Genome-wide association analysis of resistance to scald in an adapted multiparent winter malting barley population

23 Scald, caused by the fungus Rhynchosporium graminicola Heinsen 1897, is a major foliar disease in 24 winter malting barley (Hordeum vulgare L). Resistance to scald in winter malting barley is controlled by 25 major and minor resistance genes. We used a large population of lines derived from biparental crosses 26 among five winter malting barley parents to analyze resistance to scald and associated agronomic traits. 27 Increased winter survival and later heading dates were negatively correlated with increased resistance, 28 whereas increased height was positively correlated with resistance. A genome-wide association study 29 (GWAS) for resistance to scald was analyzed with multiple models, using 15,463 SNPs. The similarities 30 and differences between the models were identified in SNP trait associations and phenotypic effect sizes. 31 SNP associations identified a large region on chromosome 3H across models. FarmCPU identified 32 additional associations on chromosomes 2H, 3H, and 4H. Linkage disequilibrium on chromosome 3H and 33 GWAS for resistance to scald using the Rrs1-linked marker, HVS3, as a covariate confirmed Rrs1 was 34 segregating in this population. GWAS for winter survival, heading date and plant height identified 35 associations across the genome, with chromosome 2H showing SNP-trait colocalizations between 36 resistance to scald, winter survival, heading date and plant height. Breeding for durable resistance to 37 scald in winter malting barley can include pyramiding major resistance loci, such as Rrs1, as well as QTL 38 for disease resistance and agronomic traits. 39 40 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 2 41 42 PLAIN LANGUAGE SUMMARY 43 Genetic architecture of resistance to scald in winter malting barley 44 Scald is an important foliar pathogen in winter malting barley, affecting both grain yield and quality. 45 While resistance to scald is controlled by major and minor resistance genes, agronomic traits are also 46 known to limit the spread of scald in barley. We determined the genetic architecture using a large 47 multiparent population of winter malting barley. The FarmCPU genome-wide association model proved 48 optimal for defining the resistance genes, with the major resistance gene, Rrs1, conferring 27% of the 49 variation in this population. Fewer days to heading and taller plants contributed to plant avoidance of 50 scald. Reduced canopy coverage in plants with low winter survival led to less scald severity. A region of 51 the genome contributing a minor resistance effect was co-localized with a region for plant height, 52 heading date and winter survival. 53 54 Abbreviations 55 BLINK, Bayesian-information and linkage-disequilibrium iteratively nested keyway 56 DLA, diseased leaf area 57 FarmCPU, fixed and random model circulating probability unification 58 GAPIT, genome association and prediction integrated tool 59 GWAS, genome wide association study 60 HT, height 61 LD, linkage disequilibrium 62 LRR-RLK, leucine-rich repeat receptor-like proteins 63 MAF, minor allele frequency 64 MAGIC, multiple advanced generation inbred cross 65 MTA, marker trait association 66 MLM, mixed linear model 67 MLMM, multiple loci mixed linear model 68 PCA, principal components analysis 69 SNP, single nucleotide polymorphism 70 QTL, quantitative trait loci 71 PEI, pectin esterase inhibitor 72 QQ, quantile-quantile 73 Reml, restricted maximum likelihood model 74 SCAR, sequenced characterized amplified region 75 PVE, phenotypic variation explained 76 WS, winter survival 77 78 1 INTRODUCTION 79 Barley, Hordeum vulgare L., is a globally important crop used for livestock feed, human food, and for 80 malt used in brewed and distilled beverages. The increased demand for malting barley by the craft 81 brewing industry has led to increased malting barley production in non-traditional barley growing 82 regions (Shrestha & Lindsey, 2019). The adoption of malting barley in the Northeast US has been 83 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 3 fostered by development of improved management recommendations for barley grown in higher rainfall 84 and more humid conditions than in traditional barley growing regions (Shrestha & Lindsey, 2019; Siller et 85 al., 2021). Winter barley offers consistently higher yield and often higher quality than spring barley and 86 often avoids unfavorable spring planting conditions and summer droughts. Along with agronomic 87 management differences, a shift from spring to winter malting barley alters the disease exposure profile, 88 resulting in an environment conducive to the proliferation of scald. Barley scald is a foliar fungal 89 pathogen, that flourishes in the cooler climates reaching up to 77% diseased leaf area of upper canopy 90 during early grain filling stages in susceptible cultivars grown in New York (Kolkman et al., 2025a). Scald 91 can cause both a reduction in quality (Avrova & Knogge, 2012), and yield losses ranging from 10 to 45% 92 (Shipton et al., 1974), and up to 65% in severe epidemics (Beigi et al., 2013). 93 Scald in barley is caused by Rhynchosporium graminicola Heinsen 1897 (Crous et al., 2021; formerly 94 known as R. commune; Zaffarano, et al., 2011) a hemibiotrophic foliar fungal pathogen that thrives in 95 cooler temperatures (Zaffarano et al., 2008). Originally identified in 1897 (Frank, 1987), R. graminicola 96 was renamed as R. secalis (Oudem.) J.J. Davis in 1919 (Davis, 1919), renamed again in 2008 as R. 97 commune indicate host speciation specific to Hordeum species (Zaffarano et al., 2008, 2011). 98 Rhynchosporium graminicola is proposed to have originated in the cool climates of Scandinavia 99 approximately 2500 years ago as a host jump from an unknown grass species (Brunner et al., 2007). R. 100 graminicola spreads through previously infected barley debris and/or infected seed (Ababa et al., 2023). 101 Conidia land on leaf surfaces and produce a germ tube along the intercellular grooves that produces an 102 appressorium that penetrates through the cuticle, all within the first 24 hours of exposure (Linsell et al., 103 2011). Hyphae grow below the cuticle, above the anticlinal walls and between the epidermal cells, 104 between the pectin layer and outer cell wall, disrupting the pectic layer and cuticle for pectin 105 degradation (Ayesu-Offei & Clarke, 1970; Ryan & Grivell, 1974; Lehnackers & Knogge, 1990; Linsell et al., 106 2011). The cuticle and epidermal layers separate with the production of a packed hyphal mat, forming a 107 subcuticular stroma, inducing a water-soaked lesion, all within approximately 4 to 8 days (Ayesu-Offei & 108 Clarke, 1970; Linsell et al., 2011). Mesophyll collapse follows approximately 7 to 14 days post 109 inoculation, creating a straw-colored necrotic lesion surrounded by dark brown borders at which point 110 hyphal growth increased, likely due to the release of nutrients from the collapsed mesophyll cells (Ayesu-111 Offei & Clarke, 1970; Lehnackers & Knogge, 1990; Linsell et al., 2011). Conidia are produced from 112 subcuticular and/or substomatal stroma, which protrude through the cuticle along the leaf surface. 113 Resistance to scald in barley has been attributed to both major resistance genes and quantitative trait 114 loci (QTLs; as reviewed by Zhang et al., 2020). In total, 11 major resistance genes have been described 115 (as compiled by Noe et al., 2025) with major resistance genes located on all chromosomes except 116 chromosome 5H. Several of the Rrs genes are comprised of multiple alleles and/or loci, such as the Rrs1 117 gene complex located on chromosome 3H that includes the previously described alleles Rh, Rh1, Rh3 118 Rh4 and Rh7 (Bjørnstad et al., 2002). While no causal gene underlying the Rrs genes has been 119 determined, several Rrs genes have been fine-mapped. The Rrs1 gene region has been delimited to a 120 region of 0.8 Mb with 10 candidate genes using the Morex genome sequence. The Rrs1 gene is not, 121 however, present in the Morex genome, and is hypothesized to be presence-absence and/or a gene 122 duplication variant. There are several candidates that are plausible in this genomic region that is reticent 123 to recombination, due to its centromeric location, including a protein kinase (Looseley et al., 2020). The 124 Rrs2 resistance gene is proposed to be in a cluster of pectin esterase inhibitor (PEI) genes, either as a 125 unique gene, a combination of PEI genes and/or other unidentified gene(s) (Marzin et al., 2016). The 126 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 4 Rrs13 resistance gene, fine-mapped within a 0.58 to 1.2 Mbp region, codes for two to seven tandemly 127 repeated leucine-rich repeat receptor-like proteins (LRR-RLP) and a lectin receptor-like kinase (Eckstein 128 et al., 2024). More recently, the Rrs18 gene has been identified upstream of Rrs13 (Coulter et al., 2019). 