Pan-genome Analysis Reveals Hidden Diversity and Selection Signatures of Auxin Response Factors (ARFs) Associated with Breeding in Barley

Pan-genome Analysis Reveals Hidden Diversity and Selection Signatures of Auxin Response Factors (ARFs) Associated with Breeding in Barley | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pan-genome Analysis Reveals Hidden Diversity and Selection Signatures of Auxin Response Factors (ARFs) Associated with Breeding in Barley Kenan Tan, Zhenru Guo, Thorsten Schnurbusch This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7399203/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Apr, 2026 Read the published version in Theoretical and Applied Genetics → Version 1 posted 5 You are reading this latest preprint version Abstract Auxin Response Factors (ARFs) play a pivotal role in regulating plant growth and development, yet their evolutionary dynamics and functional divergence remain poorly understood in barley ( Hordeum vulgare L.) In this study, we conducted a comprehensive genome-wide analysis of the ARF gene family across a 76-accession barley pan-genome. By integrating gene presence/absence variation (PAV) and copy number variation (CNV), phylogeny, expression profiling, transposable element (TE)-mediated regulation, and selection signatures, we characterized 1,911 ARF-coding genes and their structural, transcriptional, and functional variation at the population level. Phylogenetic analysis revealed lineage-specific expansion and dynamic duplications, particularly within the HvARF13 clade. Co-expression networks and tissue-resolved transcriptomes showed that many HvARFs are preferentially expressed in inflorescence and meristematic tissues. Selective sweep and haplotype analyses identified HvARF3 as a candidate gene under selection during European barley breeding. A favorable haplotype of HvARF3 , enriched in European cultivars, was significantly associated with increased grain size and weight, demonstrating the utility of pan-genome-enabled frameworks for accelerating gene-trait association and candidate gene discovery. This study highlights the power of multi-omics integration in decoding gene family complexity and provides valuable insights for functional genomics and trait improvement in cereal crops. ARF gene family barley pan-genome multi-omics integration gene family evolution selective breeding Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Auxin response factors (ARFs) are a family of plant-specific transcription factors that mediate auxin signaling by binding to auxin-responsive elements in gene promoters, thereby regulating a broad array of developmental and physiological processes. These include organogenesis, embryogenesis, apical dominance, root and shoot development, and reproductive differentiation (Cancé et al. 2022 ). Typically, ARF proteins harbor three functional domains: a conserved N-terminal B3 DNA-binding domain (DBD), a variable middle region that acts as either a transcriptional activator (glutamine-rich, Q-rich) or repressor (serine/threonine/proline-rich, STP-rich), and a C-terminal PB1 (Phox and Bem1) domain that facilitates homo- and hetero-dimerization with other ARFs or Aux/IAA proteins (Chandler 2016 ). This domain architecture is remarkable conserved from early land plants to higher angiosperms, underscoring the fundamental roles ARFs play across plant evolution (Li et al. 2023 ). In numerous species—such as Arabidopsis thaliana , rice ( Oryza sativa L.), maize ( Zea mays L.), and wheat ( Triticum aestivum L.)— ARF genes have been systematically identified and functionally characterized (Gidhi et al. 2021 ; Man et al. 2025 ; Okushima et al. 2005 ; Wang et al. 2007 ; Xing et al. 2011 ). These studies revealed the involvement of specific ARF members in regulating key agronomic traits such as grain size, inflorescence architecture, and stress responses. For example, OsARF3 , OsARF4 , OsARF6 , ZmARF24 , ZmARF2 , ZmARF17 , and TaARF25 have been reported to regulate grain/kernel traits (Gao et al. 2023 ; Gu et al. 2023 ; Hu et al. 2018 ; Jia et al. 2021 ; Qiao et al. 2021 ; Wang et al. 2024 ). Notably, OsARF3 is also involved in balancing the trade-off between abiotic and biotic stress responses. OsARF12 , OsARF17 , OsARF25 , and OsARF10 have been shown to affect stigma development (Guo et al. 2022 ; Zhao et al. 2022 ). ZmARF28 can pleiotropically modify plant architecture by single amino acid substitution, its mutant shows reduced plant height, increased leaf number and changed inflorescence identity (Prigge et al. 2025 ). These functional insights demonstrate the evolutionary versatility and their potential for agronomic improvement of ARF genes across cereals. Despite its agronomic and historical significance, as one of the first domesticated crops (Lister et al. 2018 ), barley ( Hordeum vulgare L.) has not yet seen comprehensive systematic characterization of its ARF gene family. Although ARF genes have been implicated in regulating key yield and fitness traits in related cereals, their characterization in barley remains limited, highlighting a significant knowledge gap. Given the known roles of ARF genes in other species, elucidating the diversity and function of the ARF protein family in barley could reveal previously unknown regulators of important developmental processes and contribute directly to crop improvement efforts. Recent advances in high-throughput sequencing and the development of pan-genome and pan-transcriptome resources have transformed plant genomics by enabling species-wide resolution of genetic diversity (Marks et al. 2021 ). Traditional single-reference-based analyses are insufficient to capture the full extent of gene family variation, as they often miss presence/absence variants (PAVs), copy number variations (CNVs), or population-specific alleles. Pan-genomic resources—such as those recently generated for barley—offer a more comprehensive view of structural and functional diversity at both the gene and genome levels (Jayakodi et al. 2024 ; Jayakodi et al. 2020 ). In parallel, transcriptome datasets across tissues, genotypes, and even at single-cell resolution now make it possible to explore gene expression divergence, regulation, and potential adaptation at an unprecedented scale. In this study, we performed a comprehensive, population-scale analysis of the ARF protein family in barley using integrated multi-omics data. We systematically identified and annotated genes and proteins of the ARF family across the 76 barley pan-genomes, revealing approximately 30% more members than in previous studies. By constructing high-resolution phylogenies and characterizing structural variation, expression patterns, cis -regulatory elements, and selective signals, we explored the evolutionary and functional dynamics of ARFs in barley. Our findings revealed significant differences in natural selection pressures and genomic diversity across distinct barley populations. Notably, we identified a superior allele of HvARF3 that appears to have undergone selection in European cultivars, with significant association to increased grain size and weight. These findings demonstrate how integrative pan-genome and transcriptome analyses can illuminate both conserved gene family evolution and novel, population-specific adaptations with agronomic relevance. Our results provide a comprehensive panoramic view of the ARF protein family and establish a foundation for future studies on auxin signaling, inflorescence development, and molecular breeding in barley. Methods Identification of ARF genes in 76 barley pan-genome accessions Genomic and annotated protein/coding sequences for 76 barley pan-genome accessions were retrieved from a previously published study (Jayakodi et al., 2024 ). To comprehensive identify members of the ARF protein family, all candidate proteins containing ARF-related domains—including PTHR31384 from PANTHER; PF02362, PF06507, and PF02309 from Pfam, along with the B3 (PS50863) and PB1 (PS51745) domains—were identified using HMMER v3.1b2 (Finn et al. 2011 ) with an E-value threshold of < 1e − 5. Domain annotations were verified by comparing against known ARF proteins from rice and Arabidopsis using BLAST + v2.13.0 (Camacho et al. 2009 ). Identified ARF proteins were named based on their phylogenetic relationships to rice ARFs, using the barley cultivar Barke as the reference genome. To determine presence/absence variations (PAVs) and copy number variations (CNVs), we extracted 5 kb flanking sequences of loci where annotated ARF genes were missing using Bedtools v2.29.2 (Quinlan and Hall 2010 ). These regions were aligned to the Morex reference genome using Minimap2 v2.16 (Li 2018 ). Large deletions were visualized using MUMmer v3.23 (Delcher et al. 2003 ), while smaller deletions were confirmed through BLAST alignments. Phylogenetic tree construction and Ka / Ks calculation A total of 1,911 ARF protein sequences from the pan-genome were aligned using MAFFT v7.490 (Katoh and Standley 2013 ). Phylogenetic relationships were inferred using IQ-TREE v2.2.2.6 (Nguyen et al. 2015 ) with the maximum likelihood method under default parameters. For interspecies comparison, ARF protein sequences from barley (cv. Barke), rice (cv. Nipponbare), and Arabidopsis (Col-0) were used to construct a combined phylogenetic tree using the same procedure. Conserved motifs were identified using MEME v5.3.3 (Bailey et al. 2015 ). Phylogenetic trees and conserved domain architectures were visualized and edited using the iTOL platform ( https://itol.embl.de/ ). To assess selection pressures, pairwise nonsynonymous to synonymous substitution rate ratios ( Ka/Ks ) were calculated using ParaAT2.0 , based on aligned ARF ortholog sequences from the 76 barley genotypes. Statistical significance of Ka/Ks differences between groups (e.g., wild vs. domesticated accessions) was tested using a two-tailed Student’s t-test. Transcriptome data processing and co-expression analysis Raw transcriptome data from five tissues—caryopsis, coleoptile, inflorescence, root, and shoot—across 20 barley cultivars were retrieved from the recently published barley pan-transcriptome dataset (accession: PRJEB64639). Transcript abundance was quantified using Kallisto v0.46.1 (Bray et al. 2016 ), yielding TPM values and raw read counts. To minimize genotype-specific effects in the functional analysis of ARF genes, genotype-corrected residual expression values ( \(\:Residua{l}_{G}\) ) were calculated (see below) and used for downstream analyses. Weighted gene co-expression network analysis (WGCNA) was performed using the WGCNA R package with default parameters and a correlation threshold of 0.7. Gene Ontology (GO) enrichment was conducted using clusterprofiler v3.2.1 (Yu et al. 2012 ), with GO annotations sourced from the IPK barley database ( https://galaxy-web.ipk-gatersleben.de/ ). Single-cell expression patterns of ARF genes were extracted from a recently published barley inflorescence single-cell transcriptomic resource BARVISTA ( https://www.plabipd.de/projects/hannah_demo/BARVISTA/tissue_no_download.html ) (Demesa-Arevalo et al. 2025 ). Proportion of variance explained (PVE) and residual calculation To quantify the contribution of genotype and tissue to gene expression variation, a linear mixed model (LMM) was fitted for each ARF gene using the following equation: $$\:y=\mu\:+G+T+\epsilon\:$$ Where \(\:y\) represents the expression value (TPM), \(\:G\sim\:N\left(0,{\sigma\:}_{G}^{2}\right)\:\) is the random genotype effect, \(\:T\sim\:N\left(0,{\sigma\:}_{T}^{2}\right)\) is the tissue effect, and \(\:\epsilon\:\) is the residual error. The proportion of variance explained by genotype and tissue was calculated as: $$\:PV{E}_{G}=\frac{{\sigma\:}_{G}^{2}}{{\sigma\:}_{G}^{2}+{\sigma\:}_{T}^{2}+{\sigma\:}_{\epsilon\:}^{2}},\:PV{E}_{T}=\frac{{\sigma\:}_{T}^{2}}{{\sigma\:}_{G}^{2}+{\sigma\:}_{T}^{2}+{\sigma\:}_{\epsilon\:}^{2}}$$ Genotype- and tissue-corrected residuals were then extracted using: $$\:Residua{l}_{G}=y-\widehat{\mu\:}-\widehat{T},\:Residua{l}_{T}=y-\widehat{\mu\:}-\widehat{G}$$ All calculations were performed in R using lme4 package (Bates et al. 2015 ). TE identification and cis -eQTL analysis TEs were identified in 76 barley genomes using panHiTE (Hu et al. 2025 ), and further confirmed using TE annotations from the IPK panBarlex database ( https://panbarlex.ipk-gatersleben.de/# ). Only high confidence TEs present in both of datasets were retained. To determine TE insertions near ARF genes, 100 kb flanking regions were analyzed using bedtools . For cis-eQTL analysis, 1 Mb upstream and downstream of each ARF gene was defined as the cis-regulatory region. \(\:Residua{l}_{T}\) values were used as gene expression input in MatrixEQTL v2.3 (Shabalin 2012 ) under default settings. Selective sweep detection To explore potential domestication-related selection signals, raw genomic data were obtained from a recent barley population study (Guo et al. 2024 ). A 1 Mb window surrounding each ARF gene was analyzed. Genomic regions were extracted using bcftools v1.15.1, and downstream processing and visualization were carried out using Perl under the guidance of the original study’s first author (Guo et al. 2024 ). Variant calling and amino acid substitution prediction Whole-genome assemblies of the 76 barley accessions were re-mapped to the Morex V2 reference genome (Figure, S4). Genomes were split into 1 kb fragments using seqkit v0.9.1 (Shen et al. 2016 ) to simulate FASTQ reads. Variant calling was performed using BWA v0.7.17 (Li 2013 ), SAMtools v1.16.1(Li et al. 2009 ), and BCFtools v1.15.1. SNPs and InDels were annotated using SnpEff (Cingolani et al. 2012 ). Amino acid substitutions in ARF proteins were predicted with PPVED v1.0 (Gou et al. 2022 ). Correlation analysis between substitution scores and domestication degrees were calculated using Pearson’s correlation coefficient in R. Genome-wide association study (GWAS) and haplotype analysis GWAS was performed to identify genomic regions associated with 16 agronomic traits (awn length; culm dry weight; fertility rate; final spikelet number; grain area; grain length; grain number per spikelet; grain weight; grain width; heading date; plant height; potential spikelet number; pre-anthesis tip