Identification of a major QTL and candidate gene for photoperiodic flowering in industrial hemp via BSA-seq and fine mapping

Identification of a major QTL and candidate gene for photoperiodic flowering in industrial hemp via BSA-seq and fine mapping | 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 Article Identification of a major QTL and candidate gene for photoperiodic flowering in industrial hemp via BSA-seq and fine mapping Lili Tang, Chao Fan, Lie Yang, Hongmei Yuan, Lili Cheng, Dandan Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9403686/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Industrial hemp ( Cannabis sativa L.) is a photoperiod-sensitive short-day crop, yet the quantitative trait loci (QTLs) governing flowering time remain poorly characterized, limiting molecular breeding efforts. To identify genomic regions controlling photoperiodic flowering, we performed bulked segregant analysis sequencing (BSA-seq) coupled with fine linkage mapping using an F 2 population derived from a cross between day-neutral (Bubble Kush) and short-day (Aquawoman) accessions. Based on extreme flowering phenotypes, we pooled DNA from 50 early- and 50 late-flowering individuals. A major QTL, qHFX , was mapped to a 2.2 Mb region on chromosome X via ΔSNP-index and Euclidean distance algorithms. Using 15 Kompetitive allele-specific PCR (KASP) markers developed from parental polymorphisms, we refined qHFX to a 637-kb interval in 300 F 2 individuals. RNA-seq analysis of Aquawoman under short-day and long-day conditions identified five differentially expressed genes within this interval, with expression profiles validated by quantitative real-time PCR. Sequence analysis revealed a 1-bp indel in LOC115716363 , which emerged as a strong candidate gene. Notably, heterologous overexpression of LOC115716363 in rice significantly delayed flowering, further supporting its role in flowering time regulation. Collectively, these findings elucidate the molecular basis of flowering time in industrial hemp and provide valuable genomic resources for breeding broadly adapted, high-yield varieties. Biological sciences/Biotechnology Biological sciences/Genetics Biological sciences/Molecular biology Biological sciences/Plant sciences Industrial hemp Flowering time BSA-seq RNA-seq Candidate gene Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Industrial hemp ( Cannabis sativa L.) is an important multipurpose crop with a long history of use in textile, paper, food, and medicine [ 1 ]. As a short-day plant, its flowering time is tightly controlled by photoperiod, which in turn significantly affects yield and quality. Previous studies demonstrated that industrial hemp originating from high latitudes had a prolonged growth period when cultivated in low latitudes, leading to late flowering with no seed or unmatured seeds. In contrast, when industrial hemp from low latitudes is cultivated in high latitudes, its growth period is greatly shortened, resulting in early flowering or plant stunting with great losses of yield and quality [ 2 , 3 ]. Therefore, different varieties of industrial hemp respond very differently to their environment when planted in different latitudes. Among the cannabis germplasm resources, very few varieties are day-neutral or autoflowering, and will begin flowering when they are developmentally ready to do so, regardless of the photoperiod. These genetics do not depend on photoperiod changes to trigger photoperiodic flowering [ 4 ]. The discovery of autoflowering cannabis germplasm resource presents unique opportunities to change industrial hemp breeding patterns. Thus, understanding the molecular mechanisms underlying flowering is key to developing new industrial hemp varieties for growth in local climatic and photoperiod conditions. Flowering time is a complex quantitative trait controlled by multiple genes on which environmental factors including photoperiod, temperature, and hormones have a strong effect [ 5 ]. Among these factors, the photoperiod is a key factor regulating the floral transition from the vegetative stage to the reproductive stage. Current progress in next-generation sequencing (NGS) technology has led to the discovery and subsequent ability to clone many genes and loci to genetically control the flowering time. To date, many genes and loci related to flowering time have been discovered using NGS-based bulked segregant analysis (BSA-seq) in crops including soybean [ 6 ], luffa [ 7 ], cucumber [ 8 ], wheat [ 9 ], rice [ 10 ], and carnation [ 11 ]. To our knowledge, genomic regions governing flowering time in industrial hemp have not been reported. Transcriptome analysis (RNA-seq) provides sensitive, high-resolution analyses of the transcriptionally dynamic responses of plants to environmental stressors [ 12 ]. Furthermore, RNA-seq is powerful for detecting and quantifying novel or rare transcripts, with high reproducibility for both technical and biological replications [ 13 ]. Therefore, RNA-seq has been widely used to investigate the molecular mechanism of the photoperiodic flowering response in Arabidopsis [ 14 ], rice [ 15 ], soybean [ 16 ], and other crops [ 17 ]. Thousands of differentially expressed genes (DEGs) have been identified by RNA-seq; however, identifying the target genes for flowering time is still difficult. Recently, the combination of BSA-seq and RNA-seq has emerged as a popular approach for efficiently identifying and mapping genomic fragments or candidate genes that can be verified by each other. Recently, a candidate gene related to DNA mismatch repair for the restorer-of-fertility in onion has been identified using QTL-seq and RNA-seq [ 18 ]; a major QTL for capsaicinoid content has been mapped on chromosome 6 in pepper using QTL-seq and high-density genetic mapping, with candidate genes subsequently identified by combining QTL analysis with RNA-seq [ 19 ]; and a major QTL for salt tolerance at the bud burst stage has been mapped on chromosome 7 in rice using BSA-seq, followed by candidate gene identification through integration of QTL analysis and RNA-seq [ 20 ]. In this study, we combined QTL mapping with RNA-seq to dissect the genetic basis of photoperiodic flowering time using an F 2 population derived from a cross between the day-neutral strain Bubble Kush and the short-day strain Aquawoman. Furthermore, the candidate gene identified was functionally validated via heterologous overexpression in rice. 2. Results 2.1. Distribution of the flowering time in the F 2 population The frequency distribution of flowering time in the F 2 population derived from a cross between Bubble Kush (day-neutral) (Fig. 1 A) and Aquawoman (short-day) (Fig. 1 B) under natural long-day conditions (Harbin, Heilongjiang) is presented. The flowering time of the F 2 individuals exhibited a wide range of variation, from approximately 34 to 99 days, indicating substantial transgressive segregation (Fig. 1 C). The distribution was continuous and unimodal, consistent with quantitative inheritance of the photoperiodic flowering trait. A distinct skewness toward late flowering was observed, with the majority of individuals flowering later than the mid-parent value, suggesting a possible dominance effect of late-flowering alleles derived from the short-day parent Aquawoman. Based on the extreme flowering phenotypes, we pooled DNA from 50 early- (E-pool) and 50 late- (L-pool) flowering individuals for further analysis. This skewed distribution also implies that a small number of major loci may control the flowering time variation in this population. 2.2. Identification of a major QTL for flowering time by BSA-seq DNA samples from 50 F 2 progenies with either extreme late or early flowering time were bulked in equal amounts for sequencing. A total of 238-Gbp clean reads, representing 95.30% of the average genome coverage, were generated from Bubble Kush (P 1 ), Aquawoman (P 2 ), and the two extreme bulks. The clean reads Q30 reached more than 91%, with 50.19× average sequencing depth and 95.30% average genome coverage. In addition, 99.12% of the clean reads were mapped to the C. sativa genome (Table S1 ). SNPs were called by comparing sequences of the parents and the two extreme pools. A total of 12,407,613 SNPs were filtered and included in the SNP-index for QTL mapping (Table S2). In addition, the InDel-index was calculated for each bulk by aligning the sequences to the C. sativa reference genome to generate a total of 2,600,583 SNPs (Table S3). The SNP-index was estimated with GATK for each bulk by aligning the sequences to the C. sativa reference genome at a statistical confidence of P < 0.05. A 2.2-Mb genomic region (75,000,000 bp ~ 77,200,000 bp) on chromosome X with a Δ(SNP-index) of 1 was identified (Fig. 2 ). Additionally, the ED was also calculated by aligning the sequences to the C. sativa reference genome. A total of three regions on chromosome 4 and chromosome X carrying the genes for flowering time were detected (Fig. 3 ). The QTL for flowering time from the SNP-index overlapped with the genomic region for flowering time obtained using the ED method on chromosome X, which confirmed the candidate QTL position with candidate genes for flowering time on chromosome X. Therefore, this QTL on chromosome X was named qHFX. A total of 45 potential candidate genes were screened within the qHFX region using GO, Kyoto Encyclopedia of Genes and Genomes, NCBI’s nr, Swissprot, and Pfam databases (Table S4). 2.3. Fine mapping analysis of qHFX Primary mapping showed that the fragment region between 75,042,733-bp and 77,153,397-bp harbored candidate genes for the flowering time. The qHFX coding region contained 609 SNPs on chromosome X, mainly including 138 Non_synonymous_coding, 20 frame_shift, 93 intragenic, 166 intron, and 6 stop_gained (Table S5). A total of 300 F 2 progenies were genotyped with 15 highly credible SNPs to yield a qHFX linkage map using an LOD score of 5.0 as the threshold for consecutive occurrence (Fig. 4 and Table S6). The qHFX locus with a LOD score of 23.36 at peak spawned the 637-kb and harbored 14 genes associated with the flowering time ( https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_900626175.2/ ) (Fig. 4 ), which explained 28.50% of the phenotypic variation (Table 1 ). Table 1 Identification of qHFX for the flowering time by linkage analysis Trait QTL 1 Chromosome Position Left Marker Right Marker LOD 2 PVE(%) 3 Add 4 HF qHFX X 76,903,638 SNP8 SNP13 8.47 28.50 -1.21 1 QTL, quantitative trait locus; 2 LOD, logarithm of the odds; 3 PVE, phenotypic variation explained; 4 Add, additive effects. 2.4. RNA-seq data analysis RNA-seq analysis was performed to investigate the genes involved in flowering time. 3.71 ~ 4.71 million reads were generated per replicated sample. The high-quality base (% ≥ Q30) of each sample was more than 90% (Table S7). The uniquely mapped values were greater than 88.27%, while the multiple mapped values were lower than 8.25% by STAR for RNA-seq (Table S8). The number of mapped reads was normalized to fragments per kilobase of transcript per million fragments mapped. By restricting |log2 (FC)| ≥ 1, the respective numbers of DEGs in the 50SD vs. 50LD and 60SD vs. 60LD comparisons were 5,215 and 2,589, respectively. The volcano plots of DEGs in the two groups are shown in Fig. 5 A,B. 2.5. DEGs functional annotation and enrichment analysis All DEGs from the comparisons of 50SD vs. 50LD and 60SD vs. 60LD were assigned GO terms in three functional groups, including molecular function, biological processes, and cell composition (Fig. 6 A,B). Obviously, more DEGs from 50SD vs. 50LD were assigned to GO terms compared with DEGs from 60SD vs. 60LD. In the biological process category, DEGs from 60SD vs. 60LD were the most significantly enriched in the cell proliferation and rhythmic process. The results suggested that most of the DEGs were significantly enriched in the rhythmic process, with various metabolic activities under different photoperiods. DEGs were mapped to the KEGG pathway to investigate which DEGs are activated and which are suppressed in different groups of pathways under different photoperiods (Tables S9a,b). DEGS were the most significantly enriched in the metabolic pathways, regardless of whether DEGs were up- or down-regulated. DEGs, including 14 DEGs at 50 d and 8 DEGs at 60 d, were significantly enriched in the circadian rhythm (Fig. 6 C,D). 