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- Source title: IDENTIFICATION OF CANDIDATE GENES UNDERLYING A NOVEL QTL FOR ROOT LENGTH AND DIAMETER IN BARLEY IN RESPONSE TO SALINITY
- Author(s): Mather, D., Situmorang, A., Brien, C., Shelden, M.
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## **IDENTIFICATION OF CANDIDATE GENES UNDERLYING A NOVEL QTL FOR ROOT** **LENGTH AND DIAMETER IN BARLEY IN RESPONSE TO SALINITY**

### Diane Mather [1], Apriadi Situmorang [1], Chris J. Brien [2], Megan C. Shelden [1* ] 1 School of Agriculture, Food and Wine, Waite Research Institute, Adelaide University, Urrbrae, SA 5064, Australia. 2 The Plant Accelerator, School of Agriculture, Food and Wine, Adelaide University, Urrbrae, SA 5064, Australia.


*Corresponding author: megan.shelden@adelaide.edu.au

## **KEYWORDS**


barley, root length, auxin biosynthesis, cereals, root architecture


Wordcount: 7111


Figures: 7


Tables: 4


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## **ABSTRACT**


Salinity is a major agricultural problem that decreases growth and yield of crops and is expected to

worsen with climate change. Improving salt tolerance through breeding could be facilitated by

identification of genetic loci and candidate genes affecting root traits that enhance water and nutrient

uptake while limiting accumulation of toxic ions.


In this study, a Clipper x Sahara 3771 doubled haploid (DH) barley mapping population was screened

for root growth responses to saline conditions in a controlled environment. Quantitative trait loci (QTLs)

were mapped for root traits including seminal root length, root diameter and root potassium. Major

QTLs affecting seminal root length and average root diameter were detected in the distal region of the

short arm of chromosome 2H, indicating that this region is a key determinant of root architecture under

both control and saline conditions. The linkage map was improved by using newly designed assays for

SNPs on chromosome 2H and a 2.5-Mb interval was identified for investigation of candidate genes.

Bulked segregant RNA sequencing (BSR-Seq) of contrasting salt-tolerant and salt-sensitive DH lines

identified differentially expressed genes associated with root growth maintenance and ion

homeostasis in root tips. These findings identify specific region on chromosome 2H as a major genomic

region affecting barley root architecture, especially under saline conditions, and identify differentially

expressed candidate genes in that region.


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# **INTRODUCTION**


Soil salinity is a major abiotic stress that limits growth and yield of crops worldwide. Saline soils are

defined as having an electrical conductivity (EC) greater than 4 dS m [-1] (equivalent to ~40 mM NaCl), and

can lead to a reduction in crop growth of 15 - 20 % (Munns and Tester, 2008). The severity of salinity

stress on a crop depends on the timing and duration of stress, plant development stage, species and

genotype. Climate change is expected to further increase soil salinisation and reduce the availability

of arable land for crop production, highlighting the need to develop salt-tolerant crop varieties. Several

genes encoding transporters have been identified that contribute to salt tolerance in crops, including

_Nax2_ in wheat (Munns _et al._, 2012), _SKC1_ ( _OsHKT1;5_ ) in rice (Kobayashi _et al._, 2017) and members of

the _HAK_ family in maize (Zhang _et al._, 2019). However, considerably less is known about the genetic

mechanisms regulating root growth and architecture to salinity.


Barley ( _Hordeum vulgare_ L.) is the fourth most important cereal crop worldwide and is primarily

cultivated for malting and livestock feed. Although barley is relatively salt-tolerant compared with other

cereal crops, yields are constrained by salinity, as well as by drought. Salt tolerance is a complex trait,

involving both osmotic and ionic components. The initial reduction in root growth following exposure

to salinity occurs rapidly and is primarily associated with the osmotic component of salt stress,

whereas ion toxicity develops subsequently as toxic sodium (Na [+] ) and chloride (Cl [-] ) ions build up in

plant tissue (Munns and Tester, 2008). In both barley and wheat, seminal root growth is inhibited by

NaCl in a dose-dependent manner (Rahnama _et al._, 2011; Shelden _et al._, 2013), highlighting the

sensitivity of root growth to salinity and the importance of understanding the mechanisms underlying

variation in root responses. Plant hormones, such as auxin, are key regulators of root growth and

development and contribute to plant responses to abiotic stress (Yu _et al._, 2020).


Root system architecture is particularly important for plant adaptation to saline soils. While there are

many studies examining above-ground traits for salinity tolerance, studies on root traits are limited due

to the difficulties associated with measuring and phenotyping root systems. Understanding how roots

respond and adapt to salinity stress is important for the development of salt-tolerant crops (Shelden

and Munns, 2023). Soil salinity can affect the ability of the roots to capture water and nutrients from

the soil, and results in the unwanted uptake of toxic Na [+] and Cl [-] ions into root cells. Maintenance of

root elongation and continued acquisition of water and nutrients are important adaptive responses to

salinity. In a previous study, we identified barley genotypes that differed in their ability to maintain root

elongation in response to salinity (Shelden _et al._, 2013). Identifying the molecular mechanisms

underlying these contrasting responses to salinity may provide new targets for breeding crops with root

systems better adapted to saline soils.


Salt tolerance is a complex, multigenic trait and therefore it is difficult to identify specific gene targets

for improved salt tolerance in crop species. Numerous quantitative trait loci (QTLs) associated with

salinity tolerance have been reported in barley (Ellis _et al._, 2002; Rivandi _et al._, 2011; Shavrukov _et al._,

2010; Xue _et al._, 2009) but relatively few studies have identified the underlying genes for these loci.

Recently, a QTL for salinity damage score and Na [+] exclusion on chromosome 7H, was fine mapped,

revealing the most likely candidate to be _HvNCX_, a sodium/calcium exchanger (Zhu _et al._, 2023). QTLs

associated with root responses to salinity have been identified in several crop species including barley,

maize and rice (reviewed in Karahara and Horie, 2021). In barley, salinity stress-related QTLs have been

identified for root elongation (Xue _et al._, 2017), membrane potential of root epidermal cells under

hypoxia (Gill _et al._, 2017), root reactive oxygen species content (Gill _et al._, 2019) and root ion content


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and root dry weight (Nguyen _et al._, 2013). Despite the identification of numerous salt-responsive QTLs

in barley, the causal genes and molecular mechanisms underlying root growth and architecture under

saline conditions remain poorly understood.


Bulked segregant analysis (BSA), involves pooling DNA from individuals with contrasting phenotypes

to identify genetic markers linked to target loci controlling the trait of interest (Michelmore _et al._, 1991).

Application of a bulked segregant approach for RNA sequencing (BSR-Seq) enables simultaneous

identification of differentially expressed genes and sequence polymorphisms associated with the trait

of interest. For example, BSR-Seq has been used to reveal candidate genes for abiotic stress tolerance

in maize ( _Zea mays_ L.)(Du _et al._, 2017; Yan _et al._, 2023,) and sorghum ( _Sorghum bicolor L._ )(Zhang _et al._,

2022). The combination of BSR-Seq with QTL analysis provides a powerful tool to understand

molecular mechanisms associated with abiotic stress responses.


In this study, a Clipper x Sahara 3771 doubled haploid (DH) mapping population was used to investigate

the genetic basis of root responses to salinity stress. The parental genotypes exhibit contrasting growth

responses to salinity, with Clipper maintaining root elongation to a greater extent than Sahara 3771

(Shelden _et al._, 2013). Root traits were phenotyped across the DH population to identify genomic

regions associated with variation in root responses to salinity. To identify candidate genes underlying

major loci, BSR-Seq was performed using contrasting salt-tolerant and salt-sensitive lines.

