The genetic evaluation of Santa Inês sheep was performed for resistance to gastrointestinal nematode infection (RGNI) and body size using different relationship matrices to assess the efficiency of including genomic information in the analyses.A) and the hybrid matrix H, which blends A with the genomic relationship matrix (G). The BLUP and single-step genomic BLUP methods were used. The accuracies of estimated breeding values and Spearman rank correlation were also used to assess the feasibility of incorporating genomic information in the analyses.There were 1,637 animals in the pedigree and 500, 980, and 980 records of RGNI, thoracic depth (TD), and rump height (RH), respectively. The genomic data consisted of 42,748 SNPs and 388 samples genotyped with the OvineSNP50 BeadChip. The (co)variance components were estimated in single- and multi-trait analyses using the numerator relationship matrix (A matrix), to 0.38±0.08, for RH . The estimates of genetic correlation ranged from −0.65±0.31 to 0.59±0.19, using A, and from −0.42±0.30 to 0.57±0.16 using H. The gains in accuracy of estimated breeding values ranged from 2.22% to 75.00% with the inclusion of genomic information in the analyses.The heritability estimates ranged from 0.11±0.07, for TD (in single-trait analysis using the The inclusion of genomic information will benefit the direct selection for the traits in this study, especially RGNI and TD. More information is necessary to improve the understanding on the genetic relationship between resistance to nematode infection and body size in Santa Inês sheep. The genetic evaluation for the evaluated traits was more efficient when genomic information was included in the analyses. Gastrointestinal nematode infections (GNI) are one of the main constraints to sheep production, due to the high costs of anthelmintic treatments, as well as the body growth delay and mortality of severely affected animals ,2.The strategies for control of GNI rely mainly on treatments with anthelmintic drugs, which are generally expensive and labor intensive. In addition, these drugs may leave residues in meat and milk, and are subject to the resistance of worms to their active ingredients . Due to Studies on resistance to gastrointestinal nematodes are usually based on traits that in dicate worm infection, for example, color of the ocular conjunctiva, number of eggs per gram of feces, and body condition score (BCS). An alternative to quantify the effect of these characteristics together consists in using them to compose the resistance to GNI (RGNI) trait, using computational intelligence based on Fuzzy logic .In order to better assess the genetic potential of resistant individuals, another important aspect to be considered is the evaluation of the relationship between parasitic resistance and production traits. For instance, to assess the impact of parasitic infections on body development of farmed animals under natural rearing conditions, an option is to estimate (co)variance components and genetic correlations between traits that indicate body development and traits that are indicative of the level of parasitic infection . Thus, tAccurate estimates of genetic parameters for the traits of interest are essential for selection of genetically superior animals. In this sense, a multiple-trait model is expected to increase the accuracy of genetic parameters by making use of information from genetically correlated traits . FurtherA) and its combination with the genomic relationship matrix (G), i.e., the H matrix, to evaluate the efficiency of including genomic information in the genetic evaluation for the traits in study.Studies on RGNI and body size in sheep using genomic information are still scarce. To the best of our knowledge, there are no reports on the relationship between RGNI and body size in Santa Inês sheep. Therefore, the objective of this study was to estimate (co)variance components, genetic parameters and (genomic) breeding values for gastrointestinal nematode resistance and body size in Santa Inês sheep using the numerator relationship matrix  of the Federal University of Piauí, Brazil (protocol number 414/2017).Pedigree data from 1,637 Santa Inês sheep were available. A total of 206 individuals were sires and 713 were dams. The pedigree depth included eight generations, where 933 animals had both their parents known, 40 individuals had only dam information, and 664 had unknown ancestry. Data were collected from 2012 to 2018, in 18 farms located in the Mid-North sub-region of Brazil (states of Piauí and Maranhão). The animals were registered with the Brazilian Association of Sheep Breeders (ARCO) or belonged to the sheep and goat conservation nucleus of the federal research unit Embrapa Meio-Norte . The farms used semi-intensive system, in which the animals were raised receiving feed supplementation during the dry season. This system differed slightly across farms and time period, but this was accounted for in the statistical models.Fecal samples were taken directly from the rectum of the animals, using plastic bags which were then stored into Styrofoam boxes containing ice gel packs until the samples were taken to the laboratory. The parasitological evaluation was performed within 24 hours after the feces collection, through the fecal egg count (FEC) adopting the procedures described by Ueno and Gonçalves .The assessment of the color degree of the conjunctival mucous membrane was performed using the FAMACHA method . The BCSThe combination of FAMACHA score, BCS, and FEC values was used to generate a grade ranging from 0 to 10  that represented the RGNI trait for each animal. This grade was calculated using the CAPRIOVI software , based oTwo body dimensions were used to assess the relationship of body size with RGNI. The following measurements were obtained using a tape measure graduated in centimeters, while the animals were standing on a flat surface in an appropriate position: RH, measured as the distance from the highest point over the sacrum to the ground; and thoracic depth (TD), calculated as the difference between withers height and the distance from the sternum (between the fore legs) to the ground.The blood samples used for DNA extraction were collected by puncture of the jugular vein using a needle attached to vacuum tubes containing ethylenediaminetetraacetic acid anticoagulant. The DNA extraction was performed using the Qiagen DNeasy Blood &Tissue kit according to the manufacturer’s protocol. The DNA quality was checked using 1% agarose gels with 1× sodium borate buffer during 60 minutes at 60 V. The digitalization and visualization of the gels were performed using an L-PIX photo documenter . The NanoDrop 3300 Fluorospectrometer was used for the quantification of DNA, using the AccuBlue Broad Range dsDNA Quantitation Kit . Prior to genotyping, the extracted DNA was kept frozen (−20°C).A total of 389 samples were genotyped using the Ovine SNP50 BeadChip , which contains 54,241 single nucleotide polymorphisms (SNPs).Phenotypic records from animals with no information of birth year, farm, sex, and age were removed from further analyses. The contemporary groups for all traits were defined by the effects of year  and season of data collection , sex, and farm. The age of the animal at the day of measurement was fitted as a covariate (linear effect). After data editing, only contemporary groups with at least two animals were kept. A total of 500, 980, and 980 records of RGNI, TD, and RH, respectively, were used for the analyses. Data of animals aged at least six months were considered.−6, n = 8). In addition, samples with call rate lower than 0.90 were removed. After the quality control, 42,748 SNPs and 388 samples remained for further analyses.The genotyping quality control was performed using the PREGSF90 program . QualityThe (co)variance components were estimated via Bayesian inference in single- and multi-trait analyses, using the animal model, by the pedigree-based BLUP and single-step genomic BLUP (ssGBLUP) procedures. The GIBBS1F90 program  was usedThe estimates of variance components were used as starting values for calculation of (genomic) estimated breeding values, i.e., (G)EBVs, using the BLUPF90 program .The general model can be written as follows:y is the vector of phenotypic records for the trait(s); β is the vector of fixed effects (contemporary group and age as a covariate); α is the vector of genetic additive direct effects of the animal, where A is the additive-genetic numerator relationship matrix, and H is the additive-genetic relationship matrix based on both pedigree and genomic information; e is the vector of residual effects, where X is the incidence matrix of fixed effects; and Z is the incidence matrix of additive genetic effects.in which: For multi-trait analyses, the (co)variance structure among traits was modeled as follows:whereis the (co)variance matrix between traits due to animal additive genetic effects,I is the identity matrix , and ⊗ is the Kronecker product operator.is the (co)variance matrix between traits due to random error effects, A−1 was replaced by H−1 [For the inclusion of genomic information in the models using the ssGBLUP procedure, d by H−1 , calculaA−1 is the inverse of the numerator relationship matrix, G−1 is the inverse of the genomic relationship matrix, and G, we fixed α and β to 0.95 and 0.05, respectively, which are the default values of the PREGSF90 program to create a weighted G as 0.95G0+ 0.05A22 (to avoid singularity problems), in which G0 was created using the first method described by VanRaden [where VanRaden , as follM is the allele-sharing matrix with m columns  and n lines ; and P is a matrix containing the frequency of the second allele (pj), expressed as 2pj. The observed allele frequencies were used.where: A and H) were calculated using the standard errors of prediction (SEP), estimated from the inverse of the mixed model equations as follows:The theoretical accuracies of breeding values obtained in single- and multi-trait models using both relationship matrices (ij (prediction error variance) is equivalent to ij is the standard error of prediction calculated by the BLUPF90 program for the (G)EBV of animal i for the trait j and j.where PEVThe Spearman coefficients between the rank positions of each animal were calculated to investigate whether changes in the ranking of animals occurred based on their EBVs across the different scenarios using BLUP and ssGBLUP, in single-trait analyses.The descriptive statistics for the traits in study are shown in The estimates of variance components differed according to the models used in both scenarios (with and without inclusion of genomic information). Only the estimates of additive genetic variance for RGNI decreased in multi-trait models in both scenarios . This reAdditive genetic variances were higher when genomic in formation was included in single- and multi-trait analyses for all traits. Estimates of residual variance decreased in all the scenarios in which genomic information