2.6. Candidate gene analysis of DEGs within the major QTL NGS reads from the qHFX interval were annotated to the C. sativa reference genome, resulting in 14 candidate genes (Fig. 4 ). A total of 14 genes were predicted by combing the QTL-seq and RNA-seq results, among which 5 genes were differentially expressed at 50 d and 60 d under long-day and short-day periods, respectively (Table S10). Among five DEGs, four DEGs ( LOC115714749, LOC115724847, LOC115724856 and LOC115703387 ) were synonymous mutation, and one DEG ( LOC115716363 ) was a frame shift mutation. LOC115716363 had high homology to Arabidopsis AHL20/22 , which played an important role in delaying flowering according to data from the TAIR database. Therefore, LOC115716363 underlying qHFX on chromosome X was considered to be a candidate gene regulating flowering time and was designated as CsAHL . To further confirm the expression patterns of these genes, the expression levels of these five DEGs were evaluated using qRT-PCR to validate the RNA-seq results. The 18S gene was used as an internal control. The relative expression patterns of the five DEGs showed consistent trends with the RNA-seq data, although the absolute expression levels differed somewhat from the RNA-seq measurements (Fig. 7 A,B). 2.7. Identification of candidate gene loci for flowering time The genomic sequences of the candidate gene CsAHL ( LOC115716363 ) from the two parents, Aquawoman and Bubble Kush, were aligned with the CoDing Sequence (CDS) against the C. sativa reference genome. As shown in Fig. 8 , Bubble Kush carried a 1-bp insertion (A was inserted in position 439) compared with Aquawoman and the reference genome. The sequencing results agreed with the BSA-seq analysis, confirming that LOC115716363 was the most probable functional gene underlying qHFX for flowering time. 2.8. Functional validation of the candidate gene in rice via genetic transformation To investigate the conserved function of LOC115716363 in flowering regulation, we selected rice, another short‑day crop, for functional validation. Ten transgenic rice lines overexpressing LOC115716363 under the control of the CaMV 35S promoter were generated using the binary vector shown in Fig. 9 A. Molecular screening of T 3 homozygous lines identified three independent overexpression lines (OE‑2, OE‑5, and OE‑8) with high transcript levels of LOC115716363 . Phenotypic evaluation under short-day conditions revealed that all three overexpression lines exhibited significantly delayed flowering compared to wild‑type (WT) controls, as evidenced by a marked increase in days to flowering (Fig. 9 B). Specifically, WT plants flowered at approximately 100 days, whereas OE‑2, OE‑5, and OE‑8 lines flowered at around 115–120 days (Fig. 9 C). No substantial differences in flowering time were observed among the three overexpression lines, suggesting that the effect of LOC115716363 on flowering time may not be strictly dose‑dependent. Overall, these results indicate that heterologous overexpression of LOC115716363 delays floral transition in rice, implying a potential conserved role in flowering regulation across species. 3. Discussion Understanding the flowering time in industrial hemp is essential for maximizing reproductive success, as it strongly influences crop quality and yield [ 23 ]. Improving flowering time is a major goal of plant breeding aimed at developing new industrial hemp strains with better adaptation to local environments. The floral transition from vegetative to reproductive growth is a key determinant of yield potential [ 24 ]. Modulating key flowering time genes has been pivotal for crop domestication and enhancing adaptation across different climatic regions [ 25 ]. As a short-day plant, photoperiod is a critical factor regulating flowering time in industrial hemp. Under long-day conditions, plants remain vegetative, and flowering is induced only after several consecutive short days. The genetic control of flowering time in industrial hemp is strongly influenced by latitudinal adaptation to uniform growing environments. Therefore, modifying flowering time genes may facilitate the cultivation of hemp at new latitudes. A comprehensive understanding of the genetic basis of flowering time is thus important for integrating industrial hemp into modern agriculture. However, little is currently known about the underlying genetic architecture. In this study, we identified a major genomic region and a candidate gene controlling flowering time variation in a biparental F 2 population. This was achieved by first using BSA-seq on bulked samples from phenotypic extremes to delimit a QTL, followed by fine mapping using individual progeny. The findings are promising for the discovery and implementation of molecular markers that will enable the development of early- or late-flowering industrial hemp strains with improved adaptation to specific photoperiod regions. The availability of the C. sativa genome reference has facilitated the widespread use of QTL-seq coupled with transcriptome sequencing to identify potential candidate genes. In this study, we developed an F 2 population from a cross between Bubble Kush (day-neutral and light-insensitive) and Aquawoman (short-day and light-sensitive). Individuals from the F 2 population exhibited transgressive segregation and a skewed distribution toward late flowering (Fig. 1 ), suggesting possible dominance of late-flowering alleles. We employed a bulk segregant approach coupled with whole-genome sequencing (QTL-seq) and classical linkage mapping to identify a major QTL, qHFX (Figs. 2 and 3 ), which was localized to a 637-kb physical interval harboring 14 genes on chromosome X. This is the first report of QTL-seq being used to directly identify flowering time QTLs in C. sativa . Bulked segregant analysis was first applied to facilitate linkage analysis of discrete characters in F 2 populations [ 24 ]. Subsequently, QTL-seq has been used to identify the early-flowering QTL Ef1.1 on chromosome 1 in cucumber [ 8 ], to map QTLs underlying disease resistance in rice [ 27 ], and to identify QTLs for fruit weight and locule number in F 2 populations [ 28 ]. Collectively, the present study demonstrates that QTL-seq combined with traditional linkage analysis and high‑quality whole‑genome resequencing provides an efficient platform for QTL identification in F 2 populations. The molecular mechanisms underlying flowering time and candidate genes for flowering time QTLs have been extensively studied in many important crops [ 6 , 29 , 30 ]. Previous studies revealed that the CONSTANS-like ( COL ) gene family plays a crucial role in regulating flowering time in C. sativa , and CsCOL genes are unevenly distributed across seven chromosomes [ 31 ]. In this study, we used NGS‑based BSA to simultaneously map qHFX , a locus regulating flowering time in industrial hemp. To further refine candidate genes within the qHFX interval, we performed RNA-seq analysis on the parental line Aquawoman under short‑day and long‑day conditions. Among the 14 genes located in the candidate region, five showed differential expression between the two photoperiod treatments, and their expression patterns were validated by quantitative real‑time PCR. Based on its expression profile and gene structure, LOC115716363 emerged as a strong candidate gene for qHFX . NCBI homology analysis revealed that LOC115716363 encodes an AT‑hook motif nuclear‑localized protein homologous to the Arabidopsis AHL gene family. In Arabidopsis, AtAHL20 and AtAHL22 have been shown to delay flowering by repressing FT expression, acting as negative regulators of flowering [ 32 – 33 ]. Candidate gene analysis further revealed that LOC115716363 carries a 1-bp difference in the coding sequence between Bubble Kush and Aquawoman (Fig. 8 ), resulting in a frameshift mutation similar to those observed in clock genes associated with crop domestication [ 16 ]. LOC115716363 encodes an AT‑hook motif nuclear‑localized protein that contributes to functional nuclear architecture by binding to the nuclear matrix [ 34 ]. Previous studies have demonstrated the involvement of AT‑hook proteins in meristem identity maintenance, leaf longevity, photomorphogenesis, and flower development [ 35 , 36 ]. To functionally validate this candidate gene, we heterologously overexpressed LOC115716363 in rice. Under short‑day conditions, all three independent overexpression lines exhibited significantly delayed flowering compared to wild‑type controls (Fig. 9 ). This cross‑species functional conservation further supports the role of LOC115716363 as a flowering repressor in industrial hemp. Collectively, our results support the involvement of LOC115716363 in regulating flowering time in hemp. Further functional studies are required to elucidate the precise role of this gene, which will enrich the current understanding of flowering time regulation in C. sativa . 4. Materials and Methods 4.1. Development of mapping populations and phenotyping Purified seeds of 'Bubble Kush' were obtained from Oregon State University (Corvallis, OR, USA), and purified seeds of 'Aquawoman' were obtained from the Heilongjiang Academy of Agricultural Sciences (Harbin, China). Bubble Kush is a day-neutral, light-insensitive strain with 34–44 days to flowering, that is, flower time is not affected by the length of light under an environment suitable for growth of industrial hemp. While Aquawoman is a short-day, light-sensitive strain with 94–98 days to flowering in Harbin, China (45°49′44″ N, 126°48′55″ E). This flowering time refers to the flowering period from the May to August each year in Harbin, which is also the growing season for industrial hemp in northern China. In order to shorten the research period, Bubble Kusk is chosen as the mother parent and Aquawoam as the father parent to construct genetic population. On the one hand, the purified Bubble Kush seeds were planted in the glasshouse and the medium of the cultivated plants was common seedling soil in August 2020, and a healthy female plant was selected as the mother parent. On the other hand, when Aquawoman is in full bloom, a pollen plant is randomly selected and stored at 4℃ for later use. The cross was made by pollinating Bubble Kush with pollen from Aquawoman to develop an F 1 population in October 2021. From May to August 2022, a female plant from F 1 was randomly selected for self-pollination to generate a feminized F 2 generation [ 37 ]. In order to form a male flower for producing pollen, half of the selected female F 1 plant was sprayed with silver thisuflate (STS). When F 1 plant grew to 70 days, 0.92 mmol L − 1 STS was prepared and sprayed leaves and stems every 3 days with 500 mL each time. After a month, the sprayed plant could product pollen. The two parents and 300 