129 Fine-mapping and RNA expression analysis identified four candidate genes with the most likely candidate 130 gene a serine/threonine protein kinase. As well as these four genes, a stable QTL for adult plant 131 resistance to scald, QSc.VR4, was fine-mapped to a 0.38 Mbp region that included an LRR-RLK multi-gene 132 family, and a germin-like protein multigene family (Wang et al., 2020). 133 Numerous genome-wide association studies (GWAS) have been utilized to identify and characterize 134 resistance to scald in barley. Association studies have relied on a variety of populations, including 135 diversity panels (Looseley et al., 2018; Hiddar et al., 2023; Kunze et al., 2024; Noe et al., 2025), a multiple 136 advanced generation inbred cross (MAGIC) population (Hautsalo et al., 2021), the HEB25 nested 137 association mapping population (Büttner et al., 2020), and a variety of populations that included diverse 138 germplasm, including landraces, wild species and breeding lines (Looseley et al., 2018, 2020; Clare et al., 139 2023; Ababa et al., 2024; Ijaz et al., 2024). Many trials utilize greenhouse trials to ascertain seedling 140 resistance (major gene resistance), using one or more isolates to characterize resistance to specific 141 isolates. Several studies relied on (natural) field infection to identify adult plant resistance (Daba et al., 142 2019; Looseley et al., 2020; Ijaz et al., 2024). All GWAS studies used spring barley, except for two studies 143 that used spring, winter and/or facultative barley lines (Looseley et al., 2020; Kunze et al., 2024). Adult 144 plant resistance in the field encompasses natural infection, and resistance comprised of major resistance 145 (Rrs) genes and/or QTL for resistance to scald. Adult plant resistance has been associated with genes 146 coding for plant growth traits, such as sdw1, the gibberellin 2-oxidase gene, HvGA20ox2 (Xu et al., 2017), 147 implicated with plant height and susceptibility to scald (Looseley et al., 2018). Improving barley for 148 resistance to scald relies on agronomic practices as well as breeding for resistance. Due to the potential 149 for R. graminicola populations to overcome resistance (Mcdonald, 2015; Ababa et al., 2024), 150 understanding the facets of resistance is imperative in breeding for sustainable resistance. 151 Genome wide association studies are useful for the identification of marker-trait associations and 152 characterization of trait architecture across the genome. The premise of GWAS relies on maximizing 153 meiotic recombination events by utilizing populations consisting of a large diverse number of genotypes 154 in conjunction with high SNP marker density, and in many cases, using population structure and kinship 155 to minimize false positive results (Flint-Garcia et al., 2005; Yu et al., 2006). Several models of GWAS have 156 been developed that use different approaches to identify marker trait associations (Tibbs Cortes et al., 157 2021). In contrast, breeding programs are targeted to regional adaptation and are generally limited to 158 elite material based on founders and the introgression of adapted material that may have originated 159 from diverse or pre-breeding efforts (Kelly et al., 1998). Large populations are created with the goal of 160 finding the transgressive segregants that move crop development and the release of novel cultivars 161 forward. 162 In this study we characterized resistance to scald in a large unbalanced diallel breeding population of 163 winter malting barley recombinant inbred lines and doubled haploids derived from biparental crosses 164 between five founder lines that are adapted to New York environments. We examined the correlation of 165 agronomic traits, such as winter survival, days to heading and plant height with resistance to scald as a 166 means of escape and/or avoidance mechanisms. Using GWAS, we characterized the genetic 167 architecture of resistance to scald in the multiparent population, and the co-localization of SNP 168 associations between resistance to scald and winter survival, heading date and plant height. We explore 169 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 5 a suite of GWAS models available in GAPIT version 3 (Wang & Zhang, 2021) to determine the appropriate 170 GWAS model for the multiparent unbalanced diallel population, given the relatedness and limited 171 meiotic recombinant events between lines within the germplasm. 172 173 2 MATERIALS AND METHODS 174 2.1 Germplasm and trait phenotyping 175 2.1.1 Plant germplasm 176 A large population of winter malting barley breeding lines was derived from an unbalanced diallel mating 177 design using five malting barley cultivars, including ‘Flavia’, ‘Lightning’ (Hayes et al., 2021), ‘KWS Scala’, 178 ‘SY Tepee’, and ‘WintMalt’ and the corresponding 10 cross combinations. F1 plants were advanced to the 179 F5 stage through single seed descent and/or through creating doubled haploids resulting in 377 180 recombinant inbred lines and doubled haploid lines (Figure 1). 181 2.1.2 Field trials 182 The diallel population was planted in four field sites (environments) at the Cornell University Campus 183 Area Farms in Tompkins County near Ithaca, New York and included the Snyder Farm and Helfer Farm 184 field sites in 2022, and the McGowan Farm and Ketola Farm field sites in 2023. Trials were grown in an 185 augmented design with a single replication in each environment and included the parental cultivars as 186 well as check cultivars (‘KWS Scala’, ‘Lightning’ and ‘Endeavor’) within four blocks in both 2022 187 environments (Snyder Farm and Helfer Farm), and within eight and ten blocks in Ketola Farm and 188 McGowan Farm field sites in 2023, respectively. Seeds were planted in plots with 1.2 m width, 3.0 m 189 plot length, with a 17.8 cm space between rows within the plot and 25.4 cm between plots. No plant 190 growth hormone or foliar fungicides were applied. Preplant fertilizer of 10:10:10 was applied at 224 191 kg/ha (22.4 kg ha-1 N) and followed by a top dress in the spring of 67 kg ha-1 N via liquid UAN 30. The 192 spring applied herbicide regime in 2022 consisted of Harmony (35 g ha-1), bromoxynil (BROX-2-EC at 1.5 L 193 ha-1) and Induce surfactant (0.7 L ha-1). Herbicide applications in the spring of 2023 consisted of Axial XL 194 (1.2 L ha-1), Harmony Extra SG (35 g ha-1) and Induce surfactant (0.7 L ha-1). No foliar fungicide was 195 applied. 196 2.1.3 Trait phenotyping 197 Scald infections relied on the natural inoculum present at each field site. The scald susceptible cultivar 198 ‘KWS Scala’ was planted in each block of each experiment and acted as a susceptible check and spreader 199 ensuring adequate conidial inoculum across the trial. Plots were scored for scald symptoms at 200 approximately Feekes stage 11.1 to 11.3 on June 13th and 9th in 2022 at the Helfer Farm and Snyder 201 Farm field sites, respectively, and at Feekes stage 11.1-11.3 from June 26th to the 30th, and June 15th to 202 19th in 2023 at the McGowan Farm and Ketola Farm field sites respectively. Plots were scored on a 0-9 203 scale in all four environments. Severity was also scored as a percentage diseased leaf area (DLA) for the 204 upper canopy in 2023. 2022 trial scores were converted to a % DLA. Additional agronomic traits were 205 scored in the trials including winter survival (WS), measured as percentage of plants surviving taken in 206 the early spring; heading date (HD), when 50% of the heads had completely emerged from the sheath 207 (converted to Julian heading date); and plant height, measured as the height (HT) from the ground to the 208 top of the spike excluding awns. 209 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 6 2.1.4 Data analysis 210 The scald DLA, WS, HD and HT were analyzed separately in 2024 JMP® 17.1 (JMP Statistical Discovery 211 LLC, Cary NC) using a mixed model (restricted maximum likelihood; Reml) with genotype as fixed effects, 212 and environment, row within environment, and column within environment as random model effects. 213 Least squared means were used to determine frequency distributions and parental phenotypic value in 214 comparison to the population. Spearman’s correlations between DLA, WS, HD, and HT were determined 215 using JMP. Heritability was determined on an entry-means basis where h2 = σ2g/( σ2e/t+ σ2g), with σ2g, 216 and σ2e representing genetic variance, and experimental error, respectively, and t representing number 217 of test environments (Fehr, 1987). 