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. 2025 Jan 24;18(1):e20548. doi: 10.1002/tpg2.20548

A novel quantitative trait locus for barley yellow dwarf virus resistance and kernel traits on chromosome 2D of a wheat cultivar Jagger

Ruolin Bian 1, Na Liu 2,✉, Yuzhou Xu 1, Zhenqi Su 1,3, Lingling Chai 1,3, Amy Bernardo 4, Paul St Amand 4, Jessica Rupp 5, Michael Pumphrey 6, Allan Fritz 1, Guorong Zhang 1, Katherine W Jordan 4, Guihua Bai 1,4,✉
PMCID: PMC11760652  PMID: 39853960

Abstract

Barley yellow dwarf (BYD) is one of the most serious viral diseases in cereal crops worldwide. Identification of quantitative trait loci (QTLs) underlining wheat resistance to barley yellow dwarf virus (BYDV) is essential for breeding BYDV‐tolerant wheat cultivars. In this study, a recombinant inbred line (RIL) population was developed from the cross between Jagger (PI 593688) and a Jagger mutant (JagMut1095). A linkage map of 3106 cM consisting of 21 wheat chromosomes was developed using 1003 unique single nucleotide polymorphisms (SNPs) from the RIL population and was used to identify QTLs for BYDV resistance and yield‐related traits, including 1000‐kernel weight (TKW), kernel area (KA), kernel width (KW), and kernel length (KL). QByd.hwwg‐2DL, a QTL on chromosome arm 2DL for BYDV resistance, was consistently identified in three field experiments and explained 11.6%–44.5% of the phenotypic variation. For yield‐related traits, six major and repeatable QTLs were identified on 1AS (QKa.hwwg‐1AS), 2DL (QTkw.hwwg‐2DL, QKa.hwwg‐2DL, QKw.hwwg‐2DL, and QKl.hwwg‐2DL), and 5AL (QKw.hwwg‐5AL). The major QTLs on chromosome 2DL for TKW, KA, KW, and KL were mapped between 621 and 643 Mb, overlapping with QByd.hwwg‐2DL with all the favorable alleles from Jagger. This study reports the first native BYDV resistance QTL (QByd.hwwg‐2DL) originating from common wheat and tightly linked markers to the QTL for improvement of wheat BYDV resistance in wheat breeding.

Core Ideas

  • One novel quantitative trait locus (QTL) (QByd.hwwg‐2DL) on chromosome arm 2DL showed a stable major effect on barley yellow dwarf virus (BYDV) resistance in wheat.

  • Four yield‐related QTLs overlap with QByd.hwwg‐2DL under multiple environments.

  • Tightly linked markers to QByd.hwwg‐2DL were developed for improvement of wheat BYDV resistance in wheat breeding.

Abbreviations

BLUE

best linear unbiased estimator

BYD

barley yellow dwarf

BYDV

barley yellow dwarf virus

CIM

composite interval mapping

GBS

genotyping‐by‐sequencing

HD

heading date

KA

kernel area

KASP

kompetitive allele‐specific PCR

KL

kernel length

KW

kernel width

PACE

PCR allele competitive extension

PH

plant height

QTL

quantitative trait locus

RIL

recombinant inbred line

SL

spike length

SNP

single nucleotide polymorphism

SNS

spikelet number per spike

TKW

1000‐kernel weight

1. INTRODUCTION

Barley yellow dwarf (BYD), caused by barley yellow dwarf virus (BYDV), is one of the most serious and widely spread viral diseases in cereal crops worldwide. BYDV infection usually results in leaf discoloration from shades of yellow to red or purple, and sometimes stunted plants that are often misdiagnosed as nutrient deficiency (Miller & Rasochová, 1997). BYDV is phloem‐limited and transmitted by aphids in a non‐propagative, but persistent and circulated manner (Gray, 1996). Its infection can cause significant yield losses in wheat, barley, rice, maize, oat, and ryegrass (d'Arcy & Domier, 2005; Ayala et al., 2001).

Seven virus species can cause the BYD disease, including BYDV‐PAV, BYDV‐MAV, BYDV‐PAS, BYDV‐Ker II, BYDV‐Ker III, BYDV‐SGV, and BYDV‐GAP, which belong to different or unassigned genera in the family Luteoviridae (Hu et al., 2019). Among them, BYDV‐PAV is the most prevalent species to cause BYD disease worldwide and can be transmitted most efficiently by both bird cherry‐oat aphid (Rhopalosiphum padi) and English grain aphid (Sitobion avenae) (Aradottir & Crespo‐Herrera, 2021; Parry et al., 2012); thus, it has more economical impact than other viruses. In wheat, yield losses due to BYDV were 40%–50% in severely infected fields (Larkin et al., 2002) and up to 25% in the fields with minimal or near‐invisible BYD symptoms (Jarošová et al., 2016). Although BYD can be partially controlled by agronomic practices such as delaying wheat planting date to avoid winter aphid infestation and applying insecticides to kill aphids, currently using BYDV‐resistant or tolerant cultivars is the most effective way to control this disease (Choudhury et al., 2017; Royer et al., 2005).

