A total of 63 candidate genes were predicted from the 11 stable QTNs, and 10 candidate genes were highly expressed in the kernel at different time points after pollination. High prediction accuracy was achieved when the KMC-associated QTNs were included as fixed effects in genomic selection, and the best strategy was to integrate all KMC QTNs identified by all six ML-GWAS models. These results further our understanding of the genetic architecture of KMC and highlight the potential of genomic selection for KMC in maize breeding.Kernel moisture content at the harvest stage (KMC) is an important trait that affects the mechanical harvesting of maize grain, and the identification of genetic loci for KMC is beneficial for maize molecular breeding. In this study, we performed a multi-locus genome-wide association study (ML-GWAS) to identify quantitative trait nucleotides (QTNs) for KMC using an association mapping panel of 251 maize inbred lines that were genotyped with an Affymetrix CGMB56K SNP Array and phenotypically evaluated in three environments. Ninety-eight QTNs for KMC were detected using six ML-GWAS models . Eleven of these QTNs were considered to be stable, as they were detected by at least four ML-GWAS models under a uniformed environment or in at least two environments and BLUP using the same ML-GWAS model. With  Kernel moisture content at the harvest stage (KMC) is one of the important traits that influence maize mechanical harvesting, especially in high latitude areas  to a 2.05-Mb genomic region on chromosome 1 using a recombinant-derived progeny test. Li et al. (GAR2) for KMC on maize chromosome 7. Yin et al. , and hundreds of QTLs for maize KMC have been identified , an alternative GWAS method, was developed to address this issue; it considers the information from all markers simultaneously and does not require a multiple testing correction. ML-GWAS has been shown to have higher power and accuracy for the detection of quantitative trait nucleotides (QTNs) in maize. Zhang et al.  used fouGiven the lack of large-effect QTLs, the use of marker-assisted selection (MAS) for KMC is not ideal in maize breeding programs, and it is necessary to incorporate far more markers. Genomic selection (GS), an upgraded form of MAS, aims to use genetic effects of genome-wide molecular markers to estimate the genomic estimated breeding value (GEBV) of individuals based on optimum statistical models  identify significant QTNs for KMC using ML-GWAS, (ii) predict candidate genes associated with KMC, and (iii) explore the potential of GS for KMC in maize.An association mapping panel of 251 diverse maize inbred lines was used as the plant material in this study .The field experiments were performed in three environments in 2020: Nantong, Jiangsu Province , which is in mid-eastern China and has an average temperature of 15.1°C and an average rainfall of 1,040 mm per year; Xinxiang, Henan Province , which is in the middle of China and has an average temperature of 15.5°C and an average rainfall of 573.4 mm per year; and Sanya, Hainan Province , which is in southern China and has an average temperature of 25.7°C and an average rainfall of 1,347 mm per year. Each line was grown in single rows, which were 3 m in length and spaced 0.6 m apart, thereby giving a planting density of 65,000 plants/ha. The trial followed a randomized complete block design with two replicates per environment. The agronomic management of the field experiments was the same in the three environments.As described in our previous study . Analysis of variance (ANOVA) of KMC was performed using lmer function of the lme4 package based on the following model: yijk = μ + Gi + Ej + GEij + Rjk + eijk, where yijk is the KMC on the ith genotype in the jth environment and kth replication, μ is the grand mean over all environments, Gi is the genotypic effect of the ith genotype, Ej is the environmental effect of the jth environment, GEij is the genotype × environment interaction effect of the ith genotype and jth environment, Rjk is the effect of the kth replication in jth environment, and eijk is the residual error. The trait heritability (H2) was estimated following Knapp et al. (H2 (%) = r) × 100%, and the multiple environments H2 (%) = nnr) × 100%, where n is the number of environments, and r is the number of replications. To minimize the effects of the environment, the best linear unbiased prediction (BLUP) for KMC across the three environments was estimated using the lmer function of the lme4 package with the same ANOVA model. The normal distribution test  of KMC in each environment was estimated using the Shapiro.test function of the stats package.The phenotypic data were analyzed using R version 3.6.3 for Windows  and is made by China Golden Marker (Beijing) Biotech Co., Beijing, China. After quality control, 32,853 SNPs with minor allele frequencies >5% and missing data <20% were used for subsequent analysis. The genotypic data can be downloaded from the website K) was set from 1 to 10 with five independent runs for each K. Both burn-in periods and Markov chain Monte Carlo replication number were set at 100,000 in each run under the admixture model. The K value was estimated by the log likelihood of the data [LnP(D)] and an ad hoc statistic ΔK, based on the rate of change of LnP(D) between successive K values , including six statistical models: mrMLM  was selected to determine significant QTNs  ≥ 3.70].ML-GWAS was conducted using the mrMLM package (Two types of QTNs were defined as stable QTNs. One is model-stable QTN (msQTN), which is identified by at least four ML-GWAS models under a uniformed environment, another is environment-stable QTN (esQTN), which is identified by in at least two environments and BLUP using the same ML-GWAS model.https://www.maizegdb.org/gbrowse), the available genes within regions from 100 kb upstream to 100 kb downstream (LD of the association mapping panel) around the stable QTNs were regards as candidates. Candidate gene annotation was performed at NCBI (https://www.ncbi.nlm.nih.gov/). Expression data for candidate genes were collected from qTeller (https://qteller.maizegdb.org/).Based on the B73 reference genome v4 , βk is the estimated random additive marker effect of the kth marker, and εi is the residual error. To improve the prediction accuracy, the significant markers identified by ML-GWAS were included as fixed effects in the following model (model 2): m is the number of significant markers identified ML-GWAS considered as fixed effect covariates, αj is the fixed additive effect of the jth marker, and the remaining terms are the same as those described in model 1. Twelve sets of markers were included as fixed effects in model 2: the significant markers identified by mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, and ISIS EM-BLASSO; the markers identified by at least two, three, four, five, or six models; and all significant markers identified by all six models.GS was performed using the ridge regression best linear unbiased predictor (rrBLUP) model  values increased as K increased from 1 to 10 without an obvious inflection point, and ΔK reached its peak at K = 6 , Lancaster (LAN), BSSS, and X . Subgrour2) dropped to half of its maximum value  (Descriptive statistics for KMC are presented in  > 0.05) , suggest > 0.05) , and the > 0.05) .Based on the BLUP values across the three environments, 11 of the 251 lines showed low KMC (below 27% moisture) , and these are marked in bold in qKMC7.4, the remaining 6 QTNs were overlapped with those from ML-GWAS models.A total of 98 QTNs were detected in NT, XX, SY, and BLUP across all environments by the six ML-GWAS models, and each explained 0.67–26.96% of the phenotypic variation in KMC . Thirty-qKMC2.15, qKMC6.1, and qKMC8.2) were detected in two environments and BLUP.Among 98 QTNs, 44, 27, 16, 7, and 4 QTNs were co-detected by at least two, three, four, five, or six ML-GWAS models, respectively. 