Individuals within a common environment experience variations due to unique and non-identifiable micro-environmental factors. Genetic sensitivity to micro-environmental variation  can be identified in residuals, and genotypes with lower micro-environmental sensitivity can show greater resilience towards environmental perturbations. Micro-environmental sensitivity has been studied in animals; however, research on this topic is limited in plants and lacking in wheat. In this article, we aimed to (i) quantify the influence of genetic variation on residual dispersion and the genetic correlation between genetic effects on (expressed) phenotypes and residual dispersion for wheat grain yield using a double hierarchical generalized linear model (DHGLM); and (ii) evaluate the predictive performance of the proposed DHGLM for prediction of additive genetic effects on (expressed) phenotypes and its residual dispersion. Analyses were based on 2,456 advanced breeding lines tested in replicated trials within and across different environments in Denmark and genotyped with a 15K SNP-Illumina-BeadChip. We found that micro-environmental sensitivity for grain yield is heritable, and there is potential for its reduction. The genetic correlation between additive effects on (expressed) phenotypes and dispersion was investigated, and we observed an intermediate correlation. From these results, we concluded that breeding for reduced micro-environmental sensitivity is possible and can be included within breeding objectives without compromising selection for increased yield. The predictive ability and variance inflation for predictions of the DHGLM and a linear mixed model allowing heteroscedasticity of residual variance in different environments (LMM-HET) were evaluated using leave-one-line-out cross-validation. The LMM-HET and DHGLM showed good and similar performance for predicting additive effects on (expressed) phenotypes. In addition, the accuracy of predicting genetic effects on residual dispersion was sufficient to allow genetic selection for resilience. Such findings suggests that DHGLM may be a good choice to increase grain yield and reduce its micro-environmental sensitivity. Developing cultivars well adapted to the different environmental conditions where production is performed is one of the main goals of breeding programs. The performance of cultivars in different environments is affected by genotype-by-environment interactions (G×E). The G×E can result from environmental variation either due to differences in years and localities termed macro-environmental sensitivity, or from variability due to environmental disturbances within environments termed micro-environmental sensitivity. Selecting genotypes with lower macro and micro-environmental sensitivity is beneficial as they can present greater buffer capacity against external disturbances, thus contributing to a higher and more stable yield in a target population of environments. Given the global context of climate change and the expansion of agriculture to new areas, the development of climate-resilient varieties is also relevant to safeguarding the sustainability of wheat production .Micro-environmental sensitivity is defined as the genetic sensitivity to those environmental features that are specific to each individual  and that cannot be identified or measured at each particular plot . The micBefore describing methods to estimate genetic variation in residual dispersion, it is useful to distinguish between biological . Different statistical methods to estimate variance components (VCs) and breeding values in micro-environmental variance have been proposed in animal breeding . As a geGCVE), and the correlation between additive genetic effects in (expressed) phenotypes and residual dispersion . The h2 , P2 , GCVE indicates the proportion of micro-environmental variance that can be changed by selection, and the ρg,gd allows to infer how the selection on additive genetic effects on (expressed) phenotypes will affect the additive genetic effects on dispersion. Estimates for these parameters can be obtained from the output of the DHGLM model and are useful references to develop more resilient cultivars exhibiting lower micro-environmental sensitivity. In addition, the GCVE can be useful for comparison across traits and species.Several genetic parameters can be estimated to understand the implications of micro-environmental sensitivity in breeding; among the most relevant are the Mulder-Hill heritability of residual variance , so that individuals originating from the same line can be considered genetically homogeneous. Another reason that makes wheat a valuable species for this study is that there has been broad literature reporting a considerable variation due to macro-environmental sensitivity  phenotype and residual dispersion for wheat grain yield using a double hierarchical generalized linear model (DHGLM).To evaluate the predictive performance of the proposed double hierarchical generalized linear model for prediction of additive genetic effects on (expressed) phenotype and residual dispersion using cross-validation (CV) analysis.To simplify reading, in the next sections we will refer to (expressed) phenotype as EGY (i.e. expressed grain yield) and to residual dispersion as DGY .6) winter wheat lines (T. aestivum L.) and 21,894 plot observations developed by Nordic Seed A/S breeding company. The breeding lines originated from nine breeding cycles (BC) tested in the years 2013 to 2021  in three locations in Denmark (DK): Skive (north-west DK), Odder , and Holeby (south DK). In total, 26-year-location subsets were successfully assessed, and one failed  due to operational issues. The number of lines, plot observations, and average line replications per BC and for the whole population are presented together with descriptive statistics for grain yield in 6, including family-based selection until third-generation (F3) and creating single seed descent (SSD) lines in the fourth-generation (F4). The experimental trials for each year-location combination comprised 15 blocks of 46 sowing plots of 8.25 m2, containing two replications of 21 F6 lines and two checks assigned randomly within each block. All lines were phenotyped for grain yield measured as kg per plot (8.25 m2). Agronomic practices were standardized within and across year-location subsets .The data consisted of 2,456 sixth-generation , s is the vector of spatial effect with e is a vector of random residuals with n = 9), and thus, it is the sum of the effects centered on those plots. A detailed description of the model and the spatial effect is given in A-model”. Second, the raw phenotypes were corrected by subtracting the estimates of the spatial effect (s) in order to correct for spatial variability within the experimental fields. The corrected phenotypes (yc) were defined as: yc=y−Zs , where y represents the vector of raw phenotypes, and As a first step, a linear mixed model including all relevant effects for the population was utilized to analyze raw data. Such a model has been previously used in A linear mixed model allowing for heteroscedasticity of residual variance for the different environments (hereinafter LMM-HET) was utilized in order to have a reference point for assessing the DHGLM. The LMM-HET was defined as:yc is a vector for the response variable EGY (i.e. corrected phenotypes) with 21,894 observations from the 2,456 lines, X and  Zn are the design matrices for fixed and random effects, respectively; b is a vector of fixed trial effect nested within year-location; g is a vector of additive genetic values with G is the additive genomic relationship matrix (GRM) according to the first method of pj is the allele frequency of the jth SNP; Q is M−P, with M as the SNP matrix coded -1, 0, 1, and P the matrix with the allele frequency of SNP j calculated as 1(2(pi−0.5)) for column j ; l is a vector of line effect with I is an identity matrix and f is a vector of line × environment interaction effect (L×E), with “E” defined for the different year-location subsets and ei is a vector of random residuals with i=1, 2, …, 26).where h2) and broad-sense (H2) heritabilities (i) as: d(G) is the mean diagonal value of G with d(G)=1.84 , which can be interpreted as one plus the genomic inbreeding coefficient for the population (i “) calculated as: The narrow , and it was defined as: ϵ , where P is the phenotype, μ is the intercept, Am is the breeding value for the main effect, L is the polygenic genetic effect, F is the genetic × macro-environmnt interaction effect, exp specifies that the exponential function is used at the level of environmental variance, Av,exp is the breeding value for the environmental variance, and ϵ is a scaled environmental deviation with variance one. As previously commented, in the exponential model the genetic values in environmental variance are additive on a log scale, and thus, it avoids the problem of having negative variances , and a Gamma GLM at the level of the dispersion variable yd , where ycn observation, and Λn is the ‘leverage’ defined as the diagonal element of the hat-matrix H  algorithm in DMU software  by the imatrix H  correspoyc and yd are the vectors of response variables for EGY and DGY, respectively; X ,  b , Zn , l , and f were defined as in LMM-HET. Note that l and f were included only for EGY since it was not possible for DGY due to problems of convergence in the REML algorithm. The convergence problems may be caused by a random effect with a variance close to 0, by between-trait correlations of -1 or 1, or by two different parameters in the model that are difficult to separate (i.e. high correlation between random effects for the same trait). Xd is the design matrix for the fixed effects in DGY, bd is the vector of fixed trial effect nested within year-location in DGY; g and gd are the vectors of additive genetic values for EGY and DGY, respectively, following the distributionwhere σg,gd is the covariance between genetics effect in EGY and DGY, ⊗ denotes the Kronecker product, and G the GRM as previously defined for the LMM-HET model; e and ed the vectors of random residual effects for EGY and DGY, respectively, following the distributionwhere Λ being the leverage , and W and Wd until convergence of variance component estimates. Convergence was assumed when the relative difference in variance component estimates of two successive iterations was lower than 10-5. The bivariate model in Equation 2 was fitted using a reweighted least square (IRWLS) algorithm as follows:where yc.Run a linear mixed model (LMM) with the same effects as in eq. 1 but assuming homoscedasticity of residual variance on Wdn=diag(1−Λn)/2)Calculate yd and weight WdRun a weighted gamma generalized linear model (GLM) with a log-link function for response Calculate Run bivariate model in Equation 2yd, W, and WdUpdate Iterates steps 3-6 until convergenceLinear mixed model (LMM-HET)” section, but using an approximation for xij represents the residual for the j observation in the environment i , i ,  ni is the number of observations in the environment i , and npari is the number of fixed effects estimated in the model for the environment i . Thus, the residual variance approximation is based on the variance of the final vector of residuals.The model in Equation 2 and the iterative estimation procedure were developed by GCVE), and iii) genetic correlation between additive effects on mean and dispersion . A description of the estimated parameters is provided next.Three genetic parameters were computed in order to interpret the micro-environmental sensitivity in our population: i) Mulder-Hill heritability of residual variance (h2) as the regression of