Genome-wide association analysis and candidate gene identification for plant height in Shanxi local foxtail millet varieties

Genome-wide association analysis and candidate gene identification for plant height in Shanxi local foxtail millet varieties | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genome-wide association analysis and candidate gene identification for plant height in Shanxi local foxtail millet varieties Wei Zhou, Huibin Qin, Haigang Wang, Hui Zhi, Rui Huang, Sen Hou, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8422656/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Plant height is pivotal for lodging resistance, harvestability, and yield potential in foxtail millet ( Setaria italica ). Here, we evaluated plant height in 209 diverse Shanxi local foxtail millet landraces across three contrasting environments and performed whole-genome resequencing of these accessions (mean depth ~38×). After standard filtering, 833,157 high-quality single nucleotide polymorphisms (SNPs) were retained. Neighbor-joining and principal component analysis consistently resolved four genetic groups with minor differences in sample assignment. A mixed linear model GWAS detected four loci significantly associated with plant height, including three loci not reported previously. Comparative genomics with rice and Arabidopsis shortlisted ten candidate genes; notably, SETIT_033071mg showed stem-specific high expression at the shooting stage in model variety Yugu1, with low expression elsewhere. Haplotype analysis of SETIT_033071mg in our GWAS panel (209 accessions evaluated across three environments) and an expanded diversity panel (313 accessions evaluated across five field environments) resolved multiple major haplotypes, among which H2 was consistently associated with reduced plant height across environments. These findings refine the genomic basis of plant height in foxtail millet and provide actionable targets for marker development for breeding lodging-resistant cultivars. Figures Figure 1 Figure 2 Figure 3 Figure 4 Key message Multi-environment genome-wide association study (GWAS) and haplotype analysis identify a height-reducing WAK4 ( SETIT_033071mg ) haplotype as a promising target for breeding lodging-resistant foxtail millet. Introduction The mid-twentieth-century Green Revolution, driven by semi-dwarf, lodging-resistant wheat and rice cultivars and enabled by irrigation and agrochemical inputs, substantially increased cereal yields across the developing world (Ameen and Raza 2017 ). However, feeding a projected global population of 9.7 billion by 2050 will still require a further 50–60% increase in grain production (Falcon et al. 2022 ). Plant height remains a pivotal breeding target: suitably reduced plant height enhances lodging resistance, raises the harvest index, and enables fully mechanised harvesting, traits that underpinned previous yield breakthroughs and will be vital for future gains (Fernandez et al. 2009 ; Hedden 2003 ). Accordingly, dissecting the genetic architecture of plant height and deploying height-associated alleles are central to accelerating yield improvement in cereal crops. Foxtail millet ( Setaria italica ) was domesticated from green foxtail ( Setaria viridis ) in the Yellow River basin approximately 10,000 years before present. Owing to its small diploid genome, short life cycle, and predominant self-pollination, it is widely cultivated as a climate-resilient staple in arid and semi-arid regions and has also emerged as a tractable model for Panicoideae genomics (Bennetzen et al. 2012 ; Diao and Jia 2016 ; Goron and Raizada 2015 ; Panaud 2006 ; Peng et al. 1999 ). Compared with rice and wheat, foxtail millet grains are enriched in protein, vitamins, and minerals, and the crop shows strong tolerance to drought and low-fertility soils (Das and Rakshit 2016 ; Kalita et al. 2025 ). However, lodging can be severe during grain filling, particularly under high planting density combined with intrinsically tall culms (Tian et al. 2017 ). Developing shorter, lodging-resistant cultivars is therefore a practical breeding priority, and a systematic dissection of the genetic basis of plant height is essential for accelerating molecular improvement. Extensive genetic dissection of plant height has been achieved in major cereals. In rice, more than 100 QTLs have been reported across the 12 chromosomes (Sabouri et al. 2010 ; Sandhu et al. 2021 ; Sowadan et al. 2018 ; Srividhya et al. 2011 ; Zhao et al. 2011 ), including large-effect and recurrent loci such as sd1 , d18 , and d35 (Itoh et al. 2004 ; Itoh et al. 2002 ; Monna et al. 2002 ). In wheat, over 50 loci have been identified (Griffiths et al. 2012 ; Würschum et al. 2015 ), and both linkage mapping and GWAS repeatedly highlight the Rht-B1/Rht-D1 allelic series as the foundation of Green Revolution semi-dwarfism (Peng et al. 1999 ). In foxtail millet, plant-height studies have expanded in recent years using F₂, RIL, and diverse natural panels, yielding more than 100 reported QTLs distributed across nine chromosomes; nevertheless, only a limited subset has been reproducibly detected across studies or environments, while many loci exhibit strong environmental sensitivity (Fan et al. 2017 ; Han et al. 2024 ; He et al. 2021 ; Mauro-Herrera and Doust 2016 ; Zhang et al. 2017 ; Zhu et al. 2023 ). These observations underscore the need for stable loci and deployable markers supported by multi-environment evidence. Gene cloning has further transformed height improvement by revealing conserved regulatory routes. In rice, sd1 (GA20-oxidase) and SLR1 (DELLA), together with the wheat Rht-B1b/Rht-D1b alleles, illustrate that attenuation of gibberellin (GA) biosynthesis or signaling is an effective and widely conserved route to semi-dwarfism (Monna et al. 2002 ; Ikeda et al. 2001 ; Peng et al. 1999 ). In maize and sorghum, classic dwarfing genes such as D8/D9 and Dw3 point to additional layers of regulation involving auxin transport and broader hormonal crosstalk, including abscisic acid and brassinosteroid/strigolactone pathways (Lawit et al. 2010 ; Multani et al. 2003 ). By contrast, functional evidence in foxtail millet remains concentrated on a small number of GA-related candidates— SiSD1 , SiGID1 , and Sidwarf3 —with most studies relying on expression-level observations and limited mutant-level validation (Ni et al. 2017 ; Zhao et al. 2019 ; Fan et al. 2017 ). Consequently, non-GA mechanisms, including cell-wall biosynthesis/signaling and hormone crosstalk beyond GA, remain underexplored, leaving a gap between locus discovery and breeding-ready targets. Here, we conducted a multi-environment GWAS of 209 Shanxi local foxtail millet accessions genotyped with ~ 833,000 high-quality SNPs and detected four loci significantly associated with plant height. Integrating cross-species functional annotation with shoot–stem expression data from ‘Yugu1’ highlighted SETIT_033071mg ( WAK4 ) within qPH2.1 as a leading candidate gene. Coding-SNP haplotype analyses of SETIT_033071mg in both the GWAS panel (209 accessions) and an expanded diversity set (313 accessions) consistently identified an H2 haplotype that reduces plant height, providing tractable allelic variants for dissecting the genetic architecture of plant height and breeding lodging-resistant, semi-dwarf foxtail millet cultivars. Materials and methods Plant materials and field trials The association panel consisted of 209 Shanxi local foxtail millet landraces, all belonging to the traditional “Dabaigu” type that has been widely cultivated in Shanxi Province as landraces. Seeds of these accessions were obtained from the Center for Agricultural Genetic Resources Research, Shanxi Agricultural University (Taiyuan, China). Trials were conducted for three consecutive years (2020–2022) at Jinzhong, Shanxi, China (37.68°N, 112.75°E). Within each year, accessions were randomized and planted as a single 6-m row per accession with 33 cm row spacing; standard local management (irrigation, manual weeding and integrated pest control) was applied. Phenotypic analysis, BLUEs and broad-sense heritability Summary statistics (mean, SD, CV, range, skewness, kurtosis) were computed in R; histograms with overlaid density were drawn with ggplot2 (v3.5.1). Across-environment BLUEs were obtained using lme4 (v1.1-37) with the model: PH ~ Env + (1 | Genotype) + (1 | Genotype:Env) where Env and Genotype were treated as fixed effects and Genotype×Env was modeled as a random effect. Genotype marginal means (BLUEs) were extracted using emmeans. Variance components were obtained from the same mixed model, and broad-sense heritability on an entry-mean basis was computed as: DNA extraction, library preparation and whole-genome resequencing Young leaves were collected at the shooting stage in 2022 for all the samples. Genomic DNA was extracted using a modified CTAB method, quality-checked by agarose electrophoresis and Qubit fluorometry (Thermo Fisher). Libraries (insert ~500 bp) were prepared (end repair, A-tailing, adapter ligation), circularized, amplified as DNA nanoballs and sequenced on the BGI DNBSEQ™ platform to generate 150-bp paired-end reads. Read processing, alignment and variant calling Adapters, reads with >10% Ns, and reads with >50% bases of Q≤12 were removed by SOAPnuke (v0.20.0). Clean reads were aligned to the Yugu1 reference genome (GCF_000263155.2, v2.0) using BWA-MEM (v0.7.17). SAM files were converted, sorted and deduplicated with SAMtools and Picard; only reads with MAPQ ≥ 30 were retained. Variants were called per sample with GATK (v4.6.2) HaplotypeCaller (gVCF mode), combined with CombineGVCFs and jointly genotyped with GenotypeGVCFs. Hard filters followed GATK best practices (QD ≥ 2.0, FS ≤ 60, SOR ≤ 3.0, MQ ≥ 40, MQRankSum ≥ −12.5, ReadPosRankSum ≥ −8.0). SNPs were retained if biallelic, MAF ≥ 0.05, missing rate ≤ 10%; HWE filtering was not applied given the selfing mating system and population structure. Population structure, PCA and linkage disequilibrium For structure analyses, we used an LD-pruned SNP set (PLINK 1.9, --indep-pairwise 50 5 0.2). PCA was performed with PLINK; population structure was inferred by ADMIXTURE (v1.3.0) with K=2–8, and the optimal K was chosen by CV error minimization. Genome-wide LD decay was estimated by PopLDdecay (v3.43) as mean pairwise R² vs physical distance; LD decay distance was defined as the first point where mean R² dropped below 0.20. GWAS for Plant height Genome-wide association analyses were performed with EMMAX using a mixed linear model to identify SNPs associated with plant height. The genome-wide significance threshold was set to P 7.22) based on a Bonferroni correction (0.05/833,157). In addition, SNPs with P 7) were reported as suggestive associations, as used in previous studies (Kang et al. 2010; Khound et al. 2024). GWAS was conducted for four datasets (20JZ, 21JZ, 22JZ and BLUE), and loci were defined by