25, 25, 32, and 42 were detected in NT, XX, SY, and BLUP, respectively. However, no QTN was detected in three environments and BLUP, and only three QTNs (qKMC2.15) was common between msQTNs and esQTNs. Seven of them overlapped previously reported genomic regions, and the remaining four were putatively novel loci. Only one QTN, qKMC5.6, accounted for more than 10% of the phenotypic variation (12.41–23.27%), and it may be a major genetic locus for KMC.Eleven QTNs were considered to be stable; these were distributed on chromosomes 1, 2, 3, 5, 6, 7, 8, and 10 . Among tAccording to the LD in this association panel , 63 candR2 = 0.68, slope = −0.41, intercept = 53.17, P < 0.001) using the linear regression analysis, indicating that pyramiding of these favorable alleles could reduce KMC effectively. There was a significant difference in number of favorable alleles among the six subpopulations; the number of favorable alleles of subgroup X was the most (48), and that of subgroup PA was the smallest (32)  .The average prediction accuracy was 0.12, 0.11, 0.13, and 0.17 in NT, XX, SY, and BLUP, respectively, when all 32,853 markers across the entire genome were included in model 1 . To obtaTo explore whether using QTNs co-detected in multiple ML-GWAS models could improve prediction accuracy, we conducted GS using QTNs identified in at least two, three, four, five, or six models. Use of the QTNs identified in at least two, three, or four models maintain a relatively high prediction level, but QTNs identified in five or six models provided slight advantage in predicting KMC . Seven QIn this study, the 251 maize inbred lines were sown at three dates according to their growth periods, enabling us to measure KMC of each line over similar periods in each environment. The hand-held moisture meter was used to measure KMC, this is a reliable method and has been reported to be useful for evaluating genetic materials for QTL mapping  were located in the meta-QTL regions estimated by Xiang et al.  caught our attention.Candidate gene analysis of the stable QTNs is necessary for further gene cloning and functional verification. To date, only two genes underlying major QTLs for KMC have been identified , which provides the nucleotide sugar ADP-glucose and thus constitutes the first step in starch biosynthesis  far exceeds the number of individuals (n) . However, higher prediction levels were easily attained when using the ML-GWAS-derived markers included as fix effects. The prediction accuracy was still high (0.26–0.56) when only 16 stable markers identified by at least four models were included. Similar findings were reported for maize kernel row number (An et al., With advances in sequencing technology and reduction of testing costs, GS has been widely implemented in plant breeding. Fitting GS models need to face the fact that the number of markers (Gianola, . rrBLUP Gianola, . In thisNinety-eight QTNs for KMC were identified using six ML-GWAS models in three environments and BLUP across three environments. Eleven QTNs were considered to be stable. Seven stable QTNs corresponded to previously reported QTL regions, whereas the remaining four were putatively novel loci. Sixty-three candidate genes were identified within LD blocks of the 11 stable QTNs. Among these candidates, 10 may potentially affect the loss of water from the maize kernel. High prediction levels were easily reached when the KMC-associated markers were included as fixed effects in GS. The best strategy was to integrate all KMC-associated markers identified by all six ML-GWAS models. These results facilitate our understanding of the genetic basis of KMC and provide useful information for the reduction of KMC in maize breeding.The original contributions presented in the study are included in the article/GZ and DH designed the experiment and wrote the manuscript. GZ, YM, LX, GC, ZZ, XS, HZ, HL, MS, and DH performed the experiments and collected the phenotypic data. GZ and QZ analyzed the genotypic and phenotypic data. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Viticulture has to cope with climate change and to decrease pesticide inputs, while maintaining yield and wine quality. Breeding is a key lever to meet this challenge, and genomic prediction a promising tool to accelerate breeding programs. Multivariate methods are potentially more accurate than univariate ones. Moreover, some prediction methods also provide marker selection, thus allowing quantitative trait loci (QTLs) detection and the identification of positional candidate genes. To study both genomic prediction and QTL detection for drought-related traits in grapevine, we applied several methods, interval mapping (IM) as well as univariate and multivariate penalized regression, in a bi-parental progeny. With a dense genetic map, we simulated two traits under four QTL configurations. The penalized regression method Elastic Net (EN) for genomic prediction, and controlling the marginal False Discovery Rate on EN selected markers to prioritize the QTLs. Indeed, penalized methods were more powerful than IM for QTL detection across various genetic architectures. Multivariate prediction did not perform better than its univariate counterpart, despite strong genetic correlation between traits. Using 14 traits measured in semi-controlled conditions under different watering conditions, penalized regression methods proved very efficient for intra-population prediction whatever the genetic architecture of the trait, with predictive abilities reaching 0.68. Compared to a previous study on the same traits, these methods applied on a denser map found new QTLs controlling traits linked to drought tolerance and provided relevant candidate genes. Overall, these findings provide a strong evidence base for implementing genomic prediction in grapevine breeding. This requires understanding the physiological processes that determine adaptation to climate change, such as water use efficiency and their genetic basis  methods in bi-parental populations, or more recently genome-wide association studies (GWAS) in diversity panels [see e.g., e.g., Support Vector Machine, Reproducing Kernel Hilbert Space, Random Forest) have been shown to yield lower predictive ability than parametric models (frequentist or Bayesian) when the trait displayed an additive genetic architecture  as in Traits are often analyzed one by one in GP, using univariate methods. Nevertheless, breeders want to select the best genotypes that combine good performance for many favorable traits. Analyzing several traits jointly in GP allows taking into account any genetic correlation between traits . Calus aMethods designed for QTL detection are rarely used for genotypic value prediction. As they select only the largest QTLs, we hypothesize that these methods will provide an accurate prediction so long as the genetic architecture is simple, but would result in poor prediction performance otherwise, as determined in several studies  without missing data . These data were numerically recoded in biallelic doses  according to the initial biallelic segregation and phase  compare several methods for prediction accuracy, and (ii) assess the efficacy of these methods to select the markers most strongly associated with trait variation.Y is the n × k matrix of traits, X the n × p design matrix of allelic effects, B the p × k matrix of allelic effects, and E the n × k matrix of errors. For X, the 3961 SNP markers mapped for the SxG progeny were encoded in four additives and four dominance effects. Therefore n = 188, k = 2, and p = 31,688. For B, allelic effects corresponding to s additive QTLs were drawn from a matrix-variate Normal distribution, I the p × p identity matrix and k × k genetic variance-covariance matrix between traits such that Bρ is the genetic correlation among traits and k × k error variance-covariance matrix Eρ is the residual error correlation between traits, and Bρ to 0.8, Eρ to 0, and narrow-sense heritability to 0.1, 0.2, 0.4 or 0.8, and Two traits, s = 2 or 50 additive QTLs, located at s SNP markers, so that all corresponding additive allelic effects had nonzero values in B. Because all allelic effects were drawn from the same distribution, all QTLs had “major” or “minor” effects for s = 2 and s = 50, respectively. All dominant allelic effects were set to zero. Two QTL distributions across traits were also simulated. For the first one, called “same,” all QTLs were at the same markers for both traits. For the second one, called “diff,” the two traits had no QTL in common. Thus, there was genetic correlation among traits only for the “same” QTL distribution.To explore different genetic architectures, we simulated t = 10 times, each with a different seed for the pseudo-random number generator, resulting in different QTL positions and