Barley and wheat are two major cereal crops that have been mostly infected by BYDV. To date, several BYDV tolerance genes have been reported in barley, including Ryd1 (Rasmusson & Schaller, 1959; Suneson, 1955), Ryd2 (Collins et al., 1996), Ryd3 (Niks et al., 2004), and Ryd4Hb (Scholz et al., 2009). In wheat, four BYDV resistance genes, Bdv1, Bdv2, Bdv3, and Bdv4, have been named, but most of them were from wheat wild relatives (Banks et al., 2011; Singh et al., 1993; Sharma et al., 1995; Zhang et al., 2009). Only Bdv1 on 7DS that has been reported near the rust resistance Lr34/Yr18/Sr57 gene complex originated from a Brazilian spring wheat cultivar Frontana (Singh et al., 1993). However, Ayala et al. (2002) reported that Bdv1 showed a minor effect and only expressed in some wheat backgrounds; thus, it might not be sufficient to protect the losses of biomass or grain yield under severe BYDV infection. In addition to the four Bdv genes, many quantitative trait loci (QTLs) were reported from different wheat populations. For example, Ayala et al. (2002) identified 22 BYDV tolerance QTLs in Opata × Synthetic population and seven QTLs in Frontana × INIA66 population, including one overlapping with Bdv1. Choudhury et al. (2019) identified four QTLs for BYDV resistance on chromosomes 2A, 2B, 6A, and 7A in a genome‐wide association study (GWAS). Silva et al. (2022) identified significant marker‐trait associations for BYD severity on chromosome arms 5AS, 7AL, and 7DL using GWAS and confirmed 7DL QTL as Bdv2 in the 571–637 Mbp region. However, all these reported QTLs have less than 10% of the phenotypic effects and showed minor effects on BYDV resistance, and therefore, they have not been widely deployed in wheat breeding programs yet. Thus, exploring stable major QTLs for BYDV resistance from common wheat will facilitate improvement of BYDV resistance in wheat.

Expression of disease resistance genes may affect wheat grain yield. Understanding the genetic relationship between BYDV resistance and yield‐related traits is critical for selecting the right gene combinations for deployment in breeding (Curtis & Halford, 2014). To date, many yield‐related genes have been identified or cloned in rice and maize (G. Li et al., 2021; J. Liu et al., 2020). Using comparative genomics, some rice and maize gene homologs for kernel traits have been cloned in wheat (Triticum aestivum) (F. Li et al., 2018), including TaGW2 (Song et al., 2007), TaGS5 for 1000‐kernel weight (TKW) (S. Wang et al., 2016), and TaGW8 (Yan et al., 2019). In addition, QTLs for kernel and other yield‐related QTLs were reported on almost all 21 wheat chromosomes (Brinton et al., 2017; Cui et al., 2016, 2014; Q. Li et al., 2015; Wu et al., 2015; Yu et al., 2018). However, most of these QTLs or gene homologs showed inconsistent effects across environments and their effects in hexaploid wheat were much smaller than in diploid rice (Uauy, 2017). Also, most of them were mapped in large genetic intervals, which significantly reduced the selection efficiency when those linked markers were used in wheat breeding (Ma et al., 2019). Thus, it is necessary to search for novel QTLs associated with wheat kernel traits, such as TKW, kernel area (KA), kernel width (KW), and kernel length (KL), that can be pyramided with BYDV resistance QTLs in new cultivars through breeding.

Single nucleotide polymorphisms (SNPs) and insertion‐deletions are common nucleotide variations in the wheat genome (Rasheed et al., 2016) and can be detected using high‐throughput genotyping platforms such as genotyping‐by‐sequencing (GBS). These sequence variations can be used directly for QTL identification and for marker‐assisted breeding after being converted into kompetitive allele‐specific PCR (KASP) markers, a low‐cost marker system suitable for medium‐ to high‐throughput screening (Semagn et al., 2014). Currently, KASP markers are not available for any BYDV resistance genes or QTLs in wheat. In this study, we characterized BYDV resistance and yield‐related traits (TKW, KA, KW, and KL) in a recombinant inbred line (RIL) population of JagMut1095 × Jagger (PI 593688) using a GBS–SNP map and identified novel QTLs for BYDV resistance and yield‐related traits that can be used for the development of high BYDV‐resistant wheat cultivars.

2. MATERIALS AND METHODS

2.1. Plant materials

A population of 154 F5 RILs was generated from JagMut1095 × Jagger by single‐seed‐descent method. Jagger is a hard winter wheat cultivar from Kansas and has a much lower BYDV score than JagMut1095 in fields. JagMut1095 is a mutant selected from an ethyl methanesulfonate‐treated Jagger population (Rawat et al., 2019) and showed severe BYD symptoms in the field experiments. One plant per F5 line was used for DNA isolation, and the seeds from the plants were advanced for further phenotyping of BYDV resistance and yield‐related traits, including TKW, KA, KW, and KL in the fields.

Core Ideas

  • One novel quantitative trait locus (QTL) (QByd.hwwg‐2DL) on chromosome arm 2DL showed a stable major effect on barley yellow dwarf virus (BYDV) resistance in wheat.

  • Four yield‐related QTLs overlap with QByd.hwwg‐2DL under multiple environments.

  • Tightly linked markers to QByd.hwwg‐2DL were developed for improvement of wheat BYDV resistance in wheat breeding.

2.2. Evaluation of BYD severity and yield‐related traits of the RIL population in fields

The F5:8 RIL population and parents were evaluated for BYDV resistance in three field experiments from 2020 to 2022 at Kansas State University (KSU) Rocky Ford Experimental Station, Manhattan, KS. Plants were naturally infected by BYDV and scored using a 1–5 scale at the heading stage (Choudhury et al., 2019) (Figure S1). The population was also evaluated for developmental and yield‐related traits, including TKW, KA, KW, KL, spikelet number per spike (SNS), spike length (SL), heading date (HD), and plant height (PH), in three field environments at KSU Ashland Bottoms Research Farm, Manhattan, KS (2020 and 2021) and Agricultural Research Center, Hays, KS (2020). All the field experiments used a randomized complete block design with two replicates for the RILs and five replicates for each parent. About 30 seeds per line were sowed in a 1.4 m row plot with 30 cm between rows. Ten spikes per line were randomly selected and harvested at maturity. The plants were hand‐threshed for evaluation of kernel traits.

