Genome-Wide Association study in a US soft winter wheat population reveals novel and known sources of resistance to the Septoria tritici blotch pathogen Zymoseptoria tritici

Genome-Wide Association study in a US soft winter wheat population reveals novel and known sources of resistance to the Septoria tritici blotch pathogen Zymoseptoria tritici | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Genome-Wide Association study in a US soft winter wheat population reveals novel and known sources of resistance to the Septoria tritici blotch pathogen Zymoseptoria tritici View ORCID Profile Lamia Aouini , View ORCID Profile Rupesh Gaire , View ORCID Profile Steven Scofield , View ORCID Profile Gina Brown-Guedira , View ORCID Profile Mohsen Mohammadi , View ORCID Profile Stephen B. Goodwin doi: https://doi.org/10.1101/2025.09.30.679614 Lamia Aouini 1 Department of Agronomy, Purdue University , West Lafayette, IN 47907, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lamia Aouini Rupesh Gaire 1 Department of Agronomy, Purdue University , West Lafayette, IN 47907, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rupesh Gaire Steven Scofield 2 USDA-Agricultural Research Service, Crop Production and Pest Control Research Unit , West Lafayette, IN 47907-2054, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Steven Scofield Gina Brown-Guedira 3 USDA-Agricultural Research Service, Southeast Area, Plant Science Research , Raleigh, North Carolina, 27695 USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gina Brown-Guedira Mohsen Mohammadi 1 Department of Agronomy, Purdue University , West Lafayette, IN 47907, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mohsen Mohammadi Stephen B. Goodwin 2 USDA-Agricultural Research Service, Crop Production and Pest Control Research Unit , West Lafayette, IN 47907-2054, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stephen B. Goodwin For correspondence: steve.goodwin{at}usda.gov Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Key message GWAS of 337 soft winter wheat genotypes from a breeding program in Indiana, USA identified marker-trait associations that likely correspond with existing and novel resistances to Septoria tritici blotch disease. Septoria tritici blotch (STB), caused by the ascomycete fungus Zymoseptoria tritici , is a major disease of wheat worldwide. To find additional sources of resistance, Genome-Wide Association (GWA) was used to analyze 337 soft winter wheat genotypes from Indiana, USA, by inoculating seedlings with two isolates of Z. tritici in a complete randomized design. Necrosis and pycnidia development were assessed at 14, 18 and 22 days post inoculation, enabling the calculation of area under the disease progress curve (AUDPC) for each parameter. Adjusted necrosis and pycnidia AUDPC scores were compared with 14,341 high-quality SNPs in a GWA analysis using the FarmCPU and GAPIT CMLM models to identify markers associated with the resistance. Significant (p < 0.05) isolate ξ genotype interactions were identified, confirming that the phenotypic variation was caused by isolate-specific resistance genes. Overall, 9 marker-trait associations (MTAs) were identified with Z. tritici necrosis and pycnidia resistance. All mapped MTAs were isolate and necrosis/pycnidia specific. Distinct MTAs were mapped for necrosis and pycnidia on chromosome 6A, and for pycnidia on chromosomes 1A, 1D, 4A, 7A, 3B and 5B. The MTAs on chromosomes 4A, 6A, 5B and 1D likely corresponded with the known genes Stb7 , Stb15 , Stb1 and Stb19 , respectively. Those on chromosomes 1A, 7A and 3B were not associated with previously known genes and may be novel. Candidate genes near the marker locations have been identified for further investigations. Indiana soft winter wheat germplasm segregates for novel and known Stb genes and constitutes a valuable breeding resource for Z. tritici resistance. Introduction Wheat is a universal crop that has facilitated the dramatic shift from hunter-gatherer to agrarian societies during the evolution of human civilization ( Eckardt 2010 ). Since its domestication 10,000 years ago, wheat has contributed greatly to human nutrition and is currently a leading source of calories and plant-derived protein, contributing up to 35% of global dietary intake ( Ali et al. 2020 ; Shewry 2009 ). Tremendous breeding efforts have been made to meet the ever-increasing human population and concomitant rise in demand for food ( Khush 2001 ). These efforts have led to an unprecedented increase in wheat production and a steady balance of supply versus demand ( Figueroa et al. 2018 ). Nonetheless, feeding the growing global population, predicted to exceed nine billion people by 2050, will require an estimated annual yield increase of one billion tonnes. Moreover, fast-tracking climate shifts that promote the emergence of increasingly unpredictable biotic and abiotic pressures are expanding threats to wheat production that must be considered when breeding for superior wheat cultivars ( Fones et al. 2020 ). Fungal pathogens are among the most perilous biotic threats to wheat production ( Figueroa et al. 2018 ; Seybold et al. 2020 ). Zymoseptoria tritici (formerly known as Mycosphaerella graminicola and Septoria tritici ), the causal agent of Septoria tritici blotch (STB), is a devastating pathogen ( Fones et al. 2020 ; Seybold et al. 2020 ) that accounts for average yield losses of 5 to 10% per year in the main wheat-producing countries, including the U.S. ( Cowger et al. 2020 ; Fones and Gurr 2015 ). Despite its leading position in soft winter wheat with an annual production of 15.5 million tons in 2019 ( NASS 2020 ), U.S. wheat crops are constrained by frequent attacks by Z. tritici . STB has been among the most destructive wheat diseases in the Midwestern state of Indiana since 1955 ( Shaner and Buechley 1995 ). Thus far, STB has been mostly controlled with fungicides, rendering it the major target for the entire agrochemical industry ( Cools and Fraaije 2008 ; Lendenmann et al. 2015 ; Lucas et al. 2015 ; Torriani et al. 2015 ). However, increasing issues with the numbers of fungicide applications, and resistance phenomena in the fungus, underscore the necessity for more sustainable approaches to disease management ( Cheval et al. 2017 ; Heick et al. 2017 ; Lendenmann et al. 2015 ; Mundt et al. 2002 ; Omrane et al. 2015 ; Price et al. 2015 ; Torriani et al. 2015 ). Host resistance is a sustainable and potentially effective alternative to limit the devastating effects of this perilous pathogen. In Indiana, efforts began during the 1960s to breed for resistant cultivars of soft winter wheat ( Shaner and Finney 1982 ). These efforts led to the identification of Bulgaria 88 (PI94407), which carries the major resistance gene Stb1 ( Adhikari et al. 2004a ; Shaner and Finney 1982 ). This gene was transferred from Bulgaria 88 into soft red winter wheat cultivars Oasis and Sullivan, which had superior agronomic qualities and were first released during 1975 and 1979, respectively ( Adhikari et al. 2004a ). The major gene Stb1 has proven its efficacy in diverse STB-prone environments in the U.S and across the world over many years and therefore appears to be durable ( Adhikari et al. 2004a ; Brown et al. 2015 a). This success, coupled with attention brought by damaging STB epidemics such as one that occurred in North Africa between 1968 and 1969 ( Saadaoui 1987 ), has spurred international awareness and breeding efforts. This work plus an increased focus on genetic analysis