clustering significant or suggestive SNPs within the same genomic region, with the lead SNP taken as the variant showing the lowest P value. Candidate gene identification and functional annotation Candidate genes within each significant QTL region were identified using the Yugu1 reference genome (https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000263155.2/). Genes located in the defined intervals (including an additional 100-kb flanking region) were extracted and functionally annotated based on UniProt (https://www.uniprot.org/) and NCBI databases (https://www.ncbi.nlm.nih.gov/). Furthermore, orthologous gene searches were performed against rice (https://rapdb.dna.affrc.go.jp/) and Arabidopsis (https://www.arabidopsis.org/) protein sequences using the online NCBI BLASTP tool (https://blast.ncbi.nlm.nih.gov/Bla st.cgi) to infer potential gene functions. Relevant literature was also consulted to support the functional prediction and prioritize candidate genes. Tissue-Specific Expression Analysis and Haplotype Variation Analysis . Expression profiles of candidate genes in stems, leaves, nodes, panicles, and seeds at different developmental stages (seeds, three-leaf seedling, booting, shooting, flowering, and maturity) were retrieved from the Setaria-db database (http://www.setariadb.com/millet). Heatmaps illustrating the expression patterns across organs and stages were generated using the R package pheatmap (v1.0.12). Haplotype analyses of SETIT_033071mg were conducted using resequencing data from two panels: (i) the 209-accession GWAS panel used in this study and (ii) an independent set of 313 core foxtail millet accessions (unpublished). Plant height was measured across three environments for the GWAS panel (Jinzhong, 2020–2022) and across five environments for the 313-accession panel (Jinzhong, 2018–2020; Datong, 2020; and Yuncheng, 2020). Associations between haplotypes and plant height were tested using the geneHapR package. Results Phenotypic variations of plant height PH was measured in 209 foxtail millet accessions across three experimental environments (Supplementary Table 1). The PH phenotypes exhibited approximately normal distributions (Fig. 1A) with substantial variation (Fig. 1B). The mean PH ranged from 125.04 cm (22JZ) to 161.01 cm (21JZ), with overall phenotypic variation spanning 90.00–194.42 cm. The phenotypic difference between environments implys that foxtail millet plant height was contolled by multigene which is greatly influenced by environments. The coefficients of variation (CV) varied slightly across environments, ranging from 9.85% (21JZ, lowest variability) to 12.02% (20JZ, highest variability). Skewness and kurtosis values indicated that PH distributions were generally negatively skewed across all environments, with kurtosis values negative for 21JZ and 22JZ, while slightly positive for 20JZ. Plant height showed a high broad-sense heritability ( H ²) of 93.6 %, indicating that genetic effects account for the vast majority of the observed phenotypic variation. Sequencing, SNP identification Table 1 Summary of marker distribution among foxtail millet genome Chromosome Length (Bp) SNP numbers SNPs/Mb 1 42,145,358 79,358 1,882 2 49,199,692 96,374 1,959 3 50,651,948 110,235 2,177 4 40,407,787 96,163 2,380 5 47,252,504 81,593 1,726 6 36,014,412 85,545 2,375 7 35,964,117 65,385 1,818 8 40,688,952 125,255 3,079 9 58,970,299 93,249 1,581 Total 401,295,069 833,157 2,109 To comprehensively characterize the genomic variation in foxtail millet, we conducted deep resequencing of 209 accessions, generating a total of 12.33 billion clean reads, with an average of 59 million reads per accession (Supplementary Table 2). The average GC content, Q20, and Q30 values were 45.2%, 97.3%, and 91.9%, respectively. More than 98% of reads were successfully mapped to the reference genome version 2 (Bennetzen et al. 2012), with a coverage rate exceeding 93% and a mean sequencing depth of 37.6× across accessions. After stringent filtering a total of 833,157 high-quality SNPs were retained (Fig. 2A). These SNPs were uniformly distributed across the genome, with an average density of 2,109 SNPs per megabase (Mb). Chromosome 8 exhibited the highest SNP density (3,079 SNPs/Mb), whereas chromosome 9 had the lowest (1,581 SNPs/Mb) (Table 1). Based on functional annotation, the majority of SNPs (approximately 62.9%) were located in intergenic regions. Genic regions accounted for 33.6% of SNPs, while only 1.9% and 1.5% were found within pseudogenes and lncRNA regions, respectively (Fig. 2B). Phylogenetic relationships, population structure, principal component analysis, and linkage disequilibrium analysis An NJ tree constructed from pairwise genetic distances resolved four groups (Fig. 3A). Group sizes were uneven, with Group 2 the largest of sixty-six accessions and Group 1 the smallest of seven accessions (Supplementary Table 3). Principal component analysis (PCA) recapitulated the same four-cluster pattern (Fig. 3B), although sample assignments were not identical between methods, consistent with admixture and a complex genetic background. Notably, these genetic groups did not align with geographic origin, as accessions from different regions were broadly intermingled across clusters. Further analysis using ADMIXTURE revealed that population boundaries were most distinct at K = 4. At this optimal value, some accessions exhibited pure ancestry, while others displayed admixed genetic backgrounds, suggesting extensive gene flow among groups (Fig. 3D). The cross-validation (CV) error sharply decreased from K = 2 to K = 4 and reached its lowest point at K = 4, then gradually increased with higher K values (Fig. S1), supporting K = 4 as the optimal number of subpopulations. To characterize linkage disequilibrium (LD) patterns among genetic clusters, we assessed LD decay (R²) as a function of physical distance within each cluster. In the full panel, LD decayed most slowly in Cluster 1, whereas Clusters 2 and 3 showed comparable decay patterns, with Cluster 2 exhibiting the fastest LD decay (Fig. 3C). GWAS analysis Using 833,157 high-quality SNPs, we detected four QTL associated with plant height across the three environments, three of which have not been reported previously (Fig. 4; Supplementary Table 4). However, no significant QTL was identified in 21JZ. This may reflect the reduced phenotypic variation observed in this environment, where plant height had the highest mean (161.01 cm) but the lowest coefficient of variation (CV = 9.85%), thereby limiting the statistical power to detect genetic effects. On chromosome 8, four significant SNPs (chr8:910320, chr8:911022, chr8:911033, and chr8:911623) were located within a 1.3 kb interval, which is much smaller than the estimated LD decay distance (Fig. 3C). Therefore, these SNPs were merged into a single QTL locus ( qPH8 ), and chr8:911022, with the lowest P value, was considered as the lead SNP. Among the identified loci, two were located on chromosome 2 ( qPH2.1 and qPH2.2 ), while chromosomes 4 and 8 each harbored one locus ( qPH4 and qPH8 , respectively). The strongest association signal was detected at qPH4 on chromosome 4, which had the lowest P value among all loci. The QTLs qPH2.1 , qPH2.2 , qPH4 , and qPH8 were detected in one, one, two, and two environments, respectively. The PVE ranged from 10.62% ( qPH2.1 ) to 17.12% ( qPH2.2 ), with qPH2.2 exhibiting the highest PVE (Table 2). The quantile–quantile (QQ) plots indicated that the observed P value generally conformed to the expected distribution under the null hypothesis, suggesting that the population structure and relatedness were well controlled in the GWAS model (Fig. 4). Candidate gene Table 2 Stable QTL loci and candidate genes speculated in this study QTL Chr QTL region (bp) QTN marker P value Environment PVE (%) Candidate gene Annotation qPH2.1 2 5091820–5291820 chr2:5191820 7.734E-08 20JZ 10.62 SETIT_033071mg wall-associated kinase 4 SETIT_029387mg FAD-linked oxidases family protein SETIT_028865mg NB-ARC domain-containing disease resistance protein qPH2.2 2 24636879–24836879 chr2:24736879 3.20E-08 20JZ/BLUE 17.12 SETIT_030569mg Chlorophyll A-B binding family protein qPH4 4 8128049–8328049 chr4:8228049 3.25E-09 20JZ/22JZ/BLUE 13.19 SETIT_007662mg basic helix-loop-helix (bHLH) DNA-binding superfamily protein SETIT_007340mg HVA22 homologue A qPH8 8 811022–1011022 chr8:911022 4.51E-09 20JZ/22JZ/BLUE 12.14 SETIT_026086mg with no lysine (K) kinase 5 SETIT_026573mg dsRNA-binding domain-like superfamily protein SETIT_027872mg Tetratricopeptide repeat (TPR)-like superfamily protein SETIT_026360mg Protein phosphatase 2C family protein Genomic regions within ±100 kb were regarded as a QTL interval, based on previous studies (Dai et al. 2024; Jia et al. 2013; Liu et al. 2022) and the LD decay estimated from all accessions in the present study. Within these intervals we identified a total of 61 candidate genes (Supplementary Table 5). Through functional annotation of genes within these regions, several promising candidate genes potentially related to plant height regulation were identified (Table 2). Notably, in qPH2.1 on chromosome 2, we identified SETIT_033071mg , encoding a wall-associated kinase ( WAK ), which is involved in cell wall integrity and signal transduction, and SETIT_029387mg , encoding an FAD-linked oxidases family protein. Additionally, SETIT_028865mg , encoding an NB-ARC domain-containing disease resistance protein. For qPH2.2 , SETIT_030569mg , encoding a chlorophyll A-B binding family protein. In qPH4 , we identified SETIT_007662mg , encoding a basic helix-loop-helix (bHLH) DNA-binding superfamily protein, as well as SETIT_007340mg , encoding HVA22 homologue A. In qPH8 on chromosome 8, multiple candidate genes were detected, including SETIT_026086mg , encoding a with-no-lysine (WNK) kinase 5 potentially; SETIT_026573mg , encoding a dsRNA-binding domain-like superfamily protein; SETIT_027872mg , encoding a tetratricopeptide repeat (TPR)-like superfamily protein; and SETIT_026360mg , encoding a protein phosphatase 2C family protein. Tissue- and stage-specific expression profiles of candidate gene Expression profiling of ten plant-height candidate genes across six developmental stages (seedling, three-leaf, shooting, booting, flowering, and maturity) and six tissues (seed, root, stem, node, leaf, and panicle) revealed distinct spatiotemporal patterns (Fig. S2). Specifically, SETIT_029387mg exhibited elevated expression in leaves and panicles from shooting to flowering stages. Genes SETIT_026573mg , SETIT_007662mg , SETIT_026360mg , SETIT_027872mg , and SETIT_007340mg displayed high expression in panicles during booting and flowering stages. Additionally, SETIT_028865mg and SETIT_027872mg showed elevated expression in stems and nodes at the booting stage. Notably, SETIT_033071mg exhibited high expression exclusively in stems at the shooting stage, while remaining