Genome-Wide association studies (GWAS), based on testing one single nucleotide polymorphism (SNP) at a time, have revolutionized our understanding of the genetics of complex traits. In GWAS, there is a need to consider confounding effects such as due to population structure, and take groups of SNPs into account simultaneously due to the “polygenic” attribute of complex quantitative traits. In this paper, we propose a new approach SGL-LMM that puts together sparse group lasso (SGL) and linear mixed model (LMM) for multivariate associations of quantitative traits. LMM, as has been often used in GWAS, controls for confounders, while SGL maintains sparsity of the underlying multivariate regression model. SGL-LMM first sets a fixed zero effect to learn the parameters of random effects using LMM, and then estimates fixed effects using SGL regularization. We present efficient algorithms for hyperparameter tuning and feature selection using stability selection. While controlling for confounders and constraining for sparse solutions, SGL-LMM also provides a natural framework for incorporating prior biological information into the group structure underlying the model. Results based on both simulated and real data show SGL-LMM outperforms previous approaches in terms of power to detect associations and accuracy of quantitative trait prediction. Quantitative traits are important in medicine, agriculture, and evolution but, until recently, few polymorphisms have been shown to be related in these traits. Genome-wide association studies (GWAS) is a statistical technique that has been used successfully in the identification of over 65,000 single-nucleotide polymorphisms (SNPs) that are connected to various traits or diseases  far exceeds the number of samples, and one would expect only a small number of features are associated with the phenotype predictor). Therefore, as is customary for similar regression problems, it is necessary to regularize by demanding sparsity in the coefficients of the final model to prevent over-fitting and to maintain interpretability. The most popular regularizing penalty that serves this purpose is the lasso  , but on the gene level as well . Experiments on semi-empirical data showed that the combination of sparse group lasso and a linear mixed model yielded better power to identify marker associations in a large range of settings, and application to real datasets have verified that SGL-LMM generated a sparse solution with accurate prediction of phenotypes and interpretable detection of marker associations.We used a linear mixed model to model the genetic effects on the phenotypes. More precisely, we modeled the phenotype as a sum of three terms: a fixed effect determined by the association SNPs, a random confounding effect due to population structure, and an i.i.d. noise as follows:y is a vector of observed phenotypes of size m × 1 for m samples, X is a m × q matrix that consists of SNPs and other  variables of the m samples, ypop is a m × 1 random matrix with distribution K is an m by m matrix called realized relationship matrix(RRM) that captures the overall genetic similarity between all pairs of samples, and where y, one only needs β and β and δ goes as follows:β = 0, find the optimal δ.Set β, regularizing by using SGL.Use the δ from the first step to estimate To make a prediction on Now we describe each of the two steps in more detail.β was set to 0, we have:To calculate δ we use an approach similar to Fast-LMM. Because y isHence the log likelihood for a given K into K = USUT where U is orthogonal and S is diagonal, and we have:Diagonalize Substitute we have:C does not depend on δ. The optimal δ can then be calculated from above as a one dimensional optimization problem:Where β based on model described by Equation (1), then, in the next subsection, we introduce the SGL regularization.In this subsection, we describe the estimation for Equation (1) implies that:Hence, using the diagonalization we see that, after δ and β isLet 2 is the L2 norm. y and X by a rotation and a scaling, and to simplify notations we denote them as Here || · ||β, we need to add a regularizer to Equation (10). We used the SGL regularizer: let β, for each group βG be the vector that consists of these components, let λ > 1 and 0 ≤ α ≤ 1, then the regularized optimization problem becomes:To maintain sparsity in the estimated L1 and L2 regularization, with indicating how much sparsity at the SNP level is desired compared to the sparsity at the group level. From a Bayesian perspective, one can think of it as adding a regularizing prior to β of the form:Here λ is the strength of regularization, and α is the comparative strength of the β and δ, phenotype prediction follows from a straight-forward MLE using Equation (1). Suppose there are other samples with genotype X′ and unknown phenotype y′, thenWith estimated y′ isHere We can summarize the SGL-LMM significant SNPs selection in the following algorithm:n be the number of samples and s be the number of SNPs. When training the null model, the complexity is O(n3) which is from the computation of eigenvalues and eigenvectors. This is reasonable when n is about 10k but for higher n one can improve on the time complexity by only taking into account the dominant eigenevalues. The proximal gradient step has a complexity of about O(ns), and since n is usually much less than s, one can see it as more or less O(s). The prediction step has a complexity of O(nn′s), where n′ is the size of the testing set. From the complexity analysis, we can see that SGL-LMM is scalable for the genome-wide association analysis. But when analysing with a huge genome such as the human genome, we recommend to analysis each chromosome individually or doing a 2nd step based on suggested loci from GWAS.Let lambda sufficiently large to set lambda decrease until the result is close to unregularized. Taking advantage of this mechanism, we carry out feature selection using LMM-SGL through the following steps:When solving the Equation (11), we employ SGL R package. Instead of doing a two dimensional grid search of λ and α to determine the optimal parameters, the package fix the mixing parameter α and compute solutions for a path with many λ values. The path begins with (1) Finding the λ that optimizes phenotype prediction accuracyIn order to find the best λ for phenotype prediction, we first fitted the sparse group lasso model with the whole dataset to find a λ path. We then used 5-fold cross validation to find the appropriate λ, which maximize the average explained variance on the test dataset.(2) Stability selectionTo evaluate the significance of individual SNPs, we carry out stability selection  was < 0.05, if its missing rate was >0.05 of the population, or its allele frequencies were not in Hardy-Weinberg equilibrium (P < 0.0001). After filtering, there were 200155 SNPs left.To evaluate the accuracy of SGL-LMM and pervious methods for association mapping, we considered a semi-empirical example based on the genotypic and phenotypic data for up to 1307 world-wide accessions of l et al. . The datA.thaliana. Univariate analyses showed that the phenotype had an excess of associations when population structure was not taken into account  which is available for 177 plants of the 1307 plants of We then simulated the phenotype as follows:Xk is the genotype data for the k causal SNPs. By introducing the group structure, we consider a case with Ng = 200 genes(groups) on the chromsome1 which covered 2000 SNPs, we set m groups to be active. We vary the sparsity level of the active groups to get the total active SNPs to be k. where k = 15 and m = 3). To investigate the influence of the confounding effect strength and the overall noise, we considered varied σpop ∈ {0.5, 0.7, 0.9} and σsig ∈ {0.1, 0.2, 0.3, 0.4, 0.5}. For each combination of σpop and σsig, we generate 10 datasets, resulting in 120 datasets in total for the 12 combinations.The initial setting used for simulation were 3 active groups each containing 5 effective SNP . All the methods that were related to regularization were fit in identical ways (see section 2.2).In this paper, all the models output a ranking list of SNPs with their frequencies of being chosen; true significant markers were rare and accounted for only 15 out of 1,993 in our simulation datasets. Hence, we treated this as a binary classification problem with an imbalanced dataset where we assigned association markers as label 1 and background markers as label 0. The frequency of each marker was treated as the predicted probability for label 1.The ROC (Receive operating characteristic) curve and the PR  curve are commonly used to evaluate performance of classification models. The ROC curve is created by plotting the Sensitivity against the Specificity while varying the threshold settings:The PR curve is created by plotting the Precision against the Recall at various threshold settings:where TP=TruePositve, TN=TrueNegative, FP=FalsePositive, and FN=FalseNegative.In our imbalanced setting, the ROC curve was not a good visual illustration, because the False Positive Rate did not drop drastically when the True Negative was huge. Whereas, the PR curve was highly sensitive to False Positive and was not impacted by a large True Negative denominator. Hence, we chose the PR curve to evaluate the performance for all the methods, and we used the average AUC (Area Under Curve) of the PR curve to explore the impact of various simulation settings.pop in {0.5, 0.7, 0.9} with σsig set at 0.2  to identify genetic associations in quantitative traits with the presence of confounding influences, such as population structure. The approach benefits from the attractive properties of linear mixed models that allow for elegant correction of confounding effects and those of group-based, multi-marker models that not only consider the joint effects of sets of genetic markers rather than one single locus at a time, but that also incorporate biological group information as prior knowledge. As a consequence, SGL-LMM was able to better predict the phenotype and to identify true genetic associations, even in challenging scenarios with complex underlying genetic models, weak effects of individual markers, or presence of strong confounding effects.A. thaliana. First, we found that imposing gene structure as group structure into the model improved both the prediction of phenotype from genotype and the selection of association SNPs, which suggested that incorporating prior biological knowledge into models led to a better fit to real genetic architectures. Second, the combination of a random effect model and a multivariate linear model is a way to reveal the true association of complex phenotypes, especially with a medium signal to noise ratio. It is widely accepted that parts of the unexplained portion of genetic variance can be due to a large number of loci that have a joint effect on the phenotype, but which lead to only a weak signal if considered independently. In addition, SGL-LMM yields much more biologically meaningful and interpretable associations, which suits the biological assumption that complex traits are only related to a few SNPs in a few genes. Our experiments on the flowering phenotype of A. thaliana showed that SGL-LMM linked many more candidate genes, but this was true only in a smaller gene set compared with the Lasso-LMM method.SGL-LMM is useful for genome-wide association studies of complex quantitative phenotypes. In this paper, we have illustrated such practical use through a semi-empirical simulation study and retrospective analysis of The SGL-LMM included both GL-LMM (group lasso with linear mixed model) and Lasso-LMM as special cases by varying the ratio between the L1 and L2 norms. The sparsity within groups and group-wise sparsity influenced the performance of SGL-LMM. Small groups did not benefit from the within-group sparsity that led the method act as group lasso with LMM. In practical use, we recommend doing imputation first, which can ensure a moderate size for each group. The SGL-LMM can be made even more powerful by adding a strategy to deal with overlapping groups, which has been shown to be feasible by Jacob et al. . AssessiYG, AK, MG, and XL conceived and designed the project. YG and CW derived the formula of the method. YG implemented the software, performed the experiment, analyzed data, and wrote the paper with CW and QZ. All authors read, edited, and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer YWC declared a past co-authorship with one of the authors QZ to the handling editor.