has led to the discovery of at least 22 major resistance genes and more than 170 quantitative trait loci (QTL) for enhanced Z. tritici resistance in wheat, mainly identified through analyses of bi-parental mapping populations ( Brown et al. 2015 a; Langlands-Perry et al. 2022 ; Yang et al. 2018 ; 2021). Resistance to Z. tritici in wheat is a complex trait that has been investigated intensively ( Brown et al. 2015 a; Ghaffary 2011 ; Ghaffary et al. 2018 ; Goodwin 2007 ; Gurung et al. 2014 ). Despite the success of linkage mapping studies in analyzing multiple qualitative and quantitative trait loci, the genetic complexity of STB resistance has hampered an accurate understanding of the phenotypic variation, and thus an effective translation to STB breeding ( Mirdita et al. 2015 ). There remains a need for improved mapping resolution power to detect variable loci, especially in cases where the underlying genetic architecture is highly polygenic and complex ( Corwin and Kliebenstein 2017 ). This need for identification of additional loci for STB resistance can be satisfied by genome-wide association studies (GWAS), which have been used intensively to decode complex phenotypic traits and to identify genomic regions associated with variation in broad-based and diverse populations of many species ( Liu and Yan 2019 ). Originally, association studies were adopted from human medical genetics, and were first deployed in plants during 2001 to dissect sequence polymorphisms in the Dwarf8 gene of maize ( Thornsberry et al. 2001 ). Ever since, GWA studies have proliferated and have been adopted intensively to dissect the genetic architecture of multiple quantitative traits in many commercial crops ( Alqudah et al. 2020 ; Jabbari et al. 2018 ; Pantaliao et al. 2016 ; Razifard et al. 2020 ), including wheat ( Battenfield et al. 2016 ; Lopes et al. 2015 ; Neumann et al. 2011 ; Singh et al. 2018 ; Sukumaran et al. 2015 ; Tsai et al. 2020 ), where GWA studies provided a valuable tool to find molecular markers related to a number of fungal diseases including rust ( Juliana et al. 2018 ; Ledesma-Ramírez et al. 2019 ; Maccaferri et al. 2010 ; Prins et al. 2016 ; Yu et al. 2012 ), tan spot ( Galagedara et al. 2020 ; Phuke et al. 2020 ), Fusarium head blight ( Miedaner et al. 2011 ) and STB ( Ballini et al. 2020 ; Kristensen et al. 2018 ; Mikaberidze et al 2025 ; Muqaddasi et al. 2019b). A few GWA studies have been targeted at identifying the associations between genomic regions of wheat and resistance to Z. tritici in elite cultivars and landraces ( Ando et al. 2018 ; Kidane et al. 2017 ; Kollers et al. 2013 ; Mekonnen et al. 2021 ; Miedaner et al. 2013 ; Odilbekov et al. 2019 ; Yang et al. 2022 ). For instance, four significant single-nucleotide polymorphisms (SNPs) were identified in a study by Miedaner et al. (2013) and were associated with STB resistance located on chromosomes 1B, 2B, 5B, and 6A. That analysis was conducted on 1,055 elite hybrids and their corresponding 87 parental lines. Other studies have used hexaploid wheat landraces ( Odilbekov et al. 2019 ) or breeding lines (Mekonen et al. 2021; Vagndorf et al. 2017 ; Yang et al. 2022 ) in a GWA approach to detect genomic regions associated with STB resistance. The previous GWA analyses of bread wheat cultivars were performed on populations from Australia, Europe, the northwestern United States or diverse international sources; no comparable analyses have been performed on soft, red, winter wheat cultivars from the Midwestern U.S., which may be more likely to identify novel resistance alleles that would be effective against populations of Z. tritici in that region of North America. The goal of this research was to assay a population of 337 soft winter wheat lines in a GWA analysis to test the hypothesis that wheat lines and cultivars selected in Indiana, USA would contain novel sources of seedling resistance against STB caused by Z. tritici . Specific objectives to achieve this goal were to (1) identify markers associated with Z. tritici resistance; (2) compare two GWA models for a better and powerful mapping resolution; (3) test whether analyses at a single time point would give the same results as calculations based on Area Under the Disease Progress Curve using multiple scoring times; (4) compare the obtained results with the chromosomal locations of known Stb resistance genes and QTL; and (5) identify candidate STB genes in the soft winter wheat population. Materials and methods Plant materials and Zymoseptoria tritici isolates Screening for STB resistance was performed with three sets of hexaploid wheat lines. The first and second sets were comprised of 11 and 21 wheat lines, respectively, with known and predicted STB resistance ( Table 1 ). The remaining set, the primary object of this analysis, was a population of 337 soft winter wheat lines from the Purdue University small grains breeding program derived from 123 crosses conducted between 1988 and 2010 ( Gaire et al. 2020 ), designated hereafter as the “soft red winter wheat panel” population. View this table: View inline View popup Table 1 Hexaploid wheat lines screened for levels of Septoria tritici blotch with diverse isolates of Zymoseptoria tritici In the first experiment, seedlings of 11 wheat lines with known and empirical genes for resistance to STB ( Table 1 ) were inoculated with 12 isolates of Z. tritici sampled from diverse origins in the U.S. plus the sequenced reference for bread wheat isolates, IPO323, sampled from the Netherlands ( Table 2 ). This assay was conducted to select discriminating isolates for a subsequent seedling test of the soft red winter wheat panel population. Based on the results of these initial inoculations, two isolates of Z. tritici collected originally in 1995, T46 (synonym: IN95-Lafayette-1196-WW 1-2) and T2 (synonym: MN95-Marshall-Stephen-SW 3-1), sampled near West Lafayette in Tippecanoe County, Indiana and near Stephen in Marshall Co., Minnesota, respectively, were consequently selected and used to screen seedlings of the soft red winter wheat panel population for STB resistance during experiment two. View this table: View inline View popup Download powerpoint Table 2 Collection information about the 13 isolates of Zymoseptoria tritici used for resistance testing a The third experiment involved inoculating seedlings of 21 differential hexaploid wheat lines containing 17 previously mapped Stb genes plus susceptible controls with the T2 and T46 isolates of Z. tritici (Table1). Results of this last seedling experiment were used to postulate resistance genes in the soft red winter wheat panel population. Experimental design, inoculation procedure and plant maintenance All seedlings were tested for STB resistance in a complete randomized design in triplicate under the controlled conditions of a growth chamber. The susceptible check cv. Taichung 29 was included in all seedling tests. Screening tests with the 11 and 21 wheat differential lines were grown in rows in 2.5 x 2.5 x 3.5 cm (length, width, height) Coex square plastic pots (Greenhouse Megastore, USA), whereas the 337 soft red winter wheat panel lines were planted in flats containing 50 pots each (Greenhouse Megastore, USA). Flats were considered as plots and individual lines were the experimental units. For each isolate/replicate combination, wheat lines were randomly arranged in the flats. Each flat included 48 soft red winter wheat panel lines and the susceptible cultivar Taichung 29 that was in duplicate. The randomization