Identification of novel QTL for seedling root architectural traits in the D genome of natural and resynthetic allohexaploid wheat

doi:10.21203/rs.3.rs-3768535/v1

Root architectural traits at the seedling stage have been demonstrated to be crucial for the efficient uptake of nutrients and drought tolerance in wheat (Triticum aestivum L.). To dissect the genetic basis of these traits from the D genome, 182 recombinant inbred lines (RILs) derived from the common wheat TAA10 crossed with resynthesized allohexaploid wheat XX329 possessed similar AABB genomes were used for QTL mapping of five root traits in hydroponic-cultured seedlings, including lateral root number (LRN), seminal root number (SRN), root hair length (RHL), root diameter (RD), and total root volume (TRV). A total of seven QTL were identified for the five root traits, with six possible novel QTLs for LRN, RHL, RD and TRV, accounting for 4.98–12.17% of phenotypic variation. One QTL (QLrn.qau-5D.2), controlling lateral root number, was fine mapped an approximate 5.0-Mb interval harboring 80 annotated genes, including five auxin-related genes (TraesCS5D02G286000, TraesCS5D02G286100, TraesCS5D02G288000, TraesCS5D02G291800 and TraesCS5D02G293100). We further validated that QLrn.qau-5D.2 in NIL^TAA10 significantly enhanced yield-related traits, such as plant height (PH), spike length (SL), spike compactness (SC), tiller number per plant (TN) and grain yield per plant (GYP), in comparison with NIL^XX329 in the elite NIL pair. Collectively, these results provide vital insights for fine-mapping QTLs associated with LRN, SRN, RHL, RD and TRV and facilitate the root morphologic designs for enhancing yield performance.

Wheat

root architectural traits

QTL

resynthetic allohexaploid

QLrn.qau-5D.2 associated with lateral root number in wheat was validated and delimited into the physical interval of approximately 5.0 Mb containing five auxin-related genes.

Bread wheat (Triticum aestivum L.) is one of the earliest crop species to undergo domestication about 10,000 years ago in a core area of the Fertile Crescent to global environments, serving as more than 20% of human dietary calories and proteins (Salamini et al. 2002; Shiferaw et al. 2013; Zhou et al. 2020). Root system architecture (RSA, the spatial and temporal colonization of root types), encompassing the number, length, orientation, and branching of several root classes, plays a pivotal role in both nutrients and water absorption, soil anchorage, nutrient storage and rhizosphere microbial interaction, and serves as the primary plant organ responding to environmental abiotic stresses, such as drought, salinity, waterlogging, heavy metal toxicity and nutrient deficiencies (Atkinson et al. 2015; Manschadi et al. 2013; Jennylyn et al. 2016). Consequently, RSA directly influences the development and production of wheat (Bai et al. 2019; Smith and De Smet 2012). The domestication and subsequent evolutionary process of wheat has led to a great deal of phenotypic variability in root systems morphology, such as longer root hair, more lateral root number and seminal root. These changes contribute to enhanced agronomic performance and a wider range of adaptability ( Han et al. 2016; Golan et al. 2018; Wang et al. 2018). Historically, the primary emphasis of wheat selection and breeding programs has been on above-ground traits and yield. However, recent advancements in high-throughput root phenotyping and marker-assisted selections have made it increasingly feasible to genetically improve root system architecture in wheat breeding (Munns et al. 2012; Steele et al. 2013; Wasson et al. 2020). As such, the identification and use of the crucial genes or quantitative trait loci (QTL) that control root architectural traits are vital to expedite molecular improvements in wheat breeding program.

The root architectural traits of wheat which are controlled by complexes of polygenes with quantitative effects. Measuring and assessing them is challenging due to their susceptibility to environmental factors in field settings (Malamy and environment 2005; Wasson et al. 2012). Traditionally, root architecture is described by multiple root parameters, such as root length, root number, root hair length, root diameter, root surface area, and root volume (Chen et al. 2017; Maccaferri et al. 2016; Manschadi et al. 2008). Previous researches have identified hundreds of QTL associated with wheat RSA traits in variable mapping populations with various types of markers, and these findings are summarized in Soriano and Alvaro (2019). Among them, several studies have indicated that QTLs of wheat root traits overlapped with various previously reported QTLs for nutrient uptake, drought tolerance and production in wheat (An et al. 2006; Manschadi et al. 2008; Xie et al. 2017). However, little is known about the underlying genes controlling root traits in wheat, except two formally identified genes controlling wheat RSA traits through the forward-genetics approach. The first one was VERNALIZATION1 (VRN1) gene, a key regulator of flowering behavior in cereals, which has been demonstrated to modulate root angle at all growth stages of wheat (Voss-Fels et al. 2017). More recently, the ENHANCED GRAVITROPISM 2 (EGT2) gene, an evolutionary conserved regulator of root growth angle in barley and wheat, is proposed to be a valuable target for root-based crop improvement. (Kirschner et al. 2021). Meanwhile, a few genes controlling root traits in wheat have been discovered by reverse genetics approaches, focusing on wheat orthologues of genes known to affect RSA in other species, such as TaWRKY51 (Hu et al. 2018) and TaLBD16 (Wang et al. 2018) regulating lateral root formation, TaRSL4 (Han et al. 2016) and TaRSL2 (Han et al. 2017) associated with the elongation of root hair, the LOB family member TaMOR (Li et al. 2016) controlling number of roots, and transcription factors TaNAC2-5A, TaNAC69-1, and TaRNAC1 related to increased root length (Chen et al. 2018; Dandan et al. 2016; He et al. 2015). Hence, pinpointing QTLs/genes controlling root traits is of great interest in improving root function and contributing to increased wheat yields via these valuable genetic information and beneficial resources.

Bread wheat (2n = 6x = 42, AABBDD) is a typical allohexaploid species, derived from the hybridization of allotetraploid wheat (Triticum turgidum L., AABB) and diploid Aegilops tauschii (DD) (Consortium 2014; Petersen et al. 2006). Compared to the A and B sub-genomes, the D sub-genome exhibits limited genetic diversity in allohexaploid wheat, which is partly due to the involvement of only a few accessions of Ae. tauschii in the formation of allohexaploid wheat. This suggests that the D sub-genome holds significant potential for wheat improvement (Cox 1997; Hao et al. 2020; Huang et al. 2002). However, the genetic diversity of Ae. tauschii accessions has been found to be markedly higher than that of the D sub-genome from allohexaploid Triticum aestivum (Caldwell et al. 2004; Gaurav et al. 2022). Thus, integrating alleles underlying beneficial traits from Ae. tauschii into allohexaploid wheat using synthetic wheat and its introgression lines might be an efficient strategy to expand D sub-genome diversity (Gao et al. 2017; Okamoto et al. 2013). Specifically, extensive variation of seedling lateral root number observed among Ae. tauschii accessions is retained in synthetic allohexaploid wheat lines (Wang et al. 2018). However, there have been relatively fewer studies focused on identifying beneficial QTL for root morphological traits from the diploid D donor of bread wheat. For instance, three QTL for root diameter were found to be associated with P-deficiency tolerance on chromosomes (Chr.) 1D, 3D and 7D, with desirable alleles contributed by synthetic allohexaploid wheat (Wu et al. 2017). Positive alleles for two QTL controlling the fresh weight of roots and number of root tips associated with drought tolerance on Chr. 7D and 3D were derived from synthetic allohexaploid wheat (Liu et al. 2020). Similarly, the synthetic allohexaploid line (XX329) used in the current study is comprised of A and B sub-genomes from the common allohexaploid bread wheat TAA10 (cv Canthach) and D sub-genome from the Ae. tauschii subspecies strangulate with accession number of TQ18 (Kerber 1964). Since XX329 and TAA10 share nearly identical AABB sub-genomes but have diversified D sub-genome, the phenotypic variations between them are primarily due to the different genetic origin of the D sub-genome (Lei et al. 2017; Zhang et al. 2014). A recombinant inbred lines (RILs) population derived from a cross between XX329 and TAA10 has been utilized to identify QTLs for various traits, including grain size and shape (Lei et al. 2017), spike morphological traits, plant height and heading date (Xu et al. 2022), as well as heat stress tolerance (Zhang et al. 2022). In this study, to unravel the underlying genetic basis on the variations of root traits between XX329 and TAA10, we conducted QTL analysis for lateral root number (LRN), seminal root number (SRN), root hair length (RHL), root diameter (RD), and total root volume (TRV) with this RILs population. Moreover, we developed near isogenic line (NIL) populations to validate the genetic effect of QLrn.qau-5D.1 and identified possible candidate genes within the targeted QTL region. This research can offer crucial insights into the genetic basis of root traits in wheat throughout its evolution and domestication, thus laying the groundwork for future targeted selection of root traits in wheat improvement and breeding programs.