degeneration; spike length; spike weight; thousand kernels weight). All phenotypic data are collected at IPK Gatersleben in 2018, 2019 and 2020. Total 442 representative genotypes were used in this study; population structure was fixed. Some parts of the phenotypic data have been published in previous work (Huang et al. 2023 ; Kamal et al. 2022 ). After quality filtering, 33,138,758 SNPs (minor allele frequency > 0.1; missing rate < 10%) were retained. GWAS was conducted using PLINK v1.90b6.9 (Purcell et al. 2007 ) and GEMMA v0.98.5 (Team et al. 2024 ). Haplotype blocks around ARF genes (± 1 Mb) were analyzed using pipeline shared in github ( https://github.com/TKNsama/R ). Since some phenotypic data are unpublished, only trait-associated SNPs proximal to ARF loci are reported here. dCAPS marker design and validation To genotype the functional HvARF3 R475G variant, a dCAPS marker was developed targeting a SNP located at chr3H:494973445 (based on the MorexV2 reference genome). Primers (Supplementary data 5) were designed using the online tools dCAPS Finder 2.0 and Primer3, incorporating a mismatch to generate a specific restriction site for the enzyme DdeI (New England Biolabs Inc). PCR amplification was carried out using Taq DNA Polymerase (Qiagen, Cat. No. 201203) following the manufacturer’s recommended protocol. Amplified products were subsequently digested with DdeI and separated on a 2.5% agarose gel. Results Identification and validation of ARF genes and their PAVs/CNVs across 76 barley pangenome assemblies To comprehensively catalog ARF -coding genes in barley, we employed a domain-based gene discovery approach by integrating three major protein family databases—Pfam (PF02362, PF06507, PF02309), ProSiteProfiles (PS50863, PS51745), and PANTHER (PTHR31384) —using the recently released barley pan-genome v2 database (BPGv2) (Jayakodi et al. 2024 ). All candidate sequences were further validated against the PanBARLEX online resource ( https://panbarlex.ipk-gatersleben.de/# ) to ensure annotation accuracy. Given that the conventional reference genome (Morex) lacks two ARFs due to genotype-specific presence/absence variations (PAVs), we selected the European modern spring barley cultivar Barke for gene nomenclature. Barke harbors all identified ARF -coding genes and has been extensively characterized at the transcriptomic level (Coulter et al. 2022 ; Guo et al. 2025 ). To maintain cross-species consistency, gene names were assigned based on the wheat gene nomenclature system (Boden et al. 2023 ) and aligned with orthologous ARF genes in rice (Wang et al. 2007 ). Compared to previous single-genome based analyses (Tombuloglu 2019 ), our pan-genomic approach uncovered approximately 30% more ARF genes, underscoring the power of multi-genotype analysis to capture greater gene diversity. We retained 26 full-length ARFs containing at least one conserved ARF-related domain in ≥ 10% of accessions for downstream analysis. Based on their frequency across the 76 barley genomes, these genes were classified into core, soft-core, and shell categories following established dispensability thresholds (Marroni et al. 2014 ) (Fig. 1 A, E). Notably, approximately ~ 80.7% of ARFs belonged to the core or soft-core categories, being present in 76 or 75 genomes, respectively. This high evolutionary conservation highlights the essential roles of ARFs for barley development and fitness. An exception was the HvARF13 clade, which exhibited extensive structural and presence variation. While HvARF13-3 was highly conserved (found in 74 genotypes), HvARF13-1 and HvARF13-2 were present in only 47 and 59 accessions, respectively. Although HvARF13-1 and HvARF13-2 are shorter than HvARF13-3, all three retain an intact B3 DNA-binding domain in at least some genotypes and are expressed at transcriptome level, suggesting potential functionality (Fig. 1 B). This structural variation reflects dynamic gene duplication events and copy number variations (CNVs) within the barley ARF family. To validate these PAVs, we extracted 5–10 kb flanking genomic sequences and performed local alignment. A 479 bp deletion was identified at the 3′ end of HvARF13-2 in several genotypes, including Morex (Figure S1 B), likely explaining its absence in previous annotations. A larger deletion was detected for HvARF13-1 in some accessions, leading to complete gene loss (Figure S1 A). Additionally, a truncated gene (HORVU.BARKE.PROJ.7HG00820280) lacking conserved domains was likely misannotated and was excluded after sequence and domain verification. To explore functional implications of ARF protein diversity, we examined the composition of the middle region in each Barke ARF protein, combining domain annotation with phylogenetic analysis using rice homologs (Fig. 1 C). We specifically quantified glutamine (Q) and serine/threonine/proline (STP) residue enrichment, which are indicative of functional differentiation (Chandler 2016 ; Shen et al. 2010 ). ARFs with Q-rich middle regions were classified as Class I (typically transcriptional activators; e.g. HvARF19 ), whereas those enriched in STP residues were categorized as Class II or Class III (transcriptional repressors; e.g. HvARF1 ) (Fig. 1 D). In total, 26 well-characterized ARFs with high-confidence annotations were selected for our subsequent analyses. Phylogenetic and Ka / Ks analyses reveal evolutionary dynamics and selection pressures on ARF proteins in barley To elucidate the evolutionary relationships within the ARF gene family in barley, we performed a comprehensive phylogenetic analysis based on full-length ARF protein sequences derived from 76 barley genotypes. Despite extensive sequence variation among accessions, all ARF proteins could be reliably assigned to their respective clades, consistent with ARF classifications established in other plant species (Wang et al. 2007 ) (Fig. 2 A). Comparative genome analyses across Arabidopsis , rice, and barley revealed substantial changes in ARF copy number, with both gene losses and expansions observed. Notably, several Arabidopsis ARFs, including AtARF9 , AtARF13 , AtARF14 , AtARF23 , AtARF12 , AtARF22 , AtARF15 , AtARF10 , AtARF20 , and AtARF21 , were completely absent in both rice and barley (Fig. 2 A), suggesting lineage-specific gene loss events following the divergence of grasses and dicots (Aravind et al. 2000 ; Finet et al. 2013 ). This pattern likely reflects evolutionary streamlining of the ARF repertoires in grasses and possibly the adoption of distinct auxin signaling strategies (McSteen 2010 ). Interestingly, the AtARF5 clade retained a single-copy ortholog across all three species tested here, indicative of strong purifying selection and a critical, possibly irreplaceable, role in plant development. In contrast, barley showed expanded ARF families compared to rice, particularly in HvARF4 , HvARF8 , HvARF10 , and HvARF13 . Among these, HvARF4-1 and HvARF4-2 represent a rare example of full-length gene duplication, with both paralogs retaining all key functional domains, including the B3 DNA-binding domain and PB1-like domain (Fig. 2 A, 2 B). Other duplicated genes, such as members of the HvARF13 clade, often exhibited partial structures lacking one or more conserved domains, suggesting potential subfunctionalization or neofunctionalization. Notable, most of the observed barley-specific ARF duplications clustered within the AtARF17/16/10-like clade, which includes AtARF10 , AtARF16 , AtARF17 , and OsARF18 —a group known to function as negative regulators of plant development and seed germination (Huang et al. 2016 ; Liu et al. 2007 ; Liu et al. 2013 ; Mallory et al. 2005 ). The increasing copy number of this clade from Arabidopsis to rice and barley suggests an adaptive expansion, potentially driven by environmental selection pressures or functional diversification. To investigate the variation among ARF protein in barley, all ARF protein sequences were analyzed and are presented in a schematic diagram (Fig. 2 B) (Cancé et al. 2022 ). The DBD domain is relatively conserved in class I ARFs, whereas almost all class III ARFs contain a short insertion within this region, suggesting greater structural stability and conservation in class I. Notably, HvARF25, although belonging to class I, has a relatively high content of glutamine (Q) residues but does not form a poly-Q structure. Most class III ARFs possess the B3 repression domain (BRD, motif pattern: R/KLFG) (Choi et al. 2018 ), a characteristic indicator of repressor function. However, HvARF4-1 and HvARF4-2 are exceptions: instead of the canonical R/KLFG motif, each contain a single amino acid substitution that produces a modified KIFG motif, potentially indicating a functional divergence. To assess selection constraints acting on ARF proteins, we calculated the ratio of non-synonymous ( Ka ) to synonymous ( Ks ) substitution rates ( Ka/Ks ) across different barley populations. Ka/Ks values below, equal to, or above 1 are generally interpreted as indicative of purifying, neutral, or positive selections, respectively (Kimura 1985 ). Overall, most ARF proteins exhibited Ka/Ks ratios well below 1, indicating strong purifying selection acting to conserve gene function across genotypes (Fig. 2 C). This evolutionary constraint underscores the essential regulatory roles of ARF proteins during barley development and adaptation. However, HvARF8-2 was a notable exception, with Ka/Ks ratios frequently exceeding 1. The lack of a B3 domain in this gene may have relaxed functional constraints, permitting rapid evolution under natural or selective breeding. This gene may represent an example of lineage-specific divergence or a recently neofunctionalized paralog. Further comparison of Ka/Ks ratios among wild barley, landraces, and modern cultivars revealed a statistically significant decline from wild accessions to cultivars, indicative of intensified purifying selection during domestication and breeding. A similar trend was observed across geographic regions: accessions from East Asia exhibited lower Ka/Ks values than those from European and West Asia (Fig. 2 D), suggesting differential selection pressures linked to regional adaptation. Interestingly, HvARF12 deviated from this general pattern, showing elevated Ka/Ks ratios in cultivars compared to wild or landrace accessions. Its rice ortholog, OsARF12 , is functionally redundant with OsARF17 and OsARF25 , and has been implicated in auxin-mediated regulation of tiller angle, as well as in responses to viral infection and disease resistance (Li et al. 2020 ) (Zhao et al. 2022 ). These findings raise the possibility that HvARF12 has undergone adaptive evolution, possibly driven by trade-offs between agronomic performance and stress tolerance. Taken together, these results reveal diverse evolutionary dynamics within the ARF family in barley, shaped by natural selection, domestication, and breeding. The combination of phylogenetic, structural, and Ka/Ks analyses provides key insights into functional divergence and adaptation of ARF genes in cereal crops. TE identification, classification, and their regulatory influence on ARF gene expression Transposable elements (TEs) are mobile genetic elements capable of within the genome, frequently inducing mutations or altering gene expression. Representing a substantial fraction of plant genomes, TEs have been recognized as major contributors to genome evolution, structural variation, and transcriptional regulation (Bennetzen and Wang 2014 ; Hirsch and Springer 2017 ; Lisch 2013 ). To explore the potential cis -regulatory effects of TEs on ARF gene expression, we surveyed TE insertions within 100 kb upstream and downstream of all ARF genes across 76 barley accessions, using a high-confidence TE annotation dataset. In total, 516 TEs were identified near ARF loci, encompassing diverse TE families, with retrotransposons constituting the predominant class (Fig. 3 A). Interestingly, TE insertions were not randomly distributed: significantly enrichment was observed within the 50 kb upstream of many HvARF genes. A secondary enrichment peak occurred around 20 kb upstream of several loci (Fig. 3 B). Given their proximity to core promoter regions and their established roles in modulating chromatin accessibility and transcription factor binding (Hirsch and Springer 2017 ; Lönnig and Saedler 2002 ), these upstream TEs are strong candidates for modulating ARF gene expression via cis -regulatory mechanisms. The observed spatial enrichment pattern suggests non-random TE insertion or retention, possibly reflecting selection for regulatory function. To investigate the potential regulatory impact of nearby TEs, we conducted expression quantitative trait loci (eQTL) mapping (Druka et al. 2010 ) in a panel of 20 representative genotypes by integrating high-quality pan-genome and pan-transcriptome datasets (Guo et al. 2025 ). To disentangle the complex sources of expression variation, we partitioned the variance in gene expression into components attributable to genotype, tissue, and their interaction. Among the 26 ARF genes, 22 displayed sufficient expression variability and were retained for further analysis. Variance component analysis revealed that tissue and tissue × genotype interactions were the primary drivers of expression variation, consistent with the known tissue-specific expression profiles of ARF genes (Fig. 3 C). To mitigate tissue effects in subsequent association analyses, we used residual expression values—obtained by regressing out tissue effects—instead of raw expression levels. Cis -eQTLs were identified using SNPs located within a 1 Mb window flanking each ARF gene. As anticipated, genes with high genotype or genotype-by-tissue explained variance (PVE) —such as HvARF20 , HvARF13-3 , HvARF18 , and HvARF25 —displayed strong eQTL signals (–log₁₀P > 3) (Fig. 3 D, E). Notably, HvARF25 exhibited a distinct eQTL peak located 23–32 kb upstream of its transcriptional start site, coinciding with the position of a retrotransposon. This suggests that the TE insertion may act as a cis -regulatory element modulating HvARF25 expression in a genotype-dependent manner (Fig. 3 B, D). A particularly interesting outlier was HvARF11 , which showed the most significant eQTL signal despite having low genotype and genotype-by-tissue