feminized F 2 seeds were planted in greenhouses at the Modern Agriculture Demonstration Area (45°49′44″ N, 126°48′55″ E) in Harbin City, Heilongjiang Province, China, in May 2023 ~ August 2023. The light conditions are natural long-day, that is, the average light time in May was 14.46 h, the average light time in June was 15.39 h, the average light time in July was 15.18 h, and the average light time in August was 14.05 h. At the flowering stage, days to flowering (after sowing seeds to anthesis) were visually recorded for each F 2 plant. The course from first flower opening (anthesis) to fully open (the stigma protrudes completely) was monitored for each F 2 plant [ 37 ]. Frequency distribution of the flowering time in F 2 progenies were analyzed using IBM SPSS software, showing two extreme groups: (1) The early-flowering time group ranged from 35–57 days to flowering; (2) The late-flowering time group ranged from 86–99 days to flowering. The fresh leaves of 50 F 2 progenies in the early-flowering and late-flowering, were sampled, respectively. Meanwhile, the fresh leaves of the two parents, Bubble Kush and Aquawoman, were sampled. These samples are used for DNA extraction by QTL-Seq. 4.2. QTL-seq analysis Genomic DNA was isolated from fresh leaves of the two parents (Bubble Kush and Aquawoman), and two extreme bulks, each with 50 extreme early-flowering and late-flowering plants, using the CTAB method with minor modification [ 38 ]. DNA quality was assessed using the NanoPhotometer® spectrophotometer (IMPL EN, CA, USA). An 5 µg of DNA from sample (mixed individual) to generate the libraries for sequencing on an Illumina HiSeq X Ten platform at Adsen Biotechnology Co., Ltd. (Urumchi, China) [ 27 ]. The clean reads were aligned to the C. sativa L. reference genome sequence obtained from the National Center of Biotechnology Information (NCBI) database ( https://www.ncbi.nlm.nih.gov/data-hub/genome/GCF_900626175.2/ ) using Burrows- Wheeler Aligner (BWA) [ 39 ]. SNPs were called using the Genome Analysis Toolkit (GATK) [ 40 ]. Duplicate reads that located the reference genome were marked using SAMtools. Local realignment around indels and variant calling were performed with GATK LocalRealigner and BaseRecalibrator to correct artifacts between the reference and reads close to the misaligned region and to ensure the accuracy of all identified SNPs. Then, SNPs were detected and filtered with GATK to generate the final SNP locus set. The detailed steps are as follows: (1) Picard’s MarkDuplicates was applied to directly process BWA’s alignment for the removal of duplicates to shield the influence of PCR-duplication. (2) GATK IndelRealigner was applied to correct the error of comparison results caused by insertion and deletion. (3) GATK BaseRecalibrator was applied to correct the base mass value. (4) GATK was applied to call short variants (SNPs + indels). (5) GATK was used to correct the variations for the selection of reliable variations [ 41 ]. In order to obtain high quality SNP sites, SNP was filtrated by parental depth not less than 5 X, the depth of the mixing pool shall not be less than 8 X and both parental genotypes need to be homozygous and polymorphic. The Euclidean distance (ED) method and the genotype frequency difference between the mixed pools were applied for association analysis (SNP-index) [ 42 , 43 ]. The ED method evaluated the regions associated with flower time traits by looking for markers of significant difference between different color-mixing pools. The SNP-index (Δ-index) was calculated to detect the allele frequency between the mixed pools. The closer the Δ-index was to 1, the closer the association between the SNP and the color trait. 4.3. Regional linkage mapping analysis Primers for KAPS markers were designed with Premier 6.0 according to the BSA-seq results and two parental resequencing data (Table S6). The primer sequences were synthesized by Sangon Biotech Company (Shanghai, China). 300 F 2 individuals were genotyped to narrow the potential candidates with the Inclusive Composite Interval Mapping (ICIM) module of QTL IciMapping (v.4.2; https://isbreedingen.caas.cn/software/qtllcimapping/294607 ). The logarithm of the odds (LOD) score threshold for significant QTLs was determined using 1,000 permutations at P < 0.01 significance level. Annotation information for the candidate genes in the localized region was predicted using data obtained from the NCBI database ( https://www.ncbi.nlm.nih.gov/ ) and the Arabidopsis Information Resource (TAIR) database ( https://www.arabidopsis.org/ ). DNA sequences of the candidate genes were downloaded. 4.4. RNA extraction and RNA-seq analysis Aquawoman was cultured under short-day (8 h/16 h day/night) and long-day (18 h/6 h day/night) conditions with three replicates. A preliminary experiment showed that Aquawoman only flowered at 60 d under short-day conditions. Aquawoman was unable to flower at 50 d under short-day conditions, nor at either 50 d and 60 d under long-day conditions. Newly emerged leaves of Aquawoman under short-day and long-day treatments were sampled at 50 d (50SD and 50LD) and 60 d (60SD and 60LD), respectively, to generate a total of 12 samples: 50SD-1, 50SD-2, 50SD-3, 50LD-1, 50LD-2, 50LD-3, 60SD-1, 60SD-2, 60SD-3, 60LD-1, 60LD-2, and 60LD-3. All samples were flash frozen in liquid nitrogen and stored at -80℃ for RNA extraction. Total RNA was extracted by TRIzol reagent (Life Technologies, USA), following the manufacturer's instructions. The isolated total RNA was treated with DNase I to remove all DNA residues using the RNAprep Pure Plant Kit (TIANGEN, Beijing, China), following the manufacturer’s instructions. The quality of total RNA was assessed using a spectrophotometer (NanoPhotometer, Implen, CA, USA). The quantity of RNA was measured using a Fluorometer (Qubit 2.0, Life Technologies, CA, USA). The integrity of RNA was evaluated using a Bioanalyzer 2100 System (Agilent Technologies, CA, USA). The high-quality total RNA was then enriched for mRNA either by using oligo (dT) beads (for eukaryotic mRNA) or by selectively removing the rRNA (for prokaryotic mRNA). The enriched mRNA was fragmented to be reverse transcribed into cDNA with random primers, which was used as templates to synthesize the second-strand cDNA. The cDNA fragments were purified and then subjected to end repair and dA-tailing reaction. A sequencing adaptor was ligated to each fragment. The ligated fragments were separated by agarose gel-electrophoresis, amplified by PCR, and sequenced using an Illumina HiSeq™ 2500 System (Adsen Biotechnology Co., Ltd. Urumchi, China). The remaining high-quality reads were aligned against the reference transcriptome using Bowtie 2 [ 44 ]. The gene abundances were presented as reads per kb million [ 43 ]. DEGs were detected using package edgeR ( http://www.r-project.org/ ) under the threshold of fold change (FC) ≥ 2 and false discovery rate < 0.05. Gene ontology (GO) terms ( http://www.geneontology.org ) for each of the identified unigenes were assigned based on the UniProt and the Pfam databases [ 46 ] and then mapped to a plant-specific GO slim ontology ( http://www.geneontology.org/ ) [ 47 ]. All unigenes were loaded into the PathoLogic format to predict the biochemical pathways using the Pathway Tools program [ 48 ]. 4.5. Validation of RNA-seq and candidate gene prediction RNA-seq results were validated with qRT-PCR. Special primers were designed using Premier v (6.0) and synthesized by Sangon Biotech Company (Shanghai, China). The primer sequences are provided in Table S11, and 18S was used as the endogenous control. qRT-PCR was performed by RealmasterMix (SYBR Green) with PCR parameters as follows: predenaturation at 95℃ for 10 s, followed by 38 cycles of denaturation at 95℃ for 15 s, annealing at 56℃ for 30 s, and extension at 72℃ for 30 s. The temperature of the dissolution curve ranged from 55℃ to 95℃, dropping 0.5℃ every 5 s, and was finally preserved at 4℃. Each PCR reaction (20 µL) contained 10 µL SYBR Green Master Mix, 1 µL upstream and downstream primers, 5 uL cDNA, and 4 uL ddH 2 O. Three biological replicates were performed, with three technical replicates for each sample. Relative gene expression levels were measured by the 2 −ΔΔCt method [ 49 ]. 4.6. Validation of candidate gene sequencing and sequence alignment Candidate genes for the flowering time in Bubble Kush and Aquawoman were cloned and sequenced. Sequences were aligned using DNAMAN, with genes in the C. sativa genome used as a reference. Primers were designed with Primer Premier 6.0 to amplify the full-length genome sequences of target gene LOC115716363 (forward primer: GCTACTCTTCTACTACAACCT; reverse primer: GCGATAACGACAATGATGG). The PCR reaction parameters were as follows: predenaturation at 95℃ for 10 s, followed by 38 cycles of denaturation at 95℃ for 15 s, annealing at 50℃ for 30 s, and extension at 72℃ for 30 s, and then finally preserved at 4℃. The PCR reaction (20 µL) contained 10-µL 2×HiFi PCR Mix, 1 µL upstream and downstream primers, 5 µL cDNA, and 4 µL ddH 2 O. Primer synthesis and sequencing of PCR products were conducted by Sangon Biotech Company (Shanghai, China). 4.7. Rice transformation and flowering time assay The coding sequence of LOC115716363 was cloned into the binary vector pCAMBIA1301 under the control of the CaMV 35S promoter to generate the plant expression construct. The recombinant plasmid was introduced into Agrobacterium tumefaciens strain EHA105 by electroporation. Rice mature embryos were used as explants. Agrobacterial infection and co‑cultivation were performed as previously described [ 50 ]. Transgenic calli were selected on medium containing 50 mg/L hygromycin, and regenerated plantlets were transferred to soil in a greenhouse. T 0 transgenic plants were self‑pollinated to obtain T 1 seeds, and homozygous T 3 lines (OE‑2, OE‑5, and OE‑8) were identified by hygromycin resistance segregation analysis and further confirmed by PCR for the presence of LOC115716363 . For flowering time analysis, wild‑type (WT) and LOC115716363 ‑overexpressing lines (OE‑2, OE‑5, and OE‑8) were grown under short‑day conditions (10 h light/14 h dark, 28°C/24°C, 70% relative humidity) in a growth chamber. The days to flowering were recorded as the number of days from sowing to heading (defined as the emergence of the first panicle tip from the flag leaf sheath). At least 30 individual plants per line were evaluated in three independent biological replicates. 5. Conclusions In this study, QTL analysis was used to identify genes regulating flowering time in industrial hemp by utilizing day‑neutral and short‑day cannabis strains. A major QTL was mapped to chromosome X using ΔSNP‑index and Euclidean distance algorithms. Fifteen Kompetitive allele‑specific PCR (KASP) markers were developed to successfully narrow down the QTL to a 637-kb interval. Additionally, RNA-seq analysis of the parental line Aquawoman under different photoperiod conditions revealed five differentially expressed genes within the candidate region. The RNA‑seq results were validated by quantitative real‑time PCR (qRT‑PCR). Furthermore, sequence analysis revealed a 1‑bp Indel difference in LOC115716363 between the two parents. LOC115716363 was identified as a candidate gene for flowering time and was designated as CsAHL . Heterologous overexpression of CsAHL in rice significantly delayed flowering under short‑day conditions, further supporting its role as a flowering repressor. In summary, the findings of this study provide insights into the molecular mechanisms underlying flowering time and offer useful genetic resources for molecular breeding of industrial hemp. Declarations Conflicts of Interest: The authors declare that they have no conflict of interest. Funding: This research was funded by National Natural Science Foundation of China (32401879) and the Agricultural Science and Technology Basic Innovation Project (CX23YQ01) Author Contribution Conceptualization and investigation, L.T. and C.F.; methodology, L.Y, H.Y. and L.C; data curation, D.L. and W. H. writing—original draft preparation, L.T. and C.F.; writing—review and editing, L.T. Data Availability The datasets supporting the results presented in this manuscript are included within the article (and its supplement files). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9403686","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":628822776,"identity":"7d8508fc-f3a3-43a1-8f06-bd981da25cbd","order_by":0,"name":"Lili Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIie2QsUoEMRCG5whkLYKr3QiyvsJAIFyxcA9ik+UgNutxYHvFwYJX3qsIPsESWCu51jJiY3HF2lkcaHa1k8RWMF8RhmQ+Zv4AJBJ/FUdTwbOmdZrKImesdbFuMRyaUByLbk5uaeTZhs/pdwUQCqzVqettRTuhTmLKLHu0r3qJ5xyN8eOYlhYUwKq8DE4RCzMdFuPipfNZ+EJZMA46c70OLlYrGpXMXPlC3HjlgSZrG1by/bfiXfRFdd9MbjGqYC3dqByNClV3jPG48rRX8JXFf7ImLdFyRjqSJdvWsu8P5exi07TP74ePIt/u3ly/KoOKh+PPOx1uH2B9/D2RSCT+PZ8aIFRc4XefzwAAAABJRU5ErkJggg==","orcid":"","institution":"Heilongjiang Academy of Agricultural Sciences","correspondingAuthor":true,"prefix":"","firstName":"Lili","middleName":"","lastName":"Tang","suffix":""},{"id":628822777,"identity":"2901898a-4f6a-4f60-bebd-57da6d737c27","order_by":1,"name":"Chao Fan","email":"","orcid":"","institution":"Heilongjiang Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Fan","suffix":""},{"id":628822778,"identity":"a8070a72-08bb-42a2-a004-69d30fe44c1b","order_by":2,"name":"Lie Yang","email":"","orcid":"","institution":"Heilongjiang Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lie","middleName":"","lastName":"Yang","suffix":""},{"id":628822779,"identity":"3601cef4-7e6c-44d8-9cd9-c01c132b897f","order_by":3,"name":"Hongmei Yuan","email":"","orcid":"","institution":"Heilongjiang Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hongmei","middleName":"","lastName":"Yuan","suffix":""},{"id":628822780,"identity":"01e3236c-f3a8-4700-ac99-479e279d5d97","order_by":4,"name":"Lili Cheng","email":"","orcid":"","institution":"Heilongjiang Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Cheng","suffix":""},{"id":628822781,"identity":"f41f3d13-7304-40d1-8859-e7656b848061","order_by":5,"name":"Dandan Liu","email":"","orcid":"","institution":"Heilongjiang Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Dandan","middleName":"","lastName":"Liu","suffix":""},{"id":628822782,"identity":"466ee5f5-bbb0-4020-a6a4-d45f39d5fad5","order_by":6,"name":"Wenyuan He","email":"","orcid":"","institution":"Heilongjiang Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Wenyuan","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2026-04-13 11:54:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9403686/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9403686/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108193277,"identity":"179e87bb-bb3b-47c6-9bad-88ded4a3ad80","added_by":"auto","created_at":"2026-04-30 10:20:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":488665,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency distribution of the flowering time. (A) Bubble Kush - P\u003csub\u003e1\u003c/sub\u003e; (B) Aquawoman - P\u003csub\u003e2\u003c/sub\u003e; (C) F\u003csub\u003e2\u003c/sub\u003e populations. The X-axis represents days after sowing seeds to anthesis, and the Y-axis represents the number of F\u003csub\u003e2\u003c/sub\u003e progenies. The DNA of early- and late-flowering time progenies were bulked to generate an E-pool and an L-pool, respectively. E-pool, early-flowering time bulk; L-pool, late-flowering time bulk.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9403686/v1/217a1c4bdcebe05e208927b2.png"},{"id":108804120,"identity":"8c13754d-d05f-491e-9245-566b1545627a","added_by":"auto","created_at":"2026-05-08 15:16:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":562838,"visible":true,"origin":"","legend":"\u003cp\u003eSubtraction of the ΔSNP-index graph from QTL-seq analysis and SNP-index graphs of E-pool and L-pool. The ΔSNP-index graph was plotted with statistical confidence intervals under the null hypothesis of no QTL (P \u0026lt; 0.05). The calculation used for the ΔSNP-index, with subtraction of the two SNP-indices: ΔSNP-index = SNP-index (E-pool) - SNP-index (L-pool). The black lines represent the average values of the SNP-index or Δ(SNP-index) drawn from the sliding window analysis. E-pool, early-flowering time bulk; L-pool, late-flowering time bulk; QTL, quantitative trait locus; SNP, single nucleotide polymorphism.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9403686/v1/aeb455c026cab11d6296ffd8.png"},{"id":108193276,"identity":"08982435-fd5f-40bd-9eb7-1c802a77c164","added_by":"auto","created_at":"2026-04-30 10:20:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":590930,"visible":true,"origin":"","legend":"\u003cp\u003eQTLs detected by an ED algorithm. The black line indicates the fitting value of the ED; the red dashed line indicates the threshold line (threshold = 0.16); and the purple arrow represents locus regions. ED, Eucladean distance\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9403686/v1/f6bcd18622324f03725482ad.png"},{"id":108491363,"identity":"99667f1a-1d87-4dc7-a9c6-e19d4f9f33b7","added_by":"auto","created_at":"2026-05-05 09:53:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":167425,"visible":true,"origin":"","legend":"\u003cp\u003eFine mapping of QTL \u003cem\u003eqHFX\u003c/em\u003e for flowering time. The genetic map shows the fine-mapped region of \u003cem\u003eqHFX \u003c/em\u003eon chromosome X. The left panel lists the Kompetitive allele-specific PCR (KASP) markers (SNP1 - SNP15) developed from parental polymorphisms, with their physical positions shown on the right. The right panel displays candidate genes identified within the qHFX interval.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9403686/v1/11db1dbc560e532fd57ef5bb.png"},{"id":108491293,"identity":"aaad8bcb-fdcb-4cab-88df-9e02ca1102b0","added_by":"auto","created_at":"2026-05-05 09:53:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":274320,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plots showing DEGs between short‑day (SD) and long‑day (LD) conditions at 50 d (A) and 60 d (B). The X-axis represents log2 (fold change, FC) and the Y-axis represents -log10 (false discovery rate, FDR). Red dots indicate significantly upregulated genes, green dots indicate significantly downregulated genes, and black dots indicate non-significant genes.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9403686/v1/93fa14940004ed950fd00a34.png"},{"id":108803957,"identity":"36ffd3a6-7aaa-4592-8573-380d0be81770","added_by":"auto","created_at":"2026-05-08 15:12:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":513204,"visible":true,"origin":"","legend":"\u003cp\u003eGO classification and KEGG enrichment analysis of DEGs. (A, B) GO classification of DEGs in 50SD vs. 50LD (A) and 60SD vs. 60LD (B). The horizontal coordinate represents the GO term; the left vertical axis represents the percentage of genes; and the right vertical axis represents the number of genes. (C, D) KEGG enrichment analysis for DEGs in 50SD vs. 50LD (C) and 60SD vs. 60LD (D). The ordinate represents different KEGG pathways; the horizontal coordinate represents the enrichment factor; and the color indicates the significance of enrichment of DEGs in each pathway. KEGG pathway analysis was performed using the KEGG database [21-22]\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9403686/v1/76e686b35cc08f4574c3ae45.png"},{"id":108193279,"identity":"0d51cced-2142-49ae-93fe-5e365a752893","added_by":"auto","created_at":"2026-04-30 10:20:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":188549,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the expression levels of randomly selected DEGs using qRT-PCR at 50 d (A) and 60 d (B). Blue and orange blocks represent RNA-Seq results; blue and orange dotted lines represent qRT-PCR results.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9403686/v1/1873ad63cecd8806101fe9cc.png"},{"id":108193280,"identity":"9a1509f1-8e54-45fe-b7a2-89c8318b73ec","added_by":"auto","created_at":"2026-04-30 10:20:00","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":103801,"visible":true,"origin":"","legend":"\u003cp\u003eSequence difference analysis of \u003cem\u003eLOC115716363\u003c/em\u003e. The gene structure and sequence differences of \u003cem\u003eLOC115716363 \u003c/em\u003ebetween Bubble Kush and Aquawoman. Ref, reference sequence of the \u003cem\u003eC. sativa \u003c/em\u003egenome.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9403686/v1/ec4fd1763ad75c70831ce636.png"},{"id":108193282,"identity":"bf952ec0-02e9-4aca-8aab-ea588cd024c3","added_by":"auto","created_at":"2026-04-30 10:20:00","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":297271,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of \u003cem\u003eLOC115716363\u003c/em\u003e-overexpressing rice. (A) Schematic diagram of the pCAMBIA1301-LOC115716363 vector, showing expression elements and restriction sites. (B) Phenotypes of wild‑type (WT) plants and three independent overexpression lines (OE‑2, OE‑5, and OE‑8). Scale bar = 1 cm. (C) Flowering time of \u003cem\u003eLOC115716363\u003c/em\u003e-transgenic rice under short‑day conditions. **p \u0026lt; 0.01 (compared with WT).\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-9403686/v1/f4c6201477449f893295dd52.png"},{"id":108809110,"identity":"1b3e7e10-8a99-45a7-9480-90329058ffd3","added_by":"auto","created_at":"2026-05-08 15:50:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3495715,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9403686/v1/90362245-a9fa-4c22-9dba-90af6d4c259f.pdf"},{"id":108193274,"identity":"b67a2e11-09c3-4a43-9330-b3c1c2de65b5","added_by":"auto","created_at":"2026-04-30 10:20:00","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":219152,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9403686/v1/840b6c8dbc7790597809dcf3.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of a major QTL and candidate gene for photoperiodic flowering in industrial hemp via BSA-seq and fine mapping","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIndustrial hemp (\u003cem\u003eCannabis sativa\u003c/em\u003e L.) is an important multipurpose crop with a long history of use in textile, paper, food, and medicine [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As a short-day plant, its flowering time is tightly controlled by photoperiod, which in turn significantly affects yield and quality. Previous studies demonstrated that industrial hemp originating from high latitudes had a prolonged growth period when cultivated in low latitudes, leading to late flowering with no seed or unmatured seeds. In contrast, when industrial hemp from low latitudes is cultivated in high latitudes, its growth period is greatly shortened, resulting in early flowering or plant stunting with great losses of yield and quality [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, different varieties of industrial hemp respond very differently to their environment when planted in different latitudes. Among the cannabis germplasm resources, very few varieties are day-neutral or autoflowering, and will begin flowering when they are developmentally ready to do so, regardless of the photoperiod. These genetics do not depend on photoperiod changes to trigger photoperiodic flowering [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The discovery of autoflowering cannabis germplasm resource presents unique opportunities to change industrial hemp breeding patterns. Thus, understanding the molecular mechanisms underlying flowering is key to developing new industrial hemp varieties for growth in local climatic and photoperiod conditions.