Plant materials

A population of 182 F₈ RILs was used, following the protocol as described by Xu et al. (2022). The RIL population was developed from the cross of common wheat TAA10 and resynthesized allohexaploid wheat XX329 with similar AABB sub-genomes using a single seed descent method.

Heterozygous F1 plants of QLrn.qau-5D.1 were obtained from five generations (BC₅) of backcrossing with recurrent parent (TAA10) using marker-assisted selection (MAS). After self-pollination to F₂, three BC₅F₂ lines with different heterozygous fragments for the target region were generated to verify the presence of QLrn.qau-5D.1. Subsequently, the homozygotes (BC₅F_2:3) from the above segregating populations were used to develop corresponding three sets of NIL pairs (NB1, NB2, and NB3). NIL-I^TAA10 (genotype same as TAA10) and NIL-I^XX329 (genotype same as XX329) from different sets of NIL pairs were identified using markers flanking QLrn.qau-5D.1 and were subsequently used for genetic analysis. In addition, RIL131 (F₈) displayed residual heterozygosity within the candidate interval of QLrn.qau-5D.2. It was then self-pollinated twice to the F₁₀ generation. Afterwards, selfing of these plants was carried out to obtain nine sets of NIL pairs (L1-L9) with overlapping heterozygous fragments genotyped with QLrn.qau-5D.2 flanking markers. Each NIL set consisted of 30–107 TAA10-type homozygotes (NIL-II^TAA10) and 33–98 XX329-type homozygotes (NIL-II^XX329) to evaluating the effect of QLrn.qau-5D.2.

Root system architecture phenotyping

Seminal RSA traits with the exception of root hair length were evaluated according to the protocol described by Canè et al. (2014) and were further modified in the present work. Seeds for each genotype were surface sterilized in 1% sodium hypochlorite for 5 min, rinsed five times with distilled water and placed in Petri dishes with one sheet of germinating paper moistened with 5 mL of distilled water. The dishes were kept in the dark at 4°C for 2 days and then cultured at 20°C until germination. Seeds with uniform germination were cultivated for 2 days in a glasshouse under controlled conditions (55% humidity; 16-h light at 24°C and 8-h dark at 18°C). Next, the seedlings were transferred to a culture box with distilled water and were further cultured for an additional 8 days. The distilled water was refreshed every 2 days. Each genotype was performed with three biological replicates and each replicate comprised at least 8 plants. In addition, root hair phenotyping was conducted following the method described by Han et al. (2016). In brief, seeds with uniform germination were sown on a medium containing 1% agar and were cultivated for 2 days in the above glasshouse. For each genotype, we performed three biological replicates, each of which consisted of at least five plants.

Seminal RSA traits were investigated on a single-plant basis: Lateral root number (LRN) on the primary root and seminal root number (SRN) were determined by an Epson Perfection LA2400 scanner and analyzed using Adobe PHOTOSHOP. Root hair length (RHL) was recorded with a fluorescent stereomicroscope (SZX16; Olympus, Tokyo, Japan) attached to a digital imaging system (DP72; Olympus) and measured by Adobe PHOTOSHOP. Root diameter (RD) and total root volume (TRV) were measured using a WinRHIZO image analyzing system (Zealquest Scientific Technology Co., Ltd., China).

Field evaluation of yield-related traits

Phenotypic data for182 RILs as reported by Xu et al. (2022) including spike length (SL), fertile spikelet number per spike (FSN), sterile spikelet number per spike (SSN), total spikelet number per spike (TSN), spike compactness (SC), plant height (PH) and heading date (HD) were used to investigate the relationships between root traits and yield-related traits. The NIL pair (NIL-II^TAA10 and NIL-II^XX329) from NIL pair L6 were planted in the field at Qingdao (36.59°N, 120.12°E) in Shandong province in China during the 2021–2022 growing season. The field trials were conducted in randomized complete block design with three replications. Each plot consisted of two rows that were 1.5 m long and 0.3 m apart with a sowing rate of 15 seeds per row. For each replicate, 6 to 10 representative plants or spikes were selected for phenotypic measurement of yield-related traits, i.e. PH, SL, FSN, SSN, TSN, SC, thousand grain weight (TGW), grain length (GL), grain width (GW), tiller number per plant (TN), and grain yield per plant (GYP).

QTL analysis

The TAA10/XX329 genetic linkage map for the D genome used here has been reported by Xu et al. (2022). For the analysis of QTLs associated with seminal RSA traits, we employed the composite interval mapping (CIM) method using Windows QTL Cartographer software version 2.5 (Wang et al. 2010). In this method, model 6 (standard model) with forward and backward regression, five control markers as cofactors, a 10 cM scanning window size and a walk speed of 1 cM were determined as QTL detection parameters.

The empirical genome-wide LOD threshold values were estimated by conducting 1000 random permutation tests at a significance level of P ≤ 0.05. Confidence intervals were calculated based on position ± 2 LOD away from the peak of likelihood ratios. All QTLs were named following the recommendations of McIntosh et al. (2017).

InDel markers development

Whole genome re-sequencing of TAA10 and XX329 was conducted to obtain a polymorphism data set (Xu et al. 2022). Polymorphic nucleotide sequences between TAA10 and XX329 within the QTL interval were utilized to develop InDel markers in Primer3 v0.4.0 (http://bioinfo.ut.ee/primer3-0.4.0/). The Polymerase Chain Reaction (PCR) amplification was performed in a volume of 10 µL including 5 µl 2 × Taq PCR StarMix, 2 µl primer (mixture of forward and reverse primers, 2 µM), 1.5 µl DNA template and 1.5 µl sterilized ddH₂O. The PCR program was carried out at 94°C 5 min, followed by 32 cycles of 94°C 30 s, 55–60°C 30 s, 72°C 30 s and 72°C 5 min. The PCR products were separated using 2% agarose gel or 8% non-denatured polyacrylamide gel electrophoresis (PAGE) gels. The primer sequences of InDel markers used in this study were listed in the Supplementary Table S2.

Prediction of candidate genes

To predict candidate/flanking genes, the interval flanking marker sequences were blasted against the International Wheat Genome Sequencing Consortium and Ensembl Plants databases to determine the physical position with the highest identity of the identified QTL. To further obtain candidate genes among the target interval of QLrn.qau-7D.2, predicted genes of high-confidence were extracted from the JBrowse website (https://urgi.versailles.inra.fr/jbrowseiwgsc/gmod_jbrowse/). The gene expression pattern was determined in the Wheat eFP Browser (http://bar.utoronto.ca/efp_wheat/cgi-bin/efpWe b.cgi). The whole-genome resequencing data of TAA10 and XX329 were used to retrieve the sequences of candidate genes, including the 2-kb upstream region of the translation start codon, exons, introns and the 2-kb downstream region of the translation termination codon.

Auxin treatment

The seeds of NIL^TAA10 and NIL^XX329 from NIL pair L6 were surface sterilized with 1% NaClO for 10 min and were then incubated in Petri dishes at 4°C for 2 d in the dark and exposed to white light for 1 d. The 2-day-old seedlings of NIL^TAA10 and NIL^XX329 were transferred to a culture box filled with different concentrations of 2,4-D (0.01, 0.1, 1, and 10 µM) and 1/5th strength Hoagland solution for 10 days in the greenhouse. Lateral root number on the primary root was measured as described previously (Wang et al., 2018). The experiment was repeated three times, with at least eight plants per treatment group.