PVE, and lacking annotated TEs within its vicinity (Fig. 3 D, E). This implies the presence of a potent, localized regulatory variant—potentially an unannotated TE insertion, structural variation, or epigenetic modification—contributing substantially to HvARF11 expression, independent of broader genomic background effects. Overall, TE located near ARF genes are likely to play a regulatory role contributing to genotype-specific expression differences. Pan-transcriptomic analysis reveals tissue-specific expression and functional networks of ARF genes To elucidate the functional roles and tissue-specific regulatory dynamics of ARF genes in barley, we integrated population-scale pan-genomic (Jayakodi et al. 2024 ) and pan-transcriptomic (Guo et al. 2025 ) datasets. A total of 22 ARF genes with consistently detectable expression across five major tissues in 20 representative genotypes were retained for downstream analyses, minimizing noise from low-abundance transcripts. ARF genes displayed pronounced tissue-specific expression patterns, with the inflorescence exhibiting the highest genotype-dependent expression variability, followed by roots (Fig. 4 A). Notably, 12 of the 22 ARF genes were highly and specifically expressed in developing inflorescences at Waddington stages W6–W7 (Waddington et al. 1983 ), a critical window for spikelet and floret meristem differentiation. Seven genes showed peak expression in seedling roots, while HvARF2 and HvARF6 were predominantly expressed in shoots tissues (Fig. 4 B). HvARF22 exhibited unique expression in caryopsis, and no ARF genes demonstrated tissue specificity in embryonic structures such as the embryo, mesocotyl, or seminal root. To further resolve the spatial expression dynamics within inflorescence tissue, we utilized a high-resolution single-cell RNA-seq atlas of barley inflorescence (Morex) (Demesa-Arevalo et al. 2025 ). This analysis uncovered cell-type-specific expression patterns for individual ARF genes (Fig. 4 C). HvARF4-1 and HvARF4-2 were broadly expressed across diverse cell types, indicating general developmental roles. In contrast, HvARF14 , HvARF15 , and HvARF11 were enriched in vascular-associated tissues, with HvARF11 being specifically expressed during the transition from triple-spikelet meristem to floret meristem—an essential developmental checkpoint. Additionally, HvARF6 , HvARF12 , and HvARF25 were predominantly expressed in the adaxial epidermis, suggesting possible roles in organ polarity or boundary formation. These temporally and spatially resolved expression profiles support the hypothesis that ARF genes operate in multiple, partially distinct regulatory programs during inflorescence development. To further explore the potential regulatory networks associated with those ARF gene, we conducted a weighted gene co-expression network analysis (WGCNA). Residual expression values (adjusted for genotypic effects) were used as input to focus on tissue-specific transcriptional relationships. For each ARF gene, co-expressed gene modules were identified, and Gene Ontology (GO) enrichment analysis was performed (Fig. 4 D). As expected for transcription factors, most HvARF -associated modules were enriched in functions related to RNA/DNA binding and transcriptional regulation. Notably, HvARF3 , HvARF14 , and HvARF15 were co-expressed with genes involved in fungal defense responses. This observation is consistent with the functional identification of their rice orthologs ( OsARF3 , OsARF3a , OsARF3b ), which have been reported to modulate disease resistance pathways. Overall, our discovery indicates that ARF genes exhibit tissue and cell-type-specific expression patterns and are involved in distinct regulatory modules that govern a range of biological processes—from developmental patterning in inflorescence to biotic stress responses. These results insight a foundational framework for future functional studies and genetic manipulation strategies for optimizing inflorescence architecture or resistance in barley. Pangenome-based multi-omics analysis identifies HvARF3 as a key target of selection during barley breeding To investigate the role of ARF gene variation in barley domestication and improvement, we performed selective sweep analyses alongside functional predictions of amino acid (AA) substitutions. Selection signals were examined within genomic regions flanking each ARF gene using a population panel comprising wild barley, landraces, and modern cultivars (Figure S3 ). Among the 26 ARF loci, five— HvARF3 , HvARF8-2 , HvARF21 , HvARF15 , and HvARF4-1 —exhibited strong selection signatures, with marked allelic differentiation between wild and domesticated accessions (Fig. 5 A). These findings suggest that these loci may have undergone positive selection due to their contributions to agronomic traits favored during domestication and breeding. To assess the potential functional relevance of naturally occurring amino acid substitutions, we predicted the effects of all non-synonymous variants (allele frequency > 0.1) using the Plant Protein Variation Effect Detector (PPVED) (Gou et al. 2022 ). Pearson correlation analyses between amino acid variants and domestication scores identified several candidate residues under selection (Fig. 5 B). Four variants had PPVED scores > 0.5, indicating a high probability of functional impact, while the remaining substitutions were predicted to be functionally neutral. The most notable protein variant was a non-synonymous substitution in HvARF3 (R475G), which had the highest predicted functional impact across the ARF protein family. HvARF3, along with HvARF14 and HvARF15, belongs to the AtARF3/4 orthologous clade. All three members of this subclade displayed strong selection signals despite being located on different chromosomes, indicating potential convergent selection acting on this regulatory lineage (Fig. 5 B). Phylogenetic analysis of ARF proteins across the 76 barley accessions further supported this observation. While most ARF clades contained a mixture of wild and domesticated accessions, HvARF3, HvARF14, and HvARF15 formed lineage-specific clusters corresponding to domestication groups (Fig. 5 C). This convergence of selective sweep signals, phylogenetic divergence, and predicted functional variant highlights the AtARF3/4 subclade—particular HvARF3—as a key target of directional selection linked to agronomic adaptation. To investigate the phenotypic consequences of ARF gene variation, we performed genome-wide association studies (GWAS) focused on key yield components, particularly grain size and thousand-kernel weight (TKW). SNPs within 1 Mb of each ARF gene were analyzed using a significance threshold of –log₁₀(P) > 2. While most associations showed modest effect sizes, consistent with the pleiotropic and conserved roles of ARF genes, we observed consistent association peaks near HvARF3 and HvARF4-1 , particularly for TKW and grain size (Figure S2 C, Table S4). To validate the association of HvARF3 with grain traits, we performed haplotype analysis based on SNPs within the gene body and its flanking regulatory regions. Seven major haplotypes were identified and could be classified into two distinct groups based on the R475G substitution: the “R475” allele, predominantly present in European cultivars, and the “G475” allele, more frequently observed in Asian accessions. Accessions harboring the R475 allele exhibited significantly higher grain area (GA) and TKW, two key traits under strong selection during modern barley breeding (Fig. 5 D). Notably, this positive association between the R475 allele and increased grain size and weight remained significant even after correcting for the nud locus (responsible for the naked vs. hulled grain phenotype), indicating that allelic variation at HvARF3 contributes to grain trait variation independently of hull type (Figure S2 B). To facilitate marker-assisted selection, a derived cleaved amplified polymorphic sequence (dCAPS) marker was developed to discriminate between the R and G alleles at the HvARF3 R475G site (Figure S5). Collectively, this result provides indirect but convincing evidence that HvARF3 regulates grain traits and has undergone selection during barley improvement. Discussion In this study, we present a novel method of gene family analysis by utilizing abundant, publicly available genomic and transcriptomic resources to comprehensively investigate the ARF gene family in barley. Through the integration of multi-omics datasets, we performed a series of systematic analyses including PAV detection, evolutionary dynamics, TE-mediated eQTL mapping, transcriptome analysis, and domestication signal identification. A summary of ARF genes in barley has been created by using different data source and methods, which provide insights and clues for future cloning and analysis. Those analyses, which were previously impossible with a single reference genome, demonstrate the advantage of pan-genome, pan-transcriptome, and resequencing data in mining gene family complexity across diverse genotypes in barley. Recent advances in barley multi-omics resources, including high-resolution pan-genomes and population-wide transcriptomes, offer unprecedented opportunities for gene family research. However, the challenge lies in effective data integration and interpretation. Our study demonstrates that combining these layers enables the discovery of functional alleles and regulatory mechanisms otherwise inaccessible. For instance, the identification of HvARF3 R475G as a putative domestication-related variant associated with increased grain size and TKW underscores the value of population-scale pan-genomic approaches. Conventional gene family studies relying on single genomes often fail to capture intraspecies diversity— especially in crops like barley with global distribution and rich germplasm pools. Pan-genomics strategies, in contrast, enable the identification of both structural and nucleotide-level polymorphisms, accelerating trait mapping and gene discovery. Previously, genes such as Ppd-H1 and VRN-1 were identified through large-scale germplasm screenings (Santra et al. 2009 ; Turner et al. 2005 ). Today, pan-genomic datasets allow for earlier and more targeted discovery. In this study, we revealed geographic stratification of HvARF3 haplotypes—paralleling patterns observed in well-characterized flowering regulators like wheat Ppd-D1 —highlighting possible regional adaptation and breeding selection. The ARF gene family comprises a moderate number of transcription factors with essential roles in auxin-mediated growth and development. While several ARFs have been functionally characterized in Arabidopsis and rice, their roles in barley remain underexplored. Phylogenetic analyses revealed lineage-specific expansions and contractions. Notable, the AtARF17/16/10 clade exhibited multiple duplications in barley, particularly within the HvARF13 subgroup. HvARF13-3 remains highly conserved, while HvARF13-1 and HvARF13-2 show CNVs and sequence divergence—suggesting ongoing sub-functionalization or neofunctionalization (Fig. 1 B). These variations are more frequent in wild and landrace accessions, implying adaptive relevance in non-domesticated environments. Comparative genomics also revealed notable differences in ARF gene retention and loss. For example, rice contains two paralogs, OsARF23 and OsARF24 (formerly OsARF1), involved in grain formation via rice morphology determinant (RMD) regulation (Li et al. 2014 ; Waller et al. 2002 ). These genes have no direct orthologs in barley (Fig. 2 A), indicating species-specific divergence. Conversely, barley harbors a duplicated OsARF4 ortholog pair— HvARF4-1 and HvARF4-2 —both retaining full-length domain structures. Given OsARF4’s known role in negatively regulating grain size in rice (Hu et al. 2018 ), its barley counterparts represent strong candidates for functional studies. Despite previous findings that several rice ARF genes (e.g., OsARF3 , OsARF4 , OsARF6 , OsARF14 , OsARF15 ) are associated with grain-related traits (Gu et al. 2023 ; Hu et al. 2018 ; Qiao et al. 2021 ), most ARF genes in barley do not show direct expression in the caryopsis (Fig. 4 B). Instead, their strong expression in inflorescence tissues suggests that these ARF genes may regulate grain development indirectly by modulating early reproductive organogenesis and floral architecture, which likely affects final grain size or weight. Another example is HvARF3 , the selected gene during breeding, it shows high gene expression levels in the developing inflorescence while no expression in grain. In the single-cell transcriptome dataset of barley inflorescences, HvARF3 is specifically expressed in dividing cells, suggesting a potential role in regulating cell division and meristem maintenance during early stages of inflorescence development. But according to GWAS haplotype analysis and performance of its ortholog OsARF3 , it most likely affects grain size and grain traits, too. Interestingly, the favorable haplotypes of HvARF3 are rare in Asian barley accessions. This may be attributable to a regional genetic bottleneck or local adaptation, which limited the introgression of the advantageous haplotype. Alternatively, certain HvARF3 alleles may exhibit functional trade-offs similar to its rice ortholog OsARF3 , which has been shown to mediate a balance between heat tolerance and pathogen resistance (Gu et al. 2023 ). Furthermore, the Ka / Ks rate showed that HvARF3 was higher in Asian accessions, indicating that the purifying selection was not as strong as that in Europe. Thus, environmental difference between European and Asian growth conditions could have shaped regional selection pressures acting on different HvARF3 variants and reflect different breeding strategy. In conclusion, our integrative analysis highlights the critical role of ARF genes in barley development and demonstrates the utility of multi-omics strategies in gene family research. By resolving patterns of structural variation, expression regulation, and selection, we identify promising candidates like HvARF3 for targeted breeding. These findings contribute not only to our understanding of auxin signaling but also to practical efforts aimed at improving grain yield and adaptability in barley. The dCAPS marker developed for the HvARF3 R475 variant will facilitate marker-assisted selection in future breeding programs. Declarations Data Availability 76 pan-genome sequence and annotation data are from previous publication (Jayakodi et al. 2024); Genotypic data of wild barley/ domesticated barley are from previous publication (Guo et al. 2024); Genotypic data of GWAS will be released in upcoming publication; some phenotypic data (FSN, GY, HD, MYP, PH, PSN, SA) are from previous publication (Kamal et al. 2022), while rest of them will be released in upcoming publication. The re-mapping VCF files created by this study has been deposited at Zenodo (https://zenodo.org/records/16778621). Author contributions: KT: funding acquisition, conceptualization, data analysis, visualization, and writing — original draft preparation. ZG: conceptualization, investigation, validation, and writing — review and editing. TS: funding acquisition, supervision, and writing — review and editing. Acknowledgments: We thank the public databases for providing valuable resources that supported this study. We also thank Roop Kamal, Yongyu Huang for phenotypic data collection. This work was supported by the Chinese Scholarship Council (CSC) to K.T., and by the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) through infrastructure and core funding. Competing interests The authors declare no competing interests. References Aravind L, Watanabe H, Lipman DJ, Koonin EV (2000) Lineage-specific loss and divergence of functionally linked genes in eukaryotes. 