Genome-wide and candidate gene association mapping for plant height in wheat

Genome-wide and candidate gene association mapping for plant height in wheat | 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 Genome-wide and candidate gene association mapping for plant height in wheat Hafiz Muhammad Suleman, Humaira Qayyum, Sana ur-Rehman, Khawar Majeed, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4679366/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Nov, 2025 Read the published version in Molecular Breeding → Version 1 posted 4 You are reading this latest preprint version Abstract Plant architecture and yield potential of wheat are significantly influenced by the plant height (PH). In the present study, a diversity panel consisting of 199 historical wheat cultivars of Pakistan was evaluated for PH in three environments, and a genome-wide association study (GWAS) was conducted to identify loci associated with reduced height. GWAS identified 19 loci that were associated with reduced height, out of which 12 loci were consistently identified in all environments. Allelic variations were analyzed in the diversity panel for five Rht genes including Rht-B1 , Rht-D1 , Rht13 , Rht25 , and Rht26 using diagnostic KASP markers. Furthermore, a KASP marker was developed for the identification of dwarfing allele Rht-B1p in wheat. Allelic frequency of the GA-insensitive dwarfing allele Rht-B1b was pre-dominant (69.6%) followed by the GA-sensitive Rht26 mutant allele (58.5%). Five dwarfing allele of Rht25 including Rht25c , Rht25d , and Rht25e were rarely present in the cultivars with a frequency of 1.5%, 1%, 0.5%, respectively. The use of alternate dwarfing alleles to reduce PH can increase the genetic base of wheat cultivars by removing selection pressure on Rht-B1b/Rht-D1b haplotype and can lead to the development of wheat cultivars with improved characteristics such as reduced lodging risk, increased resource allocation to grain, improved harvest efficiency, enhanced crop stability, and adaptability. Plant height (PH) Reduced height (Rht) genes GWAS KASP SNP Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Wheat crop is a fundamental element of human civilization and plays an important role in feeding the hungry world and improving global food security, contributing about 20% of total dietary calories and proteins worldwide (Shiferaw et al., 2013 ). Wheat is a major crop for a global food security perspective and continuous efforts are needed to increase grain yield to feed a growing human population that recently surpassed 8 billion people (Zhang et al., 2023 ). Plant architecture has a significant impact on agronomy because it determines a plant's potential grain output and cultivation adaptability (Reinhardt and Kuhlemeier, 2002 ). Stature is a key element of plant architecture, primarily shaped by stem elongation. Short wheat plants can result in the crowding of canopy leaves, a decrease in photosynthetic rate, and insufficient biomass to provide a sufficient "source," resulting in low yield. On the other hand, too tall wheat plants can result in lodging, which directly results in lower yield (Hedden, 2003 ). Plant height should be reduced appropriately to increase wheat harvest index and lodging resistance. There are 26 Rht genes ( Rht1 - Rht26 ) for dwarfism in wheat, including gibberellin (GA)-responsive and GA-insensitive genes that have been cataloged. Some of these Rht genes were identified in the artificial mutants generated by chemical or physical mutagens, although the majority are found in natural populations (Agarwal et al., 2020 ). Lodging resistance, reduced plant height, increased tillers, and higher yield are all known to be caused by the Rht genes (Jobson et al., 2019 ). Rht-B1b ( Rht1 ), Rht-D1b ( Rht2 ), Rht-B1c ( Rht3 ), Rht8 , Rht-D1c ( Rht10 ), Rht-B1e ( Rht11 ), Rht12 , Rht-B1p (Rht17 ), Rht13 , Rht18 , Rht24b , and Rht25 are the 12 Rht genes that have been cloned (Bazhenov et al., 2015 ; Borrill et al., 2022 ; Buss et al., 2020 ; Chai et al., 2022 ; Ford et al., 2018 ; Li et al., 2012 ; Pearce et al., 2011 ; Peng et al., 1999 ; Tian et al., 2022 ; Wu et al., 2011 ; Xiong et al., 2022 ; Zhang et al., 2023 ). Of the 26 Rht genes that have been identified so far, Rht1 , Rht2 , Rht8 , and Rht24 were the most frequently utilized in breeding to reduce plant height (Xiong et al., 2022 ). A major improvement in wheat production was obtained under the ‘Green Revolution’ in the 1960s using dwarfing alleles Rht-B1b and Rht-D1b (Peng et al., 1999 ). The Rht-D1b decreased PH by 30.4% while Rht-B1b decreased PH by 24.6% because the resulting DELLA (aspartic acid–glutamic acid–leucine–leucine–alanine) proteins are less sensitive to GA (Na et al., 2009 ). But these alleles also decrease carbon fixation and nitrogen (N)-use efficiency (NUE), causing the green-revolution varieties (GRVs) to have smaller spike sizes, less biomass, and less grain weight (Liu et al., 2022 ). Rht8 encodes a protein with a zinc finger BED-type motif and an RNase H-like domain that reduces PH via regulating bioactive GA biosynthesis (Chai et al., 2022 ; Xiong et al., 2022 ). A novel gibberellin-sensitive height-reducing allele Rht12b encoding TaGA2oxA13 , a GA 2-oxidase has been identified in wheat that reduces PH with a range from 5.4 cm to 8.2 cm (Bian et al., 2023 ). The semidwarf allele Rht-B13b encodes an autoactive nucleotide-binding site/leucine-rich repeat (NB-LRR) protein that causes reduced height due to transcriptional upregulation of pathogenesis-related genes (Borrill et al., 2022 ). Rht24b increased TaGA2ox-A9 expression in stems, resulting in a decrease in bioactive gibberellin which reduces PH without yield penalty (Tian et al., 2022 ). PLATZ-A1 is the Rht25 causal gene encoding a plant-specific AT-rich sequence- and zinc-binding protein ( PLATZ ) (Zhang et al., 2023 ). PLATZ-A1 loses its function due to mutations, both natural and artificially generated, that lessen the height of the plant. On chromosome arm 3DL, a stable QTL for plant height has been identified and physically mapped, which has subsequently been designated Rht26 (Song et al., 2023 ). Rht26 dwarfing allele decreased plant height but had no significant effects on grain yield. A natural haploblock deletion of 500 kilobases r-e-z has been identified (Song et al., 2023 ). ZnF-B, Rht-B1 , and EamA-B constituted the deleted haploblock. The same height-reducing trait was induced by haploblock deletion as Rht-B1b , but r-e-z deletion lines have a more compact plant architecture and greater NUE. Recent studies have linked the GA-insensitive dwarfism genes to an increased risk of wheat Fusarium head blight (FHB), which is caused by Fusarium graminearum Schwabe (Voss et al., 2008 ). Wheat varieties carrying Rht-D1b allele are significantly more vulnerable to FHB than Rht-B1b allele-carrying varieties (Knopf et al., 2008 ). Rht-D1b had a negative association with type I resistance, whereas RhtB1b -carrying lines had increased resistance (Srinivasachary et al., 2009). A reduction in the concentration of grain micronutrients is correlated with an increase in the yield potential of wheat cultivars carrying the height-reducing dwarfing genes (Velu et al., 2017 ). The concentration of zinc (Zn), and iron (Fe) in the grain decreased by approximately 3.9 ppm and 3.2 ppm, respectively due to height-reducing genes (Velu et al., 2017 ). Genome-wide association studies (GWAS) proved to be a successful approach for genetic dissection of phenotypes in a natural population. Genetic loci and phenotypic diversity are linked through GWAS (Huang and Han, 2014 ). Systematic analysis of the genetic architecture underlying complex plant traits has been made possible through GWAS (Qiao et al., 2023 ). A GWAS was conducted for PH in 260 historical and contemporary US winter wheat accessions which identified 16 marker-trait associations (MTAs) using 38,693 single nucleotide polymorphism (SNP) markers (Daba et al., 2020 ). In European winter wheat varieties, GWAS identified 153 significant marker-trait associations (MTAs) for plant height (Zanke et al., 2014 ). This study was designed to (i) identify the key loci associated with reduced height in historical wheat cultivars of Pakistan using GWAS, (ii) characterize allelic variations of Rht genes in historical wheat cultivars of Pakistan using diagnostic markers, and (iii) develop and validate diagnostic marker for Rht-B1p allele in wheat. Materials and Methods Germplasm A panel of 199 historical Pakistani bread wheat varieties, which have been released from 1911 to 2023, are used for this study as the germplasm. A pedigree and year of release of each cultivar is given in Supplementary Table S1 . Field trials and phenotyping The field trials were carried out at the National Agricultural Research Center (NARC), Islamabad, Pakistan (33.6˚ N, 73.1˚ E). Field trials were conducted during Nov 2022 to May 2023 referred as environment 1 (E1), and during Nov 2023 to May 2024 in two different environments referred as E2 and E3. Cultivars were planted in two replicates in a 5 m long plot consisted of six rows with 25 cm between the rows. Each variety was investigated for data collection of plant height at the maturity stage of wheat. The distance between the stem base and the tip of the terminal grain of the head, excluding awns, was used to measure mature plant height in each variety. PH was measured in centimeters (cm) using a ruler. DNA extraction and genotyping DNA from 20 mg leaves of cultivars in the seedling stage was extracted using the cetyl trimethyl ammonium bromide (CTAB) method described in Dreisigacker et al. ( 2013 ). A 1% agarose gel was used in the gel electrophoresis system to check the quality of the extracted DNA samples, and a Nanodrop spectrophotometer was used to quantify the DNA by measuring its A260/A280 ratio. The above wheat cultivar panel was genotyped by a wheat 37K SNP genotyping-by-targeted sequencing (GBTS) platform (Rasheed and Fayyaz 2023 ). KASP genotyping of reduced height ( Rht ) genes Kompetitive allele-specific PCR (KASP) markers were used to genotype Rht genes in wheat. The SNPs/InDels for characteristic alleles in the Rht-B1 , Rht-D1 , Rht13 , Rht25 , and Rht26 were identified using KASP primers given in Supplementary Table S2 . A 384-well plate was used to carry out KASP assays using the LGC Genomics protocol. For a reaction mixture of 5 µL, 2 µL of DNA sample (30–50 ng) dried in the incubator at 40°C, 0.056 µL of KASP assay mix (12 µL of allele-specific primers, common reverse primer 30 µL, and 46 µL of ddH2O), 2.5 µL of KASP master mix (2x) and 2.5 µL of ddH 2 O were used. CFX384 Touch Real-Time PCR System was used with PCR