Differentially expressed genes and sequence polymorphisms identified through BSR-Seq were

integrated with QTL mapping to identify candidate genes associated with salinity-responsive root traits.

This combined approach provides new insights into the genetic and molecular mechanisms controlling

root growth and architecture under salinity stress in barley.

## **MATERIALS AND METHODS**

### **Plant materials, DNA marker data and genetic linkage map**


Plant materials consisted of Clipper (an Australian two-rowed barley cultivar), Sahara 3771 (an Algerian

six-rowed barley landrace) and 144 doubled haploid (DH) lines derived from the F 1 generation of a cross

between Clipper and Sahara 3771 (Karakousis _et al._, 2003). Seed and DNA samples of these lines,

genotypic data and a “legacy” genetic linkage map of 436 markers at 261 loci (Table S1) were all

provided by the Australian Centre for Plant Functional Genomics.

### **Phenotypic analysis of a mapping population**


Clipper, Sahara 3771 and 144 Clipper x Sahara 3771 DH lines were evaluated in a three-replicate

randomized split-unit experiment. Uniform-sized seeds were selected, surface sterilised and placed

on nutrient agar medium as described previously (Shelden _et al._, 2013). Seeds were germinated at 14°C

in darkness before transfer to nutrient agar and grown for 72 h at 17°C in darkness. The control nutrient

medium contained 0.05 mM Na [+ ] supplied as NaFe-EDTA. For salt treatment, nutrient agar was

supplemented with 100 mM NaCl and additional CaCl 2 to a final activity of 1 mM (Genc _et al._, 2010).

Experiments were conducted in a temperature-controlled growth cabinet (Conviron, Canada) located

at The Plant Accelerator® (Australian Plant Phenomics Facility, Adelaide, Australia).


A total of 900 seedlings were evaluated across the experiment, including parental controls

incorporated into each weekly batch of 50 plates, to monitor experimental consistency. Each seedling


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was grown individually on a 12 cm x 12 cm square petri dish, with 50 plates evaluated per week over 18

weeks.


The experiment employed a split-unit design in which each line was assigned to a pair of consecutive

plates (main units) using a resolved row-and-column design. Salt and control treatments were then

randomized to the two plates within each pair. As the main-unit design was resolved, each complete

replicate was processed before commencement of the subsequent replicate. Weekly batches of 50

plates were handled under standardized conditions to minimize environmental variation. The main
unit design was generated using CycDesigN (VSN International, 2013), and subplot randomization was

performed using the R package dae (Brien, 2017) within the R statistical computing environment (R

Core Team, 2021).

### _Root and shoot phenotyping_


Following 72 h of growth, seedlings were scanned using a WinRhizo® root analysis system (Regent

Instruments, Canada). Total seminal root length (SRL), average root diameter (RD) and shoot length

(SL) were recorded.


After scanning, shoots were excised from the roots using a razor blade and root and shoot fresh weight

(RFW and SFW) were recorded. The roots were briefly rinsed in dilute 0.05 mM CaSO 4 solution, placed

in paper bags and oven-dried at 60°C for 48 h, before determination of root dry weight (RDW). Shoot dry

weight (SDW) was determined using the same procedure.


Twelve phenotypic traits were measured for each line under both control and salt conditions: root fresh

weight (RFW), root dry weight (RDW), total seminal root length (SRL), average root diameter (RD), shoot

length (SL), shoot fresh weight (SFW), shoot dry weight (SDW), root and shoot Na [+] and K [+] concentration

( _Section 2.4_ ).

### _Determination of Na [+] and K [+ ] content_


Root and shoot Na [+] and K [+] concentrations were determined as previously described (Munns _et al._,

2010; Shelden _et al._, 2013). Dried tissue samples were digested in 1% (v/v) nitric acid at 85°C for 2 h

and subsequently diluted with Milli-Q water as required.


Na [+] and K [+] concentrations were quantified by atomic absorption spectrometry (AA-7000, Shimadzu

Corporation, Japan) in flame mode as described previously (Shelden _et al._, 2020). Standard curves

were prepared using 1000 ppm Na and K standard solutions (SIGMA, Australia). Caesium chloride was

added to samples and standards to a final concentration of 0.1% (w/v) to minimise ionisation

interference during analysis.

### **Statistical analysis of phenotypic data**


Phenotypic means were adjusted for the spatial variation in the growth room, using a linear-mixed
model analysis performed for each trait using the R package ASReml (Butler _et al._, 2009) and

ASRremlPlus (Brien, 2018) within the R statistical environment (R Core Team, 2021). This model

included terms for two-factor factorial model of the effects of genotype (lines), treatment (conditions),

and for large-scale spatial variation between replicates, weeks within replicates and MainUnit within

Weeks. It also allowed for the variances of the roots from control plates to differ from those from salt

plates.


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For each trait, REML ratio tests (𝛼= 0.05) were used to determine whether the variances differed

significantly between the treatments, and models were modified accordingly. Model assumptions

were assessed by residual-versus-fitted values plots, and normal probability plots of the residuals.


Wald F-tests were conducted to assess significance of genotype (lines) x treatment (conditions)

interactions. Where interactions were not significant, the main effects of genotype and treatment were

evaluated. Best linear unbiased estimates (BLUEs) were obtained for each genotype x treatment

combination and used for QTL analysis, selection of BSR-Seq bulks, and subsequent phenotypic

analyses.

### **Selection of bulks for BSR-Seq analysis**


Twenty DH lines were selected from the mapping population for bulked segregant RNA sequencing

(BSR-Seq). Ten of these lines were classified as sensitive to salinity because their roots were

significantly shorter in the salt treatment compared to the control treatment. The other ten lines were

classified as tolerant to salinity because their roots were not significantly shorter in the salt treatment

compared to the control treatment. Each of the sensitive lines was allocated to one of two bulks (S1

and S2) and each of the tolerant lines was allocated to one of two other bulks (T1 and T2) (Table S2).


Two plants of each selected line were grown in duplicates under control and salt conditions, in each of

three independent experiments, using the methods described in Section 2.2. After 72 h of treatment, 5

mm root tips were excised from the three longest seminal roots of each seedling using a razor blade

under a dissecting microscope. Root tips from the two seedlings of each DH line were pooled to form

one sample to obtain enough tissue for total RNA extraction. Equal amounts of tissue from the 4-6 DH

lines comprising each bulk were then combined to generate a single bulked biological replicate.

Samples were immediately transferred to pre-chilled microcentrifuge tubes, snap frozen in liquid N,

and stored at -80°C until RNA extraction. Four bulks were analysed under two treatments (control and

salt; total eight bulks) with three biological replicates per treatment, giving a total of 24 RNA samples.

### **RNA extraction, library construction, and sequencing**


Total RNA was extracted using the Spectrum™ Plant Total RNA Kit (Sigma-Aldrich, Missouri, USA)

according to the manufacturer’s instructions. Residual genomic DNA was removed using a Turbo

DNase™ kit (Invitrogen™, MA, USA). RNA quantity and quality were assessed using an Agilent 2100

Bioanalyzer (SA Pathology, Adelaide, SA, Australia). All samples had an RNA integrity (RIN) > 7.4.


RNA sequencing was performed at the Australian Genomics Research Facility (AGRF, Melbourne, VIC).

Twenty-four cDNA libraries were prepared and sequenced on an Illumina NovaSeq 6000 platform using

NovaSeq 6000 v1.5 chemistry to generate 2 x 100 bp paired-end reads.

