active gibberellins, thus significantly impacting plant growth and development (Yin et al. 2019 ). 4.4 QTL Meta-Analysis of PHS The ‘Berkut’ × ‘Worrakatta’ derived population exhibit significant variation in plant height (Yan et al. 2023 ), leaf area (Wang et al. 2022 ), drought resistance (Ren et al. 2021 ), and superoxide dismutase (Qu et al. 2024 ), with findings robustly supported by multi-environment data and statistical analysis. Notably, plant height (Ayık et al. 2024 ) and drought resistance (Biddulph et al. 2005 ) have been identified as factors closely associated with seed dormancy in wheat, highlighting the complexity of PHS as a trait influenced by multiple factors. This study integrates data from four research articles published for plant height, leaf area, drought stress, and superoxide dismutase, along with QTL information extracted from this research (Table 3 ). Using BioMercator V4.2.3 software for QTL comparative analysis, we identified four QTL loci on chromosome 2B (Fig. 5 ). Specifically, overlapping regions within the Xmwg546 ~ AX-108968210 marker interval on chromosome 2B encompass two QTLs ( QLAI.xjau-2BL-pre.2 for leaf area and QPHS.xjau-2BS for seed dormancy). These suggest that these hotspot regions may harbor a single gene influecing multiple agronomic traits. 5 Conclusions In this study, we conducted QTL mapping for wheat PHS using whole-spike germination rate data from 309 RILs. This analysis identified nine PHS associated QTLs distributed t, across chromosomes 1AL, 1DL, 2AL, 2AS, 2BS, 3DS, 4BL, and 7BL. Each QTL explained 2.67% to 6.39% of the phenotypic variation. Notably, the QTL QPHS.xjau-1AL.1 , detected across three environments, explained 3.86% to 6.39% of the phenotypic variation. Another QTL, QPHS.xjau-1AL.2 , identified under average conditions and in individual environments, explained 2.67% to 4.87% of the phenotypic variation. The remaining QTLs were identified in a single environment. Given wheat genome complexity, future research should focus on identifying key loci and genes associated with PHS. Additionally, efforts should be made to develop and validate both single-copy and multi-copy molecular markers within these genes to minimize linkage disequilibrium between markers and target genes. This approach will enhance the incorporation of multiple PHS resistance genes through molecular marker-assisted conventional breeding methods, ultimately facilitating the development of new wheat varieties resistant to pre-harvest sprouting. Declarations Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to infuence the work reported in this paper. FUNDING This work was supported by Xinjiang Key Research and Development Program (2022B02001-3) ,Xinjiang Young Science and Technology Top Talent Project (2022TSYCCX0079)and the earmarked fund for Basic Scientific Research Business Expenses of Autonomous Region Higher Education Institutions (XIEDU20241042). Author Contribution Author Contributions Statement YC and YX drafted the manuscript. LX and WH assisted in writing the manuscript. HZ and XL assisted in surveying data. JD Participate in supervision work. HG initiated the project, revised and finalized the manuscript. All authors reviewed the final manuscript. Acknowledgement We thank the anonymous reviewers of this paperfor their invaluable suggestions to improve the original manuscript. 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1","display":"","copyAsset":false,"role":"figure","size":324408,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of germination rat\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7708950/v1/9ea109803f80b5988edad250.png"},{"id":94407723,"identity":"3eea13f8-d37e-43c6-a580-9b0a0e223639","added_by":"auto","created_at":"2025-10-27 14:03:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":258649,"visible":true,"origin":"","legend":"\u003cp\u003eLocations of QTL for wheat sprouting trait\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7708950/v1/3330d54d01581d668cbaffc3.png"},{"id":94405489,"identity":"ee8338dd-f3cd-41e2-9490-fa1a323b12f5","added_by":"auto","created_at":"2025-10-27 14:01:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":42275,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the Expression Profiles of Candidate Genes Across Various Wheat Tissues\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7708950/v1/fa94ab88d7b4a5aaab8206c2.png"},{"id":94406258,"identity":"2b7c5111-aabe-48ac-b9ae-cad848036b67","added_by":"auto","created_at":"2025-10-27 14:02:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":87051,"visible":true,"origin":"","legend":"\u003cp\u003eExpression Analysis of Eight Candidate Genes Across Diverse Wheat Tissues\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7708950/v1/723eb4888665959b180d83a4.png"},{"id":94405661,"identity":"f3ffc25d-2542-4b20-9f10-344a98d2c479","added_by":"auto","created_at":"2025-10-27 14:01:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":74301,"visible":true,"origin":"","legend":"\u003cp\u003eConsensus map of QTL for wheat sprouting trait\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7708950/v1/071d6a1b5c2bae115f50bc9d.png"},{"id":98621815,"identity":"60178082-bd10-404d-87c3-f4dc5bc46f15","added_by":"auto","created_at":"2025-12-19 16:23:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1731056,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7708950/v1/d5687ae1-f776-4098-b9af-0b4866395632.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mapping Quantitative Trait Loci for Pre-Harvest Sprouting Resistance in Wheat Using Berkut × Worrakatta Recombinant Inbred Lines","fulltext":[{"header":"1 Background","content":"\u003cp\u003ePHS in wheat is a significant global climatic challenge caused by climatic conditions, occuring during ripening following sustained rainfall or hingh humidity (Derera et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Ogbonnaya et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In China, PHS frequently affects major wheat-growing regions including the Huang-Huai Valley, the southwestern winter wheat zone, and the middle and lower reaches of the Yangtze River, which collectively account for approximately 83% of the nation's total wheat cultivation area. Severe PHS events in 2016, 2018, and 2023 significantly reduced yields and degraded grain quality in provinces including Jiangsu, Anhui, Sichuan, Hubei, and Henan. PHS onset is influenced by multiple factors, including intrinsic wheat variety characteristics\u0026mdash;such as the water-holding capacity of spikes, glume traits, grain moisture content, endogenous hormones in seeds, and seed dormancy levels\u0026mdash;as well as environmental conditions. However, breeding for PHS resistance is difficult for its quantitatively inheritance affected by genetic and environmental factors (Zhou et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, understanding the PHS characteristics of existing wheat germplasm and the functional genes controlling these traits is crucial for strategically utilizing these resistance resources to breed new, high-quality, and high-yield varieties with PHS-resistant.