Detection of candidate gene expression

The 2-day-old seedlings of NIL^TAA10 and NIL^XX329 were transferred to a culture box filled with distilled water for 4 days in the greenhouse. The culture solution was changed every 2 days. Then, the 6-day-old uniform seedlings were treated with 0.1µM IAA. Total RNA was extracted from wheat roots with SteadyPure Plant RNA Extraction Kit (Accurate Biotechnology, Hunan, China). The RNA samples were digested with purified DNase I, and first-strand cDNA synthesis was performed using HiScript II One-Step RT-PCR Kit (Vazyme Biotech, Nanjing, China). SYBR Color qPCR Master Mix (Vazyme Biotech, Nanjing, China) was used to perform reverse transcription qPCR (RT-qPCR) assays to detect gene transcript levels. The RT-qPCR conditions and analytical methods were the same as those described by Wang et al. (2018). The wheat Actin gene was used as a standard control. The accession numbers of TaActin homoeologous genes were TraesCS1A02G274400, TraesCS1B02G283900, and TraesCS1D02G274400 (Wang et al., 2022). Three independent biological replicates were applied to each sample for RT-qPCR. A description of the RT-qPCR primers is given in Supplemental Table S2.

Statistical analysis

Basic phenotypic statistical analyses, correlation coefficients among traits and Shapiro-Wilk test for testing departures from normal distribution were conducted using IBM SPSS Statistics 26.0 (SPSS, Chicago, USA). The broad-sense heritability (H_B²) based on the mean values of each trait among the three replications was calculated using IciMapping software v4.0 following the formula: H_B² = σ_g²/(σ_g² + σ_e²/r), where σ_g² is the genetic variance, σ_e² is the residual variance, and r is the number of replications (Alemu et al. 2021). In the progeny test, the significance of the phenotypic variation between the NIL pairs was determined by Student’s t test.

Phenotypic Evaluation of Seminal Root Traits

The phenotypic performance and broad-sense heritability (H_B²) of five seminal RSA traits (LRN, SRN, RHL, RD and TRV) from the RIL population and their parents are shown in Table 1. Notably, XX329 had more LRN, SRN, RD and TRV compared to TAA10, while TAA10 had longer RHL than XX329. Significant differences in root traits were observed among the RILs. Specifically, LRN ranged from 36.98 to 2.35 roots per plant with a mean value of 8.84, SRN varied from 4.22 to 6.00 roots per plant with an average of 5.18, RHL spanned from 0.53 to 1.41 mm with a mean value of 0.96 mm, RD was between 0.20 and 0.13 mm with a mean value of 0.17 mm, and TRV varied from 0.14 to 0.02 cm³ with a mean value of 0.05 cm³. The coefficient of variance (CV) of seminal RSA traits varied from 6.37% for SRN to 52.37% for LRN. The estimated H_B² of these five root traits ranged from 76.25 to 98.49%, among which LRN, SRN and RHL exhibited higher heritability (98.01, 91.39 and 98.49%, respectively) compared to RD and TRV, which were 76.25% and 84.48% respectively. The Shapiro-Wilk test for normality distribution showed that RD displayed normal distributions (P > 0.05), while the distribution of LRN, SRN, RHL and TRV exhibited deviations from normality (P < 0.05) (Figure 1). The frequency distribution histograms showed pronounced transgressive segregation for each trait, indicating that TAA10 and XX329 contributed positive alleles for these traits.

Correlation among Seminal Root Traits and yield components

The BLUP values of yield-related traits including SL, FSN, SSN, TSN, PHT, HD from the RIL population have been reported in Xu et al. (2022). These values were utilized in the current study to calculate correlation coefficients between seminal root traits and yield components. Of all root traits, LRN exhibited a significantly positive correlation with HD (P < 0.05). SRN showed a positive correlation with FSN and TSN (P < 0.01)) (Table 2). Additionally, RD and TRV were notably positively correlated with GL, GW and TGW (P < 0.05) (Table 2).

QTL analysis

In this study, we detected seven QTL associated with LRN, SRN, RHL, RD and TRV on chromosomes 2D, 4D, 5D and 7D (Table 3). Of these identified QTL, three QTL associated with LRN (QLrn.qau-5D.1, QLrn.qau-5D.2 and QLrn.qau-7D) were identified on chromosome arms 5DS, 5DL and 7DS, respectively. QLrn.qau-5D.1 and QLrn.qau-5D.2 carried the favorable alleles contributed by TAA10, while QLrn.qau-7D had the favorable allele contributed by XX329. QLrn.qau-5D.1 had a LOD value of 2.55 and accounted for 4.98% of LRN variation. QLrn.qau-5D.2 had a LOD value of 5.78 and contributed to 12.17% of phenotypic variation (Figure 2A). QLrn.qau-7D had a LOD value of 2.84 and accounted for 5.35% of LRN variation. For SRN, a major QTL (QSrn.qau-2D) with a LOD of 7.30 was detected on chromosome arm 2DS to control SRN. This QTL explained 14.73% of the phenotypic variance for SRN and was associated with positive alleles from XX329. For RHL, a major QTL located in a region on chromosome 4DL (QRhl.qau-4D) explained 9.32% of the observed RHL variation, with the positive allele contributed by TAA10. Additionally, a minor QTL (QRd.qau-2D) with LOD of 2.86 was detected on chromosome arm 2DS, controlling RD. This QTL explained 7.16% of the phenotypic variance for RD and was associated with positive alleles from XX329. A QTL located on chromosome 5DL (QTrv.qau-5D) with a LOD score of 2.95 explained 6.97% of phenotypic variation in TRV. The positive allele for QTrv.qau-5D was contributed by XX329.

Fine mapping and verification of QLrn.qau‑5D.2

Residual heterozygous line (RHL) is an efficient tool for QTL fine-mapping without extensive backcrossing (Liu et al. 2018; Tuinstra et al. 1997). Fortunately, we successfully identified a RHL from the RIL population that carries a heterozygous segment in the target region of QLrn.qau‑5D.2. Hence, we chose to focus on fine mapping the major QTL QLrn.qau‑5D.2. The confidence interval of this QTL was determined to be between marker 5D432 and 5D483 (Figure 2A, B). To fine mapping QLrn.qau‑5D.2, six new polymorphic InDel markers between TAA10 and XX329 were developed to genotype the segregating populations the segregating progenies of RHL (Table S2). Based on genotype of these markers, nine sets of NILs with overlapping recombinant segments were developed (L1-L9) (Figure 2C). The phenotyping analysis on LRN was applied among these homozygous non-recombinant lines. In the progeny test, NIL^TAA10(with TAA10 allele) exhibited a significantly larger lateral root number than that of NIL^XX329 (with XX329 allele) within the populations L1, L2, L4, L6 and L8 (P < 0.01), while no significant differences were observed between NIL^TAA10 and NIL^XX329within the populations L3, L5, L7 and L9. Taken together, QLrn.qau‑5D.2 was precisely mapped to an approximate 5.0 Mb interval flanked by markers of 5D1204 and 5D122, corresponding to the genomic region from 386.3 Mb to 391.3 Mb in the IWGSC RefSeq v1.1. (Figure 2C).

To further validate and assess the effect of QLrn.qau‑5D.2 on LRN and yield component traits, we evaluated the effect of a set of NIL pair (L6) under field conditions. The results revealed that the average LRN of NIL^TAA10 was 33.33% higher than that of NIL^XX329 in the NIL pair derived from L6 (Figure 2C). In addition, an analysis of RIL lines possessing TAA10 or XX329 homozygous allele across the interval of QLrn.qau 5D.2 between markers 5D1204 and 5D122 revealed that the lines with the TAA10 allele exhibited a higher LRN than those with the TAA10 allele, which suggested that the 5D1204-5D122 interval contained a functional unit that controls LRN (Figure S1). To determine the possible pleiotropic effects of QLrn.qau‑5D.2 associated with the effect on LRN, we measured a series of other yield-related traits in the set of NIL pair. Notably, NIL^TAA10displayed significant increases in PHT, SL, SC, TN and GYP, with improvements of 5.7%, 9.0%, 12.0%, 23.96% and 24.44%, respectively (P < 0.05), when compared to NIL^XX329 in the L6-derived pair (Table S3).