additive genetic values in dispersion on the squared phenotypes (p2). The EGCV) allows to infer how much the micro-environmental variance could be changed by selection  was estimated as: The election , and it t -times  until all lines were predicted.The predictive performances of the LMM-HET and the DHGLM were evaluated using the leave-one-line-out (LOO) cross-validation (CV) analysis. The LOO CV was performed by randomly masking the phenotypes of all replicates of one line and using the remaining lines to predict the additive genetic values. This process was repeated ρ) between the average value of lines after correcting for fixed effects and the vector of predicted additive genetic effects in EGY [ rw,p , bw,p) was estimated as the slope of the regression of predicted values obtained with whole phenotypic information on predicted values obtained with partial phenotypic information, m = 2,456, and 10,000 replicates was used to obtain the standard error of the rPA, w,p, and bw,p. A two-tailed paired t-test  was used to compare PA,  rw,p and bw,p for the LMM-HET and DHGLM. The maximum potential PA for g predictions was computed for each year-location subset as gd the maximum potential PA was computed using the same formula but replacing Three estimates were computed to evaluate the predictive performance of the LMM-HET and the DHGLM models: i) Predictive ability (PA) calculated as the Pearson correlation (ar(g^p) . An ordi6 lines with an average number of replication of 8.91 were phenotyped for grain yield (kg grain/8.25m2), corresponding to 21,894 plot observations . The bivariate model in the DHGLM procedure allowed the estimation of the genetic correlation between additive genetic effects in EGY and DGY . Further interpretation of ρg,gd is presented in the following section. Differences in the predicted additive genetic values in DGY :The VCs presented in the previous section were utilized to estimate three genetic parameters associated with micro-environmental sensitivity  found for our population was 0.061. The GCVE is useful to infer the selection response in reducing micro-environmental variance when lines with lower additive values in DGY are selected  estimated for our population was 0.502 .The genetic correlation between additive effects in EGY and DGY ρg,gdρ) between the average value of lines after correcting for fixed effects and the vector of predictions of the genetic additive effects on EGY [g effect) in the DHGLM and the LMM-HET were 0.4292 and 0.4289, respectively, and no significant differences between models were observed in a bootstrap-based t-test . The maximum potential PA for predictions of additive values for EGY  in the DHGLM was 0.1003, with a maximum potential PA of 0.483. The PA for DGY was significantly lower in the t-test  than the PAs reported for EGY with the DHGLM and the LMM-HET.The predictive ability (PA), calculated as the Pearson correlation  between rw,p reported for additive genetic values for EGY was significantly higher for the DHGLM (0.9517) than for the LMM-HET (0.9467) in the bootstrap-based t-test . The rw,p estimated for the additive genetic values in DGY was 0.9049; this value was significantly lower (~4%), in the t-test  than predictions for additive genetic values for EGY from both models.The correlation rw,p bw,p) are presented in bw,p when there is no variance inflation in predictions is 1 .The statistic for variance inflation of predicted additive genetic values bw,p yc) and DGY (yd) variables. As far as we know, studies on micro-environmental sensitivity have been limited in plants, and this is the first time that genetic parameters for micro-environmental sensitivity have been investigated for wheat grain yield. We found that the micro-environmental sensitivity for grain yield is heritable and that there is potential for reducing micro-environmental sensitivity and increasing resilience of wheat breeding lines. In addition, we found that the DHGLM had similar performance for predictions of additive genetic effects in EGY compared to the LMM-HET, and had suitable performance for prediction of additive genetic effects in DGY.In this study, we utilized a DHGLM to investigate the genetic variation in residual variance for grain yield  and to assess the predictability of additive genetic effects in EGY , which specify the genetic effects that are not captured by markers in the genomic term, and hence mostly non-additive variance is expected in The LMM-HET and DHGLM revealed substantial variance for A/S data .×E due to additive-by-environment interactions in EGY. In this alternative formulation, the G×E effect was modelled using a genomic relationship matrix to relate lines within each environment. However, the G×E due to additive genotype-by-environment interactions was excluded from the analysis due to convergence problems in the REML algorithm. In this sense, the line × environment interaction effect (L×E) can be seen as a G×E effect capturing a combination of additive and non-additive effects, and helps to specify the G×E effects for our population.An alternative DHGLM was in addition implemented for VCs estimation to account for G6 lines. The high genetic similarity among replications occurs due to the F6 lines originated from selfing of F3:5 lines, where segregation is very low as product of the high homozygosity due to previous generations of selfing (expected ~96.9%).The DHGLM was used to estimate fixed effects  allows inferring the potential for reducing micro-environmental sensitivity through selection  it can be compared across traits and species  is relevant to infer possible directions of micro-environmental sensitivity in breeding programs and guide selection decisions. A positive-sign ρg,gd represent an unfavorable genetic correlation since it means that selecting for lines with higher additive value in EGY will result in higher micro-environmental sensitivity. In our study, we found an estimate of ρg,gd of 0.502  of the main response variable (YGD) to perform the analysis  is expected to yield a better estimate of ρg,gd as it is free from distortions caused by the scale effect, and therefore, it is a more accurate measure of correlation due to genetic effects. The negative correlation found is favorable for reducing micro-environmental sensitivity in breeding programs since selection for higher additive values in EGY will result in lower additive values in DGY for log-transformed yc. In addition, we have observed a correlation close to 1 (0.99) between predictions for additive values in yc and log-tranformed yc . Based on these results, we concluded that select on both the EGY and DGY is possible and could represent a comprehensive strategy to increase grain yield and reduce micro-environmental sensitivity in winter wheat.To evaluate how the scale effect affects our population, we have performed an additional analysis using the natural logarithm (ln) of  species . The anaP-value > 0.01) in PA for additive genetic effects in EGY were observed between the LMM-HET and DHGLM, and a similar trend was observed for the maximum potential PA  of 0.5% in rw,p was conferred by the DHGLM compared to the LMM-HET  was observed for predictions of additive effect in EGY (predictions of g effect) for any of the proposed models  CV Figure 4gd effect) was significantly lower than for additive genetic effect in EGY ; however, it was significantly lower  than for additive effects in EGY. The bw,p for predictions in DGY revealed a low over-dispersion for predicted values as observed in a bw,p value below one (0.935). A potential reason explaining over-dispersion could be related to the presence of other effects affecting DGY that could not be included in the model. For example, as earlier stated, genetic variation due to G×E interactions could be present at the level of DGY. However, those effects could not be included for our population due to problems of convergence with the REML algorithm.The PA for additive genetic effects in DGY  partially competing for the same variation . Consequently, the spatial effects were accounted for in a two-step approach, first estimating them in a separated linear mixed model and second subtracting their estimates from the raw phenotypes. If confounding between spatial effect and micro-environmental sensitivity effectively occurs, either not correcting for the spatial effect could inflate the estimates for micro-environmental sensitivity, or correcting by them beforehand (i.e. as in our study) would lead to a conservative estimate of the variance of micro-environmental sensitivity. The conservative estimation in our study could imply a stronger micro-environmental sensitivity than we estimated, and therefore, it may be even more important for wheat breeding. Therefore, further work is useful in order to improve model inference.In addition, other G×E related effects and spatial effects were initially attempted to be included in the DHGLM. However, we found limitations for algorithm convergence when increasing model complexity by adding these G×E effects and prohibitive computational times when modelling spatial effects. The prohibitive computational times could be due to the spatial effect and the additive genetic effect for DGY (GCVE) of 0.061, respectively. The genetic correlation between additive effects for (expressed) grain yield and its residual dispersion  was estimated, and we observed a correlation of 0.502. Further analysis using log-transformation of (expressed) phenotypes was performed to study a possible scale effect of higher dispersion variances induced by higher phenotypic values, and a negative genetic correlation was observed after transformation (-0.698). The estimate of ρg,gd using the log-transformation is a more reliable estimate of genetic correlation as it is free from distortions caused by the scale effect. Based on these results, we concluded that breeding for reduced micro-environmental sensitivity is possible and can be included within breeding objectives without compromising the selection for increased yield. In addition, the double hierarchical generalized linear model had a good predictive performance for additive effects on residual dispersion, and showed similar performance for predicting additive genetic effects on (expressed) grain yield compared to a linear mixed model allowing for heteroscedasticity of residual variancep in different environments (LMM-HET). Such findings showed that the double hierarchical generalized linear models could be a good choice to predict additive genetic effects on (expressed) grain yield and its residual dispersion for wheat breeding.In this work, we used a double hierarchical generalized linear model (DHGLM) to study the micro-environmental sensitivity for grain yield in wheat. As far as we know, studies on micro-environmental sensitivity have been scarcely conducted in plants, and this is the first time that genetic parameters for micro-environmental sensitivity have been investigated in wheat. We found that the micro-environmental sensitivity for grain yield is heritable and that there is potential for its reduction, according to a Mulder-Hill heritability (https://doi.org/10.7910/DVN/AB43I3.The original contributions presented in the study are publicly available. This data can be found here: JJ and MR: conceptualization. MR and PS: data curation. MR: formal analysis. JJ, MR: funding acquisition. MR, JJ, BC, PS, RR, LM, TM-H: investigation, methodology, and project administration. JJ and PS: resources and supervision. MR, JJ: software, validation, visualization, and writing—original draft. JJ, BC, PS, RR, LM, TM-H: writing—review and editing. All authors contributed to the article and approved the submitted version.