\u003c/p\u003e\u003cp\u003eSubstantial research indicates that wheat PHS is a quantitative trait influenced by multiple QTLs or genes. For example, Munkvold et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) identified a significant QTL, \u003cem\u003eQPhs.cnl-2B.1\u003c/em\u003e, on chromosome 2B using a doubled haploid (DH) population, which accounted for 5%-31% of the phenotypic variation. Torada et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) identified mitogen-activated protein kinase kinase 3 (MKK3), designated \u003cem\u003eTaMKK3-A\u003c/em\u003e, as the candidate gene for the seed dormancy locus \u003cem\u003ePhs1\u003c/em\u003e on chromosome 4A in bread wheat. Additionally, Liu and Bai (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) located another significant PHS-resistant QTL tightly linked to the marker \u003cem\u003eXbarc57\u003c/em\u003e on chromosome 3AS, and successfully cloned \u003cem\u003eTriticum aestivum Pre-Harvest Sprouting 1\u003c/em\u003e (\u003cem\u003eTaPHS1\u003c/em\u003e) from the white-grained, PHS-resistant wheat Rio Blanco. Osa et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) used a RIL population from a Zenkoujikomugi (Zen) and Chinese Spring (CS) to identify a major resistance locus, \u003cem\u003eQPhs.ocs-3A.1\u003c/em\u003e, located near the molecular marker \u003cem\u003eXfbb370\u003c/em\u003e on chromosome 3AS. Zhou et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) performed a genome-wide association study on 717 Chinese wheat landraces and detected a major QTL for PHS resistance on chromosome 3D, which co-localizes with the grain color transcription factor \u003cem\u003eTaMyb10\u003c/em\u003e. Yang et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) identified another major QTL for PHS resistance, \u003cem\u003eQPHS.sicau-3D\u003c/em\u003e, on chromosome 3D in the synthetic wheat SHW-1, which was derived from the highly dormant tetraploid AS60 and moderately PHS-resistant diploid AS2255, and this QTL accounted for 42.47% of the phenotypic variation in PHS resistance across various environments. Furthermore, Zhang et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) discovered \u003cem\u003eTaSdr-B1\u003c/em\u003e, a homolog of the rice seed dormancy gene \u003cem\u003eOryza sativa Seed Dormancy 4\u003c/em\u003e (\u003cem\u003eOsSdr4\u003c/em\u003e), located on chromosome 2BS, and they also developed a functional marker \u003cem\u003eSdr2B\u003c/em\u003e. \u003cem\u003eTaSdr-B1\u003c/em\u003e is considered a candidate gene for a previously reported major PHS resistant QTL on chromosome 2B.\u003c/p\u003e\u003cp\u003eDespite significant advances in QTL mapping and wheat PHS research (Osa et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ogbonnaya et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Munkvold et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Rasul et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Tyagi and Gupta, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zuo et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), the efficacy of PHS resistance in wheat varies significantly under different environmental conditions. Aggregating multiple PHS-resistant genes can effectively enhance the resistance levels of wheat varieties. Currently, the primary method for improving PHS resistance is the aggregation of effective PHS-resistant genes through marker-assisted selection (MAS) breeding (Chang et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To identify stable PHS resistance loci and key genes, this study focused on a recombinant inbred line (RIL) population consisting of 309 families derived from the cross between the \"Berkut\" and \"Worrakatta\" wheat varieties. QTL mapping analysis for PHS was conducted Using a wheat 50K SNP chip on this population under various environmental conditions. Our goal is to facilitate MAS breeding and the aggregation of multiple resistance genes to develop new wheat varieties with enhanced PHS resistance.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Plant materials\u003c/h2\u003e\u003cp\u003eThe parental lines and the RIL population were generously provided by the Wheat Research Institute of the Chinese Academy of Agricultural Sciences. The experimental materials consisted of 309 RILs (F\u003csub\u003e6\u003c/sub\u003e generation) derived from the wheat varieties \"Berkut\" and \"Worrakatta\". These varieties were originally sourced from the International Maize and Wheat Improvement Center (CIMMYT) and donated by the Wheat Research Institute. The experimental materials were cultivated over three consecutive years (2018 to 2020) at the Manas Experimental Station of the Xinjiang Academy of Agricultural Sciences, referred to as 2018M, 2019M, and 2020M, respectively. Additionally, in 2019, a trial was also conducted at the Sanping Farm Experimental Base of Xinjiang Agricultural University, denoted as 2019S. The average data from these three consecutive years, covering a total of four environments, was used to represent the mean environment (abbreviated as A). The experimental materials were systematically planted in single rows, each measuring 2 m in length with a spacing of 25 cm between rows. The management practices, including fertilization, drip irrigation, pest control, and weed management, conformed to the standard local field protocols.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Method\u003c/h2\u003e\u003cp\u003ePhenotypic identification of pre-harvest sprouting (PHS) was conducted on the RIL population across four environments from 2018 to 2020. At the late dough stage of wheat development, 10 main stem spikes, including the peduncle, were harvested from each line. These spikes were air-dried indoors for one day and subsequently stored at -20\u0026deg;C to preserve dormancy. After harvesting all materials, a unified PHS assessment was carried out. The process began with soaking the whole spikes in distilled water for 10\u0026ndash;12 hours. Subsequently, they were disinfected using a 0.1% sodium hypochlorite solution for 15 minutes, rinsed thoroughly with sterile water, wrapped in germination paper, placed in ventilated plastic bags (with 3\u0026ndash;5 micropunctures of 0.5 mm diameter) to maintain humidity while allowing gas exchange. These preparations were then incubated in a controlled environment chamber set at 20\u0026deg;C for seven days, with a photoperiod of 16 h of light and 8 h of darkness and relative humidity of 80%. Following this incubation period, the spikes were quickly dried in an electric constant temperature oven set at 150\u0026deg;C to halt further germination. The grains were then manually threshed, using embryo rupture as the criterion for successful germination (Groos et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The PHS percentage for each line was calculated using the average results from two replicates. The whole spike germination percentage (GP) was determined by dividing the number of germinated grains in five spikes by the total number of grains, and then multiplying by 100%.