For genomic selection in populations with a small reference population, combining populations of the same breed or populations of related breeds is an effective way to increase the size of the reference population. However, genomic predictions based on single nucleotide polymorphism (SNP)-chip genotype data using combined populations with different genetic backgrounds or from different breeds have not shown a clear advantage over using within-population or within-breed predictions. The increasing availability of whole-genome sequencing (WGS) data provides new opportunities for combined population genomic prediction. Our objective was to investigate the accuracy of genomic prediction using imputation-based WGS data from combined populations in pigs. Using 80K SNP panel genotypes, WGS genotypes, or genotypes on WGS variants that were pruned based on linkage disequilibrium (LD), three methods  were implemented with different prior information to identify the best method to improve the accuracy of genomic prediction for combined populations in pigs.In total, 2089 and 2043 individuals with production and reproduction phenotypes, respectively, from three Yorkshire populations with different genetic backgrounds were genotyped with the PorcineSNP80 panel. Imputation accuracy from 80K to WGS variants reached 92%. The results showed that use of the WGS data compared to the 80K SNP panel did not increase the accuracy of genomic prediction in a single population, but using WGS data with LD pruning and GFBLUP with prior information did yield higher accuracy than the 80K SNP panel. For the 80K SNP panel genotypes, using the combined population resulted in a slight improvement, no change, or even a slight decrease in accuracy in comparison with the single population for GBLUP and ssGBLUP, while accuracy increased by 1 to 2.4% when using WGS data. Notably, the GFBLUP method did not perform well for both the combined population and the single populations.The use of WGS data was beneficial for combined population genomic prediction. Simply increasing the number of SNPs to the WGS level did not increase accuracy for a single population, while using pruned WGS data based on LD and GFBLUP with prior information could yield higher accuracy than the 80K SNP panel. Genomic prediction (GP), a method proposed by Meuwissen et al. , in whicThe availability of next-generation sequencing technologies has made it possible to apply GP to WGS data. Generally, increasing the number of SNPs in a panel increases the level of LD between a SNP and a QTL, which should be beneficial for GP. WGS data include a large number of genomic variants, including most causal mutations. Thus, prediction depends much less on LD between SNPs and causal mutations. However, some studies have demonstrated that using WGS data did not increase prediction accuracy or increased it only slightly compared to using high-density SNP panel genotypes. For example, van Binsbergen et al.  reportedIn pigs, most previous studies on GP have concentrated on populations with the same or very similar genetic backgrounds , 12. HowOur objective was to evaluate alternate approaches for combined population GP by analyzing WGS data. Three Yorkshire pig populations with different genetic backgrounds were used to establish a combined reference population, and two reproduction traits and two production traits were investigated to assess the accuracy of GP using different methods.The procedure for collecting pig blood samples was carried out in strict accordance with the protocol approved by the Animal Care and Use Committee of China Agricultural University (Permit Number: DK996).Yorkshire populations were sampled from three breeding farms in China, designated LM, XD, and ZX. Information on the populations and phenotypes is in Table http://www.cnsge.org.cn/). Pedigree-based EBV and genetic parameters for reproduction traits were estimated based on a single-trait repeatability model, which was implemented separately for each population. In the model, the fixed effect included herd-year-season, in which season comprised four levels , and the random effects included additive genetic, residual, and permanent environment effects. For the production traits, a bivariate animal model was implemented with the fixed effect of herd-year-season-sex and the random effects of additive genetics, litter, and residual. In total, 141,876 animals were traced back to construct a pedigree relationship matrix. Corrected phenotypic values  were used as response variables in GP analyses , 12. TheWhole-genome sequence data on 289 pigs from 6 breeds were used as reference data for imputation, comprising 32 Duroc, 86 Large White, 29 Erhualian, 94 Yorkshire, 24 China South, and 24 China North pigs. All pigs had an average sequencing depth of ~ 25X and were introduced by Yan et al.  as a refFor the animals from the three populations used in this study, genomic DNA was extracted from blood samples using a TIANamp Blood DNA Kit . Genotyping was performed using the PorcineSNP80 BeadChip , which includes 68,528 SNPs across the pig genome. In total, 6103 pigs, which represented all families as best as possible, were genotyped as the target panel for imputation. Furthermore, 22 individuals in the target panel of 6103 pigs were sequenced (~ 10X) to calculate the genotype concordance rate (CR), which was defined as the proportion of genotypes of the imputed variants, which were the same as the whole-genome sequence variants.2 ≥ 0.9 [Imputation from the 80K chip to WGS genotypes was performed with Beagle 4.1 . Imputat2 ≥ 0.9 ). After Based on the 80K SNP data, the WGS data, and the pruned WGS data, three methods were used to predict breeding values using the corrected phenotypes: the GBLUP method based on the genomic relationship matrix, the single-step GBLUP (ssGBLUP) method with a combined pedigree–genomic relationship matrix, and the genomic feature BLUP (GFBLUP) method, which included an additional genomic effect that quantifies the joint effect of a group of variants associated with a genomic feature.The GBLUP  model wap matrix . \documeThe ssGBLUP model uses the phenotype information of both genotyped and non-genotyped animals in the same model as GBLUP, except that ple form –26:\docu+0.05A22 , 28, when et al.  to avoidThe GFBLUP  model, wL (v3.1) .−1 to 10−7) were used to assess the statistical significance of the effect of individual SNPs. When a SNP was determined to be significantly associated with corrected phenotypes based on the pre-specified significance cut-off level, the corresponding genomic region was considered to define a genomic feature.Two strategies were used to define genetic markers that formed the different classes of genomic features used in GFBLUP model analyses. First, the following general linear model-based association analysis (GLMA) was conducted, as implemented in the MVP software . After Lhttp://www.ensembl.org). The markers were grouped according to the genomic locations of the QTL region for a specific trait category downloaded from the database. The genomic region spanned by each individual QTL was standardized to three bins of 100, 500 and 1000 kb on each side of the QTL midpoint. The SNPs that were mapped to the QTL region were considered to define a genomic feature.Second, we derived genomic features from the summary statistics of a group of genetic markers located in a previously identified QTL region. All QTL for a specific trait were downloaded from NRSP , and gene annotation information was downloaded from Ensembl GBLUP models based on the 80K SNP panel, WGS data, and WGS data after LD pruning were termed (ss)GBLUP_80K, (ss)GBLUP_WGS, and (ss)GBLUP_LD, respectively. For GFBLUP, the genomic feature matrix was constructed based on prior information gained from QTL and GWAS strategies, and the results were termed GFBLUP_QTL and GFBLUP_GWAS, respectively.To evaluate the accuracy of genomic prediction, the younger LM animals, based on birth dates after December 2013 for reproduction traits and after August 2014 for production traits, were selected as validation populations, with sizes of 223 and 270, respectively. Considering that, only reproduction records were available from the XD population and the small number of genotyped animals from the ZX population, younger XD animals (born after April 2013) were chosen as the validation population for reproduction traits, with a size of 196, which was similar to the size of the LM validation population. The accuracy of genomic prediction was evaluated as the correlation between GEBV and To identify the population structure of the three Yorkshire populations, principal component analysis (PCA) was performed using the 80K SNP panel. Figure Estimates of heritability of the reproduction and production traits in the three Yorkshire populations are in Table To assess the imputation accuracy for the imputed variants, we removed variants with minor allele frequencies (MAF) lower than 9.9E−5 and calculated the mean AR2 and mean CR across the range of MAF for the remaining variants, as shown in Fig. Genomic relationship matrices for use with GBLUP were constructed based on the different marker densities of the 80K SNP panel, the WGS data, and the WGS data after LD pruning. Table It should be noted that, compared to GBLUP_80K and GBLUP_WGS, GBLUP_LD resulted in a higher accuracy for all scenarios Tables , 3, 4; iTo evaluate the effect of including prior information, GFBLUP methods with preselection and prior biology strategies were compared with GBLUP_WGS and other methods. Prediction accuracies differed for different QTL lengths or GWAS p values used for GFBLUP  density panel data can improve the accuracy of genomic prediction , 36, 38,When using imputed WGS data for genomic prediction, several factors can affect accuracy of resulting predictions:In this study, three different methods, GBLUP, ssGBLUP and GFBLUP, were compared. To date, the use of GFBLUP and ssGBLUP with WGS data in pigs has rarely been investigated. The ssGBLUP model uses a combined genotype-pedigree relationship matrix , 25, andmentclasspt{minimaIn this study, we investigated the efficiency of GFBLUP based on two sources of additional information: incorporating prior knowledge of QTL from the literature (GFBLUP_QTL) and including significant SNPs obtained from GWAS (GFBLUP_GWAS) as known genomic features. Our results indicate that GFBLUP yielded approximately 1 to 2% higher accuracy than GBLUP based on the WGS data for the reproduction and production traits  incorporating imputation accuracy into genomic prediction models by weighting all SNPs with imputation accuracy of AR2 for the construction of the election  and Bayeelection  for the election ; and (4)election , 51. Forelection . HoweverThe use of WGS data holds potential to increase the accuracy of combined-population genomic prediction, and ssGBLUP performed best in all scenarios. However, WGS is still much more expensive than SNP-chip assays; thus, a more acceptable approach is to sequence a subset of a population as a reference panel to perform genotype imputation with high accuracy. Our results showed that simply increasing the number of markers used for prediction to the WGS level does not increase the accuracy of single-population prediction, while pruning WGS data and using GFBLUP based on prior information could yield higher accuracy than prediction based on a SNP panel.Additional file 1: Figure S1. Imputation accuracy across chromosomes.Additional file 2: Tables S1. Accuracy of genomic prediction for days to 100 kg (AGE) with different QTL lengths as prior information in GFBLUP.Additional file 3: Tables S2. Accuracy of genomic prediction for backfat thickness (BFT) with different QTL lengths as prior information in GFBLUP.Additional file 4: Tables S3. Accuracy of genomic prediction for number of piglets born alive (NBA) with different QTL lengths as prior information in GFBLUP.Additional file 5: Tables S4. Accuracy of genomic prediction for total number of piglets born (TNB) with different QTL lengths as prior information in GFBLUP.Additional file 6: Tables S5. Accuracy of genomic prediction for days to 100 kg (AGE) with different p values of GWAS as prior information in GFBLUP.Additional file 7: Tables S6. Accuracy of genomic prediction for backfat thickness (BFT) with different p values of GWAS as prior information in GFBLUP.Additional file 8: Tables S7. Accuracy of genomic prediction for number of piglets born alive (NBA) with different p values of GWAS as prior information in GFBLUP.Additional file 9: Tables S8. Accuracy of genomic prediction for total number of piglets born (TNB) with different p values of GWAS as prior information in GFBLUP.