lowly expressed across all other tissues and developmental stages. Haplotype analysis of SETIT_033071mg Based on expression profiles and functional annotation within the qPH2.1 interval, SETIT_033071mg ( WAK4 ) was prioritized for haplotype analysis. Thirty SNPs selected from the target gene region partitioned the 209 accessions in our GWAS panel into four major haplotypes (H1–H4; Fig. 5A). Across the three field environments (20JZ, 21JZ and 22JZ), haplotype H2 consistently showed the lowest mean plant height (Fig. 5B). In each environment, H2 plants were significantly shorter than H1 (Wilcoxon tests, *** P < 0.001), and in several cases also shorter than H3 or H4 (* P < 0.05 to P < 0.01). On average, H2 reduced plant height by approximately 8–13 cm relative to the other haplotypes: in 20JZ, H2 was 12.3, 10.2 and 10.8 cm shorter than H1, H3 and H4, respectively; in 21JZ, the corresponding reductions were 12.6, 10.4 and 8.1 cm; and in 22JZ they were 9.9, 7.9 and 12.8 cm (Supplementary Table 6). Overall, the maximum haplotype-associated difference in plant height reached about 12.8 cm (22JZ, H2 vs H4). In the expanded natural population, 28 high-confidence coding SNPs partitioned 313 accessions into six major haplotypes (H1–H6; Fig. S3A). Across five field environments (18JZ, 19JZ, 20JZ, 20DT and 20YC), H2 consistently showed the lowest mean plant height (108.71–119.69 cm; Supplementary Table 7), being generally shorter than the other five haplotypes (Wilcoxon tests, P < 0.05; Fig. S3B). Discussion A stable chromosome-2 hotspot and additional loci not reported previously In a previous study, Gao et al. (2025) mapped a major QTL, qPH2-1 (8.10–28.52 Mb), on chromosome 2 in a 300 individual F₂ population from Jingu 28 × Ai 88, an interval that encompasses and extends beyond our qPH2.2 (24.64–24.84 Mb). A neighbouring segment on the same chromosome was likewise detected by He et al. (2021) in an Ai 88 × Liaogu 1 RIL population (26.04–28.91 Mb) and by Dai et al. (2024) in a 408 accession GWAS panel ( qPH02.13 , marker LDB_2_24921046) (Supplementary Table 4). Convergent evidence from F₂, RIL and natural populations thus pinpoints the 24–29 Mb region of chromosome 2 as a key hotspot controlling plant height in foxtail millet. By contrast, the additional loci we discovered— qPH2.1 , qPH4 , and qPH8 —have not appeared in previous RIL linkage maps analyses and natural-panel GWAS mining (Jaiswal et al. 2019; Jia et al. 2013), or the 1,844-accession graph-genome GWAS of He et al. (2023), and therefore represent genuinely novel additions to the foxtail-millet height landscape. Among them, qPH4 yielded the lowest P -value and, together with qPH8 , was repeatedly detected in all three environments. qPH2.2 accounted for the largest proportion of phenotypic variance (17.12 %), while qPH2.1 still explained 10.62 %, underscoring the practical breeding value of these loci. Reliance on a 209-line Shanxi local foxtail millet association panel, phenotyped for three consecutive seasons at a single dryland site, likely improved QTL resolution and reproducibility by combining spatial uniformity with inter-annual climatic variation. The new loci enlarge the current catalogue of foxtail-millet height QTL and offer fresh entry points for map-based cloning and functional marker development; which will serve as useful tools for stacking dwarf alleles, potentially underpinning lodging-resistant, high-harvest-index cultivars in arid and semi-arid regions. Future work will adopt the cross-genome collinearity framework of Sandhu et al. (2021) in rice Meta-QTL analysis, together with functional-mutant validation and marker development, to clarify the underlying biology and facilitate efficient aggregation of superior alleles. Candidate-gene landscape suggests GA-dependent and GA-independent contributions Early foxtail-millet height studies focused heavily on the GA pathway, He et al. (2021) highlighted Seita.1G242300 (GA2-oxidase-8) within qPH1.3 and proposed six additional GA-biosynthesis/signalling genes plus fifteen F-box genes across other QTL. Using RIL and F₂ data, Ni et al. (2017) mapped Z3ph1 , an sd1 / GA20ox homologue (89 % identity), in bin2021 on chromosome 2, reinforcing GA20ox as a core regulator. More recently, Dai et al. (2024) identified WAK / Bph30 , the NAC factor OMTN3, phospholipase pPLAIII, and the IAA-glucosidase TGW6 in qPH03.14 and qPH06.10 , implicating cell-wall plasticity and auxin/cytokinin homeostasis. Within qPH2.1 we pinpointed SETIT_033071mg ( WAK4 ) and SETIT_029387mg (FAD-linked oxidase); the former guards wall integrity, the latter may modulate ROS-lignin balance (Kanneganti and Gupta 2008; Schmülling et al. 2003). qPH2.2 houses SETIT_030569mg , a chlorophyll a-b binding protein gene, suggesting that photosynthetic carbon flux can indirectly influence internode elongation (Green and Durnford 1996). qPH4 contains a bHLH transcription factor ( SETIT_007662mg ) (Toledo-Ortiz et al. 2003) and HVA22a ( SETIT_007340mg ) (Brands and Ho 2002) linked to hormone crosstalk and ER–vesicle homeostasis, while qPH8 features WNK5 (Manuka et al. 2015; Urano et al. 2012), PP2C (Leung et al. 1997; Umezawa et al. 2009), and RNA-binding/TPR proteins (Allan and Ratajczak 2011; Blatch and Lässle 1999), pointing to BR/ABA signalling and RNA metabolism. Except for the functional parallel between WAK4 and the WAK reported by Dai et al. (2024) these genes have not surfaced in earlier height studies, widening the regulatory horizon beyond GA. Notably, haplotype analysis of SETIT_033071mg ( WAK4 ) in both the GWAS panel (209 Shanxi local landraces) and an expanded natural population (313 accessions) showed that the H2 haplotype consistently conferred the lowest plant height across eight field environments. In the GWAS panel, H2 reduced plant height by approximately 8–13 cm relative to the other major haplotypes, while in the expanded panel it was associated with mean plant heights of 108.7–119.7 cm, substantially lower than the remaining haplotypes. This pattern indicates that the H2 haplotype behaves as a moderate, environmentally robust semi-dwarf allele that could be directly exploited in marker-assisted selection or genomic selection schemes to breed lodging-resistant, high-harvest-index foxtail millet cultivars, particularly in dryland production systems. Collectively, GA signalling remains a major determinant of plant height in foxtail millet, but our results also implicate additional processes (cell-wall remodelling, light/energy allocation, BR/ABA signalling and RNA metabolism), which require functional validation. In contrast to previously reported GA-related major loci, the associations detected here were largely of small effect, likely reflecting the composition of our panel (209 Dabaigu Shanxi landraces) with a relatively narrow origin and simple population structure. While this design may reduce stratification and improve mapping robustness, limited allelic diversity could constrain power to detect major or rare variants; therefore, validation in broader germplasm and across environments is warranted. Well-powered GWAS enabled by multi-year phenotyping of a diverse panel The present study assessed plant height in an association panel of 209 Shanxi local foxtail millet landraces, which were grown under standardized field conditions in Jinzhong, Shanxi Province, across three consecutive seasons (2020–2022). Jinzhong, experiences a typical temperate continental monsoon climate and is representative of the northern Chinese dry-land millet agro-ecosystem (Sun et al. 2022). A mixed linear model (MLM) fitted across three successive seasons yielded a broad-sense heritability ( H ²) of 93.6% for plant height, indicating strong genetic control while retaining some environmental responsiveness. The resulting BLUE values thus provide a robust phenotypic foundation for high-resolution GWAS and reliable QTL localization. Plant stature integrates lodging resistance, canopy light capture and synchronised grain filling (Dineshkumar et al. 1992; Tian et al. 2017); moderate dwarfism therefore enhances harvest index and facilitates mechanised harvesting while reducing labour and energy inputs (Pearce 2021). In the context of increasingly volatile climates, semi-dwarf, lodging-resistant foxtail millet cultivars are vital for sustaining food and feed production across arid and semi-arid zones (Choudhary et al. 2023; Yu et al. 2024). The continual expansion of genetic resources has likely enhanced the resolution with which plant height in foxtail millet can be dissected. Three material systems now drive the progress of genetic decipher of plant height regulation in foxtail millet. (i) Biparental populations. In a 124 plant Hongmiaozhangu × Changnong35 F₂ family, Wang et al. (2017) delimited the major locus qPH1.1 . He et al. (2021) later used 333 Ai 88 × Liaogu 1 RILs to uncover 26 QTL on nine chromosomes, underscoring the value of deep recombination for capturing both large- and moderate-effect loci. (ii) Diversity panels. Resequencing 916 globally sourced accessions, Jia et al. (2013) performed five-environment GWAS and pinpointed three stable height QTL, illustrating how abundant allelic diversity and rapid LD decay deliver high-resolution mapping. (iii) Functional mutants and multi-parent resources. The dwarf mutant Sidwarf2 of Yugu 1 was resolved by BSA-seq and fine mapping to a 52.7-kb window on chromosome 3, implicating a cytochrome P450 gene (Xue et al. 2016). Together, these resources create a seamless pipeline—from primary mapping and fine localization to causal validation and elite-allele assembly. Leveraging high-stability, single-site multi-year phenotypes from 209 diverse accessions, our GWAS identified environmentally stable height loci and deployable markers. The resulting genetic targets and rich allelic variation underpin semi-dwarf breeding for northern dry-land foxtail millet and provide a solid springboard for subsequent mutant validation and allele pyramiding. Declarations Acknowledgement We thank Drs. Qiang He, Hongkai Liang and Bin Liu for their valuable support and assistance with data processing. Author contribution statement WZ, HQ and HZ performed the data analyses and drafted the manuscript. ZQ, ZM and XD conceived and supervised the study, developed the 209-line population, and revised the manuscript. RH and SH managed the field trials and conducted haplotype analysis. HW, JW, LC and XT collected the phenotypic data. All authors read and approved the final manuscript. Funding This work was supported by the National Key Project of Research and the Development Plan of China (2021YFF1000103), the Major Special Science and Technology Project of Shanxi Province (202101140601027), Science and Technology Innovation Enhancement Project of Shanxi Agricultural University (CXGC2023090), and the National Natural Science Foundation of China (32241041), Research Project for Introduced Talent of Shanxi Agricultural University(2021BQ37). Data, materials and code availability All raw resequencing genotype data and supplementary files generated in this study are available in Zenodo at https://doi.org/10.5281/zenodo.18012842. Conflict of interest The authors declare that they have no relevant financial or non-financial interests to disclose. References Allan RK, Ratajczak T (2011) Versatile TPR domains accommodate different modes of target protein recognition and function. Cell stress and chaperones 16:353-367 Ameen A, Raza S (2017) Green revolution: a review. 