### **Differential gene expression analyses**


RNA-seq data pre-processing was carried out using an in-house analysis workflow, using MultiQC for

quality reporting (Ewels _et al._, 2016), alignment to the barley reference genome assembly using STAR

(Dobin _et al._, 2013), and gene expression quantification using featureCounts (Liao _et al._, 2014).

Alignment rates exceeded 90% for all libraries and sequencing depth exceeded 45 million paired-end


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reads per library. Differential gene analysis was performed in R (R Core Team, 2021) using the edgeR

(Robinson _et al._, 2009) and limma-voom (Law _et al._, 2014; Ritchie _et al._, 2015) packages. Genes with

counts per million (CPM) > 1 in at least three libraries were retained for analysis resulting in 20,604

expressed genes. Library sizes were normalised using the trimmed mean of M-values (TMM) method

implemented in edgeR. Differential gene expression was performed using limma-voom and assessed

using empirical Bayes moderated t-tests implemented in limma. Genes with a false discovery rate

(FDR)-adjusted _p_ -value < 0.05 and log 2 fold-change |log 2 FC)| > 1 were considered differentially

expressed.


Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment

analyses of salt-responsive DEGs were performed using ShinyGO V0.86.0 (Ge _et al._, 2020), with

_Hordeum vulgare_ specified as the reference species. GO and KEGG pathways with an FDR <0.05 were

considered significantly enriched.


GO enrichment analysis of genes exhibiting a root-length-dependent response to salinity was

performed separately using g:Profiler, with _Hordeum vulgare_ specified as the reference organism

(Kolberg _et al._, 2023). Enrichment was assessed for the molecular function (GO:MF), biological process

(GO:BP), and cellular component (GO:CC) categories. GO terms with adjusted P < 0.05 were

considered significantly enriched.

### **Variant calling and annotation using BSR-Seq data**


Single nucleotide polymorphisms (SNPs) and short insertion/deletion variants (indels) were identified

from RNA-seq reads generated from the four contrasting bulks. Reads had been previously aligned to

the barley Morex V3 reference genome during transcriptome analysis. Variant discovery was performed

according to the Genome Analysis Toolkit (GATK) best practice workflow for RNA-seq short variant

discovery. Briefly, aligned reads were processed using SplitNCigarReads prior to variant calling.

Variants were called for each library using GATK HaplotypeCaller in gVCF mode, followed by joint

genotyping using CombineGVCFs and GenotypeGVCFs.


Variants were filtered according to GATK recommendations for RNA-seq data. Variants were annotated

using SnpEff (Cingolani et al., 2012) with the _Hordeum vulgare_ L. database compatible with the Morex

V3 genome assembly. Only annotated variants that passed quality and coverage filtering criteria were

retained for downstream analyses.

### **Quantitative trait loci analysis and SNP genotyping**


Quantitative trait locus analysis was conducted using simple interval mapping as implemented in the

R package R/qtl (Broman et al. 2009), with BLUEs from the phenotypic analysis used as trait values for

the DH lines. Logarithm of odds (LOD) test statistic values were compared to threshold values

determined with 10,000 permutations to achieve a genome-wide significance level of 0.05.


After QTL analysis revealed that both root length and root diameter were affected by the terminal region

of the short arm of chromosome 2H, genotype results for variants on that chromosome arm were

reviewed to identify single-nucleotide polymorphisms (SNPs) for which assays could be designed to

improve the genetic map. Competitive allele-specific PCR (KASP) assays (He et al. 2014) were designed


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for 16 SNPs. The positions of these SNPs in Version 3 of the Morex barley genome assembly (Mascher

et al. 2021) and the sequences of the KASP primers are provided in Table S3.


The KASP assays were applied to Clipper, Sahara 3771 and 144 DH lines using an automated SNPline

system (LGC Biosearch Technologies, Teddington, UK) according to the manufacturer’s instructions.

Genotypic data for the 16 SNPs were combined with genotypic data for legacy markers that had

previously been mapped on chromosome 2H. Where SNPs co-segregated with legacy markers, the

SNPs were retained and the legacy markers were removed. Based on the combined dataset, marker

loci on chromosome 2H were ordered and genetic distances calculated using the MSTmap algorithm

(Wu et al. 2008) as implemented in the R/ASMap package (Taylor et al. 2017). QTL analysis was

repeated using the SNP-enriched linkage map, with significance thresholds established based on

10,000 permutations for root length and root diameter, each under both control and salt conditions.

### **Filtering of BSR-Seq variants in a candidate region on chromosome 2H**


Variants within a candidate region (500,000 to 3,000,000 bp on chromosome 2H) were reviewed to

identify variants as candidate polymorphisms for QTL effects. For variants with high or moderate

SNPeff impact ratings, genotype results for the treatment combination (two sets of T lines and two sets

of S lines, each grown in control and salt treatments) were considered. Each treatment combination

was classified as fixed for a particular allele if all three of its replicate samples had been called as fixed

for that allele, or if two replicate samples had been called as fixed for that allele and the remaining

sample not called (null result) or called as mixed (heterozygous). Variants were then excluded from

further consideration if (1) none of the T treatment combinations were called as fixed for any allele; (2)

none of the S treatment combinations were called as fixed for any allele; (3) more than one fixed

genotype was called among the four T treatment combinations or among the four S treatment

combinations; or (4) the same allele was called as fixed in both S and T treatment combinations. This

filtered the variants to include only those for which at least one of the four T treatment combinations

seems to be fixed for one allele (deduced to come from Clipper) and at least one of the four S treatment

combinations seems to be fixed for a different allele (deduced to come from Sahara 3771), with no

strong inconsistency among replicate samples, among the T treatment combinations or among the S

combinations.

## **RESULTS**

### **Phenotyping of DH mapping population for salt tolerance**


Phenotypic variation among the DH lines and parental genotypes under control and salt conditions is

summarised in Table S4. Frequency distributions for seminal root length, average root diameter and

root Na [+] content were approximately normally distributed (Figure 1). The remaining traits also exhibited

approximately normal distributions (Figure S1). For most traits, the population mean was intermediate

between the parental values. However, for average root diameter (ARD), root Na [+] content (RNaC) and

shoot length (SL), the population mean fell outside the parental range.


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### **Phenotypic validation of selected salt-tolerant and salt-sensitive lines**


Twenty lines were classified as salt-tolerant (T1 and T2) and salt-sensitive (S1 and S2) and selected for

further analysis based on their root phenotype under saline conditions in the phenotyping experiment

described in Section 3.1. The root phenotypic responses of the selected lines are shown in Figure 2.

Both T1 and T2 maintained seminal root length in response to salt treatment relative to control at 88

and 91%, respectively, with no significant difference between salt-treated and control plants or

between T1 and T2. Seminal root length was significantly higher for T1 and T2 compared to S1 and S2

for both control and salt treatment (Figure 2A). S1 and S2 relative root length (salt relative to control)

was 71 and 73%, respectively. S1 and S2 salt-treated roots were significantly shorter than their

corresponding controls.


For all groups, salt-treated roots had significantly higher root Na [+] content (5.5 to 7.1-fold) compared to

their respective controls (Figure 2B). No significant difference in root Na [+] content was observed

between T1 and T2, however S1 and S2 were significantly different to each other (Figure 2C). The

average root diameter was lower in T1 and T2 compared to S1 and S2 (Figure 2C). These selected lines

were then used for bulk segregant RNA sequencing.