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Statistical analysis of phenotypic data\u003c/h2\u003e\u003cp\u003eBasic statistical analyses were performed using Microsoft Excel 2016 and SPSS software version 21.0. Descriptive statistics and analysis of variance were conducted using QTL IciMapping V4.1 software(Lin et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Broad-sense heritability (\u003cem\u003eH\u003c/em\u003e\u0026sup2;) was calculated using the formula: \u003cem\u003eH\u003c/em\u003e\u0026sup2; = \u003cem\u003eσ\u003c/em\u003e\u0026sup2;\u003csub\u003eg\u003c/sub\u003e/[(\u003cem\u003eσ\u003c/em\u003e\u0026sup2;\u003csub\u003eg\u003c/sub\u003e+\u003cem\u003eσ\u003c/em\u003e\u0026sup2;\u003csub\u003egt\u003c/sub\u003e)/r\u0026thinsp;+\u0026thinsp;\u003cem\u003eσ\u003c/em\u003e\u0026sup2;\u003csub\u003ee\u003c/sub\u003e/r], where \u003cem\u003eσ\u003c/em\u003e\u0026sup2;\u003csub\u003eg\u003c/sub\u003e is the genotypic variance, \u003cem\u003eσ\u003c/em\u003e\u0026sup2;\u003csub\u003ee\u003c/sub\u003e is the error variance, \u003cem\u003eσ\u003c/em\u003e\u0026sup2;\u003csub\u003egt\u003c/sub\u003e is the genotype-by-environment interaction variance, and r is the number of replicates. Variance components (\u003cem\u003eσ\u003c/em\u003e\u0026sup2;\u003csub\u003eg\u003c/sub\u003e, \u003cem\u003eσ\u003c/em\u003e\u0026sup2;\u003csub\u003egt\u003c/sub\u003e, and \u003cem\u003eσ\u003c/em\u003e\u0026sup2;\u003csub\u003ee\u003c/sub\u003e) were estimated from ANOVA using the following equations: σ\u0026sup2;\u003csub\u003eg\u003c/sub\u003e = (MS_G \u0026ndash; MS_GxE)/(rE), \u003cem\u003eσ\u003c/em\u003e\u0026sup2;\u003csub\u003egt\u003c/sub\u003e = (MS_GxE - MS_e)/r, and \u003cem\u003eσ\u003c/em\u003e\u0026sup2;\u003csub\u003ee\u003c/sub\u003e = MS_e, where MS_G is mean square for genotypes, MS_GxE is mean square for genotype \u0026times; environment interaction, MS_e is mean square for residual error, r is number of replicates, E is number of environments, and rE is the product of the number of replicates (\u003cem\u003er\u003c/em\u003e) and the number of environments (E) (Lynch and Walsh, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Linkage map construction and QTL analysis\u003c/h2\u003e\u003cp\u003eThe wheat 50K SNP chip used in the study was processed and genotyped by Beijing CapitalBio Technology Co. Ltd. After the screening of polymorphic markers, genotype data of the segregating population were imported into QTL IciMapping version 4.1 software. Redundant markers were removed using the BIN program, and the genetic linkage map for the population was constructed using the MAP program. A total of 28 linkage groups were established, covering all 21 chromosomes of common wheat. The Inclusive Composite Interval Mapping (ICIM-ADD) method was employed to identify major QTLs, with the LOD threshold set to 2.0 and default settings for other parameters. QTL loci detected on the same chromosome and sharing overlapping genetic positions of peak values were classified as the same locus, and QTLs identified in two or more environmental conditions were considered to be stably inherited. QTLs were named according to the format \"Q\" followed by the trait abbreviation, the institution abbreviation (xjau), and the chromosome where the QTL locus is located.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Prediction of candidate genes\u003c/h2\u003e\u003cp\u003eFor QTL identified as stably inherited or having significant phenotypic contributions, candidate genes were predicted. This involved mapping population markers to the wheat genome based on their physical positions and utilizing significant SNP marker sequences identified across multiple environments. BLAST comparisons were performed using the Chinese Spring wheat reference genome database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.wheatgenome.org/\u003c/span\u003e\u003cspan address=\"http://www.wheatgenome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the National Center for Biotechnology Information (NCBI) website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Functional annotations of the candidate genes were conducted to delineate their potential roles.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 QTL meta-analysis\u003c/h2\u003e\u003cp\u003eThe collected QTL data, including QTL name, QTL position, LOD score, phenotypic variation rate, closely linked markers, confidence interval, and population size, were analyzed using BioMercator V4.2.3 (Cheng et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Among these parameters, the QTL position (confidence interval and peak position) and the genetic contribution rate are critical for effectively conducting QTL meta-analysis.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Phenotypic analysis of PHS in parents and RILs population\u003c/h2\u003e\u003cp\u003eAcross four distinct environments, whole-spike pre-harvest sprouting (WSPS) rates showed generally consistent trends within the RIL population. Both parental lines exhibited high WSPS rates, and the RIL population displaying a high average rate, including weak overall PHS resistance (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Within the RIL population, the coefficient of variation for WSPS rates ranged from 0.16 to 0.19, with observed values varying widely from 24% to 100%. This substantial variation highlights significant differences in PHS rates among the RIL families, suggesting transgressive segregation. Correlation analysis revealed strong positive relationships between WSPS rates measured across the four environments ranging from 0.51 to 0.84 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Variance analysis identified extremely significant differences in WSPS rates among RILs across environments (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.0e-05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), suggesting that genotype, environmental, and their interaction all significantly influence PHS resistance. The broad-sense heritability for PHS traits across environments was 0.86, underscoring that while genetic factors are predominantly responsible for variation in resistance, environmental factors also substantially affect these traits. These results suggest that genetic factors are the primary drivers of phenotypic variation, and that selection in early generations is an effective strategy.\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\u003eRIL population wheat sprouting trait statistical analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"left\" 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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnvironment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMin(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMax(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSD(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2018M\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2019M\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2019S\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e2020M\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2018M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e82.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e14.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2019M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e99.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e82.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.84**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2019S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e98.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e81.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e13.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.63**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.51**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2020M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e96.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e98.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e78.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e14.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.77**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.67**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.76**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003eNote: 2018M、2019M and 2020M: The experimental materials were cultivated over three consecutive years (2018 to 2020) at the Manas Experimental Station of the Xinjiang Academy of Agricultural Sciences, referred to as 2018M, 2019M, and 2020M;\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e2019S: In 2019, a trial was also conducted at the Sanping Farm Experimental Base of Xinjiang Agricultural University, denoted as 2019S.