Pairwise-GWAS analysis revealed six, 14, four and five shared (pleiotropic) loci with headache and hypothyroidism, hyperthyroidism, secondary hypothyroidism, and fT4, respectively. Cross-trait GWAS meta-analysis identified novel genome-wide significant loci for headache: five with hypothyroidism, three with secondary hypothyroidism, 12 with TSH, and nine with fT4. Of the genes at these loci, six  and six  were significant in our gene-based analysis . Our causal analysis suggested a positive causal relationship between headache and secondary hypothyroidism (p = 3.64 × 10−4). The results also suggest a positive causal relationship between hypothyroidism and headache (p = 2.45 × 10−3) and a negative causal relationship between hyperthyroidism and headache (p = 1.16 × 10−13). These findings suggest a strong evidence base for a genetic correlation and complex causal relationships between headache and thyroid traits.Epidemiological studies have reported a comorbid relationship between headache and thyroid traits; however, little is known about the shared genetics and causality that contributes to this association. We investigated the genetic overlap and associations between headache and thyroid function traits using genome-wide association study (GWAS) data. We found a significant genetic correlation ( Headache is one of the most common neurological symptoms affecting the central nervous system, causing pain in the head, neck, and shoulders. Its overall estimated global prevalence accounts for 52% of the general population . It affeHeadache disorders are classified into two major categories: primary headache—when associated with no underlying cause, and secondary headache—when associated with an underlying medical illness ,3. PrimaThe endocrine diseases studied with respect to the pathological processes within the thyroid gland are related to the two most common thyroid dysfunction traits, hypothyroidism, and hyperthyroidism. However, if the pathological processes occur within the hypothalamus or pituitary gland, it results in a rare thyroid dysfunction trait, secondary hypothyroidism ,15. ThesDespite observational epidemiological evidence, a clear interpretation of the increased co-occurrence of headache and thyroid traits is lacking, including the role of genetic factors in their comorbidity. Thus in the present study, we utilise genome-wide association studies (GWAS) summary statistics to investigate the genetic overlap between headache and thyroid traits including thyroid dysfunction traits, hypothyroidism, hyperthyroidism, and secondary hypothyroidism; and thyroid hormones, TSH and fT4, at both the genome-wide as well as the regional locus level. We also used genetic approaches including cross-trait meta-analysis and gene-level genetic overlap, to identify shared genetic components and related pathophysiology between headache and thyroid traits. Finally, we utilise Mendelian Randomisation (MR) to look for causal relationships that occur between headache and thyroid traits. The genetic association between headache and thyroid traits was examined both at the single nucleotide polymorphism (SNP)-, as well as the gene-level. SNP-level genetic association was calculated using four approaches. We first estimated the SNP-level genetic correlation between headache and each thyroid trait using the linkage disequilibrium score regression (LDSC) analysis. Next, to access genetic correlation within segments across the genome we performed pairwise-GWAS (GWAS-PW). We then performed a cross-trait GWAS meta-analysis to identify novel SNPs shared by headache and each thyroid trait. Further, to infer the likely causal relationships of headache with the thyroid traits, we performed the MR analysis. To complement our SNP-level genetic association analysis, we performed a gene-based analysis to identify genome-wide significant (GWS) genes shared by each of the headache and thyroid trait combinations and then further assess the gene-level genetic overlap between the headache and each thyroid trait. Finally, we conducted pathway analysis to delve into the biological insights shared between headache and each thyroid trait.https://pan.ukbb.broadinstitute.org/ (accessed on 29 August 2020)  CEU reference population) [p-values. We note that neighbouring genes may have correlated results due to LD between the topmost significant SNP assigned to each gene.To further confirm our SNP-level genetic overlap analysis and identify shared genes across headache and thyroid traits, we performed gene-based association analysis. Our gene-based analysis was conducted using the GATES test  implemenulation) . The outWe performed independent gene-based analysis using the ‘genetic type I error calculator’ GEC software  as implep-value thresholds , were more than expected by chance. Further analysing the effective number of independent genes obtained using independent gene-based association results, we assessed whether the proportion of genes overlapping headache and thyroid traits, at three nominal p-value thresholds. We then assigned ‘headache’ as the discovery dataset and ‘hypothyroidism’ as the target dataset, to calculate the proportion of expected as well as observed genes overlapping both traits using GEC analysis. The expected proportion of overlapping genes across headache and hypothyroidism was defined as the effective number of independent genes with a p-value less than the threshold in the target dataset divided by the total effective number of independent genes in the target dataset. The observed proportion of overlapping genes across headache and hypothyroidism was defined as the observed effective number of independent overlapping genes divided by the effective number of independent genes with a p-value less than the threshold in the discovery dataset. Finally, using a one-sided binomial test, we test the statistical significance of the observed proportion of overlapping genes against the expected proportion of genes overlapping headache and hypothyroidism for the three p-value thresholds. A significant binomial test p-value indicates that the observed number of overlapping genes was more than expected by chance.To test for gene-level genetic overlap between headache and hypothyroidism, we first estimated the effective number of independent genes overlapping headache and hypothyroidism within the three A similar analysis was performed to test for gene-level genetic overlap between headache and hyperthyroidism, secondary hypothyroidism, TSH, and fT4.p < 0.05, we combined the gene-based p-value for the respective traits using Fisher’s combined p-value method (FCP) [To identify individual genes associated with headache and thyroid traits at a od (FCP) ,35. We thttp://biit.cs.ut.ee/gprofiler/ (accessed on 1 August 2022).) [p < 0.05 (FCP < 1 × 10−4). We applied the recommended ‘g:SCS algorithm’ to adjust for multiple testing and restricted the term size of the pathways to 5 and 350. In case the pathways were biased due to the presence of LD across the neighbouring genes, we ensured that the top SNPs of the genes in any respective pathway were not in LD (r2 < 0.1). Enrichmentmap within Cytoscape was utilised to interpret the enriched pathways [To identify potential biological mechanisms and pathways related to the overlapping genes of headache and thyroid traits, we performed functional enrichment analysis using ‘g:GOst’ tool implemented in the g:Profiler web server ( 2022).) . The too 2022).) . In the pathways .h2SNP) estimates from LDSC analysis of the headache and thyroid traits GWAS summary statistics are reported in p < 0.05) h2SNP and were therefore utilised in subsequent cross-trait genetic analysis.The SNP-based heritability , and headache and fT4 . We also found a significant negative genetic correlation between headache and hyperthyroidism . Additionally, although not significant we found a positive genetic correlation between headache and secondary hypothyroidism . However, we did not find a significant correlation between headache and TSH . LDSC indicated a small amount of sample overlap for headache with hypothyroidism, hyperthyroidism, and secondary hypothyroidism, although the genecov intercept were very close to zero  and when constrained to zero had a negligible impact on the results. Detailed SNP-based genetic correlation results are provided in Using LDSC, we found a significant positive genetic correlation between headache and hypothyroidism , respectively . When exp-value < 5 × 10−8 and trait specific 5 × 10−8 < pheadache/thyroid trait < 0.05.A genome-wide cross-trait meta-analysis approach, METASOFT, was utilised to identify loci that may share an association with headache and thyroid traits. We selected SNPs with a meta-analyses Applying the approach detailed in the methods section identified five, three, 12, and nine novel GWS loci from the meta-analysis of headache and hypothyroidism, headache and secondary hypothyroidism, headache and TSH, and headache and fT4, respectively .pmeta = 4.55 × 10−10) for headache and fT4, located near ITPK1. Interestingly, this gene was also associated with headache and secondary hypothyroidism . The second strongest association was at chromosome 17, rs4986170 (pmeta = 5.20 × 10−10) for headache and TSH, located near PLCD3.Among the identified novel loci throughout the headache and thyroid trait combinations, the strongest and most significant association was at chromosome 14, rs12883201 ; hyperthyroidism on headache with IVW , weighted median , and MR-Egger ; and TSH on headache with MR-PRESSO . The MR-Egger intercept for hypothyroidism on headache , hyperthyroidism on headache , and TSH on headache  did not deviate significantly from zero, suggesting no evidence for pleiotropy. Applying GSMR analysis found a significant positive causal relationship between hypothyroidism and headache  and a significant negative causal relationship between hyperthyroidism and headache , however, no causal effect was observed between TSH and headache , weighted median , and MR-PRESSO , but not with MR-Egger . The MR-Egger intercept for headache on secondary hypothyroidism  did not deviate significantly from zero, suggesting no evidence for pleiotropy. Similarly, reverse GSMR analyses found a significant positive causal relationship between headache and secondary hypothyroidism . Interestingly, the reverse GSMR analyses also found weak evidence for a causal relationship between headache and hyperthyroidism , but no evidence for headache on hypothyroidism, TSH, or fT4  of overlapping associated genes was significantly higher  than the empirically derived expected proportion (0.183). A similar pattern of results was observed at the other two thresholds (pgene < 0.01 and pgene < 0.1). We performed gene-level genetic overlap analysis to identify and assess the proportion of associated genes overlapping the headache and each thyroid trait GWAS. To determine if the proportion of overlapping genes was more than expected by chance, we performed binomial tests for genes associated at three resholds . For inspgene < 2.09 × 10−6 adjusted for testing the estimated maximum number of independent gene-based tests . Combining gene-based tests for association across headache and each thyroid trait using the FCP method, we identified 802 unique genes showing evidence of being shared by both traits in the present study.All genes overlapping headache and each thyroid trait GWAS are reported in pgene < 0.05 threshold.We performed pathway analysis using the g:GOst tool implemented in the g:Profiler software of overlapping genes associated with