conditions of 95°C for 15 minutes for enzyme activation, followed by nine cycles at 95°C for 20 s, 65 − 55°C for 60 seconds (temperature dropped by 1°C per cycle). Next 41 cycles of amplification at 95°C for 20 seconds and 57°C for 60 seconds. The FAM, HEX and ROX data from CFX384 was exported and the KlusterCaller software was used for allele calling. Development of KASP marker for Rht-B1p KASP marker was developed for the identification of dwarfing allele Rht-B1p in wheat following standard KASP guidelines ( http://www.lgcgroup.com/kasp ). A nucleotide substitution C/T at 178 bp position from the start codon was causal for Rht-B1p and alternate alleles (Bazhenov et al., 2015 ). The coding sequence of Rht-B1 was retrieved from IWGSC RefSeq v1.1 ( https://wheat-urgi.versailles.inra.fr/Tools ). Standard FAM (5′ GAAGGTGACCAAGTTCATGCT 3′) tail for wild-type allele Rht-B1a and HEX (5′ GAAGGTCGGAGTCAACGGATT 3′) tail for dwarf allele Rht-B1p were added at the 5′ end of the forward primers. Statistical analysis Jamovi 2.3.24 and R v4.3.1 software was used to perform statistical analysis. GraphPad Prism 9 was used to carry out a student’s t-test to analyze the effects of alleles on PH. Quality control of the genotypic data from the 37K SNP array was performed for further use. Genotypic data was filtered for minor allele frequency (MAF) < 5% in TASSEL v5.2. A total of 23897 SNPs were retained and used in the GWAS analysis. Based on the population structure deduced from the principal component analysis (PCA), the Q matrix was created using the first five PC scores. For calculating the kinship matrix (K), TASSEL v5.2 was used. The rMVP package in R v4.3.1 software was used to perform GWAS. The threshold was set at − log 10 P > 4 to identify the SNPs associated with tested traits. The SNPs were reported by using the Farm CPU model. RTM-GWAS software v1.2 was used to create the SNP linkage disequilibrium blocks (SNPLDBs) (He et al., 2017 ). SNPLDBs were created to determine multiple alleles in the cultivars panel to match the properties of multiple alleles per locus. Association analysis was performed in TASSEL v5.2 by using the marker dataset generated by RTM-GWAS. Results Phenotypic variation for PH in diversity panel PH was measured in three different environments under optimal field conditions in the years 2023 (E1) and 2024 (E2, E3) and their best linear unbiased estimates (BLUEs) were also calculated. The descriptive statistics and frequency distribution plots of the diversity panel used in this experiment under different environments are given in Table 1 and Fig. 1 , respectively. Mean PH in E1 was 94.5 cm with a range from 71 cm (TD-1) to 118 cm (C-250). In E2, the mean PH was 87.3 cm with a range from 68 cm (NIA-Zarkhaz) to 111 cm (Khatakwal), while in E3, the mean PH was 89.7 cm with a range from 70 cm (TD-1) to 115 cm (Abbasen-21). BLUEs across three environments was calculated, and descriptive statistics showed average BLUEs for PH was 90.5 cm with a range from 70.3 cm (TD-1) to 109 cm (Khatakwal). PH of wheat cultivars according to their year of release is given in Fig. 2. PH of the cultivars continue to reduce from 1911 to 2010 with a slight increase in PH from 2010 onwards. High variation in the PH of the 1996–2010 group was observed because this group contained some dwarf cultivars like TD-1 which has the minimum PH in our diversity panel, while this group also contained the tall stature cultivars like Khatakwal with PH above 105 cm. Table 1 Descriptive statistics of PH of historical wheat cultivars in different environments E1, E2, E3 and BLUEs during two cropping years 2023 and 2024. Environment Mean SD Minimum Maximum E1 94.5 6.98 71 118 E2 87.3 6.98 68 111 E3 89.7 6.85 70 115 BLUEs 90.5 5.95 70.3 109 . A significant positive correlation was observed for PH between all environments. The correlation between PH in E1 and E2 was (r = 0.51**). A positive correlation (r = 0.57**) was observed between PH in E1 and E3. Similarly, the correlation between PH in E2 and E3 was (r = 0.74***). SNPs associated with PH using GWAS GWAS was conducted with phenotypic data from three different environments E1, E2, E3 and their BLUEs. In total, 19 SNPs were associated with PH in this diversity panel. These SNPs were distributed on twelve chromosomes chr1A, chr1B, chr2A, chr2B, chr3A, chr3B, chr4B, chr5B, chr6D, chr7A, chr7B and chr7D, of which chr3A and chr7B harbor three SNPs each. SNP markers that were significantly associated with the PH in different environments E1, E2, E3, and BLUEs are given in Table 2 . Manhattan plots of PH under study are presented in Supplementary Figure. Twelve SNPs on chr1A, chr1B, chr2A, chr2B, chr3A, chr3B, chr4B, chr5B, and chr7B were identified in all three environments E1, E2, E3, and their BLUEs. One SNP on chr1B was identified in E1, E2 and BLUEs while one SNP on chr6D was identified in E1, E3 and BLUEs. Two SNPs on chr7B were identified in E1 and BLUEs but found to be non-significant in E2 and E3. Three SNPs on chr5B, chr7A, and chr7D were identified in both E2 and E3. SNPs identified in individual environments are given in Supplementary Table S4 . Table 2 SNPs associated with PH in historical wheat cultivars in different environments E1, E2, E3 and BLUEs. SNP CHROM POS MAF Environment p-value FarmCPU p-value MLM (Mb) BLUEs E1 E2 E3 BLUEs E1 E2 E3 1A_316961271 1A 316.96 0.06 BLUEs, E1, E2, E3 2.00E-03 - 3.00E-04 1.00E-02 3.00E-04 1.00E-03 7.00E-03 - 1B_15871850 1B 15.87 0.08 BLUEs, E1, E2 1.00E-03 1.24E-06 4.00E-03 - 2.00E-03 5.00E-03 9.00E-03 - 1B_328976984 1B 328.97 0.2 BLUEs, E1, E2, E3 - - 2.00E-02 4.00E-03 5.00E-03 9.00E-04 2.00E-03 1.00E-03 2A_28823356 2A 28.82 0.05 BLUEs, E1, E2, E3 - 1.02E-06 1.00E-02 1.00E-03 4.00E-04 1.00E-04 2.00E-02 7.00E-03 2B_144763178 2B 144.76 0.06 BLUEs, E1, E2, E3 - - 1.00E-05 4.00E-04 4.00E-02 4.00E-02 2.00E-03 2.00E-02 3A_46454688 3A 46.45 0.26 BLUEs, E1, E2, E3 - 9.00E-04 9.00E-03 6.00E-05 2.00E-02 7.00E-04 8.00E-02 1.00E-04 3A_570022479 3A 570.02 0.11 BLUEs, E1, E2, E3 - 3.00E-02 5.00E-05 6.00E-06 1.00E-02 1.00E-03 1.00E-02 9.00E-04 3A_686725071 3A 686.72 0.14 BLUEs, E1, E2, E3 - - 2.00E-05 5.00E-05 9.00E-02 2.00E-02 8.00E-04 3.00E-04 3B_19115737 3B 19.11 0.07 BLUEs, E1, E2, E3 8.00E-04 2.00E-04 2.00E-02 9.00E-02 8.00E-04 2.00E-03 - - 3B_703922938 3B 703.92 0.05 BLUEs, E1, E2, E3 2.00E-02 - 5.00E-06 1.00E-03 1.00E-03 2.00E-03 2.00E-03 2.00E-02 4B_531573948 4B 531.57 0.06 BLUEs, E1, E2, E3 5.00E-02 4.00E-02 2.00E-02 6.00E-03 3.00E-03 4.00E-04 - 8.00E-03 5B_50655398 5B 50.65 0.22 BLUEs, E1, E2, E3 - - 1.00E-05 4.00E-04 5.00E-02 3.00E-02 1.00E-03 3.00E-02 5B_51034644 5B 51.03 0.11 E2, E3 - - 1.00E-05 8.00E-04 - - 1.00E-03 2.00E-02 6D_145770177 6D 145.77 0.21 BLUEs, E1, E3 3.00E-02 3.00E-04 - - 1.00E-03 6.00E-04 - 7.00E-02 7A_540052569 7A 540.05 0.39 E2, E3 - - 5.00E-06 2.00E-04 - - 3.00E-03 3.00E-02 7B_43246561 7B 43.24 0.12 BLUEs, E1 5.00E-06 1.33E-05 - - 7.00E-02 7.00E-02 - - 7B_107565630 7B 107.56 0.09 BLUEs, E1 3.00E-08 1.57E-05 - - 5.00E-04 4.00E-04 - - 7B_687424494 7B 687.42 0.17 BLUEs, E1, E2, E3 9.00E-03 5.00E-04 4.00E-04 2.00E-03 5.00E-03 3.00E-03 3.00E-02 2.00E-02 7D_89945818 7D 89.94 0.25 E2, E3 - - 5.00E-06 5.00E-04 - - 3.00E-03 1.00E-02 Allelic variations at Rht genes associated with PH Allelic variations at the Rht genes regulating PH were determined through high-throughput KASP genotyping in the diversity panel. Allelic variations were analyzed for five Rht genes including Rht-B1 , Rht-D1 , Rht13 , Rht25 , and Rht26 . Five different alleles were identified at Rht-B1 gene including Rht-B1b , Rht-B1p , Rht-B1a-160Ins , and Rht-B1a-197Ins in the diversity panel was 69.6%, 4.5%, 17.1%, and 2.5%, respectively. The allelic frequency of the GA-insensitive height-reducing Rht-D1b allele in the diversity panel was 13.1%. To analyze the effect of Rht-B1 and Rht-D1 on the PH of the cultivars, a haplotype analysis was performed. In BLUEs, a significant effect of dwarfing alleles on PH was observed (Fig. 3). Cultivars with wild-type alleles have much higher PH (93.5 cm) as compared to mutant alleles. Rht-D1b conferred the lowest PH (87.2 cm) followed by Rht-B1-160Ins (89.3 cm), Rht-B1b (90.3 cm), and Rht-B1p (92 cm). FIGURE 3 Allelic effect of Rht-B1 and Rht-D1 haplotype on PH in historical wheat cultivars of Pakistan. The allelic frequency of GA-sensitive height-reducing allele Rht13b in the diversity panel was 7%, and wild-type allele Rht13a was 87.3% (n = 173). The presence of dwarfing alleles Rht-B1b and Rht-D1b may reduce the PH of cultivars carrying the wild-type allele Rht13a , so the allelic effect of Rht13b on PH was determined in the cultivars carrying the wild-type alleles Rht-B1a and Rht-D1a . In BLUEs, mean PH of cultivars carrying Rht13b was 87.5 cm whereas mean PH of cultivars with wild-type allele was 93.5 cm depicting Rht13b as a favorable allele as it reduced PH by 6 cm (6.4%) (Fig. 4A). Rht25 has five dwarfing alleles Rht25b , Rht25c , Rht25d , Rht25e , and Rht25f . The allelic frequency of dwarfing alleles Rht-25b , Rht-25c , Rht25d, Rht25e , and Rht25f in the diversity panel was 6%, 1.5%, 1%, 0.5% and 90.9%, respectively. Although Rht25b and Rht25f have higher frequencies but no significant effect of any Rht25 dwarfing allele was found on PH, and mean PH of the cultivars carrying dwarfing alleles is the same as the PH of the cultivars with wild-type Rht25a allele. Table 3 Allelic frequency (%) and phenotypic effect of Rht genes on PH (cm) in BLUEs. Alleles Rht13a Rht13b Rht26 wild type (GG) Rht26 mutant (AA) Rht-B1a 26.7 (91.3) NA 7 (93.5) 15 (89.8) Rht-B1b 59 (90.7) 6.5 (88) 9.5 (91.2) 42.9 (90.4) Rht-D1a 73.2 (91.4) 6.5 (88) 13.1 (92.8) 50 (90.7) Rht-D1b 12.6 (87.3) NA 3.5 (89.7) 8.5 (87.4) The allelic frequency of GA-sensitive height-reducing mutant allele (AA) of Rht26 in the diversity panel was 58.5%, and the wild-type allele (GG) of Rht26 was present in 34 cultivars (17.1%). To analyze the effect of Rht26 mutant allele on PH, only those cultivars were retained that contained wild-type alleles Rht-B1a and Rht-D1a because the presence of dwarfing alleles Rht-B1b and Rht-D1b may reduce the PH of cultivars carrying the wild-type allele of Rht26 . In BLUEs, mean PH of cultivars carrying Rht26 mutant allele was 90.2 cm whereas mean PH of cultivars with wild-type allele was 93.5 cm depicting Rht26 mutant allele as a favorable allele as it reduced PH by 3.3 cm (3.5%) (Fig. 4B). Discussion Phenotypic variation for PH in spring wheats from Pakistan In the present study, a diversity panel consisting of historical wheat cultivars of Pakistan released from 1911 to 2022 was evaluated for PH in three environments and their BLUEs. The average PH was approximately 90.5 cm which is