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eANOVA and generalized heritability of wheat sprouting in RIL population\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource of variance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e274475.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e894.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e52.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.0e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnvironment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4529.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1509.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e88.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.0e-05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG\u0026times;E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77467.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e140.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.0e-05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eError\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e858.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14709.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.14\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal variation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1720\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e371351.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 QTL loci of wheat PHS traits\u003c/h2\u003e\u003cp\u003eBy integrating PHS rates data from 309 RIL families with genotype data from the wheat 50K SNP chip, additive effect QTL mapping was performed using the inclusive composite interval mapping (ICIM) method. This analysis identified nine QTLs associated with PHS traits, distributed across seven chromosomes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Fve QTLs exhibited positive additive effects, while four displayed negative additive effects. Individual QTLs accounted for 2.67% \u0026minus;\u0026thinsp;6.39% of the observed phenotypic variation, depending on the environmental conditions. Alleles associated with increasing effects located on chromosomes 1A, 2A, and 3D originated from the 'Berkut' parent, while those on chromosomes 1D, 2B, 4B, and 7B were derived from the 'Worrakatta' parent. \u003cem\u003eQPHS.xjau-1AL.1\u003c/em\u003e, detected under three different environmental conditions (2018M, 2019M, and 2019S), was mapped within the interval \u003cem\u003eAX-110067057\u003c/em\u003e-\u003cem\u003eAX-179561683\u003c/em\u003e at a physical position of 549.08Mb-552.85Mb. This QTL explains 3.86%-6.39% of the phenotypic variation. Another QTL, \u003cem\u003eQPHS.xjau-1AL.2\u003c/em\u003e, was identified in the 2019S environment and under average environmental conditions; it was located within the interval \u003cem\u003eAX-109326239\u003c/em\u003e-\u003cem\u003eAX-94417718\u003c/em\u003e at a physical position of 535.72Mb-545.65Mb and accounted for 2.67%-4.87% of the phenotypic variation. Upon comparison with previously reported results, three of the detected QTLs were found to be proximal to markers or QTL intervals identified in previous studies, while the other six were located in chromosomal regions not previously associated with these traits (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;2). Comparison with previously reported results indicated that three of detected QTLs were located near markers/QTL intervals identified in previous studies, while the rother six QTLs were in chromosomal regions not previously reported (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eIn the 2018M, 2020M, and the average environment (A), one PHS-related QTL was detected per environment: \u003cem\u003eQPHS.xjau-1AL.1\u003c/em\u003e, \u003cem\u003eQPHS.xjau-2BS\u003c/em\u003e, and \u003cem\u003eQPHS.xjau-1AL.2\u003c/em\u003e. These QTLs exhibited LOD scores between 2.4\u0026ndash;3.1and collectively explained 4.20%-4.87% of the phenotypic variation. The alleles contributing to increased effects for \u003cem\u003eQPHS.xjau-1AL.1\u003c/em\u003e and \u003cem\u003eQPHS.xjau-1AL.2\u003c/em\u003e are derived from Berkut, while the allele contributing to increased effects for \u003cem\u003eQPHS.xjau-2BS\u003c/em\u003e is derived from Worrakatta.\u003c/p\u003e\u003cp\u003eIn the 2019M environment, two PHS related QTLs were identified: \u003cem\u003eQPHS.xjau-1AL.1\u003c/em\u003e and \u003cem\u003eQPHS.xjau-3DS\u003c/em\u003e, located on chromosomes 1AL and 3DS, respectively. \u003cem\u003eQPHS.xjau-1AL.1\u003c/em\u003e is situated within the interval \u003cem\u003eAX-110067057\u003c/em\u003e-\u003cem\u003eAX-179561683\u003c/em\u003e and has a LOD value of 2.6, explaining 6.39% of the phenotypic variation. \u003cem\u003eQPHS.xjau-3DS\u003c/em\u003e is located within the interval \u003cem\u003eAX-108907550\u003c/em\u003e-\u003cem\u003eAX-94479963\u003c/em\u003e and has a LOD value of 2.2, explaining 4.03% of the phenotypic variation. The alleles contributing to increased effects for both QTLs originate from the parent Berkut.\u003c/p\u003e\u003cp\u003eIn the 2019S environment, seven PHS-related QTLs were identifiedon chromosomes 1AL (two loci), 2AL, 2AS, 4BL, 7BL, and 1DL. Specifically, the two QTLs on chromosome 1AL, \u003cem\u003eQPHS.xjau-1AL.1\u003c/em\u003e and \u003cem\u003eQPHS.xjau-1AL.2\u003c/em\u003e, are located within the intervals \u003cem\u003eAX-110067057\u003c/em\u003e-\u003cem\u003eAX-179561683\u003c/em\u003e and \u003cem\u003eAX-109326239\u003c/em\u003e-\u003cem\u003eAX-94417718\u003c/em\u003e, respectively, with LOD values of 2.2 and 2.1, explaining 3.86% and 2.67% of the phenotypic variation. The alleles with increasing effects for both QTLs originate from the parent Berkut. Additionally, two QTLs detected on chromosome 2A, \u003cem\u003eQPHS.xjau-2AL\u003c/em\u003e and \u003cem\u003eQPHS.xjau-2AS\u003c/em\u003e, are located within the intervals \u003cem\u003eAX-89471863\u003c/em\u003e-A\u003cem\u003eX-158572584\u003c/em\u003e and \u003cem\u003eAX-111037158\u003c/em\u003e-\u003cem\u003eAX-94566723\u003c/em\u003e, respectively, with LOD values of 3.6 and 2.2, explaining 4.75% and 4.02% of the phenotypic variation. The alleles with increasing effects for these QTLs also originate from the parent Berkut. One QTL each was detected on chromosomes 4BL, 7BL, and 1DL, namely \u003cem\u003eQPHS.xjau-4BL\u003c/em\u003e, \u003cem\u003eQPHS.xjau-7BL\u003c/em\u003e, and \u003cem\u003eQPHS.xjau-1DL\u003c/em\u003e, respectively. These QTLs have LOD values ranging from 2.6 to 3.1, collectively explaining 3.42%-4.00% of the phenotypic variation. Unlike other QTLs detected, the alleles with increasing effects for these QTLs all originate from the parent Worrakatta.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eQTL locus information of wheat sprouting\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQTL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEnvironment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMarker interval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePhysical position\u003c/p\u003e\u003cp\u003e(Mb)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGenetic position\u003c/p\u003e\u003cp\u003e(cM)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLOD peak\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eR2(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAdd effect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eKnown loci\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eQPHS.xjau-1AL.