layout was generated using the Agricolae package in the R environment ( de Mendiburu 2014 ; Team 2013 ). For all assays, five seeds were planted per pot and subsequently thinned to three well-developed, homogeneous seedlings. Seeds were sown in Promix potting soil, a mixture formulated for growing plants indoors (Greenhouse Megastore, USA). Plant development was allowed for 10 days in a growth chamber adjusted to a temperature of 22°C and a relative humidity (RH) of 70%, under a fluorescent light temperature of 6400K and a photoperiod of 16/8 h (light/dark) prior to inoculation. Inoculum was initiated by pre-cultures of each isolate grown in an autoclaved 100-ml Erlenmeyer flask containing 50 ml of yeast glucose (YG) liquid medium (30 g of glucose, 10 g of yeast per liter of demineralized water). Flasks were inoculated with frozen isolate samples that were taken directly from the Z. tritici isolate culture collection maintained by the USDA-Agricultural Research Service on the Purdue University campus, stored at -80°C. Flasks were placed in a Lab-Line orbital shaker set at 120 rpm at room temperature (∼22°C) for 5–7 days. These pre-cultures were then used to inoculate four 1-L Erlenmeyer flasks containing 500 ml of YG medium per isolate that were incubated under the aforementioned conditions to provide sufficient inoculum for the seedling inoculation assays at growth stage (GS) 11 ( Zadoks et al. 1974 ). Spores were collected after overnight settling in static cultures, concentrated by decanting the supernatant medium, adjusted to 1 x 10 7 spores/ml in a total volume of 40 ml for a set of 18 pots with a haemocytometer and supplemented with two drops of Tween 20 surfactant. Inoculations were conducted by spraying the spores over the 10-day-old seedlings with a hand-held sprayer. Inoculated plants were incubated in transparent plastic bags for 48 h at 100% RH in the growth chamber. Ten days after inoculation, seedlings were trimmed to remove the second and subsequent leaves to enable sufficient light to reach the inoculated primary leaves for appropriate disease development. An instant soluble fertilizer (Miracle-Gro®, USA) (0.5 g/l) was applied to saturation once at 10 days post inoculation to maintain plant condition. Data collection and statistical analysis Disease severities were evaluated at 15, 18 and 22 days post inoculation (dpi) for three leaves per genotype over three replications for 9 leaves in total. These multiple observations enabled Area Under the Disease Progress Curve (AUDPC) calculations for quantitative analyses of temporal differences in disease progress. We estimated the quantitative presence of necrosis (N) and pycnidia (P) on the inoculated seedling leaves visually in percentages. AUDPC calculations for seedling scores followed the trapezoidal method, which approximates the time variable and calculates the average disease intensity between each pair of adjacent time points ( Madden et al. 2007 ). Analysis of variance was performed in the R environment ( Team R 2013 ) on all generated phenotypic data to determine the effects of line, isolate and any line x isolate interactions. Significance of differences between isolates was determined using the least significant difference (LSD) function in the Agricolae package in the R environment ( de Mendiburu 2014 ; Team R 2013 ). Isolate x line groupings of N and P AUDPC scores were defined based on the Bonferroni test at p <0.05. Box-plot figures were generated in the R environment using the boxplot function ( Team R 2013 ). For the N and P AUDPC scores generated on the 337 soft red winter wheat panel lines, a first check of the data was performed to assess potential variation among the three replicates for each isolate using the lme4 package in the R environment (Bates et al. 2015; Team R 2013 ). Data variation was checked based on the common susceptible cultivar Taichung 29. Subsequently, a mixed model was generated using the “lmer” command in the lme4 package on all lines over the three replicates. Lines were the fixed effect term of the model and replicates constituted the random-effect term. The least-squares means (LS means) of the N and P AUDPC scores of lines were estimated using the “fixef” statement in lme4, and were used as an estimate of phenotype for subsequent marker-trait association analyses. Heritability was estimated for N and P traits based on the line means as follows: h 2 = σg2/(σg2+ σe2/r) where σg2 is the variance component due to genotypes, σe2 is the variance component due to unexplainable error, and r is the number of replicates, which is three per isolate. Pearson correlation coefficients were determined for the N and P AUDPC LS mean scores of the soft red winter wheat panel lines inoculated with the T2 and T46 isolates of Z. tritici using the “Cor” function and visualized using the “Corrplot” package in the R environment ( Team 2013 ; Wei et al. 2017a ). Frequency distribution and the density curve figure were generated using the “hist” function in the R environment ( Team 2013 ). Genotyping and population structure Genomic DNA was extracted from leaves sampled from single plants grown in a greenhouse. Reduced-representation libraries were generated by Pst1-Msp1 digestion following the protocol of Poland et al. (2012) . Samples were pooled in 144-plex multiples to create libraries and each library was sequenced on a single lane of an Illumina Hi-Seq 2500. SNP calling was performed using the Brown5 GBSv2 pipeline1 using 64-base kmer length and minimum kmer count of 5 ( Daba et al. 2018 ; Glaubitz et al. 2014 ), and reads were aligned to the IWGSC RefSeq assembly v1.0 ( Alaux et al. 2018 ). This resulted in 169,486 unfiltered single-nucleotide polymorphisms (SNPs) that were identified after variant calling. These SNPs were subsequently filtered for missing data (≥20 %) and for minor allele frequency (MAF) of 5%. Missing genotypic data were imputed using the Linkage Disequilibrium K-number neighbour imputation method (LDKNNi) algothirm ( Money et al. 2015 ) implemented in TASSELv 5.2 ( Bradbury et al. 2007 ). Overall, a total of 14,341 SNPs were retained after filtering for MAF and missing data and were used in this study. Population structure was evaluated using principal component analysis (PCA) of the 14,341 SNP markers, implemented in TASSEL5.0 ( Bradbury et al. 2007 ). Accordingly, the eigenvalues of all the principal components and the proportions of individual eigenvalues to the total variance (component contribution rates) were calculated. To illustrate relationships among lines, a three-dimensional plot and a kinship matrix were derived from the GAPIT package in the R environment 3.4.3 ( Lipka et al. 2012 ). This analysis was performed using a compressed mixed linear model (CMLM) that replaces the genetic effects of individuals with those of the group to which each individual belongs ( Zhang et al. 2010 ). The marker-based kinship matrix was calculated using the VanRaden method, and then used to create a clustering heat map of the soft red winter wheat panel population in the GAPIT R package. Hence, individuals were clustered into groups on the basis of their relationships derived from all the available genetic markers ( Tang et al. 2016 ). The first three PCs were displayed in a three-dimensional plot and the kinship matrix was displayed as a heat map, where red indicates the highest correlation between pairs of individuals and yellow indicates the lowest correlation. A hierarchy tree among individuals was displayed based