relatively high and needs to be reduced to boost wheat harvest index and lodging resistance. Mean PH of the cultivars released after 2000 in E1, E2, and E3 was 93.8 cm, 86.4 cm, and 89 cm respectively. Mean of the current wheat varieties released after 2000 is more important because older varieties are not cultivated now and that why we divided into 5 groups. A significant difference in the range of PH was observed in all environments. Phenotypic analysis of PH of the cultivars according to their year of release showed that PH decreased from 1911 to 2010 while an increase in the PH of cultivars was observed from 2010 onwards. Correlation analysis showed that PH of cultivars is positively correlated with each other in all environments. Key loci associated with PH in Pakistani wheat GWAS is an effective way of identifying loci associated with phenotypes due to the development of high-density SNP genotyping platform. In wheat, a total of 65 QTL-rich clusters (QRC) for PH were defined from 332 QTL, 270 associated loci, and 83 genes; and 38 candidate genes were predicted for QRC from QTL linkage analysis and genome-wide association study published from 2003 to 2022 (Xu et al., 2023 ). More than 20 loci for PH were identified in each of the chr5A and chr5B while chr4B and chr4D harbor 19 loci each. In the present study, only one locus was identified on chr4B and two on chr5B, while no locus was identified on chr4D and chr5A. Association analysis and linkage mapping identified several QTLs for plant height in wheat through the recent advancement in high-throughput genotyping platforms (Lv et al., 2021 ). After that, breeders will find it convenient to tailor the appropriate plant height using gene editing technology to specifically modify the genetic material within a particular germplasm, avoiding the hybridization process used in conventional breeding (Gao, 2021 ). A GWAS was conducted for PH in 260 historical and contemporary winter wheat accessions using 38,693 SNP markers (Daba et al., 2020 ). This study reported 16 MTAs on chr1A, chr2B, chr2D, chr3B, chr4D, chr5A, chr5D, chr6A, chr6B, chr7A, and chr7D for PH. Another comprehensive GWAS identified 395 QTLs for 12 traits including PH under 7 different environments in a 768 wheat cultivars panel using 327 609 SNPs generated by genotyping-by-sequencing (Pang et al., 2020 ). Three QTL were found to be known cloned genes Rht-D1 , Vrn-B1 , and Vrn-D1 whereas 4 QTL were found as already known mapped QTL TaGA2ox8 , APO1 , TaSus1-7B , and Rht12 . An essential ingredient for crop development and growth is nitrogen. Nitrogen supplementation can have an impact on characteristics associated with plant height, so GWAS was performed on local wheat varieties treated with low nitrogen and normal (CK) treatments (Xing et al., 2022 ). A total of 86 QTL were reported out of which 13 QTL were found to be associated with PH. A comparison of PH loci identified in this study with the previous findings revealed that historical wheat cultivars of Pakistan carry novel PH loci that were not previously reported. Utilization of these height-reducing alleles identified in GWAS through breeding programs can lead to the development of wheat cultivars with improved characteristics such as reduced lodging risk, increased resource allocation to grain, improved harvest efficiency, enhanced crop stability, and adaptability. Functional characterization of genes within the identified loci can unfold the precise role of these genes in regulating PH. Fine mapping of identified loci will reveal novel candidate genes associated with reduced height. Multi-omics approaches such as genomics and transcriptomics are required to comprehensively understand the regulatory networks and molecular basis of reduced plant height. Allelic variations at Rht genes associated with PH Rht genes regulate PH in wheat through GA-sensitive and GA-insensitive pathways. Allelic frequency of Rht genes in the diversity panel showed that GA-insensitive dwarfing allele Rht-B1b was most dominant followed by GA-sensitive Rht26 mutant allele (AA) in the diversity panel. Higher frequencies of GA-insensitive dwarfing alleles Rht-B1b ( Rht1 ) and Rht-D1b (Rht2 ) as compared to GA-sensitive dwarfing alleles showed positive selection of GA-insensitive dwarfing alleles in breeding. Rht25 has five dwarfing alleles but Rht25b , Rht25c , Rht25d , and Rht25e are rarely present in the cultivars. Rht-B1 and Rht25 have a strong genetic association in terms of PH (Zhang et al., 2023 ). Rht25 must be explored for its ability to enhance wheat production under various conditions, and these mutations can be used to adjust the height of wheat plants (Zhang et al., 2023 ). In European winter wheat varieties, the frequency of Rht-D1b allele was 58%, and the frequency of the Rht-B1b allele was 7% (Zanke et al., 2014 ). The frequency of dwarfing genes Rht-B1b and Rht-D1b was determined in modern soft and hard winter wheat cultivars grown in the central and eastern USA (Guedira et al., 2010 ). Rht-D1b was dominant in soft winter wheat cultivars with a frequency of 45% while 28% of cultivars carried Rht-B1b . In hard winter wheat cultivars, Rht-B1b was dominant with a frequency of 77% while Rht-D1b was present in 8% of the cultivars. The frequency and allelic effect of Rht-B1b, Rht-D1 , Rht-B1-160Ins , and Rht-B1a-197Ins were found in a worldwide core collection of 372 wheat accessions (Wilhelm et al., 2013 ). Rht-B1b and Rht-D1b both have a frequency of 12.5%, while Rht-B1a-160Ins and Rht-B1a-197Ins have a frequency of 16.1% and 11.9%, respectively. Rht-B1b and Rht-D1b were reported in another study with a frequency of 40% and 22%, respectively in 172 wheat genotypes from 20 different countries (Tosovic-Maric et al., 2008 ). In contrast with European winter wheat, bread wheat cultivars of Pakistan have a higher frequency of Rht-B1b and a low frequency of Rht-D1b . The allelic frequency of Rht-D1b in Pakistani cultivars is comparable with the allelic frequency of Rht-D1b in worldwide wheat accessions. These findings suggested that Rht-B1b is dominant in worldwide germplasm whereas Rht-D1b allele is dominant in European wheat varieties. Allelic effect of Rht genes in the diversity panel showed a significant effect on PH. Along with the green revolution Rht-B1 and Rht-D1 genes, GA-sensitive Rht genes such as Rht13 and Rht26 showed a strong height reduction in the cultivars which is comparable with the reduction effect of Rht-B1 and Rht-D1 . In a worldwide core collection of 272 wheat accessions, Rht-B1b and Rht-D1b conferred a strong height reduction of approximately 30%, while Rht-B1a-160Ins and Rht-B1a-197Ins reduced PH by 18% and 12% respectively as compared to wild-type (Wilhelm et al., 2013 ). In the present study, strong height reduction is caused by Rht-D1b , reducing PH by 6.7%, while Rht-B1b and Rht-B1-160Ins reduce PH by approximately 4%. GA-insensitive Rht genes have detrimental impacts on seedling vigour, coleoptile length, and grain yield. GA-sensitive Rht genes mitigate the drawbacks of GA-insensitive Rht genes, so GA-sensitive Rht genes must be introduced into modern cultivars to replace GA-insensitive Rht genes. Of the 26 reduced height genes that have been identified so far, Rht1 , Rht2 , Rht8 , and Rht24 were the most frequently utilized in breeding to reduce PH (Xiong et al., 2022 ). But dwarfing alleles of Rht8 and Rht24 were not found in historical bread wheat cultivars of Pakistan. However, too few of these genes for reduced heights have been identified, and additional genes for shorter heights must be discovered (Song et al., 2023 ). Conclusion This study reports the identification of important loci associated with PH in historical wheat cultivars of Pakistan. The identification of these key loci provides valuable insight into the genetic architecture of PH in wheat. Optimized plant architecture can be achieved by the incorporation of identified loci into the wheat cultivars through breeding which will significantly enhance wheat productivity. Diagnostic KASP markers showed a higher frequency and strong height reduction impact of GA-insensitive Rht genes as compared to GA-sensitive Rht genes. These findings open new ways to discover the full potential of genetic improvements of PH in wheat using modern genomics and bioinformatics tools. Declarations Data availability statement The phenotypic data is available as Supplementary Table S1. 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Scientia Agricultura Sinica 53(15):2983–3004 Zanke CD, Ling J, Plieske J, Kollers S, Ebmeyer E, Korzun V, Röder MS (2014) Whole genome association mapping of plant height in winter wheat ( Triticum aestivum L). PLoS ONE 9(11):e113287 Zhang J, Li C, Zhang W, Zhang X, Mo Y, Tranquilli GE, Dubcovsky J (2023) Wheat plant height locus RHT25 encodes a PLATZ transcription factor that interacts with DELLA (RHT1). Proceedings of the National Academy of Sciences , 120 (19), e230020 Supplementary Figure Supplementary Figure 1 is not available with this version. Supplementary Figure S1 Manhattan plot showing the density of SNP markers associated with PH of historical wheat cultivars. (A) E1, (B) E2, (C) E3, (D) BLUEs. Supplementary Files TableS1.xlsx Table S1: A pedigree and year of release of 199 cultivars used in this study, plant height data, and KASP markers results. TableS2.xlsx Table S2: KASP primers used in this study for the identification of Rht genes. TableS3.xlsx Table S3: Genotypic data as HapMap file for all wheat cultivars used in this study. TableS4.xlsx Table S4: SNPs identified in individual environments in this study. Cite Share Download PDF Status: Published Journal Publication published 07 Nov, 2025 Read the published version in Molecular Breeding → Version 1 posted Reviewers agreed at journal 29 Jul, 2024 Reviewers invited by journal 25 Jul, 2024 Editor assigned by journal 03 Jul, 2024 First submitted to journal 03 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4679366","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":331574013,"identity":"897116ac-9a09-4f2b-9eb5-ec7a29eefd2d","order_by":0,"name":"Hafiz Muhammad Suleman","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hafiz","middleName":"Muhammad","lastName":"Suleman","suffix":""},{"id":331574014,"identity":"1745786b-4f4e-48d0-8999-e4512f76e7ce","order_by":1,"name":"Humaira Qayyum","email":"","orcid":"","institution":"Quaid-i-Azam University Islamabad: Quaid-i-Azam 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09:45:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4679366/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4679366/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11032-025-01600-1","type":"published","date":"2025-11-07T15:56:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62971278,"identity":"e056a1ec-4448-46b6-b6cb-47a7903d80fd","added_by":"auto","created_at":"2024-08-21 15:05:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":20413,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency distribution of PH in three different environments. \u003cstrong\u003e(A, B, and C)\u003c/strong\u003e shows PH in environments E1, E2 and E3, while \u003cstrong\u003e(D)\u003c/strong\u003e shows PH BLUEs across environments.