Genome-wide linkage mapping of root system architecture-related traits in common wheat (Triticum aestivum L.)

Identifying loci for root system architecture (RSA) traits and developing available markers are crucial for wheat breeding. In this study, RSA-related traits, including total root length (TRL), total root area (TRA), and number of root tips (NRT), were evaluated in the Doumai/Shi4185 recombinant inbred line (RIL) population under hydroponics. In addition, both the RILs and parents were genotyped using the wheat 90K single-nucleotide polymorphism (SNP) array. In total, two quantitative trait loci (QTLs) each for TRL (QTRL.caas-4A.1 and QTRL.caas-4A.2), TRA (QTRA.caas-4A and QTRA.caas-4D), and NRT (QNRT.caas-5B and QNRT.caas-5D) were identified and each explaining 5.94%–9.47%, 6.85%–7.10%, and 5.91%–10.16% phenotypic variances, respectively. Among these, QTRL.caas-4A.1 and QTRA.caas-4A overlapped with previous reports, while QTRL.caas-4A.2, QTRA.caas-4D, QNRT.caas-5B, and QNRT.caas-5D were novel. The favorable alleles of QTRL.caas-4A.1, QTRA.caas-4A, and QTRA.caas-5B were contributed by Doumai, whereas the favorable alleles of QTRL.caas-4A.2, QTRA.caas-4D, and QTRA.caas-5D originated from Shi 4185. Additionally, two competitive allele-specific PCR (KASP) markers, Kasp_4A_RL (QTRA.caas-4A) and Kasp_5D_RT (QNRT.caas-5D), were developed and validated in 165 wheat accessions. This study provides new loci and available KASP markers, accelerating wheat breeding for higher yields.


Introduction
Wheat production is influenced seriously by abiotic stresses.Breeding higher-yielding and more stable accessions under abiotic stress is a crucial objective in modern wheat breeding (Bai et al., 2013;Alahmad et al., 2019;Adeleke et al., 2020).Root system architecture (RSA) traits, which contribute to the shape of the root system, are pivotal for wheat agronomic performance and play vital roles in plant development (Kabir et al., 2015;Li et al., 2015;Kulkarni et al., 2017;Alemu et al., 2021).Optimizing RSA traits is important not only for abiotic stress tolerance but also for efficient nutrient and water acquisition (Gupta et al., 2017;Griffiths et al., 2022).RSA traits primarily include root length, surface area, and the number of root tips, which influence the major components and spatial arrangement of root systems, significantly affecting water and nutrient uptake (Atkinson et al., 2015;Maccaferri et al., 2016;Liu et al., 2019;Li et al., 2020;Ma et al., 2022;Maqbool et al., 2022).
Previous breeding programs predominantly focused on aboveground traits such as disease resistance, grain quality, and harvest index, while the application of RSA traits has been limited due to the complexity of phenotypic evaluation (Rogers and Benfey, 2015;Rosellóet al., 2019;Mehrabi et al., 2020;Rufo et al., 2020;Salarpour et al., 2020;Ober et al., 2021;Pariyar et al., 2021;Saini et al., 2021).Previous studies have indicated that the RSA traits are influenced by environmental factors and controlled by minor genes (Xie et al., 2017;Soriano and Alvaro, 2019;Maqbool et al., 2022).To accelerate the progress of breeding for RSA-related traits, it is imperative to identify the significant associated genomic regions (Soriano and Alvaro, 2019;Yang et al., 2021a).Nowadays, with the advancement of high-throughput genotyping, such as re-sequence and SNP assay (Wang et al., 2014), genome-wide linkage mapping has been widely employed to elucidate the genetic basis of complex traits.Over the past two decades, numerous QTLs have been identified for RSA-related traits (Maccaferri et al., 2016;Xie et al., 2017;International Wheat Genome Sequencing Consortium (IWGSC) et al., 2018;Ye et al., 2018;Soriano and Alvaro, 2019;Yang et al., 2021a;Yang et al., 2021b;Xu D et al., 2023).
In this study, we conducted linkage mapping for RSA traits using the wheat 90K assays in a biparental recombinant inbred line (RIL) population derived from the Doumai/Shi 4185 cross.The primary objective of this study is to uncover the genetic basis of these traits and develop available KASP markers for improving wheat RSA.