The original version [1] of this article unfortunately contained a mistake. The additive effects of the eQTLs of lncRNAs were flipped, meaning that the base allele in the contrast to derive the additive effects should have been B73, rather than Mo17, due to the original coding of biallele SNPs as “0s” and “1s”. Going through the entire analysis procedure, it was determined that the mistake was made while tabulating the eQTL results from QTL Cartographer. The eQTL locations, the proportion of cis- and trans- eQTLs and the eQTL effects for lncRNAs as reported in the original manuscript are all correct. The effect directions of the eQTLs for lncRNAs was not reported. Hence, the correction to reverse the effect directions of the eQTLs is reflected below and in the attached updated Additional file Mo17 increases the phenotypic value” has been corrected to “the allele from B73 increases the phenotypic value”.In table S2 footnote j and Table S3 footnote j, “the allele from The flipped additive effect mistake does not affect other portions of the paper.Additional file 6:Table S2. eQTL mapping of HC-lncRNA expressed in more than 80% of the RILs. a Chromosome position of e-traits. b Genetic position of e-traits. c The physical chromosomal location on the B73 reference genome (AGPv2) of e-traits. d The middle physical position  of e-traits. e The genetic position of the peak of the eQTL. f The genetic position of the inferior support interval left Li et al. Genome Biology 2014, 15:R40 Page 12 of 15 http://genomebiology.com/2014/15/2/R40 bound of the eQTL. g The genetic position of the inferior support interval right bound of the eQTL. h The physical position of the peak of the eQTL on the B73 reference genome (AGPv2). i The logarithm of odds (LOD) score of the eQTL. j The additive effect - the positive value indicates that the allele from B73 increases the phenotypic value. k The amount of expression variation of the e-trait explained by the eQTL. Type shows the relationship between e-traits and the eQTLs. (XLS 143 kb)Additional file 7:Table S3. eQTL mapping of HC-lncRNA expressed in more than 40% but less than 80% of the RILs. a Chromosome position of e-traits. b Genetic position of e-traits. c The physical chromosomal location on the B73 reference genome (AGPv2) of e-traits. d The middle physical position  of e-traits. e The genetic position of the peak of the eQTL. f The genetic position of the inferior support interval left bound of the eQTL. g The genetic position of the inferior support interval right bound of the eQTL. h The physical position of the peak of the eQTL on the B73 reference genome (AGPv2). i The logarithm of odds (LOD) score of the eQTL. j The additive effect - the positive value indicates that the allele from B73 increases the phenotypic value. k The amount of expression variation of the e-trait explained by the eQTL. Type shows the relationship between e-traits and the eQTLs. (XLS 143 kb)


Genome-wide association studies (GWAS) have identified associations between thousands of common genetic variants and human traits. However, common variants usually explain a limited fraction of the heritability of a trait. A powerful resource for identifying trait-associated variants is whole genome sequencing (WGS) data in cohorts comprised of families or individuals from a limited geographical area. To evaluate the power of WGS compared to imputations, we performed GWAS on WGS data for 72 inflammatory biomarkers, in a kinship-structured cohort. When using WGS data, we identified 18 novel associations that were not detected when analyzing the same biomarkers with genotyped or imputed SNPs. Five of the novel top variants were low frequency variants with a minor allele frequency (MAF) of <5%. Our results suggest that, even when applying a GWAS approach, we gain power and precision using WGS data, presumably due to more accurate determination of genotypes. The lack of a comparable dataset for replication of our results is a limitation in our study. However, this further highlights that there is a need for more genetic epidemiological studies based on WGS data. Most of the associated alleles discovered so far are common, with a minor allele frequency (MAF) above 5%2. Many SNPs are also located outside coding regions, which complicates the identification of causal mechanisms, functional variants and relevant genes. Additionally, identified SNPs collectively only explain a limited fraction of the heritability5. A number of hypotheses for this “hidden heritability” have been proposed, such that part of the heritability is due to rare variants6, that there is a non-negligible fraction of unmapped or untagged common variants7, or that variants with very low effect sizes have not been captured in current GWAS. The extent to which rare and low-frequency coding variants (<5%) influence traits and diseases is still not completely understood2. Rare variants may not be present on currently available SNP arrays nor be well tagged by the available SNPs8 on the array due to the low linkage disequilibrium (LD) between common SNPs and rare variants9. Performing GWAS with imputed or genotyped variants is therefore not ideal for detecting associations with rare variants. Additionally, rare variants are often specific to individual populations12, or even families, making them hard to detect with standard GWAS in unrelated participants.Over the past decade, genome-wide association studies (GWAS) have successfully identified associations of thousands of single-nucleotide polymorphisms (SNPs) with human traits and diseases14. Secondly, whole genome sequencing (WGS) data can be used to better capture rare and low frequency variants and variants not in LD with SNPs on a genotyping array. WGS is superior to imputation when it comes to determining genotypes of rare variants with high accuracy8. Simulation studies have shown that the mapping precision for rare variants increases considerably when using WGS data in a GWAS approach, making it an efficient way of detecting and fine-mapping rare variants simultaneously15. Hence, by shifting from genotyped and imputed data to WGS data, a standard GWAS can be performed with a likely increase in both variant capture and precision. Yet, few GWAS have been performed using WGS data to date. During the last years, WGS has been performed in a variety of different populations16. A recent study within a small kinship-structured cohort (similar to ours), tested for the burden of rare variants from WGS data on six cardiometabolic traits17. The authors found novel signals that neither were captured with low-depth sequencing, nor with genome-wide genotyping with dense imputation in the same samples17, supporting the notion that WGS data in kinship-structured cohorts can improve power to identify genetic associations. A number of studies have also performed GWAS in cohorts where the variants were imputed from unique reference panels based on WGS of a subset of the participants of the same cohorts, or performed GWAS on low coverage (~4×) WGS data22. For example, in a study on circulating lipid levels and five inflammatory biomarkers22, WGS of 2,120 Sardinians was performed to assess the impact of the variants common in the Sardinian cohort but rare in the 1000 Genomes Project. In total, 14 signals were found, including two new loci that would have been missed if data had been imputed using the 1000 Genomes reference panel, further underlining the advantages of large-scale sequencing.However, the limitations in association studies can be partly reduced. Firstly, a powerful approach to identify complex trait- and disease-associated rare variants is to use populations that comprise of families or individuals from a limited geographical area24 and have been shown to be less polygenic in comparison to complex traits and diseases, which increases the power to study the effect in a smaller cohort where WGS data is available. When used for diagnosis, an ideal biomarker should be uniquely present or overexpressed in the tissue of interest and not be influenced by confounding factors, such as genetic variants24. However, genetic factors commonly have a considerable effect on biomarker levels and introduce noise when biomarkers are used for diagnosis. Better characterization of the genetic contribution to variation in biomarker levels is therefore of great importance.Biomarkers are often strongly genetically regulated25 individuals with high coverage WGS data from the kinship-structured population-based Northern Swedish population health study (NSPHS)26 were included. This cohort has also been genotyped27 and imputed, making this a valuable opportunity to compare the relative performance of WGS and genotyping/imputation in relation to the same phenotype measurements. This study is one of few that uses WGS data with a GWAS approach in order to capture a greater number of low frequency variants associated with inflammatory protein biomarkers, and to further characterize the genetic structure underlying these associations, aiming to extend our knowledge of the genetic contribution to these biomarkers.In this project, we used a GWAS approach to test for associations with no fewer than 72 inflammatory plasma protein biomarkers, in order to investigate the gain in precision and rare variant-capture with WGS data compared to genotyped/imputed SNPs. In total, 1005A total of 1005 individuals with WGS data and biomarker data were included in this study. The age of the participants ranged from 14 to 94 years with a median of 52 years, and 50.8% of the participants were females. The biomarkers were measured at two timepoints. At the first timepoint, biomarkers from Olink’s Oncology I and Cardiovascular I panels were measured of which 31 are inflammatory . At the second timepoint, 95 inflammatory biomarkers from the Inflammatory I panel, were measured . After quality control (QC), 72 unique biomarkers remained of which 42 were only from the INF panel, while one had been measured only on ONC_CVD. As many as 29 biomarkers were included on both the INF and ONC_CVD panels and they were considered technical replicates. The average number of individual measurements per biomarker was 915  in ONC_CVD and 829  in INF.