headache and each thyroid trait at a r2 < 0.1), pathway analysis was performed for 482, 226, 69, 170, and 246 genes associated with pFCP < 1 × 10−4 overlapping headache with hypothyroidism, hyperthyroidism, secondary hypothyroidism, TSH, and fT4, respectively. Staphylococcus aureus, leukemia virus 1 infection and coronavirus disease (COVID-19).After retaining the most significant gene within sets of correlated genes with their top significant SNPs in LD , rs3957147 (headache with hyperthyroidism and secondary hypothyroidism), rs9263610 , and rs3130631 (headache with hyperthyroidism and secondary hypothyroidism) at chromosome 6 overlapped across the traits.ITPK1, ZNF462, RPL30P1, ANKDD1B, THADA, ZNF652, and C20orf203 that have been reported as genome-wide significant in the most recent migraine GWAS [PLCD3, Aff3, PDE1C, and RSPH6A. ITPK1, (Inositol-tetrakisphosphate 1-kinase 1) (lead SNP: rs12883201 and rs28540738) a protein coding gene, encodes an enzyme that is involved in inositol phosphate metabolism crucial in the development of the neural tube. It also plays a key role in both signalling and metabolic pathways [THADA, (thyroid adenoma-associated protein) (lead SNP: rs7590268) a protein coding gene, is the known target of 2p21 chromosomal aberrations in benign thyroid adenomas and the encoded protein is possibly involved in the death receptor pathway as well as apoptosis. ZNF652 (zinc finger protein 652) (lead SNP: rs6504608) is also a protein coding gene active in the nucleus and is involved in the regulation of transcription by RNA polymerase II. ITPK1, THADA, and ZNF652 have also been reported to be GWS in the recent TSH GWAS [PLCD3 (phospholipase C delta 3) (lead SNP: rs4986170) catalyses the hydrolysis of phosphatidylinositol 4,5-biphosphate generating diacylglycerol (DAG) and inositol 1,4,5-triphosphate (IP3) which mediate activation of protein kinase C (PKC) and release of calcium (Ca2+), respectively. Although the exact role of this gene in headache and thyroid traits is unknown, it is expressed in both brain as well as thyroid gland tissues.The novel loci identified in our cross-trait GWAS meta-analysis identified a number of novel loci. Interestingly, some of these loci mapped to genes including ine GWAS . Other gpathways . This geTSH GWAS  implyingpgene < 0.05). Of the genes, mapping to novel loci identified from the cross-trait meta-analysis, six  and six  were identified to be GWS in our gene-based analysis (pFCP < 2.09 × 10−6). TMX2-CTNND1 is a locus representing a naturally occurring read-through transcription between the two neighbouring genes on chromosome 11, TMX2 (thioredoxin-related transmembrane protein 2) and CTNND1 . The transcript is a candidate for nonsense-mediated mRNA decay (NMD) and thus is unlikely to produce a protein product. TMX2 has been identified as an important regulator in the developing brain and variants within the gene have been reported to cause severe neurological disorders [HMGB1P45 (high mobility group box 1 pseudogene 45) is a pseudogene that belongs to the HMGB1, which encodes a non-histone, nuclear DNA-binding protein that regulates transcription and is involved in DNA organisation. This gene has been considered a promoter in diseases such as cancers and myocardial infarction [Our gene-level analysis revealed a significant genetic overlap across headache and the tested thyroid trait , C3/C4/C5 complement system and COVID-19, overlapped across headache and thyroid traits. Due to the polygenic contribution of TSH and fT4-associated variants and their shared physiology with headache, we observed some overlapping genes between the traits which relate to some autoimmune diseases. The complement system is made up of a network of plasma and membrane proteins that interacts with the innate and adaptive immune systems. Dysregulated or impaired complement system activation is known to contribute to neurodegenerative diseases with headache as the most common symptom . SimilarOur causal analysis found a significant positive causal relationship between hypothyroidism and headache, and a significant negative causal relationship between hyperthyroidism and headache. We also found a significant positive causal relationship between headache and secondary hypothyroidism. These causal relationships suggest that their association might be due to both shared molecular genetic mechanisms as well as causality. Although the underlying mechanism(s) is unclear, the role of the hypothalamus in the comorbid state of headache and thyroid dysfunction is a possible hypothesis. A recent study has reported that cases with headache/migraine conditions are represented by neuro-endocrinological changes  and thus leads to change in the hormone status of hypothalamically regulated hormones such as TSH, growth hormones, and testosterone. The pain during headache might contribute to the increase or decrease of TSH and fT4 levels  axis) leading to thyroid dysfunction in cases with headache . This hyOur genetic results suggests that in euthyroidism it is important to maintain the levels of TSH and fT4 within the reference range and even a slight change in the levels of fT4 could be associated with headache, however other mechanisms are involved when comparing cases of primary or secondary hypothyroidism (very low levels of fT4) and cases of hyperthyroidism (very high levels of fT4). TSH levels are very sensitive to minor changes in the fT4 levels, thus in thyroid dysfunction abnormal TSH levels are detected earlier than abnormal fT4 levels and the results from our genetic study indicate that fT4 levels have a significant relationship with headache. Additionally, because of the complex relationship observed with secondary hypothyroidism, fT4 levels in blood should be measured in headache patients to understand if thyroid dysfunction causes headache. Thus, testing of blood TSH and fT4 levels should be aided, particularly when symptoms of thyroid dysfunction, e.g., fatigue, constipation, weight gain, muscle weakness, joint pain, slow heart rate depression for hypothyroidism; and/or anxiety, lack of sleep, diarrhoea, tiredness for hyperthyroidism co-occur with headache in a patient . This isThe present study has three possible limitations. First, since we utilised GWAS summary statistics from European ancestry our conclusions may not generalise to other ancestries. Second, our conclusions are limited to the general susceptibility of headache and thyroid traits as the sex-specific GWAS datasets were not available. Third, although there is a slight sample overlap between the headache and PANUK thyroid traits GWAS datasets, we do not expect it to affect our conclusions, indeed the LDSC genetic correlation analyses indicated a very small sample overlap with genecov intercepts very close to zero.The present study provides insight into the genetic and causal relationships between headache and thyroid traits which will help to understand the disease co-occurrence and their management. Our results show a significant genetic correlation between headache and hypothyroidism, hyperthyroidism, and fT4. The shared loci, genes and pathways identified between headache and thyroid traits provide biological insight into their comorbid relationship. Additionally, the causal relationship observed between headache and secondary hypothyroidism; and the genomic regions shared between headache, hypothyroidism, hyperthyroidism, secondary hypothyroidism, and fT4 indicate the importance of measuring levels of both TSH and fT4 when assessing patients with headaches.


MANBA and IRF5. Network-based in silico drug efficacy screening provided candidate agents for further study of pharmacological effect in PSC.Primary sclerosing cholangitis (PSC) is a rare autoimmune bile duct disease that is strongly associated with immune-mediated disorders. In this study, we implemented multitrait joint analyses to genome-wide association summary statistics of PSC and numerous clinical and epidemiological traits to estimate the genetic contribution of each trait and genetic correlations between traits and to identify new lead PSC risk-associated loci. We identified seven new loci that have not been previously reported and one new independent lead variant in the previously reported locus. Functional annotation and fine-mapping nominated several potential susceptibility genes such as  The genetic basis of primary sclerosing cholangitis has only been partially uncovered. Here, the authors perform a multitrait genome-wide association study to provide insight into the genetic etiology of primary sclerosing cholangitis risk and possible therapeutic drug targets. Individuals with PSC are at risk of severe liver problems including a lifetime risk of cholangiocarcinoma of between 5 and 20%4. PSC is often associated with inflammatory bowel disease (IBD). Approximately 75% of individuals with PSC have IBD2, most commonly ulcerative colitis (UC). Individuals with PSC are also more likely than those without PSC to have other autoimmune diseases, including type 1 diabetes, celiac disease, and thyroid disease. The shared etiology and underlying characteristics of these immune-mediated disorders remain incompletely understood.Primary sclerosing cholangitis (PSC) is a chronic, progressive autoimmune disorder of the bile duct5. Association analysis using the Immunochip genotype array data that specifically targeted known autoimmune-related disease regions identified three additional loci influencing PSC risk6. The development of PSC can be attributed to a combination of genetic and environmental factors7. Individuals with a family history of PSC have an increased risk of developing PSC suggesting that genetic influences play a critical role in susceptibility, which may act in concert with exposure to specific environmental factors. However, the genetic and environmental risk factors are not fully elucidated. As PSC is strongly associated with IBD2, examining two traits together may provide better genetic insight into a common genetic etiology11. Few studies have been conducted to understand the shared genetic underpinning between PSC and other associated medical conditions.Recent genome-wide association studies (GWAS) have identified ~19 loci associated with PSC among individuals of European ancestry14 , UC18, IBD18, lupus19, and primary biliary cirrhosis20 (PBC) using multitrait analysis of GWAS21 (MTAG). Although IBD is the umbrella term that includes CD and UC, we also surveyed the pairwise genetic correlation of PSC for CD and UC, respectively. We then performed functional fine-mapping analyses on the newly identified loci to elucidate potential functional characterization and biological mechanisms affecting PSC susceptibility. Since there is no medication proven to be effective for PSC treatment, we conducted network-based drug–disease proximity analysis to identify potential agents suitable for repurposing to PSC from the previously reported13 and newly identified candidate genes in this study.Leveraging publicly available GWAS summary-level dataP = 0.05/134 = 3.73 × 10−4. Since this is hypothesis-based research, we also considered P < 0.05 to identify nominally significant associations that could be examined in future studies. We considered P-values less than the Bonferroni-corrected significance level to be robustly associated in this study and the highlighted traits are displayed in Fig. P = 4.41 × 10−13), UC , CD , lupus , and PBC . Overall shared genetic contribution between PSC and a behavior parameter, general risk tolerance defined as the willingness to take risks22, showed