on their kinship ( Tang et al. 2016 ). Genome-Wide Association analysis Genome-Wide Association (GWA) analysis was computed using the 14,341 SNPs and the least-square means of the N and P AUDPC scores. Two models were implemented in the R environment to identify markers associated with the Z. tritici resistance, and results were compared. Consistent marker-trait associations (MTAs) between both models were considered. At first, a Fixed and random model Circulating Probability Unification (FarmCPU) algorithm was implemented to identify Z. tritici resistance MTAs ( Liu et al. 2016 a). This approach used a Multiple Loci Linear Mixed Model (MLMM) that incorporates multiple markers simultaneously as covariates in a stepwise Mixed Linear Model (MLM) to partially remove the confounding between testing markers and kinship ( Liu et al. 2016 a). Subsequently, a Compressed Mixed Linear Model (CMLM) was implemented in the GAPIT package available in the R environment ( Tang et al. 2016 ). This model clusters the individuals into groups and fits genetic values of groups as random effects in the model, thus reducing false negatives and enhancing the identification of true associations ( Zhang et al. 2010 ). For both implemented models, the cut-off of significant association was based on a False Discovery Rate (FDR) in the R environment. An MTA was considered significant at -log (p) > 4.0. Manhattan plots and the quantile-quantile (QQ) plot figures were subsequently derived from the FarmCPU and CMLM GAPIT output. Candidate genes for Z. tritici resistance Linkage disequilibrium (LD) was calculated based on the 14,341 SNP markers using the Tassel 5 software as described by Gaire et al. (2020) . LD was quantified using the squared allele frequency correlation (R²). The determination of the LD decay threshold was based on the reported threshold in this soft wheat panel as described by Gaire et al. (2020) to guide the definition of LD blocks in the population. Based on these parameters, LD blocks surrounding significant marker–trait associations (MTAs) were identified to delineate putative genomic regions associated with the traits of interest. Candidate genes for Z. tritici resistance were subsequently investigated using the wheat reference genome IWGSC RefSec v1.0 ( Alaux et al. 2018 ). Results Phenotypic data analysis The Z ymoseptoria tritici isolates grew successfully under laboratory conditions enabling appropriate inoculum production and phenotyping assays. None of the tested cultivars was resistant to the entire suite of Z. tritici isolates, resulting in a significant isolate-by-cultivar interaction, indicating specific gene action. Interestingly, necrosis scores were high in the majority of the tested lines and were less variable compared to pycnidia scores that were highly variable (Supplementary Table 1, Fig. 1 ). This observation was confirmed by the analysis of variance, indicating a higher significant difference for pycnidia scores compared to those for necrosis ( Table 3 ). The Isolate x Line term was large, emphasising that the observed phenotypic variation was mainly due to the action of specific genes ( Table 3 ). Download figure Open in new tab Fig 1 Box plots of percentage pycnidia coverage on resistant and susceptible cultivars of wheat inoculated with 13 diverse isolates of Zymoseptoria tritici . Dots outside the main plots indicate outliers View this table: View inline View popup Download powerpoint Table 3 Analysis of variance of the necrosis and pycnidia scores on seedlings with known and predicted sources of resistance against Septoria tritici blotch tested with 13 isolates of Zymoseptoria tritici sampled from diverse origins Selection of the Z. tritici isolates for subsequent screening of the soft red winter wheat panel lines was based mainly on isolate indicators that might potentially identify previously known Stb genes as well as potentially new genes for resistance. Hence, based on the pre-screening results, uniformly virulent or avirulent isolates were not considered further as they had lower ability to discriminate resistance genes (Supplementary Table 1). Based on these criteria, the T2 and T46 isolates of Z. tritici were chosen for further seedling screening assays. The soft red winter wheat panel lines were tested with the T2 and the T46 isolates that were sampled originally from Minnesota and Indiana, respectively. The seedling screening of the soft red winter wheat panel resulted in approximately normal frequency distributions for the necrosis and pycnidia AUDPC scores for both isolates ( Fig. 2 ). A skewed distribution towards a resistant phenotype was observed for pycnidia AUDPC scores for both isolates. In contrast, necrosis scores were higher and skewed towards a susceptible phenotype ( Fig. 2 ). Download figure Open in new tab Fig 2 Frequency distribution of the least-square means of the Necrosis and Pycnidia AUDPC scores for the T2 and T46 isolates of Zymoseptoria tritici following inoculations of the tested “Purdue Panel” lines under controlled conditions Albeit a similar phenotypic pattern was observed for both isolates, the analysis of variance of the tested soft red winter wheat panel lines indicated that the isolate x genotype term was highly significant, indicating a difference between the tested isolates and an ability to discriminate resistance genes that are specific for each isolate ( Table 4 ). Interestingly and in contrast with the pre-screening results, both necrosis and pycnidia AUDPC scores were highly significant for the isolate x line term ( Table 4 ). View this table: View inline View popup Download powerpoint Table 4 Analysis of variance of the necrosis and pycnidia Area Under the Disease Progress scores registered on seedlings of the soft red winter wheat panel population inoculated with the T2 and T46 isolates of Zymoseptoria tritici Differentiation between the isolates was clear in the overall population means and ranges for the necrosis and pycnidia AUDPC scores ( Table 5 ). Overall, higher necrosis and pycnidia AUDPC population means were observed for lines inoculated with the T2 Z. tritici isolate compared to those with T46 ( Table 5 , Supplementary Fig. 1). The soft red winter wheat panel population means of 417.4 for necrosis and 111.1 for pycnidia AUDPC scores for the T2 isolate were higher compared to the values of 324.5 and 85.3 for the same statistics on lines inoculated with the T46 isolate. A high variability demonstrated by large necrosis and pycnidia AUDPC ranges also was observed between lines inoculated with the same isolate ( Table 5 ). However, both isolates gave the same low and moderate heritabilities for necrosis and pycnidia AUDPC scores of 0.2 and 0.4, respectively ( Table 5 ). View this table: View inline View popup Download powerpoint Table 5 Summary of the necrosis and pycnidia development scores plus their heritabilities when scored on the soft red winter wheat panel population inoculated with the T46 and T2 isolates of Zymoseptoria tritici at the seedling stage A different correlation pattern was observed between the isolates for the two parameters; necrosis and pycnidia AUDPC scores were highly correlated for the T2 isolate (0.69), but only had a weak correlation for the T46 isolate (0.27) ( Fig. 3 ). Weak correlations also were observed between the same disease parameters (necrosis or pycnidia) and the different isolates ( Fig. 3 ). Download figure Open in new tab Fig 3 Pearson correlation coefficients for Area Under the Disease Progress Curve (AUDPC) between the indicated phenotypic traits after inoculation with two isolates of Zymoseptoria tritici . Each trait is named by the isolate ID over Necrosis or Pycnidia depending on what was scored. A scale bar showing the strength and direction of the correlations is on the right with positive correlations in blue, negative correlations in red and no correlation in white. The dark blue diagonal squares were each trait by itself so were always equal to 1 Population structure To evaluate population structure, principal component analysis (PCA) was performed in TASSEL 5 using 14,341 SNP markers, and a kinship matrix was generated in the GAPIT package. Based on model complexity, the panel of 337 wheat lines was classified into four subpopulations ( Fig. 4 ). A marked change was observed at the fourth principal component, which corresponded to a cumulative variance contribution of 25.73% ( Fig. 4A ). Download figure Open in new tab Fig 4 Population structure results based on a principal component analysis (PCA) of the 337 soft red winter wheat panel lines and a heatmap and dendrogram of the kinship matrix estimated using 14,341 Single-Nucleotide Polymorphism (SNP) markers. A , changes in variances in each principal component. B , a three-dimensional plot of the first three principal components. C , kinship analysis with the genetic clustering heatmap created with a matrix for evaluating the genetic differences among the 337 soft red winter wheat panel lines. The red color indicates the highest correlation between pairs of individuals and yellow indicates the lowest correlation. A hierarchy tree among individuals was displayed based on their kinship The first four principal components together explained 25.73% of the total genetic variation, with PC1 accounting for 11.82%, followed by PC2 (5.19%), PC3 (5.04%), and PC4 (3.68%) ( Fig. 4A ). A three-dimensional scatterplot based on the first three principal components further separated the wheat lines into discrete genetic clusters ( Fig. 4B ). In contrast, clustering based on the kinship matrix suggested that the soft red winter wheat panel was primarily divided into two major groups. Considerable genetic divergence among lines was evident from the color gradient ranging from red to yellow, representing high to low genetic relatedness among lines, respectively ( Fig. 4C ). Marker-trait associations and Stb gene postulations in the soft red winter wheat panel population Two models were compared to assess the association of SNP markers to Z. tritici resistance: the FarmCPU, which uses a Multiple Loci Linear Mixed Model (MLMM); and the Compressed Mixed Linear Model (CMLM) computed in GAPIT. Overall, 9 SNP markers mapped to chromosomes 1A, 4A, 6A, 7A, 3B, 5B and 1D were associated with the necrosis and pycnidia resistance against Z. tritici ( Table 6 ). All markers that were significantly associated with the Z. tritici resistance by the FarmCPU model were confirmed when the phenotypic scores also were analyzed with the CMLM model in GAPIT ( Table 6 , Fig. 5 , Supplementary Figure 2). However, significance levels of the associations were higher for the FarmCPU model compared to CMLM as revealed by the -log(p) values ( Table 6 ). Download figure Open in new tab Fig 5 Manhattan plots (panel A ) and Q-Q plots (panel B ) generated by the FarmCPU Genome-Wide Association Study analysis. Marker traits associated with necrosis and pycnidia resistance to the T2 and T46 isolates of Zymoseptoria tritici in A are indicated by arrows. Boxes in A include the marker IDs of all Single-Nucleotide Polymorphisms (SNPs) associated with resistance View this table: View inline View popup Download powerpoint Table 6 Marker-trait associations with STB resistance mapped on the soft red winter wheat panel population with the T2 and T46 isolates of Zymoseptoria tritici at the seedling stage in comparison with the positions of known Stb genes and using models from FarmCPU and GAPIT (CMLM) No marker-trait associations were mapped in common between the two tested isolates of Z. tritici (T2 and T46), highlighting the isolate-specific resistance in the Z. tritici- wheat pathosystem ( Table 6 , Fig. 5 , Supplementary Figure 2). For the T2 isolate of Z. tritici , significant marker-trait associations were solely with pycnidia resistance, in contrast to the T46 isolate, which identified markers associated with both necrosis and pycnidia resistance. Distinct markers were associated with necrosis and pycnidia resistances, which confirms that the necrosis and pycnidia phenotypes are under distinct genetic control ( Table 6 , Fig. 5 , Supplementary Figure 2). Two significant SNP markers ( S1D_8183618 and S1D_8183661 ) (- log (p) ≥ 4.0) that fell into the same LD block were mapped to the short arm of chromosome 1D and were associated with the pycnidia resistance against isolate T2, explaining 7% of the observed phenotypic variance ( Table 6 , Fig. 5 , Supplementary Figure 2). This genomic location overlaps that of the known resistance gene Stb19 , which was identified in the Australian soft wheat cultivar Lorikeet ( Yang et al. 2018 ). For the T46 isolate of Z. tritici , several chromosomes each were associated with the necrosis and pycnidia resistance. Chromosome 6A was associated with both the necrosis and pycnidia resistance but with different significant SNP markers. SNP locus S6A_611714334 was associated with the necrosis resistance and explained 4% of the observed phenotypic variation ( Table 6 , Fig. 5 and Supplementary Figure 2). Two SNP markers ( S6A_48048307 and S6A_532657432 ) were highly significant for pycnidia resistance and explained 6% of the observed phenotypic variance. Chromosomes 1A, 4A, 7A, 3B and 5B were solely associated with pycnidia resistance against the T46 isolate. Genomic regions for pycnidia resistance on chromosomes 1A, 7A and 3B, associated with the SNP markers S1A_500074551 , S1A_577377232 , S7A_719723711 and S3B_726510291 , respectively, do not contain any previously mapped Stb genes so appear to be novel. The significant markers S1A_500074551 and S1A_577377232 , mapped on chromosome 1A, fell into two distinct LD blocks, thus constituting different genomic positions associated with the pycnidia resistance to Z. tritici , explaining 4 and 5% of the observed phenotypic variance, respectively. The S7A_719723711 and S3B_726510291 SNP loci on chromosomes 7A and 3B explained the lowest phenotypic variance with only 3 and 1%, respectively. Significant SNP loci mapped on chromosomes 4A, 6A and 5B are in genomic regions that overlap with the known Stb genes Stb7 , Stb15 and Stb1 , respectively. Two significant SNP markers ( S4A_605028654 and S4A_729925867 ) that were identified on chromosome 4A and that fell into distinct LD blocks. The former MTA identified at 729,925,867 Mbp co-segregated with the known gene Stb7 identified in the hexaploid wheat line Estanzuela Federal ( Brown et al. 2015b ; McCartney et al. 2003 ), explaining up to 4% of the observed phenotypic variance. The SNP loci S6A_48048307 - S6A_532657432 and S5B_519034813 , mapped to chromosomes 6A and 5B, co-localized with the known Stb genes Stb15 and Stb1 , respectively. These significant markers explained 6 and 7% of the observed phenotypic variance. The Stb15 and the Stb1 resistance genes have been reported previously in the wheat lines Arina and Bulgaria 88, identified in Switzerland and the US, respectively ( Adhikari et al. 2004a ; Arraiano et al. 2007 ; Czembor et al. 2011 ). To confirm the co-segregation of the mapped