effects.For each configuration , the simulation procedure was replicated s = 20 and s = 200 QTLs, with QTL effects drawn from a matrix-variate distribution with Bρ = 0.5, in order to test the simulation parameters from In a first simulation set, narrow-sense heritability was assumed equal for both traits and prediction was done with all methods described below. In a second set, we simulated two traits with different heritability values (0.1 and 0.5), for the “same” QTL distribution with https://www6.montpellier.inrae.fr/lepse_eng/M3P, last accessed on 07-21-21) in Montpellier, France, as detailed in i.e. transpiration rate per leaf area unit, was measured during daytime (TrS) and night-time  was also measured and the difference between soil and leaf water potential (DeltaPsi) calculated. Soil-to-leaf hydraulic conductance on a leaf area basis (KS) was calculated as the ratio between TrS and DeltaPsi. Growth rate (DeltaBiomass) was estimated by image analysis. Transpiration efficiency (TE) was calculated over a period of 10 to 15 days as the ratio between growth and total water loss through transpiration during this period.Seven phenotypes related to drought tolerance had already been measured for 2 consecutive years on the Syrah × Grenache progeny (on 186 genotypes among the existing 188), in semi-controlled conditions on the PhenoArch platform (https://doi.org/10.15454/YTRKV6). For each trait, a linear mixed model was fitted with R/lme4 version 1.1-21 (These seven phenotypes were studied under each watering condition (WW and WD). We thus considered 14 traits in this study, a trait being defined as a phenotype × watering condition combination, and used the raw data available online  and three random effects  to obtain unbiased estimates of the variance components and empirical BLUPs (Best Linear Unbiased Predictions) of the genotypic values. The acceptability of underlying assumptions  was visually assessed by plotting residuals and BLUPs. Broad-sense heritability on a genotype-mean basis was computed assuming a balanced design [see the introduction of DeltaBiomass.We first recomputed genotypic BLUPs from the raw phenotypic data of e.g., up to 0.99 between PsiM and DeltaPsi in both conditions) when traits derived from others  to estimate than observations (in Y), known as the “Genomic prediction can be seen as a high-dimension regression problem with more allelic effects (in L2 norm and solves the following equation: λ). We tuned it by cross-validation using the cv.glmnet function of the R/glmnet package version 3.0-2 . Note that effects associated with correlated predictors are averaged so that they are close to identical, for a high level of regularization.Ridge regression RR,  adds to L1 norm and solves the following equation: i.e., provides a sparse solution of predictors included in the best model, in addition to estimating their allelic effect. The LASSO regularization parameter (λ) was tuned by cross-validation with cv.glmnet function , as above. If n < p, LASSO selects at most n predictors.Least absolute shrinkage and selection operator LASSO,  adds to We first applied LASSO for dimension reduction and then Extreme Gradient Boosting, a popular machine learning method  using thL1 and L2 penalties, the balance between them being controlled by a parameter (α); it solves the following equation: α and λ were tuned by nested cross-validation: 20 values of α were tested between 0 and 1 and, for each of them, we used cv.glmnet function to choose between 500 values of λ. EN performs predictor selection but is less sparse than LASSO.Elastic net EN,  adds to Note that RR, LASSO, and EN all assume a common variance for all allelic effects.λ and α parameters were tuned using cv.glmnet (family = “mgaussian”). MTV_RR is the multivariate extension of RR, also tuned with cv.glmnet . MTV_LASSO is the multivariate extension of LASSO, also tuned with cv.glmnet . The implementation of these three methods is identical.Multi-task group-EN MTV_EN,  is a mulλ1 parameter) for controlling sparsity (like LASSO) and a smooth λ2 parameter) for controlling the amount of structure among predictor variables (L) to add in the model, i.e., the correlation between markers according to their position on the genetic map. Both parameters λ1 and λ2 were tuned by cross-validation using cv.spring function . The regression equation can be written as: R is the covariance matrix of residuals (Gaussian noise). The allelic effects are: Xy and indirect ones Ωyy.The multivariate structured penalized regression  and the regression ones, which involve both direct and indirect effects and are best suited for prediction (spring.reg method).SPRING solves the following equation: To enhance the reliability of marker selection by penalized methods, we used two approaches: stability selection (SS)  and marge.g., LASSO, to a different subset of half the observations for all λ values from a given set, and then retains only predictors with a selection probability above a user-chosen threshold. SS is implemented in R/stabs package version 0.6-3  has been defined by n 3.12.0 . For QTLi.e., to detect QTLs. To assess the prediction of genotypic values on simulated data, we used the Pearson’s correlation coefficient between predicted and simulated genotypic values (prediction accuracy). On experimental data, we used the same criterion, but the actual genotypic values being unknown, we used their empirical BLUPs instead (predictive ability).All methods were compared on two aspects: their ability to predict genotypic values, and their ability to select relevant markers, For QTL detection on simulated data, the methods were compared using criteria of binary classification based on the numbers of true positives and false negatives. On experimental data, because true QTLs are unknown, no such comparison could be performed; instead, we compared the outcome of the different methods and QTLs were deemed reliable when found with several methods.A nested cross-validation (CV) was applied to assess prediction by the various methods.An outer er study . Both k1For IM methods, the optimal tuning parameter was the LOD threshold obtained from permutations, and the effects for the four additive genotypic classes  were estimated by fitting a multiple linear regression model with genotype probabilities at all QTL peak positions as predictors, using R/stat::lm. For penalized regression methods, parameters were optimized with specific functions such as cv.glmnet and cv.spring.As performance metrics, we used mainly Pearson’s correlation (corP) but we also calculated the root mean square predicted error (RMSPE), Spearman correlation (corS), the model efficiency , and tesr = 10), whereas for simulated data it was performed only once, but on 10 different simulation replicates (r = 1 and t = 10). The 14 traits were analyzed jointly for MTV_RR, MTV_LASSO, and MTV_EN. But for SPRING, since analyzing all traits together was computationally too heavy, we split traits into three groups by hierarchical clustering  and type I error rate . To compare methods, we thus used the ROC (receiver operating characteristic) curve  curve , and thec) curve . Any marα at 0.7 for EN and EN.mFDR, and λ2 at 10e-8 for SPRING). We tested several α values for EN but it did not change much the results (data not shown). For MIM, the maximum number of QTLs that can be integrated into the model was set to 10.For methods with two tuning parameters, one parameter was kept constant  and to test whether such splitting evidenced more reliable QTLs (for MTV_LASSO). The parameters of penalized methods were tuned by cross-validation, with MSE as the cost function. We compared predictor selection between methods in terms of the number of common selected markers per trait, romosome , which gWe deemed selected markers as highly reliable if they were either (i) selected by at least five methods, whatever the methods, (ii) or selected by both EN.mFDR and MIM (see Results). Then, we defined a highly reliable QTL as the interval of ± 3 cM around each highly reliable marker  to identify putative functions from NCBI, when available. For those genes with a putative function, we then refined the analysis to retrieve additional information about their function and expression. We searched UniProt  and TAIR  databases based on homologies to access a complete description of gene function, name, and