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2018M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eAX-110067057-AX-179561683\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e549.08-552.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eQPHS.xjau-1AL.2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eAX-109326239-AX-94417718\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e535.72-545.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSingh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eQPHS.xjau-2AS\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAX-111037158-AX-94566723\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.83\u0026ndash;33.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMohan\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eQPHS.xjau-2AL\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAX-89471863-AX-158572584\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e651.60-679.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eQPHS.xjau-2BS\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2020M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAX-94849048-AX-94632942\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.44\u0026ndash;50.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-3.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLin\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eQPHS.xjau-4BL\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAX-111489623-AX-109455736\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e602.90-605.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-2.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eQPHS.xjau-7BL\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAX-110292317-AX-179558612\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e684.86-685.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eQPHS.xjau-1DL\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019S\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAX-89314186-AX-111558345\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e365.35-368.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-2.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eQPHS.xjau-3DS\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2019M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAX-108907550-AX-94479963\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77.01\u0026ndash;99.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNote: 2018M、2019M and 2020M: The experimental materials were cultivated over three consecutive years (2018 to 2020) at the Manas Experimental Station of the Xinjiang Academy of Agricultural Sciences, referred to as 2018M, 2019M, and 2020M;\u003c/p\u003e\u003cp\u003e2019S: In 2019, a trial was also conducted at the Sanping Farm Experimental Base of Xinjiang Agricultural University, denoted as 2019S.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003ethe right side of each chromosome is the molecular marker and the QTL mapped, and the left side is the genetic location corresponding to the QTL. The red is 2018M, green is 2019M, blue is 2019S, yellow is 2020M and black is A.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;2\u003c/b\u003e Locations of QTL for wheat sprouting trait\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Prediction of candidate genes related to wheat PHS\u003c/h2\u003e\u003cp\u003eBased on the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq Annotations database, gene mining was conducted for QTLs identified as stably inherited or having significant phenotypic contributions, leading to the identification of eight candidate genes potentially linked with PHS (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These candidate genes are primarily involved in pathways related to seed dormancy, plant hormone biosynthesis, and signal transduction. The annotated candidate genes include: \u003cem\u003eTraesCS1A01G378100\u003c/em\u003e encodes a protein belonging to the zinc finger family; \u003cem\u003eTraesCS2A01G407800\u003c/em\u003e and \u003cem\u003eTraesCS7B01G701400LC\u003c/em\u003e encode F-box proteins; \u003cem\u003eTraesCS1A01G365900\u003c/em\u003e and \u003cem\u003eTraesCS2B01G082400\u003c/em\u003e are identified as encoding Myb transcription factors; \u003cem\u003eTraesCS2A01G075000\u003c/em\u003e and \u003cem\u003eTraesCS2B01G083400\u003c/em\u003e encode germin-like proteins; and \u003cem\u003eTraesCS3D01G124500\u003c/em\u003e is annotated as a gibberellin 3-β-hydroxylase, a key enzyme in gibberellin biosynthesis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eScreening for candidate gene information\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChr\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMarker interval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePosition(Mb)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGene annotation or coding protein\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1AL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eQPHS.xjau-1AL.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAX-110067057-AX-179561683\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e550.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eTraesCS1A01G378100\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ezinc finger superfamily protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eQPHS.xjau-1AL.2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAX-109326239-AX-94417718\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e544.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eTraesCS1A01G365900\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMYB transcription factor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2AL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eQPHS.xjau-2AL\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAX-89471863-AX-158572584\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e664.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eTraesCS2A01G407800\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eF-box family protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2AS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eQPHS.xjau-2AS\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAX-111037158-AX-94566723\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eTraesCS2A01G075000\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGermin-like protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2BS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eQPHS.xjau-2BS\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eAX-94849048-AX-94632942\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eTraesCS2B01G082400\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMYB-related transcription factor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eTraesCS2B01G083400\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGermin-like protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7BL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eQPHS.xjau-7BL\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAX-110292317-AX-179558612\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e685.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eTraesCS7B01G701400LC\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eF-box family protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3DS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eQPHS.xjau-3DS\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAX-108907550-AX-94479963\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e77.