a significant negative correlation . Increased body mass index (BMI) had a significant negative genetic correlation with PSC susceptibility . In epidemiological studies24, the association between PSC and cigarette smoking has been inconsistent. Among traits related to smoking behaviors in this study, smoking status25 modeled in previous smokers versus current smokers showed a strong negative genetic correlation with PSC susceptibility  while smoking initiation26, which is a binary phenotype indicating whether an individual had ever smoked regularly , reported a significant negative genetic correlation with PSC .We investigated the proportion of phenotypic variance explained by all common single-nucleotide polymorphisms (SNPs) for 134 clinical and epidemiological traits to identify potential comorbid conditions and to uncover traits that are causally involved in clinical course and epidemiologic associations using LDSR (“Methods”). We identified numerous traits showing moderate SNP-heritability in the observed scale h2). The study workflow shown in Fig. . The stu−8 (Table ). In addition, our MTAG-identified PSC-specific results confirmed 11 PSC-specific risk-associated variants that have been previously reported in a single-disease GWAS of PSC susceptibility. These include genetic variants from well-established risk loci at 1p36.32, 2q33.2, and 6p21.33-p21.32 that are strongly associated with autoimmune-related diseases28. We displayed a Manhattan plot for the MTAG-identified PSC-specific GWAS  shown in Fig. P < 5.0 × 10−8 are reported in Supplementary Data Based on findings from the genome-wide SNP-heritability and pairwise genetic correlation, we restricted our MTAG to the traits for which LDSR has suggested strong associations with PSC susceptibility, showing h2 > 0.20 and |r_g| > 0.20 . Associations at MANBA have been previously reported for multiple sclerosis29, primary biliary cirrhosis30, psoriasis31, numerous hematologic traits35, asthma37, and major depressive disorders38. Another association at rs17780429 between TNFAIP3 and LINC02528 on 6q23.3 showed a strong genetic signal (PMTAG_PSC = 2.24 × 10−10) and many associations at TNFAIP3 have been observed in autoimmune-related diseases42 and multiple blood-cell traits43. We found a new intergenic variant, rs3757387 between KCP and IRF5 on 7q32.1 (PMTAG_PSC = 2.19 × 10−14). rs3757387 has been previously reported for significant associations with systematic lupus erythematosus among diverse populations44 and in a single population45, rheumatoid arthritis in multiple populations47, and Sjögren’s syndrome48. An NKX2-3 intronic variant, rs791168 on 10q24.2, was associated with PSC susceptibility and has been reported in many autoimmune-related and blood-cell traits13 (PMTAG_PSC = 1.33 × 10−8). LocusZoom regional plots of genome-wide associations for these newly identified loci are provided in Supplementary Fig. A newly identified association of an intronic variant, rs228614, was detected in To assess whether our MTAG results were robust to strong genetic correlation and clinical relevance among IBD, UC, and CD, we repeated our MTAG analysis only including PSC, CD, UC, lupus, and PBC (MTAG_PSC⊥IBD) as a sensitivity analysis. The results from the MTAG-identified PSC-specific model excluding IBD were very similar to those from the inclusion model (MTAG_PSC) Table .14 and GWAS Catalog13, which are independent GWAS from the discovery phase (Supplementary Data PMTAG_PSC_R = 1.79 × 10−2), rs228614 in MANBA (PMTAG_PSC_R = 2.05 × 10−2), rs3757387 between KCP and IRF5 (PMTAG_PSC_R = 1.39 × 10−8), and rs791168 in NKX2-3 (PMTAG_PSC_R = 1.20 × 10−3) at the nominal significance level of 0.05 . We prioritized 406 unique genes from 20 PSC susceptibility loci reported in Supplementary Data 50  are mapped to the regions interacting with the promoter of the listed gene and of which 90 genes (32%) were found in the liver tissue in which the chromatin interaction is observed . We found five immune-related genes including IRF5 and SMO (7q32.1) and HAS3, SNTB2, and VPS4A (16q22.1), within newly identified loci that have not been previously reported , and chromatin interaction mappings, we exploited Functional Mapping and Annotation of GWAS (FUMA GWAS)55 and the multidimensional annotation class integrative estimator57 (MACIE). Out of 168 noncoding genes, we observed 14 more likely deleterious genes (CADD PHRED ≥ 12.37) and 8 and 6 genes on promoter and permissive enhancer sites, respectively.  platform58 and surveyed credible sets of plausible causal variants based on posterior inclusion probability (PIP). We then applied Conditional and Joint Analysis (COJO) using GCTA59 to refine independent associations with prioritized risk loci. Based on the single-SNP PIP with each locus, we identified 32 variants falling into the 95% credible set across eight MTAG-identified GWAS loci  and 4q24 (MANBA) with five variants, 6p21.2 (KCNK17) with two variants, 6q23.3 (TNFAIP3) and 7q32.1 (IRF5) with five variants, 10q24.2 (NKX2-3) with three variants and 16q22.1 (TANGO6) with two variants, respectively. There is no additional genome-wide significant association from GCTA-COJO analysis at the genome-wide significant level of 5 × 10−8.To nominate the candidate causal variants from each locus for further functional analysis, we implemented fine-mapping of MTAG-identified loci using FINEMAPMANBA at 4q24, IRF5 at 7q32.1, and NKX2-3 at 10q24.2, contributing to PSC risk  using coloc60 package , colocalization nominated three candidate genes, age Fig. .Fig. 4Fu61, these candidate genes are highly enriched for protein–protein interactions (P < 1.00 × 10−16), with enrichment at false discovery rate (FDR) < 0.05 of the following pathways: immune receptor activity (FDR = 3.84 × 10−2), beta-2-microglobulin binding (1.10 × 10−2), cytokine-mediated signaling pathway (1.58 × 10−13), interferon-gamma-mediated signaling pathway (1.13 × 10−11), T-cell receptor signaling pathway(2.21 × 10−11), immune response-activating cell surface receptor signaling pathway (2.65 × 10−9), interleukin-7-mediated signaling pathway (9.21 × 10−9), TNFR2 noncanonical NF-kB pathway (7.90 × 10−3), Th17 cell differentiation (2.63 × 10−6), and Th1 and Th2 cell differentiation (1.94 × 10−5) , antigen processing and presentation (8.18 × 10−15), immunoglobulin production involved in immunoglobulin mediated immune response (6.30 × 10−14), cytokine Signaling in Immune system (3.48 × 0−5), interferon Signaling (2.62 × 10−9), and interferon alpha/beta signaling (6.60 × 10−4)  networksMANBA, IRF5, and NKX2-3) to be highly enriched for PPI at FDR < 0.05. Each prioritized gene of MANBA, IRF5, and NKX2-3 reported a PPI P-value of 5.16 × 10−14, 1.00 × 10−16, and 1.13 × 10−9, respectively. We observed B and T-cell receptors, chemokine, C-type lectin receptor, cytosolic DNA-sensing, HIF-1, IL-17, JAK-STAT, MAPK, metabolic, NF-kappa B, NOD-like receptor, PD-L1 expression and PD-1 checkpoint in cancer, RIG-I-like receptor, th1-th2 cell differentiation, th17 cell differentiation, thyroid hormone, TNF, and toll-like receptor signaling pathways in the KEGG pathways at FDR < 0.05 using STRING PPI networks  between drug targets and PSC candidate genes (Supplementary Data ) and the relative proximity measure(z) capturing the statistical significance of distance between drug and disease protein derived from a permutation test ; vitamin E (z = −1.918); MLN0415, a small molecule IKK2 inhibitor downregulating the expression of a number of inflammatory proteins (z = −1.648). The proximity of UDCA showed 0.170 on PSC indicating that it may not be a genetically promising candidate drug for PSC. The FUMA platform facilitates gene mapping to the DrugBank database via GENE2FUNC reported in Supplementary Data Although there is no medication proven to treat PSC, ursodeoxycholic acid (UDCA) is a recommended treatment increasing the bile flow as well as preventing damage to liver cells. While UDCA is used to treat PBC and radiolucent gallstones with a functioning gall bladder, it does not appear to improve survival or reduce the need for liver transplant in PSC patients. From in silico network-based proximity analysisst Table . The mor z = −1.9; MLN041516. We identified significant phenotypic associations between different polygenic traits and PSC. The findings of this study enabled us to confirm previously well-established comorbid conditions and to identify polygenic traits for further study. Complementary approaches such as MTAG, which is a joint association analysis of genetically correlated traits, helped us to discover new susceptibility variants influencing PSC. In addition, LDSR-identified polygenic traits indicating a high correlation with PSC can be applied in Mendelian randomization analysis to unveil further causal relationships between PSC and the traits of interest.We leveraged publicly available GWAS summary statistics to investigate the shared genetic architecture of PSC with a variety of clinical and epidemiological traits and to identify additional PSC-risk loci. We first scrutinized the patterns of genomic overlap between PSC and numerous phenotypes using LDSR. Cross-trait LDSR estimated the genetic correlation between traits to gain insights into common etiologies66. The shared structure of the genetic susceptibility to PSC is notably overlapped with immune-mediated disorders such as CD, IBD, lupus, PBC, and UC27, which have well-established associations with PSC67. In addition, these immune-mediated disorders showed large proportions of phenotypic variance explained by all common SNPs in this study.We observed a significant positive correlation between the genomic architecture of each autoimmune-related disease and that of PSC using LDSR. In several genetic studies, PSC is driven by shared and distinct genetic determinants compared to immune-mediated diseases69. Our study found a strongly protective genetic correlation between the genomic architecture of smoking status modeled in former smokers versus current smokers and that of PSC, suggesting that the genetic contribution of current smoking is associated with a decreased risk of PSC compared to that of former smoking. Although it failed to meet the Bonferroni-corrected significance level of 3.73 × 10−4, the smoking cessation trait modeled in former smokers versus current smokers26 showed a consistent association with PSC implying that the genetic contribution of current smoking is associated with a decreased risk of PSC compared to that of former smoking23. The smoking initiation trait modeled in never-smokers versus ever-smokers26 showed a significant negative association with PSC suggesting that PSC risk among current and former smokers is significantly lower than that among never-smokers23. Smoking promotes chronic epithelial and tissue injury through chronic airway inflammation71 and the most common causes of chronic inflammation include immune-mediated disorders which could potentially contribute to PSC development. Therefore, the shared association of PSC with smoking behaviors makes disentangling such effects challenging.Several epidemiological studies have reported inverse associations between smoking and PSC risk75. However, clinical studies have shown inconsistent associations between cardiovascular disease and PSC76. Pairwise genetic correlation between PSC and cardiovascular risk demonstrated a negative association at the nominal significance level of 0.05. We also identified several suggestive polygenic traits for which the pairwise genetic correlations were nominally significant at P < 0.05. We observed