was used.A matrix, the G describes the genetic similarity between individuals more accurately, which results in more accurate estimation of variance components [Compared to the mponents . In the mponents . Thus, i2) were classified as low (<0.20), moderate (0.20 to 0.40), and high (>0.40). For RGNI, heritability estimates were low when using the A matrix . On the other hand, when using genomic information, the heritability estimates for TD had moderate magnitude in both scenarios  suggests that genes involved in thoracic development have almost no effect on RGNI. Nevertheless, there were relatively few records of the RGNI trait in the current study, even in relation to the other evaluated traits. Thus, more studies using higher number of RGNI records are necessary for more accurate assessment of the genetic relationship between RGNI and the body size traits evaluated in this study, in Santa Inês sheep.The antagonistic values of the genetic correlation estimates between RGNI and RH obtained in both scenarios suggest that taller Santa Inês sheep tend to show reduced RGNI. In addition, the correlation between RGNI and TD using the Estimates of genetic correlation between parasitic resistance indicator traits, such as FEC or FAMACHA score, and production traits are favorable if negative . As in tHaemonchus contortus egg counts per gram of feces and body weight from weaning to 550 days of age. These authors suggested that selection for resistance should not have an unfavorable effect on the growth potential of sheep. In a study with Santa Inês sheep from different herds, Oliveira et al [H. contortus , and body condition score .In an experimental herd of Santa Inês sheep n = 119) submitted to two natural challenges, Lôbo et al [ submittera et al  obtainedIn the present study, unfavorable genetic correlation estimates between RGNI and the body size traits indicate that there may be negative influence of selection for RGNI on body size of Santa Inês sheep, or decrease in parasitic resistance due to selection of bigger animals. This is evidenced by the estimates of genetic correlation between RGNI and RH in both scenarios (including or not genomic information). However, as mentioned above, to better understand the genetic relationship between body size and the RGNI trait in Santa Inês sheep, further information on these phenotypes is needed. In general, there are still divergences between different studies regarding the relationship of parasitic resistance and characteristics that indicate body development in sheep.According to Stear et al , a justiTeladorsagia circumcincta infected wild Soay sheep, in Scotland. According to these authors, the genetic relationship between body development and parasitic resistance in the Soay sheep breed is natural as it is a wild breed. Therefore, the results of Coltman et al [H. contortus are predominant.Most of the studies with sheep have shown favorable genetic correlation estimates between resistance to gastrointestinal parasites and production traits ,23. Howean et al  require Domestic sheep breeds underwent different selection processes over time and, according to Coltman et al , these pOn the other hand, the relationship between body size traits in sheep is consistent between different studies. In the present study, estimates of genetic correlation between TD and RH without inclusion of genomic information (0.59±0.19) or including genomic data in the analysis (0.57±0.16) were favorable. These positive values suggest that there are genes in common that act on the expression of these two traits, so that the selection for one will result in an increase in the other due to the pleiotropy effect. However, the low magnitude of these estimates suggests that selection for increase in TD based on RH (and vice versa), in Santa Inês sheep, may have little efficiency.In other sheep breeds, the estimates of genetic correlation between body height and chest dimensions range from low to high. Janssens and Vandepitte , for exaEstimates of theoretical accuracy of breeding value, in the present study, ranged from 0.046 for RGNI, using a single-trait model with the BLUP method, to 0.188 for RH, using a multi-trait model with the ssGBLUP method . Gains iIn general, the highest accuracy estimates were observed when the multi-trait models with the inclusion of genomic information were used . An incrA matrix were achieved using single-trait models. The lowest gains in accuracy using a multi-trait genomic model were observed for RGNI, probably because this trait showed antagonistic or close to zero genetic correlations with the other traits. According to Pszczola et al [However, in the current study, the greatest gains in accuracy when comparing the results obtained using models that included genomic information to those obtained using the la et al , it is eSpearman correlations between the ranks of animals had high magnitude using the BLUP and ssGBLUP methods . These rThe largest dispersion of (G)EBVs was observed for RGNI , which hIn conclusion, the correlation estimates between animal ranks according to their EBVs obtained using the methods in study, as well as the superiority of accuracy estimates of breeding values observed using the ssGBLUP method reinforce the idea that the inclusion of genomic information for the genetic evaluation of these traits will be favorable and, therefore, it should be used, whenever possible, to obtain more reliable results.The inclusion of genomic information will benefit the direct selection for the evaluated traits, especially RGNI and TD. The genetic evaluation of Santa Inês sheep for these traits will be benefited by using genomic data, due to the increase in accuracies. More information is necessary to improve the understanding on the genetic relationship between resistance to nematode infection and body size in Santa Inês sheep.


P < 0.001) genotype-by-environment interactions were detected regardless of same or different isolates used as an inoculum source. Joint regression analysis identified 19 genotypes that consistently expressed low disease severity under infection with different isolates in multi-locations. The CIMMYT inbred lines, 30ZJN09 and ZJN12 Qno25, were particularly pertinent as they had low SNB response and highest trait stability at two locations across 3 years. Genome wide association studies detected 20 QTL associated with SNB resistance on chromosomes 1A, 1B, 4B, 5A, 5B, 6A, 7A, 7B, and 7D. QTL on chromosomes 1B and 5B were previously reported in similar genomic regions. Multiple QTL were identified on 1B, 5B, 6A, and 5A and detected in response to SNB infection against different isolates and specific environments. Known SnTox-Snn interactions were either not evident or variable across WA environments and SNB response may involve other multiple complex biological mechanisms.The slow rate of genetic gain for improving resistance to Septoria nodorum blotch (SNB) is due to the inherent complex interactions between host, isolates, and environments. Breeding for improved SNB resistance requires evaluation and selection of wheat genotypes consistently expressing low SNB response in different target production environments. The study focused on evaluating 232 genotypes from global origins for resistance to SNB in the flag leaf expressed in different Western Australian environments. The aim was to identify resistant donor germplasm against historical and contemporary pathogen isolates and enhance our knowledge of the genetic basis of genotype-by-environment interactions for SNB response. Australian wheat varieties, inbred lines from Centro Internacional de Mejoramiento de Maiz y Trigo (CIMMYT), and International Center for Agricultural Research in the Dry Areas (ICARDA), and landraces from discrete regions of the world showed low to moderate phenotypic correlation for disease response amongst genotypes when evaluated with historical and contemporary isolates at two locations across 3 years in Western Australia (WA). Significant ( Parastagonospora  nodorum (Berk.) Quaedvlieg, Verkley, and Crous is a significant necrotrophic disease on the leaf and glume of bread wheat. Disease epidemics cause significant yield losses in wheat growing regions of the world, including annual yield losses of 12.9% in Western Australia  caused by the fungal pathogen alia WA; . SNB resalia WA; . TherefoParastagonospora nodorum have asexual pycnidiospores and sexual ascospores where the latter is the primary source of inoculum  enhances resolution in marker-trait associations whilst simultaneously evaluating a broader gene pool and alleles for desired trait variation. Alleles and marker polymorphisms in a GWAS panel are associated with traits of interest through historical recombination events and linkage disequilibrium (LD). There are increasing reports on the application of GWAS for genetic analysis of traits, including disease resistance. For example, marker-trait associations linked to resistance against stem and stripe rust  and FusaNP assay . ConsideNP assay  and enviNP assay , GWAS anNP assay  providesNP assay . It appeParastagonospora nodorum produces a range of necrotrophic effector (NE) proteins that induce necrosis in wheat that harbor corresponding sensitivity genes , 78 inbred lines from International Center for Agricultural Research in the Dry Areas (ICARDA), and 11 landraces from various origins. Inbred lines were accessed through the CIMMYT Australia ICARDA Germplasm Evaluation (CAIGE) projectParastagonospora nodorum isolates from various wheat growing regions of WA were sourced from the culture collection at the Department of Primary Industries and Regional Development (DPIRD), formerly the Department of Agriculture and Food WA. Inoculum used for evaluation of SNB in field trials each year consisted of a mixture of 16–20 isolates where at least 35% of the inoculum represented contemporary isolates collected from the previous year of the trials. Mixed inoculum used in each year included at least four of the same isolates represented in successive years. 