218 To normalize the residuals and homoscedasticity for association analysis, the DLA, WS, HD, HT 219 measurements were analyzed in a simple linear model in R to estimate the residual parameters for the 220 population of 377 without controls, with model effects of genotype, environment, row within 221 environment and column within environment as fixed effects. A constant of 1 was added to each DLA 222 measurement to avoid nulls for transformation. The best lambda for transformation of the data was 223 determined using the Box-Cox function (Box & Cox, 1964) in the MASS package in R v3.2.3 (R Core Team, 224 2015), that was used to transform for normal residuals and homoscedasticity. The DLA (with added 225 constant of 1), WS, HD and HT were transformed using respective lambda values of -0.1818, 2.9494, -226 7.0303 and 0.0606 for DLA, WS, HD and HT , respectively. The HD transformed data was multiplied by 1 x 227 1016 for further analysis. 228 The best linear unbiased predictors (BLUPs) transformed DLA scores were estimated using Reml in JMP 229 with WS, HD and HT as fixed covariate effects and genotype, environment, row within environment, and 230 column within environment as random effects. The BLUPs for the transformed values for DLA, WS, HD, 231 and HT were also calculated using Reml in JMP with genotype, environment, row within environment 232 and column within environment as random effects. 233 2.2 Genotypic Analysis 234 2.2.1 SNP Genotypic Data 235 Plant tissue was harvested from the multiparent population at the two-leaf stage. Tissue was lyophilized 236 and a modified Cetyltrimethylammonium bromide (CTAB) extraction was used for DNA extraction (Doyle 237 & Doyle,1987). DNA was genotyped with the 50K Illumina iSelect SNP array (Bayer et al., 2017; Mascher 238 et al., 2017, 2021) at the USDA-ARS North Central Small Grains Genotyping Lab in Fargo, ND and 239 resulted in high quality SNP data for 374 lines. The SNP data was trimmed from the original 43078 SNPs 240 to exclude homozygous SNPs across the population using Tassel5.0 (Bradbury et al., 2007). The 15,463 241 SNPs were used to estimate population structure. 242 2.2.2 Marker Genotypic Data 243 Leaf tissue was collected for specific marker traits associations (MTA) within known resistance and/or 244 height genes. For the MTA analysis, three kernels per line included in the multiparent population were 245 planted in a 96 cell tray and grown under a light bench. Tissue was harvested at the seedling stage and 246 lyophilized. The modified CTAB extraction was used to extract DNA (Doyle & Doyle, 1987). 247 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 7 The multi-parent population was genotyped for the plant height gene, HvGA20ox2, also known as semi-248 dwarf1 (sdw1), to test for the presence of the sdw1.d (Diamant) or sdw1.c (denso) alleles (Xu et al., 249 2017). The sdw1.d and sdw1.c alleles were amplified via polymerase chain reaction using mutant 250 specific primers based on Xu et al. (2017), with the adaptation of a fluorescent M13-tail in the forward 251 primers for both the sdw1.d allele (5401F_M13F; 5’- TGTAAAACGACGGCCAGTGGTGCTCCAGACCGCTCAG-252 3') and sdw1.c (MC40861P3F_M13F; 5'- TGTAAAACGACGGCCAGTTATGGCGTGACCAAAGGTTC-3') that 253 correspond to the reverse primers for sdw1.d (5549R; 5’-CGGCGGAGGGGTCAATG-3'), and sdw1.c 254 (MC40861P4R; 5’- CACCAATCCACCACGAAGA-3'). The PCR reaction consisted of ~30 ng genomic DNA, 255 12.5 µl of GoTaq polymerase, 0.8 µM M13F primer, 6 µM R primer, 6 µM M13-FAM primer (sdw1.d) or 256 M13-VIC primer (sdw1.c) and 8.22 µl of H20 in a 25 µl reaction. The PCR amplification protocol for the 257 sdw1.d primer pair included an initial denaturation step of 94oC for 3 m, 30 cycles (94oC for 1 m, 55oC for 258 30 s, 72oC for 30 s), 10 cycles (94oC for 1 m, 50oC for 30 s, and 72oC for 30 s), and a final extension cycle 259 of 72oC for 20 m. The PCR amplification cycle for the sdw1.c was similar to above, however the 260 annealing temperatures in the two cycles were 54oC and 53oC. Fragment analysis was performed on 261 pooled samples by the Biotechnology Resource Center (BRC) Genomics Facility (RRID:SCR_021727) at 262 the Cornell Institute of Biotechnology (http://www.biotech.cornell.edu/brc/genomics-facility) on an 263 Applied BioSystems 3730xl (Thermo Fisher Scientific, Waltham, MA), with the ABI 500LIZ size 264 standard. Results were analyzed in Genemarker (SoftGenetics, LLC, State College, Pennsylvania). 265 In addition, the HVS3 SCAR marker (Genger et al., 2003) was screened in the parental lines and 266 multiparent population using ~20ng genomic DNA, 1X GoTaq ® Green Master Mix (Promega Cooperation, 267 Madison, WI), 5µM Forward primer (5’-AAT CCT ACC TAT CCC ACC TT-3’), 5 µM Reverse primer (5’-TAT 268 TTT CAG CCT TGT TCG GC-3’) in a final reaction volume of 25 µl. The DNA was amplified via PCR using an 269 initial step of 94oC for 3 m, 35 amplification and extension cycles (94oC for 30 s, 50oC for 30 s, 72oC for 1 270 m), followed by a final extension cycle of 72oC for 20 m. The PCR products were amplified on a 2% 271 agarose gel using GelRed ® Nucleic Acid Stain (Biotium, Inc, Freemont, CA) for verification of PCR on a UV 272 lightbox. 273 2.3 Genome-wide association analysis 274 2.3.1 Genome-wide association mapping for resistance to scald with multiple models 275 Association analysis was used to identify regions of the genome that conferred resistance to the scald 276 pathogen in our adapted germplasm. Phenotypic data included the transformed DLA, WS, HD and HT 277 BLUPs. Of the diallel population, 374 lines were used that included both genotypic and phenotypic data. 278 The 43,078 SNPs (Morex version 3) derived from the 50K Illumina iSelect SNP array (Bayer et al., 2017; 279 Mascher et al., 2017, 2021) were filtered to exclude homozygous SNPs across the population in TASSEL 280 5.0 (Bradbury et al., 2007). 281 Genome-wide association for Box-Cox transformed DLA BLUPs for scald that included WS, HD and HT as 282 covariates as previously described was ascertained using GAPIT version 3 (Wang & Zhang, 2021) in R 283 Studio (RStudio 2025.09.1+401, Posit Software, PBC). The Mixed Linear Model (MLM), Multiple Locus 284 Mixed Linear Model (MLMM), Fixed and random model Circulating Probability Unification (Farm CPU) 285 and (Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) models (Yu et 286 al., 2006; Zhang et al., 2010; Segura et al., 2012; Liu et al., 2016; Huang et al., 2019), using the 15,463 287 SNPs, were used to estimate marker trait association parameters for resistance to scald. Kinship and 288 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 8 principal component analysis (PCA) were determined within GAPIT version 3 to be used in the 289 appropriate models. The Bonferroni threshold (Bonferroni, 1936) was used as an initial default 290 significance threshold for R2 (MLM) and Percent Variation Explained (PVE; MLM, MLMM, FarmCPU, and 291 BLINK). The resulting Quantile-Quantile (QQ) plots and contributing PVE were examined to determine 292 the appropriate GWAS model for this structured population. To reduce type 2 false negative errors due 293 to the conservative nature of the Bonferroni threshold, the QQ plots were examined to determine the 294 best GWAS fit that combined the reduction of spurious associations and putative significant or strong 295 associations. The MLMM, FarmCPU and BLINK GWAS model QQ plots showed a p-value differentiation 296 at the -log10(p) = 4.2. To determine the appropriate R2 (MLM only) and PVE (MLM, MLMM, FarmCPU and 297 BLINK models) the ‘N.sig = n’ prompt was used in GAPIT version 3 for the significance threshold where ‘n’ 298 equals the number of markers for each model equal or above the -log10(p) = 4.2 threshold. An additional 299 association analysis using FarmCPU with PCA = 2 as a covariate was used to validate model selection with 300 or without PCA. 301 2.3.2 The Rrs1 gene complex 302 Linkage disequilibrium (LD) surrounding the two strongest SNP associations identified on chromosome 303 3H at 172 Mbp and 442 Mbp were analyzed to determine if they were the same peak or independent 304 associations. The LD was first assessed in TASSEL 5.0 (Bradbury et al., 2007) between the SNP at 172 305 Mbp and all the other SNPs on chromosome 3H (Figure 3A), and between the SNP at 442 Mbp and all 306 the other SNPs on chromosome 3H. The Manhattan plots for the GWAS MLM model (Figure 2) were 307 overlaid with LD estimates and used to confirm the presence of a large linkage block across the 308 centromeric region of chromosome 3H, as visualized in the ‘ggplot2’ package of R v3.2.3 (R Core Team, 309 2015). The GWAS MLM model was used to assess associations using GAPIT version 3, using the same 310 methodology as above, except for only using the 354 lines in the GWAS model and using the HVS3 311 marker data as a covariate (Figure 3C). 