a nominally significant inverse genetic correlation between PSC and several serologic biomarkers including C-reactive protein, glucose, HbA1c, red blood cell distribution width, reticulocyte count, and triglycerides while alkaline phosphatase and sex hormone binding globulin were positively correlated with PSC risk. These findings through LDSR show good concordance with previous clinical and genetic epidemiologic studies77.Applying an orthogonal genomics-driven method complementing clinical epidemiologic research of PSC, we confirmed a link between PSC risk and elevated BMI and diabetesMANBA on 4q24 and IRF5 on 7q32.1 were previously shown to be associated with several hematology-related traits and immune-mediated disorders48. The previously identified phenotypes have also been reported in PSC. In addition, we prioritized candidate genes for PSC susceptibility through MTAG and inferred biological pathways identified through eQTL-colocalization analyses. PPI networks showed that candidate genes were often part of biological pathways involving metabolic processes and immune response.Implementing MTAG, we discovered seven new susceptibility loci that have not been previously reported in GWAS_PSC and, of these, we replicated three lead associations in other GWAS independent from the discovery phase. Two of the new MTAG PSC loci, 20. In this study, we implemented network-based in silico drug efficacy screening to predict agents potentially suitable for repurposing to PSC. Generally, UDCA is recommended for the treatment of cholestatic liver diseases including PSC, but it does not show any effect on the progression and survival of PSC patients78. Interestingly, the proximity of UDCA shows that it may not be a genetically promising candidate drug for PSC. In clinical trials in the U.S., UDCA did not improve the management of PSC79 and its use has been discouraged in the U.S. providers80, indicating a correct prediction of our drug screening analysis. The identified candidate drugs are relevant to lymphoma , various cancers , psoriasis and psoriatic disorders (Tapinarof), vitamin E, IBD (Declopramide), metabolic disorders (Girentuximab), rheumatoid arthritis, liver cancer (Becatecarin), chronic hepatitis C virus (HCV) . Poch et al. reported a single-cell atlas of intrahepatic T-cell landscape in PSC81. The top-ranked drug, Denileukin diftitox, which is involved in the regulation of immune tolerance by controlling regulatory T-cells activity, could be a candidate agent for further study of pharmacological effect.Recently, the identification of targets for drug repurposing (repositioning) using genome-wide approaches has become popular15. Another limitation of LDSR is that the analysis includes only common genetic variants with MAF >0.01 and therefore fails to capture shared heritability due to underlying rare variants between PSC and multiple polygenic traits.Integration, harmonization, and optimization of the existing large-scale GWAS datasets have become a popular analytical strategy to identify new genetic associations. However, access to individual-level GWAS datasets remains limited due to data use restrictions. Although LDSR can quantify the shared genetic architecture of traits having undergone GWAS analysis without requiring GWAS individual-level data, it assumes an absence of population stratification in the underlying summary statistics of the tested traits and necessitates the incorporation of GWAS data from populations expected to have homogeneous genetic structure. Furthermore, GWAS summary statistics with small sample sizes or low SNP-heritability are not amenable to LDSR. One caveat of implementing LDSR is that nonsignificant associations could be due to limited statistical power, rather than a lack of shared heritability, as cross-trait LDSR requires larger sample sizes of GWAS summary-level data to achieve equivalent standard error compared to methods that use individual-level data21 can substantially improve statistical power for detecting susceptibility loci relative to separate GWAS for the traits tested and allows potential sample overlap in numerous trait-specific summary statistics from large-scale cohort GWAS. However, replication or validation analysis is recommended to assess the credibility of each SNP association when MTAG is applied to low-powered GWAS or to GWAS that are considerably heterogeneous in statistical power. Since MTAG uses overlapping SNPs across all GWAS summary statistics, combining summary statistics with a smaller number of SNPs with those with a larger number of SNPs can reduce statistical power.MTAGIn conclusion, our findings from LDSR confirm the associations between immune-mediated disorders and PSC, and epidemiological parameters associated with PSC susceptibility. We also identified and replicated the newly MTAG-identified PSC risk loci and through eQTL-colocalization analysis helped to prioritize candidate genes for PSC susceptibility. This study emphasizes the strong evidence that exists for the shared genetic underpinning among immune-mediated diseases. While PSC GWAS have identified a few risk-associated variants, the function and identity of the causal variants are not fully explored. To address the impact of PSC risk-associated variants in the immune system and within less-well-established noncoding regions, we highlighted several in silico functional approaches to map and prioritize the variants identified. Furthermore, we exploited an immune-related gene database for deciphering how PSC risk-associated variants may alter immune networks. We also utilized the integrative functional annotations platform to functionally characterize the prioritized genes including both coding and noncoding genes, which provide numerous information on variant and indel functional annotations. Since there is no medication proven to treat PSC, we predicted many potential agents at the relative proximity capturing the statistically significant relationship between a potential drug and putative disease-associated proteins. We further carried out gene mapping to the drug database with the broad range of genes prioritized by position, eQTL, and chromatin interaction mapping. These analytical pipelines, which utilize activity maps of noncoding regions help us pinpoint their role in specific cell types. These findings can provide better functional insight into the genetic etiology of PSC susceptibility and improve our understanding of how PSC risk-associated variants alter the immune system. Finally, future studies using causal inference approaches such as Mendelian randomization or genetic instrumental variable methods may help to elucidate the causal relationship between the risk of PSC and other potential candidate phenotypes to reveal surrogate biomarkers that may improve the predictive power of polygenic risk scores.All participants for each GWAS were recruited following protocols approved by the local Ethics Committee/Institutional Review Boards. Written informed consent was obtained from each participant included in the study. All methods were performed in accordance with the ethical guidelines of the 1975 Declaration of Helsinki.2 and 134 clinical and epidemiological traits from existing data resources13. More details are shown in Supplementary Data 82  when the number of SNPs in a trait was considerably smaller compared to that in other traits, thus becoming less informative. Detailed methods are provided in We obtained the GWAS summary statistics for PSC16 . We conservatively set the test-wise significance level using Bonferroni correction to be 0.05/134, adjusting for the analysis of 134 polygenic traits in total (We estimated SNP-heritability (h2) on the observed scale and pairwise genetic correlation (r_g) between multiple polygenic traits using LDSRin total .85. We conducted MTAG  combining PSC with immune-mediated disorders selected by h2 > 0.20 and |r_g| > 0.20. MTAG was modeled for PSC versus five polygenic autoimmune-related traits: CD, UC, IBD, lupus, and PBC (MTAG_PSC). Additionally, we performed a sensitivity analysis excluding IBD (⊥IBD) from the MTAG analysis (MTAG_PSC⊥IBD) since IBD is the umbrella term mainly comprising of medical conditions under which both CD and UC fall86. The sensitivity analysis included only five autoimmune-related diseases; PSC, CD, UC, lupus, and PBC.The commonly used conventional GWAS approach is to analyze the univariate association test for a single trait/phenotype. This does not permit leveraging of genetic information from other polygenic traits. Integrating associations from other traits highly correlated with PSC can improve the statistical power to identify new polygenic variants14, and PBC87 from GWAS catalog, which are independent of those in the discovery phase. Details are reported in Supplementary Data To replicate MTAG-identified PSC risk-associated new loci, we implemented MTAG (MTAG_PSC_R) using PSC (FinnGen phenocode:K11_CHOLANGI), CD (K11_CD_NOUC), UC (K11_UC_NOCD), IBD (K11_IBD), and lupus (M13_SLE) from FinnGen repository49  platform computing LD structure, annotating functions to SNPs, and prioritizing candidate genes from MTAG-derived summary statistics49. To define genomic risk loci for MTAG-identified PSC susceptibility, we used linkage disequilibrium structure based on the European ancestry of the 1000 Genome Project phase 3. Genomic risk loci and the subsets of significant SNPs within each locus were identified using the SNP2GENE function applying the default thresholds: (1) independent significant SNPs, defined as P < 5 × 10−8 and independent from each other at r2 ≥ 0.6 (2) lead SNPs, defined as independently significant SNPs and independent from each other at r2 ≥ 0.1; (3) genomic risk loci, defined by merging lead SNPs within physically overlapped LD blocks and all SNPs in linkage disequilibrium of r2 ≥ 0.6 with one of the independent SNPs. Prioritized susceptibility variants from MTAG GWAS were mapped by positional, eQTL, and chromatin interaction mappings using the FUMA SNP2GENE function with default settings. Finally, FUMA maps the prioritized genes given by the SNP2GENE function to the drug database (DrugBank88) via the GENE2FUNC function in the FUMA platform. The gene table mapped to the DrugBank database provides gene information and the relevant DrugBank IDs that can be found at https://go.drugbank.com/drugs with the details.We mapped the genomic regions of associations by the most significant variants using FUMA GWAS52 portal. InnateDB provides the manually-curated list of genes and signaling responses involved in human innate immunity from publicly available databases including the Immunology Database and Analysis Portal (ImmPort) system, Immunogenetic Related Information Source (IRIS), MAPK/NFKB Network, and Immunome Database. The details can be found elsewhere at https://www.innatedb.com/redirect.do?go=resourcesGeneLists.We examined 406 prioritized genes to nominate innate immune genes associated with PSC using 7476 genes involved in innate immune responses from the InnateDB55  which is an open-access variant functional annotation portal for whole WGS/WES data. FAVOR provides functional annotation information of 8,812,917,339 SNVs across the human genome and 79,997898 indels from the Trans-Omics for Precision Medicine (TOPMed) BROVO variant set (Build GRCh38) based on a collection of databases such as variant category, evidence of chromatin, protein function, conservation, and Clinvar information. The details have been described elsewhere55.We annotated the 406 prioritized genes using FAVOR platform57  to analyze