MTAs on chromosomes 4A, 6A and 5B to the known Stb genes Stb7 , Stb15 and Stb1 , a seedling assay was carried out with the T2 and the T46 isolates of Z. tritici on the entire set of wheat differential lines, where a significant isolate ξ line interaction was observed (Supplementary Table 2, Fig. 6 ). According to the least significant difference calculated for the necrosis and the pycnidia scores, isolate ξ line interactions were clustered into distinct groups, which confirmed that the T2 and T46 isolates carry distinct avirulence genes and are potential indicators of known Stb genes ( Fig. 6 ). A low pycnidia level was generated by the T46 isolate on cultivars Oasis, Estanzuela Federal and Arina, carrying Stb1 , Stb7 and Stb15 , respectively, which indicates that this isolate is most likely avirulent to those genes. This indicates that the co-localized MTAs on chromosomes 4A, 6A and 5B are likely due to the known genes Stb1 , Stb7 and Stb15 . Download figure Open in new tab Fig 6 Necrosis and pycnidia percentages that developed on the differential wheat lines inoculated with the Zymoseptoria tritici isolates T2 (top panel) and T46 (bottom). A , different groups are marked by letters on top of the histogram bars based on the least-square means. B , box plots showing differences in necrosis and pycnidia development on the differential wheat cultivars between the T2 and the T46 isolates of Z. tritici Single time point versus AUDPC comparisons The single time point analysis sometimes identified additional and different genes compared to those revealed by the AUDPC values calculated over time. Although isolate-specific MTAs were revealed when analyzing both datasets with the two statistical models, a higher number of MTAs was detected when competing necrosis and pycnidia single time-point scores compared to the necrosis and pycnidia AUDPC scores (Supplementary Table 3). Necrosis and pycnidia AUDPC data revealed 12 MTAs associated with the Z. tritici resistance, but only one was identified using pycnidia AUDPC scores from the T2 isolate while eleven MTAs were associated with necrosis and pycnidia AUDPC resistance against the T46 isolate. In contrast, 14 MTAs were associated with the pycnidia and necrosis scores at a single time point with isolate T2, and 24 MTAs were associated with the T46 isolate when necrosis and pycnidia scores were evaluated at a single time point (Supplementary Table 3). The accuracy and robustness of both phenotypic datasets to detect markers associated with the Z. tritici resistance against isolate T46 were confirmed when using both phenotypic datasets (AUDPC and single time-point scores) (Supplementary Table 3). Nonetheless, the T46 single-time-point phenotypic scores compared in a GWA analysis revealed additional genomic regions, other than those revealed by AUDPC scores, associated with the Z. tritici resistance on chromosomes 3A ( S3A_700960980 ; S3A_715967971 ), 4B ( S4B_548241937 ), 2D ( S2D_29753796 ) and 4D ( S4D_439658613 ). All the additional MTAs were associated with pycnidial development at 16 ( S4B_548241937 ) and 22 dpi ( S3A_700960980, S3A_715967971, S2D_29753796, S4D_439658613 ). The additional detected MTAs might be mainly explained by the distinct phenotypic variations when using AUDPC and single-time-point scores generated by the T46 Z. tritici isolate. In contrast to the T46 Z. tritici isolate, none of the detected MTAs by the single-time-point scores generated by the T2 isolate of Z. tritici were detected by the AUDPC scores. Hence, analyzing the phenotypic variation on different scoring days versus combined into AUDPC led to different GWA results. Candidate resistance genes To provide an initial insight into the types of genes present within the identified MTAs, a preliminary candidate gene search was conducted. Candidate resistance genes were identified within the genomic regions associated with Z. tritici resistance based on the linkage disequilibrium of each chromosome ( Gaire et al. 2020 ). Among all of the candidate genes, many fell into three predominant categories of possible resistance genes ( Fig. 7 , Table 7 , Supplementary Tables 4 and 5, Supplementary Figure 3). Predominant candidate resistance gene categories were as follows: (i) Disease resistance candidates that comprised the leucine-rich-repeat (LRR) and nucleotide-binding site–leucine-rich repeat (NBS-LRR) protein families. This category represents 28% of the putative identified resistance genes and comprised the highest number of candidate resistance genes on chromosomes 4A, 6A (20862967-75370938) and 1D ( Fig. 7 , Table 7 ). (ii) The kinase/kinesin proteins that represent 19% of the identified putative resistance genes. This category is highly represented on chromosomes 6A (252830745-617935868) and 5B ( Fig. 7 , Table 7 ). (iii) Receptor lectin kinases constituted 14% of the total putative resistance genes and were the most common candidate resistance genes on chromosomes 7A and 3B ( Fig. 7 , Table 7 ). Download figure Open in new tab Fig 7 Categories for candidate genes identified in the genomic regions associated with resistance against Zymoseptoria tritici from the Genome-Wide Association analysis. Numbers and percentages of the predominant categories per chromosomal region associated with the Z. tritici resistance are indicated in panels A , B and C , which represent the disease-resistance protein families, the kinase/kinesin proteins and the receptor lectin kinases, respectively View this table: View inline View popup Table 7 Predominant candidate resistance gene categories in genomic regions associated with resistance to Zymoseptoria tritici in the genome-wide association analysis Candidate resistance gene categories were variable from one genomic region to another, likely indicating variable pathways to express resistance to Z. tritici . The highest number of putative resistance genes was identified on chromosome 5B, with a total of 759 candidate genes. In contrast, only 82 candidate resistance genes were identified on chromosome 6A (20862967-75370938) ( Table 7 , Supplementary Tables 4 and 5). Discussion Even though the soft red winter wheat panel population was composed mostly of advanced breeding lines, the GWA analysis identified significant variation for Septoria tritici blotch resistance, including putatively four previously known plus four possibly novel loci. These genes, if confirmed, are much more likely to be effective against populations of the pathogen in the Midwestern part of the United States where most of the selection to develop the lines in this wheat panel occurred over multiple years and locations. All reported MTAs were confirmed by the FarmCPU and the CMLM (GAPIT) statistical models, with higher significance levels of the associations for the FarmCPU model compared to the CMLM model in the GAPIT program. Similar observations have been reported by Wei et al. (2017 b) and Kaler et al. (2020) , indicating that the FarmCPU method detected more loci and with higher levels of significance for their associated SNPs. Different results from analyzing single-time-point scores versus AUDPC confirms that GWA results are highly contingent on the type of the inputted phenotypic data; careful consideration should be given to phenotypic data to ensure accurate GWA results ( Ibrahim et al. 2020 ; Zhu et al. 2008 ). Calculation of AUDPC has been a common measure of quantitative disease resistance in plants but it entails repeated disease assessments ( Jeger and Viljanen-Rollinson 2001 ). It has