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Omics: a journal of integrative biology 16:284-287 Zhao Z, Yin X, Li S, Peng Y, Yan X, Chen C, Hassan B, Zhou S, Pu M, Zhao J (2022) miR167d-ARF s Module Regulates Flower Opening and Stigma Size in Rice. Rice 15:40 Supplementary Files Supplementalfigure.docx Supplementarydata.xlsx Cite Share Download PDF Status: Published Journal Publication published 20 Apr, 2026 Read the published version in Theoretical and Applied Genetics → Version 1 posted Editorial decision: Major revisions 13 Oct, 2025 Reviewers agreed at journal 12 Sep, 2025 Reviewers invited by journal 12 Sep, 2025 Editor assigned by journal 18 Aug, 2025 First submitted to journal 18 Aug, 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. 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1","display":"","copyAsset":false,"role":"figure","size":57801,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and characterization of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eARF\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e genes in the barley pan-genome.\u003c/strong\u003e (A) Distribution of PAVs for each \u003cem\u003eARF\u003c/em\u003e gene across 76 barley accessions. Genes were categorized as core (present in all 76 genomes), soft-core (present in ≥95% of genomes), or shell (present in 10%-95% of genomes). (B) Amino acid sequence alignment of HvARF13-1, HvARF13-2 and HvARF13-3. Different colors represent different amino acid residuals. (C) Phylogenetic tree of \u003cem\u003eARF\u003c/em\u003e genes in the cv. Barke, classified according to gene structure. (D) Comparison of Q residual counts versus S/T/P residuals in the middle regions of ARF proteins in cv. Barke. (E) Heatmap showing the presence or absence of each \u003cem\u003eARF\u003c/em\u003e gene across the 76 barley genotypes.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7399203/v1/b02acf0b5aebe63182b51a69.png"},{"id":91871658,"identity":"c1554033-14a6-40de-a3a2-2d5a8f6799c9","added_by":"auto","created_at":"2025-09-22 13:57:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84513,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogenetic relationships, protein structure and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eKa\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e/\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eKs\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e analysis of ARF proteins.\u003c/strong\u003e (A) Phylogenetic tree of ARFs identified from 76 barley genotypes (left) and a comparative tree including ARF proteins from barley (cv. Barke), rice and \u003cem\u003eArabidopsis\u003c/em\u003e (right). Conserved protein domains of ARF proteins from Barke, rice and \u003cem\u003eArabidopsis \u003c/em\u003eare shown to the right of the phylogenetic tree. (B) Schematic organization of ARF-MR domains in barley cv. Barke. DBD, DNA-binding domain containing the B3 domain and nearby dimerization domains; MR, middle region; PLD, prion-like domain; EAR motif, putative EAR motif characterized by the short sequence LxLxL/DLNxxP; BRD, B3 repression domain; B3 domain, the core sequence of DBD domain. (C) Distribution density of \u003cem\u003eKa/Ks\u003c/em\u003e values for each ARF protein and the overall \u003cem\u003eKa/Ks\u003c/em\u003e distribution across the ARF protein family. (D) \u003cem\u003eKa/Ks\u003c/em\u003eratios of individual ARF proteins, including comparisons among cultivars, landraces and wild barley, as well as among geographic groups: East Asia, Europe and West Asia.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7399203/v1/c6061155ee6ad302a77210dc.png"},{"id":91871659,"identity":"329c7722-084d-44c7-8871-a6763cb3149a","added_by":"auto","created_at":"2025-09-22 13:57:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96773,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClassification of TEs and eQTL analysis of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eARF\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e genes.\u003c/strong\u003e (A) Number and classification of TEs located within 100 kb flanking regions of each \u003cem\u003eARF\u003c/em\u003egene across 76 barley genotypes. (B) TE types, counts, and positional distribution around \u003cem\u003eARF\u003c/em\u003e genes. Density plot shows the enrichment of TEs relative to \u003cem\u003eARF\u003c/em\u003e gene loci. (C) Proportion of expression variance explained each \u003cem\u003eARF\u003c/em\u003e gene by genotype, tissue and genotype × tissue interaction. The waffle chart displays the average contribution of each factor across all genes. (D) \u003cem\u003ecis\u003c/em\u003e-eQTL signals for \u003cem\u003eARF\u003c/em\u003egenes within a ±1 Mb region. (E) Volcano plot of eQTL-associated SNPs. The x-axis represents effect size, and the y-axis shows -log10(P-value).\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7399203/v1/f549b3c918facdf84f441a6f.png"},{"id":91871662,"identity":"17d79eb9-b182-48b3-afd1-de8442d9512b","added_by":"auto","created_at":"2025-09-22 13:57:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":355510,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePan-transcriptome analysis of each \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eARF\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e gene expression.\u003c/strong\u003e (A) PCA of \u003cem\u003eARF\u003c/em\u003e gene expression across all genotype-tissue combinations. PC1 and PC2 explain 41.5% and 24.1% of the total variance, respectively. Colors indicate tissue types; shaded areas highlight genotype-dependent variation within each tissue. (B) Summary heatmap showing expression levels of \u003cem\u003eARF\u003c/em\u003e genes across five tissues and 20 genotypes. (C) Single-cell transcriptomic visualization of \u003cem\u003eARF\u003c/em\u003e gene expression in barley inflorescence. Color intensity represents relative expression level; scale bar = 214 µm. (D) GO enrichment analysis of gene co-expression modules associated with each \u003cem\u003eARF\u003c/em\u003e gene (co-expression threshold ≥ 0.7). Color scale indicates -log\u003csub\u003e10\u003c/sub\u003e(P-value) normalized by Z-score.\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7399203/v1/6b6510455c95bdfaca4537c9.png"},{"id":91871660,"identity":"6d5f83df-b17b-4885-8f03-7bbd6df8c892","added_by":"auto","created_at":"2025-09-22 13:57:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":166157,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGermplasm analysis of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eARF\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e genes and haplotype analysis of HvARF3.\u003c/strong\u003e(A) SNP matrix showing putative selective sweep regions for five \u003cem\u003eARF\u003c/em\u003e genes. Different colors indicate nucleotide polymorphisms at each site across genotypes. (B) Bar plot showing PPVED-predicted functional impact scores (left y-axis) and Pearson’s correlation coefficients (right y-axis) for amino acid substitutions across ARF proteins. (C) Phylogenetic trees of HvARF3, HvARF14 and HvARF15, with wild (purple), landrace (blue), and cultivar (green) accessions indicated. (D) Haplotype network and geographic distribution of HvARF3 across the barley diversity panel.\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-7399203/v1/2a2c111f9a9495747080e07b.png"},{"id":107927663,"identity":"fd5b771e-c294-429d-8cfb-d85774f5982b","added_by":"auto","created_at":"2026-04-27 16:00:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1212786,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7399203/v1/86f6a307-cdc8-4753-9951-d06eed76eabb.pdf"},{"id":91871672,"identity":"d867d143-a4df-482f-bec6-dd423c5a6fbd","added_by":"auto","created_at":"2025-09-22 13:57:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5809170,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalfigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-7399203/v1/a348c2314d2794bec5f3370a.docx"},{"id":91871665,"identity":"06fa253d-f02e-4e7d-a3d0-935a2a9fb5d4","added_by":"auto","created_at":"2025-09-22 13:57:21","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":50187,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7399203/v1/88f1bdcb6c31d410482a72ae.xlsx"}],"financialInterests":"","formattedTitle":"Pan-genome Analysis Reveals Hidden Diversity and Selection Signatures of Auxin Response Factors (ARFs) Associated with Breeding in Barley","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAuxin response factors (ARFs) are a family of plant-specific transcription factors that mediate auxin signaling by binding to auxin-responsive elements in gene promoters, thereby regulating a broad array of developmental and physiological processes. These include organogenesis, embryogenesis, apical dominance, root and shoot development, and reproductive differentiation (Canc\u0026eacute; et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Typically, ARF proteins harbor three functional domains: a conserved N-terminal B3 DNA-binding domain (DBD), a variable middle region that acts as either a transcriptional activator (glutamine-rich, Q-rich) or repressor (serine/threonine/proline-rich, STP-rich), and a C-terminal PB1 (Phox and Bem1) domain that facilitates homo- and hetero-dimerization with other ARFs or Aux/IAA proteins (Chandler \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This domain architecture is remarkable conserved from early land plants to higher angiosperms, underscoring the fundamental roles ARFs play across plant evolution (Li et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn numerous species\u0026mdash;such as \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, rice (\u003cem\u003eOryza sativa\u003c/em\u003e L.), maize (\u003cem\u003eZea mays\u003c/em\u003e L.), and wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.)\u0026mdash;\u003cem\u003eARF\u003c/em\u003e genes have been systematically identified and functionally characterized (Gidhi et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Man et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Okushima et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Xing et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). These studies revealed the involvement of specific ARF members in regulating key agronomic traits such as grain size, inflorescence architecture, and stress responses. For example, \u003cem\u003eOsARF3\u003c/em\u003e, \u003cem\u003eOsARF4\u003c/em\u003e, \u003cem\u003eOsARF6\u003c/em\u003e, \u003cem\u003eZmARF24\u003c/em\u003e, \u003cem\u003eZmARF2\u003c/em\u003e, \u003cem\u003eZmARF17\u003c/em\u003e, and \u003cem\u003eTaARF25\u003c/em\u003e have been reported to regulate grain/kernel traits (Gao et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gu et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jia et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Qiao et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Notably, \u003cem\u003eOsARF3\u003c/em\u003e is also involved in balancing the trade-off between abiotic and biotic stress responses. \u003cem\u003eOsARF12\u003c/em\u003e, \u003cem\u003eOsARF17\u003c/em\u003e, \u003cem\u003eOsARF25\u003c/em\u003e, and \u003cem\u003eOsARF10\u003c/em\u003e have been shown to affect stigma development (Guo et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cem\u003eZmARF28\u003c/em\u003e can pleiotropically modify plant architecture by single amino acid substitution, its mutant shows reduced plant height, increased leaf number and changed inflorescence identity (Prigge et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These functional insights demonstrate the evolutionary versatility and their potential for agronomic improvement of \u003cem\u003eARF\u003c/em\u003e genes across cereals. Despite its agronomic and historical significance, as one of the first domesticated crops (Lister et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), barley (\u003cem\u003eHordeum vulgare\u003c/em\u003e L.) has not yet seen comprehensive systematic characterization of its \u003cem\u003eARF\u003c/em\u003e gene family. Although \u003cem\u003eARF\u003c/em\u003e genes have been implicated in regulating key yield and fitness traits in related cereals, their characterization in barley remains limited, highlighting a significant knowledge gap. Given the known roles of \u003cem\u003eARF\u003c/em\u003e genes in other species, elucidating the diversity and function of the ARF protein family in barley could reveal previously unknown regulators of important developmental processes and contribute directly to crop improvement efforts.