Plant materials
Doumai is a derivative line of Hesheng 2, which originate from Yuanfeng 6 by Cobalt-60 radiation, whereas Shi 4185 is a widely grown winter wheat cultivar.The 262 F 2:6 RILs derived from the Doumai/Shi 4185 were used for evaluating RSA-related traits.A hydroponic experiment was conducted with three replicates in a greenhouse.There were 20 seeds from each line that were surface sterilized with10% H 2 O 2 for 20 min.Subsequently, the cleaned seeds were placed in Petri dishes with moist filter paper.When the coleoptiles reached about 2 cm in length, wheat seedlings were transferred to plastic trays (53 × 27 cm) containing Hoagland's nutrient solution.Plastic trays were kept in 25°C with a 16-h light and 8-h darkness cycle.After 3 weeks, roots were evaluated for RSA-related traits (Table S1).Additionally, a diverse panel of 165 wheat cultivars primarily originating from the Yellow-Huai Wheat Region were also assessed for TRL, TRA, and NRTRAA traits to validate the effectiveness of competitive allele-specific PCR (KASP) markers.

Phenotype evaluation
Three RSA-related traits, namely, total root length (TRL), total root area (TRA), and number of root tips (NRT), were assessed using the WinRHIZO root analysis system (LA6400XL).The roots were arranged systematically in a dish and scanned using the Expression 11000XL.Subsequently, the images were imported into WinRHIZO software and analyzed using a fixed threshold parameter of 40.Each accession was scored on five plants for RSA traits, and means of three replicates were obtained.Basic statistical analyses and frequency distributions were conducted using SAS v9.3 (http://www.sas.com).

Linkage map construction
Both RILs and parents were genotyped using the wheat 90K SNP arrays (80,547 SNPs) by CapitalBio Corporation.SNPs with missing data >20% or minor allele frequency (MAF) <0.5 were filtered for further analysis.The filtered SNPs were then analyzed using the BIN function of IciMapping v4.2 (Meng et al., 2015) and grouped into bin markers, which were used to construct a linkage map employing the regression mapping algorithm by JoinMap v4.0.The linkage maps have been reported by Wen et al. (2017) and Li et al. (2018).

QTL mapping
The inclusive composite interval mapping (ICIM) method using IciMapping v4.1 (Meng et al., 2015) was applied in this study.The logarithm of odds (LOD) threshold for declaring significant QTL was set as 2.62 based on 1000 permutation.The physical positions of SNPs were based on the IWGSC v1.0.

Search for candidate genes for RSA-related traits
To identify candidate genes involved in the QTL for RSArelated traits detected in the Doumai/Shi 4185 RIL population, the genes located in the LD block region around the peak SNP ( ± 3.0 Mb kb based on previous LD decay analysis) of each QTL were extracted from the wheat IWGSC v1.1 annotation (https:// wheat.pw.usda.gov/GG3/).Genes, excluding hypothetical proteins, transposon proteins, and retrotransposon proteins with SNPs in the coding region, were considered as candidate genes.Quantitative real-time PCR (qRT-PCR) was conducted to test expression differences of the candidate genes between Doumai and Shi 4185.The roots were sampled for RNA extraction after phenotyping.RNA was extracted using the TRIzol method, and the cDNA was synthesized with the HiScript II 1st Strand cDNA Synthesis Kit.Primers were designed using the Primer Premier V5.0.PCR was conducted with a mixture of 20 ml, including 2 ml cDNA, 10 ml ChamQ Universal SYBR qPCR Master Mix, and 0.4 ml of each primer.qRT-PCR was conducted in the ABI StepOnePlus Real-Time PCR System with Tower, and the gene expression level was analyzed by the 2 -DDCT method.All assays were performed in two biological replicates and three technical replicates.TaActin1 was used as the internal control to normalize the expression levels of different samples.