functional annotation data and understand the possible mechanistic roles of individual SNPs. For each variant, MACIE utilizes a generalized linear mixed model that specifies annotation values as outcomes and unobserved latent functional classes as predictors. The posterior probabilities of these unobserved classes are then calculated for each SNP to estimate the probabilities of possessing certain functions. The calculation proceeds through an expectation-maximization (EM) algorithm until convergence. The final posterior expected value of a class is taken as the MACIE prediction. Specifically, we applied MACIE with two latent classes, (1) regulatory class informed by 28 annotations such as H3K27Ac levels and (2) conserved class informed by eight phylogenetic conservational algorithms. Predictions were only made for noncoding variants.We utilized the multidimensional annotation class integrative estimator58  to survey credible sets of plausible causal variants based on the posterior inclusion probability (PIP). We carried out the FINEMAP package with the options “--sss” to specify the “fine-mapping with shotgun stochastic search” and “--n-causal-snps 5” to set the maximum number of causal variants allowed within a locus to 5. We performed Conditional and Joint analysis using GCTA59  to select independent association signals within the prioritized risk loci with the option “--cojo-cond”.We implemented FINEMAP89 database consists of data from 49 normal tissues from 838 donors (Supplementary Data https://cran.r-project.org/web/packages/coloc/)60. We focused on the colocalizations when coloc suggested a plausible posterior probability that both PSC and a tissue from GTEx_v8 are associated and share a single functional variant (PP4 > 0.80).The Genotype-Tissue Expression (GTEx_v8)61  to explore the functional enrichment of protein–protein interaction (PPI) networks and to scrutinize the enrichment of various pathways among the prioritized genes (proteins). In addition, we surveyed the DAVID Bioinformatics Resources90  to look for enrichment of various functional annotations on the 416 prioritized genes after excluding 9 overlapped genes from 19 newly MTAG-identified and previously reported PSC risk-associated genes and 406 genes mapped from position mapping, eQTL mapping, and chromatin interaction mapping provided from FUMA.We utilized the STRING Databased), and the corresponding relative proximity (z), quantifying the network-based relationship between drugs and proteins encoded by genes associated with the disease while correcting for the known biases of the interactome64, were estimated 88.Drug–disease proximity measures, distance (stimated . To elucFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Data 16Supplementary Data 17Supplementary Data 18Reporting Summary


Genetic improvement of end-use quality is an important objective in wheat breeding programs to meet the requirements of grain markets, millers, and bakers. However, end-use quality phenotyping is expensive and laborious thus, testing is often delayed until advanced generations. To better understand the underlying genetic architecture of end-use quality traits, we investigated the phenotypic and genotypic structure of 14 end-use quality traits in 672 advanced soft white winter wheat breeding lines and cultivars adapted to the Pacific Northwest region of the United States.This collection of germplasm had continuous distributions for the 14 end-use quality traits with industrially significant differences for all traits. The breeding lines and cultivars were genotyped using genotyping-by-sequencing and 40,518 SNP markers were used for association mapping (GWAS). The GWAS identified 178 marker-trait associations (MTAs) distributed across all wheat chromosomes. A total of 40 MTAs were positioned within genomic regions of previously discovered end-use quality genes/QTL. Among the identified MTAs, 12 markers had large effects and thus could be considered in the larger scheme of selecting and fixing favorable alleles in breeding for end-use quality in soft white wheat germplasm. We also identified 15 loci (two of them with large effects) that can be used for simultaneous breeding of more than a single end-use quality trait. The results highlight the complex nature of the genetic architecture of end-use quality, and the challenges of simultaneously selecting favorable genotypes for a large number of traits. This study also illustrates that some end-use quality traits were mainly controlled by a larger number of small-effect loci and may be more amenable to alternate selection strategies such as genomic selection.In conclusion, a breeder may be faced with the dilemma of balancing genotypic selection in early generation(s) versus costly phenotyping later on.The online version contains supplementary material available at 10.1186/s12864-022-08676-5. Triticum aestivum L.) is one of the primary objectives of wheat breeding programs. End-use quality is complex and involves multiple traits. The key end-use quality parameters in soft white wheat include softer kernels, lower grain protein content and gluten strength, less damaged starch, lower non-starch polysaccharides that lead to decreased water absorption capacity, larger cookies diameter and cake volume. For some soft wheat products, starch paste viscosity is a key quality trait.End-use quality improvement in soft white wheat  of major genes. For example, the genetic architecture of grain hardness is primarily controlled by the puroindolines, of gluten strength by the high molecular weight glutenins, and of starch paste viscosity by the granule bound starch synthase (‘waxy’) genes , 26. HowA number of mapping studies for end-use quality were performed in bi-parental populations and in some cases one or both parents were either poorly adapted or would not constitute ‘elite’ germplasm for applied plant breeding , 11, 13.4:5 lines and double haploid lines selected from different crosses to represent the diversity present in the WSU winter wheat breeding program. The genotypes and the environments in which the lines were grown were described in Aoun et al. [A total of 672 soft white winter wheat breeding lines and cultivars were used in this study. The breeding lines were Fn et al. . In brien et al. . There wn et al. .The wheat genotypes were evaluated for 14 end-use quality traits that are classified into four categories which are grain characteristics, milling traits, flour characteristics, and baking parameters. The phenotypic and genotypic data were retrieved from Aoun et al.  which inMilling traits included break flour yield, flour yield, flour ash content, and milling score. Break flour yield was calculated as the percent of flour recovered from the break rolls, whereas flour yield (‘straight grade’) was determined as the proportion of grain recovered as flour (break plus reduction flour). Flour ash content is the minerals remaining after flour combustion. Milling score was a function of both flour yield and flour ash content . Higher Flour functionality plays an important role in baking performance. Flour parameters included flour protein content, flour sodium dodecyl sulfate sedimentation volume (SDS sedimentation), water solvent retention capacity (water SRC), and flour swelling volume (FSV). Unlike bread, soft white wheat products require lower grain/flour protein content, weaker gluten strength (lower SDS sedimentation volume), and low water SRC. FSV is an end-use quality parameter associated with the amount of amylose and amylopectin components in endosperm starch  and needH2Cullis) and correlations between traits were previously described by Aoun et al. [These end-use quality traits were measured following the procedures from the American Association of Cereal Chemists International  and as dn et al. .T. aestivum RefSeq v1.0 reference genome [Genotyping-by-sequencing (GBS)  was usede genome  and SNP e genome  in R vere genome .r2) was estimated using TASSEL v5 [r2 values of marker pairs were plotted against the physical distances in Mega base pairs (Mb) after randomly selecting 10% of the total SNP pairs. To visualize the LD decay across the genome and for each of the 21 chromosomes, a locally estimated scatterplot smoothing (LOESS) curve was fitted using the function ‘geom_smooth’ in R package ggplot2 [r2 threshold was derived from the 95th percentile of the distribution of unlinked r2 (for markers on different chromosomes) [r2 threshold is the value beyond which LD was likely to be caused by genetic linkage. The intersection of the horizontal line at the r2 threshold value with the LOESS curve on the LD scatter plot was considered as the estimate of the extent of LD across the genome (genome-wise LD decay plot) and across each chromosome (chromosome-wise LD decay plot).To visualize the population structure in the 672 genotypes, principal component analysis (PCA) was performed using the ‘prcomp’ function in R based on 40,518 SNPs. The population structure was visualized using the first two principal components (PCs) that explained the highest percentage of variation. Pairwise linkage disequilibrium (LD) between SNPs (ASSEL v5  by apply ggplot2 . The r2 mosomes)  that werThe BLUEs for each trait were considered as the phenotype in the GWAS. Association mapping was performed using three models 1) mixed linear model (MLM), 2) Fixed and random model Circulating Probability Unification (FarmCPU) , and 3) 10 (P) versus the observed -log10 (P). The number of PCs included in the GWAS models was limited to the first four PCs. Manhattan plots for MTAs were visualized using the R package ‘qqman’ [The GWAS models considered family relatedness (Kinship matrix or K matrix)  and popu ‘qqman’ . MTAs we ‘qqman’  of ≤ 0.0T. aestivum RefSeq v1.0 reference genome assembly (https://wheat.pw.usda.gov/jb/?data=/ggds/whe-iwgsc2018), we identified annotated genes within the genomic regions of significant SNPs that exhibit large effects . In this search, we considered only high-confidence annotated genes located within a few kilobase pairs before and after the associated SNP physical positions. The putative biological functions of the candidate genes were retrieved from this website https://wheat-urgi.versailles.inra.fr/Seq-Repository/Annotations. In addition, we extracted MTAs available at the International Wheat Genome Sequencing Consortium (IWGSC) sequence repository that were within the genomic regions of large effect MTAs. Furthermore, markers  associated with end-use quality in previous genetic studies [T. aestivum RefSeq v1.0 reference genome) to all MTAs in this study were determined.Based on the  studies , 19–22 aH2 = 0.46–0.70) was observed for all traits except for grain and flour protein content (H2 = 0.18 to 0.19) [The distributions of BLUEs for each of the 14 end-use quality traits are illustrated in Fig. to 0.19) .Fig. 1Dir2 threshold of 0.1 within 6.5 Mb on average  SNPs on the A genome, 16,626 (41.0%) SNPs on the B genome, 8,656 (21.4%) SNPs on the D genome, and 1,134 (2.8%) SNPs on unaligned (UN) chromosome(s). PCA based on the first two PCs showed minimal clustering in wheat genotypes, which was expected since the plant materials in this study were from the same wheat breeding program  model. Using FarmCPU, K + 2PCs (Kinship and Q based on the first two PCs) was selected to model SKCS hardness, SKCS weight, grain protein content, flour yield, flour ash, milling score, SDS sedimentation, water SRC, and cookie diameter, whereas K + 3PCs (Kinship and Q based on the first three PCs) was selected to model the remaining traits, SKCS