−8) associations were identified, and for 41 (56.9%) of the biomarkers, there was at least one associated SNV  were called. A MAF threshold of 0.15% was chosen in order to reach enough statistical power in the GWAS  in ONC_CVD and p-values just above the genome-wide threshold in INF  or even within the gene encoding the biomarker itself. The rest of the associations were in trans, all located on another chromosome than the gene encoding the biomarker  with at least one significant hit identified, had an association in ker Fig. . Adjustiker Fig.  and S4. In general, the biomarkers without a genome-wide significant association had heritability estimates below 0.3, i.e. less than 30% of the variation in biomarker abundance is due to genetic factors  with the top variants from our previous studies, and might therefore be considered independent associations . The top variants in the overlapping loci were more strongly associated  in the present study than in our previous GWAS, except for two biomarkers (MMP-10 and TRAIL), for which more significant GWAS top variants were found in the previous studies  with significant associations in the present study, did not have any significant associations in our previous GWAS when using genotyped/imputed SNP data29 , even if not our most significant. Our results for MCP-3, SCF and CXCL9 did not replicate in the Finnish population (P > 0.05 in the Finnish cohort for our top SNVs) and our result for TNFB was only nominally significant (P = 0.027). On the other hand, the most significant SNVs for IL-7, IL-10, IL-18 and HGF in the Finnish population, were not genome-wide significant in our cohort, even if rs5745687 (for HGF) as well as rs385076, rs17229943, and rs71478720 (for IL-18) were nominally significant (P < 0.05).Due to the lack of a similar dataset (WGS data and measured levels of the same inflammatory biomarkers) for replication, we could only test for replication for a subset of our results using GWAS results of circulating cytokines in a Finnish populationcis-eQTLs in peripheral blood was found for five of the top SNVs (Table 2 > 0.8) with a variant associated with a trans-eQTL in blood. Two secondary hits associated with IL-18R1 and TNFSF14 respectively, and one tertiary hit associated with CCL23, were also found to be colocalized with a cis-eQTL. All overlapping variants had the same direction of effect except for the overlap with the trans-eQTL, where two SNVs (rs10740118 and rs7088799) in JMJD1C was associated with increased protein levels of CXCL5, but was in LD (R2 = 0.86) with a variant, rs10761779 that was associated with decreased RNA levels of CXCL5.Colocalization (the same top variant) with Vs Table , associa2 > 0.8) with a top SNV identified in our study was also the top SNV for an inflammatory disease in the GWAS catalog (v 1.0.2). In four cases, our top SNV had been associated with an inflammatory disease in previous GWAS: rs113010081/CCL4 with inflammatory bowel disease, rs1569723/CD40 with Crohn’s disease, rs4239702/CD40 with rheumatoid arthritis and rs11230563/CD6 with ulcerative colitis. The remaining top variants are in LD (R2 > 0.8) with previously inflammatory disease-associated top variants. For example, the top variants for CXCL5 found in this study (rs352045 in ONC_CVD and rs425535 in INF) are both in high LD (R2 = 0.944) with a variant previously associated with ulcerative colitis. Some of our top SNVs were also colocalized with associations for different blood-trait  colocalized with association signals for one or several inflammatory diseases Table . Here, wcis) and the rest (32.56%) had an association on another chromosome (in trans). Many of the biomarker levels are highly heritable and some top SNVs explained as much as 25% of the variability. When comparing the results to our previous GWA analyses28 using genotyped/imputed data, novel associations were identified for 18 biomarkers when WGS data was used, 15 of which has not been identified in any previous study. Additionally, in four of the biomarkers, for which the associated loci overlapped with our previous study, the top variants in the present and former studies were not correlated (R2 < 0.8), thus making these findings potentially independent associations.We performed a GWAS on 72 inflammatory biomarkers in a Swedish cohort using WGS data, and identified SNVs that were associated with the plasma levels for as many as 41 biomarkers. Of the biomarkers with at least one significant hit, 67.44% had an association within 1 Mb of the gene encoding the biomarker , but also based on genotyped/imputed data, we contributed to the identification of 79 genome-significant loci for 83 plasma protein biomarkers for cardiovascular disease. A more recent study by Sun et al.33 identified nearly two thousand genetic associations with almost 1,500 proteins, which increased the existing knowledge about the human plasma proteome by fourfold. With the use of WGS data, we can extend our knowledge even further. In the present study, we have shown that we can increase the power in identifying novel loci by using WGS data in GWAS, instead of using genotyped or imputed SNPs. We were able to identify associations for 58% of the biomarkers, which is a considerable higher fraction compared to the 30% identified in the same cohort using genotyped/imputed data.We have previously used both mass spectrometry and the recently developed protein extension assay (PEA) to identify the genetic contribution to variation in protein levels in the NSPHS cohort, where we showed that more than 30% of the biomarkers are influenced by genetic variants28. Overall, MAF agreed well between the genotyped/imputed dataset and the WGS dataset, for all associated SNVs. The MAF threshold in our previous studies was set to at least one chromosome in the dataset, which corresponds to a lower threshold than 0.15%. This means that even the rarest variants in the present study were included in the previous analyses with genotyped or imputed SNPs, even though there was no power to identify an association with such rare variants. For the 18 associations not found in our previous studies, the imputation quality was overall good, except for the SNVs that did not pass imputation QC . This variant is quite rare in our cohort (MAF = 1.9%) and was not identified as genome-wide significant in the imputed data, even if imputation quality was suggested as good and the MAF was similar to the WGS data  with variants previously associated with an inflammatory disease, suggesting that variation in biomarker levels might mediate the disease association, although, we did not determine the direction of causality. For example, the minor allele of rs10190555 was found to be associated with higher levels of interleukin 18 receptor 1 (IL-18R1). The minor alleles of two SNPs in LD (R2 = 0.95) with rs10190555 (rs917997[T] and rs6708413[G]) are also associated with a higher risk of inflammatory bowel disease (IBD) and Crohn’s disease respectively. IL-18R1 is a cytokine receptor that binds interleukin 18 (IL-18) and is essential for IL-18 mediated signal transduction. IL18R1 has previously been found to have colocalized disease and eQTL association patterns in CD4 and CD8 cells for both ulcerative colitis and Crohn’s disease34. In that study, reduced transcript levels of IL18R1 in CD4 and CD8 cells was associated with increased risk for IBD, and the SNP most strongly associated with expression was rs11123923. However, rs11123923 is only in weak LD with the top variant (rs10190555) from our study (R2 = 0.20). In our study, the association to IL-18R1 spans over a large region with strong LD. When adjusting for the top variant, a secondary top variant (rs12999517) reached genome-wide significance. This variant is intronic in IL1RL1, which encodes the interleukin-1 receptor-like 1  protein. In a previous functional study, the C allele in rs6543115, which is located in a distal IL1RL1 promoter, was shown to confer susceptibility to ulcerative colitis as well as increase expression of the soluble ST2 isoform35. However, the top conditional variant (rs12999517) from our study is not in LD with the previously found variant, and is not in strong LD with any other variant previously associated with ulcerative colitis.We also compared our association signals for colocalization with data from the GWAS catalog. A total of eight biomarkers  had top variants previously associated with an inflammatory disease, or correlated . The top SNP (rs344560) in the primary signal is a missense variant located in the gene TNFSF14, which encodes the biomarker. This variant was found to be associated with lower TNFSF14 levels. The missense variant is neither reported in the GWAS catalog, nor is it in LD with any previously reported variants. However, the conditional SNP, rs2291668, is correlated with rs1077667 that has previously been associated with multiple sclerosis. A possible explanation is that the missense variant affects the antibody affinity for the biomarker which in turn will give lower measured protein levels. These lower levels might give a strong association signal and it is not until the top missense variant is adjusted for, that the true regulatory variant is detected.When adjusting for the effects of the top SNP (rs344560) associated with TNFSF14, the minor allele of rs2291668 was found to be significantly associated with increased TNFSF14 levels in our study. Interestingly, the secondary variant rs2291668 is found to explain significantly more of the variability in TNFSF14 levels  to complete LD (R2 = 1) in both U.S and European populations39 and are in complete LD in our cohort. The minor allele for rs425535 was previously shown to be associated with significantly higher CXCL5 plasma concentrations and the minor allele for rs352046 with higher CXCL5 expression levels, which agrees with our results where the minor alleles of rs352045 and rs425535 were associated with higher CXCL5 levels (Supplementary Table 2 = 0.97). Our top SNV is located only 137 bp from rs352046 and both are found within transcription factor binding sites. Neither the association with rs352045 nor rs352046 are reported in the GWAS catalog. However, both rs352045 and rs425535 are in high LD (R2 = 0.94) with a variant, rs2457996, that has previously been associated with ulcerative colitis. Ulcerative colitis is a sub type of IBD and CXCL5 have previously been shown to play a role in IBDs, such as ulcerative colitis and Crohn’s disease. In a study by Z’Graggen et al.40, a preferential expression of CXCL5 mRNA in the epithelium of the intestinal tissue from patients with IBD was observed. They also found a strong expression of CXCL5 at protein level. CXCL5, which encodes an epithelial cell-derived neutrophil activating peptide , has previously been suggested as a possible candidate gene for inflammatory diseases37 Since the previously mentioned variants, rs425535 and rs352046, have been shown to be associated with higher CXCL5 plasma concentrations and higher CXCL5 expression levels respectively, this further indicates that these variants might play a role in the pathogenesis of IBD.A subset of our top SNVs overlapped with eQTLs in blood. For example, the correlated variants rs352045 and rs425535 (top SNVs for CXCL5 in the INF and ONC_CVD respectively) are in high LD with an eQTL for Despite the many results, our study has some limitations with one being the limited sample size. Standard GWAS of complex traits commonly includes hundreds of thousands of samples. However, by analyzing quantitative phenotypes that are less complex, such as biomarkers, we can gain power and the sample size can be dramatically reduced. Despite this, for some biomarkers a sample size of <1000 individuals are not enough to make a robust assessment, and further studies in larger cohorts, or meta-analyzes needs to be performed. Another limitation is the lack of reproducibility, given the nature of the study. It is a small kinship-structured cohort, which makes the results not generalizable to more mixed population or a population of another ancestry. While this population structure increases power to detect rare variants that might be more common in an isolated population, it also makes it harder to reproduce in another cohort. As of now, there are also only a limited number of cohorts that have been measuring the levels of the same inflammatory biomarkers and that have WGS data available. Even within our own data, we fail to replicate some results, both in the technical replicates as well as in the novel associations with regards to the results from genotyped/imputed data. The limitations mentioned above are most likely also dependent on differences in biomarker quantifications between the biomarker panels. As with imputation, variant calling can be more or less precise. Only a genotype probability is given, with additional quality measures. Caution should be taken, until the associations have been validated. This applies especially to the associations containing only a few variants, or variants not yet given an official identifier. Validation has to be performed in a similar cohort, in order to obtain higher confidence and better understanding of the results. This limitation was especially apparent in our validation in the Finnish population, with a possible reason for lack of replication being the discrepancy in population size . Other possible explanations for the lack of replication are the different LD structures, the different techniques used for protein quantification, and that one cohort being based on WGS and the other on genotyped and imputed data.In summary, we have performed