\u003c/p\u003e \u003cp\u003eFlowering time is a complex quantitative trait controlled by multiple genes on which environmental factors including photoperiod, temperature, and hormones have a strong effect [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Among these factors, the photoperiod is a key factor regulating the floral transition from the vegetative stage to the reproductive stage. Current progress in next-generation sequencing (NGS) technology has led to the discovery and subsequent ability to clone many genes and loci to genetically control the flowering time. To date, many genes and loci related to flowering time have been discovered using NGS-based bulked segregant analysis (BSA-seq) in crops including soybean [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], luffa [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], cucumber [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], wheat [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], rice [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and carnation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. To our knowledge, genomic regions governing flowering time in industrial hemp have not been reported.\u003c/p\u003e \u003cp\u003eTranscriptome analysis (RNA-seq) provides sensitive, high-resolution analyses of the transcriptionally dynamic responses of plants to environmental stressors [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Furthermore, RNA-seq is powerful for detecting and quantifying novel or rare transcripts, with high reproducibility for both technical and biological replications [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, RNA-seq has been widely used to investigate the molecular mechanism of the photoperiodic flowering response in Arabidopsis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], rice [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], soybean [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and other crops [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Thousands of differentially expressed genes (DEGs) have been identified by RNA-seq; however, identifying the target genes for flowering time is still difficult. Recently, the combination of BSA-seq and RNA-seq has emerged as a popular approach for efficiently identifying and mapping genomic fragments or candidate genes that can be verified by each other. Recently, a candidate gene related to DNA mismatch repair for the restorer-of-fertility in onion has been identified using QTL-seq and RNA-seq [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]; a major QTL for capsaicinoid content has been mapped on chromosome 6 in pepper using QTL-seq and high-density genetic mapping, with candidate genes subsequently identified by combining QTL analysis with RNA-seq [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]; and a major QTL for salt tolerance at the bud burst stage has been mapped on chromosome 7 in rice using BSA-seq, followed by candidate gene identification through integration of QTL analysis and RNA-seq [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In this study, we combined QTL mapping with RNA-seq to dissect the genetic basis of photoperiodic flowering time using an F\u003csub\u003e2\u003c/sub\u003e population derived from a cross between the day-neutral strain Bubble Kush and the short-day strain Aquawoman. Furthermore, the candidate gene identified was functionally validated via heterologous overexpression in rice.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Distribution of the flowering time in the F\u003csub\u003e2\u003c/sub\u003e population\u003c/h2\u003e \u003cp\u003eThe frequency distribution of flowering time in the F\u003csub\u003e2\u003c/sub\u003e population derived from a cross between Bubble Kush (day-neutral) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) and Aquawoman (short-day) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) under natural long-day conditions (Harbin, Heilongjiang) is presented. The flowering time of the F\u003csub\u003e2\u003c/sub\u003e individuals exhibited a wide range of variation, from approximately 34 to 99 days, indicating substantial transgressive segregation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The distribution was continuous and unimodal, consistent with quantitative inheritance of the photoperiodic flowering trait. A distinct skewness toward late flowering was observed, with the majority of individuals flowering later than the mid-parent value, suggesting a possible dominance effect of late-flowering alleles derived from the short-day parent Aquawoman. Based on the extreme flowering phenotypes, we pooled DNA from 50 early- (E-pool) and 50 late- (L-pool) flowering individuals for further analysis. This skewed distribution also implies that a small number of major loci may control the flowering time variation in this population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Identification of a major QTL for flowering time by BSA-seq\u003c/h2\u003e \u003cp\u003eDNA samples from 50 F\u003csub\u003e2\u003c/sub\u003e progenies with either extreme late or early flowering time were bulked in equal amounts for sequencing. A total of 238-Gbp clean reads, representing 95.30% of the average genome coverage, were generated from Bubble Kush (P\u003csub\u003e1\u003c/sub\u003e), Aquawoman (P\u003csub\u003e2\u003c/sub\u003e), and the two extreme bulks. The clean reads Q30 reached more than 91%, with 50.19\u0026times; average sequencing depth and 95.30% average genome coverage. In addition, 99.12% of the clean reads were mapped to the \u003cem\u003eC. sativa\u003c/em\u003e genome (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSNPs were called by comparing sequences of the parents and the two extreme pools. A total of 12,407,613 SNPs were filtered and included in the SNP-index for QTL mapping (Table S2). In addition, the InDel-index was calculated for each bulk by aligning the sequences to the \u003cem\u003eC. sativa\u003c/em\u003e reference genome to generate a total of 2,600,583 SNPs (Table S3). The SNP-index was estimated with GATK for each bulk by aligning the sequences to the \u003cem\u003eC. sativa\u003c/em\u003e reference genome at a statistical confidence of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. A 2.2-Mb genomic region (75,000,000 bp\u0026thinsp;~\u0026thinsp;77,200,000 bp) on chromosome X with a Δ(SNP-index) of 1 was identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Additionally, the ED was also calculated by aligning the sequences to the \u003cem\u003eC. sativa\u003c/em\u003e reference genome. A total of three regions on chromosome 4 and chromosome X carrying the genes for flowering time were detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The QTL for flowering time from the SNP-index overlapped with the genomic region for flowering time obtained using the ED method on chromosome X, which confirmed the candidate QTL position with candidate genes for flowering time on chromosome X. Therefore, this QTL on chromosome X was named qHFX. A total of 45 potential candidate genes were screened within the qHFX region using GO, Kyoto Encyclopedia of Genes and Genomes, NCBI\u0026rsquo;s nr, Swissprot, and Pfam databases (Table S4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Fine mapping analysis of \u003cem\u003eqHFX\u003c/em\u003e\u003c/h2\u003e \u003cp\u003ePrimary mapping showed that the fragment region between 75,042,733-bp and 77,153,397-bp harbored candidate genes for the flowering time. The \u003cem\u003eqHFX\u003c/em\u003e coding region contained 609 SNPs on chromosome X, mainly including 138 Non_synonymous_coding, 20 frame_shift, 93 intragenic, 166 intron, and 6 stop_gained (Table S5). A total of 300 F\u003csub\u003e2\u003c/sub\u003e progenies were genotyped with 15 highly credible SNPs to yield a qHFX linkage map using an LOD score of 5.0 as the threshold for consecutive occurrence (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table S6). The \u003cem\u003eqHFX\u003c/em\u003e locus with a LOD score of 23.36 at peak spawned the 637-kb and harbored 14 genes associated with the flowering time (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/datasets/genome/GCF_900626175.2/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_900626175.2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), which explained 28.50% of the phenotypic variation (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIdentification of \u003cem\u003eqHFX\u003c/em\u003e for the flowering time by linkage analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQTL\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChromosome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLeft Marker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRight Marker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLOD\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePVE(%)\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAdd\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eqHFX\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76,903,638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSNP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSNP13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003eQTL, quantitative trait locus; \u003csup\u003e2\u003c/sup\u003eLOD, logarithm of the odds; \u003csup\u003e3\u003c/sup\u003ePVE, phenotypic variation explained; \u003csup\u003e4\u003c/sup\u003eAdd, additive effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. RNA-seq data analysis\u003c/h2\u003e \u003cp\u003eRNA-seq analysis was performed to investigate the genes involved in flowering time. 3.71\u0026thinsp;~\u0026thinsp;4.71\u0026nbsp;million reads were generated per replicated sample. The high-quality base (% \u0026ge; Q30) of each sample was more than 90% (Table S7). The uniquely mapped values were greater than 88.27%, while the multiple mapped values were lower than 8.25% by STAR for RNA-seq (Table S8). The number of mapped reads was normalized to fragments per kilobase of transcript per million fragments mapped. By restricting |log2 (FC)| \u0026ge; 1, the respective numbers of DEGs in the 50SD vs. 50LD and 60SD vs. 60LD comparisons were 5,215 and 2,589, respectively. The volcano plots of DEGs in the two groups are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA,B.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. DEGs functional annotation and enrichment analysis\u003c/h2\u003e \u003cp\u003eAll DEGs from the comparisons of 50SD vs. 50LD and 60SD vs. 60LD were assigned GO terms in three functional groups, including molecular function, biological processes, and cell composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA,B). Obviously, more DEGs from 50SD vs. 50LD were assigned to GO terms compared with DEGs from 60SD vs. 60LD. In the biological process category, DEGs from 60SD vs. 60LD were the most significantly enriched in the cell proliferation and rhythmic process. The results suggested that most of the DEGs were significantly enriched in the rhythmic process, with various metabolic activities under different photoperiods.