312 2.3.3 GWAS for resistance to scald, winter survival, heading date and plant height 313 The Box-Cox transformed DLA BLUPS (without WS, HD, and HT as covariates), as well as Box-Cox 314 transformed WS, HD, and HT BLUPs were used as independent trait variables in both MLM and FarmCPU 315 analysis using GAPIT version 3 in RStudio, with the 15,463 SNPs. The significant thresholds were based 316 on visual inspection of the QQ plots of FarmCPU and set to -log10(p) = 4.0. As in the first GWAS analysis 317 listed above, the models were run again using the ‘N.sig = n’ prompt to determine the correct PVE in 318 GAPIT version 3. The resulting Manhattan plots were created in ggplot2 in RStudio. 319 The LD along chromosome 2H was compared to the significant SNP in the marker-trait association for 320 resistance to scald, to determine if there was co-localization and/or LD between WS, HD, and HT to 321 resistance to scald. The Manhattan plot was created in ggplot2 to map the LD and SNP marker trait 322 associations from the MLM GWAS model, and included the clustered SNP regions of the WS, HD and HT 323 marker trait associations on the short end of chromosome 2H. Regions around the DLA, WS, HD and HT 324 SNP associations were based on the SNP associations in the MLM model that were above -log10(p) = 4.0, 325 as an indicator of the confidence interval for the trait. 326 327 3 RESULTS 328 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 9 3.1. The multi-parent population pedigree 329 The genetic population used in this dataset is structured as an unbalanced diallel design derived from 330 377 biparental crosses between four winter malting barley cultivars (‘Flavia’, ‘KWS Scala’, ‘SY Tepee’, and 331 ‘WintMalt’) and one facultative malting barley cultivar (‘Lightning’; Figure 1). The largest number of 332 lines was derived from ‘Lightning’ x ‘SY Tepee’ crosses, with 93 lines. ‘Lightning’ was the parent with the 333 most lines represented, with 272 (72%) of the 277 lines. ‘WintMalt’ was the parent with fewest lines 334 represented with only 95 of the 377 lines (25%). The fewest number of lines were from the cross 335 between ‘WintMalt’ x ‘SY Tepee’, with four lines. Additional parents were present in the population with 336 43.8% (165 lines), 35% (132 lines), 26% (100 lines) for ‘SY Tepee’, ‘Flavia’, and ‘KWS Scala’ respectively. 337 338 339 Figure 1. Structure of the unbalanced diallel multiparent population derived from five adapted cultivars. 340 The 377 lines consisted of recombinant inbred lines and doubled haploids. The number of lines between 341 each biparental cross is indicated along the line between the two parents, which were used for 342 statistical analysis (377 lines) and for GWAS analysis (374 lines) with three lines less as indicated by the 343 numbers in parenthesis. 344 3.2 Segregation for disease and agronomic traits in the multiparent population 345 The multi-parent population along with the corresponding parental cultivars, additional breeding lines 346 and several check cultivars were grown in four environments at the Cornell University Campus Area 347 Farms near Ithaca, NY , including two field sites (Helfer Farm and Snyder Farm) in 2022, and two field sites 348 (McGowan Farm and Ketola Farm) in 2023 (Supplemental Table S1). A priori knowledge of these field 349 sites indicated a historical presence of the scald pathogen in each environment. To ensure adequate 350 disease development in the field as well as monitor disease, the scald susceptible variety ‘KWS Scala’ 351 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 10 (Blachez et al., 2018) was grown in each experimental block, ranging from 13 entries in the Helfer Farm, 352 2022 field site to 17 entries in the McGowan Farm 2023 field site. 353 Distributions of disease and agronomic traits showed transgressive segregation for most traits from the 354 parental lines. The distribution of scald DLA was skewed towards the y-axis, with Lighting as resistant, 355 ‘SY Tepee’ as moderately resistant, and ‘WintMalt’, ‘KWS Scala’ and ‘Flavia’ as susceptible to scald 356 (Supplemental Fig1). An analysis of variance indicated significant variation for DLA for genotypes, 357 environment and rows within environment. The means and ranges of disease severity within the 377 358 lines grown across the locations varied with 50.8% (0-99%) in Helfer Farm, 2022, 19.9% (0-99%) in Snyder 359 Farm, 2022, 6.0% (0-90%) in Ketola Farm, 2023, and 19.7% (0-95%) in McGowan Farm, 2023. 360 Across all three agronomic traits, significant variation was identified for genotype, rows within 361 environment, and columns within environment, but not between environments. Across the four 362 environments, WS was skewed towards 100% survival, with ‘SY Tepee’, ‘WintMalt’ and ‘KWS Scala’ 363 showing good winter survival (>85%). ‘Lightning’ and ‘Flavia’ showed moderate winter survival (~75-364 80%), while a check cultivar, ‘Endeavor’, showed poor winter survival. The average winter survival within 365 the population ranged from 75% (Helfer Farm, 2022) to 87% (Ketola Farm, 2023) with an average across 366 the four environments of 79.9%. Differences in heading date between the parents ranged from ~143 367 days (Flavia) to ~151 days (‘WintMalt’), with ‘Lightning’, ‘SY Tepee’ and ‘KWS Scala’ showing mid-range 368 heading dates between 145 days to 147 days. The heading date (Julian) ranged from an average of 142 369 days in McGowan Farm, 2023 to 150 days to heading in Helfer Farm, 2022. Plant height ranged from an 370 average of 52 cm to 67 cm in Ketola Farm, 2023 (Supplemental Table S1 & S2). Heritability based on an 371 entry-means basis for DLA, WS, HD and HT were 0.76, 0.69, 0.83 and 0.57 respectively. 372 3.3 Scald is correlated with agronomic traits 373 Correlations between scald DLA and agronomic traits were ascertained to determine the effect of winter 374 survival, heading date and plant height on scald using the 377 lines within the multiparent population 375 (Supplemental Table S2). A highly significant negative correlation was identified between DLA and plant 376 height (P < 0.0001; R2 = -0.29), indicating that taller plants had less disease in the upper canopy. A 377 significant correlation was also identified between heading date and scald DLA (P < 0.0107; R2 = 0.13), 378 indicating that a later heading date had more DLA. The correlation between winter survival and DLA was 379 not significant across environments (P = 0.1065). Notably, there was a significant interaction between 380 381 Figure 2. Spearman’s correlation between resistance to scald (diseased leaf area), and a) winter survival, 382 b) heading date and c) plant height, in the 377 line multiparent population across four environments in 383 2022 and 2023. 384 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 11 winter survival and heading date (P=0.0229; R2 = -0.1171), and a highly significant interaction between 385 WS and plant height (P<0.0001; R2 = 0.38), and no significant correlation between heading date and 386 plant height. 387 3.4 Genome wide association mapping for resistance to scald 388 3.4.1 GWAS for resistance to scald using multiple GWAS models 389 Population structure determined via principal component analysis in GAPIT version 3 (Wang & Zhang, 390 2021; Supplemental Figure S2) indicated that the multiparent population consisted of four groupings 391 with the use of only the first and second PCAs, indicative of the relatedness between RILs and DHs. The 392 QQ plot of the MLM GWAS model showed marked deviation of the observed from expected p-values for 393 the MLM model that accounts for both population structure and kinship (Figure 3). The MLMM, 394 FarmCPU and Blink models had QQ plots that were largely in line with the observed and expected p-395 values. The FarmCPU model with PCA = 2 as a covariate resulted in a QQ plot with the observed -log10(p) 396 values skewing towards the x-axis in comparison to the FarmCPU model with no PCA covariate and was 397 not further pursued (Supplemental Figure S3). The Bonferroni threshold of -log10(p) = 5.5 was initially 398 used in GAPIT version 3 as the default to determine significance. Analysis of the QQ plots, and 399 particularly the QQ plots from the FarmCPU analysis, showed that a threshold of -log10(p) = 4.2 was more 400 appropriate and reduced type II errors, where the observed veered from the expected regression line. 401 Re-analysis of the GWAS models for MLM, MLMM, FarmCPU and Blink threshold set to -log10(p) of -4.2 402 was used to estimate the appropriate phenotypic variation explained (Table 1) and showed distinct 403 differences between models (Supplemental Table S3 & S4). 