GWAS in a family-based cohort with WGS data in relation to inflammatory biomarkers. By analyzing only sequencing data, we seek to further extend our knowledge on the genetic contribution to these important biomarkers. The cost-efficient solution of sequencing a few individuals and creating a reference panel to be able to do dense genotyping is becoming a well-established method in genetic studies. The use of low-depth sequencing as a way of increasing power is also more common today. By using high coverage WGS data, we do see an increase in both power and precision despite our limited sample size, an increase which indeed appears promising. We compared our results to earlier studies using genotyped/imputed data as well as to previously published GWAS. This study found several new loci associated with inflammatory biomarkers and nearly 50% of the associations were only detected in the present study. The associations were also stronger, with lower p-values, compared to those identified with genotyped/imputed data, suggesting that genotypes are in general more accurately determined using WGS compared to imputed data. Our results demonstrate the need of deep coverage WGS data with deeper coverage to be able to fully understand the genetic structure of common diseases and complex traits.23.The NSPHS was initiated in 2006 to provide a health survey of the population in the parish of Karesuando county of Norrbotten, Sweden, to study the medical consequences of lifestyle and genetics. Additional participants were recruited in a second phase from the neighboring village Soppero, in 2009. These parishes have about 2,000 inhabitants of which a total of 1,069 participated in the study, whereof 719 individuals participated from Karesuando (2006) while another 350 individuals participated from Soppero (2009). For each participant in the NSPHS, blood samples were taken and serum and plasma were separated and immediately frozen and stored at −70 °C41. Informed consent to the study was given by all participants, including the examination of environmental and genetic cause of disease. If a person was not of age (<18 years), a legal guardian signed additionally. The procedure that was used to obtain informed consent and the respective informed consent form has recently been discussed in light of present ethical guidelines42.The NSPHS was approved by the local ethics committee at the University of Uppsala  in compliance with the declaration of Helsinki10. Briefly, WGS data were aligned to the GR37 using bwa-mem v0.7.1243. The raw alignments were then processed according to GATK best practice44 using GATK v3.3. Variants were called by the GATK HaplotyeCaller 3.3 followed by variant quality score recalibration (VQSR). Sample quality control (QC) was then performed to remove genetic outliers and identify potentially contaminated samples and individuals with sex discordance errors. After QC, 1,021 unique samples with WGS data remained. Before analysis, the VCF-files were converted to PLINK-format with the PLINK software, version 1.90b4.945. Only autosomes and biallelic single nucleotide variants (SNVs) were included in the analysis. If a position had more than two alleles, PLINK keeps the two most common variants and sets the third one to a missing genotype. SNVs within a deletion were also excluded . In the same process, the variants without an rs-id were renamed to chr:position. MAF and deviation from HWE information were assessed with the --freq and --hardy commands in PLINK. The GWA analyses were performed using the GenABEL package in R47. To make the files compatible with GenABEL, they were first transposed with the --recode transpose command in PLINK, and then imported into GenABEL. Variants were annotated with ANNOVAR v 2017.07.1648 using the refGene database.A total of 1,041 samples were successfully sequenced using Illumina short read technology (X-ten) to 30x coverage per individual. The library preparation, sequencing, and variant calling were performed as previously describedhttps://www.olink.com/products/inflammation/). Inflammatory biomarkers have been measured at three different timepoints within the cohort. At the first two timepoints the panels Oncology I (ONC I)23 and Cardiovascular I (CVD I)25 were measured. These include 31 inflammatory biomarkers (ONC_CVD). A total of 1,00525 samples were measured at the first two timepoints. At the third timepoint28, the panel INF I was used to measure biomarker levels (INF). Here, 92 biomarkers were measured in 903 individuals28. Of the inflammatory biomarkers from the ONC_CVD dataset, 30 had overlapping measurements in the INF panel, and thus served as technical replicates. The quality control of the biomarkers has been described previously28. After quality control, up to 957 individuals had available biomarker data in ONC_CVD and up to 892 from INF. Biomarkers with measurements in less than 400 individuals were excluded from downstream analyses.Out of 1,021 individuals with WGS data, up to 1,011 individuals also have measured levels of any inflammatory biomarkers using the Proximity Extension Assay (PEA) technology provided by Olink  was calculated using PLINK within the study population itself (NSPHS). To assess the size of the associated regions, an SNV clumping was performed in PLINK. A clump kb radius of 15 Mb (--clump-kb), a p-value threshold of 1 × 10−8 (--clump-p1) and an R2 cut-off set to default (--clump-r2 0.1) was used. This function clumps SNVs together based on empirical estimates of LD. The range (in bp) of the clumps was then calculated for each biomarker and used to define biomarker-based loci. We also performed conditional analysis, where the top SNV, for each marker with a significant hit, was used as covariate in the mmscore function to see whether there was more than one independent association for each biomarker. These clump-defined loci were then used to calculate a biomarker-based significance threshold for results in the conditional analyses. SNVs with a conditional p-value below 0.05/number of SNVs tested in the predefined locus, were considered as an independent association. If the biomarker had a significant secondary  signal, a third analysis was performed, adjusting for both the primary and secondary signal. No MAF cutoff was used in the conditional analyses.QQ-plots and Manhattan plots were produced with the qqman package in R2) was estimated using the polygenic model in GenABEL. First, the heritability for the biomarker measurements was estimated only adjusting for age, sex, batch effects and kinship. Then, as an estimation of SNV heritability, the top variant was used as a covariate. The difference in heritability estimated between the models gives the variance explained by the top variant. If secondary and tertiary signals were present, they were added as additional covariates and SNV heritability was calculated for each separately. To test significance of heritability, the reported function minimum (twice the negative maximum log-likelihood) is compared to the reported function minimum in a polygenic model with a fixed heritability estimate set to zero. The difference gives a test approximately distributed as chi-squared with 1 degree of freedom.Narrow-sense heritability (h2 > 0.8), the phenotypes are considered to be colocalized. In the study cohort, the LD pattern was calculated between each top SNVs and all SNVs within 2 Mb, using PLINK. All variants that were in LD (R2 > 0.8) with a top SNV were extracted. These were used as query to test for colocalization with data from the GWAS catalog  to find out whether the variants have already been published in earlier association studies of inflammatory biomarkers. Entries with p-values up to 1 × 10−6 in the GWAS catalog (version 1.0.2 – downloaded 2018-10-29) were included in the comparison. All entries for the top variants and variants in LD found in the catalog was extracted together with their metadata.If the top SNVs of two different GWASes are in LD .Our GWAS results were also tested for colocalization with the expression quantitative trait locus (eQTL) dataset from Westra et al.29. We sought to replicate the results from the 19 inflammatory biomarkers (i.e. cytokines) that were present in both studies: bNGF, CCL11, CCL3, CCL4 (MIP1b), CXCL1 (GROa), CXCL10 (IP10), CXCL9 (MIG), HGF, IL-4, IL-5, IL-5, IL-7, IL-8, IL-10. IL-13, IL-18, MCP-1, MCP-3, SCF, TNFB and TRAIL. However, IL-4, IL-5 and IL-13 did not pass QC in our study, and thus, only 16 biomarkers could be compared. Only top SNVs were used in the replication.Further replication was made using another population from Northern Europe. Circulation cytokines have been measured in Finnish populations by Ahola-Olli 28. In the first study, the ONC_CVD biomarkers were analyzed25 in the whole cohort whereas in the second study28, the INF biomarkers were analyzed using a two-stage design by splitting the cohort into a discovery and replication cohort (genotyped using different arrays and imputed independently) followed by combined analyses for the SNVs that replicated. Even if the two previous studies were based on the same genotyping and imputation data, the quality control of the imputed genotypes where slightly different with one additional requirements of a genotype probability score of Info >0.9 in 95% of the individuals for the analyses of the INF biomarkers whereas the criteria of imputation quality >0.3 and HWE p-value >0.05/the number of SNPs within the sub cohorts based on different genotyping arrays as well as after combining the two sub cohorts. More information about genotyping, imputation and quality control of imputed variants is included in the previous articles28. In this comparison, the additional filters were also applied to the imputed data similar to the WGS data, i.e. only biallelic autosomal hits were compared. If an association was previously found with an indel or a variant in a spanning deletion, the biallelic variant with the lowest p-value after the indel or spanning deletion was used for comparison. For a subset of the non-overlapping hits, we also compared the WGS genotypes with the imputed dosage values form the previous studies.Top variants were compared to the results from previous studies with genotyped/imputed data in the same cohortSupplementary Information