\u003c/p\u003e \u003cp\u003eDEGs were mapped to the KEGG pathway to investigate which DEGs are activated and which are suppressed in different groups of pathways under different photoperiods (Tables S9a,b). DEGS were the most significantly enriched in the metabolic pathways, regardless of whether DEGs were up- or down-regulated. DEGs, including 14 DEGs at 50 d and 8 DEGs at 60 d, were significantly enriched in the circadian rhythm (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC,D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Candidate gene analysis of DEGs within the major QTL\u003c/h2\u003e \u003cp\u003eNGS reads from the \u003cem\u003eqHFX\u003c/em\u003e interval were annotated to the \u003cem\u003eC. sativa\u003c/em\u003e reference genome, resulting in 14 candidate genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A total of 14 genes were predicted by combing the QTL-seq and RNA-seq results, among which 5 genes were differentially expressed at 50 d and 60 d under long-day and short-day periods, respectively (Table S10). Among five DEGs, four DEGs (\u003cem\u003eLOC115714749, LOC115724847, LOC115724856\u003c/em\u003e and \u003cem\u003eLOC115703387\u003c/em\u003e) were synonymous mutation, and one DEG (\u003cem\u003eLOC115716363\u003c/em\u003e) was a frame shift mutation. \u003cem\u003eLOC115716363\u003c/em\u003e had high homology to Arabidopsis \u003cem\u003eAHL20/22\u003c/em\u003e, which played an important role in delaying flowering according to data from the TAIR database. Therefore, \u003cem\u003eLOC115716363\u003c/em\u003e underlying \u003cem\u003eqHFX\u003c/em\u003e on chromosome X was considered to be a candidate gene regulating flowering time and was designated as \u003cem\u003eCsAHL\u003c/em\u003e. To further confirm the expression patterns of these genes, the expression levels of these five DEGs were evaluated using qRT-PCR to validate the RNA-seq results. The 18S gene was used as an internal control. The relative expression patterns of the five DEGs showed consistent trends with the RNA-seq data, although the absolute expression levels differed somewhat from the RNA-seq measurements (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA,B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Identification of candidate gene loci for flowering time\u003c/h2\u003e \u003cp\u003eThe genomic sequences of the candidate gene \u003cem\u003eCsAHL\u003c/em\u003e (\u003cem\u003eLOC115716363\u003c/em\u003e) from the two parents, Aquawoman and Bubble Kush, were aligned with the CoDing Sequence (CDS) against the \u003cem\u003eC. sativa\u003c/em\u003e reference genome. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, Bubble Kush carried a 1-bp insertion (A was inserted in position 439) compared with Aquawoman and the reference genome. The sequencing results agreed with the BSA-seq analysis, confirming that \u003cem\u003eLOC115716363\u003c/em\u003e was the most probable functional gene underlying \u003cem\u003eqHFX\u003c/em\u003e for flowering time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Functional validation of the candidate gene in rice via genetic transformation\u003c/h2\u003e \u003cp\u003eTo investigate the conserved function of \u003cem\u003eLOC115716363\u003c/em\u003e in flowering regulation, we selected rice, another short‑day crop, for functional validation. Ten transgenic rice lines overexpressing \u003cem\u003eLOC115716363\u003c/em\u003e under the control of the CaMV 35S promoter were generated using the binary vector shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA. Molecular screening of T\u003csub\u003e3\u003c/sub\u003e homozygous lines identified three independent overexpression lines (OE‑2, OE‑5, and OE‑8) with high transcript levels of \u003cem\u003eLOC115716363\u003c/em\u003e. Phenotypic evaluation under short-day conditions revealed that all three overexpression lines exhibited significantly delayed flowering compared to wild‑type (WT) controls, as evidenced by a marked increase in days to flowering (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). Specifically, WT plants flowered at approximately 100 days, whereas OE‑2, OE‑5, and OE‑8 lines flowered at around 115\u0026ndash;120 days (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). No substantial differences in flowering time were observed among the three overexpression lines, suggesting that the effect of \u003cem\u003eLOC115716363\u003c/em\u003e on flowering time may not be strictly dose‑dependent. Overall, these results indicate that heterologous overexpression of \u003cem\u003eLOC115716363\u003c/em\u003e delays floral transition in rice, implying a potential conserved role in flowering regulation across species.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eUnderstanding the flowering time in industrial hemp is essential for maximizing reproductive success, as it strongly influences crop quality and yield [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Improving flowering time is a major goal of plant breeding aimed at developing new industrial hemp strains with better adaptation to local environments. The floral transition from vegetative to reproductive growth is a key determinant of yield potential [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Modulating key flowering time genes has been pivotal for crop domestication and enhancing adaptation across different climatic regions [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. As a short-day plant, photoperiod is a critical factor regulating flowering time in industrial hemp. Under long-day conditions, plants remain vegetative, and flowering is induced only after several consecutive short days. The genetic control of flowering time in industrial hemp is strongly influenced by latitudinal adaptation to uniform growing environments. Therefore, modifying flowering time genes may facilitate the cultivation of hemp at new latitudes. A comprehensive understanding of the genetic basis of flowering time is thus important for integrating industrial hemp into modern agriculture. However, little is currently known about the underlying genetic architecture. In this study, we identified a major genomic region and a candidate gene controlling flowering time variation in a biparental F\u003csub\u003e2\u003c/sub\u003e population. This was achieved by first using BSA-seq on bulked samples from phenotypic extremes to delimit a QTL, followed by fine mapping using individual progeny. The findings are promising for the discovery and implementation of molecular markers that will enable the development of early- or late-flowering industrial hemp strains with improved adaptation to specific photoperiod regions.\u003c/p\u003e \u003cp\u003eThe availability of the \u003cem\u003eC. sativa\u003c/em\u003e genome reference has facilitated the widespread use of QTL-seq coupled with transcriptome sequencing to identify potential candidate genes. In this study, we developed an F\u003csub\u003e2\u003c/sub\u003e population from a cross between Bubble Kush (day-neutral and light-insensitive) and Aquawoman (short-day and light-sensitive). Individuals from the F\u003csub\u003e2\u003c/sub\u003e population exhibited transgressive segregation and a skewed distribution toward late flowering (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting possible dominance of late-flowering alleles. We employed a bulk segregant approach coupled with whole-genome sequencing (QTL-seq) and classical linkage mapping to identify a major QTL, \u003cem\u003eqHFX\u003c/em\u003e (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which was localized to a 637-kb physical interval harboring 14 genes on chromosome X. This is the first report of QTL-seq being used to directly identify flowering time QTLs in \u003cem\u003eC. sativa\u003c/em\u003e. Bulked segregant analysis was first applied to facilitate linkage analysis of discrete characters in F\u003csub\u003e2\u003c/sub\u003e populations [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Subsequently, QTL-seq has been used to identify the early-flowering QTL \u003cem\u003eEf1.1\u003c/em\u003e on chromosome 1 in cucumber [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], to map QTLs underlying disease resistance in rice [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and to identify QTLs for fruit weight and locule number in F\u003csub\u003e2\u003c/sub\u003e populations [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Collectively, the present study demonstrates that QTL-seq combined with traditional linkage analysis and high‑quality whole‑genome resequencing provides an efficient platform for QTL identification in F\u003csub\u003e2\u003c/sub\u003e populations.\u003c/p\u003e \u003cp\u003eThe molecular mechanisms underlying flowering time and candidate genes for flowering time QTLs have been extensively studied in many important crops [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Previous studies revealed that the CONSTANS-like (\u003cem\u003eCOL\u003c/em\u003e) gene family plays a crucial role in regulating flowering time in \u003cem\u003eC. sativa\u003c/em\u003e, and \u003cem\u003eCsCOL\u003c/em\u003e genes are unevenly distributed across seven chromosomes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In this study, we used NGS‑based BSA to simultaneously map \u003cem\u003eqHFX\u003c/em\u003e, a locus regulating flowering time in industrial hemp. To further refine candidate genes within the \u003cem\u003eqHFX\u003c/em\u003e interval, we performed RNA-seq analysis on the parental line Aquawoman under short‑day and long‑day conditions. Among the 14 genes located in the candidate region, five showed differential expression between the two photoperiod treatments, and their expression patterns were validated by quantitative real‑time PCR. Based on its expression profile and gene structure, \u003cem\u003eLOC115716363\u003c/em\u003e emerged as a strong candidate gene for \u003cem\u003eqHFX\u003c/em\u003e. NCBI homology analysis revealed that \u003cem\u003eLOC115716363\u003c/em\u003e encodes an AT‑hook motif nuclear‑localized protein homologous to the Arabidopsis AHL gene family. In Arabidopsis, \u003cem\u003eAtAHL20\u003c/em\u003e and \u003cem\u003eAtAHL22\u003c/em\u003e have been shown to delay flowering by repressing \u003cem\u003eFT\u003c/em\u003e expression, acting as negative regulators of flowering [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCandidate gene analysis further revealed that \u003cem\u003eLOC115716363\u003c/em\u003e carries a 1-bp difference in the coding sequence between Bubble Kush and Aquawoman (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), resulting in a frameshift mutation similar to those observed in clock genes associated with crop domestication [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. \u003cem\u003eLOC115716363\u003c/em\u003e encodes an AT‑hook motif nuclear‑localized protein that contributes to functional nuclear architecture by binding to the nuclear matrix [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Previous studies have demonstrated the involvement of AT‑hook proteins in meristem identity maintenance, leaf longevity, photomorphogenesis, and flower development [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. To functionally validate this candidate gene, we heterologously overexpressed \u003cem\u003eLOC115716363\u003c/em\u003e in rice. Under short‑day conditions, all three independent overexpression lines exhibited significantly delayed flowering compared to wild‑type controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). This cross‑species functional conservation further supports the role of \u003cem\u003eLOC115716363\u003c/em\u003e as a flowering repressor in industrial hemp. Collectively, our results support the involvement of \u003cem\u003eLOC115716363\u003c/em\u003e in regulating flowering time in hemp. Further functional studies are required to elucidate the precise role of this gene, which will enrich the current understanding of flowering time regulation in \u003cem\u003eC. sativa\u003c/em\u003e.