### **BSR-Seq analysis of tolerant and sensitive lines**


RNA sequencing generated a total of 733.5 M reads across the 24 libraries representing the T1, T2, S1

and S2 pools under control and salt conditions, with three biological replicates per treatment

combination. Genes were considered differentially expressed (DE) if their FDR-adjusted _p_ -value was

<0.05 and |log 2 FC| > 1. Principal component analysis (PCA) of the RNA-seq data showed “salt

treatment” separated the samples across PC2, indicating that salt treatment is one of the main

sources of variation in the data, accounting for approximately 10% of the variation in gene expression

between samples (Figure 3A). PC1 explained 28% of the transcriptional variation and separated

tolerant and sensitive bulks.


_Salt-responsive genes in long and short roots_


In response to salinity stress, 652 and 646 genes were differentially expressed in the tolerant (long
root) and sensitive (short-root) bulks, respectively. In the tolerant bulks, 606 genes were upregulated

and 46 were downregulated, whereas the sensitive bulks exhibited 569 upregulated and 77

downregulated genes, in response to salt treatment. The tolerant and sensitive bulks shared 306 salt
responsive genes, while each bulk also exhibited distinct transcriptional responses associated with

their root-length phenotype (Figure 3B).


KEGG enrichment analysis of salt-induced genes using ShinyGO identified several pathways enriched

in both tolerant and sensitive bulks, including glutathione metabolism, plant MAPK signalling,

phenylpropanoid biosynthesis and biosynthesis of secondary metabolites. In the tolerant bulks,

glutathione metabolism showed the highest fold enrichment, followed by plant MAPK signalling and

phenylpropanoid biosynthesis (Figure 4A). Plant hormone signal transduction was also significantly

enriched. In contrast, the sensitive bulks showed strong enrichment of alpha linolenic metabolism,

and zeatin biosynthesis (Figure 4B). Genes differentially expressed between tolerant and sensitive

bulks were enriched for pathways associated with flavone and flavanol biosynthesis, carotenoid


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biosynthesis, flavonoid biosynthesis, nitrogen metabolism and phenylpropanoid synthesis (Figure

4C).


_Root-length dependent salt-response_


A total of 83 differentially expressed genes (DEGs) exhibited a root-length-dependent response to salt

stress, indicating that the expression of these genes was influenced by both factors and their combined

effects. Of these, 66 DEGs showed positive interaction effects and 17 showed negative interaction

effects (Table S5). GO enrichment analysis identified sequence-specific DNA binding (GO:0043565;
adjusted _P_ = 6.22 × 10⁻¹⁰) and DNA-binding transcription factor activity (GO:0003700; adjusted _P_ = 1.00
× 10⁻⁷) as the most significantly enriched terms. Regulation of RNA biosynthetic process (GO:2001141;
adjusted _P_ = 1.18 × 10⁻⁴) and systemic acquired resistance (GO:0009627; adjusted _P_ = 2.03 × 10⁻²)

were also significantly enriched (Figure 4D). Overall, genes exhibiting a root-length-dependent

response to salinity were predominantly enriched for DNA-binding and transcriptional regulatory

functions.

### **Quantitative Trait Loci**


With QTL analysis using the legacy linkage map, QTLs were mapped for root morphology traits, biomass

traits, and ion homeostasis traits under control and/or saline conditions (Tables S6 & S7). Significant

LOD scores ranged from 2.5 to 10.5.


_Root morphology_


The results of the QTL analysis for root traits indicate that the terminal region of the short arm of

chromosome 2H is important in determining root morphology in both control and saline conditions

(Table S6), with alleles from Sahara 3771 favouring short, thick roots and Clipper alleles favouring long,

thin roots.


_Biomass_


Near _Vrs1,_ the major gene on chromosome 2H that determines whether barley spikes have two or six

rows of spikelets (Saisho _et al._, 2009), QTLs were detected for root and shoot dry weight. Another QTL

for shoot dry weight was detected on chromosome 1H, near the _HvNax4_ sodium exclusion locus

(Rivandi _et al._, 2011). In all cases, the allele from Sahara 3771 was associated with lower dry weight.


_Ion homeostasis_


QTLs associated with Na [+] and/or K [+] content in roots and shoots were detected on chromosomes 1H,

2H and 6H (Table S7). On chromosome 1H, there were significant LOD scores for Na [+] content and

K [+] /Na [+] ratio, but the QTL peaks for those traits were not very distinct. On chromosome 2H, there are

QTLs at which the Sahara 3771 allele increases shoot K [+] concentration (at 144 cM) or root Na [+ ]


concentration (at 151 and 198 cM). On chromosome 6H, there is a minor QTL at which the Sahara 3771

allele reduces root Na [+] concentration in saline conditions.


_Refining the linkage and QTL maps for chromosome 2H_


To further investigate the root morphology QTLs on chromosome 2H, newly designed KASP assays for

16 SNPs (all discovered with BSR-Seq) were used to enrich the linkage map. All 16 assays discriminated

between the two parental lines and detected segregation in the mapping population. All 16 SNPs were

genetically mapped on the short arm of chromosome 2H (Table S8).


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With QTL analysis using the SNP-enriched map, QTLs for seminal root length and average root diameter

were mapped on the short arm of chromosome 2H (Table 1, Figure 5). Under saline conditions, both

traits had very distinct QTL peaks at 2.1 cM (Figure 5B). Under control conditions, the QTL peaks were

less distinct and had lower LOD values. Based on these results, 2.1 cM was taken to be the best

estimate for the position of a QTL that affects both seminal root length and average root diameter,

particularly under saline conditions. Based on the physical positions of four SNPs that mapped at 2.1

cM (Figure 6) and considering the sharp drops in LOD values immediately distal and proximal to the

QTL peaks (Figure 5B), the 2.5-Mb region of the 2H barley pseudomolecule between 500,000 bp and

3,000,000 bp provides an approximate 95% confidence interval for the physical position of the causal

gene(s). Consistent with the detection of a QTL at 2.1 cM, all 10 DH lines in the two tolerant groups

carry Clipper alleles at the 2.1-cM and 2.8-cM positions and all 10 lines in the two sensitive groups carry

Sahara 3771 alleles at those positions (Table S9).

### **Candidate genes for a QTL**


Within the 2.5 Mb candidate region on the short arm of chromosome 2H, there are 76 high-confidence

gene models on the Morex reference genome (Table S10). Ten of these genes were differentially

expressed between the tolerant lines (all of which carried Clipper alleles at 2.1 and 2.8 cM) and the

sensitive lines (all of which carried Sahara 3771 alleles at 2.1 and 2.8 cM). Two of these genes and two

other genes were differentially expressed in response to salt stress in either the tolerant or sensitive

lines (Table 2). Within the same candidate region, 3,876 sequence variants were identified, of which

177 variants in 31 genes (Table 3, Table S11) were identified as high- or moderate-impact variants that

are likely to be polymorphic between Clipper and Sahara 3771.


Based on genomic location, differential expression between contrasting bulks, transcriptional

responses to salinity, sequence polymorphisms and functional annotation, six genes were prioritised

as candidate genes (Table 4). HORVU.MOREX.r3.2HG0096800 is predicted to encode a flavin
containing monooxygenase homologous to YUCCA5, a key enzyme in auxin biosynthesis, and

HORVU.MOREX.r3.2HG0096550 is predicted to encode an ABCB transporter protein. Both genes are

downregulated in lines carrying the Clipper allele compared to the Sahara allele (flavin-containing

monooxygenase log 2 FC = -1.27; ABCB transporter log 2 FC = -4.33; Figure 7, Table 2) and both have

missense polymorphisms between Clipper and Sahara 3771 (Table 3). Four additional candidates, with

possible functions in auxin biosynthesis, hormone transport, cell-wall biosynthesis or stress signalling

are HORVU.MOREX.r3.2HG0096770, HORVU.MOREX.r3.2HG0096540,

HORVU.MOREX.r3.2HG0096710 and HORVU.MOREX.r3.2HG0096520, which are predicted to encode

a cellulose synthase, a glycosyltransferase, a cytochrome P450 enzyme and a pentatricopeptide

repeat-containing protein (PPR), respectively. Like the auxin-associated candidates, these genes were

differentially expressed between contrasting bulks (Table 2). Three of them (all except the cellulose

synthase) also have missense polymorphisms between the parental alleles (Table 3).