Sustaining wheat production under low-input conditions through development and identifying genotypes with enhanced nutritional quality are two current concerns of wheat breeders. Wheat grain total protein content, to no small extent, determines the economic and nutritive value of wheat. Therefore, the objectives of this study are to identify accessions with high and low grain protein content (GPC) under well-watered and water-deficit growth conditions and to locate genomic regions that contribute to GPC accumulation. Spring wheat grains obtained from 2111 accessions that were grown under well-watered and water-deficit conditions were assessed for GPC using near-infrared spectroscopy (NIR). Results indicated significant influences of moisture, genotype, and genotype × environment interaction on the GPC accumulation. Furthermore, genotypes exhibited a wide range of variation for GPC, indicating the presence of high levels of genetic variability among the studied accessions. Around 366 (166 with high GPC and 200 with low GPC) wheat genotypes performed relatively the same across environments, which implies that GPC accumulation in these genotypes was less responsive to water deficit. Genome-wide association mapping results indicated that seven single nucleotide polymorphism (SNPs) were linked with GPC under well-watered growth conditions, while another six SNPs were linked with GPC under water-deficit conditions only. Moreover, 10 SNPs were linked with GPC under both well-watered and water-deficit conditions. These results emphasize the importance of using diverse, worldwide germplasm to dissect the genetic architecture of GPC in wheat and identify accessions that might be potential parents for high GPC in wheat breeding programs. Triticum aestivum L.) is the food commodity for more than third of the world's population. Wheat grain is a rich source of starch (carbohydrate). Therefore wheat is primarily considered as a source of energy [Wheat  genotype was identified in Israel, i.e., “FA15-3” which was found to be able to accumulate 40% protein when given adequate nitrogen fertilization [GPC-B1 allele which was originally identified in wild emmer wheat, was transferred to a spring wheat genotype and increased grain total protein content by 3% [GPC-B1 allele accelerates senescence and increases mobilization of nitrogen, zinc, and iron to the developing grains [GPC-B1 [Exploring genetic resources to identify wheat genotypes with high grain protein content is the most efficient way to improve the nutritional value of wheat grains . Wheat blization . High grnt by 3% . The GPCg grains . Thus, ag grains . However [GPC-B1 . During  [GPC-B1 ,29,30,31Marker-assisted selection (MAS) was defined as one of the promising avenues to improve wheat total protein content and grain yield . The criThree strategies were adopted to select for high GPC and grain yield, i.e., selecting for high grain protein alone, selecting for high grain protein within highest yielding genotypes, and using an index to simultaneously select for both protein and yield . In the Wheat grains obtained from 2111 wheat accessions  were used in the current study. The accessions seeds were provided by the national small grains collection (NSGC) located in Aberdeen, ID, USA. The accessions were screened in Egypt during 2015/2016 and 2016/2017 growing seasons for total protein content under well-watered and water deficit conditions in Damanhour university experimental farm . During the two growing seasons, drought stress was imposed by controlling irrigation during the reproductive stage in which plants were irrigated at 40% depletion of plant available water (PAW) (well-watered), or 80% PAW (water deficit). Well-watered and water deficit treatments were applied on two sublocations within the same experimental farm to facilitate the control of water application. For both sublocations, the wheat accessions were planted in two replicates using a randomized incomplete block design [Grain protein content (% or g/100 g) was estimated using near-infrared spectroscopy (NIR) with a Perten DA7250 diode array NIR . NIR is a nondestructive technique that complies with the ISO 12099 standard method. The measurements of GPC were done in the near infrared region 950–1650 nm and readings were processed in NetPlus software , which includes validation calculation modules, such as calculations of bias, slope, and standard errors of prediction against the reference methods. However, for initial calibration of the Perten DA7250, the crude protein content of 100 wheat accessions was measured using the Kjeldahl method . The correlation coefficient (r) between the calibration set and Perten DA7250 NIR readings was 0.964 for crude protein (% dry basis).https://triticeaetoolbox.org/wheat/) according to Wang et al. [Wheat accessions included in this study were genotyped through the Triticeae Coordinated Agriculture Project (TCAP) using the Illumina iSelect 9 K  wheat array  at the Ug et al. .ijlmY is the response measured on the ijlm plot, µ is the overall mean, iE is the effect of ith environment, EBil)j( is jth incomplete block nested within lth complete block and ith environment (random), mG is the effect of mth accession, imEG is the interaction effect among ith environment and mth accession, and ijlmԑ is the experimental error. Type III expected mean square estimation was conducted as follows:Analysis of variance was carried out by fitting the following model :Yijlm = SourceType III Expected Mean SquareEnvironment (Env)Var (Error) + 45.372 Var (IBlock (Env × Rep)) + Q Incomplete block (Env × block)Var (Error) + 36.829 Var (IBlock (Env × Rep))AccessionsVar (Error) + Q Env × AccessionsVar (Error) + Q (Env × Genotypes)p-value < 0.05). Pearson correlation analysis (r) was carried out between lsmeans using R/package corr.test [2) was estimated using the following model:G2 is the genetic variance, σE2 the residual variance and ri is the number of replicates [Homogeneity and normality of variance were checked using Bartlett and Shapiro-Wilk statistics using R/package agricolae ; Least Sorr.test . Mean-baplicates .Y is a vector of the total protein content, μ is a vector of intercepts, u is an n × 1 vector of random polygene background effects, e is a vector of random experimental errors with mean 0 and covariance matrix Var(e), Z is an incidence matrix relating Y to u. Var(u) = 2 KVg, where K is a known n × n matrix of a realized relationship matrix, estimated using the A.mat function in R software [Wik = Xik + 1 − 2pk and pk is the frequency of the one allele at marker k [m is a vector of fixed effects due to SNP markers, W is incidence matrix relating Y to m. Var(e) = RVR, where R is an n × n matrix, and VR is the unknown residual variance, which is a scalar too. Furthermore, principal component analysis (PCA) was conducted using the filtered SNP markers [p-values estimated from the mixed models were subjected to false discovery rate (FDR) corrections using Q-value estimates applied in the R package q-value [2) by the significant markers, and their additive effects were estimated using the GAPIT function, according to Wray et al. [The estimated Lsmeans for GPC and SNP markers were subjected to association analysis according to the following mixed linear model (MLM) in R package GAPIT .Y = μ +software , as K = marker k , Vg is t markers  and the  q-value . The proy et al. , in R soy et al. .p-value < 0.01) for the environments, genotypes, and genotype × environment interaction  were observed across the four environments (two seasons and two water regimes). Thus, combined analysis of variance across environments was conducted. Combined analysis of variance for GPC indicated a highly significant effect . The first quartiles for the GPC across growing seasons (the cut off for the lowest 25%) under well-watered and water deficit conditions were ≤8.36 and 13.41, respectively , the LD ranged from 0 to 0.35. Overall, LD declined to 50% of its initial value at about 8 cM  in the 2111 wheat accessions. Only SNP loci having MAF ≥0.05 and missing values ≤10% were used to estimate rout 8 cM . Eigenveout 8 cM . Neverthout 8 cM  with zerAssociation mapping analysis was conducted on each environment separately (two growing seasons and two water regimes). Genome-wide association mapping (GWAS) indicated that 46 SNP markers found to be significantly linked with GPC. The significant SNP markers were located on chromosomes 1A (12 SNPs), 1B (12 SNPs), 1D (7 SNPs), 6A (6 SNPs), 6B (7 SNPs) and 6D (3 SNPs) . Out of Under well-watered conditions for the two growing seasons, seven SNP markers  found to be significantly linked with GPC. On the other hand, under water deficit conditions for the two growing seasons, six SNP markers  found to be significantly linked with GPC. In contrary, 14 markers  found to be significantly linked with GPC during only one growing season under well-watered conditions. Another ten markers  found to be significantly linked with GPC under water deficit conditions in only one growing season. Repeatability of the GPC associated loci in 2 seasons under any given water treatment suggests the feasibility of using/developing markers in LD with these loci.Protein content is an essential compositional trait in wheat, which has a broad impact in the food industry concerning human nutrition and health. Consequently, breeding for enhanced end-use quality is one of the essential breeding goals in wheat. However, GPC in wheat is positively affected by water deficit compared to well-watered conditions . In thisr = 0.23). That weak correlation implies strong genotype × environment interaction, in which genotypes responded differently concerning water treatment. Increase in GPC under water deficit conditions could be mainly due to higher rates of accumulation of grain nitrogen and lower rates of accumulation of carbohydrates. High moisture, on the other hand, may decrease GPC by dilution of nitrogen with carbohydrates [The most striking observation in this study was the weak, positive and significant correlation between GPC obtained from the well-watered condition and water deficit conditions  matrix in a mixed model indicated that K matrix was adequate in accounting for population structure . Also, tSorrells  and 0.01Sorrells  because vice versa. Moreover, ten QTLs were linked with GPC under both well-watered and water deficit conditions. The GWAS analysis suggested a significant role of genotype × environment interaction in detecting GPC associated loci. Genome-wide association studies using diverse wheat germplasm have successfully detected GPC associated loci in durum wheat [Glu-B1 and Gli-B1 genes reside [Glu-B1 and Gli-B1 genes were previously reported to contribute of about 24.6 and 19.5% of the total phenotypic variation for sedimentation volume  [Genome-wide association analysis (GWAS) was conducted on each environment separately to measure the repeatability of the significant SNPs, and the effect of moisture on the genomic regions controlling GPC. Several SNPs found to be significantly linked to the GPC under well-watered conditions but not significantly linked to GPC under water deficit conditions and um wheat , and breum wheat . Thus, ts reside . Glu-B1 f pasta) . These results together emphasized the importance of using diverse worldwide germplasm to dissect the genetic architecture of GPC in wheat and identify accessions that might be potential parents in wheat breeding programs. Ongoing multiple years, multiple replication study using 406 accessions identified in the current study is being conducted, to evaluate these genotypes for yield and validate the GPC associated loci detected herein. Furthermore, GPC estimates under well-watered and water deficit conditions was used as a selection parameter to downsize the number of accessions from 2111 to 406. Reducing the number of accessions will allow us to profoundly investigate other wheat quality aspects such as concentrations (soluble and insoluble) of glutenin, α/β, γ gliadin and albumin/globulin in addition to the total protein for high and low GPC genotypes.Based on previous research and our findings, the spring wheat collection used in this study contains high protein accessions. Furthermore, GPC measurement under well-watered and water deficit conditions was used as a selection criterion to reduce the number of accessions from 2111 to 406 accessions. This reduction in the number of studied accessions will allow us to profoundly study other wheat quality aspects such as concentrations (soluble and insoluble) of glutenin, α/β, γ gliadin and albumin/globulin in addition to the total protein for high and low GPC genotypes. It also represents a precious resource for further investigations including annotation of relevant genomic regions/genes using available wheat genomic resources to study the GPC. Results of GWAS indicated that several genomic regions were involved in GPC accumulation in wheat grains. Furthermore, GWAS results also suggested a significant role for genotype x environment interaction in the identification of GPC associated loci under well-watered and water deficit conditions. The identified loci might allow development of marker-assisted selection (MAS) for GPC and might also facilitate the development of a better understanding of the genetic architecture that controls GPC in wheat. Therefore, the high and low GPC accessions identified in the current study were included in ongoing multiple years and locations studies to evaluate them for yield and confirm the GPC associated loci detected.