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4679366/v1/59aefcb98440d2c6a3134d10.png"},{"id":62971281,"identity":"1833ee32-24ff-4e78-a879-af74e611e0df","added_by":"auto","created_at":"2024-08-21 15:05:13","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":291784,"visible":true,"origin":"","legend":"\u003cp\u003ePH of historical wheat cultivars according to their year of release from 1911 to 2023. \u003cstrong\u003e(A, B, and C)\u003c/strong\u003e shows PH of cultivars in environments E1, E2 and E3, while \u003cstrong\u003e(D)\u003c/strong\u003e represents PH of cultivars in BLUEs.\u003c/p\u003e","description":"","filename":"floatimage213.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4679366/v1/7c7a7543c51dad2833df1fb8.jpeg"},{"id":62972054,"identity":"982d4ffe-540a-4c04-a58f-909dc1687951","added_by":"auto","created_at":"2024-08-21 15:13:13","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":70918,"visible":true,"origin":"","legend":"\u003cp\u003eAllelic effect of \u003cem\u003eRht-B1\u003c/em\u003e and \u003cem\u003eRht-D1\u003c/em\u003e haplotype on PH in historical wheat cultivars of Pakistan.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4679366/v1/e6e77e5a50d5b9bb0993f0c5.jpeg"},{"id":62971282,"identity":"540fd52c-2e38-4466-9f7e-a79eea954c70","added_by":"auto","created_at":"2024-08-21 15:05:14","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":136247,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003eshows allelic effect of \u003cem\u003eRht13a\u003c/em\u003e and \u003cem\u003eRht13b\u003c/em\u003e on PH, \u003cstrong\u003e(B)\u003c/strong\u003e shows allelic effect of \u003cem\u003eRht26\u003c/em\u003e wild-type allele (GG) and mutant allele (AA) on PH in historical wheat cultivars of Pakistan.\u003c/p\u003e","description":"","filename":"floatimage47.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4679366/v1/a3700876bccb6b02cda869f7.jpeg"},{"id":95563906,"identity":"3f6a0e70-fdb0-4881-8496-0d6ae9b390a8","added_by":"auto","created_at":"2025-11-10 16:03:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1862452,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4679366/v1/fc08b441-06bf-4452-b4a0-9d1a1770ea3d.pdf"},{"id":62971283,"identity":"e98c5a17-4516-449a-bd0a-6fe79549e3ed","added_by":"auto","created_at":"2024-08-21 15:05:14","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":30921,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1: A pedigree and year of release of 199 cultivars used in this study, plant height data, and KASP markers results.\u003c/p\u003e","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4679366/v1/ad0abee404cbde8e3f676dcd.xlsx"},{"id":62971280,"identity":"061a567e-67f3-439f-9930-91c257df3742","added_by":"auto","created_at":"2024-08-21 15:05:13","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10954,"visible":true,"origin":"","legend":"\u003cp\u003eTable S2: KASP primers used in this study for the identification of Rht genes.\u003c/p\u003e","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4679366/v1/fe199838020787149bc7b4da.xlsx"},{"id":62971306,"identity":"813c21bb-a09f-42ec-8cf6-e6f6ebfaa01d","added_by":"auto","created_at":"2024-08-21 15:05:14","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17092579,"visible":true,"origin":"","legend":"\u003cp\u003eTable S3: Genotypic data as HapMap file for all wheat cultivars used in this study.\u003c/p\u003e","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4679366/v1/f72fa9fa162313ba5410343c.xlsx"},{"id":62972055,"identity":"d842fcc8-a036-4280-8849-239e3bbc657b","added_by":"auto","created_at":"2024-08-21 15:13:14","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":31395,"visible":true,"origin":"","legend":"\u003cp\u003eTable S4: SNPs identified in individual environments in this study.\u003c/p\u003e","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4679366/v1/aff5bcb7f097fa91770620c3.xlsx"}],"financialInterests":"","formattedTitle":"Genome-wide and candidate gene association mapping for plant height in wheat","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWheat crop is a fundamental element of human civilization and plays an important role in feeding the hungry world and improving global food security, contributing about 20% of total dietary calories and proteins worldwide (Shiferaw et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Wheat is a major crop for a global food security perspective and continuous efforts are needed to increase grain yield to feed a growing human population that recently surpassed 8\u0026nbsp;billion people (Zhang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Plant architecture has a significant impact on agronomy because it determines a plant's potential grain output and cultivation adaptability (Reinhardt and Kuhlemeier, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Stature is a key element of plant architecture, primarily shaped by stem elongation. Short wheat plants can result in the crowding of canopy leaves, a decrease in photosynthetic rate, and insufficient biomass to provide a sufficient \"source,\" resulting in low yield. On the other hand, too tall wheat plants can result in lodging, which directly results in lower yield (Hedden, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Plant height should be reduced appropriately to increase wheat harvest index and lodging resistance.\u003c/p\u003e \u003cp\u003eThere are 26 \u003cem\u003eRht\u003c/em\u003e genes (\u003cem\u003eRht1\u003c/em\u003e-\u003cem\u003eRht26\u003c/em\u003e) for dwarfism in wheat, including gibberellin (GA)-responsive and GA-insensitive genes that have been cataloged. Some of these \u003cem\u003eRht\u003c/em\u003e genes were identified in the artificial mutants generated by chemical or physical mutagens, although the majority are found in natural populations (Agarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Lodging resistance, reduced plant height, increased tillers, and higher yield are all known to be caused by the \u003cem\u003eRht\u003c/em\u003e genes (Jobson et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). \u003cem\u003eRht-B1b\u003c/em\u003e (\u003cem\u003eRht1\u003c/em\u003e), \u003cem\u003eRht-D1b\u003c/em\u003e (\u003cem\u003eRht2\u003c/em\u003e), \u003cem\u003eRht-B1c\u003c/em\u003e (\u003cem\u003eRht3\u003c/em\u003e), \u003cem\u003eRht8\u003c/em\u003e, \u003cem\u003eRht-D1c\u003c/em\u003e (\u003cem\u003eRht10\u003c/em\u003e), \u003cem\u003eRht-B1e\u003c/em\u003e (\u003cem\u003eRht11\u003c/em\u003e), \u003cem\u003eRht12\u003c/em\u003e, \u003cem\u003eRht-B1p (Rht17\u003c/em\u003e), \u003cem\u003eRht13\u003c/em\u003e, \u003cem\u003eRht18\u003c/em\u003e, \u003cem\u003eRht24b\u003c/em\u003e, and \u003cem\u003eRht25\u003c/em\u003e are the 12 \u003cem\u003eRht\u003c/em\u003e genes that have been cloned (Bazhenov et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Borrill et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Buss et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chai et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ford et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pearce et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Tian et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Xiong et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Of the 26 \u003cem\u003eRht\u003c/em\u003e genes that have been identified so far, \u003cem\u003eRht1\u003c/em\u003e, \u003cem\u003eRht2\u003c/em\u003e, \u003cem\u003eRht8\u003c/em\u003e, and \u003cem\u003eRht24\u003c/em\u003e were the most frequently utilized in breeding to reduce plant height (Xiong et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA major improvement in wheat production was obtained under the \u0026lsquo;Green Revolution\u0026rsquo; in the 1960s using dwarfing alleles \u003cem\u003eRht-B1b\u003c/em\u003e and \u003cem\u003eRht-D1b\u003c/em\u003e (Peng et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The \u003cem\u003eRht-D1b\u003c/em\u003e decreased PH by 30.4% while \u003cem\u003eRht-B1b\u003c/em\u003e decreased PH by 24.6% because the resulting DELLA (aspartic acid\u0026ndash;glutamic acid\u0026ndash;leucine\u0026ndash;leucine\u0026ndash;alanine) proteins are less sensitive to GA (Na et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). But these alleles also decrease carbon fixation and nitrogen (N)-use efficiency (NUE), causing the green-revolution varieties (GRVs) to have smaller spike sizes, less biomass, and less grain weight (Liu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cem\u003eRht8\u003c/em\u003e encodes a protein with a zinc finger BED-type motif and an RNase H-like domain that reduces PH via regulating bioactive GA biosynthesis (Chai et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Xiong et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A novel gibberellin-sensitive height-reducing allele \u003cem\u003eRht12b\u003c/em\u003e encoding \u003cem\u003eTaGA2oxA13\u003c/em\u003e, a GA 2-oxidase has been identified in wheat that reduces PH with a range from 5.4 cm to 8.2 cm (Bian et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The semidwarf allele \u003cem\u003eRht-B13b\u003c/em\u003e encodes an autoactive nucleotide-binding site/leucine-rich repeat (NB-LRR) protein that causes reduced height due to transcriptional upregulation of pathogenesis-related genes (Borrill et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cem\u003eRht24b\u003c/em\u003e increased \u003cem\u003eTaGA2ox-A9\u003c/em\u003e expression in stems, resulting in a decrease in bioactive gibberellin which reduces PH without yield penalty (Tian et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cem\u003ePLATZ-A1\u003c/em\u003e is the \u003cem\u003eRht25\u003c/em\u003e causal gene encoding a plant-specific AT-rich sequence- and zinc-binding protein (\u003cem\u003ePLATZ\u003c/em\u003e) (Zhang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cem\u003ePLATZ-A1\u003c/em\u003e loses its function due to mutations, both natural and artificially generated, that lessen the height of the plant. On chromosome arm 3DL, a stable QTL for plant height has been identified and physically mapped, which has subsequently been designated \u003cem\u003eRht26\u003c/em\u003e (Song et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cem\u003eRht26\u003c/em\u003e dwarfing allele decreased plant height but had no significant effects on grain yield. A natural haploblock deletion of 500 kilobases \u003cem\u003er-e-z\u003c/em\u003e has been identified (Song et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cem\u003eZnF-B, Rht-B1\u003c/em\u003e, and \u003cem\u003eEamA-B\u003c/em\u003e constituted the deleted haploblock. The same height-reducing trait was induced by haploblock deletion as \u003cem\u003eRht-B1b\u003c/em\u003e, but \u003cem\u003er-e-z\u003c/em\u003e deletion lines have a more compact plant architecture and greater NUE.