\u003c/p\u003e"},{"header":"4. Materials and Methods","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Development of mapping populations and phenotyping\u003c/h2\u003e \u003cp\u003ePurified seeds of 'Bubble Kush' were obtained from Oregon State University (Corvallis, OR, USA), and purified seeds of 'Aquawoman' were obtained from the Heilongjiang Academy of Agricultural Sciences (Harbin, China). Bubble Kush is a day-neutral, light-insensitive strain with 34\u0026ndash;44 days to flowering, that is, flower time is not affected by the length of light under an environment suitable for growth of industrial hemp. While Aquawoman is a short-day, light-sensitive strain with 94\u0026ndash;98 days to flowering in Harbin, China (45\u0026deg;49\u0026prime;44\u0026Prime; N, 126\u0026deg;48\u0026prime;55\u0026Prime; E). This flowering time refers to the flowering period from the May to August each year in Harbin, which is also the growing season for industrial hemp in northern China. In order to shorten the research period, Bubble Kusk is chosen as the mother parent and Aquawoam as the father parent to construct genetic population. On the one hand, the purified Bubble Kush seeds were planted in the glasshouse and the medium of the cultivated plants was common seedling soil in August 2020, and a healthy female plant was selected as the mother parent. On the other hand, when Aquawoman is in full bloom, a pollen plant is randomly selected and stored at 4℃ for later use. The cross was made by pollinating Bubble Kush with pollen from Aquawoman to develop an F\u003csub\u003e1\u003c/sub\u003e population in October 2021. From May to August 2022, a female plant from F\u003csub\u003e1\u003c/sub\u003e was randomly selected for self-pollination to generate a feminized F\u003csub\u003e2\u003c/sub\u003e generation [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In order to form a male flower for producing pollen, half of the selected female F\u003csub\u003e1\u003c/sub\u003e plant was sprayed with silver thisuflate (STS). When F\u003csub\u003e1\u003c/sub\u003e plant grew to 70 days, 0.92 mmol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e STS was prepared and sprayed leaves and stems every 3 days with 500 mL each time. After a month, the sprayed plant could product pollen.\u003c/p\u003e \u003cp\u003eThe two parents and 300 feminized F\u003csub\u003e2\u003c/sub\u003e seeds were planted in greenhouses at the Modern Agriculture Demonstration Area (45\u0026deg;49\u0026prime;44\u0026Prime; N, 126\u0026deg;48\u0026prime;55\u0026Prime; E) in Harbin City, Heilongjiang Province, China, in May 2023\u0026thinsp;~\u0026thinsp;August 2023. The light conditions are natural long-day, that is, the average light time in May was 14.46 h, the average light time in June was 15.39 h, the average light time in July was 15.18 h, and the average light time in August was 14.05 h. At the flowering stage, days to flowering (after sowing seeds to anthesis) were visually recorded for each F\u003csub\u003e2\u003c/sub\u003e plant. The course from first flower opening (anthesis) to fully open (the stigma protrudes completely) was monitored for each F\u003csub\u003e2\u003c/sub\u003e plant [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Frequency distribution of the flowering time in F\u003csub\u003e2\u003c/sub\u003e progenies were analyzed using IBM SPSS software, showing two extreme groups: (1) The early-flowering time group ranged from 35\u0026ndash;57 days to flowering; (2) The late-flowering time group ranged from 86\u0026ndash;99 days to flowering. The fresh leaves of 50 F\u003csub\u003e2\u003c/sub\u003e progenies in the early-flowering and late-flowering, were sampled, respectively. Meanwhile, the fresh leaves of the two parents, Bubble Kush and Aquawoman, were sampled. These samples are used for DNA extraction by QTL-Seq.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2. QTL-seq analysis\u003c/h2\u003e \u003cp\u003eGenomic DNA was isolated from fresh leaves of the two parents (Bubble Kush and Aquawoman), and two extreme bulks, each with 50 extreme early-flowering and late-flowering plants, using the CTAB method with minor modification [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. DNA quality was assessed using the NanoPhotometer\u0026reg; spectrophotometer (IMPL EN, CA, USA). An 5 \u0026micro;g of DNA from sample (mixed individual) to generate the libraries for sequencing on an Illumina HiSeq X Ten platform at Adsen Biotechnology Co., Ltd. (Urumchi, China) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe clean reads were aligned to the \u003cem\u003eC. sativa\u003c/em\u003e L. reference genome sequence obtained from the National Center of Biotechnology Information (NCBI) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/data-hub/genome/GCF_900626175.2/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/data-hub/genome/GCF_900626175.2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using Burrows- Wheeler Aligner (BWA) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. SNPs were called using the Genome Analysis Toolkit (GATK) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Duplicate reads that located the reference genome were marked using SAMtools. Local realignment around indels and variant calling were performed with GATK LocalRealigner and BaseRecalibrator to correct artifacts between the reference and reads close to the misaligned region and to ensure the accuracy of all identified SNPs. Then, SNPs were detected and filtered with GATK to generate the final SNP locus set. The detailed steps are as follows: (1) Picard\u0026rsquo;s MarkDuplicates was applied to directly process BWA\u0026rsquo;s alignment for the removal of duplicates to shield the influence of PCR-duplication. (2) GATK IndelRealigner was applied to correct the error of comparison results caused by insertion and deletion. (3) GATK BaseRecalibrator was applied to correct the base mass value. (4) GATK was applied to call short variants (SNPs\u0026thinsp;+\u0026thinsp;indels). (5) GATK was used to correct the variations for the selection of reliable variations [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In order to obtain high quality SNP sites, SNP was filtrated by parental depth not less than 5 X, the depth of the mixing pool shall not be less than 8 X and both parental genotypes need to be homozygous and polymorphic. The Euclidean distance (ED) method and the genotype frequency difference between the mixed pools were applied for association analysis (SNP-index) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The ED method evaluated the regions associated with flower time traits by looking for markers of significant difference between different color-mixing pools. The SNP-index (Δ-index) was calculated to detect the allele frequency between the mixed pools. The closer the Δ-index was to 1, the closer the association between the SNP and the color trait.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Regional linkage mapping analysis\u003c/h2\u003e \u003cp\u003ePrimers for KAPS markers were designed with Premier 6.0 according to the BSA-seq results and two parental resequencing data (Table S6). The primer sequences were synthesized by Sangon Biotech Company (Shanghai, China). 300 F\u003csub\u003e2\u003c/sub\u003e individuals were genotyped to narrow the potential candidates with the Inclusive Composite Interval Mapping (ICIM) module of QTL IciMapping (v.4.2; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://isbreedingen.caas.cn/software/qtllcimapping/294607\u003c/span\u003e\u003cspan address=\"https://isbreedingen.caas.cn/software/qtllcimapping/294607\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The logarithm of the odds (LOD) score threshold for significant QTLs was determined using 1,000 permutations at P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 significance level. Annotation information for the candidate genes in the localized region was predicted using data obtained from the NCBI database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the Arabidopsis Information Resource (TAIR) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.arabidopsis.org/\u003c/span\u003e\u003cspan address=\"https://www.arabidopsis.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). DNA sequences of the candidate genes were downloaded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.4. RNA extraction and RNA-seq analysis\u003c/h2\u003e \u003cp\u003eAquawoman was cultured under short-day (8 h/16 h day/night) and long-day (18 h/6 h day/night) conditions with three replicates. A preliminary experiment showed that Aquawoman only flowered at 60 d under short-day conditions. Aquawoman was unable to flower at 50 d under short-day conditions, nor at either 50 d and 60 d under long-day conditions. Newly emerged leaves of Aquawoman under short-day and long-day treatments were sampled at 50 d (50SD and 50LD) and 60 d (60SD and 60LD), respectively, to generate a total of 12 samples: 50SD-1, 50SD-2, 50SD-3, 50LD-1, 50LD-2, 50LD-3, 60SD-1, 60SD-2, 60SD-3, 60LD-1, 60LD-2, and 60LD-3. All samples were flash frozen in liquid nitrogen and stored at -80℃ for RNA extraction. Total RNA was extracted by TRIzol reagent (Life Technologies, USA), following the manufacturer's instructions. The isolated total RNA was treated with DNase I to remove all DNA residues using the RNAprep Pure Plant Kit (TIANGEN, Beijing, China), following the manufacturer\u0026rsquo;s instructions. The quality of total RNA was assessed using a spectrophotometer (NanoPhotometer, Implen, CA, USA). The quantity of RNA was measured using a Fluorometer (Qubit 2.0, Life Technologies, CA, USA). The integrity of RNA was evaluated using a Bioanalyzer 2100 System (Agilent Technologies, CA, USA). The high-quality total RNA was then enriched for mRNA either by using oligo (dT) beads (for eukaryotic mRNA) or by selectively removing the rRNA (for prokaryotic mRNA). The enriched mRNA was fragmented to be reverse transcribed into cDNA with random primers, which was used as templates to synthesize the second-strand cDNA. The cDNA fragments were purified and then subjected to end repair and dA-tailing reaction. A sequencing adaptor was ligated to each fragment. The ligated fragments were separated by agarose gel-electrophoresis, amplified by PCR, and sequenced using an Illumina HiSeq\u0026trade; 2500 System (Adsen Biotechnology Co., Ltd. Urumchi, China).\u003c/p\u003e \u003cp\u003eThe remaining high-quality reads were aligned against the reference transcriptome using Bowtie 2 [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The gene abundances were presented as reads per kb million [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. DEGs were detected using package edgeR (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org/\u003c/span\u003e\u003cspan address=\"http://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) under the threshold of fold change (FC)\u0026thinsp;\u0026ge;\u0026thinsp;2 and false discovery rate\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Gene ontology (GO) terms (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.geneontology.org\u003c/span\u003e\u003cspan address=\"http://www.geneontology.