404 MLM: The mixed linear model, with population structure and kinship included, resulted in 77 SNPs 405 associated with resistance to scald (Figure 3). Seventy-seven SNPs were localized in two SNP clusters on 406 chromosome 3H and spanned a region from 88 Mb to 448 Mb. Two peaks across this region were 407 identified as the significant associations, with the most significant SNP association located at 408 174,868,558 bp, with a -log10(p) = 7.3 (MAF = 0.46; PVE = 0.6%. The second SNP peak was located at 409 442,185,927 bp, with a -log10(p) = 7.0 (MAF = 0.47; PVE = 3.8%). An additional SNP association was 410 identified on chromosome 6H (MAF = 0.004; PVE = 50.9%). Despite the large phenotypic variation 411 attributed to the SNP association on chromosome 6H, it appears to be an anomaly (false positive) and 412 will be treated as such in the further interpretation of results. 413 MLMM: Using MLMM, two SNPs were found to be associated with resistance to scald (Figure 3). The 414 single peak identified was located on chromosome 3H, at 174,868,558 bp, with a-log10(p) = 11.1 (MAF = 415 0.46; PVE = 19.4%). The second association was identified as a false positive at the previously described 416 SNP on chromosome 6H: 65,563,245 bp (MAF = 0.004; PVE = 51.6%). 417 418 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 12 419 Figure 3. Manhattan plots of the GWAS models including a) MLM, b) MLMM, c) FarmCPU, and d) BLINK, with corresponding QQ plots (right) in 420 GAPIT version 3 for resistance to scald in the multiparent population with using DLA BLUPs for resistance to scald. DLA BLUPs for resistance to 421 scald were calculated using winter survival, heading date and plant height as covariates in the BLUP model in four environments across 2022 and 422 2023. The red horizontal line in the Manhattan plots indicate the QQ plot - determined threshold of -log10(p) of 4.2. 423 424 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 13 FarmCPU: The FarmCPU model identified four SNPs associated with resistance to scald (Table 1). The 425 most significant SNP was located on chromosome 3H at 442,185,927 bp with a -log10(p) = 14.8 (MAF = 426 0.47; PVE = 27.1%). The SNP association on chromosome 2H was at 18,144,981 bp (MAF = 0.426; PVE = 427 8.1%) Additional SNP associations were identified on chromosomes 3H (553,288,836 Mbp; MAF = 0.303; 428 PVE = 5.2%), 4H (MAF = 0.240; PVE = 0.8%). Two additional SNP marker trait associations were identified 429 however both had low MAF and are considered aberrations, including the association on chromosome 430 6H at 65,635,245 bp (MAF = 0.004; PVE = 6.3%) and on chromosome 5H at 329,813,939 bp (MAF = 431 0.021; PVE = 2.8%). FarmCPU with PCA=2 resulted in a QQ plot with the observed -log10(p) values below 432 the expected -log10(p) values and skewed towards the x-axis and was not considered further 433 (Supplemental Figure S3). 434 BLINK: BLINK identified three SNPs associated with resistance to scald. The most significant SNP was 435 located on chromosome 3H: 442,185,927 bp, with a -log10(p) = 17.7 (MAF = 0.47; PVE = 7.6%). The next 436 most significant SNP was located on chromosome 7H: 443,811,200 bp with a -log10(p) = 11.6 (MAF = 437 0.327; PVE = 9.0%). The third SNP marker trait association was found on chromosome 4H at 22,416,684 438 bp with a -log10(p) = 5.7 (MAF = 0.24; PVE = 4.3%). As with the other models, a SNP association was 439 identified on chromosome 6H: 65,635,245 bp with a -log10(p) of 7.2 (MAF = 0.004; PAV = 62.6%) and is 440 considered an aberration. 441 A discrepancy between the GWAS models was identified, that was important in interpreting how to 442 proceed with the analysis. The difference in SNP marker trait associations between the MLM, MLMM, 443 FarmCPU and BLINK spans both SNP location and effect size. Notably, there appeared to be two large 444 peaks on chromosome 3H (MLM) that were either an association on the first peak (MLMM) or on the 445 second peak (FarmCPU; BLINK). In addition, PVE for the large peaks on chromosome 3H (442 Mbp) 446 varied , with FarmCPU providing a PVE = 27%, indicative of a major gene such as the Rrs1, which co-447 localizes to that region. 448 Using Tassel 5.0 (Bradbury et al., 2007), the LD along chromosome 3H was compared to both peaks 449 separately (Figure 4). Using the SNP at 174,868,558 bp, the LD analysis showed that SNPs across both 450 peaks were in LD with the peak at 174 Mb, indicating a large linkage block that spanned the centromere. 451 The LD analysis comparing the SNPs at the second peak at 442,185,927 bp also indicated that SNPs 452 across both peaks were in LD with the second peak. In both scenarios of GWAS using MLM, where both 453 peaks presented as being associated with resistance, the LD patterns were tightly linked and in 454 coordination with the p-values of the surrounding SNP association landscape. As this population is 455 derived from recombinant inbred lines and/or doubled haploids, it appears that the two large peaks on 456 chromosome 3H comprise one large linkage block that spans the centromere. The SNP at 442 Mb is also 457 the most significant SNP identified via GWAS MLM, and in FarmCPU and BLINK, with FarmCPU indicating 458 the most variation explained by this SNP . 459 3.4.2 Rrs1 is segregating in a large linkage block on chromosome 3H 460 The cluster of SNPs with high marker-trait associations on chromosome 3H co-localized near the Rrs1 461 locus. To determine if the resistance in this region is due to the major resistance gene complex, Rrs1, the 462 population was scored for the HVS3 SCAR marker, located downstream of the Rrs1 gene (Genger et al., 463 2003). ‘Lightning’ harbored the resistance allele at 250 bp, while ‘Flavia’, ‘KWS Scala’, ‘SY Tepee’ and 464 ‘WintMalt’ carried the susceptible allele at ~500 bp. (Supplemental Table S5). The HVS3 marker was 465 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 14 used as a covariate in both the MLM and FarmCPU GWAS analysis in GAPIT version 3. The resulting 466 Manhattan plot along chromosome 3H (Figure 4c) indicates the collapse of the entire region of SNP 467 associations to well below the threshold level, indicating the resistance identified via GWAS model is 468 conferred via Rrs1. Using the HVS3 marker as a covariate in the GWAS model implies Rrs1 as the 469 resistance mechanism across this large region. 470 Based on the Morex version 3 genome, the SNP at 442,185,927 bp was located within a receptor-like 471 kinase (HORVU.MOREX.r3.HG0281210.1), that is one of several kinase and receptor-like kinase genes 472 clustered in the region. The SNP located on chromosome 3H at 553,288,836 bp co-localized with the 473 Rrs4 gene (Patil et al., 2003), located at 576.6 Mbp. The SNP resides in a Protein 1Q-Domain 1 gene 474 (HORVU.MOREX.r3.3HG0303590.1). The Rrs17 resistance gene is located at 10.4 Mbp (Wagner et al., 475 2008), upstream of the SNP identified on chromosome 2H located at 18.1 Mbp, that is located within a 476 leucine-rich repeat receptor-like protein kinase (HORVU.MOREX.r3.2HG0104570). The identified SNP on 477 chromosome 4H at 22,416,684 bp is located within HORVU.MOREX.r3.4HG0338290, a glucan endo-1 3-478 beta glucosidase gene. 479 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 15 Table 1. SNP-trait associations for resistance to scald using DLA BLUPs (using winter survival, heading date and plant height as covariates in the 480 BLUPs), 374 lines and 15,463 SNPs in the MLM, MLMM, FarmCPU and BLINK GWAS models using a threshold of -log10(p) > 4.2. 481 SNP Marker Name Chromosome Position (bp)a p-value MAFb Allele Effectc Phenotype Variance Explained (%) MLM JHI-Hv50k-2016-168548 3 174,868,558 5.00E-08 0.462 c/t -0.0398 0.6 JHI-Hv50k-2016-183351 3 442,550,473 2.35E-07 0.473 c/t -0.0380 3.8 JHI-Hv50k-2016-183207 3 442,203,921 1.05E-07 0.471 t/a -0.0380 0.7 SCRI_RS_221644 3 442,185,927 1.05E-07 0.471 a/g -0.0380 0.1 JHI-Hv50k-2016-166941 3 127,232,535 3.47E-07 0.460 t/c 0.0005 0.0 MLMM JHI-Hv50k-2016-168548 3 174,868,558 8.22E-12 0.462 c/t -0.0412 19.4 FarmCPU SCRI_RS_221644 3 442,185,927 1.58E-15 0.471 a/g -0.0319 27.1 JHI-Hv50k-2016-70026 2 18,144,891 5.03E-06 0.426 g/a -0.0121 8.1 JHI-Hv50k-2016-232214 4 22,416,684 1.04E-05 0.240 a/t 0.0130 0.8 JHI-Hv50k-2016-202358 3 553,288,836 1.76E-05 0.303 t/c 0.0130 5.2 BLINK SCRI_RS_221644 3 442,185,927 1.81E-18 0.471 a/g -0.0335 7.6 JHI-Hv50k-2016-514374 7 443,811,200 2.39E-12 0.327 g/n -0.0551 9.0 JHI-Hv50k-2016-232214 4 22,416,684 2.05E-06 0.240 a/t 0.0113 4.3 a SNP position based on Morex version 3 (Mascher et al., 2021) 482 b MAF, minor allele frequency 483 c Effect is based on Box Cox transformed data and as a result is a) not directly transferrable to unit measurements of DLA and b) the negative Box 484 Cox transformation of DLA results in the reverse effect direction of the DLA effect. 