Genome-Wide Association Study Reveals Major SNPs Associated with Pollen Viability and Spikelet Fertility in Wheat under Heat Stress

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This study employed genome-wide association study (GWAS) using 5,171 high-quality single nucleotide polymorphisms (SNPs) across 319 diverse wheat lines to identify genomic regions associated with pollen viability and spikelet fertility under reproductive-stage heat stress. Pollen viability was assessed from field-grown plants subjected to high temperature in a controlled setup using a thermal cycler. Spikelet fertility was evaluated in two years with heat treatment applied at the four different ear emergence stages. The wheat population showed significant genotypic and phenotypic variation. Chinese landraces formed a distinct cluster from other groups and generally exhibited lower pollen viability and spikelet fertility compared to Chinese commercial varieties. Pollen viability score showed moderate but significant correlation with spikelet fertility, especially at early ear-emergence stage. GWAS identified 15 significant SNPs associated with these two traits. Notably, AVRIG15341 on chromosome 1B and AVRIG21657 on chromosome 3A explained 20.31% and 11.9% of phenotypic variation in pollen viability, respectively. AVRIG26861 on chromosome 5A explained 17.09% of phenotypic variation in spikelet viability. This is the first report on SNPs linked to wheat pollen viability scores under heat stress. Combination of favorable alleles showed higher trait performance, suggesting strong potential for use in marker-assisted selection. Our pioneering study provides new insights into the genetic architecture of reproductive-stage heat tolerance in wheat and offers valuable genetic resources for breeding heat-resilient cultivars. Triticum aestivum L. heat stress GWAS pollen viability spikelet fertility reproductive stage heat tolerance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Global climate change is driving an increase in both mean and extreme temperatures, resulting in increased heat stress (HS) in temperate crops such as wheat ( Triticum aestivum L) (Farhad et al. 2023 ). Among various abiotic stresses, HS has become one of the major constraints to wheat productivity and is projected to intensify in the coming decades, due to current climate trends. Yield losses during the reproductive stage are particularly severe, with each 1°C rise in temperature estimated to reduce global wheat yield by approximately 6.0 ± 2.9%, and even higher losses reported in major wheat-growing regions such as India (9.1%), Russia (7.8%), and China (2.6%) (Asseng et al. 2011 ; Zhao et al. 2017 ; Hill & Li 2022 ). HS affects all aspects of physiological and developmental processes in wheat, especially during flowering and grain development. Elevated temperatures impair photosynthesis, alter metabolic pathways, degrade cellular proteins, and increase the accumulation of reactive oxygen species (ROS), collectively leading to reduced pollen viability and function (Zhang et al. 2023 ; Shenoda et al. 2021 ). The male gametophyte is especially heat sensitive due to its unicellular structure and rapid development (Ullah et al. 2022 ; Fábián et al. 2024 ). This results in impaired microsporogenesis, pollen tube growth, reduced dehiscence, and poor fertilization (Prasad et al. 2017 ; Aiqing et al. 2018 ; Bokshi et al. 2021 ; Zhao et al. 2024 ), ultimately reducing seed set and yield. Pollen viability and grain filling rate are reliable indicators for HS tolerance traits due to its positive correlation with grain number and grain yield (Shenoda et al. 2021 ; Khan et al. 2022a ; Masthigowda et al. 2022 ). Therefore, improved pollen viability under HS is increasingly adopted as a key selection trait in breeding programs. Yet, the genetic basis of these traits under reproductive-stage HS remains poorly understood. With advances in next-generation sequencing and the availability of high-density single nucleotide polymorphism (SNP) markers, genome-wide association studies (GWAS) offer a robust approach to dissect the genetic basis of complex traits across diverse germplasm. GWAS have already successfully identified loci associated with several heat stress tolerance traits including spikelet fertility (SF) and seed number (Kumar et al. 2020 ; Wang et al. 2023 ), and grain yield components under late-sown or heat-stressed conditions (Hong et al. 2024 ; Jamil et al. 2019 ). However, large scale pollen viability phenotyping in wheat under HS conditions has not been exploited, largely due to methodological issues related to such studies. All previous studies predominantly relied on late sowing to imposed HS, which exposes wheat plants to a broader window of reproductive and grain-filling stages, therefore limiting the precision of developmental-stage-specific stress assessment. At the same time, given the highly stage-specific impact of HS on SF (Chaturvedi et al. 2021 ), controlled heat treatments are essential for accurate trait dissection. In this work, we overcame the above limitations by using refined phenotyping protocols developed by Zhao et al. ( 2024 ). Two complementary approaches were combined: (i) a novel approach to score the pollen viability using thermal cycler based heat treatment using the anthers collected from field, and (ii) two years of glasshouse experiments applying HS at early and late ear emergence stages to assess SF. Using a diverse panel of 319 wheat lines genotyped with 5,171 high quality SNPs, we conducted GWAS to identify genomic regions associated with heat resilience. Significant SNPs and their associated candidate genes point to the roles in energy metabolism, stress signaling, transcriptional regulations, protein modification, and redox homeostasis, providing novel insights into the molecular mechanisms underlying thermotolerance in wheat. Overall, our work represents one of the first studies on assessing pollen viability in a large wheat panel, opening new prospects for improving HS tolerance in one of the most important staple foods in the world. 2. Materials and Methods 2.1 Plant materials A total of 319 wheat lines were used in this study. These include 127 Australian commercial varieties (ACV), 45 from Australian Grains Genebank (AGG), 62 Chinese commercial varieties (CCV), and 85 Chinese landraces (CL) ( Table S1 ). 2.2 Field trials Field experiment was conducted at the Mt Pleasant laboratories, Tasmanian Institute of Agriculture, University of Tasmania, Launceston, Tasmania, Australia (41.47°S, 147.14°E) during April 2023 to December 2023. Seeds of each accession were sown manually in a 0.6 m row with spacing of 0.35 m between rows and 15 seeds per row. There were three replications with Randomized Complete Block Design. Field management, including fertilizer use, followed local farmers’ practices. Manual weeding was carried out when required and proper irrigation was maintained throughout the season. 2.3 Phenotyping for pollen viability Single spikes or ear with yellowish mature anthers, which exhibit a split or opening on the adaxial side specifically at the stomium to allow pollen release, were collected and brought to lab for microscopic study (Fig. 1 ). All yellowish anthers were collected and subsequently transferred to the 1.5 mL Eppendorf tube containing 400 µl of pollen culture medium containing 10% sucrose, 2% agarophyte and 5% boric acid (Zhao et al. 2024 ). The tube was gently shaken for a uniform distribution of pollen as suspended in pollen culture medium, and 20 µl of suspension solution containing pollens was transferred to 96 PCR well plate. Pollen in 96 PCR plate was incubated at 37°C for 4 hours followed by imaging using 2% I 2 -KI solution. After heat incubation, 1–2 drops of suspended pollen were placed on a slide and stained with 1 drop of 2% I 2 -KI staining solution. Microscopic analysis was performed using an Olympus CKX53 microscope with 10x magnification, and three images from different regions of each sample were captured using Cell Sens software. For each line, a total of nine images (from three biological replicates) were processed and scored on a scale of 0–10, where 0 represents images with completely non-viable pollen and 10 represents the highest percentage of viable pollen (Fig. 1 ). The average pollen viability score (PVS) was calculated from the scores of the nine images and used for statistical and GWAS analyses. 2.4 Glasshouse experiment: growing condition, heat treatment, and data recording The glasshouse experiments were conducted over two consecutive years using 319 wheat lines. For each line, four seeds were sown in a single plastic pot (50 mm length × 50 mm width × 120 mm height) filled with potting mix (potting mixture, Searles, TAS, Australia). The experiments were conducted from May 2023 to December 2024. During the reproductive stage, four heads from each line at four different ear emergence stages were selected and tagged as follows: S 1 (very early booting stage), S 2 (early booting stage), S 3 (early ear emergence stage) and S 4 (very early flowering stage) ( Fig. S1 ). Tagged plants were then transferred to a controlled growth room and subjected to a two-week heat treatment at 37°C. Throughout the treatment period, plants were adequately irrigated to avoid drought stress, ensuring that the observed effects were attributable solely to HS. Day and night temperature was recorded using HOBOconnect thermometer with automatic temperature and humidity recording in every 30 minutes and data are presented in Fig. S2 . After two weeks of heat treatments, plants were transferred to normal growing conditions. After maturity, the tagged spikes (S 1 -S 4 ) from each line were harvested separately. Spikelet number per spike and seeds per spike were counted and the number of seeds per 100 spikelets were calculated to determine spikelet fertility. 2.5 Genotyping and marker selection All accessions were genotyped using the Illumina iSelect 90K SNP bead chip assay in the Agriculture Victoria sequencing facility at the Department of Energy, Environment and Climate Action (DEECA). This genotyping resulted in 24,955 SNPs which were later filtered to 5,171 high quality SNPs using the minor allele frequency (MAF 10). 2.6 Statistical analysis All statistical analyses were performed using R software (version 4.2.3). Genetic relationships among the 319 wheat lines were assessed by constructing a phylogenetic tree based on 5,171 SNP data using the ape, ggplot2, ggtree, and phangorn packages. Trait means for PVS and SF were visualized as bar plots with standard error bars using ggplot2. Group differences were analysed using the Least Significant Difference (LSD) test implemented in the agricolae package, with significance set at the 5% level. Pearson correlation coefficients between PVS and SF at early and late-stage HS were performed using corr package and visualized using ggplot2. The frequency distributions of PVS and SF were generated using the ggplot2 package, based on the mean values for each of the 319 wheat lines and were stacked based on the classification of wheat lines. 2.7 Genome-wide association study A GWAS was carried out using Genome Association and Prediction Integrated Tools (GAPIT) package in R (Lipka et al. 2012 ). We used four different models namely Bayesian information and linkage-disequilibrium iteratively nested keyway (BLINK) (Huang et al. 2019 ), fixed and random model circulating probability unification (FarmCPU) (Liu et al. 2016 ), mixed-linear model (MLM), and generalized linear model (GLM). For each of the models, we used first two PCA, the notional p-value threshold was set to suggest significant SNPs linked to traits of studied to -log 10 (p) ≥ 3, and SNP.MAF was set 0.05. Based on the QQ-plot, BLINK model was selected and output from this model were used for Manhattan plot generation, significant SNP identification, and subsequent bioinformatic analysis. Significant SNPs linked to PVS, and SF were visualized using ggplot2 package. Previously reported SNPs associated with PVS, and SF were compiled from the WheatQTLdb databases (Singh et al. 2020 ) and visualized as chromosome maps using MapChart software (Voorrips 2002). Markers or SNPs with higher phenotypic variance explained (PVE%) ≥ 10 were prioritized. A comprehensive list of previously reported markers and SNPs linked to grain yield, grain numbers, spikelet fertility, thousand grain weight, and HS tolerance traits is presented in Table S2 . 2.8 Allelic effect For each significant SNP identified through GWAS, the allele associated with a higher trait value of PVS, and SF was designated as the tolerant allele, while the allele associated with a lower trait value was designated as the sensitive allele. To evaluate the effect of individual and combined SNPs, wheat lines were grouped based on their allele composition at the respective loci. Mean trait values (PVS and SF) were calculated for each allelic group, and pairwise comparisons were conducted using two-sample t-tests to assess statistical significance. To determine the combined effect of alleles from two SNPs, lines were grouped based on the presence or absence of both allele and their mean was followed by two-way t-test. Summary of t test including number of each allele, average values, p values, and differences are presented in Table S3 . Allelic effect was visualized using ggplot2 in R including the distributions, means, error bars, and p-values. 2.9 Candidate gene identification GenomicRanges (Lawrence et al. 2013 ) and rtracklayer (Lawrence et al. 2009 ) packages in R were used to identify the candidate genes around the highly significant SNPs linked to PVS and SF. GFF3 or GTF annotation file for Triticum aestivum was downloaded from EnsemblPlants website ( https://ftp.ensemblgenomes.ebi.ac.uk/pub/plants/release-61/gff3/triticum_aestivum/ ) followed by filtering in R using the SNP position for specific chromosome. A window of 2 Mb (downstream and upstream) from the highly significant SNP position was chosen for identifying the candidate genes. Finally, the biological functions of these candidate genes were checked using Biomart package using host of Ensembl ( https://plants.ensembl.org/Triticum_aestivum/Info/Index?db=core ) by selecting Triticum aestivum as reference genome. Based on their description, identified candidate genes were categorized into broader biological functional category and visualized as bar graphs to determine the number of genes for each of the functions. 3. Results 3.1 Marker distribution and population structure Genotyping of 319 wheat lines using Illumina iSelect 90K SNP bead chip assay generated 24,955 SNPs which were filtered based on minor allele frequency (MAF 10%), resulted in 5,171 high quality SNPs for GWAS analysis. These SNPs were distributed across the 21 chromosomes with the highest number (2,361) located in sub-genome B, followed by sub-genome A (2190), and fewest (620) in sub-genome D (Fig. 2 A). The number of SNP markers on individual chromosomes ranged from 73 on Chr3D to 386 on Chr2B. PCA-biplot and NJ-phylogenetic tree using the high-quality SNPs (5,171) indicated association among the 319 wheat lines from different regions, where lines classified as CL showed well separation from the rest of three classifications, ACV, AGG, and CCV (Fig. 2 B-C). The first and second PCs were composed largely by genotypes originating from CCV (Fig. 2 B). Lines classified as ACV showed smaller separation from AGG whereas lines classified as AGG and CCV showed no separation indicating their close genetic relationship. 3.2 Mean performance of genotypic group Mean value of the PVS and SF for the 319 wheat lines, classified into four groups, are presented in Fig. 3 . For PVS, significant differences were observed among the classification groups with the highest mean value in ACV, followed by CCV, and the lowest in CL (Fig. 3 A). Under HS at early and late stages of ear emergence stage, CL exhibited the lowest mean SF while CCV showed the highest SF (Fig. 3 B-C). Significant differences in SF were observed for HS at each of the four individual stages (S 1 -S 4 ) where CL and CCV showed the lowest and the highest mean value, respectively (Fig. S3). Additionally, HS during the early stage of ear emergence resulted in lower seed set compared to late stages. 