6 spores/ml with 0.5% gelatine prior to use as inoculum in field trials.Trials in 2016 and 2017 were sown at Northam and Katanning, and Northam and Manjimup in 2018. Locations were selected based on varying mean annual temperatures, rainfall and maximum relative humidity . Trials were arranged in completely randomized block design with 3 replications for each accession with 0.6 m spacing between plots. Each plot consisted of a single 1.9 m length row with 0.4 m row spacing in 2016 and 2017 trials and two rows of 1.9 m length with 0.2 m row spacing in 2018. A spreader 2-row plot of the susceptible early maturing variety “Amery” was adjacent to each treatment plot. Susceptible check cultivars (6 reps) comprising cultivars “Amery” (early maturing), “Arrino” (early-mid maturity), “Millewa” (mid-late maturity), “EGA2248” (mid-late maturity), and “Scout” (late maturity) were included in all trials. Trials were rainfed or irrigated with above ground sprinklers to simulate a rainfall event and induce disease when required.6 spores/ml with 0.5% gelatine) was applied using a motorized mister commencing at Feekes growth stage 3–4 with a further three inoculations at 10–14 day intervals. Inoculum was applied at a rate of 28.5 m2/L prior to a rainfall event. Sprinkler irrigation was applied to trials in the absence of rainfall within 2 days post inoculation. SNB response on the flag leaf was visually assessed using percent leaf area diseased (PLAD) scale on susceptible check cultivars where 0% and 100% were scored as highly resistant and highly susceptible, respectively. All accessions were scored in each trial when at least 2 susceptible check varieties had PLAD scores >70%. Disease ratings were scored for 5 random individual plants from the middle of each plot and mean plot scores were used in statistical analysis.Inoculum (10Measurements for plant height (cm) were taken as the distance from the top of the soil to the top of the heads (excluding awns). Three measurements were taken in each plot and mean plot scores used for analysis. Heading date was measured as days from date of sowing to when 50% of the ears were fully emerged for each plot.2. Disease and agronomic data were tested for normality and error variance homogeneity across environments. Residuals plotted against fitted values revealed a random distribution (data not shown) indicating there was no need for data transformation. Generalized linear models and linear mixed models were used in phenotypic analysis of trait data. Treatment factors and co-variates were fitted to fixed models to estimate main effects and interactions. Finlay-Wilkinson joint regression analysis was used to compare genotypes for SNB response and agronomic traits at two locations across 3 years. Broad-sense heritability estimates were calculated using the formula H2 = r, where r is the number of replications.All statistical analyses for phenotypic evaluation were done using Genstat, 19th editionhttps://wheat-urgi.versailles.inra.fr/  to establish whether Tsn1 had a significant effect on PLAD scores. The genotypes for fcp620 were also compared to the marker-trait associations (MTA) co-locating with Tsn1, IWB14942, and IWB43679.The escribed . The effr2 ≤ 0.2 using the PLINK 1.9 command “–indep-pairwise 50 2 0.2,” reducing the data set to 2,941 SNPs, and a third set where the markers were filtered to include SNPs with no less than 90% call rate and pruning the data based on a LD of r2 ≤ 0.1 using the PLINK 1.9 command “–indep-pairwise 50 2 0.1,” reducing the data set to 1,142 SNPs.Monomorphic markers and markers with less than 80% call rate were removed from the dataset. Markers with minor allele frequencies (MAF) less than 5% were removed prior to analysis, reducing the total number of SNPs to 20,563. The data was then split into three separate analyses, one set containing the filtered data set of 20,563 SNPs, a set where the markers were further filtered to include SNPs with no less than 90% call rate and pruning the data based on a LD of r2 ≤ 0.1) was imported into STRUCTURE version 2.3.4.  > 5.61] using Bonferroni correction with α = 0.05. Bonferroni correction is highly conservative and reduces type I errors, however, is most practical when all tests are independent (r2 of 0.1. This returned a genome-wide threshold significance of p < 7.65 × 10–5 [−log10 (p) > 4.12] and was considered a moderate level of significance compared to the Bonferroni corrected threshold. As reported in a number of related studies a threshold of significance of p < 1 × 10–3 [−log10 (p) > 3.00] was included as a suggestive level of significance  as previously described  and similarly for heading date and plant height  accounted by genotypes (72.94–79.71%) followed by genotype-by-environment interactions (10.22–22.63%) and environment (4.43–10.07%) for each year (P < 0.001) but smaller proportion of variation was accounted by genotypes at two locations across 3 years (44.52%) whereby genotype-by-environment interactions (34.52%), and environment (20.96%) was higher compared to their respective sources of variation across environments within years  with similar negative correlation between plant height and PLAD scores  in all environments supporting potential pleiotropic effects of morphological characteristics on disease evaluation. Since heading date and plant height were significantly different (P < 0.001) between genotypes in each environment (P < 0.001) main effects on SNB response at two locations across 3 years when adding to or sequentially dropping terms from the fixed model whereas plant height had significant main effects (P < 0.05) in most environments (P > 0.05) in any of the environments  showed no difference in principal component analysis and population structure, with the top PCs accounting for slightly less of the total variation in the LD pruned datasets (R2) for the 20,563 SNP loci. LD decay was estimated by non-linear regression at 6.97 cM and 6.14 cM for the A and B subgenomes, respectively, whereas the D subgenome had a significantly higher LD of 24.62 cM for threshold R2 = 0.2 . PCA described 15.6% of the genetic variance between PC 1, 2, and 3  and regarded as having low population structure . Despitedatasets . The extR2 = 0.2 .p < 2.43 × 10–6 [−log10 (p) > 5.61], p < 7.65 × 10–5 [−log10 (p) > 4.12], and p < 1 × 10–3 [−log10 (p) > 3.00] ranking as a high, moderate and suggestive levels of significance, respectively, in Manhattan plots (A mixed linear model (MLM) was applied to account for any confounding effects of population structure and cryptic relatedness and reduce rate of false positives or spurious SNP marker associations linked to genes controlling SNB resistance. Adjusted mean PLAD values from six individual environments in 2016–2018 were used to reduce any confounding effects of heading date and height and analyzed using 20,563 filtered SNP markers. Analysis of quantile-quantile (Q-Q) plots for adjusted mean PLAD scores showed deviations of the observed association compared to the association statistics expected under the null hypothesis of no association , indicatan plots .p < 7.65 × 10–5 [−log10(p) > 4.12] so it appears that markers are associated with disease response and not a pleiotropic effect of agronomic characteristics. The number of QTL detected for each environment was variable. A minimum of two QTL were detected for Katanning 2017 and Northam in 2018 with a maximum of seven QTL detected for Katanning in 2016  reduced PLAD across all environments when stacking of more than one allele  in the EGA Blanco/Millewa population (IWA5670 (at 514.9 Mbp) associated with SNB resistance in Katanning in 2016 was identified flanking the QTL QSnl07.daw-5B in the EGA Blanco/Millewa mapping population (1WB43679 (at 539.5 Mbp) and IWB14942 (at 546.7 Mbp) on chromosome 5B associated with SNB resistance at Northam and Manjimup in 2018, respectively, aligned to the QTL, QSnl07.daw-5B in the EGA Blanco/Millewa population  associated with SNB response in Northam and Manjimup 2018  was used to genotype the GWAS panel for sensitivity or insensitivity to SnToxA with 100% genotype identity with SNP marker IWB14942 located at 546.70 Mbp  reducing PLAD in only two environments, Northam 2018 and Manjimup 2018  on chromosome 5B indicated that Tsn1 was associated with SNB resistance but only at Northam and Manjimup in 2018 and not the remaining environments. Therefore, it appears that the SnToxA-Tsn1 interaction is inconsistent across environments in WA and corroborates a similar conclusion drawn from a previous QTL study using a bi-parental mapping population (Snn loci indicating that historical and contemporary SNB isolates from WA may secrete alternative effector proteins or environmental influences may have an effect on known host-NE interactions. The inconsistent association with several known NE host susceptibility loci across environments, the preponderance of environment-specific QTL and minimal common loci across QTL from global field-based studies makes it reasonable to assume that alternative and distinct biological mechanisms are largely influenced either by the environment, isolates or their interactions. Biological mechanisms other than known SnTox-Snn interactions, including a myriad of biochemical and physiological process during infection, penetration and colonization (The co-location of QTL with the position of known SnTox-pulation . Moreovenization  may haveAdditional studies are needed to identify other possible genes involved in SNB response but would require extensive field evaluation across wider locations and contemporary isolates to elucidate their role in specific environments. In doing so, it would provide a comprehensive analysis of the role of multiple genes on chromosomal blocks that respond to SNB infection and provide further insights on environmental factors and isolates affecting different biological pathways. In the meantime, the challenge for breeding SNB resistance is to transfer chromosomal blocks from donor resistant parents to increase the probability of an individual gene contributing suitable SNB resistance in any specific environment and against contemporary isolates. EGA Blanco, 6HRWSN125, 30ZJN09, and ZJN12 Qno 25 would be logical donor parents and the SNP markers identified on chromosomes on 1B, 5A, 5B, and 6A in this study will be important for marker-assisted selection and tracking chromosomal blocks harboring resistance genes.Snn interactions are not evident with SnToxA-Tsn1 being variable in different Western Australian environments and SNB response may involve other multiple complex biological mechanisms.The wheat response to SNB infections against historical and contemporary isolates under multi-environment field conditions showed considerable genotype-by-environment-by isolate interactions within and between years. Although phenotypic correlation between wheat genotypes sourced from Australian cultivars, inbred lines from CIMMYT and ICARDA and some landraces was low, four genotypes expressed stable phenotypes with consistently low SNB symptoms at two locations across three years and identified as suitable donor parents for breeding SNB resistant wheat for WA production environments. GWAS identified 20 QTL with the majority detected in only one environment, confirming the complex genetic control for SNB response and the role of different environments and diverse isolates on expression of minor host genes. It appears that a majority of SnTox-All datasets generated for this study are included in the article/MF designed the experiments, contributed to data acquisition, analysis and interpretation, and a major contributor to writing the manuscript. EW contributed to data acquisition, analysis and interpretation, and a major contributor to writing the manuscript. CM and WM contributed to trial designs, planting, maintenance, and data acquisition. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Phaseolus vulgaris) grown for > 70 years in the United States and Canada, consisting of 20–50 entries each year at 10–20 locations. The CDBN provides a rich source of phenotypic data across entries, years, and locations that is amenable to genetic analysis. To study stable genetic effects segregating in this MET, we conducted genome-wide association studies (GWAS) using best linear unbiased predictions derived across years and locations for 21 CDBN phenotypes and genotypic data (1.2 million SNPs) for 327 CDBN genotypes. The value of this approach was confirmed by the discovery of three candidate genes and genomic regions previously identified in balanced GWAS. Multivariate adaptive shrinkage (mash) analysis, which increased our power to detect significant correlated effects, found significant effects for all phenotypes. Mash found two large genomic regions with effects on multiple phenotypes, supporting a hypothesis of pleiotropic or linked effects that were likely selected on in pursuit of a crop ideotype. Overall, our results demonstrate that statistical genomics approaches can be used on MET phenotypic data to discover significant genetic effects and to define genomic regions associated with crop improvement.Multienvironment trials (METs) are widely used to assess the performance of promising crop germplasm. Though seldom designed to elucidate genetic mechanisms, MET data sets are often much larger than could be duplicated for genetic research and, given proper interpretation, may offer valuable insights into the genetics of adaptation across time and space. The Cooperative Dry Bean Nursery (CDBN) is a MET for common bean ( Almost every crop improvement program assesses the performance of promising germplasm and breeding material via multienvironment trials (METs). The phenotypic data produced by these trials are extremely important guides to growers, private seed companies, and public institutions involved in crop improvement, because combining trial data from multiple years and locations increases the probability of identifying genotypes that perform well, or show especially desirable traits . Many coLongstanding METs such as the CDBN have often focused on breeding for crop ideotypes, in addition to breeding to eliminate defects and to select for yield. Though METs are often used to measure genetic gain over time . The freDespite these analytical issues, METs often produce decades of phenotypic data, which gives them substantial appeal for use in genetic analyses of phenotypic variation. Genetic analyses of MET data sets have recently been implemented in several crop species . In NortMore than 500 CDBN entries have been grown since the 1980s . These eHere, we demonstrate that the CDBN MET data set can be used to make genetic discoveries, despite the sparse nature of the data, by using best linear unbiased predictions (BLUPs) for entries phenotyped in the CDBN. We explore whether this approach can find genomic regions significantly associated with phenotypic variation, and compare associations found with this approach to published GWAS results obtained from more balanced trials. We also explore patterns of genomic associations with significant effects on more than one CDBN phenotype using mash. Our results demonstrate the value of adding a genetic component to data sets such as the CDBN and provide a starting point for future work that explores the genetics of phenotypes evaluated in METs.MET data sets represent substantial phenotypic resources that can aid in the genetic study of important agronomic phenotypes. Several important steps in preparing the CDBN data for analysis fall under the remit of data science, and specifically involve the data processing steps outlined here. First, when available only from printed reports, the data were rendered machine-readable. Processing of the digitized data next involved cleaning the data to remove inconsistencies and spurious data, then filtering to retain only the relevant data. The data were stored in a consistent form where the semantics of the data set matched the way it was stored. Then, various data scales for individual traits such as growth habit were standardized to create phenotypes that were more consistent across locations and years. The phenotypic data were next enriched with additional attributes that made subsequent analyses more meaningful, such as germplasm, environment, and crop management information. Then, the data were aggregated to create summary data by estimating BLUPs for each phenotype. We next used a GWAS modeling approach to determine the genomic regions associated with these data summaries. Finally, we used mash to examine the patterns of overlap between genomic associations with significant effects on one or more phenotype . Reports from 1981 to 2015 were scanned if not in digital format, digitized using optical image recognition as required, and then reformatted using custom SAS  scripts that also standardized nomenclature and units of measurement.http://rpubs.com/alice_macqueen/CDBN_Phenotype_Standardization.Much of the phenotypic data required additional processing to allow comparisons across locations and years. The long time span and large number of testing locations led to the scoring of 152 traits. Many of these traits represented distinct methods for scoring similar phenotypes; for example, lodging was scored on a percent scale, a 1–5 scale, a 0–9 scale, and a 1–9 scale at different locations and in different years; for this analysis, these lodging traits were standardized to one lodging phenotype on a 1–5 scale. From 152 traits reported, 22 phenotypes were standardized for use in GWAS, including 8 quantitative phenotypes and 14 qualitative phenotypes created from visual scores and/or specific measurements . The outWe generated phenotypes associated with location code, year, and genotype information. A total of 70 location codes were created as four-letter abbreviations with the U.S. state or Canadian province abbreviation as the first two letters, and the specific site abbreviation as the second two letters. Five location codes ending in “2” corresponded to a second trial grown at that location and year, usually with a treatment such as drought or disease applied. Location codes were associated with latitude, longitude, elevation, and other location-specific metadata (Table S2), while genotypes were associated with market class and race, as well as the availability of seed from the holdings of CDBN cooperators and SNP data, where available (Table S2).https://github.com/Alice-MacQueen/CDBNgenomics/tree/master/analysis-paper.In general, location by year (L*Y) combinations with outlier phenotypic values [values above the third quartile or below the first quartile by 1.5 times the interquartile range (IQR)] were removed for every entry in that L*Y combination. Removing outlier L*Y combinations prevented possible bias from linear models using a biased sample of data points for a L*Y, while still removing points that, by IQR measures and by knowledge of reasonable ranges for common bean quantitative phenotypes, were likely due to mismeasurement or data entry errors. The specifics of phenotype standardization for all 22 phenotypes are given in the Supplementary Note and the code is available on GitHub at Selection and breeding strategies to generate new bean entries for the CDBN varied across years and among breeding programs. However, in general, new advanced lines were selected from either single, triple, or double crosses among advanced breeding material and released cultivars, which in most cases were already tested within the CDBN in previous years. These lines were bulked to increase seed supply, then field tested to ensure consistency of phenotypic responses in the advanced lines. Entries with favorable characteristics were often entered into the CDBN to be phenotyped in multiple environments. Consequently, most CDBN entries were members of a complex pedigree that has had novel, favorable alleles recombined or introgressed into it over time.It is clear that the CDBN is not a randomly mating, homogeneous population, and the breeding and selection strategy in the CDBN likely impacts GWAS on this material in a number of ways. Presumably, breeders have increased the frequency of alleles that favorably affect phenotypes over time, which should aid in the detection of these genomic regions via GWAS. The multiple generations of inbreeding should reduce allelic heterogeneity, which should also aid GWAS. Indeed, we find few heterozygous regions in our SNP data set and few examples of multiallelic loci. By the same token, the frequent inbreeding may also increase the size of linkage disequilibrium (LD) blocks or cause spurious patterns of LD, which may cause nonsyntenic associations and make candidate gene identification more difficult. In addition, the infrequent crosses between the gene pools from the two independent domestication events, and the assortative mating practiced as part of the breeding strategy, could lead to an inflated false-positive rate and create correlations between previously uncorrelated traits  , defined as Va / (Va + Ve), where Va is the restricted maximum likelihood (REML) estimate of the additive genetic variance from rrBLUP and Ve is the REML estimate of the error variance.To explore consistent genetic effects that could be compared to balanced genetic trials, analyses were performed on genetic BLUPs for each phenotype. BLUPs were calculated in the rrBLUP package in R using a kinship matrix, and treating location and the interaction between location and year as fixed effects. The R code to generate the BLUPs is available on GitHub at P-values above a Benjamini–Hochberg false discovery rate (FDR) threshold of 0.1 were examined further. In a balanced GWAS panel that had many identical genotypes, this interval size was suitable for capturing LD in the data  under submission number SUB6162710. Code for SNP calling is available at https://doi.org/10.18738/T8/RTBTIR. Raw phenotypic data are available in the National Agricultural Library: https://www.nal.usda.gov/. Code used to generate data used in this analysis from the raw phenotypic data are available at Rpubs, found at: http://rpubs.com/alice_macqueen/CDBN_Phenotype_Standardization. Code and data necessary to replicate this analysis are available as part of the R package CDBNgenomics, found at: https://github.com/Alice-MacQueen/CDBNgenomics. Supplemental material available at Texas Data Repository Dataverse: https://doi.org/10.18738/T8/KZFZ6K.Aligned SNP data are available at The CDBN contains a wealth of data to study the genetics of phenotypes and phenotypic correlations . We wereh2, potentially explainable by GWAS. h2 varied between 6 and 73% in the 21 phenotypic BLUPs . To determine if any SNP frequencies had changed over the duration of the CDBN, we also conducted GWAS on the earliest year that each germplasm entry was present in the CDBN as a proxy for the age of the entry. This GWAS was analogous to an environmental GWAS that uses climatic variables associated with a genotype’s location of origin  had one significant