\u003c/p\u003e\u003cp\u003eRecent advances in high-throughput sequencing and the development of pan-genome and pan-transcriptome resources have transformed plant genomics by enabling species-wide resolution of genetic diversity (Marks et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Traditional single-reference-based analyses are insufficient to capture the full extent of gene family variation, as they often miss presence/absence variants (PAVs), copy number variations (CNVs), or population-specific alleles. Pan-genomic resources\u0026mdash;such as those recently generated for barley\u0026mdash;offer a more comprehensive view of structural and functional diversity at both the gene and genome levels (Jayakodi et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jayakodi et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In parallel, transcriptome datasets across tissues, genotypes, and even at single-cell resolution now make it possible to explore gene expression divergence, regulation, and potential adaptation at an unprecedented scale.\u003c/p\u003e\u003cp\u003eIn this study, we performed a comprehensive, population-scale analysis of the ARF protein family in barley using integrated multi-omics data. We systematically identified and annotated genes and proteins of the ARF family across the 76 barley pan-genomes, revealing approximately 30% more members than in previous studies. By constructing high-resolution phylogenies and characterizing structural variation, expression patterns, \u003cem\u003ecis\u003c/em\u003e-regulatory elements, and selective signals, we explored the evolutionary and functional dynamics of ARFs in barley. Our findings revealed significant differences in natural selection pressures and genomic diversity across distinct barley populations. Notably, we identified a superior allele of \u003cem\u003eHvARF3\u003c/em\u003e that appears to have undergone selection in European cultivars, with significant association to increased grain size and weight. These findings demonstrate how integrative pan-genome and transcriptome analyses can illuminate both conserved gene family evolution and novel, population-specific adaptations with agronomic relevance. Our results provide a comprehensive panoramic view of the ARF protein family and establish a foundation for future studies on auxin signaling, inflorescence development, and molecular breeding in barley.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of ARF genes in 76 barley pan-genome accessions\u003c/h2\u003e\u003cp\u003eGenomic and annotated protein/coding sequences for 76 barley pan-genome accessions were retrieved from a previously published study (Jayakodi et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To comprehensive identify members of the ARF protein family, all candidate proteins containing ARF-related domains\u0026mdash;including PTHR31384 from PANTHER; PF02362, PF06507, and PF02309 from Pfam, along with the B3 (PS50863) and PB1 (PS51745) domains\u0026mdash;were identified using \u003cem\u003eHMMER\u003c/em\u003e v3.1b2 (Finn et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) with an E-value threshold of \u0026lt;\u0026thinsp;1e\u0026thinsp;\u0026minus;\u0026thinsp;5. Domain annotations were verified by comparing against known ARF proteins from rice and \u003cem\u003eArabidopsis\u003c/em\u003e using \u003cem\u003eBLAST\u0026thinsp;+\u003c/em\u003e\u0026thinsp;v2.13.0 (Camacho et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Identified ARF proteins were named based on their phylogenetic relationships to rice ARFs, using the barley cultivar Barke as the reference genome. To determine presence/absence variations (PAVs) and copy number variations (CNVs), we extracted 5 kb flanking sequences of loci where annotated ARF genes were missing using \u003cem\u003eBedtools\u003c/em\u003e v2.29.2 (Quinlan and Hall \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). These regions were aligned to the Morex reference genome using \u003cem\u003eMinimap2\u003c/em\u003e v2.16 (Li \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Large deletions were visualized using \u003cem\u003eMUMmer\u003c/em\u003e v3.23 (Delcher et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), while smaller deletions were confirmed through BLAST alignments.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhylogenetic tree construction and\u003c/b\u003e \u003cb\u003eKa\u003c/b\u003e\u003cb\u003e/\u003c/b\u003e\u003cb\u003eKs\u003c/b\u003e \u003cb\u003ecalculation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 1,911 ARF protein sequences from the pan-genome were aligned using MAFFT v7.490 (Katoh and Standley \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Phylogenetic relationships were inferred using \u003cem\u003eIQ-TREE\u003c/em\u003e v2.2.2.6 (Nguyen et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) with the maximum likelihood method under default parameters. For interspecies comparison, ARF protein sequences from barley (cv. Barke), rice (cv. Nipponbare), and \u003cem\u003eArabidopsis\u003c/em\u003e (Col-0) were used to construct a combined phylogenetic tree using the same procedure. Conserved motifs were identified using MEME v5.3.3 (Bailey et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Phylogenetic trees and conserved domain architectures were visualized and edited using the iTOL platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://itol.embl.de/\u003c/span\u003e\u003cspan address=\"https://itol.embl.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To assess selection pressures, pairwise nonsynonymous to synonymous substitution rate ratios (\u003cem\u003eKa/Ks\u003c/em\u003e) were calculated using \u003cem\u003eParaAT2.0\u003c/em\u003e, based on aligned ARF ortholog sequences from the 76 barley genotypes. Statistical significance of \u003cem\u003eKa/Ks\u003c/em\u003e differences between groups (e.g., wild vs. domesticated accessions) was tested using a two-tailed Student\u0026rsquo;s t-test.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eTranscriptome data processing and co-expression analysis\u003c/h3\u003e\n\u003cp\u003eRaw transcriptome data from five tissues\u0026mdash;caryopsis, coleoptile, inflorescence, root, and shoot\u0026mdash;across 20 barley cultivars were retrieved from the recently published barley pan-transcriptome dataset (accession: PRJEB64639). Transcript abundance was quantified using Kallisto v0.46.1 (Bray et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), yielding TPM values and raw read counts. To minimize genotype-specific effects in the functional analysis of \u003cem\u003eARF\u003c/em\u003e genes, genotype-corrected residual expression values ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Residua{l}_{G}\\)\u003c/span\u003e\u003c/span\u003e) were calculated (see below) and used for downstream analyses. Weighted gene co-expression network analysis (WGCNA) was performed using the \u003cem\u003eWGCNA\u003c/em\u003e R package with default parameters and a correlation threshold of 0.7. Gene Ontology (GO) enrichment was conducted using \u003cem\u003eclusterprofiler\u003c/em\u003e v3.2.1 (Yu et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), with GO annotations sourced from the IPK barley database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://galaxy-web.ipk-gatersleben.de/\u003c/span\u003e\u003cspan address=\"https://galaxy-web.ipk-gatersleben.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Single-cell expression patterns of \u003cem\u003eARF\u003c/em\u003e genes were extracted from a recently published barley inflorescence single-cell transcriptomic resource BARVISTA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.plabipd.de/projects/hannah_demo/BARVISTA/tissue_no_download.html\u003c/span\u003e\u003cspan address=\"https://www.plabipd.de/projects/hannah_demo/BARVISTA/tissue_no_download.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Demesa-Arevalo et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eProportion of variance explained (PVE) and residual calculation\u003c/h3\u003e\n\u003cp\u003eTo quantify the contribution of genotype and tissue to gene expression variation, a linear mixed model (LMM) was fitted for each ARF gene using the following equation:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:y=\\mu\\:+G+T+\\epsilon\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:y\\)\u003c/span\u003e\u003c/span\u003e represents the expression value (TPM), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:G\\sim\\:N\\left(0,{\\sigma\\:}_{G}^{2}\\right)\\:\\)\u003c/span\u003e\u003c/span\u003eis the random genotype effect, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:T\\sim\\:N\\left(0,{\\sigma\\:}_{T}^{2}\\right)\\)\u003c/span\u003e\u003c/span\u003e is the tissue effect, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\epsilon\\:\\)\u003c/span\u003e\u003c/span\u003e is the residual error. The proportion of variance explained by genotype and tissue was calculated as:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:PV{E}_{G}=\\frac{{\\sigma\\:}_{G}^{2}}{{\\sigma\\:}_{G}^{2}+{\\sigma\\:}_{T}^{2}+{\\sigma\\:}_{\\epsilon\\:}^{2}},\\:PV{E}_{T}=\\frac{{\\sigma\\:}_{T}^{2}}{{\\sigma\\:}_{G}^{2}+{\\sigma\\:}_{T}^{2}+{\\sigma\\:}_{\\epsilon\\:}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eGenotype- and tissue-corrected residuals were then extracted using:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:Residua{l}_{G}=y-\\widehat{\\mu\\:}-\\widehat{T},\\:Residua{l}_{T}=y-\\widehat{\\mu\\:}-\\widehat{G}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAll calculations were performed in R using \u003cem\u003elme4\u003c/em\u003e package (Bates et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eTE identification and\u003c/b\u003e \u003cb\u003ecis\u003c/b\u003e\u003cb\u003e-eQTL analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTEs were identified in 76 barley genomes using \u003cem\u003epanHiTE\u003c/em\u003e (Hu et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and further confirmed using TE annotations from the IPK panBarlex database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://panbarlex.ipk-gatersleben.de/#\u003c/span\u003e\u003cspan address=\"https://panbarlex.ipk-gatersleben.de/#\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Only high confidence TEs present in both of datasets were retained. To determine TE insertions near ARF genes, 100 kb flanking regions were analyzed using \u003cem\u003ebedtools\u003c/em\u003e. For cis-eQTL analysis, 1 Mb upstream and downstream of each ARF gene was defined as the cis-regulatory region. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Residua{l}_{T}\\)\u003c/span\u003e\u003c/span\u003e values were used as gene expression input in \u003cem\u003eMatrixEQTL\u003c/em\u003e v2.3 (Shabalin \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) under default settings.\u003c/p\u003e\n\u003ch3\u003eSelective sweep detection\u003c/h3\u003e\n\u003cp\u003eTo explore potential domestication-related selection signals, raw genomic data were obtained from a recent barley population study (Guo et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A 1 Mb window surrounding each \u003cem\u003eARF\u003c/em\u003e gene was analyzed. Genomic regions were extracted using \u003cem\u003ebcftools\u003c/em\u003e v1.15.1, and downstream processing and visualization were carried out using Perl under the guidance of the original study\u0026rsquo;s first author (Guo et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eVariant calling and amino acid substitution prediction\u003c/h3\u003e\n\u003cp\u003eWhole-genome assemblies of the 76 barley accessions were re-mapped to the Morex V2 reference genome (Figure, S4). Genomes were split into 1 kb fragments using \u003cem\u003eseqkit\u003c/em\u003e v0.9.1 (Shen et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) to simulate FASTQ reads. Variant calling was performed using \u003cem\u003eBWA\u003c/em\u003e v0.7.17 (Li \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), \u003cem\u003eSAMtools\u003c/em\u003e v1.16.1(Li et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and \u003cem\u003eBCFtools\u003c/em\u003e v1.15.1. SNPs and InDels were annotated using \u003cem\u003eSnpEff\u003c/em\u003e (Cingolani et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Amino acid substitutions in ARF proteins were predicted with \u003cem\u003ePPVED\u003c/em\u003e v1.0 (Gou et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Correlation analysis between substitution scores and domestication degrees were calculated using Pearson\u0026rsquo;s correlation coefficient in R.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eGenome-wide association study (GWAS) and haplotype analysis\u003c/h2\u003e\u003cp\u003eGWAS was performed to identify genomic regions associated with 16 agronomic traits (awn length; culm dry weight; fertility rate; final spikelet number; grain area; grain length; grain number per spikelet; grain weight; grain width; heading date; plant height; potential spikelet number; pre-anthesis tip degeneration; spike length; spike weight; thousand kernels weight). All phenotypic data are collected at IPK Gatersleben in 2018, 2019 and 2020. Total 442 representative genotypes were used in this study; population structure was fixed. Some parts of the phenotypic data have been published in previous work (Huang et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kamal et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). After quality filtering, 33,138,758 SNPs (minor allele frequency\u0026thinsp;\u0026gt;\u0026thinsp;0.1; missing rate\u0026thinsp;\u0026lt;\u0026thinsp;10%) were retained. GWAS was conducted using \u003cem\u003ePLINK\u003c/em\u003e v1.90b6.9 (Purcell et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and \u003cem\u003eGEMMA\u003c/em\u003e v0.98.5 (Team et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Haplotype blocks around \u003cem\u003eARF\u003c/em\u003e genes (\u0026plusmn;\u0026thinsp;1 Mb) were analyzed using pipeline shared in github (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/TKNsama/R\u003c/span\u003e\u003cspan address=\"https://github.com/TKNsama/R\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Since some phenotypic data are unpublished, only trait-associated SNPs proximal to \u003cem\u003eARF\u003c/em\u003e loci are reported here.