2.3. Genotyping the parents and RIL population

Methods for DNA isolation, genotyping, and map construction were described previously (Bian et al., 2023). Briefly, leaf tissues were collected from a single plant of each F5 RIL and their parents at the three‐leaf stage for DNA isolation using a modified cetyltrimethyl ammonium bromide method (Bai et al., 1999). DNA concentration was estimated and normalized to 20 ng/µL for construction of GBS library (Poland & Rife, 2012) that was sequenced in an Ion Torrent Proton sequencer (ThermoFisher Scientific). The SNPs were called from GBS reads using a reference‐based pipeline in TASSEL 5.0 (Bradbury et al., 2007) and the IWGSC reference genome RefSeq v2.1 (International Wheat Genome Sequencing Consortium [IWGSC], 2018). The raw data were filtered by removing SNPs with >20% missing data, >10% heterozygotes, or <20% minor allele frequency. Additionally, previously generated exome‐capture data of JagMut1095 and Jagger (Jordan et al., 2015) were used to increase marker density in the target QTL regions.

2.4. KASP marker development and validation

In the interval of the major QTLs for BYDV resistance and yield‐related traits, KASP primers were designed based on the sequences harboring the SNPs identified from both GBS and exome capture using a web version of Primer3 v.4.1.0 (http://primer3.wi.mit.edu/). The newly designed primers were screened in the parents and RILs using PCR allele competitive extension (PACE) assay mix (3CR Bioscience). Each KASP assay includes 1.94 µL of 2X PACE reaction mix, 0.06 µL of primer assay mix, and 2 µL of 20 ng/µL DNA. The polymerase chain reaction (PCR) profile started initially at 94°C for 15 min, then 10 cycles at 94°C for 20 s, 65°C for 1 min with −0.8°C in each subsequent cycle to 57°C, followed by 30 cycles of 94°C for 20 s and 57°C for 1 min. The normally segregated markers among the RILs were used to refine the genetic map.

2.5. QTL analysis

The previously constructed linkage map (Bian et al., 2023) was used for initial QTL mapping. The phenotypic values of each trait over two replications in each environment and the adjusted mean values of the best linear unbiased estimators (BLUEs) across all environments were calculated for the RILs and used for QTL mapping. QTL analysis was performed using the composite interval mapping (CIM) module in QTL Cartographer v2.5 (J. Wang, 2009) with a walking speed of 1.0 cM. A significant QTL was claimed for a trait based on 1000 times of permutations (Doerge & Churchill, 1996) using the QTL Cartographer v2.5. QTL names were designated using standard QTL nomenclature, starting with Q for QTLs, followed by the abbreviations of a trait name, such as BYD for barley yellow dwarf, and an institute name (Hwwg for Hard Winter Wheat Genetic Research Unit), then by a chromosome (or chromosome arm) location where the QTL is located after a dash line. QTLs that were mapped at the same or overlapping locations were considered as the same QTL and a QTL that was significant in two or more experiments was considered as a repeatable QTL. The putative candidate genes in the QTL region were selected based on the Chinese Spring RefSeq v.2.1 developed by the International Wheat Genome Sequencing Consortium (International Wheat Genome Sequencing Consortium [IWGSC], 2018) and Jagger genome assembly PGSBv2.1 (Walkowiak et al., 2020).

2.6. Statistical analysis

Analysis of variance and calculation of mean, standard deviation, correlation coefficients, broad‐sense heritability, and BLUEs across environments were carried out using QTL IciMapping V4.2 (Meng et al., 2015). The broad‐sense heritability for each trait was calculated using the formula H 2 = VG/(VG + VG×E/E + Ve/ER), where VG represents the genotypic variance, VG×E was the variance in genetics by environment, Ve was the residual variance, E was the number of environments, and R was the number of replications. The BLUE was estimated using a mixed linear model, Y = G + E + G × E + R, assuming fixed effects for the genotype.

3. RESULTS

3.1. BYDV severity and yield traits in parents and RILs

The mean BLUE values of BYD scores from three field experiments were significantly different between the two parents (p < 0.001; Figure 1a), with an average of 3.7 for JagMut1095 and 1.6 for Jagger across all environments in a 1–5 scale (Figure 1b). Although variation in BYD severities was observed due to inconsistent natural infection conditions among 3 years of field experiments, the correlations in BYD scores were significant among years (0.25–0.50, p < 0.001). Additionally, the broad‐sense heritability for BYDV resistance was medium high (0.66). The effects of genotype, environment, and genotype‐by‐environment interaction on BYD severity were highly significant at p < 0.0001 across the three environments (Table S1).

FIGURE 1.

FIGURE 1

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(a) Frequency distribution of mean barley yellow dwarf (BYD) severity of JagMut1095 × Jagger recombinant inbred lines (RILs) evaluated in three field experiments and the best linear unbiased estimator (BLUE); (b) barley yellow dwarf virus (BYDV) symptoms on JagMut1095 (left) and Jagger (right).

In addition to the BYDV resistance, Jagger had higher BLUE values of TKW, KA, KW, and KL, but lower BLUE values of PH, HD, SNS, and SL than JagMut1095 (Table 1). The broad‐sense heritability was high for all these traits ranging from 0.79 for SNS to 0.92 for PH. Additionally, TKW showed significantly high positive correlations with KA and KW and a moderate positive correlation with KL (Table 2). All four kernel‐related traits were negatively correlated with HD and BYD severity, although those traits were evaluated in fields different from the BYD nursery where the BYDV infections were not observed.

TABLE 1.

The best linear unbiased estimators (BLUEs) of JagMut1095 and Jagger and the heritability of nine traits estimated in the population of JagMut1095 × Jagger recombinant inbred lines (RILs).