## **DISCUSSION**


Barley is the most salt-tolerant cereal crop compared to other major cereals (Munns and Tester, 2008).

While considerable research has focussed on understanding the mechanisms underlying salt


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tolerance in shoots, the molecular and physiological mechanisms governing root responses remain

comparatively poorly understood (Shelden and Munns, 2023). Identifying the molecular mechanisms

that contribute to the maintenance of root growth and function during salt stress is important for

improving seedling establishment and crop performance in saline soils.


Previous studies showed that Clipper maintains root elongation under salt stress and accumulates

higher Na [+] in the roots than the more sensitive genotype Sahara 3771 (Shelden _et al._, 2020; Shelden _et_

_al._, 2013). These findings suggest that Na [+] accumulation may contribute to osmotic adjustment and

maintenance of water uptake in Clipper, rather than directly inhibiting growth, together with the

synthesis of compatible solutes (Shelden _et al._, 2016). Consistent with this hypothesis, salt-tolerant

accessions from a global barley core population maintained greater root biomass, which was positively

correlated with root Na [+] content (Tu _et al._, 2021).


**Root responses to salinity and bulk segregant RNA-seq analysis**


In this study, we used the parental genotypes, Clipper and Sahara 3771 and144 doubled haploid (DH)

lines derived from a Clipper x Sahara 3771 cross to evaluate phenotypic variation in root traits under

control and saline conditions. Root traits exhibited quantitative variation in the population, with

approximately normal distributions (Figure 1, Figure S1). To minimise background genetic variation in

non-targeted traits, we used a pooling strategy to generate tolerant and sensitive bulks for RNA

sequencing (Michelmore _et al._, 1991). Bulked segregant RNA-Seq analysis was then used to identify

genes differentially expressed between the two groups. This strategy has been successfully used in

other studies to identify candidate genes underlying stress adaptation traits, including waterlogging

and chilling tolerance in maize (Du _et al._, 2017; Yan _et al._, 2023).


We identified 652 differentially expressed genes in tolerant bulks and 646 in sensitive bulks with 306

overlapping genes, in response to salt stress (Figure 3B). Consistent with previous studies of salinity

responses in cereals, salt-responsive genes were enriched for glutathione metabolism, MAPK

signalling and phenylpropanoid biosynthesis pathways (Figure 4A and 4B). Glutathione plays a central

role in stress tolerance by maintaining redox homeostasis and detoxifying reactive oxygen species

(Bose Mazumdar and Chattopadhyay, 2025). MAPK signalling pathways mediate the perception and

transduction of stress signals, regulating downstream responses including hormone biosynthesis,

transporter activity, and transcriptional reprogramming (Shi _et al._, 2026). Phenylpropanoid-derived

compounds, including lignin and suberin, are important components of cell walls and apoplastic

barriers that influence water and ion movement. Consistent with this role, previous studies reported

enrichment of suberin-related metabolites in Sahara 3771 and lignin metabolites in Clipper (Ho _et al._,

2020), with genotypic differences also reported in wheat (Dissanayake _et al._, 2024). These pathways

have established roles in oxidative stress protection and stress signalling, indicating that lines in both

bulks activated core salinity-response pathways despite exhibiting contrasting root-growth

phenotypes. Flavonoid biosynthesis showed the highest enrichment in tolerant relative to sensitive

roots. Flavenoids are polyphenolic secondary metabolites that contribute to antioxidant defence and

abiotic stress tolerance in plants, including barley (Mia _et al._, 2026).


In contrast, genes showing a root-length-dependent response to salinity were enriched for

transcription factor activity and regulation of RNA biosynthesis, suggesting that differences between

tolerant and sensitive lines involve alterations in transcriptional regulatory networks (Figure 4D). This


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pattern is consistent with the complex genetic architecture of root development and stress adaptation,

where changes in regulatory pathways can have substantial downstream effects on root growth and

morphology.


**Integration of QTL mapping and BSR-Seq identifies candidate genes**


QTL analysis identified loci associated with root morphology, biomass and ion homeostasis, primarily

on chromosome 2H but also on chromosomes 1H and 6H. Several of the loci identified in this study

overlap genomic regions associated with salinity tolerance in barley. In particular, the chromosome 1H

shoot K [+] : Na [+] ratio co-localises with the _HvNax4/HvCBL4_ _(HvSOS_ 3) which controls Na [+] exclusion under

saline conditions (Rivandi _et al._, 2011). Chromosome 2H has also been repeatedly implicated in salinity

tolerance across diverse mapping populations, including a QTL for root dry weight identified in the

Steptoe x Morex population, although at a different position from the loci detected here (Nguyen _et al._,

2013).


Using a legacy Clipper x Sahara 3771 linkage map, we mapped QTLs for seminal root length and

average root diameter between 0 and 10 cM on the short arm of chromosome 2H cM. The Clipper

haplotype was associated with longer, thinner roots under both control and saline conditions, whereas

the Sahara 3771 haplotype was associated with shorter, thicker roots, especially under salt treatment.

To further investigate this region, we designed and applied SNP assays and revised the linkage map of

chromosome 2H. This improved the map by replacing existing markers (restriction fragment length

polymorphisms and simple sequence repeats) that could not be anchored to the genome assembly

and that had missing data. With QTL analysis using the improved map, it became evident that a single

QTL (estimated to be at 2.1 cM) affect both root length and diameter (especially under saline

conditions), possibly by influencing the balance between longitudinal and radial root growth.


While a similar short-root phenotype has previously been reported in barley under salt stress, the

associated QTL was located on chromosome 7H (Xue _et al._, 2017). Identification of a distinct root

architecture QTL region on chromosome 2H in the present study therefore reveals an additional

genomic region contributing to variation in root architectural responses to salinity in barley.


Improvement of the linkage map also made it possible to anchor the genetic map to the barley genome

assembly. The 2.1 cM position corresponded with a 1.3 Mb region (between 1.2 and 2.5 Mb). To allow

for the causal gene to be in an adjacent (distal or proximal) region, we conservatively defined a 2.5 Mb

region (between 0.5 and 3.0 Mb) as the candidate region. To identify candidate genes within this region,

we drew on information from RNA sequence analysis. Of 76 predicted genes in the interval, only a small

number exhibited differential expression, sequence polymorphisms with potentially moderate or high

impact, or both (Table S10, Table 2-4).


Bulked segregant RNA-seq identified 12 differentially expressed genes within the interval, 10 of which

were differentially expressed between long- and short- root phenotypes, and four of which genes were

differentially expressed in response to salt treatment in long and/or short roots, respectively (Table 2).

Thirty-one genes in the interval were found to contained polymorphic variants with high or moderate

impact (Table 3). Six genes were prioritised for further investigation based on expression profiles,

sequence variation and predicted biological function. These genes represent strong candidates for

regulating root architectural responses to salinity and provide a foundation for future functional

validation studies.


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**Auxin-associated genes as candidates for root length and diameter**


Among the candidates identified within the 2H QTL interval, two genes associated with auxin

biosynthesis and transport are particularly compelling given the central role in auxin homeostasis in

regulating root elongation and architecture (Korver _et al._, 2018).