\u003c/p\u003e \u003cp\u003eRecent studies have linked the GA-insensitive dwarfism genes to an increased risk of wheat Fusarium head blight (FHB), which is caused by \u003cem\u003eFusarium graminearum Schwabe\u003c/em\u003e (Voss et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Wheat varieties carrying \u003cem\u003eRht-D1b\u003c/em\u003e allele are significantly more vulnerable to FHB than \u003cem\u003eRht-B1b\u003c/em\u003e allele-carrying varieties (Knopf et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). \u003cem\u003eRht-D1b\u003c/em\u003e had a negative association with type I resistance, whereas \u003cem\u003eRhtB1b\u003c/em\u003e-carrying lines had increased resistance (Srinivasachary et al., 2009). A reduction in the concentration of grain micronutrients is correlated with an increase in the yield potential of wheat cultivars carrying the height-reducing dwarfing genes (Velu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The concentration of zinc (Zn), and iron (Fe) in the grain decreased by approximately 3.9 ppm and 3.2 ppm, respectively due to height-reducing genes (Velu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenome-wide association studies (GWAS) proved to be a successful approach for genetic dissection of phenotypes in a natural population. Genetic loci and phenotypic diversity are linked through GWAS (Huang and Han, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Systematic analysis of the genetic architecture underlying complex plant traits has been made possible through GWAS (Qiao et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A GWAS was conducted for PH in 260 historical and contemporary US winter wheat accessions which identified 16 marker-trait associations (MTAs) using 38,693 single nucleotide polymorphism (SNP) markers (Daba et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In European winter wheat varieties, GWAS identified 153 significant marker-trait associations (MTAs) for plant height (Zanke et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This study was designed to (i) identify the key loci associated with reduced height in historical wheat cultivars of Pakistan using GWAS, (ii) characterize allelic variations of \u003cem\u003eRht\u003c/em\u003e genes in historical wheat cultivars of Pakistan using diagnostic markers, and (iii) develop and validate diagnostic marker for \u003cem\u003eRht-B1p\u003c/em\u003e allele in wheat.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGermplasm\u003c/h2\u003e \u003cp\u003eA panel of 199 historical Pakistani bread wheat varieties, which have been released from 1911 to 2023, are used for this study as the germplasm. A pedigree and year of release of each cultivar is given in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eField trials and phenotyping\u003c/h2\u003e \u003cp\u003eThe field trials were carried out at the National Agricultural Research Center (NARC), Islamabad, Pakistan (33.6˚ N, 73.1˚ E). Field trials were conducted during Nov 2022 to May 2023 referred as environment 1 (E1), and during Nov 2023 to May 2024 in two different environments referred as E2 and E3. Cultivars were planted in two replicates in a 5 m long plot consisted of six rows with 25 cm between the rows. Each variety was investigated for data collection of plant height at the maturity stage of wheat. The distance between the stem base and the tip of the terminal grain of the head, excluding awns, was used to measure mature plant height in each variety. PH was measured in centimeters (cm) using a ruler.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction and genotyping\u003c/h2\u003e \u003cp\u003eDNA from 20 mg leaves of cultivars in the seedling stage was extracted using the cetyl trimethyl ammonium bromide (CTAB) method described in Dreisigacker et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). A 1% agarose gel was used in the gel electrophoresis system to check the quality of the extracted DNA samples, and a Nanodrop spectrophotometer was used to quantify the DNA by measuring its A260/A280 ratio. The above wheat cultivar panel was genotyped by a wheat 37K SNP genotyping-by-targeted sequencing (GBTS) platform (Rasheed and Fayyaz \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eKASP genotyping of reduced height (\u003c/b\u003e \u003cb\u003eRht\u003c/b\u003e \u003cb\u003e) genes\u003c/b\u003e \u003c/p\u003e \u003cp\u003eKompetitive allele-specific PCR (KASP) markers were used to genotype \u003cem\u003eRht\u003c/em\u003e genes in wheat. The SNPs/InDels for characteristic alleles in the \u003cem\u003eRht-B1\u003c/em\u003e, \u003cem\u003eRht-D1\u003c/em\u003e, \u003cem\u003eRht13\u003c/em\u003e, \u003cem\u003eRht25\u003c/em\u003e, and \u003cem\u003eRht26\u003c/em\u003e were identified using KASP primers given in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. A 384-well plate was used to carry out KASP assays using the LGC Genomics protocol. For a reaction mixture of 5 \u0026micro;L, 2 \u0026micro;L of DNA sample (30\u0026ndash;50 ng) dried in the incubator at 40\u0026deg;C, 0.056 \u0026micro;L of KASP assay mix (12 \u0026micro;L of allele-specific primers, common reverse primer 30 \u0026micro;L, and 46 \u0026micro;L of ddH2O), 2.5 \u0026micro;L of KASP master mix (2x) and 2.5 \u0026micro;L of ddH\u003csub\u003e2\u003c/sub\u003eO were used. CFX384 Touch Real-Time PCR System was used with PCR conditions of 95\u0026deg;C for 15 minutes for enzyme activation, followed by nine cycles at 95\u0026deg;C for 20 s, 65\u0026thinsp;\u0026minus;\u0026thinsp;55\u0026deg;C for 60 seconds (temperature dropped by 1\u0026deg;C per cycle). Next 41 cycles of amplification at 95\u0026deg;C for 20 seconds and 57\u0026deg;C for 60 seconds. The FAM, HEX and ROX data from CFX384 was exported and the KlusterCaller software was used for allele calling.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDevelopment of KASP marker for\u003c/b\u003e \u003cb\u003eRht-B1p\u003c/b\u003e\u003c/p\u003e \u003cp\u003eKASP marker was developed for the identification of dwarfing allele \u003cem\u003eRht-B1p\u003c/em\u003e in wheat following standard KASP guidelines (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.lgcgroup.com/kasp\u003c/span\u003e\u003cspan address=\"http://www.lgcgroup.com/kasp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A nucleotide substitution C/T at 178 bp position from the start codon was causal for \u003cem\u003eRht-B1p\u003c/em\u003e and alternate alleles (Bazhenov et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The coding sequence of \u003cem\u003eRht-B1\u003c/em\u003e was retrieved from IWGSC RefSeq v1.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wheat-urgi.versailles.inra.fr/Tools\u003c/span\u003e\u003cspan address=\"https://wheat-urgi.versailles.inra.fr/Tools\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Standard FAM (5\u0026prime; GAAGGTGACCAAGTTCATGCT 3\u0026prime;) tail for wild-type allele \u003cem\u003eRht-B1a\u003c/em\u003e and HEX (5\u0026prime; GAAGGTCGGAGTCAACGGATT 3\u0026prime;) tail for dwarf allele \u003cem\u003eRht-B1p\u003c/em\u003e were added at the 5\u0026prime; end of the forward primers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eJamovi 2.3.24 and R v4.3.1 software was used to perform statistical analysis. GraphPad Prism 9 was used to carry out a student\u0026rsquo;s t-test to analyze the effects of alleles on PH. Quality control of the genotypic data from the 37K SNP array was performed for further use. Genotypic data was filtered for minor allele frequency (MAF)\u0026thinsp;\u0026lt;\u0026thinsp;5% in TASSEL v5.2. A total of 23897 SNPs were retained and used in the GWAS analysis.\u003c/p\u003e \u003cp\u003eBased on the population structure deduced from the principal component analysis (PCA), the Q matrix was created using the first five PC scores. For calculating the kinship matrix (K), TASSEL v5.2 was used. The rMVP package in R v4.3.1 software was used to perform GWAS. The threshold was set at \u0026minus;\u0026thinsp;log\u003csub\u003e10\u003c/sub\u003e P\u0026thinsp;\u0026gt;\u0026thinsp;4 to identify the SNPs associated with tested traits. The SNPs were reported by using the Farm CPU model. RTM-GWAS software v1.2 was used to create the SNP linkage disequilibrium blocks (SNPLDBs) (He et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). SNPLDBs were created to determine multiple alleles in the cultivars panel to match the properties of multiple alleles per locus. Association analysis was performed in TASSEL v5.2 by using the marker dataset generated by RTM-GWAS.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic variation for PH in diversity panel\u003c/h2\u003e \u003cp\u003ePH was measured in three different environments under optimal field conditions in the years 2023 (E1) and 2024 (E2, E3) and their best linear unbiased estimates (BLUEs) were also calculated. The descriptive statistics and frequency distribution plots of the diversity panel used in this experiment under different environments are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, respectively. Mean PH in E1 was 94.5 cm with a range from 71 cm (TD-1) to 118 cm (C-250). In E2, the mean PH was 87.3 cm with a range from 68 cm (NIA-Zarkhaz) to 111 cm (Khatakwal), while in E3, the mean PH was 89.7 cm with a range from 70 cm (TD-1) to 115 cm (Abbasen-21). BLUEs across three environments was calculated, and descriptive statistics showed average BLUEs for PH was 90.5 cm with a range from 70.3 cm (TD-1) to 109 cm (Khatakwal). PH of wheat cultivars according to their year of release is given in Fig.\u0026nbsp;2. PH of the cultivars continue to reduce from 1911 to 2010 with a slight increase in PH from 2010 onwards. High variation in the PH of the 1996\u0026ndash;2010 group was observed because this group contained some dwarf cultivars like TD-1 which has the minimum PH in our diversity panel, while this group also contained the tall stature cultivars like Khatakwal with PH above 105 cm.