Traits JagMut1095 Jagger Heritability
BYD 3.1 1.4 0.66
TKW (g) 23.4 31.9 0.81
KA (mm2) 12.0 14.1 0.84
KW (mm) 2.7 3.1 0.87
KL (mm) 6.1 6.3 0.88
SNS 18.7 16.6 0.79
SL (cm) 9.4 9.0 0.86
HD (days) 136.1 126.6 0.80
PH (cm) 104.5 89.9 0.92
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Note: Barley yellow dwarf severity was evaluated in Rocky Ford, Manhattan, KS and other agronomic traits were evaluated in three Kansas locations (Rocky Ford, Ashland Bottoms, and Hays).

Abbreviations: BYD, barley yellow dwarf severity evaluated in Rocky Ford, Manhattan, KS; HD, heading date; KA, kernel area; KL, kernel length; KW, kernel width; PH, plant height.; SL, spike length; SNS, spikelet number per spike; TKW, 1000‐kernel weight.

TABLE 2.

The correlation coefficients of nine traits in the population of JagMut1095 × Jagger recombinant inbred lines (RILs) evaluated across three Kansas field environments.

TKW KA KW KL SNS SL HD PH
KA 0.95***
KW 0.92*** 0.88***
KL 0.54*** 0.70*** 0.31***
SNS −0.15 −0.10 −0.15 0.04
SL 0.06 0.16* −0.07 0.41*** 0.36***
HD −0.38*** −0.34*** −0.32*** −0.17* 0.32*** −0.06
PH 0.32*** 0.29*** 0.20* 0.30*** 0.28*** 0.24** 0.19*
BYD −0.44*** −0.42*** −0.35*** −0.25** 0.26*** −0.06 0.29*** 0.06
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Note: Barley yellow dwarf severity was evaluated in Rocky Ford, Manhattan, KS and other agronomic traits were evaluated in three Kansas locations (Rocky Ford, Ashland Bottoms, and Hays).

Abbreviations: BYD, barley yellow dwarf severity; HD, heading date; KA, kernel area; KL, kernel length; KW, kernel width; PH, plant height.; SL, spike length; SNS, spikelet number per spike; TKW, 1000‐kernel weight.

Significant at *p < 0.05, **p < 0.01, ***p < 0.001 level.

3.2. QTLs for BYDV resistance and yield‐related traits

CIM using the SNP map and the BYD data from the three field experiments identified four QTLs for BYDV resistance on chromosome arms 1DL, 2AL, 2DL, and 5AL (Table S2). Among them, only QByd.hwwg‐2DL was consistently detected in all three experiments and for the BLUE values. QByd.hwwg‐2DL was located between markers S2D_642415923 (642,415,923 bp) and Exon2D‐19 (642,998,925 bp) and explained 11.6%–44.5% of the phenotypic variations for BYDV resistance (Table 3; Figure 2). Jagger contributes the allele for low BYD severity, consistent with parental BYD scores. Besides the 2DL QTL, the other three QTLs were unstable with minor effects, which were detected in only one of the three field experiments (Table S2). QByd.hwwg‐2AL and QByd.hwwg‐5AL were detected in the 2020 experiment and mapped between 748.38 and 754.79 Mb on 2AL, and between 472.15 and 547.10 Mb on 5AL, respectively. QByd.hwwg‐1DL was detected in the 2021 experiment and mapped between 466.62 and 484.15 Mb on 1DL. The results indicated that QByd.hwwg‐2DL is the only major stable QTL across environments and the other three QTLs might be spurious showing minor effects only in a single experiment

TABLE 3.

Quantitative trait loci (QTLs) detected in multiple experiments for barley yellow dwarf virus (BYDV) resistance, 1000‐kernel weight (TKW), kernel area (KA), kernel width (KW), kernel length (KL), spikelet number per spike (SNS), spike length (SL), heading date (HD), and plant height (PH) using the population of JagMut1095 × Jagger recombinant inbred lines (RILs) evaluated in Hays (H), Ashland (A), and Rocky Ford (R) field environments in years 2020 (20), 2021 (21), and 2022 (22).