peak after FDR correction, on Pv01 at 42.2 Mb , affects flowering time by activating Flower Locus C, which is a repressor of flowering  had associations on nine chromosomes that were significant after FDR ; the stromplexes . The assPhvul.001G087500, is located at 13.76 Mb in the V2.0 annotation for P. vulgaris. Gene model Phvul.001G087500 is an ortholog of KNUCKLES (KNU), a protein that is part of the Polycomb repressive complex 2, a complex that affects both flowering time and floral meristem development . It was correlated with a difference in flowering time of 2–3 days, depending on the population . A candidate gene model hypothesized to affect days to flowering, Phvul.004G025600; the A. thaliana homolog of this gene is involved in the biosynthesis of inositol pyrophosphate, a cellular signaling molecule involved in metabolism and energy sensing . In A. thaliana, SAR1 increases plant height and internode distance, and appears to affect stem thickness . In total, these three associations explained 8% of the variation in lodging . The signal on Pv04 fell within gene model Phvul.003G023000, a cellulose synthase-like protein highly expressed in green mature pods, whole roots, and leaf tissue at the second trifoliate leaf stage of development . The alternate allele was associated with an increase in harvest index of 1.5–3.5%, and associated with bean race . This allele was 20 kb from gene model Phvul.001G055600, a RING-CH-type zinc finger protein expressed highly in roots and in stem internodes above the cotyledon at the second trifoliate stage (−1). The association on Pv09 was 5-kb upstream of model Phvul.009G204100, which encodes a signal peptide peptidase A highly expressed in pods associated with stage-2 seeds and in stem internodes above the cotyledon at the second trifoliate stage , as well as upright and prostrate indeterminate types (II and III). Growth habit had significant associations on every chromosome after FDR; the strongest four associations were on Pv01 at 6.2 and 42.2 Mb, on Pv09 at 30.9 Mb, and Pv10 at 42.7 Mb . There are known to be multiple determinacy loci segregating in different gene pools of common bean (Uromyces appendiculatus) causes leaf and pod pustules, and leads to losses in vigor and seed yield. Higher plant damage caused by rust was indicated by a higher rust score. Rust score had significant associations on 10 chromosomes after FDR . However, the strongest association was located on Pv11 at 50.6 Mb and overlapped a major cluster of disease resistance genes containing the rust resistance genes Ur-3, Ur-6, Ur-7, and Ur-11 . The association on Pv01 was 0.5-kb upstream of gene model stresses . The assung pods .Ur-3, one of the two genes pyramided in the 1980s in bean breeding to provide comprehensive rust resistance.The presence of many associations above the FDR threshold supports using MET data for genetic analyses. However, the assortative mating employed purposefully by breeders of entries in the CDBN could potentially lead to a high rate of false positives . However, as the balanced studies often did not conduct GWAS on similar phenotypes, our “expected” rate of overlap is likely to be biased. Thus, we consider the fact that this study found the same three 20-kb regions that overlap in balanced GWAS comparisons to be stronger evidence than the large number of overlaps at the 200-kb level of the conclusion that this panel can yield similar associations to balanced GWAS of common bean diversity panels.In comparisons involving only the 11 balanced studies, 9 of 80 associations fell into three 20-kb regions, while 15 of the 80 associations fell into six 200-kb regions. When this study was added, seven additional associations fell into four 20-kb regions, while 12 additional associations fell into 14 overlapping 200-kb regions . This stP-values for phenotypes from the CDBN as well as 4000 SNPs for the earliest year an entry was grown in the CDBN  or due to multiple linked genetic loci affecting multiple phenotypes. To formally compare these overlaps, we used mash on 19 sets of 4000 SNPs with the smallest the CDBN . Mash sh on Pv01 . Outside2 affected ≥ 10 phenotypes (Table S5) and typically affected phenotypes in the two groups in similar ways. However, a few exceptions included Pv03 at 10.64 Mb, which affected only plant height; Pv04 at 17.77 Mb, which affected seed weight and varied with earliest year in the CDBN; Pv07 at 13.94 Mb, which affected biomass; and Pv08 at 33.18 Mb, which affected days to flowering, plant height, and seed appearance.The alternate allele for the SNP on Pv01 at 15.4 Mb was associated with significant decreases in biomass, days to flowering, days to maturity, plant height, and seed appearance score. It was also associated with increases in common bacterial blight (CBB) damage score, harvest index, root rot damage score, rust damage score, seed fill duration, white mold damage score, and zinc deficiency damage score . Here, hThe genes and genomic regions affecting phenotypic variation in common bean are now being narrowed down with the aid of a recently released high-quality reference genome , significant SNPs nonetheless highlight two, large linked regions on chromosome 1 that have effects on many phenotypes. Regions of this size (15 and 11 Mb) with evidence for significant effects almost certainly require the presence of multiple linked genes with effects on multiple phenotypes. Linked effects do not preclude pleiotropic effects occurring at single loci within this large region, though molecular confirmation of pleiotropy is still necessary. We suggest that the two major genomic regions on Pv01 associated with many CDBN phenotypes were major targets of selection by breeders for entries that matched an “ideotype” for common bean. The original ideotype had a long hypocotyl, many nodes carrying long pods and without side branches, small leaves, and determinate growth   on Pv01, a major determinacy gene in common bean,  was a major disease in North America in the 20th century (Phvul.011G193100, which maps in the interval suggested to contain the resistance gene Ur-3 (Ur-3 was the first gene aggressively used by US breeders to address bean rust in the mid-1980s (Ur-3 and Ur-11 provides resistance against all known rust races (The primary disease resistance phenotype introduced into entries in the CDBN over the past 30 years was bean rust resistance. Bean rust ( century . Althoug century , rust wa century . Our strFinally, this work allowed us to characterize the patterns of sharing of genetic effects on phenotypes in the CDBN, using an exciting new method to determine if this data set could uncover novel results. Selection for the common bean ideotype is known to have led to pleiotropic effects on other traits, such as seed yield, biomass, and plant height . Our masOverall, METs such as the CDBN offer a remarkable opportunity to identify candidate genes underlying phenotypic variation and phenotypic plasticity, and to identify how artificial selection has affected crop phenotypes through time. We note that the genomic regions found with this approach are likely to have consistent, stable phenotypic effects across a large range of environments. These genomic regions are thus likely to be generally useful to bean breeding. Detailed mapping and cloning of the causative genes in these regions will provide insight into molecular mechanisms that control these critical phenotypes important for high productivity of common bean. In the future, we also believe that it would be of great value to crop breeding and genetics to archive DNA from all material used in breeding programs and MET trials.Many crops, both in the U.S. and worldwide, have public trials that could be mined in a manner similar to our approach. This work will require collaborative efforts between crop breeders and bioinformaticians to digitize, clean, and analyze phenotypic data from METs, and to obtain genetic material from successful and unsuccessful trial entries. Phenotypic and genetic data can be combined using genomic selection approaches, or by GWAS using models that adjust BLUPs for effects of kinship, trial location, and trial year (


Many conifer breeding programs are paying increasing attention to breeding for resistance to needle disease due to the increasing importance of climate change. Phenotyping of traits related to resistance has many biological and temporal constraints that can often confound the ability to achieve reliable phenotypes and consequently, reliable genetic progress. The development of next generation sequencing platforms has also enabled implementation of genomic approaches in species lacking robust reference genomes. Genomic selection is, therefore, a promising strategy to overcome the constraints of needle disease phenotyping.We found high accuracy in the prediction of genomic breeding values in the disease-related traits that were well characterized, reaching 0.975 for genotyped individuals and 0.587 for non-genotyped individuals. This compared well with pedigree-based accuracies of up to 0.746. Surprisingly, poorly phenotyped disease traits also showed very high accuracy in terms of correlation of predicted genomic breeding values with pedigree-based counterparts. However, this was likely caused by the fact that both were clustered around the population mean, while deviations from the population mean caused by genetic effects did not appear to be well described. Caution should therefore be taken with the interpretation of results in poorly phenotyped traits.Pinus radiata resulted in a relatively high prediction accuracy of needle loss due to Dothistroma septosporum compared with a pedigree-based approach. Using genomics to avoid biological/temporal constraints where phenotyping is reliable appears promising. Unsurprisingly, reliable phenotyping, resulting in good heritability estimates, is a fundamental requirement for the development of a reliable prediction model. Furthermore, our results are also specific to the single pathogen mating-type that is present in New Zealand, and may change with future incursion of other pathogen varieties. There is no doubt, however, that once a robust genomic prediction model is built, it will be invaluable to not only select for host tolerance, but for other economically important traits simultaneously. This tool will thus future-proof our forests by mitigating the risk of disease outbreaks induced by future changes in climate.Implementation of genomic selection in this test population of  Dothistroma needle blight (DNB), also known as red band needle blight, is one of the most important needle diseases that affect conifer species (pines) across the world –4. FrequDothistroma septospora (Dorog.) Morelet and characterized by 1–3 mm wide brick-red bands around the needles, caused by the release of a mycotoxin (dothistromin) . This challenge in consistent phenotyping is seen in other conifer diseases . This follows the concept that resolution of genetic differences in tree disease tolerance/resistance can depend greatly on disease incidence –42. Thisdle cast ; Swiss ndle cast  or red ndle cast ).]