\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\u003eDescriptive statistics of PH of historical wheat cultivars in different environments E1, E2, E3 and BLUEs during two cropping years 2023 and 2024.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"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 \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\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBLUEs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e .\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA significant positive correlation was observed for PH between all environments. The correlation between PH in E1 and E2 was (r\u0026thinsp;=\u0026thinsp;0.51**). A positive correlation (r\u0026thinsp;=\u0026thinsp;0.57**) was observed between PH in E1 and E3. Similarly, the correlation between PH in E2 and E3 was (r\u0026thinsp;=\u0026thinsp;0.74***).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSNPs associated with PH using GWAS\u003c/h2\u003e \u003cp\u003eGWAS was conducted with phenotypic data from three different environments E1, E2, E3 and their BLUEs. In total, 19 SNPs were associated with PH in this diversity panel. These SNPs were distributed on twelve chromosomes chr1A, chr1B, chr2A, chr2B, chr3A, chr3B, chr4B, chr5B, chr6D, chr7A, chr7B and chr7D, of which chr3A and chr7B harbor three SNPs each. SNP markers that were significantly associated with the PH in different environments E1, E2, E3, and BLUEs are given in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Manhattan plots of PH under study are presented in Supplementary Figure. Twelve SNPs on chr1A, chr1B, chr2A, chr2B, chr3A, chr3B, chr4B, chr5B, and chr7B were identified in all three environments E1, E2, E3, and their BLUEs. One SNP on chr1B was identified in E1, E2 and BLUEs while one SNP on chr6D was identified in E1, E3 and BLUEs. Two SNPs on chr7B were identified in E1 and BLUEs but found to be non-significant in E2 and E3. Three SNPs on chr5B, chr7A, and chr7D were identified in both E2 and E3. SNPs identified in individual environments are given in Supplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\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\u003eSNPs associated with PH in historical wheat cultivars in different environments E1, E2, E3 and BLUEs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\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=\"left\" 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 \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHROM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMAF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnvironment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003ep-value FarmCPU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003ep-value MLM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Mb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBLUEs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBLUEs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1A_316961271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e316.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLUEs, E1, E2, E3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e7.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1B_15871850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLUEs, E1, E2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.24E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1B_328976984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e328.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLUEs, E1, E2, E3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.00E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2A_28823356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLUEs, E1, E2, E3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.02E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7.00E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2B_144763178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e144.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLUEs, E1, E2, E3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.00E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3A_46454688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLUEs, E1, E2, E3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.00E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.00E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3A_570022479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e570.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLUEs, E1, E2, E3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.00E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.00E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e9.00E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3A_686725071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e686.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLUEs, E1, E2, E3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.00E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.00E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.00E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3B_19115737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLUEs, E1, E2, E3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3B_703922938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e703.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLUEs, E1, E2, E3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.00E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.00E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4B_531573948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e531.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLUEs, E1, E2, E3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e8.00E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5B_50655398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLUEs, E1, E2, E3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.00E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5B_51034644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eE2, E3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.00E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6D_145770177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e145.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLUEs, E1, E3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7.00E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7A_540052569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e540.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eE2, E3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.00E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.00E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7B_43246561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLUEs, E1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.00E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.33E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7B_107565630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLUEs, E1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.00E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.57E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7B_687424494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e687.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBLUEs, E1, E2, E3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.00E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.00E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7D_89945818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eE2, E3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.00E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.00E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAllelic variations at\u003c/b\u003e \u003cb\u003eRht\u003c/b\u003e \u003cb\u003egenes associated with PH\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAllelic variations at the \u003cem\u003eRht\u003c/em\u003e genes regulating PH were determined through high-throughput KASP genotyping in the diversity panel. Allelic variations were analyzed for five \u003cem\u003eRht\u003c/em\u003e genes including \u003cem\u003eRht-B1\u003c/em\u003e, \u003cem\u003eRht-D1\u003c/em\u003e, \u003cem\u003eRht13\u003c/em\u003e, \u003cem\u003eRht25\u003c/em\u003e, and \u003cem\u003eRht26\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFive different alleles were identified at \u003cem\u003eRht-B1\u003c/em\u003e gene including \u003cem\u003eRht-B1b\u003c/em\u003e, \u003cem\u003eRht-B1p\u003c/em\u003e, \u003cem\u003eRht-B1a-160Ins\u003c/em\u003e, and \u003cem\u003eRht-B1a-197Ins\u003c/em\u003e in the diversity panel was 69.6%, 4.5%, 17.1%, and 2.5%, respectively. The allelic frequency of the GA-insensitive height-reducing \u003cem\u003eRht-D1b\u003c/em\u003e allele in the diversity panel was 13.1%. To analyze the effect of \u003cem\u003eRht-B1\u003c/em\u003e and \u003cem\u003eRht-D1\u003c/em\u003e on the PH of the cultivars, a haplotype analysis was performed. In BLUEs, a significant effect of dwarfing alleles on PH was observed (Fig.\u0026nbsp;3). Cultivars with wild-type alleles have much higher PH (93.5 cm) as compared to mutant alleles. \u003cem\u003eRht-D1b\u003c/em\u003e conferred the lowest PH (87.2 cm) followed by \u003cem\u003eRht-B1-160Ins\u003c/em\u003e (89.3 cm), \u003cem\u003eRht-B1b\u003c/em\u003e (90.3 cm), and \u003cem\u003eRht-B1p\u003c/em\u003e (92 cm).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFIGURE 3\u003c/b\u003e Allelic effect of \u003cem\u003eRht-B1\u003c/em\u003e and \u003cem\u003eRht-D1\u003c/em\u003e haplotype on PH in historical wheat cultivars of Pakistan.\u003c/p\u003e \u003cp\u003eThe allelic frequency of GA-sensitive height-reducing allele \u003cem\u003eRht13b\u003c/em\u003e in the diversity panel was 7%, and wild-type allele \u003cem\u003eRht13a\u003c/em\u003e was 87.3% (n\u0026thinsp;=\u0026thinsp;173). The presence of dwarfing alleles \u003cem\u003eRht-B1b\u003c/em\u003e and \u003cem\u003eRht-D1b\u003c/em\u003e may reduce the PH of cultivars carrying the wild-type allele \u003cem\u003eRht13a\u003c/em\u003e, so the allelic effect of \u003cem\u003eRht13b\u003c/em\u003e on PH was determined in the cultivars carrying the wild-type alleles \u003cem\u003eRht-B1a\u003c/em\u003e and \u003cem\u003eRht-D1a\u003c/em\u003e. In BLUEs, mean PH of cultivars carrying \u003cem\u003eRht13b\u003c/em\u003e was 87.5 cm whereas mean PH of cultivars with wild-type allele was 93.5 cm depicting \u003cem\u003eRht13b\u003c/em\u003e as a favorable allele as it reduced PH by 6 cm (6.4%) (Fig.\u0026nbsp;4A).