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Supplementary Files TableS1Phenotypicvariationinplantheightamong209foxtailmilletaccessionsacrossthreeenvironments.xlsx TableS4TheoverlappingQTLsbetweenpresentandpreviousstudies.xlsx TableS5Geneannotationsfor61genesintheremainingfourQTLregions.xlsx TableS7Plantheightcmof313accessionsgroupedbyhaplotypeH1H6acrossfivefieldenvironments.xlsx TableS6Plantheightcmof209accessionsgroupedbyhaplotypeH1H4acrossthreefieldenvironments.xlsx TableS3Geographicaloriginsofthe209foxtailmilletaccessionsusedinthisstudy.xlsx TableS2Summaryofsequencingdataqualitystatisticsforeachsample.xlsx FigureS1.CrossvalidationErrorAcrossKValuesinPopulationstructureAnalysis.tif FigureS3.HaplotypeanalysisofSETIT033071mg..tif FigureS2.Expressionprofilesofcandidategenesacrosstissuesanddevelopmentalstagesinfoxtailmillet.tif Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Mar, 2026 Reviews received at journal 09 Mar, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviews received at journal 27 Jan, 2026 Reviewers agreed at journal 14 Jan, 2026 Reviewers invited by journal 12 Jan, 2026 Editor assigned by journal 29 Dec, 2025 Submission checks completed at journal 29 Dec, 2025 First submitted to journal 22 Dec, 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. 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1","display":"","copyAsset":false,"role":"figure","size":154405,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequency distribution and descriptive statistics of plant height across three environments.\u003c/strong\u003eA: Frequency distribution of PH in 209 foxtail millet accessions evaluated in three environments: 20JZ (Jinzhong, 2020), 21JZ (Jinzhong, 2021), and 22JZ (Jinzhong, 2022). Curves indicate kernel-density estimates. B: Statistical summary of PH data across the three environments. CV, coefficient of variation; \u003cem\u003eH\u003c/em\u003e², broad-sense heritability.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8422656/v1/e9833212fd94e8f7f4a7c5bc.png"},{"id":100224877,"identity":"f8329ffd-6610-45c8-af4d-814decc5555e","added_by":"auto","created_at":"2026-01-14 10:11:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":153487,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenome-wide distribution of SNPs in 209 foxtail millet.\u003c/strong\u003e A: SNP density distribution across the nine chromosomes of foxtail millet. The red lines represent SNP density along each chromosome. B: Proportion of SNPs distributed in different genomic regions, including intergenic, genic region, lncRNA, and pseudogene.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8422656/v1/c913d6dd85048368b18ac9a4.png"},{"id":100224856,"identity":"bdae6030-9582-4f41-a62e-baa394daae87","added_by":"auto","created_at":"2026-01-14 10:11:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":190246,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenomic diversity and population structure of 209 Shanxi local foxtail millet landraces based on SNP data. \u003c/strong\u003eA: Neighbor-joining phylogenetic tree of the 209 foxtail millet accessions, with four major groups indicated by different colors. B: Principal component analysis (PCA) of the 209 accessions, showing the distribution of individuals along the first three principal components (PC1, PC2, PC3). Colors represent genetic clusters inferred from population structure analysis, and point shapes correspond to phylogenetic groups. C: Decay of linkage disequilibrium (LD, measured as R²) with physical distance (kb) for each subpopulation. D: Population structure inferred from ADMIXTURE analysis at K = 4. 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10:11:45","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":13160,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6Plantheightcmof209accessionsgroupedbyhaplotypeH1H4acrossthreefieldenvironments.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8422656/v1/6a42629a73d61b1a993a044b.xlsx"},{"id":100224854,"identity":"e8fca95f-2340-4a85-b1b0-6899ad99f44f","added_by":"auto","created_at":"2026-01-14 10:11:43","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":15868,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3Geographicaloriginsofthe209foxtailmilletaccessionsusedinthisstudy.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8422656/v1/3a99a4e2b4c3c90851c9dcc4.xlsx"},{"id":100224857,"identity":"8df6de3b-2e2c-4629-930e-10ff1a5d54d9","added_by":"auto","created_at":"2026-01-14 10:11:44","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":40790,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2Summaryofsequencingdataqualitystatisticsforeachsample.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8422656/v1/ebe1f462a4d901038fa40928.xlsx"},{"id":100370773,"identity":"3c1b7399-a300-4259-a1a4-58ac480c04d4","added_by":"auto","created_at":"2026-01-16 08:08:12","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":4084674,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.CrossvalidationErrorAcrossKValuesinPopulationstructureAnalysis.tif","url":"https://assets-eu.researchsquare.com/files/rs-8422656/v1/dbdba3e1a662c4f833c2224c.tif"},{"id":100371284,"identity":"d970ff0b-392b-43e0-95b6-8a49a70b0bbb","added_by":"auto","created_at":"2026-01-16 08:09:46","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":3957922,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.HaplotypeanalysisofSETIT033071mg..tif","url":"https://assets-eu.researchsquare.com/files/rs-8422656/v1/88d3ef0eed0be0b422d686fd.tif"},{"id":100224866,"identity":"452084b2-68c1-41ba-b621-5e91d000d221","added_by":"auto","created_at":"2026-01-14 10:11:44","extension":"tif","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":8374630,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.Expressionprofilesofcandidategenesacrosstissuesanddevelopmentalstagesinfoxtailmillet.tif","url":"https://assets-eu.researchsquare.com/files/rs-8422656/v1/ceeb50af60f0fc9c1d8794c2.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-wide association analysis and candidate gene identification for plant height in Shanxi local foxtail millet varieties","fulltext":[{"header":"Key message","content":"\u003cp\u003eMulti-environment genome-wide association study (GWAS) and haplotype analysis identify a height-reducing \u003cem\u003eWAK4\u003c/em\u003e (\u003cem\u003eSETIT_033071mg\u003c/em\u003e) haplotype as a promising target for breeding lodging-resistant foxtail millet.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eThe mid-twentieth-century Green Revolution, driven by semi-dwarf, lodging-resistant wheat and rice cultivars and enabled by irrigation and agrochemical inputs, substantially increased cereal yields across the developing world (Ameen and Raza \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, feeding a projected global population of 9.7\u0026nbsp;billion by 2050 will still require a further 50\u0026ndash;60% increase in grain production (Falcon et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Plant height remains a pivotal breeding target: suitably reduced plant height enhances lodging resistance, raises the harvest index, and enables fully mechanised harvesting, traits that underpinned previous yield breakthroughs and will be vital for future gains (Fernandez et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Hedden \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Accordingly, dissecting the genetic architecture of plant height and deploying height-associated alleles are central to accelerating yield improvement in cereal crops.\u003c/p\u003e \u003cp\u003eFoxtail millet (\u003cem\u003eSetaria italica\u003c/em\u003e) was domesticated from green foxtail (\u003cem\u003eSetaria viridis\u003c/em\u003e) in the Yellow River basin approximately 10,000 years before present. Owing to its small diploid genome, short life cycle, and predominant self-pollination, it is widely cultivated as a climate-resilient staple in arid and semi-arid regions and has also emerged as a tractable model for Panicoideae genomics (Bennetzen et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Diao and Jia \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Goron and Raizada \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Panaud \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Peng et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Compared with rice and wheat, foxtail millet grains are enriched in protein, vitamins, and minerals, and the crop shows strong tolerance to drought and low-fertility soils (Das and Rakshit \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kalita et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, lodging can be severe during grain filling, particularly under high planting density combined with intrinsically tall culms (Tian et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Developing shorter, lodging-resistant cultivars is therefore a practical breeding priority, and a systematic dissection of the genetic basis of plant height is essential for accelerating molecular improvement.\u003c/p\u003e \u003cp\u003eExtensive genetic dissection of plant height has been achieved in major cereals. In rice, more than 100 QTLs have been reported across the 12 chromosomes (Sabouri et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Sandhu et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sowadan et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Srividhya et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), including large-effect and recurrent loci such as \u003cem\u003esd1\u003c/em\u003e, \u003cem\u003ed18\u003c/em\u003e, and \u003cem\u003ed35\u003c/em\u003e (Itoh et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Itoh et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Monna et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In wheat, over 50 loci have been identified (Griffiths et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; W\u0026uuml;rschum et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and both linkage mapping and GWAS repeatedly highlight the \u003cem\u003eRht-B1/Rht-D1\u003c/em\u003e allelic series as the foundation of Green Revolution semi-dwarfism (Peng et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). In foxtail millet, plant-height studies have expanded in recent years using F₂, RIL, and diverse natural panels, yielding more than 100 reported QTLs distributed across nine chromosomes; nevertheless, only a limited subset has been reproducibly detected across studies or environments, while many loci exhibit strong environmental sensitivity (Fan et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Han et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; He et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mauro-Herrera and Doust \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These observations underscore the need for stable loci and deployable markers supported by multi-environment evidence.