QTL name Traits Genetic interval (cM) Physical interval (Mb) LOD Add R 2
QTkw.hwwg‐2DL TKW‐20H 80.1–95.5 623.58–643.10 6.61 −1.29 0.163
TKW‐20A 95.9–98.8 7.52 −1.34 0.159
TKW‐21A 95.5–98.4 6.99 −1.61 0.157
TKW‐BLUEs 98.8–99.9 6 −0.95 0.119
QKa.hwwg‐1AS KA‐20H 2.2–7.6 3.44–9.48 4.44 −0.3 0.096
KA‐20A 0.3–7.7 7.17 −0.35 0.136
KA‐BLUEs 0–2.6 6.04 −0.33 0.125
QKa.hwwg‐2DL KA‐20H 78.8–94.4 623.58–643.10 7.06 −0.38 0.181
KA‐20A 91.8–94.9 6.23 −0.35 0.143
KA‐21A 98.8–99.9 5.44 −0.28 0.11
KA‐BLUEs 86.5–96.0 5.66 −0.29 0.136
QKw.hwwg‐2DL KW‐20H 87.3–95.5 630.91–643.10 4.81 −0.04 0.101
KW‐20A 87.0–95.9 5.02 −0.06 0.142
KW‐21A 98.8–99.8 6.61 −0.05 0.11
KW‐BLUEs 98.8–99.8 7.28 −0.04 0.119
QKw.hwwg‐5AL KW‐20H 37.3–39.7 455.34–407.29 15 −0.08 0.316
KW‐20A 36.1–41.4 10.39 −0.06 0.194
KW‐21A 33.0–37.5 8.46 −0.06 0.156
KW‐BLUEs 37.8–39.6 17.58 −0.07 0.333
QKl.hwwg‐2DL KL‐20H 76.2–85.4 621.81–641.82 8.86 −0.1 0.234
KL‐20A 78.5–89.0 5.74 −0.08 0.124
KL‐21A 77.2–85.1 5.33 −0.06 0.116
KL‐BLUEs 75.0–85.1 8.57 −0.08 0.198
QByd.hwwg‐2DL BYDV‐20R 95.9–98.2 642.42–643.00 5.04 0.26 0.116
BYDV‐21R 95.9–98.3 19.46 1.01 0.383
BYDV‐22R 97.2–98.8 11.49 0.44 0.251
BYDV‐BLUEs 96.1–98.3 21.55 0.51 0.445
QSns.hwwg‐2BS SNS‐20H 117.4–122.4 46.34–57.83 6.51 0.7 0.155
SNS‐20A 117.6–123.4 7.99 0.38 0.17
SNS‐21A 120.3–123.7 11.65 0.39 0.214
SNS‐BLUEs 120.1–123.6 10.33 0.35 0.19
QSns.hwwg‐7BS SNS‐20H 113.0–118.9 116.51–245.74 6.16 0.33 0.135
SNS‐20A 4.41 0.29 0.091
SNS‐21A 9.83 0.37 0.182
SNS‐BLUEs 7.17 0.29 0.127
QSl.hwwg‐1AS SL‐20H 0–13.7 3.44–11.01 5.04 −0.24 0.11
SL‐20A 0–8.0 4.7 −0.25 0.096
SL‐21A 0–12.1 4.31 −0.21 0.097
SL‐BLUEs 2.2–8.3 6.51 −0.22 0.129
QSl.hwwg‐5AL SL‐20H 32.7–44.9 455.34–445.42 3.76 0.22 0.078
SL‐20A 34.5–38.4 9.27 0.35 0.199
SL‐21A 36.1–39.7 4.92 0.24 0.103
SL‐BLUEs 33.5–39.7 6.63 0.22 0.121
QSl.hwwg‐7AL SL‐20H 9.3–24.3 624.05–656.40 5.39 −0.28 0.117
SL‐21A 14.9–22.1 4.79 −0.23 0.096
SL‐BLUEs 9.3–22.1 3.75 −0.16 0.067
QHd.hwwg‐1DL HD‐20H 74.8–84.0 466.62–491.18 4.87 0.62 0.132
HD‐20A 72.3–84.0 4.83 0.74 0.099
HD‐BLUEs 63.6–84.0 4.36 0.52 0.088
QHd.hwwg‐7BL HD‐20H 85.8–112.6 474.37–640.88 4.9 1.36 0.116
HD‐21A 5.17 0.69 0.09
HD‐BLUEs 6.49 0.63 0.127
QPh.hwwg‐1AS PH‐20H 20.9–42.7 3.44–23.55 3.87 −2.08 0.068
PH‐20A 0–8.7 5.26 −3.53 0.111
QPh.hwwg‐4BS PH‐20H 50.4–59.3 31.18–38.24 16.54 4.5 0.339
PH‐20A 50.1–61.0 19.14 7.07 0.435
PH‐21A 50.6‐59.4 26.37 8.5 0.474
PH‐BLUEs 50.6–52.3 24.78 5.32 0.421
QPh.hwwg‐5AL PH‐20A 68.7–76 547.10–552.73 4.13 2.83 0.072
PH‐21A 36.1–38.6 415.34–417.22 4.26 3.01 0.056
PH‐BLUEs 34–39.7 4.24 1.89 0.053
QPh.hwwg‐7BL PH‐20H 85.8–112.6 474.37–640.88 3.63 1.74 0.059
PH‐20A 4 2.42 0.055
PH‐BLUEs 3.7 1.79 0.048
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Abbreviations: ADD, additive effect in which a positive value indicates alleles for increased value contributed by the JagMut1095; BLUEs, best linear unbiased estimators.; LOD, log likelihood ratios, which is significant when comparing hypothesis H1 (there is QTL linked) versus H0 (there is no QTL linked) (Churchill & Doerge, 1994); R 2, phenotypic variation explained by the QTL.

FIGURE 2.

FIGURE 2

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Map of quantitative trait loci (QTL) for barley yellow dwarf virus (BYDV) resistance, 1000‐kernel weight (TKW), kernel area (KA), kernel width (KW), and kernel length (KL) in the population of JagMut1095 × Jagger recombinant inbred lines (RILs). LOD is the log likelihood ratio. Centimorgan (cM) is a unit to measure the frequency of genetic recombination. BLUE, best linear unbiased estimator.

For the yield‐related traits, five QTLs were detected for TKW on chromosome arms 1AS, 2DL, 4BS, 5AL, and 5DL. QTkw.hwwg‐2DL mapped between 623.58 and 643.10 Mb was the only repeatable QTL for TKW across all environments. It overlapped with QByd.hwwg‐2DL and explained 11.9%–16.3% of the phenotypic variations for TKW. Among the other TKW QTLs, QTkw.hwwg‐1AS and QTkw.hwwg‐5DL were detected only in 2020 Ashland and explained 13.1% and 12.8% of the phenotypic variations, respectively. QTkw.hwwg‐5AL was detected in 2020 Hays and for BLUE values and explained 15.8% and 10.1% of the phenotypic variations, respectively. QTkw.hwwg‐4BS was mapped at the 31.18–38.24 Mb interval on 4BS where a reduced height (Rht) gene, Rht1, is located. This QTL explained 20.3% of the phenotypic variation in 2021 Ashland and is the only TKW QTL with the beneficial allele from JagMut1095.

Six QTLs were detected for KA on chromosome arms 1AS, 2DL, 4BS, 5AL, 5DL, and 6AS. QKa.hwwg‐2DL was in the same interval as QTkw.hwwg‐2DL and was significant in all three environments and for BLUE values. The QTL explained 11.0%–18.1% of the phenotypic variations. QKa.hwwg‐1AS between 3.44 and 9.48 Mb bp on 1AS was detected in two environments and by BLUE values and explained 9.6%–13.6% of the phenotypic variations. The other four QTLs (QKa.hwwg‐4BS, QKa.hwwg‐5AL, QKa.hwwg‐5DL, and QKa.hwwg‐6AS) were detected in a single environment and explained 6.9%–17.9% of the phenotypic variations. Same as for TKW, Jagger contributed the positive alleles at all these QTLs for KA except QKa.hwwg‐4BS that overlaps with Rht1 and was contributed by JagMut1095.