485 486 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 16 487 Figure 4. Dissection of the SNP-trait association (MLM) cluster on chromosome 3H for resistance to scald 488 DLA, where a) characterizes linkage disequilibrium (LD) in relation to the first SNP trait association at 489 174 Mbp, b) characterizes LD in relation to the second SNP-trait association at 442 Mbp, and c) the 490 Manhattan plot shows SNP-trait associations across chromosome 3H using the MLM GWAS model in 491 GAPIT version 3 using the HVS3 (Rrs1) scar marker as a covariate). SNP associations are labeled as black 492 dots. Linkage disequilibrium indicated as R2 is shown in grey dots in a) and b). The most significant SNP-493 trait association at 174 Mbp and 442 Mbp chromosome 3H are indicated as red dots in all three panels. 494 Four GWAS models were used to validate SNP associations across the genome for resistance to scald. All 495 four models have different approaches for estimating associations and are available to avoid overfitting 496 analysis. The most significant SNP association(s) in this study resided on chromosome 3H at either 174.8 497 Mbp or 422.1 Mbp. FarmCPU and Blink associations at these peaks had the lowest p-values (i.e., the 498 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 17 most significant) across the models. Importantly, the PVE greatly differed between the models for these 499 two SNPs. The PVE at 174.8 Mbp ranged from 0.6% to 19.4% for MLM and MLMM, respectively. The 500 PVE at 442.1 Mbp was 0.1%, 27.1% and 7.6% for the MLM, FarmCPU and Blink models, respectively. The 501 FarmCPU model PVE of 27.1% is indicative of major gene resistance such as Rrs1. The SNP association 502 on chromosome 6H, explained ~50% of the phenotypic variation in MLM, MLMM, and BLINK despite the 503 very low MAF (0.004) and given the variation in the few SNPs at this locus, appears to be a type I error. 504 The FarmCPU model constrains the PVE of this SNP at 6%. Due to the PVE allocated for the Rrs1 region, 505 as well as for the aberrant SNP on chromosome 6H, and for simplicity, the FarmCPU was the model that 506 was used in the further analysis, being considered the best fit for this population. The MLM model was 507 also used for reference. 508 3.4.3 GWAS for resistance to scald compared to agronomic traits via FarmCPU 509 Association analysis across disease and agronomic traits for the 374 lines was used to identify SNP 510 associations for resistance to scald, winter survival, heading date and plant height to determine if there 511 was any colocalization of marker associations that explained the phenotypic traits (Table 2). The BLUPs 512 were estimated as mentioned above and included the scald DLA BLUP estimate (without using the WS, 513 HD, or HT as covariates; Supplemental Table S3). The QQ plots for the FarmCPU analysis (Figure 5) 514 indicated an adherence of the observed to expected -log10(p) values except for several SNPs at the tail of 515 the slope, as expected. The separation of the observed to expected levels at the thresholds for each of 516 the traits was analyzed separately, and based on the QQ plots, a -log10(p) = 4.0 was used as the threshold 517 for each trait (Figure 5). 518 DLA: In general, the SNP associations identified using the scald DLA BLUPs (without the agronomic traits 519 as covariates) using FarmCPU (Figure 5; Table 2) were similar to the previous identified SNPs (using the 520 agronomic traits as covariates; Table 1). The SNP associations using the correlated traits had slightly 521 lower p-values (i.e., more significant), except for the Rrs1 region on chromosome 3H. The SNP 522 associations on chromosome 3H included the main association at 442,550, 473 bp (PAV=20.7%) and 523 553,388, 8365 (PAV = 6.2%). The same SNP associations were identified on Chromosome 2H and 4H, 524 with slightly adjusted PVE of 6.7% and 1.2%, respectively. Additionally, a SNP association was identified 525 on chromosome 7H (MAF = 0.326; PVE = 0.2%; g/n) and may be a presence/absence variation. This SNP 526 association was present in the previous BLINK analysis but was below the -log10(p) = 4.0 significance 527 threshold for FarmCPU. The SNP association aberrations identified in the previous analysis were also 528 identified in this analysis and not considered for further discussion based on low MAF and exaggerated 529 PVE for the SNP on Chromosome 6H. 530 Winter Survival: A SNP association was identified on Chromosome 2H, at 4,217,244 bp (MAF = 0.432; 531 PVE = 0.25%). Two additional SNPs were identified in FarmCPU that did not contribute to PVE (0%) 532 including SNPs on chromosome 1H at 9,315,052 bp (MAF = 0.161) and chromosome 5H at 84,325,845 bp 533 (MAF = 0.318), which are not considered further. 534 Heading date: The most significant SNP association with heading date co-localized at Ppd-H1 (Turner et 535 al., 2005), on chromosome 2H at 26, 107,957 (MAF = 0.389; PVE = 18.4%). Two SNP associations were 536 identified on chromosome 1H at 9,315052 (MAF = 0.161; PVE = 13.1%) and 408,764,804 bp (MAF = 537 0.311; PVE = 9.8%). Two SNP associations were identified on chromosome 4H (558,482,572 bp) and 538 chromosome 5H (468,299,945 bp) however both contributed 0% to phenotypic variation. 539 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 18 Plant Height: Eight SNP associations were identified for HT (Table 1). The most significant SNP , 540 chromosome 2H: 10,011,392 contributes approximately 8.7% to the phenotypic variation and is 541 localized just upstream of Ppd-H1 and the SNP association peak identified for heading date. The SNP 542 variant is g/r suggesting possible gene duplication in the region. The MLM GWAS model showed a 543 defined peak with multiple significant SNPs that are not segregating for heterozygousity (Supplemental 544 Table S6). Two SNP locations are located on Chromosome 7H at 510,346,749 (MAF = 0.083; PVE = 545 18.1%) and 568,195,700 (MAF = 0.2645; PVE = 7.5%), with the latter segregating for heterozygousity 546 indicating putative gene duplication. Four additional SNP associations were identified on chromosome 547 1H, 3H, 4H and 5H that accounted for PVE from 0.7 to 2.5%, with the chromosome 4H SNP association 548 identified as a heterozygote. The SNP association located on chromosome 3H with a PVE = 0.0% was 549 also driven by a heterozygous SNP variation and may be considered a false positive. 550 The HvGA20ox2 gene, also known as sdw1, is located on chromosome 3H at 563.9 Mb and, in previous 551 GWAS studies, was suggested to contribute towards reaction of barley to scald (Looseley et al., 2018). In 552 our study, there were no SNP associations for plant height or resistance to scald located within this 553 region. The multiparent population and parental lines were genotyped using the sdw1.d and sdw1.c 554 markers (Xu et al., 2017), all of which carried the wild type allele at sdw1. 555 The correlation between resistance to scald and agronomic traits resulted in the co-localization of GWAS 556 SNP associations. The inclusion of agronomic traits in the DLA BLUPs as covariates in the initial analysis 557 (Table 1; Figure 3) appeared to lower the p-values for identified SNP associations and resulted in a higher 558 significance threshold in comparison to DLA BLUPs without covariates. While there was no overlap 559 between the identified SNP associations for resistance to scald (without using the agronomic traits in the 560 calculation of DLA). There were, however, distinct SNP associations on the short arm of chromosome 2H 561 for resistance to scald (chromosome 2H: 18.1 Mbp; Figure 6), winter survival (chromosome 2H: 4,2 562 Mbp), heading date (chromosome 2H; 26.1 Mbp) plant height (chromosome 2H: 10.1 Mbp), however 563 there appeared to be little genomic overlap between these peaks, as seen in the Manhattan plots for the 564 GWAS MLM model (Supplemental Fig S4). The linkage disequilibrium between the significant peak for 565 resistance to scald (DLA) and the remaining SNPs along chromosome 2H indicated that this region may 566 harbor LD, and that the scald DLA association should likely be considered a QTL related to architectural 567 avoidance (Figure 6). Alternatively, the SNP association is located within a gene encoding agmatine 568 coumaroyltransferase-2 (ACT-2), known to produce antifungal compounds (Burhenne et al., 2003). The 569 resistance gene, Rrs17, is also located at 10.4 Mbp (Wagner et al., 2008) 570 571 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 19 572 Figure 5: Manhattan plots for a) resistance to scald (DLA), b) winter survival (WS), c) heading date (HD), and d) plant height (HT) using the 374 573 lines and 15,463 SNPs in the FarmCPU GWAS model, and corresponding QQ plots (right). DLA BLUPs for resistance to scald were calculated 574 without using winter survival, heading date and plant height as covariates in the BLUP model. All traits BLUPs were calculated in four 575 environments across 2022 and 2023. The red horizontal line indicates the QQ plot – determined threshold of -log10(p) of 4.0. 