Analysis of candidate genes

To identify the candidate genes in the 5.0-Mb interval of QLrn.qau-5D.2 (5D1204-5D122), we searched the annotated genes based on the gene annotations of the Chinese Spring reference genome (IWGSC RefSeq v1.1). This interval contained 80 high-confidence genes (Table S4). Utilizing the Wheat eFP Browser database, we analyzed the expression patterns of these genes in different tissues. Among them, 39 genes were primarily expressed in root tissues (Table S5). It is well-established that Auxin plays a pivotal role in the formation of lateral and adventitious roots (Lavenus et al. 2013; Lee et al. 2019; Wang et al. 2022). The orthologs of the above 39 genes in Arabidopsis and rice were analyzed by using Triticeae-GeneTribe (Table S6) (Chen et al. 2020; Xu et al. 2022). Interestingly, five predicted genes (TraesCS5D02G286000, TraesCS5D02G286100, TraesCS5D02G288000, TraesCS5D02G291800 and TraesCS5D02G293100) have been identified to be associated with the auxin-related pathway, suggesting that they might be the candidate genes (Table S7). DNA sequence analysis uncovered SNP variants of TraesCS5D02G286000 between TAA10 and XX329. These variants were found in the upstream region of the translation start codon and the downstream region of the termination codon, respectively. For TraesCS5D02G286100, only one single InDel was found in the upstream region of translation start codon and downstream region of the termination codon between TAA10 and XX329, respectively. As for TraesCS5D02G288000, 6 SNPs were observed between two parent lines. In particular, one SNP in the exon 3 was found in XX329, as compared with TAA10, which led to the substitution of amino acid (Asn/Ile). For TraesCS5D02G291800, a total of 17 SNPs and 11 InDels were found between TAA10 and XX329. These variants included 3 SNPs in the upstream region of translation start codon, 1 SNP in the intron, as well as 13 SNPs and 11 InDels in the downstream region of the termination codon. For TraesCS5D02G293100, altogether 17 SNPs and 3 InDels were identified between two parents. Notably, 4 SNPs were located in the exon 1 and exon 3, resulting in 3 amino acid substitutions.

Given the missense mutations in TraesCS5D02G288000 and TraesCS5D02G293100, which involved in auxin-associated pathways, between TAA10 and XX329, we hypothesize that these genes may play a role in auxin-induced lateral root development. We first examined the expression patterns of these two candidate genes in roots of NIL^TAA10 and NIL^XX329 from one NIL pair of L6 under 0.1 μM IAA treatment. The results showed that the accumulation of TraesCS5D02G28800 mRNA continued to increase in NIL^TAA10after 3 h of IAA treatment. In contrast, there was no dramatic change in the transcript level of this gene in NIL^XX329following IAA treatment (Figure 3A). Compared with NIL^XX329, the transcript level of TraesCS5D02G28800 remained consistently higher in NIL^TAA10throughout the 3 h to 12 h period of IAA treatment (Figure 3A). As for TraesCS5D02G293100 mRNA, there was a significant increase in the transcript level of this gene in NIL^TAA10 and NIL^XX329 after a 6-h IAA treatment (Figure 3B). Moreover, the relative expression level of TraesCS5D02G293100 was significantly higher under IAA treatment for different durations in NIL^TAA10 than that of NIL^XX329(Figure 3B). Next, we examined how NIL^TAA10 and NIL^XX329 plants response to different concentrations of IAA treatments. There was a noticeable difference in lateral root number between NIL^TAA10 and NIL^XX329 in response to increasing IAA concentrations from 0.01 to 1 μM. The NIL^TAA10 plants exhibited more lateral root number compared to the NIL^XX329plants (Figure 3C). These data indicated that TraesCS5D02G288000 and TraesCS5D02G293100 may be involved in the development of lateral roots, partially in an auxin-associated manner.

Novel QTLs for root-related traits in the D genome of Wheat

Allohexaploid common wheat is a product of hybridization between Triticum turgidum and a single lineage of Ae. tauschii only once or at best a few times (Gaurav et al. 2022; Wang et al. 2013). Possibly as a result of this, the genetic diversity of the D genome is far less than that of the A and B genomes (Chao et al. 2010; Dubcovsky and Dvorak 2007). Synthetic allohexaploid wheats (AABBDD) provide promising resources to widen the genetic diversity associated with the D genome of Ae. tauschii (Liu et al. 2020; Mares and Mrva 2008). To date, a number of genes/QTL controlling important agronomic traits have been found in the D genome of synthetic allohexaploid wheat, such as pest and disease resistance, stress tolerance, quality and yield (Li et al. 2012; Liu et al. 2020; Okamoto et al. 2013; Singh et al. 2004; Tadesse et al. 2007). Plant roots are important organs in determining grain productivity. Early vigorous growth of roots can improve the efficient acquisition of water and nutrients, and is closely related to plant adaptation and yield (Liao et al. 2004; Liu et al. 2013). Although root traits in the field are difficult to be directly selected by wheat breeders, identification of QTLs for root traits and development of marker-assisted selection (MAS) will greatly help breeders to select wheat root traits desirable for improving wheat water-nutrient use efficiencies (Den Herder et al. 2010; Wachsman et al. 2015; Xu et al. 2023). Especially, some QTLs for root characteristics were co-located with loci affecting plant height, grain-related traits or drought tolerance (Bai et al. 2013; Liu et al. 2019; Liu et al. 2020). Thus, the loci for root traits could be selected to enhance the potential and stability of yield and drought tolerance by MAS.

Since the genome (AABB) of the resynthesized allohexaploid wheat XX329 is near-identical to that of the allohexaploid common wheat TAA10 (Yan et al. 2017), the observed phenotypic variation of root-related traits in this study could primarily be attributed to differences on the D genome between TAA10 and XX329. Here, we identified 7 QTL for root traits (LRN, SRN, RHL, RD and TRV) using the TAA10/XX329 RIL population, and these QTLs were primarily located on chromosomes 2D, 4D, 5D and 7D (Table 3). Previous studies showed that QTL for root traits (LRN, SRN, RHL, RD and TRV) have been detected on various chromosomes (Liu et al. 2019; Soriano and Alvaro 2019; Zheng et al. 2019). To the best of our knowledge, only one of the seven QTLs (QTrv.qau-5D) coincided with that of a previously reported QTL controlling root volume (Ibrahim et al. 2012), and the remaining six QTLs identified in this study, namely QLrn.qau-5D.1, QLrn.qau-5D.2, QLrn.qau-7D, QSrn.qau-2D, QRhl.qau-4D and QRd.qau-2D, appear to be novel. These findings provide valuable insights into the underlying mechanisms of root traits (Soriano and Alvaro 2019). Among these QTLs, three novel ones (QLrn.qau-5D.1, QLrn.qau-5D.2 and QRhl.qau-4D) had positive alleles originating from TAA10, while QLrn.qau-7D, QSrn.qau-2D, and QRd.qau-2D were also novel QTLs with the increasing allele from XX329 (Table 3). Similarly, in a study by Liu et al. (2020), two novel QTLs for root fresh weight and root diameter, along with six novel QTLs for root fresh weight, the ratio of root water loss, total root surface area, number of root tips, and number of root forks were detected in a synthetic allohexaploid wheat line. Among these QTLs, four novel QTLs contributed by positive alleles from the synthetic allohexaploid wheat line were mapped to the D genome (Liu et al. 2020). Therefore, the presence of novel QTLs detected in the D genome indicated that synthetic allohexaploid wheat contains some new QTL/genes for root traits obtained from Ae. tauschii, which can provide more desirable targets for improving wheat root traits through molecular breeding.