been well demonstrated that the power of detecting QTLs and MTAs is highly increased with repeated measurements ( Arbelbide et al. 2006 ; Yu et al. 2005 ; Zhu et al. 2008 ). Therefore, AUDPC measures are potentially more accurate phenotypes for MTA discovery. Nonetheless, implementing different phenotypic variations may lead to different GWA results as confirmed by our data. Population structure is one of the key factors that may affect resolution of a GWAS so must be considered carefully to identify true associations ( Alqudah et al. 2020 ; Mohammadi et al. 2020 ; Pritchard et al. 2000 ). Population structure can be addressed using a statistical approach that aims to calculate relatedness correlations among individuals within the population due to admixture and/or historical structure ( Alqudah et al. 2020 ). An unrecognized population structure will negatively impact the GWAS resolution, resulting in false-positive associations ( Hayes 2013 ; Pritchard et al. 2000 ). To improve the resolution power of our GWA study, the Purdue population structure was investigated through a principal component analysis that resulted in the identification of four subpopulations. This observation agreed with that of Gaire et al. (2020) that there were four subpopulations in the soft red winter wheat panel population, consisting of two North American subpopulations and Australian and Chinese subpopulations. Structure of this population was investigated using the STRUCTURE software by Gaire et al. (2020) . Both STRUCTURE ( Pritchard et al. 2000 ) and principal component analysis (PCA) ( Price et al. 2006 ) are common approaches that use genetic markers to determine population structure ( Kaler et al. 2020 ). As observed in our analysis, results from STRUCTURE and PCA are usually similar ( Kaler et al. 2020 ). However, due to lower required computational resources, PCA is generally used more commonly to generate covariates ( Kaler et al. 2020 ). The number of sub-populations was used subsequently as a covariate in our GWAS statistical model to control for false positives that can arise from population structure and family relatedness ( Kaler et al. 2020 ). Two approaches were used to detect any possible false associations ( Alqudah et al. 2020 ; Wei et al. 2017 b). Application of mixed-model methods to correct for population structure using a PCA ( Alqudah et al. 2016 ) or kinship matrix ( Nagel et al. 2019 ) is used commonly ( Alqudah et al. 2020 ; Crowell et al. 2016 ; Wallace et al. 2014 ). While both the FarmCPU and CMLM models control for population structure and relatedness to reduce the detection of false-negative associations ( Kaler et al. 2020 ), these two statistical models handle the testing of markers, population structure and kinship differently ( Wei et al. 2017 b). The CMLM model implemented in the GAPIT package is based on the mixed linear model (MLM) and deals with large and computationally challenging datasets by clustering individuals into groups in the kinship matrix ( Lipka et al. 2012 ). Hence, this model fits genetic values of groups as random effects in the model, thus reducing false negatives and enhancing the identification of true associations ( Kaler et al. 2020 ). The FarmCPU model is based on a Multiple Loci Linear Mixed Model (MLMM) that incorporates multiple markers simultaneously as covariates in a stepwise Mixed Linear Model (MLM) to partially remove the confounding among markers that occurs with kinship ( Kaler et al. 2020 ; Liu et al. 2016 b). Compared to CMLM, in which the kinship matrix remains constant for all the markers, FarmCPU adjusts its kinship matrix based on the tested markers and covariates in the fixed-effects model ( Wei et al. 2017 b). Another advantage of the FarmCPU model compared to CMLM is the use of selected associated markers as covariates and the simultaneous testing of multiple markers, which significantly improves the control of both false-positive and false-negative associations ( Liu et al. 2016 b). Therefore, despite the popular use of the CMLM model for GWAS, FarmCPU provides for better control of the confounding among population structure, kinship, and the simultaneous testing of markers, leading to an increased statistical power with fewer false positives ( Liu et al. 2016 b; Wei et al. 2017 b). Nonetheless, it is always advisable and beneficial to implement different models to test for associations to increase the confidence in the overlapping loci, and to carefully choose the best approach to conduct GWAS based on the species and traits being analyzed ( Wei et al. 2017 b). Another potential stumbling block for identifying resistance genes is selection of the most appropriate isolate(s) for phenotypic testing. As cultivar-specific interactions are observed commonly between Z. tritici and wheat ( Ahmed et al. 1995 ; Brown et al. 2001 ; Cowger et al. 2000 ; Kelm et al. 2012 ; Kema et al. 1996a ; Kema et al. 1996b ; Kema et al. 2018 ; Kema and van Silfhout 1997 ; Kema et al. 2000 ), a careful selection of the isolates for testing is essential for successful identification and postulation of genes ( Adhikari et al 2004 ; Ghaffary 2011 ; Ghaffary et al. 2018 ). To address this issue, a seedling pre-screening assay was conducted on a suite of differential bread wheat cultivars harboring known Stb major resistance genes to identify the best testers among Z. tritici isolates sampled from diverse U.S. regions. This assay revealed a variable pathogenicity pattern, particularly for pycnidia development, which confirmed the specific cultivar · Z. tritici interaction in the wheat– Z. tritici pathosystem, thus indicating that specific gene actions govern STB resistance. This pre-screening enabled us to select two Z. tritici isolates that were used subsequently to evaluate seedlings of the soft red winter wheat panel population. These preliminary tests identified pycnidia development as the most discriminating trait among all tested isolates compared to necrosis development. Hence, pycnidia development appears to be the most accurate phenotypic criterion to use for isolate selection with our materials, and also has been shown to give the best discrimination among resistance genes in other Z. tritici -wheat interaction analyses ( Dalvand et al. 2016 ; Ghaffary 2011 ; Ghaffary et al. 2018 ; Kema et al. 1996a ; Kema et al. 1996b ; Kema and van Silfhout 1997 ). Even so, the subsequent seedling assays revealed specific interactions for both necrosis and pycnidia development. Therefore, the tested population appears to harbor divergent resistance loci that are specific for each isolate, with necrosis and pycnidia development likely under different genetic control. This result agrees with previous demonstrations that necrosis and pycnidia resistance are controlled by divergent genetic factors ( Stewart et al. 2016 ; Stewart and McDonald 2014 ). Comparisons with genomic locations of known major Stb resistance genes revealed that, irrespective to the phenotypic datasets used, the GWA analysis unveiled multiple novel locations associated with Z. tritici resistance and others that corresponded to previously mapped genes. While AUDPC measures generated with the T2 isolate of Z. tritici indicated that the soft red winter wheat panel lines probably contain the major gene Stb19 ( Yang et al. 2018 ), novel locations for STB resistance were detected when using single time-point scores generated by the same isolate, on chromosomes 3A ( S3A_699078407 ), 3B ( S3B_736712248 ), 4B ( S4B_660493440 ), 5A ( S5A_666525700 ), 6A ( S6A_14386770 ), 6D ( S6D_447068059 ), 7A ( S7A_38234502 ; S7A_38238713 ), and 7B ( S7B_11882709 , S7B_605313385 , S7B_711755909 ), as well as the MTAs S7A_84027035 , S7A_85912163 , and S7A_91722899 on chromosome 7A that overlapped with the location of the known major resistance gene Stb3 ( Goodwin and Thompson 2011 ; Goodwin et al 2015 ). Novel genomic locations also were revealed by the T46 AUDPC scores on chromosomes 1A (two locations: S1A_500074551; and S1A_577377232 ), 3B ( S3B_726510291 ), 5B ( S5B_519034813 ), 6A ( S6A_611714334, S6A_48048307, S6A_532657432 ), and 7A ( S7A_719723711 ). These MTAs were confirmed using the single-time-point scores that additionally detected the new genomic positions on chromosomes 2D (S2D_29753796), 4B (S4B_548241937) and 4D (S4D_439658613). Previous analyses also identified markers on chromosomes 1A, 3A, 5A, 6A, 7A, 3B, 4B, 5B, 7B, 2D, 4D and 6D that were associated with STB resistance ( Gurung et al. 2014 ; Muqaddasi et al. 2019a ; Odilbekov et al. 2019 ; Vagndorf et al. 2017 ; Yates et al. 2019 ). However, we could not ascertain whether these positions overlap with our reported locations due to the lack of the physical position information and /or the sequence data of the previously reported MTAs. Hence, the only accurate and possible comparison was towards the major known Stb resistance genes. Locating the associated SNPs on the bread wheat genome sequence IWGSC RefSec v1.0 allowed candidate resistance genes to be identified within the LD blocks of the MTAs on each chromosome. Many of the candidate genes that were identified encode classes of proteins known to be involved in plant disease resistance pathways (Supplementary Tables 4 and 5). By far the largest class encodes proteins with leucine-rich repeat domains and nucleotide-binding domains. NB-LRR proteins are frequently identified as conferring gene-for-gene resistance (Eitas and Dangl, 2010). Mechanistically, they interact either directly or indirectly with effector proteins produced by avirulent pathogens, triggering a signaling cascade that ultimately causes cell death. The high frequency of NB-LRR genes being associated with STB resistance loci is consistent with the fact that this type of gene is very often found in clusters that undergo frequent recombination and sometimes generate new R-gene specificities ( McHale et al., 2006 ). In addition, many receptor kinases were found ( Table 7 ), which is consistent with several recently cloned Stb genes ( Hafeez et al 2025 ; Saintenac et al 2018 ; Saintenac et al 2021 ), including wall-associated receptor kinases like Stb6 ( Saintenac et al 2018 ) and lectin receptor kinases (Supplementary Table 4) that may be similar to Stb15 ( Hafeez et al 2025 ). This study is not able to discern which of these genes might have essential functions in STB resistance but does identify numerous candidates for future research. This can include functional analyses, such as loss of function studies with mutants, gene editing, or expression knockdown by RNAi or Virus-Induced Gene Silencing (Scofield et al 2006). In conclusion, our work shed light into the genetic make-up of septoria tritici blotch resistance factors in U.S. soft winter wheat elite lines. Known and novel genomic regions were identified which could contribute to enhance breeding for septoria tritici-mediated resistance. Funding This research was supported by USDA-Agricultural Research Service research project 3602–22000-017-00D. Supplementary Information The online version contains supplementary material available at URL. Author contributions LA mostly designed the experiments, performed the analyses and wrote the first draft of the manuscript. RG helped with data analysis. GB-G supervised generation of the molecular genotyping data. SS provided funding and edited the manuscript. MM hired LA to do the work, helped with analyses and with improving the manuscript. SBG helped design the experiments, provided funding, wrote parts and edited the entire manuscript. Competing interests The authors have no relevant financial or non-financial interests to disclose. Data availability The datasets generated and/or analyzed during this research are available from the corresponding author on reasonable request. Supplementary information Supplementary Table 1 . Phenotypic response at 21 days post inoculation, in percentage necrosis and pycnidia on the inoculated primary leaves, of differential lines to 13 Zymoseptoria tritici isolates. Colored cells indicate significant differences (LSDs; P=0.05) with resistant in green (not significantly different from 0 %), intermediate in yellow (significantly different from 0 % and 100%) and susceptible in red (not significantly different from 100%). Supplementary Table 2 . Analysis of variance of the necrosis and pycnidia scores on seedlings of the differential wheat set tested with the T2 and T46 isolates of Zymospetoria tritici . Supplementary Table 3 . Marker-trait associations with STB resistance mapped on the soft red winter wheat panel population with the T2 and the T46 isolates of Zymoseptoria tritici at the single time point score (16, 18 and 22 days-post inoculation) using models from FarmCPU and GAPIT (CMLM). Supplementary Table 4. List of candidate resistance genes identified using the wheat reference genome IWGSC RefSec v1.0. Supplementary Table 5. Resistance candidate gene IDs and coordinates. Supplementary Figure 1 . Box plots of the Necrosis (panel A ) and Pycnidia (panel B ) AUDPC scores showing significant differences between the Zymoseptoria tritici isolates T2 and T46 when tested on the soft red winter wheat panel population Supplementary Figure 2 . Manhattan plots and Q-Q plots of the GWAS analysis generated by the GAPIT MCLM model Supplementary Figure 3 . Categories for candidate genes identified in the indicated genomic regions associated with resistance against Zymoseptoria tritici from the Genome-Wide Association analysis Acknowledgements The authors thank Ian Thompson for locating and helping acquire space to perform the inoculations. Funder Information Declared USDA-Agricultural research Service - West Lafayette, IN , 3602–22000-017-00D Footnotes Corrected errors and clarified some parts in response to reviewer comments; improved several of the figures. References Adhikari TB , Anderson JM , Goodwin SB ( 2003 ) Identification and molecular mapping of a gene in wheat conferring resistance to Mycosphaerella graminicola . Phytopathology 93 : 1158 – 1164 OpenUrl CrossRef PubMed ↵ Adhikari TB , Cavaletto JR , Dubcovsky J , Gieco JO , Schlatter AR , Goodwin SB ( 2004a ) Molecular mapping of the Stb4 gene for resistance to Septoria tritici blotch in wheat . 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Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Genome-Wide Association study in a US soft winter wheat population reveals novel and known sources of resistance to the Septoria tritici blotch pathogen Zymoseptoria tritici Lamia Aouini , Rupesh Gaire , Steven Scofield , Gina Brown-Guedira , Mohsen Mohammadi , Stephen B. Goodwin bioRxiv 2025.09.30.679614; doi: https://doi.org/10.1101/2025.09.30.679614 Share This Article: Copy Citation Tools Genome-Wide Association study in a US soft winter wheat population reveals novel and known sources of resistance to the Septoria tritici blotch pathogen Zymoseptoria tritici Lamia Aouini , Rupesh Gaire , Steven Scofield , Gina Brown-Guedira , Mohsen Mohammadi , Stephen B. 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