3.3 Frequency and Correlation of pollen viability score and spikelet fertility The 319 wheat lines exhibited significant variations in PVS and SF (Fig. 3 D-F). Only a few lines exhibited extreme scores, with two lines (0.62% of total lines) showing PVS of 1 and one line (0.31% of total lines) showing PVS of 8 (Fig. 3 D). A total of 83 (26.02%) lines showed moderate PVS (PVS = 5), followed by 72 lines (22.57%) with PVS = 4 and 62 lines (19.44%) with PVS = 6. Moreover, 51 lines (15.99%) showed a high PVS = 7. Overall, a greater proportion of ACV showed moderate to high PVS whereas tended to cluster in the low-to- moderate PVS range. For SF, the distribution was highly skewed towards low fertility under early ear emergence stage-HS (Fig. 3 E) whereas distribution late-stage HS was flatter and slightly more spread out across the fertility values, suggesting less severe damage (Fig. 3 F). Under early-stage HS, most lines had moderate SF whereas late-stage heat treatment conditions resulted in higher number lines with high SF followed by moderate SF. To understand the relationship between PVS and SF of early-stage and late-stage HS, Pearson correlation coefficients were calculated and visualized in Figure S4. PVS showed a moderate but significant positive correlation with both SF at early-stage HS (0.137*, p = 0.023) and late-stage HS (0.127, p = 0.027) (Figure S4 A, B). When analyzed for each individual ear emergence stage heat treatment, PVS showed significant and positive correlation with S 1 , S 2 , and S 3 stage, with the highest correlation (r = 0.18, p = 0.002) with S 1 stage (Fig. S4 C-F). 3 .4 GWAS for pollen viability score and spikelet fertility GWAS with BLINK model identified 15 significant SNPs associated with PVS and SF (early ear emergence stage) (Table 1 , Fig. 4 ). Ten significant SNPs were detected for PVS, distributed across chromosomes 1B, 2A, 3A, 4A, 4B, 6B, 6D, 7A, and 7B (Fig. 4 A). Among them, the most significant SNPs, AVRIG15341 on chromosome 1B (-log 10 p = 7.23, position 43119347) and AVRIG21657 on chromosome 3A (-log 10 p = 5.05, position 106776234), explained 20.31% and 11.90% of phenotypic variation, respectively (PVE%) (Fig. 4 A, Table 1 ). Other highly significant SNPs included AVRIG25720 on chromosome 4B (–log 10 p = 4.88) and AVRIG32251 on chromosome 6D (–log 10 p = 4.69). Table 1 List of significant SNPs linked to pollen viability score and spikelet fertility in wheat under heat stress Traits SNP chr Pos p.value -log 10 P MAF nobs effect PVE (%) Pollen viability score AVRIG15341 1B 43119347 5.90E-08 7.23 0.5 319 0.37 20.31 AVRIG21657 3A 106776234 9.00E-06 5.05 0.06 319 -0.52 11.9 AVRIG25720 4B 42028226 1.30E-05 4.88 0.06 319 -0.49 AVRIG32351 6D 427650981 2.10E-05 4.69 0.25 319 -0.3 AVRIG17433 2A 34649309 2.80E-04 3.55 0.11 319 0.32 AVRIG34123 7B 3672642 5.10E-04 3.3 0.3 319 0.25 AVRIG31940 6B 663589169 7.40E-04 3.13 0.27 319 -0.28 Wx-B1-SNP2 4A 690269864 8.00E-04 3.1 0.24 319 -0.29 AVRIG34072 7A 717567620 8.90E-04 3.05 0.33 319 -0.23 AVRIG18520 2A 748854583 9.50E-04 3.02 0.08 319 0.31 Spikelet fertility (early-stage HS) AVRIG26861 5A 24509229 3.20E-08 7.5 0.14 271 25.88 17.09 AVRIG29108 5B 595297562 5.10E-05 4.29 0.33 271 12.78 AVRIG25392 4A 680096353 1.00E-04 3.98 0.42 271 -11.36 AVRIG25715 4B 40141266 2.80E-04 3.55 0.23 271 -14.63 IWB4227 3D 368660644 5.20E-04 3.29 0.27 271 -19.66 Five significant SNPs were identified for SF under early ear emergence HS treatment. AVRIG26861 (-log 10 P = 7.5) was detected on chromosome 5A, showing the highest PVE (17.09%) (Fig. 4 B, Table 1 ). Additional significant SNPs were detected on chromosomes 3D, 4A, 4B, and 5B, including the highly significant AVRIG29108 on chromosome 5B (-log 10 p = 4.29). All significant SNPs identified from this study and previously reported significant SNPs (PVE ≥ 10%) associated with different heat stress tolerance traits in wheat are listed in Table S2 and presented in a physical chromosome map (Fig. 5 ). Several major SNPs (PVE > 10%) associated with different HS traits have been reported near the significant SNPs (this study) associated with PVS and SF. 3.5 Allelic effect on pollen viability score and spikelet fertility Alleles of significant SNPs associated with PVS, and SF showed significant differences in trait means between favorable (tolerant) and unfavorable (sensitive) alleles (Fig. 6 , Table S3 ). Three significant SNPs, AVRIG15341, AVRIG17433, and AVRIG18520, associated with PVS showed significant differences between their tolerant and -sensitive alleles (Fig. 6 A, B). Alleles of AVRIG15341 showed the highest with mean differences (1.13, p = 2.24 × 10⁻¹⁴) with mean values of 5.95 and 4.82 for the tolerant and sensitive alleles, respectively. AVRIG18520 displayed a mean difference of 0.84 (p = 0.0002) with mean value of 6.17 and 5.32 (Table S3) for the tolerance and the sensitive groups, respectively. Another significant SNP AVRIG17433 also showed significant differences (0.74, p = 0.009) between mean value of tolerant (6.06) and sensitive (5.32) alleles. Combined allelic effects of AVRIG15341 and AVRIG17433 showed significantly higher differences (1.88, p = 2.27 × 10⁻ 10 ) with increase mean value of PVS in tolerant allele (6.69) and decrease in sensitive allele (4.81) (Fig. 6 A, Table S3). The combination of AVRIG15341 and AVRIG18520 also showed highly significant differences (2.05, p = 6.88 × 10⁻ 06 ) between mean PVS of tolerant (6.75) and sensitive (4.70) alleles (Fig. 6 B, Table S3). Tolerant and sensitive alleles of the two significant SNPs AVRIG268761 and AVRIG29108 showed significant differences on SF (Table S3). Highest mean differences (43.95, p = 23.97× 10⁻ 05 ) were observed between the mean SF of tolerant (121.75) and sensitive (77.80) alleles of SNP AVRIG26861. Significant differences (25.43, p = 0.0008) were also observed between the mean SF of tolerant (132.17) and sensitive (106.74) allele of AVRIG29108. Combination of alleles from these two SNPs resulted in higher differences (66.56, p = 0.0002) increase in mean SF in tolerant allele (141.21) and decrease in sensitive allele (74.65). 3.6 Candidate genes associated with pollen viability score and spikelet fertility Seven significant SNPs, including four associated with PVS and three associated with SF were used to identify candidate genes located near these SNPs, resulting in 44 candidate genes with a diverse range of functions (Table 2 , Fig. S5). Based on their functional description, eight candidate genes are involved in energy metabolism, followed by a similar number of candidate genes involved in stress signaling and transcription regulation (Fig. S5). The second dominant group (seven candidate genes) is related to protein modification and folding. Remaining candidate genes were involved in redox homeostasis and detoxification (6), RNA processing and regulation (6), transport and trafficking (6) and cell wall modification (4). Table 2 List of identified candidate genes using the highly significant SNPs associated with pollen viability score and spikelet fertility. SNP Gene_id start end Description Functional category AVRIG15341 TraesCS1B02G059400 41982414 41987242 Formin-like protein Cytoskeleton Organization TraesCS1B02G059800 42347605 42351160 Nudix hydrolase 9 Nucleotide Metabolism / Detoxification AVRIG21657 TraesCS3A02G128600 106303465 106307935 NADH-cytochrome b5 reductase Electron Transport / Redox Metabolism TraesCS3A02G130500 107418238 107424746 NADPH:adrenodoxin oxidoreductase, mitochondrial Mitochondrial Electron Transport / Redox Enzyme TraesCS3A02G131000 107732788 107739023 Pentatricopeptide repeat-containing protein chloroplastic RNA Processing / Organelle Gene Expression TraesCS3A02G129900 107052572 107054254 Peroxidase Reactive Oxygen Species (ROS) Scavenging TraesCS3A02G130000 107076189 107080320 Phospholipase D Lipid Signaling / Membrane Dynamics TraesCS3A02G128500 105946479 105949850 Probable prolyl 4-hydroxylase 12 Post-translational Modification TraesCS3A02G128200 105111804 105113961 Protein kinase PINOID 2 Signal Transduction / Protein Phosphorylation AVRIG25720 TraesCS4B02G053800 42396312 42401199 Adenylyl cyclase-associated protein cAMP Signaling / Cytoskeletal Regulation TraesCS4B02G054900 43720172 43729286 Alanine–tRNA ligase Protein Translation / Aminoacylation TraesCS4B02G052700 41555365 41556689 Calcium load-activated calcium channel Calcium Signaling TraesCS4B02G053000 41777831 41783720 DNA-binding family protein DNA Binding / Transcription Regulation TraesCS4B02G052300 41009664 41014024 Glutamate decarboxylase Amino Acid Metabolism / GABA Biosynthesis TraesCS4B02G054800 43593682 43595526 Glycosyltransferase Cell Wall Biosynthesis / Glycan Modification TraesCS4B02G052900 41743521 41744496 Histone H2A Chromatin Structure / DNA Packaging TraesCS4B02G054200 43061143 43061867 Histone H2B Chromatin Structure / DNA Packaging TraesCS4B02G054300 43104629 43107091 Pentatricopeptide repeat-containing protein RNA Editing / Organelle RNA Metabolism TraesCS4B02G052000 40780124 40786295 Phytochrome A, Photoreceptor Light Perception / Photoreceptor AVRIG32351 TraesCS6D02G322300 429383239 429384224 17.4 kDa class III heat shock protein Heat Stress Response / Molecular Chaperone TraesCS6D02G321600 429010152 429011840 3-ketoacyl-CoA synthase Fatty Acid Elongation / Lipid Metabolism TraesCS6D02G320500 428475669 428482477 ATP-dependent DNA helicase DNA Repair / Replication TraesCS6D02G318100 426335420 426337884 Endoglucanase Cell Wall Degradation / Carbohydrate Metabolism TraesCS6D02G318500 426983867 426984875 Xyloglucan endotransglucosylase/hydrolase Cell Wall Remodeling / Growth Regulation AVRIG26861 TraesCS5A02G027400 22976211 22985551 Methyl-Binding Domain 6-5AS DNA Methylation Binding / Epigenetic Regulation TraesCS5A02G030200 26153880 26157442 Nucleolar GTP-binding protein 1 Ribosome Biogenesis / Nucleolar Function TraesCS5A02G030300 26440090 26449927 Serine/threonine-protein phosphatase Signal Transduction / Dephosphorylation TraesCS5A02G030500 26466490 26468864 Tubulin-specific chaperone A Cytoskeletal Protein Folding AVRIG29108 TraesCS5B02G420400 595676670 595685625 DNA-directed RNA polymerase subunit Transcription Machinery TraesCS5B02G418600 594867010 594872193 Inosine-5'-monophosphate dehydrogenase Purine Biosynthesis / Nucleotide Metabolism TraesCS5B02G419000 594913464 594916850 Obg-like ATPase 1 Organelle Biogenesis / Stress Signaling TraesCS5B02G419100 594916943 594919492 Pentatricopeptide repeat-containing protein RNA Stability / Organelle RNA Processing TraesCS5B02G422000 597218457 597222722 Phytochrome-interacting factor-like bHLH protein, PIF Light Signal Transduction / Transcription Factor Abiotic Stress Response/ Gene Regulation Growth Regulation / Drought Response TraesCS5B02G420300 595661801 595664844 Reticulon-like protein Endoplasmic Reticulum Morphology TraesCS5B02G419900 595630894 595633310 Serpin-Z2A Protease Inhibitor / Stress Defense TraesCS5B02G418700 594873314 594876320 Tubulin beta chain Cytoskeleton Assembly / Microtubule Dynamics TraesCS5B02G420800 596493692 596499867 Vacuolar protein sorting-associated protein Protein Trafficking / Vacuole Function AVRIG25392 TraesCS4A02G407600 680372860 680376810 Mitochondrial substrate carrier family protein Metabolite Transport / Mitochondrial Function TraesCS4A02G409000 681710999 681715958 PHD finger protein ING Chromatin Remodeling / Transcription Regulation TraesCS4A02G404500 678127054 678131552 Protein DETOXIFICATION Xenobiotic Detoxification / Stress Response TraesCS4A02G407500 680370183 680371906 RAB GTPase homolog H1E Vesicle Trafficking / Intracellular Transport TraesCS4A02G407900 680451111 680453753 Stomatal Density and Distribution 1 Stomatal Development / Cell Fate Determination TraesCS4A02G408900 681661869 681680991 transcription activators Gene Expression Regulation TraesCS4A02G409300 681896628 681900476 Xyloglucan galactosyltransferase MUR3 Cell Wall Biosynthesis / Glycosylation Several candidate genes were located near the SNP position associated with PVS. TraesCS1B02G059800 (location 42,347,605–42,351,160) on chromosome 1B is located 768 kb upstream from the highly significant SNP AVRIG15341 (Table 2 ). This gene involves maintenance of cellular homeostasis by removing the potential harmful nucleotide metabolites during pollen development and germination. Another candidate gene, TraesCS3A02G129900, on chromosome 3A (location 107,052,572–107,054,254) is located ~ 276 kb downstream from the significant SNP AVRIG21657, which is involved in phenolic compound metabolism in pollen pistil and regulates the pollen tube growth. TraesCS4B02G053800 on chromosome 4B (location 42,396,312–42,401,199) was identified ~ 368 kb downstream from the significant SNP, AVRIG25720, which participates in catalyzing the transfer of sugar moieties to various acceptor molecule, modify their structure, and functions in pollen development and maturation. Three highly significant SNPs associated with SF led to the identification of 20 candidate genes (Table 2 ). However, the candidate genes are located more than 1 Mb downstream or upstream from the significant SNPs. For example, TraesCS5A02G027400 on chromosome 5A (location 22,976,211–22,985,551) is located ~ 1.5 Mb upstream of highly significant SNP AVRIG2681 and is involved in reproductive development and abiotic stress responses. Another candidate gene, TraesCS5A02G030200 (location:26,153,880–26,157,442), on chromosome 5A is located ~ 1.6 Mb downstream of same significant SNP and plays roles in cell cycle regulation and seed development. 4. Discussion Heat stress (HS) significantly reduces grain yield by adversely affecting multiple stages of reproductive development, including pollen viability and germination, fertilization, and seed formation (Djanaguiraman et al. 2020 ; Ullah et al. 2022 ; Zhao et al. 2024 ). Given the high sensitivity of pollen and seed development to HS, these traits are critical targets for breeding heat-tolerant wheat cultivars. Enhanced pollen survival and improved grain filling rates contribute directly to increased yield potential (Shenoda et al. 2021 ; Masthigowda et al. 2022 ). However, the genetic regulation of pollen viability and grain filling under abiotic stress remains poorly understood due to the complex nature of these traits and the varying effects of HS on different phases of pollen development and ear emergence. Assessing phenotypic variation among diverse germplasm is essential to capture differential responses to HS. Previous studies have rarely used PVS as a phenotypic trait under heat stress conditions, and no GWAS has yet been conducted to dissect the genomic regions associated with this critical trait mainly due to the previous methodological limitations for rapid testing of PVS. Additionally, most of the GWAS study used for seed set traits focused on single phage of ear emergence stage while the HS effect on smaller phase of each reproductive stage varies significantly. In this context, the present study aimed to: (i) evaluate the impact of HS on pollen viability and SF; (ii) identify genomic regions associated with pollen survival and higher SF; and (iii) assess the potential integration of these loci into breeding programs for developing HS-tolerant elite wheat lines. The study revealed a stage-specific impact of HS on SF and successfully identified several significant SNPs associated with PVS and SF. 4.1 Novel aspects of phenotyping and screening for pollen viability and spikelet fertility Two independent yet complementary experiments were designed to address the research gaps. The first experiment assessed pollen survival through PVS using a PCR thermal cycler-based heat treatment method developed by Zhao et al. ( 2024 ) applied to pollen collected from field-grown plants. Most of the pollen study in cereal crops rely on the natural coincidence of heat waves with critical developmental stages. However, variability in flowering time and the unpredictability of weather often result in plants being exposed to HS across a broad range of stages, complicating interpretation. The PCR thermal cycler-based method overcomes these limitations by allowing targeted heat exposure at specific pollen developmental stages. To our knowledge, this is the one of the first GWAS studies to phenotype pollen viability under HS in a larger number of diverse wheat lines using the PCR thermal cycler-based method to identify SNPs associated with PVS. A prior GWAS for wheat pollen viability (Ei-Hanafi et al. 2021 ) analyzed 196 wheat lines grown under field conditions without implying HS, identifying only one SNP associated with pollen viability. While other studies (e.g., Masthigowda et al. 2022 ; Khan et al. 2022a ; Groli et al. 2024 ) have evaluated pollen viability under HS, these studies did not use GWAS to identify genomic region associated with the trait. Here, PCR thermal cycler-based PVS under heat incubation captured extensive phenotypic variation among wheat lines and allowed the detection of genomic regions associated with PVS, resulting in multiple significant SNPs (Table 1 , Fig. 4 , Fig. 5 ). The method can be broadly applied to precisely phenotype pollen viability in other crop species. In the glasshouse experiments, we evaluated SF under controlled HS applied at four different stages of ear emergence stage. Individual stages differ in sensitivity to HS, but most of the previous studies considered or targeted single or broad range of ear emergence stage for determining the HS impact such as vegetative (Khan et al. 2022b ; Lu et al. 2022 ), reproductive (Kumar et al. 2023 ; Xu et al. 2022 ), or grain-filling (Zhao et al. 2022 ). Our study addressed this gap by imposing HS at each specific ear emergence stage to determine its impact on seed set as a form of SF. HS on early ear emergence (S 1 and S 2 ) stage caused more sterile spikelets compared to HS at late stages (S 3 , and S 4 ) (Fig. 3 and S3). The lower SF in early-stage HS is likely due to the higher impact on pollen viability. Pollen during early ear emergence (booting stage) is at meiosis stage which is more sensitive to HS compared to late pollen development stage, which is less sensitive to HS (Xu et al. 2022 ; Ullah et al. 2024 ). HS during early booting—particularly around meiosis—can disrupt pollen cell division and viability, leading to pollen sterility, reduced pollen numbers, and ultimately lower seed set (Erena et al. 2021 ; Browne et al. 2021; Xu et al. 2022 ). These findings underscore the importance of precisely targeting specific development stages when assessing heat tolerance and selecting thermotolerant genotypes in crop breeding programs. Our findings also highlight the necessity of conducting GWAS focused on individual phases of ear emergence, which was the key component of this study. 