The first candidate, HORVU.MOREX.r3.2HG0096800, encodes a flavin-containing monooxygenase of

the YUCCA (YUC) family and is homologous to YUCCA genes in rice and Arabidopsis (reference).

YUCCA flavin-containing monooxygenases catalyse the conversion of indole-3-pyruvic acid (IPA) to

indole 3-acetic acid (IAA), the final and rate-limiting step in the auxin biosynthesis pathway

(Vijayanathan _et al._, 2025). The importance of YUCCA-meditated auxin biosynthesis in root

development is illustrated in rice where _YUC8/REIN7_, which promotes auxin production and inhibits

epidermal cell elongation, results in shorter, swollen roots (Qin _et al._, 2023). Furthermore, increased

expression of YUCCA genes under drought and salinity have been associated with enhanced auxin

biosynthesis and increased stress tolerance (Marzi _et al._, 2024). Given the central role of auxin in

regulating root cell division and elongation, HORVU.MOREX.r3.2HG0096800 represents a strong

candidate underlying _QArd.ClSa-2H_ and _QSrl.ClSa-2H._


Expression profiling provides additional support for HORVU.MOREX.r3.2HG0096800 as a candidate

gene. Transcript abundance was 2.4-fold lower in lines carrying the Clipper allele compared to those

carrying the Sahara 3771 allele (log 2 FC = -1.28, Table 2, Figure 7). In addition, the gene contained a

missense polymorphism resulting in an Asp (D) to Asn (N) substitution at amino acid position 65. The

Asp residue is highly conserved among homologous proteins in barley, rice and Arabidopsis and in the

Clipper allele, suggesting that the variant warrants further investigation for effects on protein function.

Notably, the Sahara 3771 allele is associated with the shorter, thicker root phenotype, raising the

possibility that increased expression of this auxin biosynthesis gene alters local auxin homeostasis in

the root apex and consequently influences the balance between longitudinal and radial root growth.


Although expression of HORVU.MOREX.r3.2HG0096800 was not significantly altered by salt treatment,

the strong difference between allelic classes suggests that constitutive differences in auxin regulation,

rather than salt-induced transcription of this gene, may contribute to the contrasting root phenotypes.

This distinction is important because root responses to salinity involve not only changes in auxin

biosynthesis but also changes in auxin redistribution within the root. Salt-induced inhibited of root

elongation has been associated with reduced auxin accumulation (Liu _et al._, 2015) which appears to

result from altered polar auxin transport, as salinity affects the localisation of auxin transporters within

the root apex (Jiang _et al._, 2016).


The second candidate gene, HORVU.MOREX.r3.2HG0096550, is predicted to encode an ATP-binding

cassette subfamily B (ABCB) transporter. Members of the ABCB transporter family are key components

of the polar transport of auxin and have important roles in the regulation of root growth, lateral root

development and root architecture (Devi _et al._, 2025; Geisler _et al._, 2017). They have also recently been

shown to transport other phytohormones including brassinosteroid (Ying _et al._, 2024). ABCB

transporters also function cooperatively with PIN transporters to regulate the spatial distribution of

auxin in root tissues (Mellor _et al._, 2022). In barley, the ABCB transporter HvABCB25, contributes to

aluminium tolerance, and its overexpression alleviates aluminium-induced inhibition of root

elongation (Liu _et al._, 2021), demonstrating that ABCB transporters can influence root growth

responses to abiotic stress in cereal crops.


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In the present study, HORVU.MOREX.r3.2HG0096550 was more highly expressed in root tips of lines

carrying the Sahara 3771 allele than those carrying the Clipper allele (Figure 7). Expression increased

under salt treatment in lines carrying the Clipper allele. As observed for the YUCCA-like candidate, the

clearest expression difference was associated with genotype rather than salt-treatment. If

HORVU.MOREX.r3.2HG0096550 functions in auxin transport, variation in its expression or activity

could alter auxin distribution within the root apex and contribute to differences in root elongation and

radial growth. Consequently, this gene also represents a plausible candidate underlying chromosome

2HS QTL interval.


Together, these results suggest that variation in auxin homeostasis may contribute to the contrasting

root architectures of Clipper and Sahara 3771. Both the YUCCA like and the ABCB transporter were

more highly expressed in the root tips of lines carrying the Sahara 3771 allele which is associated with

shorter, thicker roots. As these genes occupy complementary positions with the auxin pathway,

affecting auxin biosynthesis and transport respectively, coordinated variation in their activity could

influence auxin abundance and distribution within the root apex, altering the balance between

longitudinal and radial root growth. However, because the QTL effects were detected under both

control and saline conditions, these loci may regulate constitutive differences in root development

rather than a salt-specific response. Functional validation of both, together with measurements of

auxin distribution and root cellular anatomy, will be required to test this hypothesis.


**Other gene candidates**


Although the two auxin-associated genes provide biologically compelling hypotheses for the

chromosome 2H root phenotype, several other genes within the QTL interval warrant consideration.

HORVU.MOREX.r3.2HG0096540 encodes a UDP-glucosyltransferase (UGT), that may play a role in the

glycosylation of metabolites, including phytohormones, as has been shown in other species (Wang _et_

_al._, 2023). Through their effects on hormone homeostasis, UGTs have been implicated in plant growth,

development and responses to abiotic stress. Consequently, variation in the expression or activity of

this gene could influence root development either directly or through modification of hormone

signalling pathways.


Another candidate, HORVU.MOREX.r3.2HG0096770, is predicted to encode a DUF1644-containing

protein associated with cellulose synthase. Cellulose synthases are essential for cell wall formation

and root cell expansion (Polko and Kieber, 2019). Consistent with this, RNAi-mediated silencing of a

cellulose synthase gene in barley resulted in slower root growth, linked with a shorter elongation zone

and a significant reduction in root system size (Lou _et al._, 2022). Variation in the expression or function

of a cellulose synthase gene could therefore contribute to differences in root elongation.


A further notable candidate, HORVU.MOREX.r3.2HG0096710, which exhibited large difference in

expression between the contrasting root phenotypes, is predicted to encode a member of the

cytochrome P450 superfamily. Members of the P450 superfamily have previously been associated with

primary and lateral root development (Julkowska _et al._, 2017; Tu _et al._, 2021). Several cytochrome P450

families participate in hormone metabolism. For example, CYP79B2 and CYP79B3 convert tryptophan

to indole-3-acetaldoxime (IOX), linking P450 activity with pathways contributing to auxin metabolism

(Hull _et al._, 2000), while other P450 enzymes regulate ABA homeostasis through ABA catabolism

(Kushiro _et al._, 2004)). Furthermore, disruption of CYP83B ( _Sur2_ ) in Arabidopsis increases free IAA


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levels and promotes lateral root development (Barlier _et al._, 2000). Although these observations make

HORVU.MOREX.r3.2HG0096710 an interesting candidate, its biochemical function cannot be inferred

from its general P450 annotation, and identification of its CYP subfamily will be required to establish a

role in hormone metabolism.


HORVU.MOREX.r3.2HG0096520 is predicted to encode a pentatricopeptide repeat protein (PPR).

These proteins are typically targeted to the mitochondria or chloroplast, where they regulate RNA

processing, editing and translation and have been implicated in abiotic stress tolerance. Notably, a

PPR gene was identified within a QTL associated with root growth under salinity stress in barley (Cope

_et al._, 2022), providing support for a possible role in the regulation of root responses to salinity.


Together, these candidate genes identified within the QTL interval represent several plausible

biological mechanisms that could influence root architecture, including auxin homeostasis, hormone

modification, and cell wall biosynthesis. The development of thicker roots under salinity may represent

an adaptive response that alters radial transport pathways, ion uptake, or root hydraulic properties.