576 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 20 577 578 579 580 Figure 6. Association and linkage disequilibrium (LD) analysis across chromosome 2H for resistance to scald (DLA; black dots). The Manhattan 581 plot (left y axis) with black dots depicts the SNP associations for resistance to diseased leaf area (DLA) using FarmCPU in GAPIT version 3 with the 582 most significant SNP association identified as a red dot across the first 1 Mb of chromosome 2H. Grey vertical shaded regions in the main graph 583 represent confidence intervals based on significant MLM associations (Supplemental Figure S) of co-localized SNP-trait association clusters for 584 winter survival (left, grey bar), heading date (right, grey bar) and plant height (left, dark grey bar). The LD estimate scatter plot (right y-axis) with 585 grey dots indicates the LD of neighboring SNPs in relation to the SNP associated with resistance to scald (in red). The top right graph insert 586 indicates the LD across chromosome 2H with respect to the most significant associated SNP for DLA. The vertical grey shaded box indicates the 1 587 Mb region shown in the main graph. 588 589 590 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 21 Table 2. SNP-trait associations for diseased leaf area for scald, winter survival, heading date and plant height BLUPs, for the 374 lines and 15,463 591 SNPs in the FarmCPU GWAS model using a threshold of -log10(p) > 4.0. using the FarmCPU model 592 SNP Chromosome Position (bp) a p-value MAFb Allele Effectc Phenotype Variance Explained(%) Scald DLA JHI-Hv50k-2016-183351 3 442,550,473 1.28E-15 0.473 c/t -0.0335 20.7 JHI-Hv50k-2016-202358 3 553,288,836 8.77E-06 0.300 t/c 0.0162 6.2 JHI-Hv50k-2016-232214 4 22,416,684 1.55E-05 0.240 a/t 0.0127 1.2 JHI-Hv50k-2016-70026 2 18,144,891 2.45E-05 0.425 g/a -0.0119 6.7 JHI-Hv50k-2016-514674 7 443,811,200 3.76E-05 0.326 g/n 0.1888 0.2 Winter survival JHI-Hv50k-2016-61826 2 4,217,244 8.22E-29 0.432 a/g -36892.4 0.25 Heading date JHI-Hv50k-2016-73663 2 26,107,957 3.59E-31 0.389 g/t 0.5854 18.4 BOPA1_4178-1592 1 408,764,804 4.96E-13 0.311 a/g 0.2405 9.8 JHI-Hv50k-2016-9518 1 931,5052 2.00E-07 0.161 a/g -0.1999 13.1 Plant height JHI-Hv50k-2016-64130 2 10,119,392 3.40E-15 0.288 g/r -0.0676 8.7 JHI-Hv50k-2016-175127 3 322,820,460 1.71E-07 0.499 c/n/y -0.3922 2.2 JHI-Hv50k-2016-335573 5 527,330,768 2.68E-06 0.201 a/g 0.0264 2.5 JHI-Hv50k-2016-7860 1 7,633,880 5.75E-06 0.499 a/g -0.0211 1.5 JHI-Hv50k-2016-250342 4 494,857,832 6.47E-06 0.198 g/r 0.0379 0.7 JHI-Hv50k-2016-493281 7 568,195,700 7.29E-06 0.264 a/g -0.0261 7.5 JHI-Hv50k-2016-490693 7 510,346,749 7.53E-06 0.083 t/w 0.0507 18.1 a SNP position based on Morex version 3 (Mascher et al., 2021) 593 b MAF, minor allele frequency 594 c Effect is based on Box Cox transformed data and as a result is a) not directly transferrable to unit measurements of traits and b) the negative Box 595 Cox transformation of DLA and HD results in the reverse effect direction of effects. 596 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 22 4 DISCUSSION 597 4.1. Breeding for resistance to scald in winter malting barley is important. 598 Malting barley is a commercially important crop due to its value in the brewing and distilling industries. 599 The malting process demands uniformly high quality grain. Winter malting barley, grown in New York for 600 the craft brewing industry, faces unique challenges due to the wet humid weather in this region. Foliar 601 fungal diseases affect both spring and winter malting barley, resulting in yield loss and kernel quality 602 issues. The environmental conditions encountered by winter malting barley grown in New York are more 603 conducive to scald infections than spring barley, even when grown in similar regions (Kolkman et al., 604 2025a, b). 605 In this study, ambient inoculum of R. graminicola was relied upon for field infections. Winter malting 606 barley is planted in the fall and develops in the early spring season in New York when weather is 607 favorable for development, reaching levels of up to 77% severity in susceptible germplasm (Kolkman et 608 al., 2025a). The distribution of the susceptible parent check, ‘KWS Scala’, across each of the 609 environments in this study validated the consistent natural infection across the experiment, while early 610 season infections in ‘KWS Scala’ provided spore production across the experiment that resulted in 611 consistent disease severity in highly susceptible genotypes. 612 4.2 Resistance in the multiparent population is comprised of both major resistance genes and 613 quantitative disease resistance. 614 The Rrs1 gene complex is an important source of resistance to scald. We identified a large linkage block 615 on chromosome 3H that spanned the centromere and initially appeared as two separate peaks in the 616 MLM GWAS model. Linkage disequilibrium analysis validated that the large region was in LD with the 617 most significant peak that co-localized near the Rrs1 gene complex. Additional evidence of the large 618 linkage blocks was seen in the deviation of the observed: expected p-values as seen by the heavily 619 skewed slope in the QQ plots for the GLM and MLM models. The evidence of a large linkage block at 620 Rrs1 has been seen in previous GWAS studies, where a large linkage block was identified on 621 chromosome 3H for seedling resistance in a diversity panel of spring barley (Hiddar et al., 2023) which 622 was accompanied by QQ plots that showed a large deviation from the expected, and where there were 623 two peaks in LD that spanned the centromeric region (Kunze et al., 2024). 624 Fine-mapping Rrs1 to a 0.8 cM has identified ten candidate genes, however the Rrs1 allele is in a 625 centromeric region with high linkage disequilibrium and is not present in the reference Morex genome 626 (Looseley et al., 2020). Further association analysis derived three markers near a protein kinase gene, 627 indicating the Rrs1 may be an allelic a member of the protein kinase gene family (Looseley et al., 2020). 628 Examination of the mode of action of the Rrs1 gene upon infection reveals that the resistance is 629 incomplete, but that hyphal development is restricted, random in the direction of growth, and exhibits a 630 delay in the collapse of the epidermal layer (Thirugnanasambandam et al., 2011). Additionally, marker 631 trait associations on chromosome 3H have been found to contribute ~27 to 30% of the phenotypic 632 variation for scald (Kunze et al., 2024; Noe et al., 2025), similar to the 27% phenotypic variation 633 explained at Rrs1 in this study. Importantly, additional SNP marker trait associations were found across 634 the genome, including one that co-localized with Rrs4 on chromosome 3H, and regions on chromosomes 635 2H, 4H, and 7H that can add to durable long term resistance. 636 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 23 4.3 Agronomic traits can reduce disease infection 637 Rhynchosporium graminicola is a slow growing pathogen, requiring a three-week incubation period 638 between infection and lesion development. It capitalizes on the long early cold days of spring to 639 increase in spore load and infection. Agronomic variables can influence the rate of upward dispersal of 640 spores in the plant canopy. Winter survival is an important trait in crop improvement in northern 641 environments. The positive correlation between reduced plant density from poor winter survival (and 642 resulting reduced plot canopy) and reduced disease is an indicator of a microclimate and putative 643 reduced R. graminicola growth and/or spore load that leads to less disease. While not useful for 644 breeding purposes regarding management of scald through reduced canopy density, it is important to 645 include in GWAS models as a covariate for the phenotypic trait to improve SNP association identification. 646 Additional agronomic traits can help reduce infection. The negative correlation between heading date 647 and scald is indicative of an escape or avoidance mechanism, limiting the ability of R. graminicola conidia 648 to reach the upper leaves and spikes. The negative correlation between plant height and diseased leaf 649 area for scald also suggests an avoidance mechanism in plant defense, where elongative growth of plants 650 may outpace pathogen reproductive cycles thus delaying the rain splash dispersal of conidia to upper 651 leaves and glumes. The sdw1 gene has previously been identified in GWAS for resistance to scald 652 (Loosely et al., 2018). In this study we identified a region on the short arm of chromosome 2H that 653 contributed to plant height and heading date (via Ppd-H1). The SNP association in this region may 654 contribute to either plant architectural traits, and/or active resistance, however it provides a target for 655 selection. With a rapidly mutating pathogen, breeding for earlier heading date and taller plants would 656 help provide additional mechanisms for plant defense. The implications for disease management as 657 shorter plants are often selected or desired for reduced lodging should include selection for resistance to 658 scald. 