4.2 Early stage of ear emergence correlated with PVS under HS PCR thermal cycler-based PVS from field grown wheat lines, and SF recorded from controlled glasshouse experiment showed variable correlation depending on the stage at which HS was imposed (Fig. S4). The highest correlation was found between PVS and SF when HS was imposed at early pollen development stage (S 1 ) (Fig. S4). At the early booting stage (S 1 ), pollen mother cells undergo meiosis, which is highly sensitive to HS (Aiqing et al. 2018 ). Consequently, SF is more dependent on pollen viability, leading to a stronger correlation, with higher pollen viability at S 1 being associated with increased seed set. However, pollen at the late- stage ear emergence has already completed its development and is undergoing fertilization, making seed set less dependent on pollen survival and more influenced by other mechanisms. Similarly, Xu et al. ( 2022 ) reported HS-specific correlations between pollen viability and grain number with HS during early booting stage resulting in less fertile pollen. While our findings are supported by previous studies, the significance of correlation between these two traits is relatively low compared to those studies (Zhao et al. 2024 ; Khan et al. 2022a ; Xu et al. 2022 ). This is likely due to the greater variation in pollen viability across the 319 wheat lines, which may introduce additional variability in trait relationships. Overall, these results reinforce the importance of precise stress timing in phenotyping and breeding trials for wheat. We thus propose that wheat genotypes maintaining high pollen viability under HS at early booting stage are likely to exhibit better reproductive resilience, which can be prioritized for development of heat-tolerant wheat cultivars. 4.3 Past breeding programs contribute significantly to improving pollen viability score and spikelet fertility under heat stress The genetic structure of 319 wheat lines revealed clear separation of ACV, AGG, and CCV from CL (Fig. 2 B-C), consistent with previous studies showing divergence between the landraces and modern commercial cultivars (Rufo et al. 2019 ; Royo et al. 2020 ). Evidence of gene flow between the Chinese landraces (CL) and Chinese commercial cultivars was observed, reflecting the historical use of landraces in developing modern wheat lines adapted to local conditions. The genetic differentiation was mirrored at phenotypic level of PVS and SF. Modern cultivars consistently exhibited higher PVS and SF upon exposure to HS compare with China landraces, which generally performed poorly (Fig. 3 , Fig. S3). The superior performance of ACV, CCV, and AGG lines under HS aligns with previous findings linking modern cultivars to improved grain yield and yield-related traits through targeted breeding for reproductive success, abiotic stress tolerance, and nutrient use efficiency (Frankin et al. 2021 ; Royo et al. 2020 ; Eser et al. 2024 ). Frequency distributions further illustrated that ACV and CCV lines were enriched in the mid-to-high PVS and SF classes, whereas CL contributed largely to the lower-performing categories (Fig. 3 ). Notably, CCV demonstrated higher SF particularly at the S 1 –S 3 stages, highlighting breeding gains in reproductive resilience. Nevertheless, the wide phenotypic diversity present in landraces still offers valuable allelic variation that could be harnessed to introduce novel tolerance mechanisms into elite cultivars. For example, some CLs, such as H-002, H-068, and H-045 showed higher PVS (6.44, 7.67, and 6.78 respectively) carrying the favorable alleles of highly significant SNP (AVRIG15341) which can be transferred to elite commercial cultivars in future breeding endeavors. In terms of SF, several CLs, such as H-002, H-058, H-079, and H-111 carried favorable alleles of both significant SNPs (AVRIG26861 and AVRIG29108) with higher SF (194.17, 178.92, 223.86, and 179.44). These lines, which carry favorable alleles contributing to improved PVS and SF, can be utilized in backcrossing and introgression breeding programs. 4.4 Significant novel SNPs associated with pollen viability score and spikelet fertility Pollen viability is a key trait for HS tolerance in wheat, but its genetic basis of pollen viability under biotic and abiotic stress conditions is yet to be conducted. To date, one study (El-Hanafi et al. 2022) reported a single SNP associated with pollen viability in field grown wheat lines, and this study is limited by the lack of HS treatment. In contrast, our study is the first to conduct GWAS on pollen viability under HS using a larger, and diverse wheat panel. This study successfully identified 10 significant SNPs associated with PVS across different chromosomes (Fig. 4 , 5 , Table 1 ). Notably, SNPs AVRIG15341 (1B) and AVRIG21657 (3A) contributed to 20.31% and 11.90% of phenotypic variation, respectively (Table 1 ), suggesting major genetic effects of these two SNPs. Several major SNPs and markers have been reported near the highly significant SNP AVRIG15341 on chromosome 1B, including heat tolerance (Qaseem et al. 2019 ), grain yield per plant (Li et al. 2019 ), productive tiller and grain number per plant (Sharma et al. 2016 ), and flag leaf and spike temperature, and heat tolerance index for thousand grain weight (Mason et al. 2011 ) (Fig. 5 , Table S2 ). The second most significant SNP AVRIG21657 on chromosome 3A located closely to several major SNP/markers associated with HS tolerance index (Qaseem et al. 2019 ), grain yield and yield contributing traits (Bennett et al. 2012 ). Another significant SNP on chromosome 4B showed close location with major markers linked to heat stress tolerance index (Valluru et al. 2016 ), grain yield (Guan et al. 2018 ), grain filling duration (Tiwari et al. 2013 ), and thousand grain weight (Bennett et al. 2012 ). SF determines the grain yield in wheat. We identified a highly significant SNP, AVRIG26861 (5A) as a major locus for SF under HS, explaining 25.88% of phenotypic variation (Fig. 4 , Table 1 ). This SNP is close to some previously reported major SNPs and markers for normalized difference vegetation index (NDVI) of wheat under HS (Liu et al. 2019 ), stress tolerance index (Qaseem et al. 2019 ), thousand kernel weight (Li et al. 2019 ), temperature depression in flag leaf (Mondal et al. 2015 ), grain yield (Tiwari et al. 2013 ), and grain number per spike (Guan et al. 2018 ). The physical chromosome map developed in this study clearly demonstrates the clustering of significant markers in this region, supporting the idea of a potential hotspot for SF regulation (Fig. 5 ). Further fine mapping and functional validation of candidate genes within this region could provide deeper insights into the underlying genetic mechanisms and facilitate the development of heat-resilient wheat cultivars. 4.5 Favorable allele combinedly improves PVS and spikelet fertility The present study demonstrated that favorable alleles at key SNP loci significantly improve reproductive traits, PVS and SF, highlighting their genetic control and potential for breeding applications. For PVS, tolerant alleles of three significant SNPs, AVRIG15341 (1B), AVRIG17433 (2A) and AVRIG18520 (2A), individually increased PVS by 1.13, 0.74, and 0.84 units, respectively, compared to their sensitive counterparts (Fig. 6 , Table S3). Combinations of tolerant alleles showed additive or synergistic effects, confirming the cumulative benefit of pyramiding tolerant alleles to improve PVS under HS. For example, the combination of tolerant allele from AVRIG15341 and AVRIG17433 resulted in a significant increase of PVS (1.88 higher than sensitive alleles) whereas combination of AVRIG15341 and AVRIG18520 resulted in 2.05 higher PVS compared to sensitive alleles (Fig. 6 A-B; Table S3). Some of the lines for an example, EMS Summito70-8, Eagle, Federation, Halberd, and Heron, carried three favorable alleles from the three significant SNPs and exhibited higher PVS (5.33, 7.67, 7.17, 7.11, and 7). These varieties can be utilized as pre-breeding material for developing climate-resilient wheat variety. Similarly, for SF, favorable allele of AVRIG26861 (5A) and AVRIG29108 (5B) possessed significantly higher SF (43.95 and 25.43, respectively) compared to their sensitive alleles (Table S3). Combination of tolerant alleles from these two SNPs further enhance the fertility to 66.56, exceeding both individual effects (Table S3). This finding aligns with previous findings (e.g. Katz et al 2022 ) showing that multiple superior alleles increase spikelet numbers, highlighting the polygenic nature of the trait. 4.5 Candidate genes and their predicted function under HS Pollen survival and seed set under HS are controlled by multiple genes involved in stress perception, signal transduction, and stress responses (Table 2 , Fig. S5). The highly significant SNPs associated with PVS and SF provided several candidate gene and gene families (Table 2 ) which are reported to involve energy metabolism, stress signaling and transcription regulation, protein folding and modification, redox homeostasis, and ion transport and trafficking (Fig. S5). These well-known molecular mechanisms contributed to stress tolerance in plants. Here, we discuss the predicted functions and potential roles of a few selected genes in wheat heat tolerance. One of the candidate genes, TraesCS1B02G059400 (near SNP AVRIG15341 on 1B) belongs to Formin-like proteins (Table 2 ). Formin-like proteins play a critical role in pollen cell division and cytoskeleton organization. There are 25 candidate formin like genes that have been identified in wheat with their different expressions and function in pollen viability. They are highly expressed in wheat reproductive tissues and show upregulation under stress, contributing to pollen viability by impacting pollen cytoskeleton distribution (Duan et al., 2021 ), supporting actin polymerization, pollen tube growth, and cell wall organization (Kollárová et al. 2021 ). These studies suggest that formin genes may play a key role in maintaining pollen viability under HS, although functional studies in wheat remain limited. Another candidate gene for the SNP AVRIG15341 is TraesCS1B02G059800 , which belongs to Nudix hydrolase 9 protein. This protein is involved in several biochemical and physiological processes including metabolic regulation, plant immunity, and stress responses (Wang et al. 2022 ). In Arabidopsis and cereals, overexpression of a Nudix gene family improved oxidative stress and is expressed in pollen and stamen (Tanaka et al. 2015 ; Kondo et al. 2022 ). However, the functional roles of the Nudix hydrolase gene family in reproductive success under abiotic stresses remain largely unexplored. Peroxidases contribute to ROS detoxification and maintaining ion homeostasis, which is vital for the evolutionary success of most plant species (Singh et al. 2024 ). A candidate gene on chromosome 3A (near SNP AVRIG21657) belongs to this family (Table 2 ). Abiotic stresses including HS causes overproduction of ROS in leaf and reproductive organs and disrupts cellular ion-homeostasis (Xu et al. 2024 ). Overproduction of ROS leads to programmed cell death of tapetum, resulting in lower nutrient translocation to pollen and causes pollen sterility (Chaturvedi et al. 2021 ). Peroxidase is involved in neutralizing the ROS and reducing the oxidative damage. The peroxidase gene family has been reported as essential for anther and pollen development in Arabidopsis (Jacobowitz et al. 2019 ), with the mutation of two peroxidase genes resulting in sterile pollen, disrupted cell wall integrity, and swollen tapetum cells. The stress-related upregulation of peroxidase genes in wheat and other cereals, e.g. drought tolerance (Su et al. 2023 ; Jiao et al. 2024 ) and salinity tolerance (Su et al. 2020 ), highlights potential for improving HS tolerance. Overall, the identified candidate genes are associated with reproductive and seed development wheat under HS. While related gene families have been validated in Arabidopsis and some cereal crops for their role in enhancing stress tolerance, targeted characterization in wheat is crucial to exploit these genes for improving pollen survival and seed set under HS. 5. Conclusion and Future Prospective Heat stress, exacerbated by climate change, poses a major challenge to wheat production by impairing reproductive development and reducing yield. This study demonstrated substantial genetic variation in PVS and SF within a diverse wheat panel, highlighting their potential as key selection traits in breeding programs targeting heat resilience. Importantly, this is the first study involving the PCR thermal cycler-based pollen viability screening for HS tolerance, successfully identifying several genomic regions and favorable alleles associated with these traits. Complementary study also demonstrated that HS effects vary between early and late ear emergence stages, with significant genotype-specific responses. The significant SNPs and favorable allele combinations provide a foundation for marker-assisted selection. Further work should focus on validating these SNPs using bi-parental mapping populations and evaluate their stability through multi-year and multi-location trials. Favorable alleles can be pyramided into elite wheat lines to accelerate the development of heat tolerant cultivars. Further, identified genes need to be further characterized for their roles in pollen survival and heat tolerance, facilitating their use in breeding programs. Collectively, these efforts will enhance the development of heat-tolerant wheat cultivars, thus contributing to sustainable wheat production under rising global temperatures. Declarations Data availability The datasets used in this study are available from the corresponding authors (Professor Meixue Zhou) upon reasonable request. Supplementary materials Supplementary tables and figures can be found in Supplementary Figures and Supplementary Tables. Acknowledgement We would like to thank the Grains Research and Development Corporation (GRDC) for providing funds and the Tasmanian Graduate Research Scholarship (TGRS, TIA) to provide funds for PhD candidate (ABS). Funding The work was funded by the Grains Research and Development Corporation (WSU2303-001RTX). Author Contributions A.B.S., M.Z., and C.Z., conceptualized and designed the experiments; A.B.S., performed all the experiments, statistical analysis, visualization, and wrote the manuscript; O.S., X.W., and M.S., assisted in harvesting and data collection; C.L., R.K.V., Z.H.C., S.S., M.L., M.Z., and C.Z., reviewed and edited manuscript; C.Z., Z.H.C., S.S., R.K.V., and M.Z., Supervision and fund acquisition. Ethics declarations Competing interests Authors declare no competing interests. This article has not been submitted to another journal simultaneously. All authors contributed to, read, and approved this submitted manuscript in its current form. References Aiqing S, Somayanda I et al (2018) Heat stress during flowering affects time of day of flowering, seed set, and grain quality in spring wheat. Crop Sci 58(1):380–392. https://doi.org/10.2135/cropsci2017.04.0221 Asseng S, Foster I, Turner NC (2011) The impact of temperature variability on wheat yields. 