\u003c/p\u003e \u003cp\u003e \u003cem\u003eRht25\u003c/em\u003e has five dwarfing alleles \u003cem\u003eRht25b\u003c/em\u003e, \u003cem\u003eRht25c\u003c/em\u003e, \u003cem\u003eRht25d\u003c/em\u003e, \u003cem\u003eRht25e\u003c/em\u003e, and \u003cem\u003eRht25f\u003c/em\u003e. The allelic frequency of dwarfing alleles \u003cem\u003eRht-25b\u003c/em\u003e, \u003cem\u003eRht-25c\u003c/em\u003e, \u003cem\u003eRht25d, Rht25e\u003c/em\u003e, and \u003cem\u003eRht25f\u003c/em\u003e in the diversity panel was 6%, 1.5%, 1%, 0.5% and 90.9%, respectively. Although \u003cem\u003eRht25b\u003c/em\u003e and \u003cem\u003eRht25f\u003c/em\u003e have higher frequencies but no significant effect of any \u003cem\u003eRht25\u003c/em\u003e dwarfing allele was found on PH, and mean PH of the cultivars carrying dwarfing alleles is the same as the PH of the cultivars with wild-type \u003cem\u003eRht25a\u003c/em\u003e allele.\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\u003eAllelic frequency (%) and phenotypic effect of \u003cem\u003eRht\u003c/em\u003e genes on PH (cm) in BLUEs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlleles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRht13a\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRht13b\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eRht26 wild type (GG)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eRht26 mutant (AA)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRht-B1a\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.7 (91.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (93.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15 (89.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRht-B1b\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59 (90.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5 (88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.5 (91.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.9 (90.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRht-D1a\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73.2 (91.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5 (88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.1 (92.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50 (90.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRht-D1b\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.6 (87.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.5 (89.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.5 (87.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe allelic frequency of GA-sensitive height-reducing mutant allele (AA) of \u003cem\u003eRht26\u003c/em\u003e in the diversity panel was 58.5%, and the wild-type allele (GG) of \u003cem\u003eRht26\u003c/em\u003e was present in 34 cultivars (17.1%). To analyze the effect of \u003cem\u003eRht26\u003c/em\u003e mutant allele on PH, only those cultivars were retained that contained wild-type alleles \u003cem\u003eRht-B1a\u003c/em\u003e and \u003cem\u003eRht-D1a\u003c/em\u003e because the presence of dwarfing alleles \u003cem\u003eRht-B1b\u003c/em\u003e and \u003cem\u003eRht-D1b\u003c/em\u003e may reduce the PH of cultivars carrying the wild-type allele of \u003cem\u003eRht26\u003c/em\u003e. In BLUEs, mean PH of cultivars carrying \u003cem\u003eRht26\u003c/em\u003e mutant allele was 90.2 cm whereas mean PH of cultivars with wild-type allele was 93.5 cm depicting \u003cem\u003eRht26\u003c/em\u003e mutant allele as a favorable allele as it reduced PH by 3.3 cm (3.5%) (Fig.\u0026nbsp;4B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic variation for PH in spring wheats from Pakistan\u003c/h2\u003e \u003cp\u003eIn the present study, a diversity panel consisting of historical wheat cultivars of Pakistan released from 1911 to 2022 was evaluated for PH in three environments and their BLUEs. The average PH was approximately 90.5 cm which is relatively high and needs to be reduced to boost wheat harvest index and lodging resistance. Mean PH of the cultivars released after 2000 in E1, E2, and E3 was 93.8 cm, 86.4 cm, and 89 cm respectively. Mean of the current wheat varieties released after 2000 is more important because older varieties are not cultivated now and that why we divided into 5 groups. A significant difference in the range of PH was observed in all environments. Phenotypic analysis of PH of the cultivars according to their year of release showed that PH decreased from 1911 to 2010 while an increase in the PH of cultivars was observed from 2010 onwards. Correlation analysis showed that PH of cultivars is positively correlated with each other in all environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eKey loci associated with PH in Pakistani wheat\u003c/h2\u003e \u003cp\u003eGWAS is an effective way of identifying loci associated with phenotypes due to the development of high-density SNP genotyping platform. In wheat, a total of 65 QTL-rich clusters (QRC) for PH were defined from 332 QTL, 270 associated loci, and 83 genes; and 38 candidate genes were predicted for QRC from QTL linkage analysis and genome-wide association study published from 2003 to 2022 (Xu et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). More than 20 loci for PH were identified in each of the chr5A and chr5B while chr4B and chr4D harbor 19 loci each. In the present study, only one locus was identified on chr4B and two on chr5B, while no locus was identified on chr4D and chr5A. Association analysis and linkage mapping identified several QTLs for plant height in wheat through the recent advancement in high-throughput genotyping platforms (Lv et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). After that, breeders will find it convenient to tailor the appropriate plant height using gene editing technology to specifically modify the genetic material within a particular germplasm, avoiding the hybridization process used in conventional breeding (Gao, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA GWAS was conducted for PH in 260 historical and contemporary winter wheat accessions using 38,693 SNP markers (Daba et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This study reported 16 MTAs on chr1A, chr2B, chr2D, chr3B, chr4D, chr5A, chr5D, chr6A, chr6B, chr7A, and chr7D for PH. Another comprehensive GWAS identified 395 QTLs for 12 traits including PH under 7 different environments in a 768 wheat cultivars panel using 327 609 SNPs generated by genotyping-by-sequencing (Pang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Three QTL were found to be known cloned genes \u003cem\u003eRht-D1\u003c/em\u003e, \u003cem\u003eVrn-B1\u003c/em\u003e, and \u003cem\u003eVrn-D1\u003c/em\u003e whereas 4 QTL were found as already known mapped QTL \u003cem\u003eTaGA2ox8\u003c/em\u003e, \u003cem\u003eAPO1\u003c/em\u003e, \u003cem\u003eTaSus1-7B\u003c/em\u003e, and \u003cem\u003eRht12\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eAn essential ingredient for crop development and growth is nitrogen. Nitrogen supplementation can have an impact on characteristics associated with plant height, so GWAS was performed on local wheat varieties treated with low nitrogen and normal (CK) treatments (Xing et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A total of 86 QTL were reported out of which 13 QTL were found to be associated with PH. A comparison of PH loci identified in this study with the previous findings revealed that historical wheat cultivars of Pakistan carry novel PH loci that were not previously reported. Utilization of these height-reducing alleles identified in GWAS through breeding programs can lead to the development of wheat cultivars with improved characteristics such as reduced lodging risk, increased resource allocation to grain, improved harvest efficiency, enhanced crop stability, and adaptability. Functional characterization of genes within the identified loci can unfold the precise role of these genes in regulating PH. Fine mapping of identified loci will reveal novel candidate genes associated with reduced height. Multi-omics approaches such as genomics and transcriptomics are required to comprehensively understand the regulatory networks and molecular basis of reduced plant height.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAllelic variations at\u003c/b\u003e \u003cb\u003eRht\u003c/b\u003e \u003cb\u003egenes associated with PH\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eRht\u003c/em\u003e genes regulate PH in wheat through GA-sensitive and GA-insensitive pathways. Allelic frequency of \u003cem\u003eRht\u003c/em\u003e genes in the diversity panel showed that GA-insensitive dwarfing allele \u003cem\u003eRht-B1b\u003c/em\u003e was most dominant followed by GA-sensitive \u003cem\u003eRht26\u003c/em\u003e mutant allele (AA) in the diversity panel. Higher frequencies of GA-insensitive dwarfing alleles \u003cem\u003eRht-B1b\u003c/em\u003e (\u003cem\u003eRht1\u003c/em\u003e) and \u003cem\u003eRht-D1b (Rht2\u003c/em\u003e) as compared to GA-sensitive dwarfing alleles showed positive selection of GA-insensitive dwarfing alleles in breeding. \u003cem\u003eRht25\u003c/em\u003e has five dwarfing alleles but \u003cem\u003eRht25b\u003c/em\u003e, \u003cem\u003eRht25c\u003c/em\u003e, \u003cem\u003eRht25d\u003c/em\u003e, and \u003cem\u003eRht25e\u003c/em\u003e are rarely present in the cultivars. \u003cem\u003eRht-B1\u003c/em\u003e and \u003cem\u003eRht25\u003c/em\u003e have a strong genetic association in terms of PH (Zhang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cem\u003eRht25\u003c/em\u003e must be explored for its ability to enhance wheat production under various conditions, and these mutations can be used to adjust the height of wheat plants (Zhang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn European winter wheat varieties, the frequency of \u003cem\u003eRht-D1b\u003c/em\u003e allele was 58%, and the frequency of the \u003cem\u003eRht-B1b\u003c/em\u003e allele was 7% (Zanke et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The frequency of dwarfing genes \u003cem\u003eRht-B1b\u003c/em\u003e and \u003cem\u003eRht-D1b\u003c/em\u003e was determined in modern soft and hard winter wheat cultivars grown in the central and eastern USA (Guedira et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). \u003cem\u003eRht-D1b\u003c/em\u003e was dominant in soft winter wheat cultivars with a frequency of 45% while 28% of cultivars carried \u003cem\u003eRht-B1b\u003c/em\u003e. In hard winter wheat cultivars, \u003cem\u003eRht-B1b\u003c/em\u003e was dominant with a frequency of 77% while \u003cem\u003eRht-D1b\u003c/em\u003e was present in 8% of the cultivars. The frequency and allelic effect of \u003cem\u003eRht-B1b, Rht-D1\u003c/em\u003e, \u003cem\u003eRht-B1-160Ins\u003c/em\u003e, and \u003cem\u003eRht-B1a-197Ins\u003c/em\u003e were found in a worldwide core collection of 372 wheat accessions (Wilhelm et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). \u003cem\u003eRht-B1b\u003c/em\u003e and \u003cem\u003eRht-D1b\u003c/em\u003e both have a frequency of 12.5%, while \u003cem\u003eRht-B1a-160Ins\u003c/em\u003e and \u003cem\u003eRht-B1a-197Ins\u003c/em\u003e have a frequency of 16.1% and 11.9%, respectively. \u003cem\u003eRht-B1b\u003c/em\u003e and \u003cem\u003eRht-D1b\u003c/em\u003e were reported in another study with a frequency of 40% and 22%, respectively in 172 wheat genotypes from 20 different countries (Tosovic-Maric et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In contrast with European winter wheat, bread wheat cultivars of Pakistan have a higher frequency of \u003cem\u003eRht-B1b\u003c/em\u003e and a low frequency of \u003cem\u003eRht-D1b\u003c/em\u003e. The allelic frequency of \u003cem\u003eRht-D1b\u003c/em\u003e in Pakistani cultivars is comparable with the allelic frequency of \u003cem\u003eRht-D1b\u003c/em\u003e in worldwide wheat accessions. These findings suggested that \u003cem\u003eRht-B1b\u003c/em\u003e is dominant in worldwide germplasm whereas \u003cem\u003eRht-D1b\u003c/em\u003e allele is dominant in European wheat varieties.