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eTraesCS3D01G124500\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGibberellin 3-beta-hydroxylase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Analysis of Relative Expression Levels of Candidate Genes\u003c/h2\u003e\u003cp\u003eUtilizing gene annotation data from the wheat genome database, we analyzed the expression levels of these eight candidate genes across various wheat tissues and generated a heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results indicated that two genes consistently show high expression in both stem and leaf tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). \u003cem\u003eTraesCS7B02G701400LC\u003c/em\u003e exhibits negligible expression, with faint activity observed only in the spike tissue (spike_Z39). \u003cem\u003eTraesCS3D02G124500\u003c/em\u003e demonstrates relatively high expression levels in stem tissues (stem_Z30, stem_Z32) and spike tissues (spike_Z39, spike_Z65), moderate expression in roots (root_Z10, root_Z13, root_Z39), and lower expression in leaves (leaf_Z23, leaf_Z71) and grains (grain_Z71, grain_Z75). \u003cem\u003eTraesCS2A02G075000\u003c/em\u003e shows extremely high expression in the stem (stem_Z65) and leaves (leaf_Z10, leaf_Z23), especially during leaf development stages, while exhibiting lower expression levels in other tissues such as roots, spikes, and grains. \u003cem\u003eTraesCS2B02G083400\u003c/em\u003e displays elevated expression in the spike (spike_Z32, spike_Z39, spike_Z65) and grain (grain_Z71), with minimal expression detected in tissues like roots, stems, and leaves. \u003cem\u003eTraesCS1A02G365900\u003c/em\u003e is notably expressed during early root development phases (root_Z10, root_Z13), and also shows high expression in leaves (leaf_Z23, leaf_Z71) and stems (stem_Z30, stem_Z32, stem_Z65), but displays lower levels in spike and grain tissues. \u003cem\u003eTraesCS1A02G378100\u003c/em\u003e exhibits relatively high expression levels in roots (root_Z13, root_Z39) and stems (stem_Z30, stem_Z65), with moderate expression observed in spikes (spike_Z32, spike_Z39, spike_Z65). Its expression in grains and leaves remains comparatively low. \u003cem\u003eTraesCS2B02G082400\u003c/em\u003e shows significant expression in roots (root_Z10, root_Z13, root_Z39) and spikes (spike_Z32, spike_Z39), while its activity in other tissues is minimal. \u003cem\u003eTraesCS2A02G407800\u003c/em\u003e displays elevated expression during the late stage of spike development (spike_Z65), with negligible expression detected in roots, stems, and leaves.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Phenotypic analysis of PHS in RIL population of wheat\u003c/h2\u003e\u003cp\u003eWheat resistance to PHS is an extremely complex, influenced by both genotype and environmental factors. (Detje \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Groos et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Himi and Noda \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhou et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Wang et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) have highlighted that the dormancy characteristics of seeds are a major genetic determinant of PHS resistance. Zhou et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) have identified temperature and moisture as the predominant environmental factors affecting PHS, with higher risks associated with conditions of elevated temperature and humidity. Additionally, Pu et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) emphasized that the coefficient of variation (CV) for population quantitative traits serves as a critical measure of trait variability, while heritability is indicative of the differences in trait expression and development within a population. In this study, both parental lines exhibited high PHS rates across four different environments. Similarly, most RIL families displaying high average PHS rates, indicating generally weak PHS resistance within the population. This suggests a generally weak PHS resistance within the RIL population. Specifically, PHS rates within the RIL population ranged from 23.92%to 100%, with coefficients of variation (CV) between 0.16 and 0.19. This wide range indicates substantial segregation of alleles contributing to PHS resistance from the parents to their offspring, resulting in pronounced transgressive segregation within the RIL population. Comparing 2019S and 2019M, PHS rates of both the parents and the RIL population decreased, albeit with a widened range. The correlation coefficient between these two environments was the lowest at 0.51. This discrepancy may be attributed to the frequent precipitation and temperature drops experienced in the Sanping area in 2019, which led to two cold waves and a delayed wheat growth period compared to typical years. The RIL population exhibited some resistance to the cold wave conditions, contributing to an overall enhancement of PHS resistance. However, compared to 2018 and 2020, the RIL population exhibited increased PHS rates and a narrower range of variation in 2019 (including 2019S and 2019M), indicating a weakened overall PHS resistance. According to the \"2019 Climate Bulletin and Impact Assessment of Xinjiang Uyghur Autonomous Region\" released by the Xinjiang Meteorological Bureau, the average temperature in northern Xinjiang during midsummer (July to August) of 2019 was 24.4\u0026deg;C, which is 1.6\u0026deg;C higher than usual, marking it as the hottest midsummer on record in northern Xinjiang. This increase in temperature likely contributed to the observed decrease in overall PHS resistance in the RIL population for that year.\u003c/p\u003e\u003cp\u003eCorrelation analysis revealed significantly higher coefficients among the 2018M, 2019M, and 2020M environments compared to 2019S. This indicates a relatively stable PHS resistance performance of the RIL population under different environmental conditions at the same location. Collectively, our findings suggest that the PHS resistance in the RIL population, while influenced by environmental factors, is predominantly determined by genetic makeup. This is supported by the high broad-sense heritability (0.86) observed across four environments, indicating that genetic factors are the primary drivers of phenotypic variation. RIL families exhibiting lower germination rates represent promising candidates for resistant varieties within breeding programs. Furthermore, although some families displayed elevated PHS rates after seven days of germination, their initial lower germination rates during the early days of the whole-spike sprouting test. This suggests an ability to withstand short-term high temperature and high humidity conditions, making them suitable candidates for developing resistant varieties.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.2 QTL analysis of PHS characters of wheat\u003c/h2\u003e\u003cp\u003eAdvancements in molecular biology techniques have significantly enhanced the identification of QTLs and candidate genes associated with PHS. Previous linkage studies by Munkvold et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), Osa et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), and Lin et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) using different wheat populations combined with gene chip technology have identified major QTLs on chromosomes 2AL, 2BS, 3AS, 3AL, 4AL, 5D, 6B, and 7D. Notably, the loci on 2AL and 2BS identified in these studies align with those found in our study, further validating the reliability of these QTLs.