corresponding locus in Arabidopsis. In addition, we used the GREAT (GRape Expression Atlas) RNA-seq data analysis workflow , which gathers published expression data, to assess the level of expression of our candidate genes in grapevine leaves and shoots, relevant organs for the traits under study. RNA-seq data are normalized as detailed in the ‘User manual’ section of the GREAT platform: “from the raw read counts, the normalized counts  and the RPKM  are calculated for each gene in each sample.” Data were retrieved with all filters set to “Select All” except for the organ considered that was restricted to ‘Leaves’ and ‘Shoot’.After merging the most highly reliable QTLs colocalized between traits, we searched for underlying candidate genes. We retrieved the list of genes overlapping the intervals of our QTLs from the reference Methods were compared for prediction accuracy by applying cross-validation on simulated data with four different configurations and four heritability values.Bρ=0.8) and no correlation between errors.Mean Pearson’s correlation coefficient varied from 0.16 to 0.98, with a strong effect of heritability on prediction accuracy in all configurations, for the seven main methods . As expespring.dir.ols was always lower than with spring.reg, and even very low for “minor” configurations. With 100 or 1000 simulated QTLs (under each QTL distribution) the ranking of methods based on prediction accuracy did not change compared to “minor” configurations , the better is the method’s selection performance. As expected, IM methods (SIM and MIM) showed low selection performance when many minor QTLs were simulated and high selection performance when a few major QTLs were simulated. Note that the MIM curve was hardly visible; it roughly overlapped with the SIM curve but stopped at a low FPR because it could not select many QTLs by design.We compared the main methods mentioned above (except RR that does not perform marker selection), as well as some robust extensions, for their marker selection performance, by means of ROC curves, using the same simulated data in the four configurations . The clospring.dir.ols, displayed high variability in classification results with two simulated QTLs (“major” configurations). Indeed, when one QTL was not detected in one trait, impact on TPR was stronger than with 50 simulated QTLs.The penalized regression methods always performed at least as well as the IM methods and even much better in the case of “minor” configurations. Among penalized methods, none was clearly better than the others in all configurations, except for a slight superiority of MTV_LASSO in the “same_minor” configuration. These methods, and particularly i.e., that with a low FPR . We thus calculated the partial Area Under the Curve (pAUC) for FPR between 0 and 0.1 for methods reaching that value , LASSO (0.329), LASSO.GB (0.313), MIM (0.200), and SIM (0.162). However, based on the number of traits for which each method gave the best prediction, spring.reg had the highest score, with 6 traits out of 14, followed by MTV_EN (3 out of 14) and EN (2 out of 14).Mean genomic predictive ability per trait ranged from −0.10 to 0.68 . It decrspring.reg outperformed them for some traits, its performance was unstable and especially low for DeltaBiomass.WW, DeltaBiomass.WD, DeltaPsi.WW, and DeltaPsi.WD. For computational reasons, all traits could not be analyzed together with spring.reg, but were divided into three groups. These four traits with low predictive ability belonged to the same group. Yet, the effect of group membership on predictive ability was not significant at 5% .In a nutshell, MTV_EN and RR, tied with MTV_RR, provided the best mean predictive ability across traits. Even though TrS_night.WD) and in To address the intersection of SNP selection by all methods, and determine the number of reliable intervals (QTLs) and their position, we examined in detail marker selection for each trait and chromosome. These results are given in Most of the time, more markers were selected for traits under water deficit than for traits in well-watered conditions, and they were most often selected by several methods. Penalized methods tended to select exactly the same markers, not only close ones; for example, for  methods .vs penalized regression). We also considered as highly reliable those markers selected by at least five methods. These criteria resulted in a set of 59 highly reliable selected markers, which were converted to genetic intervals of ± 3 cM around each selected marker. Overlapping intervals per trait were merged, resulting in 25 highly reliable QTLs.We considered markers selected by both MIM and EN.mFDR as highly reliable ones for three reasons: (1) markers selected by both MIM and EN were considered as reliable ones, because most markers selected by LASSO were also selected by EN, whereas MIM marker selection was quite different; (2) simulations showed that MIM and mFDR methods led to a very low FPR; (3) these methods belong to different method classes  per trait was 51.3% for TrS_night.WD  and six to drought stress signaling and drought-related hormones (relevant for TrS_night).After merging the QTLs colocalized between traits, we obtained 12 intervals, located on chromosomes 1, 4, 10, 12, 13, 17, and 18, harboring a total of 3461 genes according to the VCost.v3 annotation . We thenetrieved  and S21.etrieved . For a sThis study contributes to our knowledge of the complex genetic determinism of vegetative traits under different watering conditions in three different ways. We compared by simulation several univariate and multivariate methods for genomic prediction and QTL detection, and re-analyzed grapevine phenotypes obtained under semi-controlled conditions. In particular, we showed that penalized methods are valuable not only for prediction but also for QTL detection. Indeed, we found new QTLs using these methods and identified relevant candidate genes.IM methods estimate genotypic probabilities between markers according to a genetic map which is computationally costly to build. On the other hand, most penalized methods do not require any previous knowledge on LD.e.g., SNPs in LD), EN selects either no or all predictors within the group with close estimated effects  and the method used .Unexpectedly, when reanalyzing the data simulated by TE.WW, colocalizes on chromosome 4 with QTLs for four high-to-moderate-H2 traits . This improvement was not found in For experimental data, we observed that MTV_LASSO (respectively MTV_EN) was superior to LASSO (resp. EN) for the five traits with the smallest heritability . Such thFurthermore, we simulated data with various levels of residual correlation among traits  but this did not significantly change prediction results (data not shown). A more detailed methodological analysis is out of the scope of the current work.Comparison with the ROC curve between IM and penalized regression methods for marker selection has not been extensively studied before. As expected, we found that IM methods are well adapted to detect a few major QTLs but not many minor QTLs . Yi et aAs MTV_LASSO selects one predictor for all traits, its superiority over univariate LASSO depends on QTL distribution across traits, notably on the amount of genetic basis shared by the traits . NeverthSPRING had never been evaluated before for its quality of predictor selection. As for prediction, SPRING showed unstable results across our simulation replicates and hyper-parameter values. However, for the ROC curve, we did not include predictor structure in the model, which may have hampered marker selection quality.IM methods were designed for marker selection; hence they are not expected to be optimal for prediction, as confirmed in our study. Among penalized regression methods, some may be better at prediction than at marker selection, and vice versa. For example, our results showed that EN performed well across several configurations for both aims. Some methods such as SPRING are specially adapted to handle both purposes but this method produced too variable results for either prediction or QTL detection. However, SPRING is a recent method that still can be improved to correct this drawback.L0 regularization that limits the number of nonzero coefficients in the model  recently proved to outperform existing methods for both prediction and predictor