\u003c/p\u003e \u003cp\u003eGene cloning has further transformed height improvement by revealing conserved regulatory routes. In rice, \u003cem\u003esd1\u003c/em\u003e (GA20-oxidase) and \u003cem\u003eSLR1\u003c/em\u003e (DELLA), together with the wheat \u003cem\u003eRht-B1b/Rht-D1b\u003c/em\u003e alleles, illustrate that attenuation of gibberellin (GA) biosynthesis or signaling is an effective and widely conserved route to semi-dwarfism (Monna et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Ikeda et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Peng et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). In maize and sorghum, classic dwarfing genes such as \u003cem\u003eD8/D9\u003c/em\u003e and \u003cem\u003eDw3\u003c/em\u003e point to additional layers of regulation involving auxin transport and broader hormonal crosstalk, including abscisic acid and brassinosteroid/strigolactone pathways (Lawit et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Multani et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). By contrast, functional evidence in foxtail millet remains concentrated on a small number of GA-related candidates\u0026mdash;\u003cem\u003eSiSD1\u003c/em\u003e, \u003cem\u003eSiGID1\u003c/em\u003e, and \u003cem\u003eSidwarf3\u003c/em\u003e\u0026mdash;with most studies relying on expression-level observations and limited mutant-level validation (Ni et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fan et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Consequently, non-GA mechanisms, including cell-wall biosynthesis/signaling and hormone crosstalk beyond GA, remain underexplored, leaving a gap between locus discovery and breeding-ready targets.\u003c/p\u003e \u003cp\u003eHere, we conducted a multi-environment GWAS of 209 Shanxi local foxtail millet accessions genotyped with ~\u0026thinsp;833,000 high-quality SNPs and detected four loci significantly associated with plant height. Integrating cross-species functional annotation with shoot\u0026ndash;stem expression data from \u0026lsquo;Yugu1\u0026rsquo; highlighted \u003cem\u003eSETIT_033071mg\u003c/em\u003e (\u003cem\u003eWAK4\u003c/em\u003e) within \u003cem\u003eqPH2.1\u003c/em\u003e as a leading candidate gene. Coding-SNP haplotype analyses of \u003cem\u003eSETIT_033071mg\u003c/em\u003e in both the GWAS panel (209 accessions) and an expanded diversity set (313 accessions) consistently identified an H2 haplotype that reduces plant height, providing tractable allelic variants for dissecting the genetic architecture of plant height and breeding lodging-resistant, semi-dwarf foxtail millet cultivars.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003ePlant materials and field trials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe association panel consisted of 209 Shanxi local foxtail millet landraces, all belonging to the traditional \u0026ldquo;Dabaigu\u0026rdquo; type that has been widely cultivated in Shanxi Province as landraces. Seeds of these accessions were obtained from the Center for Agricultural Genetic Resources Research, Shanxi Agricultural University (Taiyuan, China). Trials were conducted for three consecutive years (2020\u0026ndash;2022) at Jinzhong, Shanxi, China (37.68\u0026deg;N, 112.75\u0026deg;E). Within each year, accessions were randomized and planted as a single 6-m row per accession with 33 cm row spacing; standard local management (irrigation, manual weeding and integrated pest control) was applied.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhenotypic analysis, BLUEs and broad-sense heritability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSummary statistics (mean, SD, CV, range, skewness, kurtosis) were computed in R; histograms with overlaid density were drawn with ggplot2 (v3.5.1). Across-environment BLUEs were obtained using lme4 (v1.1-37) with the model:\u003c/p\u003e\n\u003cp\u003ePH ~ Env + (1 | Genotype) + (1 | Genotype:Env)\u003c/p\u003e\n\u003cp\u003ewhere Env and Genotype were treated as fixed effects and Genotype\u0026times;Env was modeled as a random effect. Genotype marginal means (BLUEs) were extracted using emmeans.\u003c/p\u003e\n\u003cp\u003eVariance components were obtained from the same mixed model, and broad-sense heritability on an entry-mean basis was computed as:\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1768384308.png\" width=\"267\" height=\"130\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA extraction, library preparation and whole-genome resequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYoung leaves were collected at the shooting stage in 2022 for all the samples. Genomic DNA was extracted using a modified CTAB method, quality-checked by agarose electrophoresis and Qubit fluorometry (Thermo Fisher). Libraries (insert ~500 bp) were prepared (end repair, A-tailing, adapter ligation), circularized, amplified as DNA nanoballs and sequenced on the BGI DNBSEQ\u0026trade; platform to generate 150-bp paired-end reads.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRead processing, alignment and variant calling \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdapters, reads with \u0026gt;10% Ns, and reads with \u0026gt;50% bases of Q\u0026le;12 were removed by SOAPnuke (v0.20.0). Clean reads were aligned to the Yugu1 reference genome (GCF_000263155.2, v2.0) using BWA-MEM (v0.7.17). SAM files were converted, sorted and deduplicated with SAMtools and Picard; only reads with MAPQ \u0026ge; 30 were retained. \u003c/p\u003e\n\u003cp\u003eVariants were called per sample with GATK (v4.6.2) HaplotypeCaller (gVCF mode), combined with CombineGVCFs and jointly genotyped with GenotypeGVCFs. Hard filters followed GATK best practices (QD \u0026ge; 2.0, FS \u0026le; 60, SOR \u0026le; 3.0, MQ \u0026ge; 40, MQRankSum \u0026ge; \u0026minus;12.5, ReadPosRankSum \u0026ge; \u0026minus;8.0). SNPs were retained if biallelic, MAF \u0026ge; 0.05, missing rate \u0026le; 10%; HWE filtering was not applied given the selfing mating system and population structure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation structure, PCA and linkage disequilibrium \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor structure analyses, we used an LD-pruned SNP set (PLINK 1.9, --indep-pairwise 50 5 0.2). PCA was performed with PLINK; population structure was inferred by ADMIXTURE (v1.3.0) with K=2\u0026ndash;8, and the optimal K was chosen by CV error minimization.\u003c/p\u003e\n\u003cp\u003eGenome-wide LD decay was estimated by PopLDdecay (v3.43) as mean pairwise R\u0026sup2; vs physical distance; LD decay distance was defined as the first point where mean R\u0026sup2; dropped below 0.20. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGWAS for Plant height\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenome-wide association analyses were performed with EMMAX using a mixed linear model to identify SNPs associated with plant height. The genome-wide significance threshold was set to \u003cem\u003eP\u003c/em\u003e \u0026lt; 6.00 \u0026times; 10⁻⁸ (\u0026minus;log₁₀(\u003cem\u003eP\u003c/em\u003e) \u0026gt; 7.22) based on a Bonferroni correction (0.05/833,157). In addition, SNPs with \u003cem\u003eP\u003c/em\u003e \u0026lt; 1 \u0026times; 10⁻⁷ (\u0026minus;log₁₀(\u003cem\u003eP\u003c/em\u003e) \u0026gt; 7) were reported as suggestive associations, as used in previous studies (Kang et al. 2010; Khound et al. 2024). GWAS was conducted for four datasets (20JZ, 21JZ, 22JZ and BLUE), and loci were defined by clustering significant or suggestive SNPs within the same genomic region, with the lead SNP taken as the variant showing the lowest \u003cem\u003eP\u003c/em\u003e value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCandidate gene identification and functional annotation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCandidate genes within each significant QTL region were identified using the \u003cem\u003eYugu1\u003c/em\u003e reference genome (https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000263155.2/). Genes located in the defined intervals (including an additional 100-kb flanking region) were extracted and functionally annotated based on UniProt (https://www.uniprot.org/) and NCBI databases (https://www.ncbi.nlm.nih.gov/). Furthermore, orthologous gene searches were performed against rice (https://rapdb.dna.affrc.go.jp/) and Arabidopsis (https://www.arabidopsis.org/) protein sequences using the online NCBI BLASTP tool (https://blast.ncbi.nlm.nih.gov/Bla st.cgi) to infer potential gene functions. Relevant literature was also consulted to support the functional prediction and prioritize candidate genes. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTissue-Specific Expression Analysis and Haplotype Variation Analysis\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eExpression profiles of candidate genes in stems, leaves, nodes, panicles, and seeds at different developmental stages (seeds, three-leaf seedling, booting, shooting, flowering, and maturity) were retrieved from the Setaria-db database (http://www.setariadb.com/millet). Heatmaps illustrating the expression patterns across organs and stages were generated using the R package pheatmap (v1.0.12).\u003c/p\u003e\n\u003cp\u003eHaplotype analyses of \u003cem\u003eSETIT_033071mg\u003c/em\u003e were conducted using resequencing data from two panels: (i) the 209-accession GWAS panel used in this study and (ii) an independent set of 313 core foxtail millet accessions (unpublished). Plant height was measured across three environments for the GWAS panel (Jinzhong, 2020\u0026ndash;2022) and across five environments for the 313-accession panel (Jinzhong, 2018\u0026ndash;2020; Datong, 2020; and Yuncheng, 2020). Associations between haplotypes and plant height were tested using the geneHapR package.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePhenotypic variations of plant height\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePH was measured in 209 foxtail millet accessions across three experimental environments (Supplementary Table 1). The PH phenotypes exhibited approximately normal distributions (Fig. 1A) with substantial variation (Fig. 1B). The mean PH ranged from 125.04 cm (22JZ) to 161.01 cm (21JZ), with overall phenotypic variation spanning 90.00\u0026ndash;194.42 cm. The phenotypic difference between environments implys that foxtail millet plant height was contolled by multigene which is greatly influenced by environments. The coefficients of variation (CV) varied slightly across environments, ranging from 9.85% (21JZ, lowest variability) to 12.02% (20JZ, highest variability). Skewness and kurtosis values indicated that PH distributions were generally negatively skewed across all environments, with kurtosis values negative for 21JZ and 22JZ, while slightly positive for 20JZ.