size, test weight, break flour yield, flour protein content, and FSV. For BLINK, we selected K + 4PCs (Kinship and Q based on the first four PCs) for all traits.P-value distribution from the expected P-value distribution , whereas chromosome 4D had only a single MTA. The favorable alleles and their corresponding frequencies are described in Tables Based on the LD between markers, each MTA identified from the FarmCPU models represent a distinct locus or QTL. Considering all traits together, a total of 178 significant MTAs were identified across all wheat chromosomes Tables ,3,4. SixS5A_480515221 and S6B_705613777 were associated with SKCS hardness and impacted the hardness index by 6.7 and 7.9 units on average, respectively. Marker S2B_533178165 was associated with SKCS weight and influenced the phenotype by 3.55 mg. For test weight, S4B_413497949 had the largest effect and resulted in 1.2 kg/hL difference in the phenotype on average, whereas for grain protein content, S1B_46883868 had the largest effect with 0.85% increase/decrease in the phenotype. S1B_46883868 was also associated with flour protein content and affected the trait value by 0.63% on average.For grain characteristics, five MTAs with large effects were detected on chromosome 1B, 2B, 4B, 5A, and 6B  in this soft white wheat germplasm.For flour parameters, there were four markers with large effects located on chromosomes 1B, 1D, and 4A Table . In addiS1B_46883868 and S1B_653681752) and ten small-effect markers that were associated with at least two end-use quality traits. For each of these 12 markers, there was desirable linkage between the favorable alleles. This suggests that these markers are having desirable pleiotropic effects and could be useful to simultaneously breed for more than a single end-use quality trait. Based on pairwise LD estimates between physically close markers, there were additionally five loci on chromosomes 1A, 1B, 6B, 7A, and 7B that were associated with more than a single end-use quality trait . For instance, SKCS hardness associated markers in this study, S2B_154846350, associated with SKCS size in this study, was located at 4–5 Mb from IWB30179 [QKd.cob-1A  that we 552 Mb, ) and QKD, respec  and QWgc.caas-1B (~ 628–634 Mb), which were associated with grain protein content and wet gluten content, respectively [S4B_63121316 and S5D_543253602 were found close to associated markers with grain protein content gwm368  and wPt, which S3B_630394456 was at the physical position of IWA6254 (630 Mb) also associated with break flour yield [S1A_9128313 and S1A_15686346 were in close proximity to QFY.ksu-1A  and TraesCS1A01G039600 (16 Mb), respectively. TraesCS1A01G010900 (5 Mb) and TraesCS1A01G039600 were annotated as low molecular weight glutenin subunit and high molecular weight glutenin subunit, respectively. Another MTA on chromosome 1B associated with flour yield, S1B_653681752, was identified close to QFY.ksu-1B (649 Mb) [S5A_382294123 and S6B_19335996 were found in proximity to IWB76667  and Qgac.cob-5B.1 , respectively, which were previously identified by El-Feki et al. [For break flour yield, the associated locus tagged by marker ur yield . For floA . S1A_91649 Mb) . Similari et al.   and S5B_539075483, which mapped close to QSsd.caas-5B , IWB27057  is a rational strategy. This study identified the genetic architecture underlying 14 end-use quality traits among recent breeding lines and cultivars from a soft white winter wheat breeding program. Prior to this study, Jernigan et al.  investigS1B_653681752 is useful to breed for higher break flour yield and flour yield and lower water SRC. Similarly, S1B_46883868 is associated with both grain protein content and flour protein. The favorable alleles of nine of the large effect markers were present in high frequencies in this germplasm. This suggested that these markers were under high selection pressure in the soft white wheat breeding program, likely the result of long-term phenotyping and selection, and the pyramiding of favorable alleles across the breeding populations. Based on our comparative mapping, eight of the large effect markers including S1B_46883868, S1D_14707739, S1D_121990680, S1D_411063068, S2B_533178165, S4A_120144412, S4B_413497949, and S5A_20640566 were not reported in previous studies, thus should be prioritized for MAS. Only a few loci were found to have large effects, suggesting that many end-use quality traits have complex genetic architecture and are mainly controlled by several minor genes with small effects. For some traits like SKCS diameter, FSV, and cookie diameter, all identified markers had small effects, suggesting that MAS may not be useful for these traits. Therefore, genomic selection might be a better approach to implement for such traits [Identified MTAs in this study as well as genotypes with favorable alleles will be useful for end-use quality improvement in soft white and other types of wheat. The 12 large effect markers can be converted into Kompetitive Allele Specific PCR (KASP) or thermal asymmetric reverse PCR (STARP) markers for use in marker-assisted selection (MAS). Among these large effect markers, h traits , 55.Ha locus are generally fixed. This is consistent that no MTAs were identified on chromosome 5DS in this study. Other grain characteristics including SKCS size, SKCS weight, test weight, and grain protein influence wheat milling performance [r = 0.8; [S2D_563799166 and S6B_583281710 were found to be associated with both traits.Grain characteristics greatly influence wheat end-use quality , 30, 47.formance , 30. SKCr = 0.8; ) and thir = 0.4–05; [S1B_46883868.Grain protein content is an essential quality trait that affects flour functionality. Unlike bread, soft wheat products often require lower protein levels to minimize gluten formation and mixing strength . The pos 0.4–05; ) between 0.4–05; . This re 0.4–05; . ConsequS1B_653681752 and S5B_508665777 favorable alleles for break flour yield were also associated with higher flour yield. Similarly, S6D_471614981, a favorable allele for flour yield was also associated with higher milling score. Negative correlations between milling score and ash in this germplasm (r = -0.7) were discussed in Aoun et al. [S5B_68052478 minor allele was associated with lower ash and higher milling score. We found that S1B_100055026, which was associated with break flour yield, was located close to Glu-B3 gene flanked by the DArT marker wPt-1317 . It is well known that glutenin subunit families are major components of wheat endosperm storage proteins and are associated with many end-use quality traits. The presence of break flour yield and flour yield associated loci close to Glu-B1 and Glu-B3 may suggest that there is a genetic association between endosperm storage proteins and endosperm structure as evidenced by Boehm Jr et al. [Higher break flour yield, flour yield, lower flour ash, and higher milling score are desirable traits in soft wheat. Cultivars with alleles that increase these traits could lead to higher milling performance and thus greater profit for flour millers. Moderate to high heritability estimates and positive correlations among milling traits in this germplasm  suggest n et al. . This der et al. . The comS1B_653681752, S5A_382294123, and S6B_27918221/ S6B_29771821. Negative correlations between water SRC and milling traits previously discussed by Aoun et al. [S1B_653681752 and S5A_382294123.Unlike bread, confectionary products require lower gluten strength and water absorption capacity, which were measured using SDS sedimentation and water SRC, respectively. Higher water SRC is in part due to starch damage from milling and non-starch polysaccharides , 33, 60 S1A_534055653, which was associated with FSV in our study was near the gene Glu-A1 flanked by the SSR marker wmc312 . This r is rare . Similarectively . MutatioIn this study we investigated the phenotypic and genotypic structure of 14 end-use quality traits in 672 soft white winter wheat breeding lines and cultivars adapted to the Pacific Northwest region of the United States. A total of 178 MTAs were identified across all wheat chromosomes of which 40 MTAs were positioned within genomic regions of previously discovered end-use quality genes/QTL. These results highlight the fact that among the multitude of traits that a wheat breeder selects for, end-use quality is a relatively large proportion. The high heritability of most traits underscores the success of long-term phenotypic selection. Among the identified MTAs, 12 markers had large effects (eight of them were previously uncharacterized) and thus could be prioritized in breeding programs. For example, a relatively manageable number of lines, say, those resulting from head row selection, could be subjected to a single round of genotypic selection to fix the favorable allele at one or more of the large effect loci. Such a strategy could return benefits later on as a greater proportion of lines would meet end-use quality targets during subsequent replicated yield trials. This study also revealed that for some end-use quality traits , only small effect markers were identified, suggesting that these traits are controlled by multiple minor genes in this germplasm, and that alternative selection strategies such as genomic selection could augment traditional and laborious phenotyping.Additional file 1: Supplementary Fig. S1. Principal component(PC) analysis obtained from 40,518 SNPs in 672 soft white winter wheat genotypes. The first two PCs, PC1 and PC2 explaining 5.3% and 4.0% of the variation, respectively. Supplementary Fig. S2. Scatter plot representing the genome-wise linkage disequilibrium (LD) decay. The LD estimate (r2)for pairs of SNPs was plotted against the corresponding physical positions inmega base pairs (Mb) based on IWGSC Wheat Chinese Spring IWGSC RefSeq v1.0. The dashed red line represents the population-specific critical value of r2=0.1.Supplementary Fig. S3. Scatter plot representing the chromosome-wise linkage disequilibrium (LD) decay. The LD estimate (r2) for pairs of SNPs was plotted against the corresponding physical positions in megabase pairs (Mb) based on Wheat Chinese Spring IWGSC RefSeq v1.0. The dashed redline represents the LD population threshold of 0.1. Supplementary Fig. S4. Quantile-Quantile plots of the expected -log10(P) versus the observed -log10(P) for association mapping model MLM for the14 end-use quality traits. Supplementary Fig. S5. Quantile-Quantile plots of the expected -log10 (P) versus the observed -log10(P) for association mapping model FarmCPU for the 14 end-use quality traits. Supplementary Fig. S6. Quantile-Quantile plots of the expected-log10 (P) versus the observed -log10(P) for association mapping model BLINK for the 14 end-use quality traits. Supplementary Fig. S7. Summary of genome-wide association studies for 14 end-use quality traits in 672 soft winter wheat genotypes based on Fixed and random model Circulating Probability Unification model. The horizontal red line indicates significance level at FDR ≤ 0.05.Additional file 2: Table S1. Summary of GWAS for 14 end-use quality in softwhite winter wheat using mixed linear model (MLM). Table S2. Summary of GWAS for 14 end-use quality in soft whitewinter wheat using Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) model. Table S3. Annotated genes within the genomic regions of large-effect markers identified in this study for end-use quality traits in soft white winter wheat. Table S4. Previously identified QTL within the genomic regions of Marker-Trait-Associations identified in this study.