selection, thanks to a λ value controls sparsity . Thus, the optimal λ value might not be the same if the aim is to limit FPR or to maximize predictive ability  . For EN,As a consequence, as an efficient default method, we advise at this stage to apply EN for performing genomic prediction, and its extension EN.mFDR for performing sparser marker selection.Based on experimental data on the Syrah × Grenache progeny , we compared the same methods as above for both prediction and marker selection. To the best of our knowledge, grapevine GP within a bi-parental family has only been applied to a limited number of traits, with very few methods and never multivariate GP. e.g., PsiM.WD, DeltaPsi.WD, and TE.WW, predictive abilities with sparse methods  were better than with RR, suggesting a genetic architecture with few major QTLs rather than an infinitesimal one in those cases.Among univariate penalized methods, RR generally had equivalent or better predictive ability compared to LASSO. For the traits with the largest discrepancy between RR and LASSO, this suggests that trait variability was rather due to many minor QTLs than to a few major ones. On the other hand, for a few traits, Finally, while not considered by the penalized methods used, nonadditive genetic effects such as epistasis could be involved. We, therefore, tested the superiority of LASSO.GB over LASSO. Extreme Gradient Boosting methods are indeed among the best machine learning methods . LASSO.GTrS_night, TE, and DeltaBiomass. TrS_night QTL was previously described as a promising target for marker-assisted selection, as alleles limiting night-time transpiration also favor plant growth, resulting in a doubly, beneficial impact on improving TE , was specifically shown to impact transpiration and TE in Arabidopsis by acting as a negative regulator of stomatal density. On the other hand, 27 genes in the list are directly related to growth, development, or photosynthesis, hinting to a possible direct impact on DeltaBiomass. A histidine kinase 1 (Vitvi04g01483) may be a particularly interesting candidate for its multiple roles in Arabidopsis in ABA signaling, stomatal development, and plant growth, hence potentially simultaneously acting on both components of TE. Both these likely candidates were often highly expressed in grapevine leaves. More precise analyses of these candidate genes, including functional genomics work and possibly gene editing, will now be necessary to identify the causative polymorphisms under these new QTLs.The thorough methodology deployed for candidate genes analysis allowed us not only to retrieve a list of genes potentially underlying the QTLs of interest, but also to classify them based on their function and expression to point at the most likely candidates. We focused on the highly reliable QTL detected on chromosome 4 for elopment . One of Rather than decoupling genomic prediction from the identification of major QTLs, we argue for the need to pursue both goals jointly. Indeed, they provide complementary information on the genetic architecture of the target traits, as well the key underlying functions. Our study provided encouraging findings for further implementing genomic prediction in grapevine breeding programs. Applied to both simulated and 14 experimental traits, univariate and multivariate Elastic Net proved to be efficient for both goals, followed by mFDR control for the robust identification of QTLs. Moreover, of interest to plant biologists seeking to understand the response to water stress, our results highlighted several candidate genes underlying integrated traits such as night-time transpiration, TE, and biomass production. For some, their putative functions suggest causal links with stomatal functioning, trichome development, or the ABA pathway.i.e. genetic mapping, simulation, phenotypic analysis, prediction, and QTL detection, are available online at https://doi.org/10.15454/NOUQY2. Many of the custom functions we used are available in a package for reproducibility purposes, R/rutilstimflutre (All software we used was free and open-source and most analyses were done with R , notablyimflutre .T.F., A.D., and L.L.C. conceived the idea of the study and contributed to funding acquisition; A.C.L. and T.S. obtained the phenotypic data used in this work; C.B. and A.D. carried out the analysis of the genotypic data, with input data previously analyzed by T.F.; T.F. and J.C. performed preliminary multivariate genomic prediction analysis; A.C.L. and T.S. interpreted the results from the candidate gene analysis and wrote the corresponding parts in the manuscript; PT is the PhD supervisor of CB; CB wrote the original draft, which was reviewed and edited by all authors. All authors read and approved the final manuscript.The authors declare that the experiments comply with the current laws of the country in which they were carried out.jkab248_Supplementary_DataClick here for additional data file.


In response to major challenges regarding the supply and sustainability of marine ingredients in aquafeeds, the aquaculture industry has made a large-scale shift toward plant-based substitutions for fish oil and fish meal. But, this also led to lower levels of healthful n−3 long-chain polyunsaturated fatty acids (PUFAs)—especially eicosapentaenoic (EPA) and docosahexaenoic (DHA) acids—in flesh. One potential solution is to select fish with better abilities to retain or synthesise PUFAs, to increase the efficiency of aquaculture and promote the production of healthier fish products. To this end, we aimed i) to estimate the genetic variability in fatty acid (FA) composition in visceral fat quantified by Raman spectroscopy, with respect to both individual FAs and groups under a feeding regime with limited n-3 PUFAs; ii) to study the genetic and phenotypic correlations between FAs and processing yields- and fat-related traits; iii) to detect QTLs associated with FA composition and identify candidate genes; and iv) to assess the efficiency of genomic selection compared to pedigree-based BLUP selection.Proportions of the various FAs in fish were indirectly estimated using Raman scattering spectroscopy. Fish were genotyped using the 57 K SNP Axiom™ Trout Genotyping Array. Following quality control, the final analysis contained 29,652 SNPs from 1382 fish. Heritability estimates for traits ranged from 0.03 ± 0.03 (n-3 PUFAs) to 0.24 ± 0.05 (n-6 PUFAs), confirming the potential for genomic selection. n-3 PUFAs are positively correlated to a decrease in fat deposition in the fillet and in the viscera but negatively correlated to body weight. This highlights the potential interest to combine selection on FA and against fat deposition to improve nutritional merit of aquaculture products. Several QTLs were identified for FA composition, containing multiple candidate genes with indirect links to FA metabolism. In particular, one region on Omy1 was associated with n-6 PUFAs, monounsaturated FAs, linoleic acid, and EPA, while a region on Omy7 had effects on n-6 PUFAs, EPA, and linoleic acid. When we compared the effectiveness of breeding programmes based on genomic selection  or on pedigree-based selection, we found that the former yielded increases in selection accuracy of 12 to 120% depending on the FA trait.This study reveals the polygenic genetic architecture for FA composition in rainbow trout and confirms that genomic selection has potential to improve EPA and DHA proportions in aquaculture species.The online version contains supplementary material available at 10.1186/s12864-021-08062-7. Over the last several decades, aquatic food production has evolved away from capture fisheries toward the culture of increasing numbers of farmed fish species . HoweverTo meet consumer demand, producers are constantly making improvements in husbandry techniques, nutrition, and genetic management. One potential strategy for meeting the demand for farmed fish without compromising nutritional value could be to combine genetic selection with changes in commercial feed formulations. Recent investigations of the genetic variability underlying lipid deposition and metabolism in fish have identified a highly heritable genetic component that governs the capacity to synthesise and/or deposit LC-PUFAs –22. In tIn the literature, the technology most commonly used for FA characterisation is chemical extraction and gas