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003edCAPS marker design and validation\u003c/h3\u003e\n\u003cp\u003eTo genotype the functional HvARF3 R475G variant, a dCAPS marker was developed targeting a SNP located at chr3H:494973445 (based on the MorexV2 reference genome). Primers (Supplementary data 5) were designed using the online tools dCAPS Finder 2.0 and Primer3, incorporating a mismatch to generate a specific restriction site for the enzyme \u003cem\u003eDdeI\u003c/em\u003e (New England Biolabs Inc). PCR amplification was carried out using \u003cem\u003eTaq\u003c/em\u003e DNA Polymerase (Qiagen, Cat. No. 201203) following the manufacturer\u0026rsquo;s recommended protocol. Amplified products were subsequently digested with \u003cem\u003eDdeI\u003c/em\u003e and separated on a 2.5% agarose gel.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eIdentification and validation of\u003c/b\u003e \u003cb\u003eARF\u003c/b\u003e \u003cb\u003egenes and their PAVs/CNVs across 76 barley pangenome assemblies\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo comprehensively catalog \u003cem\u003eARF\u003c/em\u003e-coding genes in barley, we employed a domain-based gene discovery approach by integrating three major protein family databases\u0026mdash;Pfam (PF02362, PF06507, PF02309), ProSiteProfiles (PS50863, PS51745), and PANTHER (PTHR31384) \u0026mdash;using the recently released barley pan-genome v2 database (BPGv2) (Jayakodi et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). All candidate sequences were further validated against the PanBARLEX online resource (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://panbarlex.ipk-gatersleben.de/#\u003c/span\u003e\u003cspan address=\"https://panbarlex.ipk-gatersleben.de/#\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to ensure annotation accuracy. Given that the conventional reference genome (Morex) lacks two \u003cem\u003eARFs\u003c/em\u003e due to genotype-specific presence/absence variations (PAVs), we selected the European modern spring barley cultivar Barke for gene nomenclature. Barke harbors all identified \u003cem\u003eARF\u003c/em\u003e-coding genes and has been extensively characterized at the transcriptomic level (Coulter et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). To maintain cross-species consistency, gene names were assigned based on the wheat gene nomenclature system (Boden et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and aligned with orthologous ARF genes in rice (Wang et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCompared to previous single-genome based analyses (Tombuloglu \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), our pan-genomic approach uncovered approximately 30% more \u003cem\u003eARF\u003c/em\u003e genes, underscoring the power of multi-genotype analysis to capture greater gene diversity. We retained 26 full-length \u003cem\u003eARFs\u003c/em\u003e containing at least one conserved ARF-related domain in \u0026ge;\u0026thinsp;10% of accessions for downstream analysis. Based on their frequency across the 76 barley genomes, these genes were classified into core, soft-core, and shell categories following established dispensability thresholds (Marroni et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, E). Notably, approximately\u0026thinsp;~\u0026thinsp;80.7% of ARFs belonged to the core or soft-core categories, being present in 76 or 75 genomes, respectively. This high evolutionary conservation highlights the essential roles of ARFs for barley development and fitness.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAn exception was the HvARF13 clade, which exhibited extensive structural and presence variation. While \u003cem\u003eHvARF13-3\u003c/em\u003e was highly conserved (found in 74 genotypes), \u003cem\u003eHvARF13-1\u003c/em\u003e and \u003cem\u003eHvARF13-2\u003c/em\u003e were present in only 47 and 59 accessions, respectively. Although HvARF13-1 and HvARF13-2 are shorter than HvARF13-3, all three retain an intact B3 DNA-binding domain in at least some genotypes and are expressed at transcriptome level, suggesting potential functionality (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). This structural variation reflects dynamic gene duplication events and copy number variations (CNVs) within the barley ARF family. To validate these PAVs, we extracted 5\u0026ndash;10 kb flanking genomic sequences and performed local alignment. A 479 bp deletion was identified at the 3\u0026prime; end of \u003cem\u003eHvARF13-2\u003c/em\u003e in several genotypes, including Morex (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB), likely explaining its absence in previous annotations. A larger deletion was detected for \u003cem\u003eHvARF13-1\u003c/em\u003e in some accessions, leading to complete gene loss (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). Additionally, a truncated gene (HORVU.BARKE.PROJ.7HG00820280) lacking conserved domains was likely misannotated and was excluded after sequence and domain verification.\u003c/p\u003e\u003cp\u003eTo explore functional implications of ARF protein diversity, we examined the composition of the middle region in each Barke ARF protein, combining domain annotation with phylogenetic analysis using rice homologs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). We specifically quantified glutamine (Q) and serine/threonine/proline (STP) residue enrichment, which are indicative of functional differentiation (Chandler \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Shen et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). ARFs with Q-rich middle regions were classified as Class I (typically transcriptional activators; e.g. \u003cem\u003eHvARF19\u003c/em\u003e), whereas those enriched in STP residues were categorized as Class II or Class III (transcriptional repressors; e.g. \u003cem\u003eHvARF1\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). In total, 26 well-characterized ARFs with high-confidence annotations were selected for our subsequent analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhylogenetic and\u003c/b\u003e \u003cb\u003eKa\u003c/b\u003e\u003cb\u003e/\u003c/b\u003e\u003cb\u003eKs\u003c/b\u003e \u003cb\u003eanalyses reveal evolutionary dynamics and selection pressures on ARF proteins in barley\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo elucidate the evolutionary relationships within the ARF gene family in barley, we performed a comprehensive phylogenetic analysis based on full-length ARF protein sequences derived from 76 barley genotypes. Despite extensive sequence variation among accessions, all ARF proteins could be reliably assigned to their respective clades, consistent with ARF classifications established in other plant species (Wang et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Comparative genome analyses across \u003cem\u003eArabidopsis\u003c/em\u003e, rice, and barley revealed substantial changes in ARF copy number, with both gene losses and expansions observed. Notably, several \u003cem\u003eArabidopsis\u003c/em\u003e ARFs, including \u003cem\u003eAtARF9\u003c/em\u003e, \u003cem\u003eAtARF13\u003c/em\u003e, \u003cem\u003eAtARF14\u003c/em\u003e, \u003cem\u003eAtARF23\u003c/em\u003e, \u003cem\u003eAtARF12\u003c/em\u003e, \u003cem\u003eAtARF22\u003c/em\u003e, \u003cem\u003eAtARF15\u003c/em\u003e, \u003cem\u003eAtARF10\u003c/em\u003e, \u003cem\u003eAtARF20\u003c/em\u003e, and \u003cem\u003eAtARF21\u003c/em\u003e, were completely absent in both rice and barley (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), suggesting lineage-specific gene loss events following the divergence of grasses and dicots (Aravind et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Finet et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This pattern likely reflects evolutionary streamlining of the \u003cem\u003eARF\u003c/em\u003e repertoires in grasses and possibly the adoption of distinct auxin signaling strategies (McSteen \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eInterestingly, the AtARF5 clade retained a single-copy ortholog across all three species tested here, indicative of strong purifying selection and a critical, possibly irreplaceable, role in plant development. In contrast, barley showed expanded \u003cem\u003eARF\u003c/em\u003e families compared to rice, particularly in \u003cem\u003eHvARF4\u003c/em\u003e, \u003cem\u003eHvARF8\u003c/em\u003e, \u003cem\u003eHvARF10\u003c/em\u003e, and \u003cem\u003eHvARF13\u003c/em\u003e. Among these, \u003cem\u003eHvARF4-1\u003c/em\u003e and \u003cem\u003eHvARF4-2\u003c/em\u003e represent a rare example of full-length gene duplication, with both paralogs retaining all key functional domains, including the B3 DNA-binding domain and PB1-like domain (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Other duplicated genes, such as members of the HvARF13 clade, often exhibited partial structures lacking one or more conserved domains, suggesting potential subfunctionalization or neofunctionalization. Notable, most of the observed barley-specific ARF duplications clustered within the AtARF17/16/10-like clade, which includes \u003cem\u003eAtARF10\u003c/em\u003e, \u003cem\u003eAtARF16\u003c/em\u003e, \u003cem\u003eAtARF17\u003c/em\u003e, and \u003cem\u003eOsARF18\u003c/em\u003e\u0026mdash;a group known to function as negative regulators of plant development and seed germination (Huang et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mallory et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The increasing copy number of this clade from \u003cem\u003eArabidopsis\u003c/em\u003e to rice and barley suggests an adaptive expansion, potentially driven by environmental selection pressures or functional diversification. To investigate the variation among ARF protein in barley, all ARF protein sequences were analyzed and are presented in a schematic diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) (Canc\u0026eacute; et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The DBD domain is relatively conserved in class I ARFs, whereas almost all class III ARFs contain a short insertion within this region, suggesting greater structural stability and conservation in class I. Notably, HvARF25, although belonging to class I, has a relatively high content of glutamine (Q) residues but does not form a poly-Q structure. Most class III ARFs possess the B3 repression domain (BRD, motif pattern: R/KLFG) (Choi et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), a characteristic indicator of repressor function. However, HvARF4-1 and HvARF4-2 are exceptions: instead of the canonical R/KLFG motif, each contain a single amino acid substitution that produces a modified KIFG motif, potentially indicating a functional divergence.\u003c/p\u003e\u003cp\u003eTo assess selection constraints acting on ARF proteins, we calculated the ratio of non-synonymous (\u003cem\u003eKa\u003c/em\u003e) to synonymous (\u003cem\u003eKs\u003c/em\u003e) substitution rates (\u003cem\u003eKa/Ks\u003c/em\u003e) across different barley populations. \u003cem\u003eKa/Ks\u003c/em\u003e values below, equal to, or above 1 are generally interpreted as indicative of purifying, neutral, or positive selections, respectively (Kimura \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). Overall, most ARF proteins exhibited \u003cem\u003eKa/Ks\u003c/em\u003e ratios well below 1, indicating strong purifying selection acting to conserve gene function across genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). This evolutionary constraint underscores the essential regulatory roles of ARF proteins during barley development and adaptation. However, \u003cem\u003eHvARF8-2\u003c/em\u003e was a notable exception, with \u003cem\u003eKa/Ks\u003c/em\u003e ratios frequently exceeding 1. The lack of a B3 domain in this gene may have relaxed functional constraints, permitting rapid evolution under natural or selective breeding. This gene may represent an example of lineage-specific divergence or a recently neofunctionalized paralog. Further comparison of \u003cem\u003eKa/Ks\u003c/em\u003e ratios among wild barley, landraces, and modern cultivars revealed a statistically significant decline from wild accessions to cultivars, indicative of intensified purifying selection during domestication and breeding. A similar trend was observed across geographic regions: accessions from East Asia exhibited lower \u003cem\u003eKa/Ks\u003c/em\u003e values than those from European and West Asia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), suggesting differential selection pressures linked to regional adaptation.\u003c/p\u003e\u003cp\u003eInterestingly, \u003cem\u003eHvARF12\u003c/em\u003e deviated from this general pattern, showing elevated \u003cem\u003eKa/Ks\u003c/em\u003e ratios in cultivars compared to wild or landrace accessions. Its rice ortholog, \u003cem\u003eOsARF12\u003c/em\u003e, is functionally redundant with \u003cem\u003eOsARF17\u003c/em\u003e and \u003cem\u003eOsARF25\u003c/em\u003e, and has been implicated in auxin-mediated regulation of tiller angle, as well as in responses to viral infection and disease resistance (Li et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) (Zhao et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These findings raise the possibility that \u003cem\u003eHvARF12\u003c/em\u003e has undergone adaptive evolution, possibly driven by trade-offs between agronomic performance and stress tolerance.\u003c/p\u003e\u003cp\u003eTaken together, these results reveal diverse evolutionary dynamics within the \u003cem\u003eARF\u003c/em\u003e family in barley, shaped by natural selection, domestication, and breeding. The combination of phylogenetic, structural, and \u003cem\u003eKa/Ks\u003c/em\u003e analyses provides key insights into functional divergence and adaptation of \u003cem\u003eARF\u003c/em\u003e genes in cereal crops.