Discussion
Optimization of crop root systems has long been proposed.However, genetic improvement of crop roots has been rarely attempted (Maccaferri et al., 2016;Yang et al., 2021a).A comprehensive understanding of the genetic basis of RSA traits would facilitate the optimization of root systems under nutrient  The identified QTLs for RSA related traits in the Doumai × Shi 4185 RIL population.
In our study, we conducted linkage mapping for agronomic traits in the Doumai/Shi 4185 RIL population (Li et al., 2018) and identified several regions associated with both RSA-related traits and agronomic traits.Specifically, we found that QTRL.caas-4A.1 (702.3Mb-721.3Mb) co-located influencing QTL clusters related to thousand kernel weight (QTKW.caas-4AL.1,708.6 Mb, Validating the efficiency of KASP markers for discriminating RSA-related traits.Different lowercase letters means significant different at p<0.05 level. IWB42202) (Li et al., 2018).Additionally, the loci for RSA traits on chromosomes 5D (IAAV6218, 449.6 Mb-454.1 Mb) colocated with regions affecting QTL clusters related to yield, including SN, KNS, TKW, FLW, and KL (IWB61072-IWB49479, 382.9 Mb-465.6 Mb) (Li et al., 2018).These results indicated that RSA traits loci could also be targeted to enhance yield potential and stability.Four genes involved in the biological metabolism of plant hormones, cellulose, and the ubiquitin pathway were identified as high confidence candidate genes.Among these, TraesCS4A01G436700 of QTRL.caas-4A.1 encodes the CDPKrelated kinase.In plants, CDPKs play a critical role in various signaling pathways and are pivotal in root growth and development (Yip Delormel and Boudsocq, 2019;Dekomah et al., 2022).Silencing CDPK in Medicago truncatula led to a significant reduction in root hair growth and cell length (Ivashuta et al., 2005).TRA Another candidate gene for QTRL.caas-4A.1 is TraesCS4A01G456300, encoding a cellulose synthase-like protein, which holds importance in plant growth-related and stressresponsive activities (Karas et al., 2021;Lou et al., 2022).TraesCS4A01G296500, associated with QTRA.caas-4AS, encodes an ethylene-regulated nuclear protein (ERT2)-like protein.
Ethylene, a simple small molecule, plays a vital role in signal transduction, cell differentiation, division, and elongation (Qin et al., 2019).Ethylene has also been reported to influence traits related to RSA, leading to root hair and cluster root formation (Xu et al., 2020).Additionally, TraesCS5D01G380200, linked to QNRT.caas-5D, encodes an E3 ubiquitin-protein ligase, which plays essential roles in plant development (Chen et al., 2018) and is crucial for primary root growth and shoot development (Ramaiah et al., 2022).
Although conventional breeding has contributed to the enhancement of root system, the selection process is timeconsuming and less efficient due to the challenges in field measurements of RSA traits (Rasheed et al., 2017).KASP offers a cost-effective, flexible, and highly accurate approach for MAS breeding.In this study, Kasp_4A_RL and Kasp_5D_RT were successfully developed based on tightly linked SNP markers, proving to be valuable tools for MAS in breeding programs.Additionally, accessions with more favorable alleles, superior RSA traits and appropriate agronomic traits, such as Yumai 18, Jinhe 9123, Yumai 34, Bainong 3217, Lumai 6, Jinmai 61, Lumai 5, Lumai 23, and Yumai 57 are recommended as parental lines for improvement of RSA traits.

Conclusion
In this study, we identified six QTLs associated with wheat RSA traits and successfully developed two KASP markers that can be utilized in wheat breeding programs aimed at achieving higher and more stable yields.This research serves as a foundational step towards gene cloning and enhancement of wheat root systems.

TABLE 2
Effects of Kasp_4A_RL and Kasp_5D_RT on RSA-related traits in the natural population.
a TT is a favorable allele; CC is an unfavorable allele.b GG is favorable allele, AA is unfavorable allele.* Significant at P < 0.05.

TABLE 3
The candidate genes for RSA-related traits identified in the Doumai/Shi 4185 RIL population.