In crop genetic studies, the mapping of longitudinal data describing the spatio-temporal nature of agronomic traits can elucidate the factors influencing their formation and development. Here, we combine the mapping power and precision of a MAGIC wheat population with robust computational methods to track the spatio- temporal dynamics of traits associated with wheat performance. NIAB MAGIC lines were phenotyped throughout their lifecycle under smart house conditions. Growth models were fitted to the data describing growth trajectories of plant area, height, water use and senescence and fitted parameters were mapped as quantitative traits. Trait data from single time points were also mapped to determine when and how markers became and ceased to be significant. Assessment of temporal dynamics allowed the identification of marker-trait associations and tracking of trait development against the genetic contribution of key markers. We establish a data-driven approach for understanding complex agronomic traits and accelerate research in plant breeding. In crop genetics, the formation of dynamic biological traits such as height, size and color are usually governed by multiple temporal and spatial factors. Their genetic variation can be attributed to the collective response of multiple small effects associated to those dynamic traits  underlying the dynamics of a developmental trait. For example, Yang et al.  fitted lIn this study we analyzed phenotypic profiles derived from the daily screening of a large, eight-founder “NIAB elite MAGIC” wheat population to evaluate the genetic factors underlying the temporal changes in key traits associated with wheat performance such as plant height and water use. Growth curve models were fitted to plant area, height, water use and senescence over time and the fitted parameters of the growth trajectories were mapped to the wheat genome. Single time points were also individually mapped to the genomes and results from both analytic approaches compared. We demonstrate that both methods provide key insights that could not be captured otherwise. The growth curve approach identifies crucial markers through the crop growth time line and the single time point approach gives an indication of when and how those markers become and then cease to be significant.www.niab.com/MAGIC.Phenotypes extracted from a subset of the NIAB Elite eight-founder MAGIC population described in Mackay et al.  were evaAs described in Camargo et al. , plants Daily imaging commenced when the plants were transferred to the conveyor belt following vernalization using a LemnaTec 3D Scanalyzer  for image acquisition. Four RGB pictures  were taken of each plant, one top view and three side view with a 45° horizontal rotation. All manually acquired and digitally derived traits and their abbreviations are provided in Table https://github.com/NPPC-UK/PAT64V3_W8). Briefly, image color classification was used to separate plant from background and a binary image was produced where “1” corresponded to plant and “0” to anything else. Then, image features accounting for plant height, plant area and plant yellow area were calculated from the analysis of “1” area. Height was measured from the surface of the soil (the front edge of the pot) to the uppermost part of the plant. Area was the total amount of segmented area. Yellow area was the total amount of regions of yellow color in the segmented area and was used as a proxy for Senescence  was used to identify and remove outliers.A trait is called time-dependent, or longitudinal, if measured over time. Time-dependent traits can be analyzed by; (1) using a repeated measurements framework; (2) treating phenotypes measured across time as different traits or; (3) fitting growth curves to the phenotypic values measured over time then analyzing the parameters describing growth's trajectory , C is the inflection point , and B is the hill's slope, or the steepness of the curve. It could either be positive or negative.a is an asymptote, .where, y are assumed to come from m distinct classes f1, f2,…, fm in proportions π1, π2,…, πg. In its simplest form, the density of a m-component mixture model as:A finite mixture model (FMM)  belongs to class m is given by:The posterior probability that observation , …, } is given by:The log-likelihood of a sample of N observations {(FMM uses the Expectation–maximization (EM) algorithm to refine starting values before maximum likelihood estimation via two steps:Estimate-step: estimate the posterior class probabilities for each observation:Using Equation (2) and derive the prior class probabilities asMaximize-step: maximize the log-likelihood for each component separately using the posterior probabilities as weights:The E- and M-steps are repeated until either the likelihood improvement falls under a given threshold or a number of searches is reached.R2) is used for model selection:Coefficient of determination (H2) (Equation 9) and genetic (g) and environmental (e) covariances (Equation 10) and correlations (Equation 11) between parameters were estimated.Growth curve parameters were estimated for each trait and for each plant, and parameters' heritability  and Where S is the covariance between two traits , s2 is the variance and i = g or e, genetic or environmentWhere r is the correlation between two traits .where Analysis of trait across time indicated that in general all the MAGIC lines and their parents maintained steady growth and water use up until 138 DAS Figures . Figure In addition, the time point at which the Water Use curve starts to rise for the second time, 170 DAS Figure .Area, Height and Senescence (TYP) showed a logistic growth . Figures Growth parameters (Equation 2) were estimated for each trait and for each plant. Using data from the RILs, the heritability of these parameters and their genetic and environmental co-variances and correlations were estimated. Results are shown in Tables S1–S6.B (the curve's slope) has the highest heritability (>0.8). The remaining parameters have heritability below 0.5 indicating that the rate at which a plant grows is highly heritable. However, B did not show strong genetic or environmental correlations with the other parameters. A and C  were highly correlated both genetically and environmentally (r > 0.9) and A and D were negatively correlated (<−0.6) at the genetic level. Senescence growth curve parameters did not show high heritability for any parameter. However, the genetic and environment correlations between A and D were negative (<−0.6) and there was a large positive environment correlation (0.9) between B and D.In the analysis of Area and Height growth curve parameters, In comparison to Area, Height and Senescence's curves, the Water Use curve was bi-modal Figures –C. The p1, λ1, and σ1, corresponding to the first mode, μ2, and σ2, corresponding to the second mode, were estimated for each trait and for each plant. Parameters for the RILs were then used to estimate heritabilities, co-variances and correlations. Results shown in Tables S7, S8 indicate that none of the growth parameters were highly heritable (H2 < 0.2). With such low heritabilities, genetic correlations are very poorly estimated and correlations between parameters are largely environmental. The pattern of these correlations indicates that early peak Water Use  is associated with more water being used in the early period  and that this delays the second water uptake phase (high μ2). Xie et al. , described in Yang et al.  at early plant development (~97 DAS) but became high at around 148 DAS and reached their peak at 189 DAS, at the end of the plant's life . Significance level of the QTL on chr2D was high at around 136 DAS and reached its maximum at 166DAS   Figure . For Wat) Figure .This work aimed to understand the genetic elements influencing the temporal and spatial changes in key traits such as plant height and area, the timing of senescence and the dynamics of water use. Since plant traits were measured across time, we used two different approaches to analyse growth trajectories: first, growth curves were fitted to the phenotypic values of each trait measured over time. Second, phenotypic measurements were treated as different traits and analyzed individually.For the analysis of growth curves, we initially fitted second, third and fourth degree polynomials of the form Poorter, , and perHigher order polynomials are difficult to interpret and are unlikely to have an obvious biological meaning: they describe the form of the curve rather than offering any natural explanation. In contrast, the logistic have been used successfully to model growth, and the parameters better describe the biology of growth  as the most heritable (>0.8) for area and height but not for senescence. However, “B” was not correlated to any other parameter in contrast to “A” and “C,” which were highly correlated both genetically and environmentally (r > 0.9), indicating that phenotypic outcome was highly dependent on genotype.Comparison between growth curve parameters, identified curve steepness  locus. The Rht-D1b allele at this locus has been associated with a reduction in plant height and several other morphological traits and is segregating in the MAGIC population . Table Genetic mapping analysis identified peak QTLs for the traits Senescence (Q1) and Water Use (Q1) on chr2D at the location of Vrn-A1 which plays an important role in the vernalization process in diploid Einkorn wheat (Triticum monococcum) and polyploid common wheat  on chr5A associated with plant Height at markers IAAV1650 and BS00011360_51. We believe these markers are closely linked to the vernalization gene Rht-B1b gene) marker showed a allelic effect on area, height, senescence and negative on Water Use, indicating a positive correlation with the trait. The Kukri_c27309_590 marker (likely to be related to the Ppd-D1 gene) showed a positive effect on area, height, and water use, indicating a positive correlation with the trait. However, for Senescence, the marker effect was positive up until 136 DAS but its significance level was lower after that time point which coincided with the average plant initiating Senescence. The marker IAAV1650  showed completely different patterns for