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTE identification, classification, and their regulatory influence on\u003c/b\u003e \u003cb\u003eARF\u003c/b\u003e \u003cb\u003egene expression\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTransposable elements (TEs) are mobile genetic elements capable of within the genome, frequently inducing mutations or altering gene expression. Representing a substantial fraction of plant genomes, TEs have been recognized as major contributors to genome evolution, structural variation, and transcriptional regulation (Bennetzen and Wang \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hirsch and Springer \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lisch \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). To explore the potential \u003cem\u003ecis\u003c/em\u003e-regulatory effects of TEs on \u003cem\u003eARF\u003c/em\u003e gene expression, we surveyed TE insertions within 100 kb upstream and downstream of all \u003cem\u003eARF\u003c/em\u003e genes across 76 barley accessions, using a high-confidence TE annotation dataset.\u003c/p\u003e\u003cp\u003eIn total, 516 TEs were identified near \u003cem\u003eARF\u003c/em\u003e loci, encompassing diverse TE families, with retrotransposons constituting the predominant class (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Interestingly, TE insertions were not randomly distributed: significantly enrichment was observed within the 50 kb upstream of many \u003cem\u003eHvARF\u003c/em\u003e genes. A secondary enrichment peak occurred around 20 kb upstream of several loci (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Given their proximity to core promoter regions and their established roles in modulating chromatin accessibility and transcription factor binding (Hirsch and Springer \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; L\u0026ouml;nnig and Saedler \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), these upstream TEs are strong candidates for modulating \u003cem\u003eARF\u003c/em\u003e gene expression via \u003cem\u003ecis\u003c/em\u003e-regulatory mechanisms. The observed spatial enrichment pattern suggests non-random TE insertion or retention, possibly reflecting selection for regulatory function. To investigate the potential regulatory impact of nearby TEs, we conducted expression quantitative trait loci (eQTL) mapping (Druka et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) in a panel of 20 representative genotypes by integrating high-quality pan-genome and pan-transcriptome datasets (Guo et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). To disentangle the complex sources of expression variation, we partitioned the variance in gene expression into components attributable to genotype, tissue, and their interaction. Among the 26 \u003cem\u003eARF\u003c/em\u003e genes, 22 displayed sufficient expression variability and were retained for further analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eVariance component analysis revealed that tissue and tissue \u0026times; genotype interactions were the primary drivers of expression variation, consistent with the known tissue-specific expression profiles of \u003cem\u003eARF\u003c/em\u003e genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). To mitigate tissue effects in subsequent association analyses, we used residual expression values\u0026mdash;obtained by regressing out tissue effects\u0026mdash;instead of raw expression levels. \u003cem\u003eCis\u003c/em\u003e-eQTLs were identified using SNPs located within a 1 Mb window flanking each \u003cem\u003eARF\u003c/em\u003e gene. As anticipated, genes with high genotype or genotype-by-tissue explained variance (PVE) \u0026mdash;such as \u003cem\u003eHvARF20\u003c/em\u003e, \u003cem\u003eHvARF13-3\u003c/em\u003e, \u003cem\u003eHvARF18\u003c/em\u003e, and \u003cem\u003eHvARF25\u003c/em\u003e\u0026mdash;displayed strong eQTL signals (\u0026ndash;log₁₀P\u0026thinsp;\u0026gt;\u0026thinsp;3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, E). Notably, \u003cem\u003eHvARF25\u003c/em\u003e exhibited a distinct eQTL peak located 23\u0026ndash;32 kb upstream of its transcriptional start site, coinciding with the position of a retrotransposon. This suggests that the TE insertion may act as a \u003cem\u003ecis\u003c/em\u003e-regulatory element modulating \u003cem\u003eHvARF25\u003c/em\u003e expression in a genotype-dependent manner (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, D).\u003c/p\u003e\u003cp\u003eA particularly interesting outlier was \u003cem\u003eHvARF11\u003c/em\u003e, which showed the most significant eQTL signal despite having low genotype and genotype-by-tissue PVE, and lacking annotated TEs within its vicinity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, E). This implies the presence of a potent, localized regulatory variant\u0026mdash;potentially an unannotated TE insertion, structural variation, or epigenetic modification\u0026mdash;contributing substantially to \u003cem\u003eHvARF11\u003c/em\u003e expression, independent of broader genomic background effects. Overall, TE located near \u003cem\u003eARF\u003c/em\u003e genes are likely to play a regulatory role contributing to genotype-specific expression differences.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePan-transcriptomic analysis reveals tissue-specific expression and functional networks of\u003c/b\u003e \u003cb\u003eARF\u003c/b\u003e \u003cb\u003egenes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo elucidate the functional roles and tissue-specific regulatory dynamics of \u003cem\u003eARF\u003c/em\u003e genes in barley, we integrated population-scale pan-genomic (Jayakodi et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and pan-transcriptomic (Guo et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) datasets. A total of 22 \u003cem\u003eARF\u003c/em\u003e genes with consistently detectable expression across five major tissues in 20 representative genotypes were retained for downstream analyses, minimizing noise from low-abundance transcripts.\u003c/p\u003e\u003cp\u003e\u003cem\u003eARF\u003c/em\u003e genes displayed pronounced tissue-specific expression patterns, with the inflorescence exhibiting the highest genotype-dependent expression variability, followed by roots (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Notably, 12 of the 22 \u003cem\u003eARF\u003c/em\u003e genes were highly and specifically expressed in developing inflorescences at Waddington stages W6\u0026ndash;W7 (Waddington et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1983\u003c/span\u003e), a critical window for spikelet and floret meristem differentiation. Seven genes showed peak expression in seedling roots, while \u003cem\u003eHvARF2\u003c/em\u003e and \u003cem\u003eHvARF6\u003c/em\u003e were predominantly expressed in shoots tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). \u003cem\u003eHvARF22\u003c/em\u003e exhibited unique expression in caryopsis, and no ARF genes demonstrated tissue specificity in embryonic structures such as the embryo, mesocotyl, or seminal root. To further resolve the spatial expression dynamics within inflorescence tissue, we utilized a high-resolution single-cell RNA-seq atlas of barley inflorescence (Morex) (Demesa-Arevalo et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This analysis uncovered cell-type-specific expression patterns for individual ARF genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). \u003cem\u003eHvARF4-1\u003c/em\u003e and \u003cem\u003eHvARF4-2\u003c/em\u003e were broadly expressed across diverse cell types, indicating general developmental roles. In contrast, \u003cem\u003eHvARF14\u003c/em\u003e, \u003cem\u003eHvARF15\u003c/em\u003e, and \u003cem\u003eHvARF11\u003c/em\u003e were enriched in vascular-associated tissues, with \u003cem\u003eHvARF11\u003c/em\u003e being specifically expressed during the transition from triple-spikelet meristem to floret meristem\u0026mdash;an essential developmental checkpoint. Additionally, \u003cem\u003eHvARF6\u003c/em\u003e, \u003cem\u003eHvARF12\u003c/em\u003e, and \u003cem\u003eHvARF25\u003c/em\u003e were predominantly expressed in the adaxial epidermis, suggesting possible roles in organ polarity or boundary formation. These temporally and spatially resolved expression profiles support the hypothesis that ARF genes operate in multiple, partially distinct regulatory programs during inflorescence development.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further explore the potential regulatory networks associated with those ARF gene, we conducted a weighted gene co-expression network analysis (WGCNA). Residual expression values (adjusted for genotypic effects) were used as input to focus on tissue-specific transcriptional relationships. For each ARF gene, co-expressed gene modules were identified, and Gene Ontology (GO) enrichment analysis was performed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). As expected for transcription factors, most \u003cem\u003eHvARF\u003c/em\u003e-associated modules were enriched in functions related to RNA/DNA binding and transcriptional regulation. Notably, \u003cem\u003eHvARF3\u003c/em\u003e, \u003cem\u003eHvARF14\u003c/em\u003e, and \u003cem\u003eHvARF15\u003c/em\u003e were co-expressed with genes involved in fungal defense responses. This observation is consistent with the functional identification of their rice orthologs (\u003cem\u003eOsARF3\u003c/em\u003e, \u003cem\u003eOsARF3a\u003c/em\u003e, \u003cem\u003eOsARF3b\u003c/em\u003e), which have been reported to modulate disease resistance pathways.\u003c/p\u003e\u003cp\u003eOverall, our discovery indicates that \u003cem\u003eARF\u003c/em\u003e genes exhibit tissue and cell-type-specific expression patterns and are involved in distinct regulatory modules that govern a range of biological processes\u0026mdash;from developmental patterning in inflorescence to biotic stress responses. These results insight a foundational framework for future functional studies and genetic manipulation strategies for optimizing inflorescence architecture or resistance in barley.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePangenome-based multi-omics analysis identifies HvARF3 as a key target of selection during barley breeding\u003c/h2\u003e\u003cp\u003eTo investigate the role of \u003cem\u003eARF\u003c/em\u003e gene variation in barley domestication and improvement, we performed selective sweep analyses alongside functional predictions of amino acid (AA) substitutions. Selection signals were examined within genomic regions flanking each \u003cem\u003eARF\u003c/em\u003e gene using a population panel comprising wild barley, landraces, and modern cultivars (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Among the 26 \u003cem\u003eARF\u003c/em\u003e loci, five\u0026mdash;\u003cem\u003eHvARF3\u003c/em\u003e, \u003cem\u003eHvARF8-2\u003c/em\u003e, \u003cem\u003eHvARF21\u003c/em\u003e, \u003cem\u003eHvARF15\u003c/em\u003e, and \u003cem\u003eHvARF4-1\u003c/em\u003e\u0026mdash;exhibited strong selection signatures, with marked allelic differentiation between wild and domesticated accessions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). These findings suggest that these loci may have undergone positive selection due to their contributions to agronomic traits favored during domestication and breeding.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo assess the potential functional relevance of naturally occurring amino acid substitutions, we predicted the effects of all non-synonymous variants (allele frequency\u0026thinsp;\u0026gt;\u0026thinsp;0.1) using the Plant Protein Variation Effect Detector (PPVED) (Gou et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Pearson correlation analyses between amino acid variants and domestication scores identified several candidate residues under selection (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Four variants had PPVED scores\u0026thinsp;\u0026gt;\u0026thinsp;0.5, indicating a high probability of functional impact, while the remaining substitutions were predicted to be functionally neutral. The most notable protein variant was a non-synonymous substitution in HvARF3 (R475G), which had the highest predicted functional impact across the ARF protein family. HvARF3, along with HvARF14 and HvARF15, belongs to the AtARF3/4 orthologous clade. All three members of this subclade displayed strong selection signals despite being located on different chromosomes, indicating potential convergent selection acting on this regulatory lineage (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Phylogenetic analysis of ARF proteins across the 76 barley accessions further supported this observation. While most ARF clades contained a mixture of wild and domesticated accessions, HvARF3, HvARF14, and HvARF15 formed lineage-specific clusters corresponding to domestication groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). This convergence of selective sweep signals, phylogenetic divergence, and predicted functional variant highlights the AtARF3/4 subclade\u0026mdash;particular HvARF3\u0026mdash;as a key target of directional selection linked to agronomic adaptation.\u003c/p\u003e\u003cp\u003eTo investigate the phenotypic consequences of \u003cem\u003eARF\u003c/em\u003e gene variation, we performed genome-wide association studies (GWAS) focused on key yield components, particularly grain size and thousand-kernel weight (TKW). SNPs within 1 Mb of each \u003cem\u003eARF\u003c/em\u003e gene were analyzed using a significance threshold of \u0026ndash;log₁₀(P)\u0026thinsp;\u0026gt;\u0026thinsp;2. While most associations showed modest effect sizes, consistent with the pleiotropic and conserved roles of \u003cem\u003eARF\u003c/em\u003e genes, we observed consistent association peaks near \u003cem\u003eHvARF3\u003c/em\u003e and \u003cem\u003eHvARF4-1\u003c/em\u003e, particularly for TKW and grain size (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e C, Table S4). To validate the association of \u003cem\u003eHvARF3\u003c/em\u003e with grain traits, we performed haplotype analysis based on SNPs within the gene body and its flanking regulatory regions. Seven major haplotypes were identified and could be classified into two distinct groups based on the R475G substitution: the \u0026ldquo;R475\u0026rdquo; allele, predominantly present in European cultivars, and the \u0026ldquo;G475\u0026rdquo; allele, more frequently observed in Asian accessions. Accessions harboring the R475 allele exhibited significantly higher grain area (GA) and TKW, two key traits under strong selection during modern barley breeding (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Notably, this positive association between the R475 allele and increased grain size and weight remained significant even after correcting for the \u003cem\u003enud\u003c/em\u003e locus (responsible for the naked vs. hulled grain phenotype), indicating that allelic variation at \u003cem\u003eHvARF3\u003c/em\u003e contributes to grain trait variation independently of hull type (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eTo facilitate marker-assisted selection, a derived cleaved amplified polymorphic sequence (dCAPS) marker was developed to discriminate between the R and G alleles at the HvARF3 R475G site (Figure S5). Collectively, this result provides indirect but convincing evidence that \u003cem\u003eHvARF3\u003c/em\u003e regulates grain traits and has undergone selection during barley improvement.