Candidate genes of QLrn.qau-5D.2

To date, several wheat genes controlling lateral root number have been characterized using a homology-based approach, such as TaWRKY51, TaLBD16, TaSNAC8 and TaMOR (Hu et al. 2018; Li et al. 2022; Mao et al. 2020; Wang et al. 2018). To identify the candidate genes for LRN, we conducted an analysis of the annotated genes within the confidence interval of QLrn.qau-5D.2 which controls lateral root number. Given that many genetic studies highlighted the pivotal role of the plant hormone auxin in regulating lateral root development (Rogg and Bartel 2001; Tian and Reed 1999; Wang et al. 2018; Wilmoth et al. 2010), this study focused on auxin-related genes that might be the candidate genes of QLrn.qau-5D.2. Based on functional annotations and transcriptome analyses of 80 high confidence candidate genes in the genetic interval of QLrn.qau-5D.2 (Table S4), this study identified five genes (TraesCS5D02G286000, TraesCS5D02G286100, TraesCS5D02G288000, TraesCS5D02G291800, and TraesCS5D02G293100 that have the potential to affect lateral root development. These genes encode proteins involved in the auxin-related pathway (Table S4; Table S6). Of these, two genes (TraesCS5D02G286000 and TraesCS5D02G286100) encode glycosylphosphatidylinositol tail anchored proteins related to root growth, which were both orthologs of Arabidopsis AUXIN INDUCED IN ROOTS12 (AtAIR12). In Arabidopsis, the auxin-responsive gene AtAIR12 plays an important role in regulating the formation of lateral roots (Gibson et al. 2015; Neuteboom et al. 1999). Additionally, TraesCS5D02G288000 encodes a TRANSPORT INHIBITOR RESISTANT 1 (TIR1)/AUXIN SIGNALLING F-BOX (AFB) protein. The sequence variation of TaTIR1/AFB (TraesCS5D02G288000) including one SNP in the exon and 4 SNPs in the upstream region of the translation start codon was detected between two parents. Notably, the SNP in the exon caused an amino acid substitution (Asn/Ile). Recent studies have demonstrated that the TIR1/AFB proteins are essential in very rapid auxin responses in root growth, such as lateral root formation (Fendrych et al. 2018; Prigge et al. 2020). In rice, downregulation of the auxin receptor genes OsTIR1 and OsAFB2 by overexpression of OsMIR393 affects plant height, tillering, grain yield, and primary and crown root growth (Bian et al. 2012; Guo et al. 2021). In wheat, the wheat auxin receptor TaTIR1 (TraesCS1A02G091300) negatively regulates defense against Fusarium graminearum (Su et al. 2021), but the function of this gene (TraesCS5D02G288000) has not been reported in wheat. Interestingly, our results revealed that the positive allele of QLrn.qau-5D.2 increased not only lateral root number, but also plant height, tillering number and grain yield per plant (Table S3), suggesting that TraesCS5D02G288000 (TaTIR1/AFB) may play a similar role in wheat, root system, plant height, tillering, and grain yield. Analysis of another two candidate genes (TraesCS5D02G291800 and TraesCS5D02G293100) showed that both genes encode an ER-localized protein, and its homologous gene OsPIN5b in rice. OsPIN5b participates in auxin homeostasis and regulates plant architecture and yield. In particular, overexpression of the OsPIN5b leads to reduced plant height, tiller number, yield, root length and root tips number. In contrast, reduced expression of OsPIN5b results in higher tiller number, more vigorous root system and increased yield (Lu et al. 2015). Moreover, both TraesCS5D02G291800 and TraesCS5D02G293100 have several sequence variations between TAA10 and XX329, but only TraesCS5D02G293100 has 4 SNPs in exon that caused the substitution of 3 amino acids (Table S7). Notably, TraesCS5D02G288000 (CTIR1/AFB) and TraesCS5D02G293100 (TaPIN5b) have missense mutations between TAA10 and XX329 (Table S?). The transcript level of TraesCS5D02G288000 (TaTIR1/AFB) continued to increase in NIL^TAA10 after 3 h IAA treatment, whereas no drastic change in NIL^XX329 was detected in the transcript level of this gene after IAA treatment (Fig. 3A). The relative expression level of TraesCS5D02G288000 (TaTIR1/AFB) at peaks was significantly higher throughout the period of 3 h-12 h IAA treatment in NIL^TAA10 than that of NIL^XX329 (Fig. 3A). TraesCS5D02G293100 (TaPIN5b) mRNA showed that there was a remarkable increase in the transcript level of this gene in NILTAA10 and NILXX329 after a 6-h IAA treatment, respectively (Fig. 3B). The transcript level of this gene was significantly higher under IAA treatment for different durations in NIL^TAA10 than that of NIL^XX329 (Fig. 3B). Interestingly, there was a noticeable difference in lateral root number between NIL^TAA10 and NIL^XX329 in response to increasing IAA concentrations from 0.01 to 1 µM, and The NIL^TAA10 plants exhibited more lateral root number compared to the NIL^XX329 plants (Fig. 3C). This indicates that TraesCS5D02G288000 (TaTIR1/AFB) and TraesCS5D02G293100 (TaPIN5b) may be involved in lateral root development partly in an auxin-associated manner. In summary, these genes may be the candidate genes and further studies are required to confirm the true candidate gene for QLrn.qau-5D.2.

Authors` Contributions YZ designed the research. HW, BY, XZ, HC, FL, YR, XW, XF, WG, and XL carried out. HW analyzed data and wrote the first draft. HW, BY, XZ, HC prepared the tables and figures. All authors assisted in revising the manuscript.

Acknowledgements We are grateful to Professor Zhongfu Ni providing the recombinant inbred line population and genotyping. This work was supported by grants from the Key Research and Development Project of Shandong Province (2021LZGC025, 2023LZGC002 and 2021LZG013), and the Foundation for High-level Talents of Qingdao Agriculture University (6631119057).

Availability of data and material All data generated or analyzed during this study are included in the main text article and its supplementary files.

Conflict of interest The authors declare that they have no conflicts of interest.