Further functional characterisation of these candidate genes will be required to identify the causal

gene and establish whether auxin-mediated changes in root architecture contribute directly to

improved tolerance of saline conditions.


**Conclusion**


In summary, the integration of QTL mapping, BSR-sequencing and sequence variant analysis identified

a small number of candidate genes in a region of barley chromosome 2H that affects root architecture

traits. Several candidates have functions consistent with roles in root development and stress

responses, including genes involved in auxin biosynthesis and transport. These genes represent

plausible candidates for the observed differences in longitudinal and radial root growth between

Clipper and Sahara 3771, potentially by affecting auxin homeostasis. These findings provide new

insight into the genetic regulation of root architecture and establish a foundation for future studies

investigating the role of root traits in adaptation to saline soil.

## **ACKNOWLEDGEMENTS**


We would like to acknowledge the contribution of the South Australian Genomics Centre (SAGC) in the

generation of data used in this publication. The SAGC is supported by its founding partner institutes

and by BioPlatforms Australia as part of the Australian Government’s National Collaborative Research

Infrastructure Strategy (NCRIS). We also acknowledge the Australian Centre for Plant Functional

Genomics for providing the mapping population and associated data. The authors wish to thank to

Professor Matthew Tucker and Dr Nick Collins for helpful discussions and Dr. Adriane Piechatzek, Dr

Jessey George and Ms. Jodie Kretschmer for technical assistance. MS acknowledges financial support

from the Australian Research Council (DE140100575) and Adelaide University Mortlock Bequest.

## **CONFLICTS OF INTEREST**


No conflict of interest declared.


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## **TABLES**

**Table 1.** Quantitative trait loci detected for length and diameter in a Clipper/Sahara 3771 barley population, using a linkage map that includes the positions of 16

SNP markers on the short arm of chromosome 2H

**Trait** **Condition** **Position (cM) on chromosome 2H** **LOD** **Significance threshold** [a] **Source of positive-eBect QTL allele**

Root length Control 6.5 4.3 2.8 Clipper

Salt 2.1 5.3 2.8 Clipper

Root diameter Control 2.8 6.0 2.8 Sahara 3771

Salt 2.1 12.7 2.7 Sahara 3771


a Established by permutation with a genome-wide significance level of 0.05



21


**Table 2.** High-confidence predicted genes in the candidate region between 500,000 – 3,000,000 bp on chromosome 2H that are differentially expressed.



**Start (bp)** **End (bp)** **Gene ID** **Description**



**Log** **2** **FC** **[a]**

**Tolerant vs** **Sensitive S vs C** **Tolerant S vs C**


**Sensitive**



1,227,414 1,231,905 HORVU.MOREX.r3.2HG0095950 NBS-LRR disease resistance protein-like protein 1.14


1,512,496 1,513,768 HORVU.MOREX.r3.2HG0096100 3'-N-debenzoyl-2'-deoxytaxol N-benzoyltransferase -2.85


1,524,394 1,525,030 HORVU.MOREX.r3.2HG0096120 WEB family protein (DUF827)



1.21



1,818,365 1,818,761 HORVU.MOREX.r3.2HG0096230 Phosphatidylinositol 3- and 4-kinase family protein 1.00


2,503,905 2,506,708 HORVU.MOREX.r3.2HG0096520 Pentatricopeptide repeat-containing protein (PPR) -3.35 -2.38


2,516,439 2,517,917 HORVU.MOREX.r3.2HG0096540 Glycosyltransferase -1.89



2,523,096 2,530,883 HORVU.MOREX.r3.2HG0096550 ABC transporter B family-like protein -4.33


2,697,963 2,703,943 HORVU.MOREX.r3.2HG0096670 Mediator of RNA polymerase II transcription subunit 15a 3.18

2,770,503 2,772,104 HORVU.MOREX.r3.2HG0096710 Cytochrome P450 -2.84 1.39


2,813,143 2,816,820 HORVU.MOREX.r3.2HG0096730 Agenet domain, putative 1.32


2,872,655 2,876,682 HORVU.MOREX.r3.2HG0096770 cellulose synthase, putative (DUF1644) 3.18


2,893,692 2,895,546 HORVU.MOREX.r3.2HG0096800 Flavin-containing monooxygenase (YUCCA 5) -1.28


a Log2FC, Tolerant – T1 and T2 bulk, Sensitive – S1 and S2 bulk, S – salt treatment, C – control.



22


**Table 3.** High-confidence predicted genes in the candidate region between 500,000 – 3,000,000 bp on chromosome 2H for which transcriptomic sequence data
indicate that Clipper and Sahara 3771 alleles differ for variants of types expected to have high or moderate impact.

**Gene ID** **Description** **High-impact variants** **Moderate-impact variants**



HORVU.MOREX.r3.2HG0095600 NBS-LRR resistance-like protein 1 missense

HORVU.MOREX.r3.2HG0095610 YIF1b 1 missense



HORVU.MOREX.r3.2HG0095610 YIF1b 1 missense


HORVU.MOREX.r3.2HG0095620 Curved DNA binding protein 2 in-frame indel

HORVU.MOREX.r3.2HG0095670 Cysteine synthase 1 missense



HORVU.MOREX.r3.2HG0095670 Cysteine synthase 1 missense


HORVU.MOREX.r3.2HG0095710 Disease resistance protein (NBS-LRR class) family 2 missense

HORVU.MOREX.r3.2HG0095930 NBS-LRR disease resistance protein-like protein 1 missense



HORVU.MOREX.r3.2HG0095930 NBS-LRR disease resistance protein-like protein 1 missense


HORVU.MOREX.r3.2HG0095950 NBS-LRR disease resistance protein-like protein 2 stop codon gain/loss; 2 frameshift 38 missense

HORVU.MOREX.r3.2HG0095960 NBS-LRR disease resistance protein-like protein 3 frameshift 15 missense

HORVU.MOREX.r3.2HG0095970 NBS-LRR disease resistance protein-like protein 1 stop codon gain/loss; 1 frameshift 9 missense; 1 in-frame indel

HORVU.MOREX.r3.2HG0096080 NBS-LRR disease resistance protein-like protein 3 stop codon gain/loss; 2 frameshift 1 in-frame indel

HORVU.MOREX.r3.2HG0096100 3'-N-debenzoyl-2'-deoxytaxol N-benzoyltransferase 8 missense

HORVU.MOREX.r3.2HG0096180 Histone-lysine N-methyltransferase 1 frameshift



HORVU.MOREX.r3.2HG0096210 Phosphotransferase 1 frameshift

HORVU.MOREX.r3.2HG0096220 WEB family protein



HORVU.MOREX.r3.2HG0096220 WEB family protein 1 missense


HORVU.MOREX.r3.2HG0096230 Phosphatidylinositol 3- and 4-kinase family protein 1 missense

HORVU.MOREX.r3.2HG0096290 Late embryogenesis abundant (LEA) hydroxyproline-rich 1 missense
glycoprotein family member



1 missense



HORVU.MOREX.r3.2HG0096350 3'-N-debenzoyl-2'-deoxytaxol N-benzoyltransferase 1 frameshift 1 missense

HORVU.MOREX.r3.2HG0096400 Cortactin-binding protein 2 1 stop codon gain/loss 6 missense;1 in-frame indel

HORVU.MOREX.r3.2HG0096440 tRNA-dihydrouridine(47) synthase [NAD(P)(+)] 1 in-frame indel



HORVU.MOREX.r3.2HG0096440 tRNA-dihydrouridine(47) synthase [NAD(P)(+)] 1 in-frame indel