Four QTLs were identified for KW on 1AS, 2DL, 4BS, and 5AL. Two of them (QKw.hwwg‐2DL and QKw.hwwg‐5AL) were significant in all the three environments and for BLUE values. QKw.hwwg‐5AL in the interval between 407.29 and 455.33 Mb showed the largest effect, explaining 15.6%–33.3% of the phenotypic variations. The second stable QTL, QKw.hwwg‐2DL, was physically mapped between 630.91 and 643.10 Mb and explained 10.1%–14.2% of the phenotypic variations. The other two QTLs were significant only in one or two experiments. QKw.hwwg‐1AS and QKw.hwwg‐4BS were detected in 2020 and 2021 Ashland experiments and explained 7.8% and 14.5% of the phenotypic variations, respectively. Jagger contributed positive alleles at all the QTLs for KW except for QKw.hwwg‐4BS overlapping with Rht1.

Five QTLs were detected for KL on chromosome arms 1AS, 1DL, 2DL, 4BS, and 5AL. QKl.hwwg‐2DL within the interval between 621.8 and 641.8 Mb was the only stable QTL that was detected in all three environments and by BLUE values. This QTL explained 11.6%–23.4% of the phenotypic variations. The other four QTLs, QKl.hwwg‐1AS, QKl.hwwg‐1DL, QKl.hwwg‐4BS, and QKl.hwwg‐5AL, were detected only in 2020 or 2021 in Ashland and explained 7.5%–15.1% of the phenotypic variations. QKl.hwwg‐1AS and QKl.hwwg‐2DL had the preferred alleles from Jagger, and the positive alleles in other three QTLs were from JagMut1095.

All the four yield‐related QTLs on chromosome arm 2DL (QTkw.hwwg‐2DL, QKa.hwwg‐2DL, QKw.hwwg‐2DL, and QKl.hwwg‐2DL) overlapped with QByd.hwwg‐2DL for BYDV resistance with the preferred alleles from Jagger. However, QTLs were not significant for SNS, SL, HD, and PH in the 2DL interval (Table S2). These results indicate that the 2DL QTL for BYDV resistance might have pleiotropic effects on or tight linkage with kernel traits (TKW, KA, KW, and KL), but not on SNS, SL, HD, and PH. Interestingly, the major QTL QPh.hwwg‐4BS, which contributed up to 47.4% of the phenotypic variation for reduced PH, overlaps with the green revolution gene Rht1 and four yield‐related QTLs on chromosome 4BS (QTkw.hwwg‐4BS, QKa.hwwg‐4BS, QKw.hwwg‐4BS, and QKl.hwwg‐4BS) that were detected in a single environment. JagMut1095 contributed the taller allele for PH and favorable alleles for TKW, KA, KW, and KL.

3.3. Development of tightly linked markers to QByd.hwwg‐2DL

To develop KASP markers that are tightly linked to the QTLs on 2DL, the flanking sequences of four GBS–SNPs in the QTL interval were selected to design primers. Genotyping of the RIL population using the four KASP markers (2D‐641819218, 2D‐642415923, 2D‐642166246, and 2D‐643101947) showed the same segregation pattern as the original GBS–SNPs, but the KASP markers filled missing datapoints of the corresponding GBS–SNPs. To increase SNP density in the QByd.hwwg‐2DL region, four additional co‐dominant KASP markers between 642.42 and 643.12 Mb were developed using sequence information discovered from an exome capture assay of the two parents. These four markers were polymorphic between the two parents and segregated in the RIL population; thus, were also used to reconstruct the 2DL linkage map (Table S3). All the RILs were grouped into allelic group of Jagger or JagMut1095 based on their marker alleles within the 2DL overlapping QTL region in the RIL population (Table S4). The BLUE values were compared between the two allelic groups for each marker. Overall, the Jagger group (B) had significantly better BYDV resistance and higher values of TKW, KA, KW, and KL than the JagMut1095 group (A).

3.4. Candidate genes analysis of QByd.hwwg‐2DL

Searching for candidate genes in the IWGSC reference genome RefSeq v2.1 (International Wheat Genome Sequencing Consortium [IWGSC], 2018) identified a total of 14 high‐confidence (HC) genes in the QByd.hwwg‐2DL interval (Table 4). Meanwhile, sequences of the QByd.hwwg‐2DL flanking markers were blasted in the Jagger genome assembly PGSBv2.1 (Walkowiak et al., 2020), which identified 16 genes with 10 genes in common with Chinese Spring. The six other HC genes from the QByd.hwwg‐2DL interval of Jagger were mapped on chromosome 2B instead of 2D of Chinese Spring based on the IWGSC reference RefSeq v2.1.

TABLE 4.

A list of putative candidate genes identified in the candidate interval of QByd.hwwg‐2DL in Chinese Spring reference genome RefSeq v.2.1 and Jagger genome assembly PGSBv2.1.