each trait: for Area, the effect was positive; for height, the effect was negative; for senescence the effect became positive after the average plant started senescence; and for water use the effect became negative at around 158 DAS. The BS00087197_51 (on chr7B) showed a positive effect on area and water use and negative on height. However, for senescence, the marker effect was negative up until 136 DAS but its significance level was lower after that time point, this pattern was the opposite of that showed by the Kukri_c27309_590 marker. Regardless of the marker, this analysis together with QTL mapping seems to suggest that fundamental changes take place around 136 DAS.To look further into the effect of key markers over trait development, we fitted a regression model of the trait against the marker, for each time point. Results are summarized in Figures S7–S11. Briefly, RAC875_rep_c105718_585 , there are no striking differences between these 16 plants representing all possible combination. This demonstrates the merit of accurate measurement taken here to quantify the parameters responsible for plant form and development.Genetic mapping of growth curves parameters fitted to the same traits confirmed the identification of major QTLs such as those on chr2D, chr4D, and on chr7B. This result demonstrate the power of analyzing growth curves that carry all the information about the plant's development as opposed to the analysis of single time points. However, it also showed that the analysis of single time points helps to describe how traits and their marker effects evolve over time. Figure This study used a new data driven approach to analyse the longitudinal data capturing the process of formation of dynamic traits such as senescence or water use. We combined the mapping power and precision of a MAGIC wheat population with robust computational methods to track the spatio-temporal dynamics of traits associated with wheat performance. We demonstrated that the combination of these two methodologies can be used as a powerful strategy for fine-tuning wheat's response to the environment.AC and IM conceived analysis. AB and JD developed and obtained funding for the project. JH processed digital images. AC analyzed data. RM, IM, and AC assisted with the analysis. KA, FC, and KW provided technical support in the collection of phenotypic data. All authors provided comments and corrected the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


SOCS2 gene, which influences body growth, milk production and somatic cell scores, a proxy for mastitis, was studied as an example in dairy sheep.Genomic evaluation is usually based on a set of markers assumed to be linked with causal mutations. Selection and precise management of major genes and the remaining polygenic component might be improved by including causal polymorphisms in the evaluation models. In this study, various methods involving a known mutation were used to estimate prediction accuracy. The SOCS2 SNP was genotyped for 4297 animals and imputed in the above 9844 animals. Breeding values and their accuracies were estimated for each of nine traits by using single-step approaches. Pedigree-based BLUP, single-step genomic BLUP (ssGBLUP) involving the 54K ovine SNPs chip, and four weighted ssGBLUP (WssGBLUP) methods were compared. In WssGBLUP methods, weights are assigned to SNPs depending on their effect on the trait. The ssGBLUP and WssGBLUP methods were again tested after including the SOCS2 causal mutation as a SNP. Finally, the Gene Content approach was tested, which uses a multiple-trait model that considers the SOCS2 genotype as a trait.The data comprised 1,503,148 phenotypes and 9844 54K SNPs genotypes. The SOCS2 SNP to ssGBLUP methods led to an average gain of 0.26%. Construction of the kinship matrix and estimation of breeding values was generally improved by placing emphasis on SNPs in regions with a strong effect on traits. In the absence of chip data, the Gene Content method, compared to pedigree-based BLUP, efficiently accounted for partial genotyping information on SOCS2 as accuracy was increased by 6.25%. This method also allowed dissociation of the genetic component due to the major gene from the remaining polygenic component.EBV accuracies were increased by 14.03% between the pedigree-based BLUP and ssGBLUP methods and by 3.99% between ssGBLUP and WssGBLUP. Adding the Causal mutations with a moderate to strong effect can be captured with conventional SNP chips by applying appropriate genomic evaluation methods. The Gene Content method provides an efficient way to account for causal mutations in populations lacking genome-wide genotyping.The online version of this article (10.1186/s12864-019-6068-4) contains supplementary material, which is available to authorized users. Loci (QTLs) or major genes. Studies of this complex architecture have been facilitated by new technologies and molecular markers such as microsatellites or single nucleotide polymorphisms (SNPs) which make it possible to detect the regions of the genome responsible for genetic variation and measure their respective effects , [0.04–0.06 Mb], [0.06–0.08 Mb], and [0.08–0.10 Mb], respectively, and < 0.08 for all the other distance categories. A visualization of r2 according to distance between SNP is provided in Additional file The map coverage of the chip attained 2444 Mb and the mean SNP interval was 0.064 Mb. The mean r2 measure of LD between the 40 markers closest to SOCS2 is represented in Additional file SOCS2 with the other SNP markers ranged from zero to 0.47 with OAR3_138135461.1, which was 0.229 Mb pairs away from SOCS2. The average LD between the SOCS2 SNP and the 10 previous SNPs on the chip was 0.17. The category containing the distance between the SOCS2 SNP and the SNP most linked to the SOCS2 SNP (0.229 Mb) was the interval , for which we obtained a mean r2 of 0.049 on the whole chip. The SOCS2 SNP mutation was therefore in strong LD with some of the other SNPs in the region.The The (co)variance parameters estimated and used in this study are presented in Additional file SOCS2 gene content trait and the other traits, and the genetic variances explained by the SOCS2 gene using the pedigree-based Gene Content method are presented in Table SOCS2 gene content trait were: LSCS (rg = 0.34), PY (rg = 0.29), MY (rg = 0.25), UD (rg = − 0.19), FY (rg = 0.18) and FC (rg = − 0.14). This was confirmed by the genetic variances explained by the SOCS2 gene that ranged from 0.05% (TA) to 11.24% (LSCS).Genetic correlations between the The prediction accuracies and gains obtained with the different evaluation methods and traits are shown in Table SOCS2 genotype was + 1.06% and the best accuracy (0.471) was obtained with the WssGBLUP method.In our study, the highest accuracies were obtained by using alternative WssGBLUP approaches. Figure SOCS2 was + 0.26%. The highest gain (+ 1.06%) was obtained for LSCS.The gain in prediction accuracy from pedigree-based BLUP to ssGBLUP, the currently used genomic method, was on average + 14.03% Table . An averThe average gain in prediction accuracy between pedigree-based BLUP and pedigree-based Gene Content was + 6.25%. This gain represents the improvement in prediction accuracy for a trait when genome-wide data (54 K herein) are not available and information about a point mutation is known and is included in a multi-trait genetic evaluation model.polygen), excluding the effect of the SOCS2 gene, EBVs associated with the SOCS2 gene (EBVsSOCS2), as well as estimated breeding values for the trait of interest (EBVstrait), for each trait.The EBVspolygen and EBVstrait values were very similar for all the traits (Spearman correlation of 0.99), except for LSCS (Spearman correlation of 0.87), the trait most associated with SOCS2. The genetic trends of both the polygenic (EBVpolygen) and gene (EBVSOCS2) components of the LSCS trait over the years are provided in Fig. SOCS2 gene mutation on the trait , but also to the reduction (improvement) of the polygenic component determining the trait.The Gene Content method was used to obtain EBVs for the polygenic component (EBVsSOCS2 genotype among the markers. The QTL regions (positions on ovine genome assembly v4.0) found by applying the best alternative WssGBLUP method for each trait (SOCS2 SNP included in the markers), based on a threshold of 1% of genetic variance explained, are presented in Table SOCS2 gene region (Table SOCS2 SNP) explaining as much as 12.00% of the genetic variance. Moreover, this region also influenced PY (4.91% of the variance explained), UD (4.02%), MY (3.94%), FC (2.57%), and UC (1.84%). In addition, among the surrounding SNPs, the SOCS2 SNP exhibited the strongest (or second strongest) effect  was previously estimated at between 0.12 and 0.15 by Barillet et al. (2001), Rupp et al. (2003) and Barillet et al. (2007) : \dVariance and (co)variance (for the Gene Content method) components for models , 2), ,  for eachSOCS2 gene for each trait was calculated by applying the following equation [α the allele substitution effect of the SOCS2 gene mutation on the trait described previously.Based on these variance component estimations, the genetic variance explained by the equation : \documerg) between the SOCS2 gene content trait and the trait of interest with the following equation:We also used the estimated variance components to derive the genetic correlation  between the 40 markers closest to the SOCS2 point mutation . Figure S2 Visualization of linkage disequilibrium measured as squared correlation coefficient (r2) according to distance between markers on the 50 K ovine SNP chip. Figure S3 Components estimations according to the different models. One-trait methods correspond to eqs. . On the left are presented analysis without the SOCS2 genotype among the markers and on the right, with the SOCS2 genotype (green point). Figure S5 Manhattan plots of estimated variance explained by 20 adjacent SNPs using the best WssGBLUP approach for each phenotype (second iteration). The horizontal red line represents the threshold of 1% adopted in this study. On the left are presented the analyses without the SOCS2 genotype among the markers and on the right, with the SOCS2 genotype. (DOCX 2190 kb)librium r × 100 be