\u0026nbsp;Plant height showed a high broad-sense heritability (\u003cem\u003eH\u003c/em\u003e\u0026sup2;) of 93.6 %, indicating that genetic effects account for the vast majority of the observed phenotypic variation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSequencing, SNP identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Summary of marker distribution among foxtail millet genome\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eChromosome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eLength (Bp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eSNP numbers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eSNPs/Mb\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e42,145,358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e79,358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e1,882\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e49,199,692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e96,374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e1,959\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e50,651,948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e110,235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e2,177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e40,407,787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e96,163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e2,380\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e47,252,504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e81,593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e1,726\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e36,014,412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e85,545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e2,375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e35,964,117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e65,385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e1,818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e40,688,952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e125,255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e3,079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e58,970,299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e93,249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e1,581\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e401,295,069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e833,157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e2,109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTo comprehensively characterize the genomic variation in foxtail millet, we conducted deep resequencing of 209 accessions, generating a total of 12.33 billion clean reads, with an average of 59 million reads per accession (Supplementary Table 2). The average GC content, Q20, and Q30 values were 45.2%, 97.3%, and 91.9%, respectively. More than 98% of reads were successfully mapped to the reference genome version 2 (Bennetzen et al. 2012), with a coverage rate exceeding 93% and a mean sequencing depth of 37.6\u0026times; across accessions.\u003c/p\u003e\n\u003cp\u003eAfter stringent filtering a total of 833,157 high-quality SNPs were retained (Fig. 2A). These SNPs were uniformly distributed across the genome, with an average density of 2,109 SNPs per megabase (Mb). Chromosome 8 exhibited the highest SNP density (3,079 SNPs/Mb), whereas chromosome 9 had the lowest (1,581 SNPs/Mb) (Table 1).\u003c/p\u003e\n\u003cp\u003eBased on functional annotation, the majority of SNPs (approximately 62.9%) were located in intergenic regions. Genic regions accounted for 33.6% of SNPs, while only 1.9% and 1.5% were found within pseudogenes and lncRNA regions, respectively (Fig. 2B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhylogenetic relationships, population structure, principal component analysis, and linkage disequilibrium analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn NJ tree constructed from pairwise genetic distances resolved four groups (Fig. 3A). Group sizes were uneven, with Group 2 the largest of sixty-six accessions and Group 1 the smallest of seven accessions (Supplementary Table 3). Principal component analysis (PCA) recapitulated the same four-cluster pattern (Fig. 3B), although sample assignments were not identical between methods, consistent with admixture and a complex genetic background.\u0026nbsp;Notably, these genetic groups did not align with geographic origin, as accessions from different regions were broadly intermingled across clusters.\u003c/p\u003e\n\u003cp\u003eFurther analysis using ADMIXTURE revealed that population boundaries were most distinct at K = 4. At this optimal value, some accessions exhibited pure ancestry, while others displayed admixed genetic backgrounds, suggesting extensive gene flow among groups (Fig. 3D). The cross-validation (CV) error sharply decreased from K = 2 to K = 4 and reached its lowest point at K = 4, then gradually increased with higher K values (Fig. S1), supporting K = 4 as the optimal number of subpopulations.\u003c/p\u003e\n\u003cp\u003eTo characterize linkage disequilibrium (LD) patterns among genetic clusters, we assessed LD decay (R\u0026sup2;) as a function of physical distance within each cluster. In the full panel, LD decayed most slowly in Cluster 1, whereas Clusters 2 and 3 showed comparable decay patterns, with Cluster 2 exhibiting the fastest LD decay (Fig. 3C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGWAS analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing 833,157 high-quality SNPs, we detected four QTL associated with plant height across the three environments, three of which have not been reported previously (Fig. 4; Supplementary Table 4). However, no significant QTL was identified in 21JZ. This may reflect the reduced phenotypic variation observed in this environment, where plant height had the highest mean (161.01 cm) but the lowest coefficient of variation (CV = 9.85%), thereby limiting the statistical power to detect genetic effects.\u003c/p\u003e\n\u003cp\u003eOn chromosome 8, four significant SNPs (chr8:910320, chr8:911022, chr8:911033, and chr8:911623) were located within a 1.3 kb interval, which is much smaller than the estimated LD decay distance (Fig. 3C). Therefore, these SNPs were merged into a single QTL locus (\u003cem\u003eqPH8\u003c/em\u003e), and chr8:911022, with the lowest \u003cem\u003eP\u003c/em\u003e value, was considered as the lead SNP.\u003c/p\u003e\n\u003cp\u003eAmong the identified loci, two were located on chromosome 2 (\u003cem\u003eqPH2.1\u003c/em\u003e and \u003cem\u003eqPH2.2\u003c/em\u003e), while chromosomes 4 and 8 each harbored one locus (\u003cem\u003eqPH4\u003c/em\u003e and \u003cem\u003eqPH8\u003c/em\u003e, respectively). The strongest association signal was detected at \u003cem\u003eqPH4\u003c/em\u003e on chromosome 4, which had the lowest \u003cem\u003eP\u003c/em\u003e value among all loci. The QTLs \u003cem\u003eqPH2.1\u003c/em\u003e, \u003cem\u003eqPH2.2\u003c/em\u003e, \u003cem\u003eqPH4\u003c/em\u003e, and \u003cem\u003eqPH8\u003c/em\u003e were detected in one, one, two, and two environments, respectively. The PVE ranged from 10.62% (\u003cem\u003eqPH2.1\u003c/em\u003e) to 17.12% (\u003cem\u003eqPH2.2\u003c/em\u003e), with \u003cem\u003eqPH2.2\u003c/em\u003e exhibiting the highest PVE (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe quantile\u0026ndash;quantile (QQ) plots indicated that the observed \u003cem\u003eP\u003c/em\u003e value generally conformed to the expected distribution under the null hypothesis, suggesting that the population structure and relatedness were well controlled in the GWAS model (Fig. 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCandidate gene\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Stable QTL loci and candidate genes speculated in this study\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"784\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 47px;\"\u003e\n \u003cp\u003eQTL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003eChr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003eQTL region (bp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003eQTN marker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003eEnvironment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003ePVE (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003eCandidate gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eAnnotation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cem\u003eqPH2.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 38px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5091820\u0026ndash;5291820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 76px;\"\u003e\n \u003cp\u003echr2:5191820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 57px;\"\u003e\n \u003cp\u003e7.734E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 94px;\"\u003e\n \u003cp\u003e20JZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 57px;\"\u003e\n \u003cp\u003e10.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003eSETIT_033071mg\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003ewall-associated kinase 4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003eSETIT_029387mg\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eFAD-linked oxidases family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003eSETIT_028865mg\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eNB-ARC domain-containing disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cem\u003eqPH2.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e24636879\u0026ndash;24836879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003echr2:24736879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3.20E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e20JZ/BLUE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e17.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003eSETIT_030569mg\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eChlorophyll A-B binding family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cem\u003eqPH4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 38px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e8128049\u0026ndash;8328049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003echr4:8228049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.25E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 94px;\"\u003e\n \u003cp\u003e20JZ/22JZ/BLUE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003e13.