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Proc Natl Acad Sci USA 114(35):9326–9331. https://doi.org/10.1073/pnas.1701762114 Zhao C, Siddique AB et al (2024) A high-throughput protocol for testing heat-stress tolerance in pollen. aBIOTECH 6: 63–71 (2025). https://doi.org/10.1007/s42994-024-00183-3 Zhao K, Tao Y et al (2022) Does temporary heat stress or low temperature stress similarly affect yield, starch, and protein of winter wheat grain during grain filling? J Cereal Sci 103:103408. https://doi.org/10.1016/j.jcs.2021.103408 Additional Declarations No competing interests reported. Supplementary Files SupplemenatryTables.xlsx SuplementaryFigures.pptx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Feb, 2026 Reviews received at journal 27 Jan, 2026 Reviews received at journal 23 Jan, 2026 Reviewers agreed at journal 05 Jan, 2026 Reviewers agreed at journal 05 Nov, 2025 Reviewers invited by journal 02 Oct, 2025 Editor assigned by journal 29 Sep, 2025 Submission checks completed at journal 09 Sep, 2025 First submitted to journal 09 Sep, 2025 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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1","display":"","copyAsset":false,"role":"figure","size":402778,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the field experiment involving pollen collection, PCR-induced heat treatment, staining, microscopy, and scoring of PVS. Pollen from mature anthers was collected in pollen culture media followed by heat treatment in PCR machine. Heat treated pollen was stained for microscopic study followed by pollen viability scoring based on the percentage of viable pollen.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7577963/v1/41f69da0fa57f676b0c1c95a.png"},{"id":93617776,"identity":"e2cb1520-b800-4b26-a56e-ed421db02ff2","added_by":"auto","created_at":"2025-10-15 17:13:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":210940,"visible":true,"origin":"","legend":"\u003cp\u003eMarker distribution, PCA-biplot, and NJ phylogenetic tree showing the population structure of 319 wheat lines. A total of 5171 high quality markers across 21 wheat chromosomes were used to construct the PCA-biplot and NJ phylogenetic trees and showed genotypic relationships among 319 wheat lines.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7577963/v1/da869bc1674d760b8dd603b0.png"},{"id":93617778,"identity":"fdae6f59-77cc-496f-8317-f9a964901069","added_by":"auto","created_at":"2025-10-15 17:13:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":95510,"visible":true,"origin":"","legend":"\u003cp\u003e(A-C) Mean comparison (D-F) frequency distribution of pollen viability score and spikelet fertility of 319 wheat lines classified as ACV, AGG, CCV, and CL. Bar graph refers to the mean value with standard error bar and letter refers to the statistical differences at 5% of significance. Frequency distribution is presented as a stack bar with four different colours representing the lines belonging to four classifications.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7577963/v1/5aadea1c02f7556effd56c9b.png"},{"id":93618196,"identity":"37d0048d-8606-4a38-8ee3-bacb41c4c3ee","added_by":"auto","created_at":"2025-10-15 17:21:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":106690,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eQQ plot (left) and Manhattan plot using the BLINK model showing the significant SNPs associated with pollen viability score and spikelet fertility. Highly significant SNP AVRIG15341 and AVRIG26861 associated with pollen viability score and spikelet fertility were located on chromosome 1B and 5A respectively.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7577963/v1/b622af9ed06916d2fd051958.png"},{"id":93617782,"identity":"b80fe3d3-0c5b-4201-9fcd-3cccbecac4c2","added_by":"auto","created_at":"2025-10-15 17:13:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":209221,"visible":true,"origin":"","legend":"\u003cp\u003eChromosome map showing identified SNPs (this study) and previously reported SNPs/markers associated with different heat stress tolerance traits. Significant SNPs linked to pollen viability score (PVS) and spikelet fertility (SF) are highlighted.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7577963/v1/eaa63827b6b601043b3e2fc8.png"},{"id":93619350,"identity":"0ccdc2ce-7feb-4181-86f7-eb4763954c2e","added_by":"auto","created_at":"2025-10-15 17:37:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":123449,"visible":true,"origin":"","legend":"\u003cp\u003eSingle and combined effect of tolerant and sensitive allele on pollen viability score. For combined SNP, data is from lines have either both repetitive tolerant, or both sensitive alleles. For Pollen viability score: n=319 for AVRIG15341 (160 and 159 for sensitive and tolerant allele), n=314 for AVRIG17433 (282 and 32 for sensitive and tolerant allele), n=317 for AVRIG18520 (291 and 26 for sensitive and tolerant allele). For combined effect of AVRIG15341 and AVRIG17433, n=164 (144 and 20 for sensitive and tolerant allele) and for combined of AVRIG15341 and AVRIG18520, n=151 (142 and 9 for sensitive and tolerant allele).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7577963/v1/e5959fceb7c03af87cc14a22.png"},{"id":93620076,"identity":"504355a2-61d4-41c8-baf3-ecaeb8daba23","added_by":"auto","created_at":"2025-10-15 17:45:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2491323,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7577963/v1/9dfbcd8f-0f2a-4911-836d-3610bf376ae3.pdf"},{"id":93617774,"identity":"54ac3540-55c7-408e-bfc4-f401fca87956","added_by":"auto","created_at":"2025-10-15 17:13:41","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":36799,"visible":true,"origin":"","legend":"","description":"","filename":"SupplemenatryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7577963/v1/3adc4c1cb79633abe9111ccd.xlsx"},{"id":93617804,"identity":"4240b19b-26e7-4e39-bfb9-2edd77733e12","added_by":"auto","created_at":"2025-10-15 17:13:42","extension":"pptx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":35962202,"visible":true,"origin":"","legend":"","description":"","filename":"SuplementaryFigures.pptx","url":"https://assets-eu.researchsquare.com/files/rs-7577963/v1/d37f627b199c2796cda28796.pptx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-Wide Association Study Reveals Major SNPs Associated with Pollen Viability and Spikelet Fertility in Wheat under Heat Stress","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobal climate change is driving an increase in both mean and extreme temperatures, resulting in increased heat stress (HS) in temperate crops such as wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L) (Farhad et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Among various abiotic stresses, HS has become one of the major constraints to wheat productivity and is projected to intensify in the coming decades, due to current climate trends. Yield losses during the reproductive stage are particularly severe, with each 1\u0026deg;C rise in temperature estimated to reduce global wheat yield by approximately 6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9%, and even higher losses reported in major wheat-growing regions such as India (9.1%), Russia (7.8%), and China (2.6%) (Asseng et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hill \u0026amp; Li \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHS affects all aspects of physiological and developmental processes in wheat, especially during flowering and grain development. Elevated temperatures impair photosynthesis, alter metabolic pathways, degrade cellular proteins, and increase the accumulation of reactive oxygen species (ROS), collectively leading to reduced pollen viability and function (Zhang et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Shenoda et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The male gametophyte is especially heat sensitive due to its unicellular structure and rapid development (Ullah et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; F\u0026aacute;bi\u0026aacute;n et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This results in impaired microsporogenesis, pollen tube growth, reduced dehiscence, and poor fertilization (Prasad et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Aiqing et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bokshi et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), ultimately reducing seed set and yield.\u003c/p\u003e\u003cp\u003ePollen viability and grain filling rate are reliable indicators for HS tolerance traits due to its positive correlation with grain number and grain yield (Shenoda et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Khan et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Masthigowda et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, improved pollen viability under HS is increasingly adopted as a key selection trait in breeding programs. Yet, the genetic basis of these traits under reproductive-stage HS remains poorly understood. With advances in next-generation sequencing and the availability of high-density single nucleotide polymorphism (SNP) markers, genome-wide association studies (GWAS) offer a robust approach to dissect the genetic basis of complex traits across diverse germplasm. GWAS have already successfully identified loci associated with several heat stress tolerance traits including spikelet fertility (SF) and seed number (Kumar et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and grain yield components under late-sown or heat-stressed conditions (Hong et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jamil et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, large scale pollen viability phenotyping in wheat under HS conditions has not been exploited, largely due to methodological issues related to such studies. All previous studies predominantly relied on late sowing to imposed HS, which exposes wheat plants to a broader window of reproductive and grain-filling stages, therefore limiting the precision of developmental-stage-specific stress assessment. At the same time, given the highly stage-specific impact of HS on SF (Chaturvedi et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), controlled heat treatments are essential for accurate trait dissection.\u003c/p\u003e\u003cp\u003eIn this work, we overcame the above limitations by using refined phenotyping protocols developed by Zhao et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Two complementary approaches were combined: (i) a novel approach to score the pollen viability using thermal cycler based heat treatment using the anthers collected from field, and (ii) two years of glasshouse experiments applying HS at early and late ear emergence stages to assess SF. Using a diverse panel of 319 wheat lines genotyped with 5,171 high quality SNPs, we conducted GWAS to identify genomic regions associated with heat resilience. Significant SNPs and their associated candidate genes point to the roles in energy metabolism, stress signaling, transcriptional regulations, protein modification, and redox homeostasis, providing novel insights into the molecular mechanisms underlying thermotolerance in wheat. Overall, our work represents one of the first studies on assessing pollen viability in a large wheat panel, opening new prospects for improving HS tolerance in one of the most important staple foods in the world.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Plant materials\u003c/h2\u003e\u003cp\u003eA total of 319 wheat lines were used in this study. These include 127 Australian commercial varieties (ACV), 45 from Australian Grains Genebank (AGG), 62 Chinese commercial varieties (CCV), and 85 Chinese landraces (CL) (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e2.2 Field trials\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eField experiment was conducted at the Mt Pleasant laboratories, Tasmanian Institute of Agriculture, University of Tasmania, Launceston, Tasmania, Australia (41.47\u0026deg;S, 147.14\u0026deg;E) during April 2023 to December 2023. Seeds of each accession were sown manually in a 0.6 m row with spacing of 0.35 m between rows and 15 seeds per row. There were three replications with Randomized Complete Block Design. Field management, including fertilizer use, followed local farmers\u0026rsquo; practices. Manual weeding was carried out when required and proper irrigation was maintained throughout the season.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Phenotyping for pollen viability\u003c/h2\u003e\u003cp\u003eSingle spikes or ear with yellowish mature anthers, which exhibit a split or opening on the adaxial side specifically at the stomium to allow pollen release, were collected and brought to lab for microscopic study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All yellowish anthers were collected and subsequently transferred to the 1.5 mL Eppendorf tube containing 400 \u0026micro;l of pollen culture medium containing 10% sucrose, 2% agarophyte and 5% boric acid (Zhao et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The tube was gently shaken for a uniform distribution of pollen as suspended in pollen culture medium, and 20 \u0026micro;l of suspension solution containing pollens was transferred to 96 PCR well plate. Pollen in 96 PCR plate was incubated at 37\u0026deg;C for 4 hours followed by imaging using 2% I\u003csub\u003e2\u003c/sub\u003e-KI solution. After heat incubation, 1\u0026ndash;2 drops of suspended pollen were placed on a slide and stained with 1 drop of 2% I\u003csub\u003e2\u003c/sub\u003e-KI staining solution. Microscopic analysis was performed using an Olympus CKX53 microscope with 10x magnification, and three images from different regions of each sample were captured using Cell Sens software. For each line, a total of nine images (from three biological replicates) were processed and scored on a scale of 0\u0026ndash;10, where 0 represents images with completely non-viable pollen and 10 represents the highest percentage of viable pollen (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The average pollen viability score (PVS) was calculated from the scores of the nine images and used for statistical and GWAS analyses.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Glasshouse experiment: growing condition, heat treatment, and data recording\u003c/h2\u003e\u003cp\u003eThe glasshouse experiments were conducted over two consecutive years using 319 wheat lines. For each line, four seeds were sown in a single plastic pot (50 mm length \u0026times; 50 mm width \u0026times; 120 mm height) filled with potting mix (potting mixture, Searles, TAS, Australia). The experiments were conducted from May 2023 to December 2024. During the reproductive stage, four heads from each line at four different ear emergence stages were selected and tagged as follows: S\u003csub\u003e1\u003c/sub\u003e (very early booting stage), S\u003csub\u003e2\u003c/sub\u003e (early booting stage), S\u003csub\u003e3\u003c/sub\u003e (early ear emergence stage) and S\u003csub\u003e4\u003c/sub\u003e (very early flowering stage) (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Tagged plants were then transferred to a controlled growth room and subjected to a two-week heat treatment at 37\u0026deg;C. Throughout the treatment period, plants were adequately irrigated to avoid drought stress, ensuring that the observed effects were attributable solely to HS. Day and night temperature was recorded using HOBOconnect thermometer with automatic temperature and humidity recording in every 30 minutes and data are presented in \u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e. After two weeks of heat treatments, plants were transferred to normal growing conditions. After maturity, the tagged spikes (S\u003csub\u003e1\u003c/sub\u003e-S\u003csub\u003e4\u003c/sub\u003e) from each line were harvested separately. Spikelet number per spike and seeds per spike were counted and the number of seeds per 100 spikelets were calculated to determine spikelet fertility.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Genotyping and marker selection\u003c/h2\u003e\u003cp\u003eAll accessions were genotyped using the Illumina iSelect 90K SNP bead chip assay in the Agriculture Victoria sequencing facility at the Department of Energy, Environment and Climate Action (DEECA). This genotyping resulted in 24,955 SNPs which were later filtered to 5,171 high quality SNPs using the minor allele frequency (MAF\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and percentage of missing values (\u0026gt;\u0026thinsp;10).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using R software (version 4.2.3). Genetic relationships among the 319 wheat lines were assessed by constructing a phylogenetic tree based on 5,171 SNP data using the ape, ggplot2, ggtree, and phangorn packages. Trait means for PVS and SF were visualized as bar plots with standard error bars using ggplot2. Group differences were analysed using the Least Significant Difference (LSD) test implemented in the agricolae package, with significance set at the 5% level. Pearson correlation coefficients between PVS and SF at early and late-stage HS were performed using corr package and visualized using ggplot2. The frequency distributions of PVS and SF were generated using the ggplot2 package, based on the mean values for each of the 319 wheat lines and were stacked based on the classification of wheat lines.