HORVU.MOREX.r3.2HG0096450 Peptidyl-prolyl cis-trans isomerase 1 missense



HORVU.MOREX.r3.2HG0096450 Peptidyl-prolyl cis-trans isomerase 1 missense


HORVU.MOREX.r3.2HG0096460 NBS-LRR-like resistance protein 1 missense


HORVU.MOREX.r3.2HG0096520 Pentatricopeptide repeat-containing protein 1 missense


HORVU.MOREX.r3.2HG0096530 Arginine/serine-rich splicing factor (putative) 1 missense


HORVU.MOREX.r3.2HG0096540 Glycosyltransferase 4 missense


HORVU.MOREX.r3.2HG0096550 ABC transporter B family-like protein 4 missense


HORVU.MOREX.r3.2HG0096600 Pyruvate dehydrogenase E1 component subunit alpha



HORVU.MOREX.r3.2HG0096600 Pyruvate dehydrogenase E1 component subunit alpha 1 in-frame indel


HORVU.MOREX.r3.2HG0096610 StAR-related lipid transfer protein 1 missense



HORVU.MOREX.r3.2HG0096610 StAR-related lipid transfer protein 1 missense


HORVU.MOREX.r3.2HG0096760 TOM1-like protein 2 3 missense



3 missense



HORVU.MOREX.r3.2HG0096780 Evolutionarily conserved C-terminal region 2



HORVU.MOREX.r3.2HG0096780 Evolutionarily conserved C-terminal region 2 9 missense


HORVU.MOREX.r3.2HG0096800 Flavin-containing monooxygenase 2 missense



2 missense



23


HORVU.MOREX.r3.2HG0096810 Werner-Syndrome-like exonuclease



1 missense



24


**Table 4** . List of candidate genes for _QArd.ClSa-2H_ and _QSrl.ClSa-2H_ between 500,000 – 3,000,000 bp on chromosome 2H.

**Rank** **Gene ID** **Description** **Wheat Gene ID** **Rice Gene ID** **Arabidopsis Gene ID**

1 HORVU.MOREX.r3.2HG0096800 Flavin-containing monooxygenase (YUCCA 5) TraesCS2D02G012100.1 Os04g0128900 AT5G43890

(YUCCA7)


2 HORVU.MOREX.r3.2HG0096550 ABC transporter B family-like protein TraesCS2B02G012100 [Os05g0548500](https://www.ensembl.org/feature-explorer/80c925c8-5c7d-4757-8a04-a53d2a689719/gene:Os05g0548500)

(ABC19)


3 HORVU.MOREX.r3.2HG0096770 Cellulose synthase, putative (DUF1644) TraesCS2A02G011300.1 Os01g0612600 AT3G24740

4 HORVU.MOREX.r3.2HG0096540 Glycosyltransferase TraesCS2B02G012000 Os01g0697100

5 HORVU.MOREX.r3.2HG0096710 Cytochrome P450 enzyme 71D11

6 HORVU.MOREX.r3.2HG0096520 Pentatricopeptide repeat-containing protein (PPR) Os04t0118700 AT3G57430


25


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preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
[perpetuity. It is made available under a CC-BY 4.0 International license.](http://creativecommons.org/licenses/by/4.0/)

## **FIGURES**


**Figure 1** . Root growth under control and salt conditions (Clipper, Sahara 3771, tolerant, sensitive DH

line). Frequency distributions of (A) seminal root length (SRL), (B) average root diameter (ARD) and (C)

root Na [+] content for the Clipper x Sahara 3771 DH population under control (purple) and salt treatment

(blue). Arrows indicate the parental values under salt-treatment. Phenotypic values represent BLUEs

calculated from three independent experiments.


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preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
[perpetuity. It is made available under a CC-BY 4.0 International license.](http://creativecommons.org/licenses/by/4.0/)


**Figure 2.** Graphs showing the distribution of root traits in salt-sensitive (S1 and S2) and salt-tolerant

(T1 and T2) barley lines used as bulks for BSR-seq. Root traits were measured after 3 d of growth on

control (purple) or 100 mM NaCl (blue) nutrient media. (A) Total seminal root length, (B) root Na [+]

content and (C) average root diameter. Each bar represents the mean of the BLUE values for each bulk,

n = 5 (S1), 5 (S2), 6 (T1), 4 (T2) ± SEM. Asterisks indicate significant differences between groups (* p <

0.05, ** p < 0.01, **** p < 0.0001; two-way ANOVA, Tukey’s multiple comparison test).


27


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preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
[perpetuity. It is made available under a CC-BY 4.0 International license.](http://creativecommons.org/licenses/by/4.0/)


**Figure 3.** Differential expression in barley root tips. (A) Principal component analysis (PCA) plot of RNA
seq TMM read counts (gene expression) showed separation of control (C) and salt-treated (S) samples

on PC1 and separation of tolerant (T1 and T2) and sensitive (S1 and S2). Each point represents one

bulked sample with R1, R2 and R3 representing samples from three independent experiments. (B) Venn

diagram showing the number of differentially expressed genes (DEGs) in long roots (T1 and T2) and

short roots (S1 and S2) in response to salt stress. The number in overlapping areas represent the

number of shared DEGs.


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preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
[perpetuity. It is made available under a CC-BY 4.0 International license.](http://creativecommons.org/licenses/by/4.0/)


**Figure 4.** Profiling of DEGs in barley roots under control and salt stress in the tolerant and sensitive

bulks. KEGG enrichment analysis of DEGs using ShinyGO tool. The enriched KEGG pathway in (A)

tolerant and (B) sensitive bulks under salt stress. (C) DEGs enriched in tolerant bulks compared to

sensitive bulks. False discovery rate (FDR; coloured dot) represents the degree of enrichment. The size

of the dot indicates the number of DEGs involved in the pathway. (D) GO analyses of DEGs in root length

dependent salt response.


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preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
[perpetuity. It is made available under a CC-BY 4.0 International license.](http://creativecommons.org/licenses/by/4.0/)


**Figure 5.** LOD test statistic scans for barley chromosome 2H for (A) total seminal root length and (B)

average root diameter of Clipper x Sahara 3771 doubled haploid lines grown for 72 hours on control

(dashed line) or saline (solid line) nutrient medium. Tick marks on the horizontal axis show the genetic

positions at which markers were mapped. The horizontal dashed line shows the genome-wide

significance threshold (LOD= 2.8).


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preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
[perpetuity. It is made available under a CC-BY 4.0 International license.](http://creativecommons.org/licenses/by/4.0/)


**Figure 6.** Genetic linkage map (on the left) showing the positions in the distal 5 cM of the short arm of

chromosome 2HS at which SNPs were mapped using assays wri956 through wri966 and (on the right)

the physical positions of those SNPs on the 2H pseudomolecule of Version 3 of the Morex barley

genome assembly, and the names of the predicted genes in which the SNPs are located. The estimated

genetic position of the QTL affecting total seminal root length and average root diameter under saline

conditions is marked with an arrow and the candidate region of the pseudomolecule is shaded.


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preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
[perpetuity. It is made available under a CC-BY 4.0 International license.](http://creativecommons.org/licenses/by/4.0/)


**Figure 7** . Bulk segregant RNA-Seq shows differentially expressed genes (DEGs) in the QTL interval

between 50,000 - 300,000 bp, between the sensitive (S1 and S2) and tolerant (T1 and T2) bulks. Gene

expression (Log2 counts per million (CPM) was assessed under control and 100 mM NaCl salt

treatment for 3 days. Asterisk indicates a significant difference in the response to salt treatment

compared to control.


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