. Such factors further promote spatially dependent disease expression, resulting in even larger environmental heterogeneity which in turn underestimates heritability and the potential response to selection . In contrast, Li et al.  reported genotype by environment interaction (GxE) among environments across New Zealand using a factor analytic approach . However, this statistical approach is confounded with connectivity so that lower genetic correlations may reflect low levels of genetic relatedness (low number of shared parents) among environments. Due to small data set, incompatible with a comprehensive approach, and previous studies of the same needle disease with extensive data, showing low GxE (when genetic connectivity between environments is sufficient) , replacing the subjective visual assessment methods presently in place. In addition, screening systems where environmental conditions are controlled could be advantageous in discovering the underlying genetic signal. The use of laboratory-based assays are under development as a possible option for discovering a stronger genetic signal, although even within more controlled conditions, variation remains a challenge [Phytophthora pluvialis and Phaeocryptopus gaeumannii causing Swiss needle cast in Douglas-fir [Improvements in capturing phenotypes in the field could be enhanced through the development of more quantitative assessments using remote sensing –58, replhallenge . Detacheglas-fir .Dothistroma septosporum possibly due to the missing sexual stage Mycosphaerella pini Rostr. ex Munk, identified only in Europe and Canada to date [,Dothistroma has found very limited genetic diversity, supporting conclusion that all isolates belonged to only one mating type . This single mating type has subsequently been shown to be the same type detected in Australia . Therefore, the results of this study should be considered mating-type specific and not necessarily applicable in environments affected by other mating types. Thus, when interpreting our results, we must consider not only the context of the trajectory of climate change but also the biology and genetic diversity of the pathogen population. [The lack of GxE interaction in resistance to DNB may also be explained by the low genetic diversity of  to date ,3. Analying type . This siustralia . Therefoulation. .Implementation of genomic selection in the test population achieved a relatively high prediction accuracy compared with a pedigree-based approach. This result is promising and implies that genomics is a promising option to minimise the current biological/time constraints on reliable disease phenotyping. Reliable phenotyping, however, has to be available in the first instance, for the successful training of the prediction model. Finding the best ontogenetic stage for phenotyping plants and exposure to optimal weather conditions for disease expression, as well as the use of statistical approaches to remove environmental heterogeneity, are all important for successful prediction of resistance to needle diseases.∘ 22’ 19.5”, Longitude E 175∘ 55’ 41.5”) was established using an optimal design [∘ 26’ 44.0”, Longitude E 176∘ 30’ 22.9”) and Kinleith 2  sites were established using the optimal design  with single tree plot, incomplete blocks with 86 and 100 blocks. respectively, with 36 trees per block. There were 1270 genotypes with 1 - 9 ramets each (mean 2.02) at Kinleith 1, with a total of 3,095 trees. At Kinleith 2, there were 653 genotypes with 5 ramets each and 340 control individuals with no replicates, giving a total of 3,590 trees. The number of individuals that were genotyped was 720 at Kaingaroa, 647 at Kinleith 1, and 342 at Kinleith 2. The level of DNB expression was scored as a percentage of the crown that was affected in 5% steps [The genomic prediction model was trained on three clonally replicated full-sib experiments established and managed by New Zealand Radiata Pine Breeding Company Ltd (RPBC). The Kaingaroa site  ∼N ∼N is the first-order autoregressive correlation matrix, a) ∼N ∼N ∼MVN*0.5), and H is the relationship matrix, incorporating information from both the pedigree and genomic markers, and is constructed as follows:A11 is the pedigree-based relationship matrix for non-genotyped individuals, A22 is the pedigree-based matrix for genotyped individuals, A12 and A21 are the pedigree-based matrices between genotyped and non-genotyped individuals, Gw is the rescaled and weighted marker-based relationship matrix. The marker-based relationship matrix was estimated according to [Z = M - P, M is the genotype matrix with the reference allele homozygote coded as 0, a heterozygote as 1 and the alternative allele homozygote as 2  ∼MVN*0.5). The number of iterations was set to 120,000, burnIn to 20,000, and thinning set to 10. Additionally, the same mixed linear model was performed using pedigree-based relationship matrix [H matrix to investigate pedigree-based scenario (ABLUP).The single-step genomic evaluation was performed using a mixed linear model based on a Gibbs sampling algorithm implemented in "BGLR" R package  as follording to  as follov. 1.01e ) and P ipedigree ,73, thero et al.  as folloIndependent evaluation of the prediction model was performed through 10-fold cross-validation, where one tenth of individuals were iteratively defined as the validation population, and all the phenotypes from these individuals were masked as missing values. Predicted values from these individuals were then correlated with both genotypic values to determine predictive ability (PA) and with pedigree-based estimated breeding values to determine prediction accuracy (r1). Alternatively, the prediction accuracy (r2) was estimated as follows:tability . The her


Phaseolus vulgaris L.) are considered to have poor biological nitrogen (N2) fixation (BNF) capabilities although variation in N2 fixing capabilities exists within the species. The availability of genetic panel varying in BNF capacity and a large-scale single nucleotide polymorphism (SNP) data set for common bean provided an opportunity to discover genetic factors associated with N2 fixation among genotypes in the Middle American gene pool. Using nodulation and percentage of N2-derived from atmosphere (%NDFA) data collected from field trials, at least 11 genotypes with higher levels of BNF capacity were identified. Genome-wide association studies (GWASs) detected both major and minor effects that control these traits. A major nodulation interval at Pv06:28.0–28.27 Mbp was discovered. In this interval, the peak SNP was located within a small GTPase that positively regulates cellular polarity and growth of root hair tips. Located 20 kb upstream of this peak SNP is an auxin-responsive factor AUX/indole acetic auxin (IAA)-related gene involved in auxin transportation during root nodulation. For %NDFA, nitrate (NO3−) transporters, NRT1:2 and NRT1.7 (Pv02:8.64), squamosa promoter binding transcriptome factor (Pv08:28.42), and multi-antimicrobial extrusion protein (MATE) efflux family protein (Pv06:10.91) were identified as candidate genes. Three additional QTLs were identified on chromosomes Pv03:5.24, Pv09:25.89, and Pv11: 32.89 Mbp. These key candidate genes from both traits were integrated with previous results on N2 fixation to describe a BNF pathway.Among grain legume crops, common beans ( Phaseolus vulgaris L.) is a major crop of smallholder farmers in Latin America, the Caribbean, and Eastern and Southern Africa. In these regions, common beans are often grown on soils deficient in nitrogen  capacity . This process generates a usable form of nitrogenous compounds for the plant in an economical and environmentally friendly manner through nodulation . This procedure compares the amount of 15N (heavy N2 isotope) for a genotype relative to the 15N value of a control genotype that does not have the capacity to nodulate and fix N2. This value, known as δ15N, is obtained from the ratio of 15N: 14N. The abundance of 15N in different parts of a legume plant can be obtained by calculating the relative deviation from the 15N: 14N ratio. In a recent study, the significant genetic variation among recombinant inbred lines (RILs) derived from Middle American genotypes for %NDFA demonstrated that the genetic variation among RILs would be mostly additive (Glycine max L. Merr.) confirmed that the differences in δ15N between nodulated roots from different lines or tissues are a robust means for quantifying %NDFA  discover the genomic regions and candidate genes associated with nodulation and %NDFA under tropical soil conditions.Given the importance of the nodule numbers and %NDFA in BNF estimation, genetic mapping these two traits may lead to a better understanding of the genetic factors affecting BNF capacity in common bean. In this study, we evaluated a panel specifically developed for abiotic stress tolerance. The aim of this study was to: (1) characterize common bean genotypes with high capacity for NBean Abiotic Stress Evaluation (BASE_120) field trials  were conPhaseolus acutifolius L.) genotypes  of two strains of Rhizobium; R. etli (CIAT 632) and R. tropici (CIAT 899). The use of inoculant containing a mixture of more than one strain of Rhizobium is a recommended agronomic practice in common bean  to fit the model, estimate the main effects and interactions, and to compare the least squares (LS) means. Planting dates and bean genotypes were considered fixed effects, whereas replication within planting date was considered a random effect. A 95% probability level was used to establish statistical differences.n = 2,05,293) generated from genotype-by-sequencing (GBS) reads of 469 Middle American genotypes . These will typically pass the conservative Bonferroni test. But for other traits, we believe the best way is to define the cut off value based on the trait and the specific population under study. That is why in the current study, 1,000 permutation tests and a p value bootstrap of two cutoff values were used. The amount of phenotypic variation explained by these significant SNPs was estimated by a likelihood-ratio-based (R2LR) analysis in R using the GenABEL package that included population structure and/or relatedness effects (A genome-wide association study (GWAS) was performed using the phenotypic data for the BASE_120 genotypes, and their corresponding SNPs. The Base_120 was genotyped with SNPs  production and general NO3− uptake and mobilization. Integrating nodulation and %NDFA data with genotyping data in a genetic analysis enabled the identification of genomic intervals and candidate genes associated with BNF capacity and N metabolism under tropical soil conditions in common bean.The BNF capacity of a legume is associated with nodule-mediated nitrate .Based on results from previous studies, 2 fixation in common beans are sensitive to higher temperatures (r = 0.60). There were significant differences between seasons, entries, and