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003eSETIT_007662mg\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003ebasic helix-loop-helix (bHLH) DNA-binding superfamily protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003eSETIT_007340mg\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eHVA22 homologue A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cem\u003eqPH8\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 38px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 104px;\"\u003e\n \u003cp\u003e811022\u0026ndash;1011022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 76px;\"\u003e\n \u003cp\u003echr8:911022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4.51E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 94px;\"\u003e\n \u003cp\u003e20JZ/22JZ/BLUE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 57px;\"\u003e\n \u003cp\u003e12.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003eSETIT_026086mg\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003ewith no lysine (K) kinase 5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003eSETIT_026573mg\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003edsRNA-binding domain-like superfamily protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003eSETIT_027872mg\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eTetratricopeptide repeat (TPR)-like superfamily protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003eSETIT_026360mg\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eProtein phosphatase 2C family protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGenomic regions within \u0026plusmn;100 kb were regarded as a QTL interval, based on previous studies\u0026nbsp;(Dai et al. 2024; Jia et al. 2013; Liu et al. 2022) and the LD decay estimated from all accessions in the present study. Within these intervals we identified a total of 61 candidate genes (Supplementary Table 5).\u003c/p\u003e\n\u003cp\u003eThrough functional annotation of genes within these regions, several promising candidate genes potentially related to plant height regulation were identified (Table 2). Notably, in \u003cem\u003eqPH2.1\u003c/em\u003e on chromosome 2, we identified \u003cem\u003eSETIT_033071mg\u003c/em\u003e, encoding a wall-associated kinase (\u003cem\u003eWAK\u003c/em\u003e), which is involved in cell wall integrity and signal transduction, and\u003cem\u003e\u0026nbsp;SETIT_029387mg\u003c/em\u003e, encoding an FAD-linked oxidases family protein. Additionally, \u003cem\u003eSETIT_028865mg\u003c/em\u003e, encoding an NB-ARC domain-containing disease resistance protein.\u003c/p\u003e\n\u003cp\u003eFor \u003cem\u003eqPH2.2\u003c/em\u003e, \u003cem\u003eSETIT_030569mg\u003c/em\u003e, encoding a chlorophyll A-B binding family protein. In \u003cem\u003eqPH4\u003c/em\u003e, we identified \u003cem\u003eSETIT_007662mg\u003c/em\u003e, encoding a basic helix-loop-helix (bHLH) DNA-binding superfamily protein, as well as \u003cem\u003eSETIT_007340mg\u003c/em\u003e, encoding HVA22 homologue A. In \u003cem\u003eqPH8\u003c/em\u003e on chromosome 8, multiple candidate genes were detected, including \u003cem\u003eSETIT_026086mg\u003c/em\u003e, encoding a with-no-lysine (WNK) kinase 5 potentially; \u003cem\u003eSETIT_026573mg\u003c/em\u003e, encoding a dsRNA-binding domain-like superfamily protein; \u003cem\u003eSETIT_027872mg\u003c/em\u003e, encoding a tetratricopeptide repeat (TPR)-like superfamily protein; and \u003cem\u003eSETIT_026360mg\u003c/em\u003e, encoding a protein phosphatase 2C family protein.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTissue- and stage-specific expression profiles of candidate gene\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExpression profiling of ten plant-height candidate genes across six developmental stages (seedling, three-leaf, shooting, booting, flowering, and maturity) and six tissues (seed, root, stem, node, leaf, and panicle) revealed distinct spatiotemporal patterns (Fig. S2). Specifically, \u003cem\u003eSETIT_029387mg\u003c/em\u003e exhibited elevated expression in leaves and panicles from shooting to flowering stages. Genes \u003cem\u003eSETIT_026573mg\u003c/em\u003e, \u003cem\u003eSETIT_007662mg\u003c/em\u003e, \u003cem\u003eSETIT_026360mg\u003c/em\u003e, \u003cem\u003eSETIT_027872mg\u003c/em\u003e, and \u003cem\u003eSETIT_007340mg\u003c/em\u003e displayed high expression in panicles during booting and flowering stages. Additionally, \u003cem\u003eSETIT_028865mg\u003c/em\u003e and \u003cem\u003eSETIT_027872mg\u003c/em\u003e showed elevated expression in stems and nodes at the booting stage. Notably, \u003cem\u003eSETIT_033071mg\u003c/em\u003e exhibited high expression exclusively in stems at the shooting stage, while remaining lowly expressed across all other tissues and developmental stages.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHaplotype analysis of \u003cem\u003eSETIT_033071mg\u003c/em\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on expression profiles and functional annotation within the \u003cem\u003eqPH2.1\u003c/em\u003e interval, \u003cem\u003eSETIT_033071mg\u003c/em\u003e (\u003cem\u003eWAK4\u003c/em\u003e) was prioritized for haplotype analysis.\u0026nbsp;Thirty SNPs selected from the target gene region partitioned the 209 accessions in our GWAS panel into four major haplotypes (H1\u0026ndash;H4; Fig. 5A). Across the three field environments (20JZ, 21JZ and 22JZ), haplotype H2 consistently showed the lowest mean plant height (Fig. 5B). In each environment, H2 plants were significantly shorter than H1 (Wilcoxon tests, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and in several cases also shorter than H3 or H4 (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 to \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). On average, H2 reduced plant height by approximately 8\u0026ndash;13 cm relative to the other haplotypes: in 20JZ, H2 was 12.3, 10.2 and 10.8 cm shorter than H1, H3 and H4, respectively; in 21JZ, the corresponding reductions were 12.6, 10.4 and 8.1 cm; and in 22JZ they were 9.9, 7.9 and 12.8 cm (Supplementary Table 6). Overall, the maximum haplotype-associated difference in plant height reached about 12.8 cm (22JZ, H2 vs H4).\u003c/p\u003e\n\u003cp\u003eIn the expanded natural population, 28 high-confidence coding SNPs partitioned 313 accessions into six major haplotypes (H1\u0026ndash;H6; Fig. S3A). Across five field environments (18JZ, 19JZ, 20JZ, 20DT and 20YC), H2 consistently showed the lowest mean plant height (108.71\u0026ndash;119.69 cm; Supplementary Table 7), being generally shorter than the other five haplotypes (Wilcoxon tests, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Fig. S3B).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eA stable chromosome-2 hotspot and additional loci not reported previously\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn a previous study, Gao et al. (2025) mapped a major QTL, \u003cem\u003eqPH2-1\u003c/em\u003e (8.10\u0026ndash;28.52 Mb), on chromosome 2 in a 300 individual F₂ population from Jingu 28 \u0026times; Ai 88, an interval that encompasses and extends beyond our \u003cem\u003eqPH2.2\u003c/em\u003e (24.64\u0026ndash;24.84 Mb). A neighbouring segment on the same chromosome was likewise detected by He et al. (2021) in an Ai 88 \u0026times; Liaogu 1 RIL population (26.04\u0026ndash;28.91 Mb) and by Dai et al. (2024) in a 408 accession GWAS panel (\u003cem\u003eqPH02.13\u003c/em\u003e, marker LDB_2_24921046) (Supplementary Table 4). Convergent evidence from F₂, RIL and natural populations thus pinpoints the 24\u0026ndash;29 Mb region of chromosome 2 as a key hotspot controlling plant height in foxtail millet.\u003c/p\u003e\n\u003cp\u003eBy contrast, the additional loci we discovered\u0026mdash;\u003cem\u003eqPH2.1\u003c/em\u003e, \u003cem\u003eqPH4\u003c/em\u003e, and \u003cem\u003eqPH8\u003c/em\u003e\u0026mdash;have not appeared in previous RIL linkage maps analyses and natural-panel GWAS mining (Jaiswal et al. 2019; Jia et al. 2013), or the 1,844-accession graph-genome GWAS of He et al. (2023), and therefore represent genuinely novel additions to the foxtail-millet height landscape. Among them, \u003cem\u003eqPH4\u003c/em\u003e yielded the lowest \u003cem\u003eP\u003c/em\u003e-value and, together with \u003cem\u003eqPH8\u003c/em\u003e, was repeatedly detected in all three environments. \u003cem\u003eqPH2.2\u003c/em\u003e accounted for the largest proportion of phenotypic variance (17.12 %), while \u003cem\u003eqPH2.1\u003c/em\u003e still explained 10.62 %, underscoring the practical breeding value of these loci.\u003c/p\u003e\n\u003cp\u003eReliance on a 209-line Shanxi local foxtail millet association panel, phenotyped for three consecutive seasons at a single dryland site, likely improved QTL resolution and reproducibility by combining spatial uniformity with inter-annual climatic variation. The new loci enlarge the current catalogue of foxtail-millet height QTL and offer fresh entry points for map-based cloning and functional marker development; which will serve as useful tools for stacking dwarf alleles, potentially underpinning lodging-resistant, high-harvest-index cultivars in arid and semi-arid regions. Future work will adopt the cross-genome collinearity framework of Sandhu et al. (2021) in rice Meta-QTL analysis, together with functional-mutant validation and marker development, to clarify the underlying biology and facilitate efficient aggregation of superior alleles.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCandidate-gene landscape suggests GA-dependent and GA-independent contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEarly foxtail-millet height studies focused heavily on the GA pathway, He et al. (2021) highlighted \u003cem\u003eSeita.1G242300\u003c/em\u003e (GA2-oxidase-8) within \u003cem\u003eqPH1.3\u003c/em\u003e and proposed six additional GA-biosynthesis/signalling genes plus fifteen F-box genes across other QTL. Using RIL and F₂ data, Ni et al. (2017) mapped \u003cem\u003eZ3ph1\u003c/em\u003e, an \u003cem\u003esd1\u003c/em\u003e/\u003cem\u003eGA20ox\u003c/em\u003e homologue (89 % identity), in bin2021 on chromosome 2, reinforcing GA20ox as a core regulator. More recently, Dai et al. (2024) identified \u003cem\u003eWAK\u003c/em\u003e/\u003cem\u003eBph30\u003c/em\u003e, the NAC factor OMTN3, phospholipase pPLAIII, and the IAA-glucosidase TGW6 in \u003cem\u003eqPH03.14\u003c/em\u003e and \u003cem\u003eqPH06.10\u003c/em\u003e, implicating cell-wall plasticity and auxin/cytokinin homeostasis.