\u003c/p\u003e\u003cp\u003eComparative analysis of the QTLs detected in this research with those reported in previous studies revealed some notable relocations of marker loci/QTLs. For example, \u003cem\u003eQPHS.xjau-1AL.2\u003c/em\u003e (535.72Mb-545.65Mb), located on the long arm of chromosome 1A in our study, is approximately 6Mb away from the major QTL \u003cem\u003eQphs.ahau-1A\u003c/em\u003e (530.2Mb) reported by Singh et al. (Singh et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) on the same chromosome. In addition, both \u003cem\u003eQPHS.xjau-1AL.1\u003c/em\u003e (549.08Mb-552.85Mb) and \u003cem\u003eQPHS.xjau-1AL.2\u003c/em\u003e (535.72Mb-545.65Mb), also located on the long arm of chromosome 1A, were between the QTLs \u003cem\u003eMQTL.PHS-1A.3\u003c/em\u003e (517.48Mb) and \u003cem\u003eMQTL.PHS-1A.4\u003c/em\u003e (581.87Mb) reported by Li et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The location of the two QTLs identified in this research suggest that they may represent new QTLs for PHS.\u003c/p\u003e\u003cp\u003e\u003cem\u003eQPHS.xjau-2AS\u003c/em\u003e, located on the short arm of chromosome 2A between 31.83-33.78Mb, is approximately 1Mb from the marker IWA1152 (33.3Mb) identified on chromosome 2A by Mohan et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). These QTLs could potentially be the same, though variations in their phenotypic contribution rates are observed across different studies. For example, in this study, the QTLs on chromosomes 1A and 2A contributed 2.67%-4.87% and 4.02% to their respective phenotypic effects, whereas the corresponding QTLs identified by Singh et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Mohan et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) contributed significantly higher rates of 8.7%-13.7% and 6.3%-13.6%, respectively. The discrepancies in phenotypic contribution observed could be attributed to several factors including differences in experimental methods, the genetic makeup of the mapping populations, environmental conditions where the mapping populations were planted, and variations in gene chip throughput.\u003c/p\u003e\u003cp\u003eSix QTLs we detected are notably distanced\u0026mdash;ranging from 20Mb to 40Mb\u0026mdash;away from previously reported loci on the same chromosomes. For instance, \u003cem\u003eQPHS.xjau-2AL\u003c/em\u003e located at 651.60Mb-679.54Mb on chromosome 2A and \u003cem\u003eQPHS.xjau-7BL\u003c/em\u003e at 684.86Mb-685.78Mb on chromosome 7B are approximately 30Mb and 25Mb away from the \u003cem\u003eQphs.ahau-2A.2\u003c/em\u003e (709.0Mb\u0026ndash;712.7Mb) and barc340 (727.1Mb) identified by Kumar et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Zhu et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), respectively. These are likely new QTLs associated with PHS, contributing novel insights into the genetic basis of PHS resistance.\u003c/p\u003e\u003cp\u003eAdditionally, \u003cem\u003eQPHS.xjau-1AL.1\u003c/em\u003e was consistently detected across three environments (2018M, 2019M, and 2019S), it is located at 549.08Mb-552.85Mb and explains 3.86% to 6.39% of the phenotypic variation. This suggests that this locus is a critical site influencing wheat PHS resistance.\u003c/p\u003e\u003cp\u003eCandidate gene mining within this interval identified: \u003cem\u003eTraesCS1A01G378100\u003c/em\u003e, a gene encoding a zinc finger family protein. This protein regulates seed dormancy by modulating the sensitivity of the plant to the endogenous hormone ABA (Lee et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), highlighting its significance in PHS resistance mechanisms. Moving forward, we plan to conduct map-based cloning and functional validation on this and other candidate genes that show large effect sizes and stable inheritance to further elucidate the genetic mechanisms underpinning wheat PHS.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Functional analysis of wheat PHS candidate genes\u003c/h2\u003e\u003cp\u003eCandidate gene mining was performed for QTLs that exhibiting stable inheritance or significant contributions to phenotypic variance,. Using the Chinese Spring wheat genome database, relevant QTL sequences were obtained. Based on gene function annotation information, eight candidate genes potentially related to wheat pre-harvest sprouting traits were identified. Notably, the genes \u003cem\u003eTraesCS1A01G365900\u003c/em\u003e and \u003cem\u003eTraesCS2B01G082400\u003c/em\u003e, located on chromosomes 1AL and 2BS of wheat respectively, encode proteins belonging to the Myb transcription factor family,These proteins are known to regulate the biosynthesis of flavonoids in seeds, significantly impacting grain color(Zhang et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Himi and Noda༈2005) have shown that an \u003cem\u003eR\u003c/em\u003e gene enhances the transcription of flavonoid biosynthesis genes. However, this gene is not expressed in white-grained wheat varieties. Red-grained wheat exhibits enhanced resistance to PHS, which is primarily attributed to the regulatory effects of three \u003cem\u003eR\u003c/em\u003e genes that control seed coat color (Himi and Noda, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Additionally, \u003cem\u003eR\u003c/em\u003e genes are closely associated with dormancy loci, and \u003cem\u003eMyb\u003c/em\u003e family transcription factors have been identified within this interval, with corresponding genetic markers developed successfully (Himi and Noda, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Furthermore, the genes \u003cem\u003eTraesCS2A01G407800\u003c/em\u003e and \u003cem\u003eTraesCS7B01G701400LC\u003c/em\u003e, located on chromosomes 2AL and 7BL respectively, encode proteins from the F-box protein family. These proteins are crucial for several physiological processes, including plant hormone signal transduction, light signal transduction, and floral organ development (Wu et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Additionally, the genes \u003cem\u003eTraesCS2A01G075000\u003c/em\u003e and \u003cem\u003eTraesCS2B01G083400\u003c/em\u003e, situated on chromosomes 2AS and 2BS respectively, encode germin-like proteins. These proteins are regulated by external environmental signals and play crucial roles throughout the entire growth and development of the plant, particularly in responding to various stress conditions (Li and Liu, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The gene \u003cem\u003eTraesCS1A01G378100\u003c/em\u003e, found on chromosome 1AL, encodes a zinc finger protein that regulates seed dormancy by modulating the sensitivity to ABA༈Zhang et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Another gene, \u003cem\u003eTraesCS3D01G124500\u003c/em\u003e, located on chromosome 3DS, is annotated as encoding gibberellin 3-β-hydroxylase. This enzyme is pivotal in the biosynthesis pathway of Gas. GAs are essential for breaking seed dormancy, controlling internode elongation, managing leaf growth, as well as influencing flowering and fruiting, and gibberellin 3-β-hydroxylase specifically catalyzes the conversion to active gibberellins, thus significantly impacting plant growth and development (Yin et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.4 QTL Meta-Analysis of PHS\u003c/h2\u003e\u003cp\u003eThe \u0026lsquo;Berkut\u0026rsquo; \u0026times; \u0026lsquo;Worrakatta\u0026rsquo; derived population exhibit significant variation in plant height (Yan et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), leaf area (Wang