season × entry interaction for nodulation scores. Nodulation and Neratures  and soileratures , which m10(P) = 7.46] with the trait (10 (P) = 7.54] were located inside the gene model Phvul.006G18000, an ortholog of the Arabidopsis RAB GTPase. The auxin-responsive factor AUX/indole acetic auxin (IAA)-related gene model (Phvul.006G181200) was located 20 Kb upstream of RAB GTPase related gene model.After filtering for MAF ≥ 0.05, a total of 1,25,745 SNPs out of 2,11,764 were extracted from the original HapMap . Two prihe trait . These She trait , 3. A to10 (P) > 5.31] was detected at Pv09:25.89 Mbp and was located ~189 kb upstream of another annotated RAB GTPase gene. The second most significant SNP interval was 98 bp in length at Pv08:28.42 Mbp and was located ~2.5 kb upstream region of gene model Phvul.008G157200, an ortholog of a squamosa promoter binding protein-like 8 (SPL8). The SNPEff results revealed that this SNP was placed in the promotor regions of SPL8 and potentially has a modifier effect. A SNP at Pv11:32.89 Mbp was detected 17 Mbp downstream of a small auxin up RNA (SAUR)-like auxin responsive protein family cluster. NO3− transporters, NRT1:7 and NRT1.2  flanked the Pv02:8.64 Mbp peak SNP within the heterochromatic region of Pv02. Finally, the Pv06:10.91 Mbp SNP interval is located ~40 kb downstream of a MATE efflux protein  known to be involved in flavonoid metabolism , phosphatidylinositide, and Ca+2 signaling pathways establishes the symbiotic interactions when legumes are infected by rhizobacteria , protein positively nodule senescence genes in common bean. In mature nodules, AP2-1 is silenced by miR172  and mutant (nrt1.7) Arabidopsis  produced in this process can be converted to NO3− by nitrifying bacteria. The NO3− will then be transported from root to shoot by NRT1.2, and later, remobilized by NRT1.7 from older leaves to the younger leaves. During plant maturation, NO3− will be distributed to the seeds which increases %NDFA (Integrating the information from both nodulation score and %NDFA shows that the SNPs which mapped in or adjacent to candidate genes are associated with BNF in common bean. es RLKs; . This coes RLKs; , which aes RLKs; . The ovees %NDFA . As a fies %NDFA . HoweverThe original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.AO, AG, RM, and CJ performed data analysis and JB, TP, and PM supervised the data analysis. JB and TP designed the phenotypic experiment and AO and PM designed the genomics approach for the association mapping. AO, AG, and RM discussed the results and the interpretation of the final data and JB, TP, and PM provided suggestions to improve it. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Triticum aestivum L.) is one of the world’s most important crops. Maintaining wheat yield gains across all of its major production areas is a key target toward underpinning global food security. Brazil is a major wheat producer in South America, generating grain yields of around 6.8 million tons per year. Here, we establish and genotype a wheat association mapping resource relevant to contemporary Brazilian wheat breeding programs. The panel of 558 wheat accessions was genotyped using an Illumina iSelect 90,000 single nucleotide polymorphism array. Following quality control, the final data matrix consisted of 470 accessions and 22,475 polymorphic genetic markers . Principal component analysis identified distinct differences between materials bred predominantly for the northern Cerrado region, compared to those bred for southern Brazilian agricultural areas. We augmented the genotypic data with 26 functional Kompetitive Allele-Specific PCR (KASP) markers to identify the allelic combinations at genes with previously known effects on agronomically important traits in the panel. This highlighted breeding targets for immediate consideration – notably, increased Fusarium head blight resistance via the Fhb1 locus. To demonstrate the panel’s likely future utility, genome-wide association scans for several phenotypic traits were undertaken. Significant (Bonferroni corrected P < 0.05) marker-trait associations were detected for Fusarium kernel damage (a proxy for type 2 Fusarium resistance), identifying previously known quantitative trait loci in the panel. This association mapping panel represents an important resource for Brazilian wheat breeding, allowing future genetic studies to analyze multiple agronomic traits within a single genetically diverse population.Bread wheat ( The majority of wheat cultivation in Brazil takes place in temperate climate zones of the southern states of Rio Grande do Sul and Paraná. However, wheat is also cultivated in the Cerrado climatic zone of Minas Gerais, Goiás, Mato Grosso and Mato Grosso do Sul in central Brazil. The development of new wheat varieties for cultivation in Brazil is undertaken by public institutes as well as private companies. Various studies have shown that the genetic gains in grain production arising from these breeding activities over the last 40 years has been between 1.2% , predominantly caused in southern Brazil by the fungus Fusarium graminearum Schw. (Teleomorph Gibberella zeae (Schw.) Petch.)  analyses via genome-wide association scans (GWAS) . Such analyses rely on the availability of genome-wide genetic marker datasets, commonly consisting of thousands of single nucleotide polymorphism (SNP) markers, with various SNP arrays  and awning  , for exaThe aim of this study was to (1) assemble a wheat association mapping panel of relevance to Brazilian wheat breeding, (2) genotypically characterize the panel using a high-density SNP array, (3) augment the genome-wide SNP datasets via the addition of genotypic data for functional SNP markers, (4) demonstrate the utility of the association mapping panel by conducting GWAS for exemplar phenotypic traits, and (5) make these resources publicly available. To this end, we assembled a panel of 470 wheat varieties and breeders’ lines and genotyped it with a 90,000 feature SNP array. The panel was assessed for its genetic diversity, and the potential utility of the resource for quantitative trait locus (QTL) analysis demonstrated via preliminary genome wide association mapping of FHB resistance and awn presence/absence. To help further inform future exploitation of the panel for wheat breeding and genetic analyses a set of 26 functional SNP markers tagging known genetic loci were genotyped. Here, we make these germplasm, SNP and genetic resources publicly available, providing useful resources for wheat research and development in Brazil and beyond. The results are discussed in the context of current and future Brazilian wheat breeding targets.In total, 558 bread wheat accessions were collated from the collections maintained by Brazilian Agricultural Research Corporation (EMBRAPA)-Trigo, Brazil. The panel represents wheat germplasm released between 1852 and 2013, and was selected to be broadly representative of the genetic diversity used within Brazilian wheat breeding programs over the last 75 years at EMBRAPA-Trigo . After quality control filtering, the panel consists of 470 accessions with 364 varieties and 64 breeders’ lines, as well as 42 synthetic lines (Supplementary Table 1), with 327 of the accessions of Brazilian origin.Fusarium infected wheat grain was spread between the rows of the trial 60 days after sowing. Percentage of Fusarium damaged kernels (%FDK), a proxy of disease spread within the ear (type 2 resistance), was assessed by sampling 1,000 seeds after harvest and manually identifying the proportion of Fusarium infected seeds. The number of genotypes was variable between years  hence a between year analysis was performed. This took the best linear unbiased predictions (BLUPs) for genotypes in a design that was unbalanced across years from a model fitted to the FDK data with genotype and year as random effects, implemented using the package lme4  of presence or absence was used as an input to subsequent analyses. Using Genstat (VSN 2H) was estimated on a line mean basis following Field trials for assessment of FHB resistance in the 2011, 2012 and 2013 seasons were undertaken at Embrapa-Trigo, Passo Fundo, Brazil . Each genotype was planted, without replication, in a single plot consisting of 3 rows of 3 meters length, with 20 cm between rows. Following common protocols for assessment of FHB, the trial was divided into three sections according to the maturity group of each line: ‘early’, ‘medium’ and ‘late’ flowering, and entries randomized within each block. To aid FHB infection, plots were watered using mist irrigation for ten minutes six times per day  in order to guarantee humidity during the whole night. Additionally, stat VSN , generalLUPs VSN .Genomic DNA was extracted from leaf material harvested from a single individual per accession using previously described protocols (r) > 0.75, only a single marker from the comparison was kept. PCA plots were generated using the R packages ggplot2 (2r) using the function ‘LD.Measures’ implemented by the package LDcorSV  to fit a line to the data, implemented by the ‘lowess’ function in R. For the A and B genomes, a smoothing span of 5% of data points was used, while for the D genome a smoothing span of 20% of data points was used.All analyses were undertaken using the software, R v3.3.3 . Of the  ggplot2  and ggpu ggplot2 . Intra-cy is the vector of observed phenotypes, X is the incidence matrix of fixed effects β is a vector of coefficients of the fixed effects (here the mean and one SNP), Z is the incidence matrix mapping for individual genetic effects, μ is the vector of individual genetic effects with Var(μ) = Kσg2 where K is the kinship matrix estimated from the markers e is an n × n matrix of residual effect such that Var(e) = Iσe2. These analyses excluded the synthetic wheat lines due to their highly differentiated genetic structure. Correction for kinship was undertaken using the default kinship matrix constructed in GAPIT, from a subset of 4,313 high quality SNPs parsed from the full quality-controlled data matrix as follows: missing data <5%, MAF >5% and thinned by LD at r < 0.75 using a custom script. Skimming by r was undertaken to reduce the effect of uneven LD and SNP density on the calculation of kinship . The wheat reference genome assembly  markers of interest. SNP flanking DNA sequences were acquired from GrainGenes (https://wheat.pw.usda.gov/GG3/) and TriticeaeToolbox (https://triticeaetoolbox.org/), and BLASTn (-20).GWAS was undertaken with the package, GAPIT , Oceania, and pre-1970 . A subset of 4,313 SNPs parsed from the full Illumina iSelect dataset  was used to investigate the genetic substructure of the panel. Principal component (PC) analysis found the first three principal components to capture 11% of the total variance  controlling a range of agronomically relevant traits were used to genotype 349 accessions of the AM panel , 59% of accessions were classified as being ‘semi-dwarf’ in stature, while the remaining lines that lacked semi-dwarfing alleles at