Ethical approval These experiments complied with the ethical standards in China

Alemu A, Feyissa T, Maccaferri M, Sciara G, Tuberosa R, Ammar K, Badebo A, Acevedo M, Letta T, Abeyo B (2021) Genome-wide association analysis unveils novel QTLs for seminal root system architecture traits in Ethiopian durum wheat. BMC Genomics22:1-16
An D, Su J, Liu Q, Zhu Y, Tong Y, Li J, Jing R, Li B, Li Z (2006) Mapping QTLs for nitrogen uptake in relation to the early growth of wheat (Triticum aestivum L.). Plant Soil 284:73-84
Atkinson JA, Wingen LU, Griffiths M, Pound MP, Gaju O, Foulkes MJ, Le Gouis J, Griffiths S, Bennett MJ, King J, Wells DM (2015) Phenotyping pipeline reveals major seedling root growth QTL in hexaploid wheat. J Exp Bot 66:2283-2292
Bai C, Ge Y, Ashton R, Evans J, Milne A, Hawkesford M, Whalley W, Parry M, Melichar J, Feuerhelm D, Bansept P, Bartsch M (2019) The relationships between seedling root screens, root growth in the field and grain yield for wheat. Plant Soil 440:311-326
Bai C, Liang Y, Hawkesford MJ (2013) Identification of QTLs associated with seedling root traits and their correlation with plant height in wheat. J Exp Bot 64:1745-1753
Bian H, Xie Y, Guo F, Han N, Ma S, Zeng Z, Wang J, Yang Y, Zhu M (2012) Distinctive expression patterns and roles of the miRNA393/TIR1 homolog module in regulating flag leaf inclination and primary and crown root growth in rice (Oryza sativa). New Phytol 196:149-161
Caldwell KS, Dvorak J, Lagudah ES, Akhunov E, Luo MC, Wolters P, Powell W (2004) Sequence polymorphism in polyploid wheat and their D-genome diploid ancestor. Genetics 167:941-947
Canè MA, Maccaferri M, Nazemi G, Salvi S, Francia R, Colalongo C, Tuberosa R (2014) Association mapping for root architectural traits in durum wheat seedlings as related to agronomic performance. Mol Breed 34:1629-1645
Chao S, Dubcovsky J, Dvorak J, Luo MC, Baenziger SP, Matnyazov R, Clark DR, Talbert LE, Anderson JA, Dreisigacker S, Glover K, Chen J, Campbell K, Bruckner PL, Rudd JC, Haley S, Carver BF, Perry S, Sorrells ME, Akhunov ED (2010) Population-and genome-specific patterns of linkage disequilibrium and SNP variation in spring and winter wheat (Triticum aestivum L.). BMC Genomics 11:1-17
Chen D, Chai S, McIntyre CL, Xue GP (2018) Overexpression of a predominantly root-expressed NAC transcription factor in wheat roots enhances root length, biomass and drought tolerance. Plant Cell Rep 37:225-237
Chen X, Ding Q, Błaszkiewicz Z, Sun J, Sun Q, He R, Li Y (2017) Phenotyping for the dynamics of field wheat root system architecture. Sci Rep 7:37649
Chen Y, Song W, Xie X, Wang Z, Guan P, Peng H, Jiao Y, Ni Z, Sun Q, Guo W (2020) A collinearity-incorporating homology inference strategy for connecting emerging assemblies in the triticeae tribe as a pilot practice in the plant pangenomic era. Mol Plant 13:1694-1708
International Wheat Genome Sequencing Consortium (2014) A chromosome-based draft sequence of the hexaploid bread wheat (Triticum aestivum) genome. Science 345: doi/10.1126/science.1251788
Cox TS (1997) Deepening the wheat gene pool. J crop prod 1:1-25
Chen D, Richardson T, Chai S, Lynne McIntyre C, Rae AL, Xue GP (2016) Drought-up-regulated TaNAC69-1 is a transcriptional repressor of TaSHY2 and TaIAA7, and enhances root length and biomass in wheat. Plant Cell Physiol 10:2076-2090
Den Herder G, Van Isterdael G, Beeckman T, De Smet I (2010) The roots of a new green revolution. Trends Plant Sci 15:600-607
Dubcovsky J, Dvorak J (2007) Genome plasticity a key factor in the success of polyploid wheat under domestication. Science 316:1862-1866
Fendrych M, Akhmanova M, Merrin J, Glanc M, Hagihara S, Takahashi K, Uchida N, Torii KU, Friml J (2018) Rapid and reversible root growth inhibition by TIR1 auxin signalling. Nat Plants 4:453-459
Gao L, Zhao G, Huang D, Jia J (2017) Candidate loci involved in domestication and improvement detected by a published 90K wheat SNP array. Sci Rep 7:44530
Gaurav K, Arora S, Silva P, Sánchez-Martín J, Horsnell R, Gao L, Brar GS, Widrig V, John Raupp W, Singh N, Wu S, Kale SM, Chinoy C, Nicholson P, Quiroz-Chávez J, Simmonds J, Hayta S, Smedley MA, Harwood W, Pearce S, Gilbert D, Kangara N, Gardener C, Forner-Martínez M, Liu J, Yu G, Boden SA, Pascucci A, Ghosh S, Hafeez AN, O'Hara T, Waites J, Cheema J, Steuernagel B, Patpour M, Justesen AF, Liu S, Rudd JC, Avni R, Sharon A, Steiner B, Kirana RP, Buerstmayr H, Mehrabi AA, Nasyrova FY, Chayut N, Matny O, Steffenson BJ, Sandhu N, Chhuneja P, Lagudah E, Elkot AF, Tyrrell S, Bian X, Davey RP, Simonsen M, Schauser L, Tiwari VK, Randy Kutcher H, Hucl P, Li A, Liu DC, Mao L, Xu S, Brown-Guedira G, Faris J, Dvorak J, Luo MC, Krasileva K, Lux T, Artmeier S, Mayer KFX, Uauy C, Mascher M, Bentley AR, Keller B, Poland J, Wulff BBH (2022) Population genomic analysis of Aegilops tauschii identifies targets for bread wheat improvement. Nat Biotechnol 40:422-431
Gibson SW, Todd CD (2015) Arabidopsis AIR12 influences root development. Physiol Mol Biol Plants 21:479-489
Golan G, Hendel E, Méndez Espitia GE, Schwartz N, Peleg Z (2018) Activation of seminal root primordia during wheat domestication reveals underlying mechanisms of plant resilience. Plant Cell Environ 41:755-766
Guo F, Huang Y, Qi P, Lian G, Hu X, Han N, Wang J, Zhu M, Qian Q, Bian H (2021) Functional analysis of auxin receptor OsTIR1/OsAFB family members in rice grain yield, tillering, plant height, root system, germination, and auxinic herbicide resistance. New Phytol 229:2676-2692
Han H, Wang H, Han Y, Hu Z, Xin M, Peng H, Yao Y, Sun Q, Ni Z (2017) Altered expression of the TaRSL2 gene contributed to variation in root hair length during allopolyploid wheat evolution. Planta 246: 1019-1028
Han Y, Xin M, Huang K, Xu Y, Liu Z, Hu Z, Yao Y, Peng H, Ni Z, Sun Q (2016) Altered expression of TaRSL4 gene by genome interplay shapes root hair length in allopolyploid wheat. New Phytol 209:721-732
Hao C, Jiao C, Hou J, Li T, Liu H, Wang Y, Zheng J, Liu H, Bi Z, Xu F, Zhao J, Ma L, Wang Y, Majeed U, Liu X, Appels R, Maccaferri M, Tuberosa R, Lu H, Zhang X (2020) Resequencing of 145 landmark cultivars reveals asymmetric sub-genome selection and strong founder genotype effects on wheat breeding in China. Mol Plant 13:1733-1751
He X, Qu B, Li W, Zhao X, Teng W, Ma W, Ren Y, Li B, Li Z, Tong Y (2015) The nitrate-inducible NAC transcription factor TaNAC2-5A controls nitrate response and increases wheat yield. Plant Physiol 2015:1991-2005
Hu Z, Wang R, Zheng M, Liu X, Meng F, Wu H, Yao Y, Xin M, Peng H, Ni Z, Sun Q (2018) TaWRKY51 promotes lateral root formation through negative regulation of ethylene biosynthesis in wheat (Triticum aestivum L.). Plant J 96:372-388
Huang Q, Börner A, Röder S, Ganal W (2002) Assessing genetic diversity of wheat (Triticum aestivum L.) germplasm using microsatellite markers. Theor Appl Genet 105:699-707
Ibrahim S, Schubert A, Pillen K, Léon J (2012) QTL analysis of drought tolerance for seedling root morphological traits in an advanced backcross population of spring wheat. Int J AgriScience 2: 619-629
Jennylyn LT, Herra LG, Jose BA, Ajay K (2016) Cereal root proteomics for complementing the mechanistic understanding of plant abiotic stress tolerance. Agric Proteomics 2:19-51
Kerber ER (1964) Wheat: reconstitution of the tetraploid component (AABB) of hexaploids. Science 143:253-255
Kirschner GK, Rosignoli S, Guo L, Vardanega I, Imani J, Altmüller J, Milner SG, Balzano R, Nagel KA, Pflugfelder D, Forestan C, Bovina R, Koller R, Stöcker TG, Mascher M, Simmonds J, Uauy C, Schoof H, Tuberosa R, Salvi S, Hochholdinger F (2021) ENHANCED GRAVITROPISM 2 encodes a STERILE ALPHA MOTIF-containing protein that controls root growth angle in barley and wheat. Proc Natl Acad Sci USA 118(35): e2101526118
Lavenus J, Goh T, Roberts I, Guyomarc'h S, Lucas M, De Smet I, Fukaki H, Beeckman T, Bennett M, Laplaze L (2013) Lateral root development in Arabidopsis: fifty shades of auxin. Trends Plant Sci 18:450-458
Lee HW, Cho C, Pandey SK, Park Y, Kim MJ, Kim J (2019) LBD16 and LBD18 acting downstream of ARF7 and ARF19 are involved in adventitious root formation in Arabidopsis. BMC Plant Biol 19:1-11
Li B, Liu D, Li Q, Mao X, Li A, Wang J, Chang X, Jing R (2016) Overexpression of wheat gene TaMOR improves root system architecture and grain yield in Oryza sativa. J Exp Bot 14:4155-4167
Li C, Wang J, Li L, Li J, Zhuang M, Li B, Li Q, Huang J, Du Y, Wang J, Fan Z, Mao X, Jing R (2022) TaMOR is essential for root initiation and improvement of root system architecture in wheat. Plant Biotechnol J 20:862-875
Li Y, Zhou R, Wang J, Liao X, Branlard G, Jia J (2012) Novel and favorable QTL allele clusters for end-use quality revealed by introgression lines derived from synthetic wheat. Mol Breed 29:627-643
Liao M, Fillery IRP, Palta JA (2004) Early vigorous growth is a major factor influencing nitrogen uptake in wheat. Funct Plant Biol 31:121-129
Liu N, Liu J, Li W, Pan Q, Liu J, Yang X, Yan J, Xiao Y (2018) Intraspecific variation of residual heterozygosity and its utility for quantitative genetic studies in maize. BMC Plant Biol 18:1-15