chromatography. However, such analyses are expensive, invasive, and time-consuming, and are thus not easily applicable to a breeding programme. In this context, what is needed are alternative methods that are affordable and potentially non-invasive, which could be used to estimate the proportions of different FAs  in a population of fish that is large enough to enable effective analyses of the genetic architecture of traits. One potential approach could be the use of Raman scattering spectroscopy, a rapid, non-destructive method for molecular characterisation based on vibrational spectrometry. Specifically, this method enables the qualitative and quantitative characterisation of molecules through analyses of spectral bands with respect to the fundamental vibrational modes of their chemical bonds. This technology has already been used to determine total lipid concentration in minced salmon flesh  and FA cAs a step toward the long-term objective of increasing the nutritional value of farmed rainbow trout and ensuring high levels of n-3 PUFAs in the flesh, the main goal of the present study was to investigate the genetics of n-3 LC PUFA composition. Specifically, we used a commercially selected population of rainbow trout to 1) estimate the genetic variability of the FA composition of visceral adipose tissue, as indirectly estimated by Raman spectroscopy; (2) analyse genetic and phenotypic correlations among different FA traits and between FAs and production traits linked to yields and fat deposition, (3) detect QTLs associated with FA proportions and identify candidate genes present within those regions, and (4) estimate the efficiency of genomic selection (GS) compared to pedigree-based BLUP selection using phenotypes predicted by Raman spectroscopy. In this study, we targeted quantification of FA proportions in adipocytes from visceral fat. A reason was that Raman spectroscopy performed on adipocytes is less tedious and expensive than the successive mincing and lyophilisation of flesh reported in previous studies. A second one, was that the procedure proposed could also be usable as highthroughput phenotyping technology by biopsy in a non-destructive manner on live candidates as Raman spectroscopy required a limited size of sample (< 1 g). The methodology of the study encompassing gaz chromatography, Raman spectroscopy, MRI, microwaves and genotyping for calibration step and commercially selected fish was illustrated in Fig.  estimateDescriptive statistics of the fish fatty acids (FAs) proportions predicted by Raman spectroscopy in this study are presented in Table In this study, the most abundant individual fatty acid was oleic acid , which represented about 90% of all monounsaturated fatty acids (MUFAs) present in visceral fat. Saturated fatty acids (SFAs) made up 22.69% of total FA content, while polyunsaturated fatty acids (PUFAs) represented 27.05%. This latter group was composed of approximately 33% n-3 PUFAs (sum of omega-3 fatty acids) and 64% n-6 PUFAs (sum of omega-6 fatty acids). Of the individual n-6 PUFAs, the most abundant was linoleic acid , which represented ca. 60% of all PUFAs and about 93% of n-6 PUFAs. Of the individual n-3 PUFAs, alpha-linolenic acid (ALA) had the highest concentration, accounting for 50% of n-3 PUFAs and about 17% of total PUFA content. DHA was the second most abundant n-3 PUFA, representing ca. 22% of n-3 PUFAs and 7% of total PUFAs. Taken together, the sum of EPA + DHA corresponded to 35% of n-3 PUFAs and 11% of PUFAs as a whole. Overall, the FA composition of fish was similar to that of the feed  ranging from 2 to 5% for SFAs, MUFAs, and PUFAs). Instead, much more variation was detected among individuals in the estimated proportions of individual FAs (CV ranging from 3 to 31%), with the largest CV found for DHA. In general, proportions of n-3 PUFAs were more variable among fish than those of n-6 PUFAs .h2) estimates based on pedigree information for individual FAs and FA groups, as well as phenotypic correlations (rp) among the 12 traits.Figure h2 = 0.16 ± 0.05) compared to SFAs (h2 = 0.08 ± 0.04) and MUFAs (h2 = 0.12 ± 0.04). Of the two types of PUFAs, n-6 PUFAs had the higher heritability (h2 = 0.24 ± 0.05). For individual fatty acids, the highest heritabilities were estimated for arachidonic acid (ARA), LA, and EPA .In general, heritability estimates were low and varied from 0.02 ± 0.03 to 0.24 ± 0.05. With respect to FA groups, higher heritability was estimated for PUFAs (p > 0.5) than to n-3 PUFAs and ALA (rp < 0.5).Our analysis of phenotypic correlations among traits revealed that, as a group, values for PUFAs as a whole were more strongly correlated to those of n-3 PUFAs than of n-6 PUFAs. Concentrations of ARA, an individual n-6 PUFA, were highly correlated to those of the larger PUFA group, n-3 PUFAs, and the individual FAs EPA, DHA, and sum EPA + DHA. The two types of n-3 LC PUFAs, EPA and DHA, were highly correlated with each other. EPA and sum EPA + DHA were more strongly correlated to n-6 PUFAs, LA, and ARA (rg) among FAs, the results were similar to the phenotypic correlations. From a group perspective, SFAs were highly positively genetically correlated to MUFAs (rg = 1 ± 0) and highly negatively genetically correlated to PUFAs (− 0.99 ± 0.22). EPA was highly genetically correlated to LA (0.89 ± 0.08). DHA and the sum of EPA + DHA were positively correlated to LA , while the sum of EPA + DHA was also highly correlated to ARA (rg = 1 ± 0.53). However, for the majority of FAs the standard errors of the genetic correlations were high (up to ±2.86 for ALA) and some parameters did not converge, thus some results would need confirmation.When we examined the genetic correlations . Instead, generally speaking those same production traits tended to be negatively correlated with all PUFA groups and individual PUFAs (− 0.11 ± 0.6 to − 0.93 ± 1.25). BW was highly positively correlated with SFAs and MUFAs  and highly negatively correlated with PUFAs, n-3 PUFAs, and n-6 PUFAs . Fat percentage correlated positively with SFAs and MUFAs (0.55 ± 0.25 and 0.25 ± 0.21), and negatively with PUFAs as a group (− 0.44 ± 0.16) as well as all individual PUFAs (− 0.11 ± 0.6 to − 0.74 ± 0.91).PUFAs, n-3 PUFAs, and n-6 PUFAs were positively genetically correlated with yield traits (HGCarc% and Carc%), with the highest positive correlation found between Carc% and n-6 PUFAs (0.47 ± 0.13). These yield traits tended to be negatively correlated with SFAs and MUFAs, with the highest negative correlation found between Carc% and SFAs (− 0.44 ± 0.36).DHA and the sum of EPA + DHA showed a moderate, positive correlation with HGCarc% and Carc% (0.17 ± 0.48 to 0.25 ± 0.57).We must reiterate here that the standard errors of the genetic correlations were generally quite high; estimates of genetic correlations were thus relatively imprecise.GWAS analyses were performed for the 12 individual FAs or FA groups using approximately 30,000 SNPs. We identified 10 QTLs with evidence effects on the fatty acids proportions as well as 13 putative QTLs with only putative evidence  and slc7a14b (probable cationic amino acid transporter) genes), and 9.88 Mb (intergenic region between ino1a (inositol-3-phosphate synthase 1-A-like) and gnao1a  genes) respectively.Within a single region on Omy7, we identified with evidence effect a QTL for n-6 PUFAs and putative QTLs for LA and EPA. This region explained the highest percentage of genetic variance for any of the traits investigated—1.67, 0.86, and 1.02%, respectively—and also had the largest credibility interval, between 2653 kb and 3705 kb wide. The peak SNPs for n-6 PUFAs, LA, and EPA were located, respectively, at 8.34 Mb (coiled coil domain-containing protein 122-like), 8.76 Mb (intergenic region between tlx1 (T-cell leukaemia homeobox protein 1 isoform X1) and lbx1 (transcription factor LBX1-like). The peak SNP for MUFAs and EPA, at 63.63 Mb, was located in the PH and SEC7 domain-containing protein 1-like isoform region.Between 63.06 Mb and 64.29 Mb on Omy 1, we detected QTLs for n-6 PUFAs and LA, and putative QTLs for MUFAs and EPA. Depending on the trait, this region explained between 0.59 and 1.08% of the genetic variance and had a credibility interval that was between 688 kb and 1232 kb wide. The peak SNP for n-6 PUFAs and LA, at 64.29 Mb, was located in the intergenic region between tmtops2a (vertebrate ancient