either of the two loci were classified as ‘tall’. Mutant Rht-B1b or Rht-D1b alleles were responsible for the semi-dwarf phenotype in 75% and 25% of the accessions carrying dwarfing alleles, respectively. Genotyping ten markers assessing eight disease resistance loci found resistance alleles to be present at relatively low frequency, with eye spot resistance alleles conferred by Pch1 representing the most infrequent . Resistance alleles for Fusarium head blight conferred by the major resistance locus Fhb1 were predicted in 3% of lines . Resistance alleles to stem rust conferred by Stem rust 36 (Sr36) were predicted to be present at 3% (wMAS000015). The most frequent resistance alleles were identified for the soil borne mosaic virus resistance locus Sbm1  and the yellow rust resistance gene Lr34 . Four loci controlling grain quality traits were assessed: (1) via the two-locus haplotypes derived from the assays targeting the Pina-D1 and Pinb-D1 loci (wMAS000018 and wMAS000019), 55% accessions were classified as having ‘hard’ grain  and 45% as ‘soft’ grain (Pina-D1a + Pinb-D1b or Pina-D1b + Pinb-D1a). (2) Alleles contributing to the composition of high molecular weight glutenin subunits (HMW-GS) in the grain were predicted via assays for the Glu-A1  and Glu-D1 (wMAS000014) loci. The two-SNP haplotypes generated allowed classification of Glu-A1 alleles as ‘null’ (16%), ‘1Ax1’ (24% of lines assayed) or ‘1Ax2*’ (59%), ordered here in increasing positive effect on bread making quality. Genotyping of the SNP linked to Glu-D1 predicted 51% of lines to be classified as ‘5+10’, indicative of improved bread making quality, while the remaining 49% of lines were predicted to carry allele(s) for the ‘2+12 and others’ gluten class associated with weaker gluten. (3) Via assessment of the KASP marker assaying a SNP linked to the Gpc-B1 locus (wMAS000017), the allele conferring high grain protein content was predicted to be present in 1% of accessions. (4) Assessment of allelic state at the sucrose synthase Sus2-2B locus (wMAS000021) found 8% of lines to carry the T586C_hapH allele associated with higher thousand kernel weight. Finally, four major flowering time loci were assayed for a subset of known alleles using ten KASP markers: the photoperiod response loci PPD-D1 and PPD-A1, and the vernalization response loci VRN-A1 and VRN-B1. At PPD-D1, assessment for the presence of the 2,089 bp promoter deletion first characterized in the variety Ciano67 identified photoperiod insensitive alleles in 48% of lines (wMAS000024). The photosensitive insensitive allele conferred by the insertion of a Mariner transposable element within PPD-D1 intron-1 was identified at low frequency  in the lines assayed. The PPD-D1 5 bp exon 7 deletion conferring photoperiod insensitivity was identified in 40% of lines (wMAS000026). Collectively, PPD-D1 photoperiod insensitive alleles were present in 91% of the accessions screened. Allelic variation at the T  to C  SNP in exon-4 of the AP1-like gene underlying the VRN-A1 locus found 19% of the accessions tested to carry ‘spring’ alleles (wMAS000034), while the assay targeting the ‘spring’ Vrn-A1a allele conferred by a deletion within the promoter was present in 23% of lines (wMAS000035). Based on these two assays, a total of 42% of lines were predicted to carry ‘spring’ alleles at VRN-A1. The SNP previously reported to be associated with ‘spring’ and ‘winter’ alleles at VRN-A1 .Twenty-six published KASP markers diagnostic for allelic state, or closely linked to previously identified loci (adj < 0.01), BobWhite_c8266_227 at 703.91cM and RAC875_c8642_231 at 709.71cM. For the meta-analysis of %FDK, which had a generalized heritability of 0.68, four significant markers (padj < 0.05) were identified on chromosome 6B, between 218.86 and 226.64 cM  was phenotyped for %FDK in 2011, 2012 and 2013, as well as for awn presence/absence (n = 199) (Supplementary Table 2). For %FDK a meta-analysis was performed between years for GWAS. The overall mean of the resulting BLUPs for %FDK was 5.46 with a standard deviation of 0.69 and the data followed a normal distribution (Supplementary Table 2). For the qualitative awn trait, the number of genotypes with and without awns was 175 and 24, respectively. Of the traits studied, both showed significant marker trait associations . For awn26.64 cM .The analyses in this study centered on wheat germplasm used in Brazilian breeding programs since 1935. These genotypes represent the diversity of lines employed in breeding at Embrapa, and include varieties released for cultivation across all Brazilian wheat growing regions. In addition, lines were also included from global germplasm that had specific ongoing utility to Brazilian germplasm, or which may have relevance in the near future. Our aim was to provide a comprehensive survey of genetic variation of relevance to the Brazilian wheat gene pool, understand how this total variability is distributed in the more recently developed genotypes, and to demonstrate the utility of the resources generated for genetic analysis of agronomic traits.Fusarium disease prevalence means that a broad range of international material, particularly lines adapted to drought environments and CIMMYT material, is suitable for breeding for improved yield in this more northerly Brazilian environment. In contrast, the major Fusarium disease pressure experienced in the southern Brazilian wheat growing regions have thus far limited the use of some international material for breeding of varieties targeting the more temperate climates. The potential long term effects of breeding for these two distinct agricultural environments is likely reflected in the PCA  on chromosome 3B, controls resistance predominantly through limiting the spread of disease through the rachis from an infected floret (type 2 resistance) via the action of a pore-forming toxin-like (PFT) gene  toxins reducing pathogen spread within the ear , Europe  and Asia , as well as panels derived from accessions around the world . While Fhb2 has previously been reported using bi-parental populations, our work is the first time that Fhb2 has been identified by GWAS as far as we are aware. The information on which FHB resistance genes are present, or absent, in the association mapping panel used in our study will inform future Brazilian wheat breeding approaches. The ability to identify QTL by GWAS depends on various factors, including marker allele frequencies in the panel. Thus, while the GWAS undertaken for FHB and awning demonstrates the general utility of the panel for use in GWAS, successful QTL identification for other phenotypes will of course depend on a trait-by-trait and QTL-by-QTL basis. Indeed, we note that the inability to detect Fhb1 via GWAS in our panel was likely due to the low frequency of resistance alleles at this locus in the association mapping panel, which was below the 5% MAF threshold for inclusion of SNPs in the association scans. As an infection of the wheat ear, FHB directly impacts grain yield , this has the potential to enhance breeding of future Brazilian wheat varieties with enhanced disease resistance and grain quality. A locus conferring type 1 FHB susceptibility has previously been reported closely linked to the semi-dwarf Rht-D1b allele on chromosome 4D . While semi-dwarfing alleles were found to be more commonly conferred by Rht-B1b alleles in the panel, the Rht-D1b semi-dwarfing allele was nevertheless observed at a frequency of 16%. GWAS did not detect a susceptibility locus linked to RHT-D1, and further investigation would be needed to clarify the allelic status of the 4D FHB susceptibility locus in this panel.Meta-analysis of three years of field data on the percentage of  the ear  span theHd , B1  and B2  , which was further fine mapped to a 2.2 cM interval , located at 698.529 Mb in the reference wheat genome sequence assembly. The significant SNP for awning identified here by GWAS .GWAS for awning identified a major genetic locus on the long arm of chromosome 5A at approximately 704 cM. In wheat, three dominant inhibitors of awning have previously been identified: B1 region    which control flowering time and seasonal growth habit in response to low temperatures . This indicates it could be of interest to explore whether the preference for awned varieties in warmer climates might also be due to linkage to specific seasonal growth habit alleles at VRN-2. By anchoring SNPs identified here as highly significant to the wheat reference genome, as well as those previously shown to have significant association with awn length on chromosome 5A  is mediated via the soil borne fungus Polymyxa graminis Led. with losses of up to 50% reported in infected wheat fields in southern Brazil  genes, with mutation in one or both genes resulting in hard grain. While differences in Pin gene expression has also been associated with grain hardness  or mutant Pina-D1b/Pinb-D1b alleles, previously reported to be the most frequently observed mutant alleles in worldwide wheat collections  mirrors previous observations in which 86% of hard wheat lines from the CIMMYT breeding program were found to carry the Pinb-D1b allele , the low molecular weight glutenin subunits (LMW-GS) and the gliadins. Alleles at several genetic loci, including VERNALIZATION-A1 [VRN-A1], VRN-B1, VRN-D1) and photoperiod  have a major influence on wheat flowering time, and thus adaptation and grain yield. Recessive wild-type vrn-A1, vrn-B1 and vrn-D1 alleles confer the vernalization responsive phenotype. Mutations in the promotors, introns and exons of VRN-1 are known to result in the loss or reduction of the vernalization requirement  were identified as carrying photoperiod insensitive alleles. This is as expected, as in Brazil wheat is typically grown as part of a cycle of three crop rotations per year, suiting rapidly maturing photoperiod insensitive lines. Of those lines found to lack insensitive alleles at PPD-D1, five possessed insensitive alleles at PPD-A1. These were all synthetic wheat lines originating from the wheat breeding institute CIMMYT, and carried the 1,117 bp promotor deletion first identified in line GS-105 by Homeologous genes controlling flowering time in response to vernalization (Characterization of genetic variation can increase the efficiency of breeding programs, helping selection of lines that maximize genetic diversity, and the introduction of specific traits and characteristics beneficial to regional agricultural environments. The wheat association mapping panel assembled here is the largest and best characterized of its kind specifically aimed to be relevant to Brazilian wheat breeding. We provide detailed genotypic datasets and analyses using the panel and demonstrate the utility of the resources generated by identifying significant marker-trait association via GWAS. Collectively, the resources generated here will help underpin future breeding programs, and aid future genetic analysis of agronomic traits of importance to Brazilian agricultural environments.