Gene‐ID (CSv2.1) Description Jagger PGSBv2.1 RefSeq v2.1 Description
TraesCS2D03G1277500 Wall‐associated receptor kinase‐like protein – – –
TraesCS2D03G1277600 AT hook motif DNA‐binding family protein, putative TraesJAG2D01G630600 TraesCS2D03G1277600 AT hook motif DNA‐binding family protein, putative
TraesCS2D03G1277700 Glutathione peroxidase TraesJAG2D01G630800 TraesCS2D03G1277700 Glutathione peroxidase
– – TraesJAG2D01G630700 TraesCS2B03G1512800 Glutathione peroxidase
TraesCS2D03G1277800 DNA‐directed RNA polymerase subunit beta TraesJAG2D01G630900 TraesCS2D03G1277800 DNA‐directed RNA polymerase subunit beta
TraesCS2D03G1277900 C2 calcium/lipid‐binding and GRAM domain protein TraesJAG2D01G631000 TraesCS2D03G1277900 C2 calcium/lipid‐binding and GRAM domain protein
TraesCS2D03G1278100 Oxidoreductase/transition metal ion binding protein TraesJAG2D01G631100 TraesCS2D03G1278100 Oxidoreductase/transition metal ion binding protein
TraesCS2D03G1278200 Envelope glycoprotein B TraesJAG2D01G631200 TraesCS2D03G1278200 Envelope glycoprotein B
TraesCS2D03G1278500 Acetyl‐coenzyme A synthetase TraesJAG2D01G631300 TraesCS2D03G1278500 Acetyl‐coenzyme A synthetase
TraesCS2D03G1278700 Defensin – – –
TraesCS2D03G1278900 Defensin TraesJAG2D01G631400 TraesCS2D03G1278900 Defensin
TraesCS2D03G1279000 lectin‐receptor kinase – – –
– – TraesJAG2D01G631500 TraesCS2B03G1516800 Tyrosine decarboxylase
– – TraesJAG2D01G631600 TraesCS2B03G1517000 Agmatine coumaroyltransferase‐2
– – TraesJAG2D01G631700 TraesCS2B03G1516600 O‐methyltransferase‐like protein
– – TraesJAG2D01G631800 TraesCS2B03G1517200 Cytochrome P450
– – TraesJAG2D01G631900 TraesCS2B03G1517300 Pleiotropic drug resistance ABC transporter
TraesCS2D03G1279200 Dihydroflavonol 4‐reductase TraesJAG2D01G632000 TraesCS2D03G1279200 Dihydroflavonol 4‐reductase
TraesCS2D03G1279300 Cytochrome P450 TraesJAG2D01G632100 TraesCS2D03G1279300 Cytochrome P450
TraesCS2D03G1279400 Cytochrome P450 – – –
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4. DISCUSSION

As one of the most widespread and economically important plant viral diseases, BYD has caused significant yield losses in wheat and other cereal crops (Perry et al., 2000). This study used a RIL population from the cross between Jagger and its mutant to identify novel BYDV resistance loci in wheat. The population segregated for the BYD severity and yield‐related traits in multiple field experiments. Medium‐high (0.66) heritability of BYDV resistance suggested relatively high consistency of BYD phenotypic data across three environments in this study even under field natural infection conditions. Usually, plant resistance to insect‐transmitted diseases has a low heritability since the transmission is highly influenced by the environments, especially under natural infection conditions where insect infestation on host plants occurs randomly (Price & Schluter, 1991).

Using the CIM method, one stable major QTL (QByd.hwwg‐2DL) controlling BYDV resistance was consistently detected in three field environments. This major QTL located in a short interval of 0.58 Mb between 642.42 and 643.00 Mb on 2DL explained up to 51% of the phenotypic variance, indicating a stable major effect on BYDV resistance under multiple environments. To date, four wheat BYDV resistance genes (Bdv1, Bdv2, Bdv3, and Bdv4) have been reported. Among them, Bdv4 originated from the intermediate wheatgrass Thinopyrum intermedium and was transferred to wheat chromosome 2D through centric fusion as 2D‐2Ai‐2 (Choudhury et al., 2019; Zhang et al., 2009). QByd.hwwg‐2DL in the present study has the resistance allele from Jagger, which does not carry any known translocation from T. intermedium. In addition, all reported QTLs from wheat showed minor effects on BYDV resistance (Ayala et al., 2002; Choudhury et al., 2019; Silva et al., 2022). Therefore, QByd.hwwg‐2DL identified in this study is most likely a novel major QTL for BYDV resistance in wheat.

Compared to those resistance genes identified from wheat relatives, QByd.hwwg‐2DL did not show linkage drag. Instead, its resistance allele co‐segregated with preferred alleles at the QTLs for kernel traits (TKW, KA, KW, and KL) and did not show any negative effect on SNS, SL, HD, and PH. The co‐segregated QTLs in the region might be due to either tightly linked QTLs or pleiotropic effects. All the favorable alleles at the five overlapping QTLs are from Jagger; therefore, incorporation of QByd.hwwg‐2DL into new cultivars could simultaneously improve wheat BYDV resistance and kernel traits to increase grain yield, suggesting high application potential of QByd.hwwg‐2DL in breeding.

We blasted the sequences of the flanking markers from the QByd.hwwg‐2DL interval in the IWGSC RefSeq 2.1 (International Wheat Genome Sequencing Consortium [IWGSC], 2018) and found 14 annotated HC genes (Table 4). Eight of them were predicted to be pathogen‐defense‐related genes in different plant species, including TraesCS2D03G1277500 encoding a wall‐associated receptor kinase‐like protein, TraesCS2D03G1277700 encoding a glutathione peroxidase, TraesCS2D03G1277900 encoding a C2 calcium/lipid‐binding and GRAM domain protein, TraesCS2D03G1278700 and TraesCS2D03G1278900 encoding defensin, TraesCS2D03G1279000 encoding a lectin‐receptor kinase, and TraesCS2D03G1279300 and TraesCS2D03G1279400 encoding cytochrome P450 (Table 4). Among these genes, the lectin‐receptor kinase (Shumayla et al., 2016; Xiao et al., 2021) and cytochrome P450 (Gunupuru et al., 2018; Y. Li & Wei, 2020) have been reported to enhance both plant defense and grain yield in wheat previously, which could potentially confer pleiotropic effects on both BYDV resistance and TKW, KA, and KW traits. Additionally, TraesCS2D03G1277500, TraesCS2D03G1277700, and TraesCS2D03G1279400 have SNPs between two parents from the exome‐capture assays and these SNPs have been successfully converted to KASP markers as Exon2D‐3, Exon2D‐13, and Exon2D‐19 (Table S3), respectively.