659 4.4 When your population is not that diverse: Selection of GWAS model for adapted 660 multiparent populations 661 GWAS is a powerful tool for discovery of trait genetic architecture within a population. The 662 identification of SNP marker trait associations can also aid in confirmation of previously identified 663 genetic elements, if the allele is segregating in the population at the appropriate frequency and 664 amplitude to be detected. Understanding the allele effect, or percent variation explained by the 665 SNP/marker trait association can have implications that are important to move forward with more basic 666 genetic studies, or in selection for breeding programs. Most GWAS studies utilize diverse genetic 667 populations for the pursuit of trait association analysis, relying on the thousands of meiotic 668 recombination events between the diverse lines to determine the precise genetic location of marker trait 669 associations. The availability of high-density genotyping platforms for breeding programs offers the 670 ability to use GWAS for crop improvement (Spindel et al., 2013). Breeding programs often cycle many 671 semi-related genetic materials through preliminary breeding trials to select a set number of lines to 672 move forward for selection. While early generation selection of specifically targeted and agronomically 673 important high heritability traits may limit the germplasm pool, the preliminary breeding trials are often 674 replicated across locations. Harnessing the power of GWAS in large breeding populations can be very 675 useful for understanding the genetic architecture of segregating traits in breeding programs 676 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 24 The multiparent population was derived from a series of biparental crosses and presented a challenge in 677 how to separate population structure from effect. In this study, the SNP associations near Rrs1 on 678 chromosome 3H varied from 174 Mb accounted for 19.4% in MLMMPVE), whereas the SNP association 679 at 442Mb accounted for 0.1% (MLM), 27.1% (FarmCPU) and 7.6% (BLINK). The FarmCPU model 680 appeared to be the least likely to overfit or overamplify type 1 associations in the structured population. 681 The MLM and MLMM GWAS models include both kinship and population structure whereas FarmCPU 682 includes kinship of associated markers and BLINK includes neither in their respective models. Population 683 structure and kinship are useful tools to reduce the confounding effects and to target unbiased marker 684 trait associations in large diverse populations. FarmCPU detected the largest amount of variation for 685 resistance to scald at the Rrs1 locus on chromosome 3H, using more limited kinship but not population 686 structure. 687 Breeding for durable resistance to R. graminicola should encompass pyramiding a variety of mechanisms 688 to reduce the loss of yield and quality to scald. Major genes, such as Rrs1, provide a high level of 689 resistance and can be combined with QTLs and additional major resistance genes, especially those with 690 differing modes of action, to increase the resistance profile and decrease scald. In addition, selection for 691 agronomic traits, such as early flowering time and increased plant height can aid in limiting the vertical 692 spread of disease in the canopy and spike, in order to limit loss of quality and yield to scald. 693

694 The authors thank the field staff at the Cornell University Love Lab and Research Farm, and the small 695 grains field manager and crew, including David Benscher, Jason Schiller, Priscilla Thompson and James 696 Tanaka. We also thank Drs. Tyr Wieser-Hanks and Zhiwu Zhang for technical and theoretical help with 697 GAPIT version 3/FarmCPU, respectively. This work was supported by the USDA Barley Pest Initiative, 698 Hatch Project 149-950 and the New York State Department of Agriculture and Markets. 699 CONFLICT OF INTEREST 700 The authors declare no conflict of interest 701 AUTHOR CONTRIBUTIONS 702 Judith M. Kolkman: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; 703 Visualization; Writing – original draft; Writing – review & editing. 704 Siim. S. Sepp: Investigation; Writing – review & editing. 705 Karl Kunze: Conceptualization; Investigation; Writing – review & editing. 706 Gary C. Bergstrom: Conceptualization; Funding acquisition; Project administration; Supervision; Writing – 707 review & editing. 708 Mark E. Sorrells: Conceptualization; Funding acquisition; Project administration; Supervision; Writing – 709 review & editing. 710 ORCID 711 Judith M. Kolkman: https://orcid.org/0000-0001-7388-7245 712 Siim S. Sepp: https://orcid.org/0000-0003-1181-7924 713 Karl H. Kunze: https://orcid.org/0000-0003-4548-1808 714 Gary Bergstrom: https://orcid.org/0000-0001-9613-270X 715 Mark E. Sorrells: https://orcid.org/0000-0002-7367-2663 716 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 25 717 718 SUPPLEMENTAL MATERIAL 719 720 DATA AVAILABILITY 721 Phenotypic data and GWAS results are available in supplemental tables; genotypic SNP dataset is 722 available at (https://ics.hutton.ac.uk/50k) 723 724

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Nature Genetics, 42, 355–360. https://doi.org/10.1038/ng.546 935 936 937 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 31 938 Supplemental figures 939 940 941 Figure S1. Frequency distribution for a) scald diseased leaf area, b) winter survival, c) heading date 942 (Julian), and d) plant height shows transgressive segregation in the population of 377 lines derived from 943 the parental lines. Values for each parental line are indicated by the blue arrows for each of the disease 944 and agronomic traits. ‘Endeavor’, a check cultivar grown across blocks and experiments is also shown for 945 comparison. 946 947 948 949 950 951 952 953 954 955 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 32 956 957 Figure S2. Principal component analysis for the 374 lines of the multiparent population displaying a) PCA 958 eigenvalues and b) genotype clustering for PCA = 2, indicating clustering into 4 subgroups. 959 960 961 962 963 964 965 966 967 968 969 970 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 33 971 Figure S3. Quantile-quantile plot for FarmCPU GWAS model for resistance to scald in winter 972 malting barley using PCA=2 as a covariate. 973 974 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 34 975 Figure S4: Manhattan plots for a) resistance to scald (DLA), b) winter survival (WS), c) heading date (HD), and d) plant height (HT) using the 374 976 lines and 15,463 SNPs in the MLM GWAS mode, with corresponding QQ plots (right). DLA BLUPs for resistance to scald were calculated without 977 using WS, HD date and HT as covariates in the BLUP model. All traits BLUPs were calculated in four environments across 2022 and 2023. The red 978 horizontal line indicates the QQ plot – determined threshold of -log10(p) of 4.0. 979 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint 35 Supplemental Tables 980 Table S1. Diseased leaf area for scald, winter survival, heading date and plant height in the multiparent 981 population across four environments. 982 Table S2. Least square means of diseased leaf area for scald, winter survival, heading date and plant 983 height for the multiparent population across four environments. 984 Table S3. Transformed diseased leaf area BLUPs with and without agronomic covariates, as well as the 985 transformed winter survival, heading date and plant height across four environments. 986 Table S4. SNP association statistics for diseased leaf area for scald (with agronomic trait covariates) in the 987 multiparent population for the MLM, MLMM, FarmCPU and BLINK GWAS models. 988 Table S5. HVS3 SCAR marker allele sizes linked to Rrs1 in the multiparent population. 989 Table S6. SNP association statistics for diseased leaf area for scald (without agronomic trait covariates), 990 winter survival, heading date and plant height in the multiparent population for MLM and FarmCPU 991 GWAS models. 992 .CC-BY-NC-ND 4.0 International licenseavailable under a (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 The copyright holder for this preprintthis version posted March 13, 2026. ; https://doi.org/10.64898/2026.03.12.711358doi: bioRxiv preprint