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Genome-wide association study\u003c/h2\u003e\u003cp\u003eA GWAS was carried out using Genome Association and Prediction Integrated Tools (GAPIT) package in R (Lipka et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). We used four different models namely Bayesian information and linkage-disequilibrium iteratively nested keyway (BLINK) (Huang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), fixed and random model circulating probability unification (FarmCPU) (Liu et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), mixed-linear model (MLM), and generalized linear model (GLM). For each of the models, we used first two PCA, the notional p-value threshold was set to suggest significant SNPs linked to traits of studied to -log\u003csub\u003e10\u003c/sub\u003e(p)\u0026thinsp;\u0026ge;\u0026thinsp;3, and SNP.MAF was set 0.05. Based on the QQ-plot, BLINK model was selected and output from this model were used for Manhattan plot generation, significant SNP identification, and subsequent bioinformatic analysis. Significant SNPs linked to PVS, and SF were visualized using ggplot2 package.\u003c/p\u003e\u003cp\u003ePreviously reported SNPs associated with PVS, and SF were compiled from the WheatQTLdb databases (Singh et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and visualized as chromosome maps using MapChart software (Voorrips 2002). Markers or SNPs with higher phenotypic variance explained (PVE%)\u0026thinsp;\u0026ge;\u0026thinsp;10 were prioritized. A comprehensive list of previously reported markers and SNPs linked to grain yield, grain numbers, spikelet fertility, thousand grain weight, and HS tolerance traits is presented in \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Allelic effect\u003c/h2\u003e\u003cp\u003eFor each significant SNP identified through GWAS, the allele associated with a higher trait value of PVS, and SF was designated as the tolerant allele, while the allele associated with a lower trait value was designated as the sensitive allele. To evaluate the effect of individual and combined SNPs, wheat lines were grouped based on their allele composition at the respective loci. Mean trait values (PVS and SF) were calculated for each allelic group, and pairwise comparisons were conducted using two-sample t-tests to assess statistical significance. To determine the combined effect of alleles from two SNPs, lines were grouped based on the presence or absence of both allele and their mean was followed by two-way t-test. Summary of t test including number of each allele, average values, p values, and differences are presented in \u003cb\u003eTable S3\u003c/b\u003e. Allelic effect was visualized using ggplot2 in R including the distributions, means, error bars, and p-values.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Candidate gene identification\u003c/h2\u003e\u003cp\u003eGenomicRanges (Lawrence et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and rtracklayer (Lawrence et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) packages in R were used to identify the candidate genes around the highly significant SNPs linked to PVS and SF. GFF3 or GTF annotation file for \u003cem\u003eTriticum aestivum\u003c/em\u003e was downloaded from EnsemblPlants website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ftp.ensemblgenomes.ebi.ac.uk/pub/plants/release-61/gff3/triticum_aestivum/\u003c/span\u003e\u003cspan address=\"https://ftp.ensemblgenomes.ebi.ac.uk/pub/plants/release-61/gff3/triticum_aestivum/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) followed by filtering in R using the SNP position for specific chromosome. A window of 2 Mb (downstream and upstream) from the highly significant SNP position was chosen for identifying the candidate genes. Finally, the biological functions of these candidate genes were checked using Biomart package using host of Ensembl (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://plants.ensembl.org/Triticum_aestivum/Info/Index?db=core\u003c/span\u003e\u003cspan address=\"https://plants.ensembl.org/Triticum_aestivum/Info/Index?db=core\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) by selecting \u003cem\u003eTriticum aestivum\u003c/em\u003e as reference genome. Based on their description, identified candidate genes were categorized into broader biological functional category and visualized as bar graphs to determine the number of genes for each of the functions.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Marker distribution and population structure\u003c/h2\u003e\u003cp\u003eGenotyping of 319 wheat lines using Illumina iSelect 90K SNP bead chip assay generated 24,955 SNPs which were filtered based on minor allele frequency (MAF\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and missing values (\u0026gt;\u0026thinsp;10%), resulted in 5,171 high quality SNPs for GWAS analysis. These SNPs were distributed across the 21 chromosomes with the highest number (2,361) located in sub-genome B, followed by sub-genome A (2190), and fewest (620) in sub-genome D (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The number of SNP markers on individual chromosomes ranged from 73 on Chr3D to 386 on Chr2B.\u003c/p\u003e\u003cp\u003ePCA-biplot and NJ-phylogenetic tree using the high-quality SNPs (5,171) indicated association among the 319 wheat lines from different regions, where lines classified as CL showed well separation from the rest of three classifications, ACV, AGG, and CCV (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-C). The first and second PCs were composed largely by genotypes originating from CCV (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Lines classified as ACV showed smaller separation from AGG whereas lines classified as AGG and CCV showed no separation indicating their close genetic relationship.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Mean performance of genotypic group\u003c/h2\u003e\u003cp\u003eMean value of the PVS and SF for the 319 wheat lines, classified into four groups, are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. For PVS, significant differences were observed among the classification groups with the highest mean value in ACV, followed by CCV, and the lowest in CL (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Under HS at early and late stages of ear emergence stage, CL exhibited the lowest mean SF while CCV showed the highest SF (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-C). Significant differences in SF were observed for HS at each of the four individual stages (S\u003csub\u003e1\u003c/sub\u003e-S\u003csub\u003e4\u003c/sub\u003e) where CL and CCV showed the lowest and the highest mean value, respectively (Fig. S3). Additionally, HS during the early stage of ear emergence resulted in lower seed set compared to late stages.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Frequency and Correlation of pollen viability score and spikelet fertility\u003c/h2\u003e\u003cp\u003eThe 319 wheat lines exhibited significant variations in PVS and SF (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-F). Only a few lines exhibited extreme scores, with two lines (0.62% of total lines) showing PVS of 1 and one line (0.31% of total lines) showing PVS of 8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). A total of 83 (26.02%) lines showed moderate PVS (PVS\u0026thinsp;=\u0026thinsp;5), followed by 72 lines (22.57%) with PVS\u0026thinsp;=\u0026thinsp;4 and 62 lines (19.44%) with PVS\u0026thinsp;=\u0026thinsp;6. Moreover, 51 lines (15.99%) showed a high PVS\u0026thinsp;=\u0026thinsp;7. Overall, a greater proportion of ACV showed moderate to high PVS whereas tended to cluster in the low-to- moderate PVS range.\u003c/p\u003e\u003cp\u003eFor SF, the distribution was highly skewed towards low fertility under early ear emergence stage-HS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE) whereas distribution late-stage HS was flatter and slightly more spread out across the fertility values, suggesting less severe damage (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Under early-stage HS, most lines had moderate SF whereas late-stage heat treatment conditions resulted in higher number lines with high SF followed by moderate SF.\u003c/p\u003e\u003cp\u003eTo understand the relationship between PVS and SF of early-stage and late-stage HS, Pearson correlation coefficients were calculated and visualized in Figure S4. PVS showed a moderate but significant positive correlation with both SF at early-stage HS (0.137*, p\u0026thinsp;=\u0026thinsp;0.023) and late-stage HS (0.127, p\u0026thinsp;=\u0026thinsp;0.027) (Figure S4 A, B). When analyzed for each individual ear emergence stage heat treatment, PVS showed significant and positive correlation with S\u003csub\u003e1\u003c/sub\u003e, S\u003csub\u003e2\u003c/sub\u003e, and S\u003csub\u003e3\u003c/sub\u003e stage, with the highest correlation (r\u0026thinsp;=\u0026thinsp;0.18, p\u0026thinsp;=\u0026thinsp;0.002) with S\u003csub\u003e1\u003c/sub\u003e stage (Fig. S4 C-F).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3\u003cb\u003e.4 GWAS for pollen viability score and spikelet fertility\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eGWAS with BLINK model identified 15 significant SNPs associated with PVS and SF (early ear emergence stage) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Ten significant SNPs were detected for PVS, distributed across chromosomes 1B, 2A, 3A, 4A, 4B, 6B, 6D, 7A, and 7B (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Among them, the most significant SNPs, AVRIG15341 on chromosome 1B (-log\u003csub\u003e10\u003c/sub\u003ep\u0026thinsp;=\u0026thinsp;7.23, position 43119347) and AVRIG21657 on chromosome 3A (-log\u003csub\u003e10\u003c/sub\u003ep\u0026thinsp;=\u0026thinsp;5.05, position 106776234), explained 20.31% and 11.90% of phenotypic variation, respectively (PVE%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Other highly significant SNPs included AVRIG25720 on chromosome 4B (\u0026ndash;log\u003csub\u003e10\u003c/sub\u003ep\u0026thinsp;=\u0026thinsp;4.88) and AVRIG32251 on chromosome 6D (\u0026ndash;log\u003csub\u003e10\u003c/sub\u003ep\u0026thinsp;=\u0026thinsp;4.69).\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\u003eList of significant SNPs linked to pollen viability score and spikelet fertility in wheat under heat stress\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePos\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep.value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-log\u003csub\u003e10\u003c/sub\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMAF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003enobs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eeffect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003ePVE (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003ePollen viability score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAVRIG15341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43119347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.90E-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e20.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAVRIG21657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e106776234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.00E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e11.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAVRIG25720\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42028226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.30E-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAVRIG32351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e427650981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.10E-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAVRIG17433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34649309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.80E-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAVRIG34123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3672642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.10E-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAVRIG31940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e663589169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.40E-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWx-B1-SNP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e690269864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.00E-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAVRIG34072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e717567620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.90E-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAVRIG18520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e748854583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.50E-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eSpikelet fertility \u003c/p\u003e\u003cp\u003e(early-stage HS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAVRIG26861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24509229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.20E-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e25.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e17.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAVRIG29108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e595297562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.10E-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e12.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAVRIG25392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e680096353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00E-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-11.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAVRIG25715\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40141266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.80E-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-14.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIWB4227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e368660644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.20E-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-19.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFive significant SNPs were identified for SF under early ear emergence HS treatment. AVRIG26861 (-log\u003csub\u003e10\u003c/sub\u003eP\u0026thinsp;=\u0026thinsp;7.5) was detected on chromosome 5A, showing the highest PVE (17.09%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additional significant SNPs were detected on chromosomes 3D, 4A, 4B, and 5B, including the highly significant AVRIG29108 on chromosome 5B (-log\u003csub\u003e10\u003c/sub\u003ep\u0026thinsp;=\u0026thinsp;4.29).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAll significant SNPs identified from this study and previously reported significant SNPs (PVE\u0026thinsp;\u0026ge;\u0026thinsp;10%) associated with different heat stress tolerance traits in wheat are listed in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e and presented in a physical chromosome map (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Several major SNPs (PVE\u0026thinsp;\u0026gt;\u0026thinsp;10%) associated with different HS traits have been reported near the significant SNPs (this study) associated with PVS and SF.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Allelic effect on pollen viability score and spikelet fertility\u003c/h2\u003e\u003cp\u003eAlleles of significant SNPs associated with PVS, and SF showed significant differences in trait means between favorable (tolerant) and unfavorable (sensitive) alleles (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, \u003cb\u003eTable S3\u003c/b\u003e). Three significant SNPs, AVRIG15341, AVRIG17433, and AVRIG18520, associated with PVS showed significant differences between their tolerant and -sensitive alleles (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B). Alleles of AVRIG15341 showed the highest with mean differences (1.13, p\u0026thinsp;=\u0026thinsp;2.24 \u0026times; 10⁻\u0026sup1;⁴) with mean values of 5.95 and 4.82 for the tolerant and sensitive alleles, respectively. AVRIG18520 displayed a mean difference of 0.84 (p\u0026thinsp;=\u0026thinsp;0.0002) with mean value of 6.17 and 5.32 (Table S3) for the tolerance and the sensitive groups, respectively. Another significant SNP AVRIG17433 also showed significant differences (0.74, p\u0026thinsp;=\u0026thinsp;0.009) between mean value of tolerant (6.06) and sensitive (5.32) alleles.