Interestingly, comparison of the annotated genes in the QByd.hwwg‐2DL interval between Jagger and Chinese Spring reference genomes identified only 10 genes in common, whereas the six other genes from Jagger reference were mapped in one block on chromosome 2B in Chinese Spring reference, the IWGSC RefSeq 2.1 (Table 4). All the six genes were predicted to have functions on stress responses or disease defense and four of them have functions different from the 14 candidate genes identified in the QTL region of Chinese Spring based on IWGSC RefSeq 2.1. The four unique genes include TraesCS2B03G1516800 encoding a tyrosine decarboxylase, TraesCS2B03G1517000 encoding an agmatine coumaroyltransferase‐2, TraesCS2B03G1516600 encoding a o‐methyltransferase‐like protein, and TraesCS2B03G1517300 encoding a pleiotropic drug resistance ABC transporter (Bureau et al., 2007; Gao et al., 2021; Muroi et al., 2009; Nuruzzaman et al., 2014). Further fine mapping of the region will narrow down the list of candidate genes to eventually identify the causal gene for QByd.hwwg‐2DL.

QTL intervals for the yield‐related traits in this region were much larger than for QByd.hwwg‐2DL, which might be due to a much larger QTL effect of BYDV than the yield‐related traits (Figure 2). The overlapping QTL region (621,811,756–643,101,947 bp) for yield traits contains 568 annotated HC genes. This interval remains too large to determine the causal genes for these yield‐related traits. However, several genes have been reported as the candidate genes for yield and kernel traits in this interval. For instance, the E3 ubiquitin ligase related to the ubiquitin‐proteasome pathway that has the potential to modulate crop productivity by influencing agronomic traits (Varshney & Majee, 2022); serine/threonine‐protein kinase, SHAGGY‐like kinase, phosphatase and auxin response factors related to the phytohormone signaling and homeostasis (Kong et al., 2015); growth‐regulating factor zinc finger family protein related to plant development and stress response (Huang et al., 2021); B3 domain transcription factor related to grain development and filling (Y. C. Liu et al., 2023); basic helix‐loop‐helix transcription factors involved in stress response, plant development, metabolite biosynthesis and trait development (Guo & Wang, 2017); and α‐tubulin and cytochrome P450 related to plant development.

To date, QTL mapping has identified many important QTLs for disease resistance, yield, and other agronomic traits from Aegilops tauschii and confirmed that 2D had a higher gene density and harbored more yield‐related genes than other A. tauschii chromosomes (Qu et al., 2022; Zhao et al., 2017). Meanwhile, many QTL clusters related to kernel traits on 2D have been reported. For example, several QTLs for the wheat yield components were located between 481 and 601 Mb (Li et al., 2021; Qu et al., 2022, 2021). In our study, the 2DL QTL cluster (621–643 Mb) was located at least 20 Mb away from the previously reported 2DL region, indicating that the 2DL cluster identified in this study is more likely a novel genomic region for both kernel traits and BYDV resistance with a high potential to be used in wheat breeding. Further fine‐mapping and functional studies are required to narrow down the interval and characterize the causal genes for both kernel traits and BYDV resistance, which will also provide significant insight into the genetic relationships between QByd.hwwg‐2DL and these kernel traits. Significant QTLs were not detected for SNS, SL, HD, and PH, suggesting deployment of QByd.hwwg‐2DL in breeding will not negatively affect these traits.

AUTHOR CONTRIBUTIONS

Ruolin Bian: Formal analysis; investigation; validation; visualization; writing—original draft. Na Liu: Investigation; validation; visualization. Yuzhou Xu: Investigation. Zhenqi Su: Investigation. Lingling Chai: Investigation. Amy Bernando: Investigation. Paul St. Amand: Investigation. Jessica Rupp: Investigation. Michael Pumphrey: Resources. Allan Fritz: Writing—review and editing. Guorong Zhang: Writing—review and editing. Katherine W. Jordan: Data curation; writing—review and editing. Guihua Bai: Conceptualization; funding acquisition; project administration; resources; writing—review and editing.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

Supporting information

Supplemental Material

The supplementary material is available in the online version. All data analyzed during this study were included in this article and its supplementary information files. The original GBS sequence data were available at https://doi.org/10.5061/dryad.8sf7m0d0b.

TPG2-18-e20548-s001.docx (2.7MB, docx)

ACKNOWLEDGMENTS

This is contribution number 24‐257‐J from the Kansas Agricultural Experiment Station. This project was partially supported by the National Research Initiative Competitive Grants 2022‐68013‐36439 from the National Institute of Food and Agriculture, US Department of Agriculture (USDA). Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the US Department of Agriculture. The USDA is an equal opportunity provider and employer.

Bian, R. , Liu, N. , Xu, Y. , Su, Z. , Chai, L. , Bernardo, A. , St. Amand, P. , Rupp, J. , Pumphrey, M. , Fritz, A. , Zhang, G. , Jordan, K. W. , & Bai, G. (2025). A novel quantitative trait locus for barley yellow dwarf virus resistance and kernel traits on chromosome 2D of a wheat cultivar Jagger. The Plant Genome, 18, e20548. 10.1002/tpg2.20548

Assigned to Associate Editor Devinder Sandhu.

Contributor Information

Na Liu, Email: naliu@henau.edu.cn.

Guihua Bai, Email: guihua.bai@usda.gov.

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The supplementary material is available in the online version. All data analyzed during this study were included in this article and its supplementary information files. The original GBS sequence data were available at https://doi.org/10.5061/dryad.8sf7m0d0b.

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