opsin-like) and jam2b .A region on Omy3 (located between 75.82 Mb and 75.96 Mb) contained QTLs for PUFAs and ARA, but these explained less than 0.5% of the genetic variance in each trait. The peak SNP for these QTLs was located in the intergenic region between rims2b (regulating synaptic membrane exocytosis protein 2-like) and rims2a (regulating synaptic membrane exocytosis protein 2-like isoform X1).A region on Omy14 (located between 2.57 and 2.73 Mb) contained a QTL for n-6 PUFAs and putative QTLs for LA and EPA. This region explained a low percentage of the genetic variance in these traits—0.42, 0.45, and 0.59%, respectively—and had credibility intervals that spanned from 50 to 165 kb. The peak SNP for this region was located at 2.73 Mb, in the intergenic region between rbtn1 (rhombotin-1) and rergl (ras-related and estrogen-regulated growth inhibitor-like protein).On Omy26, one region was identified as a putative QTL for both n-6 PUFAs and LA. This region explained 0.56 and 0.19% of genetic variance, respectively, and was located between 24.05 Mb and 25.30 Mb. The peak SNP for this QTL was located at 24.05 Mb, in the intergenic region between znf1007 (zinc finger protein 595-like) gene. The second putative QTL was located on Omy22 between 4.94 and 8.31 Mb, and explained 1.6% of genetic variance. The peak SNP for this QTL was located in the lmo7 (LIM domain only protein 7) gene at 6.74 Mb.Two other putative QTLs were detected that each explained more than 1% of the genetic variance associated with PUFAs as a group. The first was located on Omy7, had a credibility interval of 1239 kb, and explained 1.29% of the genetic variance for this trait. The peak SNP for this QTL was located at 63.78 Mb, in the No significant QTL was found for SFAs, n-3 PUFAs, OA, ALA, DHA, DPA, and EPA + DHA, which suggests that the underlying genetic architecture for these traits is very polygenic and affected by multiple loci with small effects in this population of rainbow trout.The accuracies of estimated breeding values (EBVs) and genomic estimated breeding values (GEBVs) are shown in Table Regardless of the trait in question, all inflation coefficients were statistically indistinguishable from 1 , while the lowest was obtained for ARA (+ 11.8%).In this study, we estimated the genetic parameters of FA composition predicted by Raman spectroscopy and the correlations between FA traits of visceral adipocytes and those associated with production and quality traits  in a commercial population of rainbow trout. We identified quantitative trait loci and evaluated the potential for genomic selection aimed at increasing the abundance of long-chain omega-3 polyunsaturated fatty acids in these fish.3) that could be frozen. Thus it could be a non-lethal biopsy applied on live candidates, making it affordable in breeding programs.In the majority of previous studies on aquatic species, genetic variability in FA composition has been evaluated using chemical analyses and gas chromatography , 52, 53.2) ≥ 0.75, as estimated using ridge regression methods). Only three of the FA traits tested  demonstrated weaker predictive power, with R2 < 0.75 . For the majority of FAs , the quality of prediction was high ; they play an important role in the long-term storage of FAs as triglycerides (lipogenesis) and their subsequent release (lipolysis) in the blood for use in cell growth, endocrine regulation, reproduction, or as energy sources after β-oxidation , 55. TheLitopenaeus vannemei, published heritability estimates were even lower, ranging from 0 to 0.19 ± 0.07 for all fatty acids, 0.07 ± 0.05 for EPA, and 0.12 ± 0.06 for DHA , with gas chromatography  as a reference method . The det− 1 and 2610 to 3100 cm− 1. The calibration by means of a ridge regression model was used to predict FA composition in the 1410 rainbow trout. Specifically, the proportions of saturated fatty acids (SFAs), mono-unsaturated fatty acids (MUFAs), poly-unsaturated fatty acids (PUFAs), omega-3 (n-3 PUFAs) and omega-6 (n-6 PUFAs) fatty acids, and several individual fatty acids were calculated as a percentage of total FA content. The acquisition parameters, pre-processing treatment, and statistical analyses are described in detail by Prado et al. (2021) .Visceral fat (2 g), mostly composed of adipocytes, was collected from the “front” lobe of visceral adipose tissue from 1410 commercial trout and preserved in liquid nitrogen. Raman measurements were acquired on 1410 sibs  using a 10x objective and an excitation wavelength of 785 nm. Two spectral ranges were recorded: 550 to 1800 cmFish were genotyped using the 57 K SNP Axiom® Trout Genotyping array from Thermofisher  at the Ip-value > 0.0001), and a minor allele frequency (MAF) higher than 0.05 for further analysis. In addition, samples in which less than 90% of SNPs were genotyped were removed from the analysis. Finally, all missing SNP genotypes of the remaining individuals were imputed using FImpute software 2.0 [SNPs were subjected to several steps of quality-control filtering, as described in D’Ambrosio et al. (2019) , in partware 2.0 . A totalh2) and phenotypic and genetic correlations  were estimated using the restricted maximum likelihood method (AIREML) and BLUPF90 software [i is the performance of the ith animal, μ is the overall mean of the population, ai is the additive effect of the ith animal, and ei is the residual random error term. The pedigree under consideration contained 17,235 animals over 9 generations. The maternal variance was not significantly different from zero and thus maternal effects were not included in the final models for all traits. All fish were female and reared in the same raceway, so there was no need for a fixed effect to be included in the model.Heritability  divided by the total phenotypic variance (Vp). For the genetic correlation among FAs, data were transformed using a logarithmic function to improve the convergence of estimates.GWAS was performed using BayesCπ , a Bayes2a) and proportion 1 – π have zero effect. The general model used is:Y the phenotype observed for the ith individual, μ the overall mean in the population, n the total number of SNPs in the analysis, aj the additive effect of the reference allele for the jth SNP, gij the genotype for individual i , and εik the residual effect for the ith individual in the kth iteration. The vector of residual effects is normally and independently distributed, 2e the residual variance.In the BayesCπ model, only a certain proportion of SNPs (π) are assumed to have a non-zero effect on the phenotype. The marker effects are estimated through an MCMC algorithm that considers a mixture of markers, of which proportion π have effects that follow a normal distribution N  between the genomic estimated breeding values (GEBVs) estimated from the different chains of the MCMC algorithm.th SNP in the model depended on the indicator variable δjk: if δjk was equal to 1, the effect of the jth SNP was estimated as aj, while if δjk was equal to 0, no effect was estimated. This indicator variable was sampled from a binomial distribution with a probability π that δjk was equal to 1 (i.e. the SNP has a non-zero effect) and a probability 1-π that δjk was equal to 0. The proportion 1-π was sampled from a beta distribution, B, with α = 300 and β = 29,652; the value of π was kept almost constant at 1%, corresponding to approximately 300 SNPs selected at each cycle from the 29,652 markers. This value of π was considered to be a good compromise in our variable selection algorithm between the high degree of polymorphism of the traits under study and the limited number of individuals (n = 1382) in our dataset.At each cycle k, the decision to include the ji is the probability that the ith SNP has a non-zero effect.The degree of association between each SNP and a given phenotype was assessed using the Bayes Factor (BF): Following Kass and Raftery (1995) , QTLs weOncorhynchus mykiss genome assembly release 100 (GCF_002163495.1), and gene symbols from Lallias et al. (2020) [All candidate genes that were located within the confidence or credibility intervals established using the BayesCπ approach are listed in Additional file . (2020) .To assess the relative efficiencies of a pedigree-based selection approach  and a genomic selection approach (GBLUP), estimated breeding values  were derived via consideration of either the