\u003c/p\u003e\u003cp\u003eCombined allelic effects of AVRIG15341 and AVRIG17433 showed significantly higher differences (1.88, p\u0026thinsp;=\u0026thinsp;2.27 \u0026times; 10⁻\u003csup\u003e10\u003c/sup\u003e) with increase mean value of PVS in tolerant allele (6.69) and decrease in sensitive allele (4.81) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, Table S3). The combination of AVRIG15341 and AVRIG18520 also showed highly significant differences (2.05, p\u0026thinsp;=\u0026thinsp;6.88 \u0026times; 10⁻\u003csup\u003e06\u003c/sup\u003e) between mean PVS of tolerant (6.75) and sensitive (4.70) alleles (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, Table S3).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTolerant and sensitive alleles of the two significant SNPs AVRIG268761 and AVRIG29108 showed significant differences on SF (Table S3). Highest mean differences (43.95, p\u0026thinsp;=\u0026thinsp;23.97\u0026times; 10⁻\u003csup\u003e05\u003c/sup\u003e) were observed between the mean SF of tolerant (121.75) and sensitive (77.80) alleles of SNP AVRIG26861. Significant differences (25.43, p\u0026thinsp;=\u0026thinsp;0.0008) were also observed between the mean SF of tolerant (132.17) and sensitive (106.74) allele of AVRIG29108. Combination of alleles from these two SNPs resulted in higher differences (66.56, p\u0026thinsp;=\u0026thinsp;0.0002) increase in mean SF in tolerant allele (141.21) and decrease in sensitive allele (74.65).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Candidate genes associated with pollen viability score and spikelet fertility\u003c/h2\u003e\u003cp\u003eSeven significant SNPs, including four associated with PVS and three associated with SF were used to identify candidate genes located near these SNPs, resulting in 44 candidate genes with a diverse range of functions (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. S5). Based on their functional description, eight candidate genes are involved in energy metabolism, followed by a similar number of candidate genes involved in stress signaling and transcription regulation (Fig. S5). The second dominant group (seven candidate genes) is related to protein modification and folding. Remaining candidate genes were involved in redox homeostasis and detoxification (6), RNA processing and regulation (6), transport and trafficking (6) and cell wall modification (4).\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\u003eList of identified candidate genes using the highly significant SNPs associated with pollen viability score and spikelet fertility.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"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\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\u003eGene_id\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003estart\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eend\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFunctional category\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAVRIG15341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS1B02G059400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41982414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41987242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFormin-like protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCytoskeleton Organization\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS1B02G059800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42347605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42351160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNudix hydrolase 9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNucleotide Metabolism / Detoxification\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eAVRIG21657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS3A02G128600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e106303465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e106307935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNADH-cytochrome b5 reductase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eElectron Transport / Redox Metabolism\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS3A02G130500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107418238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e107424746\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNADPH:adrenodoxin oxidoreductase, mitochondrial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMitochondrial Electron Transport / Redox Enzyme\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS3A02G131000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107732788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e107739023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePentatricopeptide repeat-containing protein chloroplastic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRNA Processing / Organelle Gene Expression\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS3A02G129900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107052572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e107054254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePeroxidase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReactive Oxygen Species (ROS) Scavenging\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS3A02G130000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107076189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e107080320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePhospholipase D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLipid Signaling / Membrane Dynamics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS3A02G128500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e105946479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e105949850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eProbable prolyl 4-hydroxylase 12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePost-translational Modification\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS3A02G128200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e105111804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e105113961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eProtein kinase PINOID 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSignal Transduction / Protein Phosphorylation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003eAVRIG25720\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS4B02G053800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42396312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42401199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAdenylyl cyclase-associated protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ecAMP Signaling / Cytoskeletal Regulation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS4B02G054900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43720172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43729286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAlanine\u0026ndash;tRNA ligase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eProtein Translation / Aminoacylation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS4B02G052700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41555365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41556689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCalcium load-activated calcium channel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCalcium Signaling\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS4B02G053000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41777831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41783720\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDNA-binding family protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDNA Binding / Transcription Regulation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS4B02G052300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41009664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41014024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGlutamate decarboxylase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAmino Acid Metabolism / GABA Biosynthesis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS4B02G054800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43593682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43595526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGlycosyltransferase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCell Wall Biosynthesis / Glycan Modification\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS4B02G052900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41743521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41744496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHistone H2A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eChromatin Structure / DNA Packaging\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS4B02G054200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43061143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43061867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHistone H2B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eChromatin Structure / DNA Packaging\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS4B02G054300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43104629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43107091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePentatricopeptide repeat-containing protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRNA Editing / Organelle RNA Metabolism\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS4B02G052000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40780124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40786295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePhytochrome A, Photoreceptor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLight Perception / Photoreceptor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eAVRIG32351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS6D02G322300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e429383239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e429384224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.4 kDa class III heat shock protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHeat Stress Response / Molecular Chaperone\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS6D02G321600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e429010152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e429011840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3-ketoacyl-CoA synthase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFatty Acid Elongation / Lipid Metabolism\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS6D02G320500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e428475669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e428482477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eATP-dependent DNA helicase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDNA Repair / Replication\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS6D02G318100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e426335420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e426337884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEndoglucanase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCell Wall Degradation / Carbohydrate Metabolism\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS6D02G318500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e426983867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e426984875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eXyloglucan endotransglucosylase/hydrolase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCell Wall Remodeling / Growth Regulation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eAVRIG26861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS5A02G027400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22976211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22985551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMethyl-Binding Domain 6-5AS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDNA Methylation Binding / Epigenetic Regulation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS5A02G030200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26153880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26157442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNucleolar GTP-binding protein 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRibosome Biogenesis / Nucleolar Function\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS5A02G030300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26440090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26449927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSerine/threonine-protein phosphatase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSignal Transduction / Dephosphorylation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS5A02G030500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26466490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26468864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTubulin-specific chaperone A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCytoskeletal Protein Folding\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e\u003cp\u003eAVRIG29108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS5B02G420400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e595676670\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e595685625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDNA-directed RNA polymerase subunit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTranscription Machinery\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS5B02G418600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e594867010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e594872193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInosine-5'-monophosphate dehydrogenase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePurine Biosynthesis / Nucleotide Metabolism\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS5B02G419000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e594913464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e594916850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eObg-like ATPase 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOrganelle Biogenesis / Stress Signaling\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS5B02G419100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e594916943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e594919492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePentatricopeptide repeat-containing protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRNA Stability / Organelle RNA Processing\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS5B02G422000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e597218457\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e597222722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePhytochrome-interacting factor-like bHLH protein, PIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLight Signal Transduction / Transcription Factor\u003c/p\u003e\u003cp\u003eAbiotic Stress Response/ Gene Regulation\u003c/p\u003e\u003cp\u003eGrowth Regulation / Drought Response\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS5B02G420300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e595661801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e595664844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReticulon-like protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEndoplasmic Reticulum Morphology\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS5B02G419900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e595630894\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e595633310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSerpin-Z2A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eProtease Inhibitor / Stress Defense\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS5B02G418700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e594873314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e594876320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTubulin beta chain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCytoskeleton Assembly / Microtubule Dynamics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS5B02G420800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e596493692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e596499867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVacuolar protein sorting-associated protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eProtein Trafficking / Vacuole Function\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eAVRIG25392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS4A02G407600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e680372860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e680376810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMitochondrial substrate carrier family protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMetabolite Transport / Mitochondrial Function\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS4A02G409000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e681710999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e681715958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePHD finger protein ING\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eChromatin Remodeling / Transcription Regulation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS4A02G404500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e678127054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e678131552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eProtein DETOXIFICATION\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eXenobiotic Detoxification / Stress Response\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS4A02G407500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e680370183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e680371906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRAB GTPase homolog H1E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eVesicle Trafficking / Intracellular Transport\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS4A02G407900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e680451111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e680453753\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStomatal Density and Distribution 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStomatal Development / Cell Fate Determination\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS4A02G408900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e681661869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e681680991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003etranscription activators\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGene Expression Regulation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraesCS4A02G409300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e681896628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e681900476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eXyloglucan galactosyltransferase MUR3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCell Wall Biosynthesis / Glycosylation\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\u003eSeveral candidate genes were located near the SNP position associated with PVS. TraesCS1B02G059800 (location 42,347,605\u0026ndash;42,351,160) on chromosome 1B is located 768 kb upstream from the highly significant SNP AVRIG15341 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This gene involves maintenance of cellular homeostasis by removing the potential harmful nucleotide metabolites during pollen development and germination. Another candidate gene, TraesCS3A02G129900, on chromosome 3A (location 107,052,572\u0026ndash;107,054,254) is located\u0026thinsp;~\u0026thinsp;276 kb downstream from the significant SNP AVRIG21657, which is involved in phenolic compound metabolism in pollen pistil and regulates the pollen tube growth. TraesCS4B02G053800 on chromosome 4B (location 42,396,312\u0026ndash;42,401,199) was identified\u0026thinsp;~\u0026thinsp;368 kb downstream from the significant SNP, AVRIG25720, which participates in catalyzing the transfer of sugar moieties to various acceptor molecule, modify their structure, and functions in pollen development and maturation.\u003c/p\u003e\u003cp\u003eThree highly significant SNPs associated with SF led to the identification of 20 candidate genes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, the candidate genes are located more than 1 Mb downstream or upstream from the significant SNPs. For example, TraesCS5A02G027400 on chromosome 5A (location 22,976,211\u0026ndash;22,985,551) is located\u0026thinsp;~\u0026thinsp;1.5 Mb upstream of highly significant SNP AVRIG2681 and is involved in reproductive development and abiotic stress responses. Another candidate gene, TraesCS5A02G030200 (location:26,153,880\u0026ndash;26,157,442), on chromosome 5A is located\u0026thinsp;~\u0026thinsp;1.6 Mb downstream of same significant SNP and plays roles in cell cycle regulation and seed development.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eHeat stress (HS) significantly reduces grain yield by adversely affecting multiple stages of reproductive development, including pollen viability and germination, fertilization, and seed formation (Djanaguiraman et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ullah et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Given the high sensitivity of pollen and seed development to HS, these traits are critical targets for breeding heat-tolerant wheat cultivars. Enhanced pollen survival and improved grain filling rates contribute directly to increased yield potential (Shenoda et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Masthigowda et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the genetic regulation of pollen viability and grain filling under abiotic stress remains poorly understood due to the complex nature of these traits and the varying effects of HS on different phases of pollen development and ear emergence. Assessing phenotypic variation among diverse germplasm is essential to capture differential responses to HS. Previous studies have rarely used PVS as a phenotypic trait under heat stress conditions, and no GWAS has yet been conducted to dissect the genomic regions associated with this critical trait mainly due to the previous methodological limitations for rapid testing of PVS. Additionally, most of the GWAS study used for seed set traits focused on single phage of ear emergence stage while the HS effect on smaller phase of each reproductive stage varies significantly. In this context, the present study aimed to: (i) evaluate the impact of HS on pollen viability and SF; (ii) identify genomic regions associated with pollen survival and higher SF; and (iii) assess the potential integration of these loci into breeding programs for developing HS-tolerant elite wheat lines. The study revealed a stage-specific impact of HS on SF and successfully identified several significant SNPs associated with PVS and SF.\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Novel aspects of phenotyping and screening for pollen viability and spikelet fertility\u003c/h2\u003e\u003cp\u003eTwo independent yet complementary experiments were designed to address the research gaps. The first experiment assessed pollen survival through PVS using a PCR thermal cycler-based heat treatment method developed by Zhao et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) applied to pollen collected from field-grown plants. Most of the pollen study in cereal crops rely on the natural coincidence of heat waves with critical developmental stages. However, variability in flowering time and the unpredictability of weather often result in plants being exposed to HS across a broad range of stages, complicating interpretation. The PCR thermal cycler-based method overcomes these limitations by allowing targeted heat exposure at specific pollen developmental stages. To our knowledge, this is the one of the first GWAS studies to phenotype pollen viability under HS in a larger number of diverse wheat lines using the PCR thermal cycler-based method to identify SNPs associated with PVS. A prior GWAS for wheat pollen viability (Ei-Hanafi et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) analyzed 196 wheat lines grown under field conditions without implying HS, identifying only one SNP associated with pollen viability. While other studies (e.g., Masthigowda et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Khan et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Groli et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) have evaluated pollen viability under HS, these studies did not use GWAS to identify genomic region associated with the trait. Here, PCR thermal cycler-based PVS under heat incubation captured extensive phenotypic variation among wheat lines and allowed the detection of genomic regions associated with PVS, resulting in multiple significant SNPs (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The method can be broadly applied to precisely phenotype pollen viability in other crop species.\u003c/p\u003e\u003cp\u003eIn the glasshouse experiments, we evaluated SF under controlled HS applied at four different stages of ear emergence stage. Individual stages differ in sensitivity to HS, but most of the previous studies considered or targeted single or broad range of ear emergence stage for determining the HS impact such as vegetative (Khan et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), reproductive (Kumar et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), or grain-filling (Zhao et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our study addressed this gap by imposing HS at each specific ear emergence stage to determine its impact on seed set as a form of SF. HS on early ear emergence (S\u003csub\u003e1\u003c/sub\u003e and S\u003csub\u003e2\u003c/sub\u003e) stage caused more sterile spikelets compared to HS at late stages (S\u003csub\u003e3\u003c/sub\u003e, and S\u003csub\u003e4\u003c/sub\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and S3).\u003c/p\u003e\u003cp\u003eThe lower SF in early-stage HS is likely due to the higher impact on pollen viability. Pollen during early ear emergence (booting stage) is at meiosis stage which is more sensitive to HS compared to late pollen development stage, which is less sensitive to HS (Xu et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ullah et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). HS during early booting\u0026mdash;particularly around meiosis\u0026mdash;can disrupt pollen cell division and viability, leading to pollen sterility, reduced pollen numbers, and ultimately lower seed set (Erena et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Browne et al. 2021; Xu et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These findings underscore the importance of precisely targeting specific development stages when assessing heat tolerance and selecting thermotolerant genotypes in crop breeding programs. Our findings also highlight the necessity of conducting GWAS focused on individual phases of ear emergence, which was the key component of this study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Early stage of ear emergence correlated with PVS under HS\u003c/h2\u003e\u003cp\u003ePCR thermal cycler-based PVS from field grown wheat lines, and SF recorded from controlled glasshouse experiment showed variable correlation depending on the stage at which HS was imposed (Fig. S4). The highest correlation was found between PVS and SF when HS was imposed at early pollen development stage (S\u003csub\u003e1\u003c/sub\u003e) (Fig. S4). At the early booting stage (S\u003csub\u003e1\u003c/sub\u003e), pollen mother cells undergo meiosis, which is highly sensitive to HS (Aiqing et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Consequently, SF is more dependent on pollen viability, leading to a stronger correlation, with higher pollen viability at S\u003csub\u003e1\u003c/sub\u003e being associated with increased seed set. However, pollen at the late- stage ear emergence has already completed its development and is undergoing fertilization, making seed set less dependent on pollen survival and more influenced by other mechanisms. Similarly, Xu et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported HS-specific correlations between pollen viability and grain number with HS during early booting stage resulting in less fertile pollen.\u003c/p\u003e\u003cp\u003eWhile our findings are supported by previous studies, the significance of correlation between these two traits is relatively low compared to those studies (Zhao et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Khan et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This is likely due to the greater variation in pollen viability across the 319 wheat lines, which may introduce additional variability in trait relationships. Overall, these results reinforce the importance of precise stress timing in phenotyping and breeding trials for wheat. We thus propose that wheat genotypes maintaining high pollen viability under HS at early booting stage are likely to exhibit better reproductive resilience, which can be prioritized for development of heat-tolerant wheat cultivars.\u003c/p\u003e\u003cp\u003e\u003cb\u003e4.3 Past breeding programs contribute significantly to improving pollen viability score and spikelet fertility under heat stress\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe genetic structure of 319 wheat lines revealed clear separation of ACV, AGG, and CCV from CL (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-C), consistent with previous studies showing divergence between the landraces and modern commercial cultivars (Rufo et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Royo et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Evidence of gene flow between the Chinese landraces (CL) and Chinese commercial cultivars was observed, reflecting the historical use of landraces in developing modern wheat lines adapted to local conditions.\u003c/p\u003e\u003cp\u003eThe genetic differentiation was mirrored at phenotypic level of PVS and SF. Modern cultivars consistently exhibited higher PVS and SF upon exposure to HS compare with China landraces, which generally performed poorly (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig. S3). The superior performance of ACV, CCV, and AGG lines under HS aligns with previous findings linking modern cultivars to improved grain yield and yield-related traits through targeted breeding for reproductive success, abiotic stress tolerance, and nutrient use efficiency (Frankin et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Royo et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Eser et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Frequency distributions further illustrated that ACV and CCV lines were enriched in the mid-to-high PVS and SF classes, whereas CL contributed largely to the lower-performing categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Notably, CCV demonstrated higher SF particularly at the S\u003csub\u003e1\u003c/sub\u003e\u0026ndash;S\u003csub\u003e3\u003c/sub\u003e stages, highlighting breeding gains in reproductive resilience. Nevertheless, the wide phenotypic diversity present in landraces still offers valuable allelic variation that could be harnessed to introduce novel tolerance mechanisms into elite cultivars. For example, some CLs, such as H-002, H-068, and H-045 showed higher PVS (6.44, 7.67, and 6.78 respectively) carrying the favorable alleles of highly significant SNP (AVRIG15341) which can be transferred to elite commercial cultivars in future breeding endeavors. In terms of SF, several CLs, such as H-002, H-058, H-079, and H-111 carried favorable alleles of both significant SNPs (AVRIG26861 and AVRIG29108) with higher SF (194.17, 178.92, 223.86, and 179.44). These lines, which carry favorable alleles contributing to improved PVS and SF, can be utilized in backcrossing and introgression breeding programs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Significant novel SNPs associated with pollen viability score and spikelet fertility\u003c/h2\u003e\u003cp\u003ePollen viability is a key trait for HS tolerance in wheat, but its genetic basis of pollen viability under biotic and abiotic stress conditions is yet to be conducted. To date, one study (El-Hanafi et al. 2022) reported a single SNP associated with pollen viability in field grown wheat lines, and this study is limited by the lack of HS treatment. In contrast, our study is the first to conduct GWAS on pollen viability under HS using a larger, and diverse wheat panel. This study successfully identified 10 significant SNPs associated with PVS across different chromosomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Notably, SNPs AVRIG15341 (1B) and AVRIG21657 (3A) contributed to 20.31% and 11.90% of phenotypic variation, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting major genetic effects of these two SNPs. Several major SNPs and markers have been reported near the highly significant SNP AVRIG15341 on chromosome 1B, including heat tolerance (Qaseem et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), grain yield per plant (Li et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), productive tiller and grain number per plant (Sharma et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and flag leaf and spike temperature, and heat tolerance index for thousand grain weight (Mason et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The second most significant SNP AVRIG21657 on chromosome 3A located closely to several major SNP/markers associated with HS tolerance index (Qaseem et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), grain yield and yield contributing traits (Bennett et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Another significant SNP on chromosome 4B showed close location with major markers linked to heat stress tolerance index (Valluru et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), grain yield (Guan et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), grain filling duration (Tiwari et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and thousand grain weight (Bennett et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSF determines the grain yield in wheat. We identified a highly significant SNP, AVRIG26861 (5A) as a major locus for SF under HS, explaining 25.88% of phenotypic variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This SNP is close to some previously reported major SNPs and markers for normalized difference vegetation index (NDVI) of wheat under HS (Liu et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), stress tolerance index (Qaseem et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), thousand kernel weight (Li et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), temperature depression in flag leaf (Mondal et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), grain yield (Tiwari et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and grain number per spike (Guan et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The physical chromosome map developed in this study clearly demonstrates the clustering of significant markers in this region, supporting the idea of a potential hotspot for SF regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Further fine mapping and functional validation of candidate genes within this region could provide deeper insights into the underlying genetic mechanisms and facilitate the development of heat-resilient wheat cultivars.