Liu P, Jin Y, Liu J, Liu C, Yao H, Luo F, Guo Z, Xia X, He Z (2019) Genome-wide association mapping of root system architecture traits in common wheat (Triticum aestivum L.). Euphytica 215:1-12
Liu R, Wu F, Yi X, Lin Y, Wang Z, Liu S, Deng M, Ma J, Wei Y, Zheng Y, Liu Y (2020) Quantitative trait loci analysis for root traits in synthetic hexaploid wheat under drought stress conditions. Int J Mol Sci 19:1947-1960
Liu X, Li R, Chang X, Jing R (2013) Mapping QTLs for seedling root traits in a doubled haploid wheat population under different water regimes. Euphytica 189:51-66
Lu G, Coneva V, Casaretto JA, Ying S, Mahmood K, Liu F, Nambara E, Bi YM, Rothstein SJ (2015) OsPIN5b modulates rice (Oryza sativa) plant architecture and yield by changing auxin homeostasis, transport and distribution. Plant J 83:913-925
Maccaferri M, El-Feki W, Nazemi G, Salvi S, Canè MA, Colalongo MC, Stefanelli S, Tuberosa R (2016) Prioritizing quantitative trait loci for root system architecture in tetraploid wheat. J Exp Bot 67:1161-1178
Malamy JE (2005) Intrinsic and environmental response pathways that regulate root system architecture. Plant Cell Environ 28:67-77
Manschadi A, Manske G, Vlek PLG (2013) Root architecture and resource acquisition: Wheat as a model plant. Plant roots 1-22
Manschadi AM, Hammer GL, Christopher JT, Devoil P (2008) Genotypic variation in seedling root architectural traits and implications for drought adaptation in wheat (Triticum aestivum L.). Plant Soil 303:115-129
Mao H, Li S, Wang Z, Cheng X, Li F, Mei F, Chen N, Kang Z (2020) Regulatory changes in TaSNAC8‐6A are associated with drought tolerance in wheat seedlings. Plant Biotechnol J 18:1078-1092
Mares D, Mrva K (2008) Genetic variation for quality traits in synthetic wheat germplasm. BMC Genomics 59: 406-412
Mcintosh RA, Dubcovsky J, Rogers WJ, Morris C, Appels R, Xia XC (2017) Catalogue of gene symbols for wheat: 2017 Supplement.
Munns R, James RA, Xu B, Athman A, Conn SJ, Jordans C, Byrt CS, Hare RA, Tyerman SD, Tester M, Plett D, Gilliham M (2012) Wheat grain yield on saline soils is improved by an ancestral Na+ transporter gene. Nat Biotechnol 30:360-364
Neuteboom LW, Ng JM, Kuyper M, Clijdesdale OR, Hooykaas PJ, van der Zaal BJ (1999) Isolation and characterization of cDNA clones corresponding with mRNAs that accumulate during auxin-induced lateral root formation. Plant Mol Biol 39:273-287
Okamoto Y, Nguyen AT, Yoshioka M, Iehisa JC, Takumi S (2013) Identification of quantitative trait loci controlling grain size and shape in the D genome of synthetic hexaploid wheat lines. Breed Sci 63:423-429
Petersen G, Seberg O, Yde M, Berthelsen K (2006) Phylogenetic relationships of Triticum and Aegilops and evidence for the origin of the A, B, and D genomes of common wheat (Triticum aestivum). Mol Phylogenet Evol 39:70-82
Prigge MJ, Platre M, Kadakia N, Zhang Y, Greenham K, Szutu W, Pandey BK, Bhosale RA, Bennett MJ, Busch W, Estelle M (2020) Genetic analysis of the Arabidopsis TIR1/AFB auxin receptors reveals both overlapping and specialized functions. Elife 9:e54740
Rogg LE, Lasswell J, Bartel B (2001) A gain-of-function mutation in IAA28 Suppresses lateral root development. Plant Cell 13:465-480
Salamini F, Ozkan H, Brandolini A, Schäfer-Pregl R, Martin W (2002) Genetics and geography of wild cereal domestication in the near east. Nat Rev Genet 3:429-441
Shiferaw B, Smale M, Braun HJ, Duveiller E, Muricho G (2013) Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security. Food Sec 5: 291–317
Singh S, Franks CD, Huang L, Brown-Guedira GL, Marshall DS, Gill BS, Fritz A (2004) Lr41, Lr39, and a leaf rust resistance gene from Aegilops cylindrica may be allelic and are located on wheat chromosome 2DS. Theor Appl Genet 108:586-591
Smith S, De Smet I (2012) Root system architecture: insights from Arabidopsis and cereal crops. Philos Trans R Soc Lond B Biol Sci 367:1441-1452
Soriano JM, Alvaro F (2019) Discovering consensus genomic regions in wheat for root-related traits by QTL meta-analysis. Sci Rep 9:10537
Steele KA, Price AH, Witcombe JR, Shrestha R, Singh BN, Gibbons JM, Virk DS (2013) QTLs associated with root traits increase yield in upland rice when transferred through marker-assisted selection. Theor Appl Genet 126:101-108
Su P, Zhao L, Li W, Zhao J, Yan J, Ma X, Li A, Wang H, Kong L (2021) Integrated metabolo‐transcriptomics and functional characterization reveals that the wheat auxin receptor TIR1 negatively regulates defense against Fusarium graminearum. J Integr Plant Biol 63:340-352
Tadesse W, Schmolke M, Hsam SL, Mohler V, Wenzel G, Zeller FJ (2007) Molecular mapping of resistance genes to tan spot [Pyrenophora tritici-repentis race 1] in synthetic wheat lines. Theor Appl Genet 114:855-862
Tian Q, Reed JW (1999) Control of auxin-regulated root development by the Arabidopsis thaliana SHY2/IAA3 gene. Development 126:711-721
Tuinstra MR, Ejeta G, Goldsbrough PB (1997) Heterogeneous inbred family (HIF) analysis: a method for developing near-isogenic lines that differ at quantitative trait loci. Theor Appl Genet 95:1005-1011
Voss-Fels KP, Robinson H, Mudge SR, Richard C, Newman S, Wittkop B, Stahl A, Friedt W, Frisch M, Gabur I, Miller-Cooper A, Campbell BC, Kelly A, Fox G, Christopher J, Christopher M, Chenu K, Franckowiak J, Mace ES, Borrell AK, Eagles H, Jordan DR, Botella JR, Hammer G, Godwin ID, Trevaskis B, Snowdon RJ, Hickey LT (2017) VERNALIZATION1 modulates root system architecture in wheat and barley. Mol Plant 11: 226-229
Wachsman G, Sparks EE, Benfey PN (2015) Genes and networks regulating root anatomy and architecture. New Phytol 208:26-38
Wang H, Han X, Fu X, Sun X, Chen H, Wei X, Cui S, Liu Y, Guo W, Li X, Xing J, Zhang Y (2022)Overexpression of TaLBD16-4D alters plant architecture and heading date in transgenic wheat. Front Plant Sci 13:911993
Wang H, Hu Z, Huang K, Han Y, Zhao A, Han H, Song L, Fan C, Li R, Xin M, Peng H, Yao Y, Sun Q, Ni Z (2018) Three genomes differentially contribute to the seedling lateral root number in allohexaploid wheat: evidence from phenotype evolution and gene expression. Plant J 95:976-987
Wang J, Luo MC, Chen Z, You FM, Wei Y, Zheng Y, Dvorak J (2013) Aegilops tauschii single nucleotide polymorphisms shed light on the origins of wheat D‐genome genetic diversity and pinpoint the geographic origin of hexaploid wheat. New Phytol 198:925-937
Wang S, Basten CJ, Zeng ZB (2012) Windows QTL Cartographer2.5. Department of Statistics, North Carolina State University, Raleigh. 2010
Wasson AP, Nagel KA, Tracy S, Watt M (2020) Beyond digging: noninvasive root and rhizosphere phenotyping. Trends Plant Sci 25:119-120
Wasson AP, Richards RA, Chatrath R, Misra SC, Prasad SV, Rebetzke GJ, Kirkegaard JA, Christopher J, Watt M (2012) Traits and selection strategies to improve root systems and water uptake in water-limited wheat crops. J Exp Bot 63:3485-3498
Wilmoth JC, Wang S, Tiwari SB, Joshi AD, Hagen G, Guilfoyle TJ, Alonso JM, Ecker JR, Reed JW (2010) NPH4/ARF7 and ARF19 promote leaf expansion and auxin-induced lateral root formation. Plant J 43:118-130
Wu F, Yang X, Wang Z, Deng M, Ma J, Chen G, Wei Y, Liu Y (2017) Identification of major quantitative trait loci for root diameter in synthetic hexaploid wheat under phosphorus-deficient conditions. J Appl Genet 58:437-447
Xie Q, Fernando KMC, Mayes S, Sparkes DL (2017) Identifying seedling root architectural traits associated with yield and yield components in wheat. Ann Bot 119:1115–1129
Xu H, Zhang R, Wang M, Li L, Yan L, Wang Z, Zhu J, Chen X, Zhao A, Su Z, Xing J, Sun Q, Ni Z (2022) Identification and characterization of QTL for spike morphological traits, plant height and heading date derived from the D genome of natural and resynthetic allohexaploid wheat. Theor Appl Genet 135:389-403
Xu Y, Yang Y, Wu S, Liu D, Ren Y (2023) QTL mapping for root traits and their effects on nutrient uptake and yield performance in common wheat (Triticum aestivum L.). Agriculture 13:210
Yan L, Liang F, Xu H, Zhang X, Zhai H, Sun Q, Ni Z (2017) Identification of QTL for grain size and shape on the D genome of natural and synthetic allohexaploid wheats with near-identical AABB genomes. Front Plant Sci 8:1705
Zhang H, Zhu B, Qi B, Gou X, Dong Y, Xu C, Zhang B, Huang W, Liu C, Wang X, Yang C, Zhou H, Kashkush K, Feldman M, Wendel JF, Liu B (2014) Evolution of the BBAA component of bread wheat during its history at the allohexaploid level. Plant Cell 26:2761
Zhang R, Liu G, Xu H, Lou H, Zhai S, Chen A, Hao S, Xing J, Liu J, You M, Zhang Y, Xie C, Ma J, Liang R, Sun Q, Zhai H, Ni Z, Li B (2022) Heat Stress Tolerance 2 confers basal heat stress tolerance in allohexaploid wheat (Triticum aestivum L.). J Exp Bot 73:6600-6614
Zheng X, Wen X, Qiao L, Zhao J, Zhang X, Li X, Zhang S, Yang Z, Chang Z, Chen J, Zheng J (2019) A novel QTL QTrl. saw-2D. 2 associated with the total root length identified by linkage and association analyses in wheat (Triticum aestivum L.). Planta 250:129-143
Zhou Y, Zhao X, Li Y, Xu J, Bi A, Kang L, Xu D, Chen H, Wang Y, Wang YG, Liu S, Jiao C, Lu H, Wang J, Yin C, Jiao Y, Lu F (2020) Triticum population sequencing provides insights into wheat adaptation. Nat Genet 52:1412-1422