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for each of the identified unigenes were assigned based on the UniProt and the Pfam databases [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and then mapped to a plant-specific GO slim ontology (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.geneontology.org/\u003c/span\u003e\u003cspan address=\"http://www.geneontology.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. All unigenes were loaded into the PathoLogic format to predict the biochemical pathways using the Pathway Tools program [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Validation of RNA-seq and candidate gene prediction\u003c/h2\u003e \u003cp\u003eRNA-seq results were validated with qRT-PCR. Special primers were designed using Premier v (6.0) and synthesized by Sangon Biotech Company (Shanghai, China). The primer sequences are provided in Table S11, and 18S was used as the endogenous control. qRT-PCR was performed by RealmasterMix (SYBR Green) with PCR parameters as follows: predenaturation at 95℃ for 10 s, followed by 38 cycles of denaturation at 95℃ for 15 s, annealing at 56℃ for 30 s, and extension at 72℃ for 30 s. The temperature of the dissolution curve ranged from 55℃ to 95℃, dropping 0.5℃ every 5 s, and was finally preserved at 4℃. Each PCR reaction (20 \u0026micro;L) contained 10 \u0026micro;L SYBR Green Master Mix, 1 \u0026micro;L upstream and downstream primers, 5 uL cDNA, and 4 uL ddH\u003csub\u003e2\u003c/sub\u003eO. Three biological replicates were performed, with three technical replicates for each sample. Relative gene expression levels were measured by the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Validation of candidate gene sequencing and sequence alignment\u003c/h2\u003e \u003cp\u003eCandidate genes for the flowering time in Bubble Kush and Aquawoman were cloned and sequenced. Sequences were aligned using DNAMAN, with genes in the \u003cem\u003eC. sativa\u003c/em\u003e genome used as a reference. Primers were designed with Primer Premier 6.0 to amplify the full-length genome sequences of target gene \u003cem\u003eLOC115716363\u003c/em\u003e (forward primer: GCTACTCTTCTACTACAACCT; reverse primer: GCGATAACGACAATGATGG). The PCR reaction parameters were as follows: predenaturation at 95℃ for 10 s, followed by 38 cycles of denaturation at 95℃ for 15 s, annealing at 50℃ for 30 s, and extension at 72℃ for 30 s, and then finally preserved at 4℃. The PCR reaction (20 \u0026micro;L) contained 10-\u0026micro;L 2\u0026times;HiFi PCR Mix, 1 \u0026micro;L upstream and downstream primers, 5 \u0026micro;L cDNA, and 4 \u0026micro;L ddH\u003csub\u003e2\u003c/sub\u003eO. Primer synthesis and sequencing of PCR products were conducted by Sangon Biotech Company (Shanghai, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.7. Rice transformation and flowering time assay\u003c/h2\u003e \u003cp\u003eThe coding sequence of \u003cem\u003eLOC115716363\u003c/em\u003e was cloned into the binary vector pCAMBIA1301 under the control of the CaMV 35S promoter to generate the plant expression construct. The recombinant plasmid was introduced into \u003cem\u003eAgrobacterium tumefaciens\u003c/em\u003e strain EHA105 by electroporation. Rice mature embryos were used as explants. Agrobacterial infection and co‑cultivation were performed as previously described [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Transgenic calli were selected on medium containing 50 mg/L hygromycin, and regenerated plantlets were transferred to soil in a greenhouse. T\u003csub\u003e0\u003c/sub\u003e transgenic plants were self‑pollinated to obtain T\u003csub\u003e1\u003c/sub\u003e seeds, and homozygous T\u003csub\u003e3\u003c/sub\u003e lines (OE‑2, OE‑5, and OE‑8) were identified by hygromycin resistance segregation analysis and further confirmed by PCR for the presence of \u003cem\u003eLOC115716363\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eFor flowering time analysis, wild‑type (WT) and \u003cem\u003eLOC115716363\u003c/em\u003e‑overexpressing lines (OE‑2, OE‑5, and OE‑8) were grown under short‑day conditions (10 h light/14 h dark, 28\u0026deg;C/24\u0026deg;C, 70% relative humidity) in a growth chamber. The days to flowering were recorded as the number of days from sowing to heading (defined as the emergence of the first panicle tip from the flag leaf sheath). At least 30 individual plants per line were evaluated in three independent biological replicates.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn this study, QTL analysis was used to identify genes regulating flowering time in industrial hemp by utilizing day‑neutral and short‑day cannabis strains. A major QTL was mapped to chromosome X using ΔSNP‑index and Euclidean distance algorithms. Fifteen Kompetitive allele‑specific PCR (KASP) markers were developed to successfully narrow down the QTL to a 637-kb interval. Additionally, RNA-seq analysis of the parental line Aquawoman under different photoperiod conditions revealed five differentially expressed genes within the candidate region. The RNA‑seq results were validated by quantitative real‑time PCR (qRT‑PCR). Furthermore, sequence analysis revealed a 1‑bp Indel difference in \u003cem\u003eLOC115716363\u003c/em\u003e between the two parents. \u003cem\u003eLOC115716363\u003c/em\u003e was identified as a candidate gene for flowering time and was designated as \u003cem\u003eCsAHL\u003c/em\u003e. Heterologous overexpression of \u003cem\u003eCsAHL\u003c/em\u003e in rice significantly delayed flowering under short‑day conditions, further supporting its role as a flowering repressor. In summary, the findings of this study provide insights into the molecular mechanisms underlying flowering time and offer useful genetic resources for molecular breeding of industrial hemp.\u003c/p\u003e"},{"header":"Declarations","content":" \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was funded by National Natural Science Foundation of China (32401879) and the Agricultural Science and Technology Basic Innovation Project (CX23YQ01)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization and investigation, L.T. and C.F.; methodology, L.Y, H.Y. and L.C; data curation, D.L. and W. H. writing\u0026mdash;original draft preparation, L.T. and C.F.; writing\u0026mdash;review and editing, L.T.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets supporting the results presented in this manuscript are included within the article (and its supplement files). The raw reads for the BSA and RNA-seq sequenced are available in the NCBI Sequence Read Archive database PRJNA1045517 and PRJNA1035970, respectively.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAndre, C. M., Hausman, J. F. \u0026amp; Guerriero, G. \u003cem\u003eCannabis sativa\u003c/em\u003e: The plant of the thousand and one molecules. \u003cem\u003eFront. Plant. Sci.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 19\u0026ndash;47 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHall, J., Bhattarai, S. P. \u0026amp; Midmore, D. J. Review of flowering control in industrial hemp. \u003cem\u003eJ. Nat. 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Analysis of relative gene expression data using real-time quantitative PCR and the 2\u003csup\u003e\u0026ndash;∆∆ Ct\u003c/sup\u003e method. \u003cem\u003eMethods\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 402\u0026ndash;408 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuan, Y. B. et al. An efficient and high-throughput protocol for \u003cem\u003eAgrobacterium\u003c/em\u003e - mediated transformation based on phosphomannose isomerase positive selection in Japonica rice (\u003cem\u003eOryza sativa\u003c/em\u003e L). \u003cem\u003ePlant. Cell. Rep.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e, 1611\u0026ndash;1624 (2012).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Industrial hemp, Flowering time, BSA-seq, RNA-seq, Candidate gene","lastPublishedDoi":"10.21203/rs.3.rs-9403686/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9403686/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIndustrial hemp (\u003cem\u003eCannabis sativa\u003c/em\u003e L.) is a photoperiod-sensitive short-day crop, yet the quantitative trait loci (QTLs) governing flowering time remain poorly characterized, limiting molecular breeding efforts. To identify genomic regions controlling photoperiodic flowering, we performed bulked segregant analysis sequencing (BSA-seq) coupled with fine linkage mapping using an F\u003csub\u003e2\u003c/sub\u003e population derived from a cross between day-neutral (Bubble Kush) and short-day (Aquawoman) accessions. Based on extreme flowering phenotypes, we pooled DNA from 50 early- and 50 late-flowering individuals. A major QTL, \u003cem\u003eqHFX\u003c/em\u003e, was mapped to a 2.2 Mb region on chromosome X via ΔSNP-index and Euclidean distance algorithms. Using 15 Kompetitive allele-specific PCR (KASP) markers developed from parental polymorphisms, we refined \u003cem\u003eqHFX\u003c/em\u003e to a 637-kb interval in 300 F\u003csub\u003e2\u003c/sub\u003e individuals. RNA-seq analysis of Aquawoman under short-day and long-day conditions identified five differentially expressed genes within this interval, with expression profiles validated by quantitative real-time PCR. Sequence analysis revealed a 1-bp indel in \u003cem\u003eLOC115716363\u003c/em\u003e, which emerged as a strong candidate gene. Notably, heterologous overexpression of \u003cem\u003eLOC115716363\u003c/em\u003e in rice significantly delayed flowering, further supporting its role in flowering time regulation. Collectively, these findings elucidate the molecular basis of flowering time in industrial hemp and provide valuable genomic resources for breeding broadly adapted, high-yield varieties.\u003c/p\u003e","manuscriptTitle":"Identification of a major QTL and candidate gene for photoperiodic flowering in industrial hemp via BSA-seq and fine mapping","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-30 10:19:55","doi":"10.21203/rs.3.rs-9403686/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-15T00:26:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300062383751023084890070128181169674744","date":"2026-04-24T04:39:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248287020821708017131122745359713840286","date":"2026-04-22T04:31:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-22T04:27:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-20T16:07:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-20T14:21:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-18T08:36:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-18T08:30:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fed6bf2f-4704-485d-9806-5d45ca1e24b8","owner":[],"postedDate":"April 30th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-15T00:26:48+00:00","index":42,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66928647,"name":"Biological sciences/Biotechnology"},{"id":66928648,"name":"Biological sciences/Genetics"},{"id":66928649,"name":"Biological sciences/Molecular biology"},{"id":66928650,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-04-30T10:19:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-30 10:19:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9403686","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9403686","identity":"rs-9403686","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}