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Favorable allele combinedly improves PVS and spikelet fertility\u003c/h2\u003e\u003cp\u003eThe present study demonstrated that favorable alleles at key SNP loci significantly improve reproductive traits, PVS and SF, highlighting their genetic control and potential for breeding applications. For PVS, tolerant alleles of three significant SNPs, AVRIG15341 (1B), AVRIG17433 (2A) and AVRIG18520 (2A), individually increased PVS by 1.13, 0.74, and 0.84 units, respectively, compared to their sensitive counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Table S3). Combinations of tolerant alleles showed additive or synergistic effects, confirming the cumulative benefit of pyramiding tolerant alleles to improve PVS under HS. For example, the combination of tolerant allele from AVRIG15341 and AVRIG17433 resulted in a significant increase of PVS (1.88 higher than sensitive alleles) whereas combination of AVRIG15341 and AVRIG18520 resulted in 2.05 higher PVS compared to sensitive alleles (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B; Table S3). Some of the lines for an example, EMS Summito70-8, Eagle, Federation, Halberd, and Heron, carried three favorable alleles from the three significant SNPs and exhibited higher PVS (5.33, 7.67, 7.17, 7.11, and 7). These varieties can be utilized as pre-breeding material for developing climate-resilient wheat variety.\u003c/p\u003e\u003cp\u003eSimilarly, for SF, favorable allele of AVRIG26861 (5A) and AVRIG29108 (5B) possessed significantly higher SF (43.95 and 25.43, respectively) compared to their sensitive alleles (Table S3). Combination of tolerant alleles from these two SNPs further enhance the fertility to 66.56, exceeding both individual effects (Table S3). This finding aligns with previous findings (e.g. Katz et al \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) showing that multiple superior alleles increase spikelet numbers, highlighting the polygenic nature of the trait.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Candidate genes and their predicted function under HS\u003c/h2\u003e\u003cp\u003ePollen survival and seed set under HS are controlled by multiple genes involved in stress perception, signal transduction, and stress responses (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. S5). The highly significant SNPs associated with PVS and SF provided several candidate gene and gene families (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) which are reported to involve energy metabolism, stress signaling and transcription regulation, protein folding and modification, redox homeostasis, and ion transport and trafficking (Fig. S5). These well-known molecular mechanisms contributed to stress tolerance in plants. Here, we discuss the predicted functions and potential roles of a few selected genes in wheat heat tolerance.\u003c/p\u003e\u003cp\u003eOne of the candidate genes, \u003cem\u003eTraesCS1B02G059400\u003c/em\u003e (near SNP AVRIG15341 on 1B) belongs to Formin-like proteins (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Formin-like proteins play a critical role in pollen cell division and cytoskeleton organization. There are 25 candidate formin like genes that have been identified in wheat with their different expressions and function in pollen viability. They are highly expressed in wheat reproductive tissues and show upregulation under stress, contributing to pollen viability by impacting pollen cytoskeleton distribution (Duan et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), supporting actin polymerization, pollen tube growth, and cell wall organization (Koll\u0026aacute;rov\u0026aacute; et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These studies suggest that formin genes may play a key role in maintaining pollen viability under HS, although functional studies in wheat remain limited.\u003c/p\u003e\u003cp\u003eAnother candidate gene for the SNP AVRIG15341 is \u003cem\u003eTraesCS1B02G059800\u003c/em\u003e, which belongs to Nudix hydrolase 9 protein. This protein is involved in several biochemical and physiological processes including metabolic regulation, plant immunity, and stress responses (Wang et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In Arabidopsis and cereals, overexpression of a Nudix gene family improved oxidative stress and is expressed in pollen and stamen (Tanaka et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kondo et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the functional roles of the Nudix hydrolase gene family in reproductive success under abiotic stresses remain largely unexplored.\u003c/p\u003e\u003cp\u003ePeroxidases contribute to ROS detoxification and maintaining ion homeostasis, which is vital for the evolutionary success of most plant species (Singh et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A candidate gene on chromosome 3A (near SNP AVRIG21657) belongs to this family (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Abiotic stresses including HS causes overproduction of ROS in leaf and reproductive organs and disrupts cellular ion-homeostasis (Xu et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Overproduction of ROS leads to programmed cell death of tapetum, resulting in lower nutrient translocation to pollen and causes pollen sterility (Chaturvedi et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Peroxidase is involved in neutralizing the ROS and reducing the oxidative damage. The peroxidase gene family has been reported as essential for anther and pollen development in Arabidopsis (Jacobowitz et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), with the mutation of two peroxidase genes resulting in sterile pollen, disrupted cell wall integrity, and swollen tapetum cells. The stress-related upregulation of peroxidase genes in wheat and other cereals, e.g. drought tolerance (Su et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jiao et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and salinity tolerance (Su et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), highlights potential for improving HS tolerance.\u003c/p\u003e\u003cp\u003eOverall, the identified candidate genes are associated with reproductive and seed development wheat under HS. While related gene families have been validated in Arabidopsis and some cereal crops for their role in enhancing stress tolerance, targeted characterization in wheat is crucial to exploit these genes for improving pollen survival and seed set under HS.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion and Future Prospective","content":"\u003cp\u003eHeat stress, exacerbated by climate change, poses a major challenge to wheat production by impairing reproductive development and reducing yield. This study demonstrated substantial genetic variation in PVS and SF within a diverse wheat panel, highlighting their potential as key selection traits in breeding programs targeting heat resilience. Importantly, this is the first study involving the PCR thermal cycler-based pollen viability screening for HS tolerance, successfully identifying several genomic regions and favorable alleles associated with these traits. Complementary study also demonstrated that HS effects vary between early and late ear emergence stages, with significant genotype-specific responses. The significant SNPs and favorable allele combinations provide a foundation for marker-assisted selection. Further work should focus on validating these SNPs using bi-parental mapping populations and evaluate their stability through multi-year and multi-location trials. Favorable alleles can be pyramided into elite wheat lines to accelerate the development of heat tolerant cultivars. Further, identified genes need to be further characterized for their roles in pollen survival and heat tolerance, facilitating their use in breeding programs. Collectively, these efforts will enhance the development of heat-tolerant wheat cultivars, thus contributing to sustainable wheat production under rising global temperatures.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eData availability\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in this study are available from the corresponding authors (Professor Meixue Zhou) upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eSupplementary materials\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary tables and figures can be found in Supplementary Figures and Supplementary Tables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eAcknowledgement\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the Grains Research and Development Corporation (GRDC) for providing funds and the Tasmanian Graduate Research Scholarship (TGRS, TIA) to provide funds for PhD candidate (ABS).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eFunding\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work was funded by the Grains Research and Development Corporation (WSU2303-001RTX).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eAuthor Contributions\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.B.S., M.Z., and C.Z., conceptualized and designed the experiments; A.B.S., performed all the experiments, statistical analysis, visualization, and wrote the manuscript; O.S., X.W., and M.S., assisted in harvesting and data collection; C.L., R.K.V., Z.H.C., S.S., M.L., M.Z., and C.Z., reviewed and edited manuscript; C.Z., Z.H.C., S.S., R.K.V., and M.Z., Supervision and fund acquisition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eEthics declarations\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no competing interests. This article has not been submitted to another journal simultaneously. All authors contributed to, read, and approved this submitted manuscript in its current form.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAiqing S, Somayanda I et al (2018) Heat stress during flowering affects time of day of flowering, seed set, and grain quality in spring wheat. Crop Sci 58(1):380\u0026ndash;392. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2017.04.0221\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2017.04.0221\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAsseng S, Foster I, Turner NC (2011) The impact of temperature variability on wheat yields. 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J Cereal Sci 103:103408. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jcs.2021.103408\u003c/span\u003e\u003cspan address=\"10.1016/j.jcs.2021.103408\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-growth-regulation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"grow","sideBox":"Learn more about [Plant Growth Regulation](https://www.springer.com/journal/10725)","snPcode":"10725","submissionUrl":"https://submission.nature.com/new-submission/10725/3","title":"Plant Growth Regulation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Triticum aestivum L., heat stress, GWAS, pollen viability, spikelet fertility, reproductive stage heat tolerance","lastPublishedDoi":"10.21203/rs.3.rs-7577963/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7577963/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHeat stress during the reproductive stage significantly reduces wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) productivity, primarily by impairing pollen viability and grain filling rate. This study employed genome-wide association study (GWAS) using 5,171 high-quality single nucleotide polymorphisms (SNPs) across 319 diverse wheat lines to identify genomic regions associated with pollen viability and spikelet fertility under reproductive-stage heat stress. Pollen viability was assessed from field-grown plants subjected to high temperature in a controlled setup using a thermal cycler. Spikelet fertility was evaluated in two years with heat treatment applied at the four different ear emergence stages. The wheat population showed significant genotypic and phenotypic variation. Chinese landraces formed a distinct cluster from other groups and generally exhibited lower pollen viability and spikelet fertility compared to Chinese commercial varieties. Pollen viability score showed moderate but significant correlation with spikelet fertility, especially at early ear-emergence stage. GWAS identified 15 significant SNPs associated with these two traits. Notably, AVRIG15341 on chromosome 1B and AVRIG21657 on chromosome 3A explained 20.31% and 11.9% of phenotypic variation in pollen viability, respectively. AVRIG26861 on chromosome 5A explained 17.09% of phenotypic variation in spikelet viability. This is the first report on SNPs linked to wheat pollen viability scores under heat stress. Combination of favorable alleles showed higher trait performance, suggesting strong potential for use in marker-assisted selection. Our pioneering study provides new insights into the genetic architecture of reproductive-stage heat tolerance in wheat and offers valuable genetic resources for breeding heat-resilient cultivars.\u003c/p\u003e","manuscriptTitle":"Genome-Wide Association Study Reveals Major SNPs Associated with Pollen Viability and Spikelet Fertility in Wheat under Heat Stress","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 17:13:36","doi":"10.21203/rs.3.rs-7577963/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-02T06:24:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-27T19:37:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-23T11:54:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87931886210608153920482984096010852150","date":"2026-01-05T18:01:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300198047079684951376969202221363663300","date":"2025-11-05T10:02:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-02T07:33:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-29T06:50:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-10T03:46:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant Growth Regulation","date":"2025-09-10T02:24:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-growth-regulation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"grow","sideBox":"Learn more about [Plant Growth Regulation](https://www.springer.com/journal/10725)","snPcode":"10725","submissionUrl":"https://submission.nature.com/new-submission/10725/3","title":"Plant Growth Regulation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0c2893e5-7e16-47de-aae3-f7a0c3304e82","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T00:53:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 17:13:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7577963","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7577963","identity":"rs-7577963","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}