\u003c/p\u003e\n\u003cp\u003eWithin \u003cem\u003eqPH2.1\u003c/em\u003e we pinpointed \u003cem\u003eSETIT_033071mg\u003c/em\u003e (\u003cem\u003eWAK4\u003c/em\u003e) and \u003cem\u003eSETIT_029387mg\u003c/em\u003e (FAD-linked oxidase); the former guards wall integrity, the latter may modulate ROS-lignin balance (Kanneganti and Gupta 2008; Schm\u0026uuml;lling et al. 2003). \u003cem\u003eqPH2.2\u003c/em\u003e houses \u003cem\u003eSETIT_030569mg\u003c/em\u003e, a chlorophyll a-b binding protein gene, suggesting that photosynthetic carbon flux can indirectly influence internode elongation (Green and Durnford 1996). \u003cem\u003eqPH4\u003c/em\u003e contains a bHLH transcription factor (\u003cem\u003eSETIT_007662mg\u003c/em\u003e) (Toledo-Ortiz et al. 2003) and \u003cem\u003eHVA22a\u003c/em\u003e (\u003cem\u003eSETIT_007340mg\u003c/em\u003e) (Brands and Ho 2002) linked to hormone crosstalk and ER\u0026ndash;vesicle homeostasis, while \u003cem\u003eqPH8\u003c/em\u003e features \u003cem\u003eWNK5\u0026nbsp;\u003c/em\u003e(Manuka et al. 2015; Urano et al. 2012), \u003cem\u003ePP2C\u003c/em\u003e (Leung et al. 1997; Umezawa et al. 2009), and RNA-binding/TPR proteins (Allan and Ratajczak 2011; Blatch and L\u0026auml;ssle 1999), pointing to BR/ABA signalling and RNA metabolism. Except for the functional parallel between\u003cem\u003e\u0026nbsp;WAK4\u003c/em\u003e and the \u003cem\u003eWAK\u003c/em\u003e reported by Dai et al. (2024) these genes have not surfaced in earlier height studies, widening the regulatory horizon beyond GA.\u003c/p\u003e\n\u003cp\u003eNotably, haplotype analysis of \u003cem\u003eSETIT_033071mg\u003c/em\u003e (\u003cem\u003eWAK4\u003c/em\u003e) in both the GWAS panel (209 Shanxi local landraces) and an expanded natural population (313 accessions) showed that the H2 haplotype consistently conferred the lowest plant height across eight field environments. In the GWAS panel, H2 reduced plant height by approximately 8\u0026ndash;13 cm relative to the other major haplotypes, while in the expanded panel it was associated with mean plant heights of 108.7\u0026ndash;119.7 cm, substantially lower than the remaining haplotypes. This pattern indicates that the H2 haplotype behaves as a moderate, environmentally robust semi-dwarf allele that could be directly exploited in marker-assisted selection or genomic selection schemes to breed lodging-resistant, high-harvest-index foxtail millet cultivars, particularly in dryland production systems.\u003c/p\u003e\n\u003cp\u003eCollectively, GA signalling remains a major determinant of plant height in foxtail millet, but our results also implicate additional processes (cell-wall remodelling, light/energy allocation, BR/ABA signalling and RNA metabolism), which require functional validation. In contrast to previously reported GA-related major loci, the associations detected here were largely of small effect, likely reflecting the composition of our panel (209 Dabaigu Shanxi landraces) with a relatively narrow origin and simple population structure. While this design may reduce stratification and improve mapping robustness, limited allelic diversity could constrain power to detect major or rare variants; therefore, validation in broader germplasm and across environments is warranted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWell-powered GWAS enabled by multi-year phenotyping of a diverse panel\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study assessed plant height in an association panel of 209 Shanxi local foxtail millet landraces, which were grown under standardized field conditions in Jinzhong, Shanxi Province, across three consecutive seasons (2020\u0026ndash;2022). Jinzhong, experiences a typical temperate continental monsoon climate and is representative of the northern Chinese dry-land millet agro-ecosystem (Sun et al. 2022). A mixed linear model (MLM) fitted across three successive seasons yielded a broad-sense heritability (\u003cem\u003eH\u003c/em\u003e\u0026sup2;) of 93.6% for plant height, indicating strong genetic control while retaining some environmental responsiveness. The resulting BLUE values thus provide a robust phenotypic foundation for high-resolution GWAS and reliable QTL localization.\u003c/p\u003e\n\u003cp\u003ePlant stature integrates lodging resistance, canopy light capture and synchronised grain filling (Dineshkumar et al. 1992; Tian et al. 2017); moderate dwarfism therefore enhances harvest index and facilitates mechanised harvesting while reducing labour and energy inputs (Pearce 2021). In the context of increasingly volatile climates, semi-dwarf, lodging-resistant foxtail millet cultivars are vital for sustaining food and feed production across arid and semi-arid zones (Choudhary et al. 2023; Yu et al. 2024).\u003c/p\u003e\n\u003cp\u003eThe continual expansion of genetic resources has likely enhanced the resolution with which plant height in foxtail millet can be dissected. Three material systems now drive the progress of genetic decipher of plant height regulation in foxtail millet. (i) Biparental populations. In a 124 plant Hongmiaozhangu\u003cem\u003e\u0026nbsp;\u0026times;\u0026nbsp;\u003c/em\u003eChangnong35 F₂ family, Wang et al. (2017) delimited the major locus\u003cem\u003e\u0026nbsp;qPH1.1\u003c/em\u003e. He et al. (2021) later used 333 Ai 88 \u0026times; Liaogu 1 RILs to uncover 26 QTL on nine chromosomes, underscoring the value of deep recombination for capturing both large- and moderate-effect loci. (ii) Diversity panels. Resequencing 916 globally sourced accessions, Jia et al. (2013) performed five-environment GWAS and pinpointed three stable height QTL, illustrating how abundant allelic diversity and rapid LD decay deliver high-resolution mapping. (iii) Functional mutants and multi-parent resources. The dwarf mutant \u003cem\u003eSidwarf2\u003c/em\u003e of Yugu 1 was resolved by BSA-seq and fine mapping to a 52.7-kb window on chromosome 3, implicating a cytochrome P450 gene (Xue et al. 2016).\u003c/p\u003e\n\u003cp\u003eTogether, these resources create a seamless pipeline\u0026mdash;from primary mapping and fine localization to causal validation and elite-allele assembly. Leveraging high-stability, single-site multi-year phenotypes from 209 diverse accessions, our GWAS identified environmentally stable height loci and deployable markers. The resulting genetic targets and rich allelic variation underpin semi-dwarf breeding for northern dry-land foxtail millet and provide a solid springboard for subsequent mutant validation and allele pyramiding.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Drs. Qiang He, Hongkai Liang and Bin Liu for their valuable support and assistance with data processing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWZ, HQ and HZ performed the data analyses and drafted the manuscript. ZQ, ZM and XD conceived and supervised the study, developed the 209-line population, and revised the manuscript. RH and SH managed the field trials and conducted haplotype analysis. HW, JW, LC and XT collected the phenotypic data. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key Project of Research and the Development Plan of China (2021YFF1000103), the Major Special Science and Technology Project of Shanxi Province (202101140601027), Science and Technology Innovation Enhancement Project of Shanxi Agricultural University (CXGC2023090), and the National Natural Science Foundation of China (32241041),\u0026nbsp;Research Project for Introduced Talent of Shanxi Agricultural University(2021BQ37).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData, materials and code availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll raw resequencing genotype data and supplementary files generated in this study are available in Zenodo at https://doi.org/10.5281/zenodo.18012842.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no relevant financial or non-financial interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllan RK, Ratajczak T (2011) Versatile TPR domains accommodate different modes of target protein recognition and function. 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The Crop Journal 11:593-604\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"","lastPublishedDoi":"10.21203/rs.3.rs-8422656/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8422656/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePlant height is pivotal for lodging resistance, harvestability, and yield potential in foxtail millet (\u003cem\u003eSetaria italica\u003c/em\u003e). Here, we evaluated plant height in 209 diverse Shanxi local foxtail millet landraces across three contrasting environments and performed whole-genome resequencing of these accessions (mean depth ~38×). After standard filtering, 833,157 high-quality single nucleotide polymorphisms (SNPs) were retained. Neighbor-joining and principal component analysis consistently resolved four genetic groups with minor differences in sample assignment. A mixed linear model GWAS detected four loci significantly associated with plant height, including three loci not reported previously. Comparative genomics with rice and Arabidopsis shortlisted ten candidate genes; notably, \u003cem\u003eSETIT_033071mg\u003c/em\u003eshowed stem-specific high expression at the shooting stage in model variety Yugu1, with low expression elsewhere. Haplotype analysis of \u003cem\u003eSETIT_033071mg\u003c/em\u003e in our GWAS panel (209 accessions evaluated across three environments) and an expanded diversity panel (313 accessions evaluated across five field environments) resolved multiple major haplotypes, among which H2 was consistently associated with reduced plant height across environments. These findings refine the genomic basis of plant height in foxtail millet and provide actionable targets for marker development for breeding lodging-resistant cultivars.\u003c/p\u003e","manuscriptTitle":"Genome-wide association analysis and candidate gene identification for plant height in Shanxi local foxtail millet varieties","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-14 10:11:19","doi":"10.21203/rs.3.rs-8422656/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-23T06:39:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-09T04:00:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"258403073127523974338665550783078198689","date":"2026-02-16T07:55:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-27T11:36:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191759647318916307846482330566615086285","date":"2026-01-14T11:56:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-12T07:29:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-29T22:57:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-29T11:49:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2025-12-22T08:03:22+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":"3577c15f-0c3b-4015-aaf9-d587409f9166","owner":[],"postedDate":"January 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T07:38:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-14 10:11:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8422656","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8422656","identity":"rs-8422656","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}