\u003c/p\u003e \u003cp\u003eAllelic effect of \u003cem\u003eRht\u003c/em\u003e genes in the diversity panel showed a significant effect on PH. Along with the green revolution \u003cem\u003eRht-B1\u003c/em\u003e and \u003cem\u003eRht-D1\u003c/em\u003e genes, GA-sensitive \u003cem\u003eRht\u003c/em\u003e genes such as \u003cem\u003eRht13\u003c/em\u003e and \u003cem\u003eRht26\u003c/em\u003e showed a strong height reduction in the cultivars which is comparable with the reduction effect of \u003cem\u003eRht-B1\u003c/em\u003e and \u003cem\u003eRht-D1\u003c/em\u003e. In a worldwide core collection of 272 wheat accessions, \u003cem\u003eRht-B1b\u003c/em\u003e and \u003cem\u003eRht-D1b\u003c/em\u003e conferred a strong height reduction of approximately 30%, while \u003cem\u003eRht-B1a-160Ins\u003c/em\u003e and \u003cem\u003eRht-B1a-197Ins\u003c/em\u003e reduced PH by 18% and 12% respectively as compared to wild-type (Wilhelm et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In the present study, strong height reduction is caused by \u003cem\u003eRht-D1b\u003c/em\u003e, reducing PH by 6.7%, while \u003cem\u003eRht-B1b\u003c/em\u003e and \u003cem\u003eRht-B1-160Ins\u003c/em\u003e reduce PH by approximately 4%. GA-insensitive \u003cem\u003eRht\u003c/em\u003e genes have detrimental impacts on seedling vigour, coleoptile length, and grain yield. GA-sensitive \u003cem\u003eRht\u003c/em\u003e genes mitigate the drawbacks of GA-insensitive \u003cem\u003eRht\u003c/em\u003e genes, so GA-sensitive \u003cem\u003eRht\u003c/em\u003e genes must be introduced into modern cultivars to replace GA-insensitive \u003cem\u003eRht\u003c/em\u003e genes. Of the 26 reduced height genes that have been identified so far, \u003cem\u003eRht1\u003c/em\u003e, \u003cem\u003eRht2\u003c/em\u003e, \u003cem\u003eRht8\u003c/em\u003e, and \u003cem\u003eRht24\u003c/em\u003e were the most frequently utilized in breeding to reduce PH (Xiong et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). But dwarfing alleles of \u003cem\u003eRht8\u003c/em\u003e and \u003cem\u003eRht24\u003c/em\u003e were not found in historical bread wheat cultivars of Pakistan. However, too few of these genes for reduced heights have been identified, and additional genes for shorter heights must be discovered (Song et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study reports the identification of important loci associated with PH in historical wheat cultivars of Pakistan. The identification of these key loci provides valuable insight into the genetic architecture of PH in wheat. Optimized plant architecture can be achieved by the incorporation of identified loci into the wheat cultivars through breeding which will significantly enhance wheat productivity. Diagnostic KASP markers showed a higher frequency and strong height reduction impact of GA-insensitive \u003cem\u003eRht\u003c/em\u003e genes as compared to GA-sensitive \u003cem\u003eRht\u003c/em\u003e genes. These findings open new ways to discover the full potential of genetic improvements of PH in wheat using modern genomics and bioinformatics tools.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe phenotypic data is available as Supplementary Table S1. The genotypic data used in this study in the form of vcf file is available at DryAd, https://datadryad.org/stash/dataset/doi:10.5061/dryad.xwdbrv1kb (Rasheed and Fayyaz, 2023), and also available as Supplementary Table S3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the Science and Technology Partnership Program from the Ministry of Science and Technology (MoST) of China (KY202201001) for financial support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.M.S, K.M, M.M, S.Z, S.R, collected the phenotypic data; H.Q, did the KASP genotyping; Z.H, M. F, conducted the field trials, H.M.S. wrote the manuscript; S.C, A.R, Z.H designed the experiment and edited the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare no conflict of interest.\u003c/p\u003e "},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgarwal P, Balyan HS, Gupta PK (2020) Identification of modifiers of the plant height in wheat using an induced dwarf mutant controlled by RhtB4c allele. Physiol Mol Biology Plants 26:2283\u0026ndash;2289\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBazhenov MS, Divashuk MG, Amagai Y, Watanabe N, Karlov GI (2015) Isolation of the dwarfing Rht-B1p (Rht17) gene from wheat and the development of an allele-specific PCR marker. 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PLoS ONE 9(11):e113287\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Li C, Zhang W, Zhang X, Mo Y, Tranquilli GE, Dubcovsky J (2023) Wheat plant height locus RHT25 encodes a PLATZ transcription factor that interacts with DELLA (RHT1). \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, \u003cem\u003e120\u003c/em\u003e(19), e230020\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Supplementary Figure","content":"\u003cp\u003eSupplementary Figure 1 is not available with this version.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSupplementary Figure S1\u003c/p\u003e \u003cp\u003eManhattan plot showing the density of SNP markers associated with PH of historical wheat cultivars. \u003cb\u003e(A)\u003c/b\u003e E1, \u003cb\u003e(B)\u003c/b\u003e E2, \u003cb\u003e(C)\u003c/b\u003e E3, \u003cb\u003e(D)\u003c/b\u003e BLUEs.\u003c/p\u003e "}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-breeding","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"molb","sideBox":"Learn more about [Molecular Breeding](https://www.springer.com/journal/11032)","snPcode":"11032","submissionUrl":"https://submission.nature.com/new-submission/11032/3","title":"Molecular Breeding","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Plant height (PH), Reduced height (Rht) genes, GWAS, KASP, SNP","lastPublishedDoi":"10.21203/rs.3.rs-4679366/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4679366/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePlant architecture and yield potential of wheat are significantly influenced by the plant height (PH). In the present study, a diversity panel consisting of 199 historical wheat cultivars of Pakistan was evaluated for PH in three environments, and a genome-wide association study (GWAS) was conducted to identify loci associated with reduced height. GWAS identified 19 loci that were associated with reduced height, out of which 12 loci were consistently identified in all environments. Allelic variations were analyzed in the diversity panel for five \u003cem\u003eRht\u003c/em\u003e genes including \u003cem\u003eRht-B1\u003c/em\u003e, \u003cem\u003eRht-D1\u003c/em\u003e, \u003cem\u003eRht13\u003c/em\u003e, \u003cem\u003eRht25\u003c/em\u003e, and \u003cem\u003eRht26\u003c/em\u003e using diagnostic KASP markers. Furthermore, a KASP marker was developed for the identification of dwarfing allele \u003cem\u003eRht-B1p\u003c/em\u003e in wheat. Allelic frequency of the GA-insensitive dwarfing allele \u003cem\u003eRht-B1b\u003c/em\u003e was pre-dominant (69.6%) followed by the GA-sensitive \u003cem\u003eRht26\u003c/em\u003e mutant allele (58.5%). Five dwarfing allele of \u003cem\u003eRht25\u003c/em\u003e including \u003cem\u003eRht25c\u003c/em\u003e, \u003cem\u003eRht25d\u003c/em\u003e, and \u003cem\u003eRht25e\u003c/em\u003e were rarely present in the cultivars with a frequency of 1.5%, 1%, 0.5%, respectively. The use of alternate dwarfing alleles to reduce PH can increase the genetic base of wheat cultivars by removing selection pressure on \u003cem\u003eRht-B1b/Rht-D1b\u003c/em\u003e haplotype and can lead to the development of wheat cultivars with improved characteristics such as reduced lodging risk, increased resource allocation to grain, improved harvest efficiency, enhanced crop stability, and adaptability.\u003c/p\u003e","manuscriptTitle":"Genome-wide and candidate gene association mapping for plant height in wheat","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-21 15:05:09","doi":"10.21203/rs.3.rs-4679366/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-07-29T12:25:32+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-25T07:45:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-03T10:53:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Breeding","date":"2024-07-03T06:49:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-breeding","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"molb","sideBox":"Learn more about [Molecular Breeding](https://www.springer.com/journal/11032)","snPcode":"11032","submissionUrl":"https://submission.nature.com/new-submission/11032/3","title":"Molecular Breeding","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e802be23-95e8-4e95-bb94-260202469ff9","owner":[],"postedDate":"August 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T15:58:55+00:00","versionOfRecord":{"articleIdentity":"rs-4679366","link":"https://doi.org/10.1007/s11032-025-01600-1","journal":{"identity":"molecular-breeding","isVorOnly":false,"title":"Molecular Breeding"},"publishedOn":"2025-11-07 15:56:58","publishedOnDateReadable":"November 7th, 2025"},"versionCreatedAt":"2024-08-21 15:05:09","video":"","vorDoi":"10.1007/s11032-025-01600-1","vorDoiUrl":"https://doi.org/10.1007/s11032-025-01600-1","workflowStages":[]},"version":"v1","identity":"rs-4679366","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4679366","identity":"rs-4679366","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}