Table 1 Two parental and RIL population means, ranges and broad-sense heritability for LRN, SRN, RHL, RD and TRV

Traits	Parents			RIL population					H_B²(%)
Traits	TAA10	XX329	Difference	Max	Min	Average	SD	CV (%)	H_B²(%)
LRN	6.62	8.86	-2.24**	36.96	2.35	8.84	4.63	52.37	98.01
RHL	1.16	0.81	0.35***	1.41	0.53	0.96	0.17	17.75	98.49
SRN	4.90	5.40	-0.50**	6.00	4.22	5.18	0.33	6.37	91.39
RD	0.16	0.20	-0.04*	0.20	0.05	0.17	0.07	41.18	76.25
TRV	0.05	0.06	-0.01*	0.14	0.02	0.05	0.01	27.92	84.48

Table 2 Phenotypic correlations of seedling root traits with yield components in the TAA10/XX329 RIL population

Traits	SL	FSN	SSN	TSN	SC	PHT	HD	GL	GW	TGW
LRN	0.022	-0.077	-0.003	-0.076	- 0.077	0.145	0.151*	0.044	-0.018	-0.031
RHL	-0.034	-0.047	-0.042	-0.063	- 0.004	-0.083	-0.144	-0.003	-0.023	0.043
SRN	0.029	0.251**	-0.075	0.214**	0.077	-0.081	-0.011	0.126	0.126	0.046
RD	-0.066	-0.028	0.003	-0.026	0.077	-0.015	0.028	0.148*	0.209**	0.306**
RV	0.015	0.014	0.082	0.046	0.011	0.101	-0.003	0.168*	0.211**	0.203**

Table 3 The QTL regions associated with LRN, SRN, RHL, RD and TRV in the TAA10/XX329 RIL population

Trait	QTL	Nearest marker	Position(cM)	LOD	Additive value	CI (cM)	R² (%)	Positive Allele
LRN	QLrn.qau-5D.1	gwm190	3.95	2.55	-1.06	1.4-10.2	4.98	TAA10
	QLrn.qau-5D.2	5D432	73.80	5.78	-1.64	65.7-82.6	12.17	TAA10
	QLrn.qau-5D.3	cfd68	92.29	2.84	1.11	90.9-97.1	5.35	XX329
SRN	QSrn.qau-2D	SSR2433	16.11	7.30	0.13	14.7-16.9	14.73	XX329
RHL	QRhl.qau-4D	4D237	16.60	4.24	-0.06	15.5-17.3	9.32	TAA10
RD	QRd.qau-3D	2S250	56.20	2.86	0.0045	53.8-57.4	7.16	XX329
TRV	QTrv.qau-5D	cfd183	121.90	2.95	0.0046	118.5-125.1	6.97	XX329

Identification of novel QTL for seedling root architectural traits in the D genome of natural and resynthetic allohexaploid wheat

Status:

Version 1

Abstract

Figures

Key message

Introduction

Materials and methods

Plant materials

Root system architecture phenotyping

Field evaluation of yield-related traits

QTL analysis

InDel markers development

Prediction of candidate genes

Auxin treatment

Detection of candidate gene expression

Statistical analysis

Results

Discussion

Novel QTLs for root-related traits in the D genome of Wheat

Declarations

References

Tables

Supplementary Files

Status:

Version 1