et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), drought resistance (Ren et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and superoxide dismutase (Qu et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with findings robustly supported by multi-environment data and statistical analysis. Notably, plant height (Ayık et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and drought resistance (Biddulph et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) have been identified as factors closely associated with seed dormancy in wheat, highlighting the complexity of PHS as a trait influenced by multiple factors. This study integrates data from four research articles published for plant height, leaf area, drought stress, and superoxide dismutase, along with QTL information extracted from this research (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Using BioMercator V4.2.3 software for QTL comparative analysis, we identified four QTL loci on chromosome 2B (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Specifically, overlapping regions within the \u003cem\u003eXmwg546\u003c/em\u003e\u0026thinsp;~\u0026thinsp;\u003cem\u003eAX-108968210\u003c/em\u003e marker interval on chromosome 2B encompass two QTLs (\u003cem\u003eQLAI.xjau-2BL-pre.2\u003c/em\u003e for leaf area and \u003cem\u003eQPHS.xjau-2BS\u003c/em\u003e for seed dormancy). These suggest that these hotspot regions may harbor a single gene influecing multiple agronomic traits.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eIn this study, we conducted QTL mapping for wheat PHS using whole-spike germination rate data from 309 RILs. This analysis identified nine PHS associated QTLs distributed t, across chromosomes 1AL, 1DL, 2AL, 2AS, 2BS, 3DS, 4BL, and 7BL. Each QTL explained 2.67% to 6.39% of the phenotypic variation. Notably, the QTL \u003cem\u003eQPHS.xjau-1AL.1\u003c/em\u003e, detected across three environments, explained 3.86% to 6.39% of the phenotypic variation. Another QTL, \u003cem\u003eQPHS.xjau-1AL.2\u003c/em\u003e, identified under average conditions and in individual environments, explained 2.67% to 4.87% of the phenotypic variation. The remaining QTLs were identified in a single environment. Given wheat genome complexity, future research should focus on identifying key loci and genes associated with PHS. Additionally, efforts should be made to develop and validate both single-copy and multi-copy molecular markers within these genes to minimize linkage disequilibrium between markers and target genes. This approach will enhance the incorporation of multiple PHS resistance genes through molecular marker-assisted conventional breeding methods, ultimately facilitating the development of new wheat varieties resistant to pre-harvest sprouting.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/b\u003e \u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to infuence the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFUNDING\u003c/h2\u003e\u003cp\u003eThis work was supported by Xinjiang Key Research and Development Program (2022B02001-3) ,Xinjiang Young Science and Technology Top Talent Project (2022TSYCCX0079)and the earmarked fund for Basic Scientific Research Business Expenses of Autonomous Region Higher Education Institutions (XIEDU20241042).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions Statement YC and YX drafted the manuscript. LX and WH assisted in writing the manuscript. HZ and XL assisted in surveying data. JD Participate in supervision work. HG initiated the project, revised and finalized the manuscript. All authors reviewed the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the anonymous reviewers of this paperfor their invaluable suggestions to improve the original manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAyık B, G\u0026uuml;le\u0026ccedil; T, Aydın N, T\u0026uuml;rkoğlu A, Bocianowski J (2024) Pre-harvest sprouting resistance in bread wheat: A speed breeding approach to assess dormancy QTL in backcross lines. 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Acta Agron Sin 40:1725\u0026ndash;1732.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZuo J, Lin CT, Cao H, Chen F, Liu Y, Liu J (2019) Genome-wide association study and quantitative trait loci mapping of seed dormancy in common wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.). Planta 250:187\u0026ndash;198.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang HP, Chang C, Xiao SH (2006) Proteome analysis of ABA signaling in wheat germ dormancy. Acta Agron Sin 32:690\u0026ndash;697.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Wheat, Pre-harvest sprouting, QTL, Candidate genes","lastPublishedDoi":"10.21203/rs.3.rs-7708950/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7708950/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePre-harvest sprouting (PHS) in wheat is a significant global challenge influenced by climate. This study aimed to decipher the genetic underpinnings of PHS and identify resistance genes using 309 recombinant inbred lines (RILs) derived from the \"Berkut \u0026times; Worrakatta\" cross. Methods:Phenotypic assessment of PHS traits was performed using the whole-spike sprouting method across various environments, complemented by quantitative trait loci (QTL) analysis employing a wheat 50K SNP chip.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eResults showed high PHS rates in both parental lines across multiple environments. Progeny exhibited substantial variation in PHS rates, with coefficients of variation ranging from 0.16 to 0.19 and a phenotypic variation ranging from 23.92% to 100%, suggesting pronounced transgressive segregation. Nine QTLs associated with PHS were identified on chromosomes 1AL, 1DL, 2AL, 2AS, 2BS, 3DS, 4BL, and 7BL. These loci accounted for 2.67% to 6.39% of the phenotypic variation. Notably, the enhancer alleles at four loci\u0026mdash;1DL, 2BS, 4BL, and 7BL\u0026mdash;originated from 'Worrakatta', and 'Berkut' contributed the enhancer alleles at the remaining five loci. Two QTLs, \u003cem\u003eQPHS.xjau-1AL.1\u003c/em\u003e and \u003cem\u003eQPHS.xjau-1AL.2\u003c/em\u003e, were stable across multiple environments. Specifically, \u003cem\u003eQPHS.xjau-1AL.1\u003c/em\u003e was present in three environments and explained 3.86% to 6.39% of the phenotypic variation, while \u003cem\u003eQPHS.xjau-1AL.2\u003c/em\u003e appeared in one environment under average conditions, explaining 2.67% to 4.87% of the variation.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur study also identified eight candidate genes associated with wheat PHS, including those encoding MYB transcription factors that influence flavonoid biosynthesis and grain color, as well as genes involved in stress response and gibberellin biosynthesis, which are crucial for plant growth and development. These genes represent vital targets for enhancing wheat PHS resistance.\u003c/p\u003e","manuscriptTitle":"Mapping Quantitative Trait Loci for Pre-Harvest Sprouting Resistance in Wheat Using Berkut × Worrakatta Recombinant Inbred Lines","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-26 10:21:05","doi":"10.21203/rs.3.rs-7708950/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"74b11740-0486-439a-8797-4de0a4de8f65","owner":[],"postedDate":"October 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-04T16:08:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-26 10:21:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7708950","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7708950","identity":"rs-7708950","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}