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we present a novel method of gene family analysis by utilizing abundant, publicly available genomic and transcriptomic resources to comprehensively investigate the \u003cem\u003eARF\u003c/em\u003e gene family in barley. Through the integration of multi-omics datasets, we performed a series of systematic analyses including PAV detection, evolutionary dynamics, TE-mediated eQTL mapping, transcriptome analysis, and domestication signal identification. A summary of \u003cem\u003eARF\u003c/em\u003e genes in barley has been created by using different data source and methods, which provide insights and clues for future cloning and analysis. Those analyses, which were previously impossible with a single reference genome, demonstrate the advantage of pan-genome, pan-transcriptome, and resequencing data in mining gene family complexity across diverse genotypes in barley.\u003c/p\u003e\u003cp\u003eRecent advances in barley multi-omics resources, including high-resolution pan-genomes and population-wide transcriptomes, offer unprecedented opportunities for gene family research. However, the challenge lies in effective data integration and interpretation. Our study demonstrates that combining these layers enables the discovery of functional alleles and regulatory mechanisms otherwise inaccessible. For instance, the identification of HvARF3\u003csup\u003eR475G\u003c/sup\u003e as a putative domestication-related variant associated with increased grain size and TKW underscores the value of population-scale pan-genomic approaches. Conventional gene family studies relying on single genomes often fail to capture intraspecies diversity\u0026mdash; especially in crops like barley with global distribution and rich germplasm pools. Pan-genomics strategies, in contrast, enable the identification of both structural and nucleotide-level polymorphisms, accelerating trait mapping and gene discovery. Previously, genes such as \u003cem\u003ePpd-H1\u003c/em\u003e and \u003cem\u003eVRN-1\u003c/em\u003e were identified through large-scale germplasm screenings (Santra et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Turner et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Today, pan-genomic datasets allow for earlier and more targeted discovery. In this study, we revealed geographic stratification of HvARF3 haplotypes\u0026mdash;paralleling patterns observed in well-characterized flowering regulators like wheat \u003cem\u003ePpd-D1\u003c/em\u003e\u0026mdash;highlighting possible regional adaptation and breeding selection.\u003c/p\u003e\u003cp\u003eThe \u003cem\u003eARF\u003c/em\u003e gene family comprises a moderate number of transcription factors with essential roles in auxin-mediated growth and development. While several \u003cem\u003eARFs\u003c/em\u003e have been functionally characterized in \u003cem\u003eArabidopsis\u003c/em\u003e and rice, their roles in barley remain underexplored. Phylogenetic analyses revealed lineage-specific expansions and contractions. Notable, the AtARF17/16/10 clade exhibited multiple duplications in barley, particularly within the \u003cem\u003eHvARF13\u003c/em\u003e subgroup. \u003cem\u003eHvARF13-3\u003c/em\u003e remains highly conserved, while \u003cem\u003eHvARF13-1\u003c/em\u003e and \u003cem\u003eHvARF13-2\u003c/em\u003e show CNVs and sequence divergence\u0026mdash;suggesting ongoing sub-functionalization or neofunctionalization (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). These variations are more frequent in wild and landrace accessions, implying adaptive relevance in non-domesticated environments. Comparative genomics also revealed notable differences in \u003cem\u003eARF\u003c/em\u003e gene retention and loss. For example, rice contains two paralogs, \u003cem\u003eOsARF23\u003c/em\u003e and \u003cem\u003eOsARF24\u003c/em\u003e (formerly OsARF1), involved in grain formation via \u003cem\u003erice morphology determinant (RMD)\u003c/em\u003e regulation (Li et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Waller et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). These genes have no direct orthologs in barley (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), indicating species-specific divergence. Conversely, barley harbors a duplicated \u003cem\u003eOsARF4\u003c/em\u003e ortholog pair\u0026mdash;\u003cem\u003eHvARF4-1\u003c/em\u003e and \u003cem\u003eHvARF4-2\u003c/em\u003e\u0026mdash;both retaining full-length domain structures. Given \u003cem\u003eOsARF4\u0026rsquo;s\u003c/em\u003e known role in negatively regulating grain size in rice (Hu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), its barley counterparts represent strong candidates for functional studies.\u003c/p\u003e\u003cp\u003eDespite previous findings that several rice \u003cem\u003eARF\u003c/em\u003e genes (e.g., \u003cem\u003eOsARF3\u003c/em\u003e, \u003cem\u003eOsARF4\u003c/em\u003e, \u003cem\u003eOsARF6\u003c/em\u003e, \u003cem\u003eOsARF14\u003c/em\u003e, \u003cem\u003eOsARF15\u003c/em\u003e) are associated with grain-related traits (Gu et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Qiao et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), most \u003cem\u003eARF\u003c/em\u003e genes in barley do not show direct expression in the caryopsis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Instead, their strong expression in inflorescence tissues suggests that these \u003cem\u003eARF\u003c/em\u003e genes may regulate grain development indirectly by modulating early reproductive organogenesis and floral architecture, which likely affects final grain size or weight. Another example is \u003cem\u003eHvARF3\u003c/em\u003e, the selected gene during breeding, it shows high gene expression levels in the developing inflorescence while no expression in grain. In the single-cell transcriptome dataset of barley inflorescences, \u003cem\u003eHvARF3\u003c/em\u003e is specifically expressed in dividing cells, suggesting a potential role in regulating cell division and meristem maintenance during early stages of inflorescence development. But according to GWAS haplotype analysis and performance of its ortholog \u003cem\u003eOsARF3\u003c/em\u003e, it most likely affects grain size and grain traits, too. Interestingly, the favorable haplotypes of \u003cem\u003eHvARF3\u003c/em\u003e are rare in Asian barley accessions. This may be attributable to a regional genetic bottleneck or local adaptation, which limited the introgression of the advantageous haplotype. Alternatively, certain \u003cem\u003eHvARF3\u003c/em\u003e alleles may exhibit functional trade-offs similar to its rice ortholog \u003cem\u003eOsARF3\u003c/em\u003e, which has been shown to mediate a balance between heat tolerance and pathogen resistance (Gu et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, the \u003cem\u003eKa\u003c/em\u003e/\u003cem\u003eKs\u003c/em\u003e rate showed that \u003cem\u003eHvARF3\u003c/em\u003e was higher in Asian accessions, indicating that the purifying selection was not as strong as that in Europe. Thus, environmental difference between European and Asian growth conditions could have shaped regional selection pressures acting on different \u003cem\u003eHvARF3\u003c/em\u003e variants and reflect different breeding strategy.\u003c/p\u003e\u003cp\u003eIn conclusion, our integrative analysis highlights the critical role of \u003cem\u003eARF\u003c/em\u003e genes in barley development and demonstrates the utility of multi-omics strategies in gene family research. By resolving patterns of structural variation, expression regulation, and selection, we identify promising candidates like \u003cem\u003eHvARF3\u003c/em\u003e for targeted breeding. These findings contribute not only to our understanding of auxin signaling but also to practical efforts aimed at improving grain yield and adaptability in barley. The dCAPS marker developed for the HvARF3\u003csup\u003eR475\u003c/sup\u003e variant will facilitate marker-assisted selection in future breeding programs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e76 pan-genome sequence and annotation data are from previous publication (Jayakodi et al. 2024); Genotypic data of wild barley/ domesticated barley are from previous publication (Guo et al. 2024); Genotypic data of GWAS will be released in upcoming publication; some phenotypic data (FSN, GY, HD, MYP, PH, PSN, SA) are from previous publication (Kamal et al. 2022), while rest of them will be released in upcoming publication. The re-mapping VCF files created by this study has been deposited at Zenodo (https://zenodo.org/records/16778621).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions: \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKT: funding acquisition, conceptualization, data analysis, visualization, and writing\u0026nbsp;\u0026mdash; original draft preparation. ZG: conceptualization, investigation, validation, and writing\u0026nbsp;\u0026mdash; review and editing. TS: funding acquisition, supervision, and writing\u0026nbsp;\u0026mdash; review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe thank the public databases for providing valuable resources that supported this study. We also thank Roop Kamal, Yongyu Huang for phenotypic data collection. This work was supported by the Chinese Scholarship Council (CSC) to K.T., and by the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) through infrastructure and core funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAravind L, Watanabe H, Lipman DJ, Koonin EV (2000) Lineage-specific loss and divergence of functionally linked genes in eukaryotes. Proceedings of the National Academy of Sciences 97:11319-11324\u003c/li\u003e\n \u003cli\u003eBailey TL, Johnson J, Grant CE, Noble WS (2015) The MEME suite. Nucleic acids research 43:W39-W49\u003c/li\u003e\n \u003cli\u003eBates D, M\u0026auml;chler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. 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Rice 15:40\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"ARF gene family, barley pan-genome, multi-omics integration, gene family evolution, selective breeding","lastPublishedDoi":"10.21203/rs.3.rs-7399203/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7399203/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAuxin Response Factors (ARFs) play a pivotal role in regulating plant growth and development, yet their evolutionary dynamics and functional divergence remain poorly understood in barley (\u003cem\u003eHordeum vulgare\u003c/em\u003e L.) In this study, we conducted a comprehensive genome-wide analysis of the ARF gene family across a 76-accession barley pan-genome. By integrating gene presence/absence variation (PAV) and copy number variation (CNV), phylogeny, expression profiling, transposable element (TE)-mediated regulation, and selection signatures, we characterized 1,911 ARF-coding genes and their structural, transcriptional, and functional variation at the population level. Phylogenetic analysis revealed lineage-specific expansion and dynamic duplications, particularly within the HvARF13 clade. Co-expression networks and tissue-resolved transcriptomes showed that many \u003cem\u003eHvARFs\u003c/em\u003e are preferentially expressed in inflorescence and meristematic tissues. Selective sweep and haplotype analyses identified \u003cem\u003eHvARF3\u003c/em\u003e as a candidate gene under selection during European barley breeding. A favorable haplotype of \u003cem\u003eHvARF3\u003c/em\u003e, enriched in European cultivars, was significantly associated with increased grain size and weight, demonstrating the utility of pan-genome-enabled frameworks for accelerating gene-trait association and candidate gene discovery. This study highlights the power of multi-omics integration in decoding gene family complexity and provides valuable insights for functional genomics and trait improvement in cereal crops.\u003c/p\u003e","manuscriptTitle":"Pan-genome Analysis Reveals Hidden Diversity and Selection Signatures of Auxin Response Factors (ARFs) Associated with Breeding in Barley","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 13:57:16","doi":"10.21203/rs.3.rs-7399203/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2025-10-13T13:56:31+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-09-13T02:46:19+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-12T23:38:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-18T14:21:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2025-08-18T07:25:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4d719922-2776-41aa-9632-32cf1b497d59","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:00:31+00:00","versionOfRecord":{"articleIdentity":"rs-7399203","link":"https://doi.org/10.1007/s00122-026-05234-5","journal":{"identity":"theoretical-and-applied-genetics","isVorOnly":false,"title":"Theoretical and Applied Genetics"},"publishedOn":"2026-04-20 15:57:16","publishedOnDateReadable":"April 20th, 2026"},"versionCreatedAt":"2025-09-22 13:57:16","video":"","vorDoi":"10.1007/s00122-026-05234-5","vorDoiUrl":"https://doi.org/10.1007/s00122-026-05234-5","workflowStages":[]},"version":"v1","identity":"rs-7399203","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7399203","identity":"rs-7399203","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}