pedigree relationship matrix (A) or the genomic matrix (G) using the software package BLUPf90 .i is the performance of the ith animal, μ is the overall mean of the population, and ai and ei are the vectors of additive genetic effects and residual effects that explain the performance of all phenotyped animals, respectively. Z is the incidence matrix for ai. In our model, vector ai corresponded to the breeding values of 17,235 individuals related through the pedigree relationship matrix A of the 1382 phenotyped fish.For pedigree-based evaluation, the following BLUP model was applied for the estimation of breeding values:i corresponding to the breeding values of 1382 phenotyped and genotyped individuals related through the genomic relationship matrix G.For genomic selection, the genomic relationship matrix G was used in place of the pedigree matrix A . The folTo assess the accuracy of (G) EBVs, 40 replicates of Monte Carlo ‘leave-one-group-out’ cross validation tests  were perv and y, (G) EBVv represents the (genomic) breeding values of individuals belonging to the validation dataset, y is their phenotype, and h2 is the heritability estimated using pedigree information. In addition to evaluating selection accuracy, we also assessed the quality of BLUP and GBLUP evaluation by deriving the inflation coefficient of EBVs as a measure of selection bias. The inflation coefficient is the slope of the regression of the phenotypes on the (G)EBVs. In the absence of selection bias, this coefficient is expected to be equal to 1; in case of EBV over-dispersion (inflation), the coefficient is below 1 and in the case of EBV under-dispersion the value is above 1.For each replicate, the selection accuracy (Acc) was computed as:The values obtained for selection accuracy and the inflation coefficient were averaged over the 40 replicates.Additional file 1. Fatty acid composition of the two final diets .Additional file 2. Estimates of phenotypic correlations between proportions of fatty acids and production or quality traits.Additional file 3.Candidate genes from the NCBI Oncorhynchus mykiss Annotation Release 100 (GCF_002163495.1) that were located within the confidence or credibility intervals estimated using BCπ analysis.Additional file 4. Summary statistics for 11 production and quality traits in rainbow trout.


PCSK6 locus affects PCSK6 splicing in human liver tissue and induced pluripotent derived hepatocyte-like cells. These results show that joint analysis of related atherosclerotic disease traits and their risk factors allowed identification of unified biology that may offer the opportunity for therapeutic manipulation. The signal at PCSK6 represent possible shared causal biology where existing inhibitors may be able to be leveraged for novel therapies.Although affecting different arterial territories, the related atherosclerotic vascular diseases coronary artery disease (CAD) and peripheral artery disease (PAD) share similar risk factors and have shared pathobiology. To identify novel pleiotropic loci associated with atherosclerosis, we performed a joint analysis of their shared genetic architecture, along with that of common risk factors. Using summary statistics from genome-wide association studies of nine known atherosclerotic  and atherosclerosis risk factors , we perform 15 separate multi-trait genetic association scans which resulted in 25 novel pleiotropic loci not yet reported as genome-wide significant for their respective traits. Colocalization with single-tissue eQTLs identified candidate causal genes at 14 of the detected signals. Notably, the signal between PAD and LDL-C at the  Atherosclerotic vascular disease is a leading cause of death worldwide  and can g = 0.67) (The genetics of CAD have been well characterized and a number of genome-wide association studies (GWAS) have identified over 200 genetic risk loci with robust connections to CAD . For mos = 0.67) . This ge = 0.67) .The development of statistical approaches for multi-trait GWAS meta-analysis has facilitated joint analyses of traits with substantial evidence for a common pathophysiological basis to elucidate shared genetic etiology . FurtherPCSK6. Ultimately, this study provides a better understanding of biological pathways underlying atherosclerosis to inform future therapeutic development.In this study, we performed a series of N-weighted multivariate genome-wide-association meta-analyses (N-GWAMA)  using diThis study was approved by the U.S. Department of Veterans Affairs Central Institutional Review Board. All participants gave written informed consent for study participation.We collected the summary statistics from the largest published GWAS to maximize our power for novel discovery. PAD summary statistics were the European ancestry subjects from the recent VA Million Veteran Program analysis which consisted of 24,009 PAD cases and 150,983 PAD controls . These dUsing the summary statistics from publicly available single-trait GWAS , we perfr2 > 0.2 with the sentinel SNP using the 1,000 Genomes European ancestry cohort (1 kG EUR) (p < 5 × 10−3) with all the individual traits involved in the multi-trait analysis. We also required that none of the variants at an independent locus were previously associated with any of the traits used in the N-GWAMA multi-trait GWAS by applying two filters. First, we required each sentinel SNP was not genome-wide significant for any of the individual traits (p > 5 × 10−8). It was also necessary that none of the SNPs at the independent locus were previously reported to be genome-wide significant for any of the individual traits involved in the multi-trait analysis in the GWAS Catalog  of the sentinel SNP and any other variants that were in linkage diseqilibrium (LD) of  kG EUR) . We then Catalog   for each trait combination test is conservative. Thus, we constructed a null distribution Z-score sampling strategy to estimate an α = 0.05 p-value threshold given the set of N-GWAMA multi-trait GWAS that we performed. Under the assumption that the correlation of the Z-scores across the entire genome that resulted from the N-GWAMA multi-trait GWAS are a reasonable estimate of the correlation in the multivariate null distribution of Z-scores, we can use the correlation matrix of the Z-scores from the 15 multi-trait GWAS to model the 15-dimentional multivariate standard normal distribution that is the theoretical null distribution of these results.Given that we performed 15 multi-trait GWAS for combinations of related traits, we next implemented a multiple testing correction procedure to assess significance. Due to the high correlation between each of the multi-trait GWAS we performed, a Bonferroni correction  project . We idenn = 503) as a reference panel  signal with the intron ID: 101365044:101366196:clu_14775. We also identified this intron signal in the Phenotyping Lipid traits in iPS derived hepatocytes Study (PhLiPS) hepatocyte-like cell (HLC) sQTL data by lifting over the start and stop of this intron to hg19 (101905249:101906401)  . We then1906401) .p-values between <5 × 10−3 and >5 × 10−8 (p < 5 × 10−8) and met our trait-to-trait colocalization criteria, and thirteen were experiment-wide significant (multi-trait p < 4.3 × 10−9) and met our trait-to-trait colocalization criteria. If we had used the Bonferroni correction threshold, only one locus, NUP85 (p = 3.55 × 10−9), would change from experiment-wide significant to genome-wide significant. The nominal genome-wide significant sentinel SNPs represent 25 independent loci and 11 experiment-wide significant independent loci  using the summary statistics files for these GWAS using LD score regression . We then5 × 10−8 . Of thesp = 7.6 × 10−10) or PAD, CAD, and LDL (bivariate p = 4 × 10−9). This variant mapped to the nearby gene PNPLA3, a well-established locus associated for non-alcoholic fatty liver disease (r2 = 0.73 1 kG EUR), the previously reported sentinel variant associated with these additional traits. A previous multi-trait scan for T2D and LDL reported strong association for both traits at this locus  at the PCSK6 locus. A rare coding variant in this region has been reported to associate with LDL-C (r2 = 0.0086 1 kG EUR) based on data from the 1,000 Genomes Project , extra-large subfractions (XL), HDL, intermediate density lipoprotein (IDL), LDL, and very-low density lipoprotein (VLDL) (p = 9 × 10−3), total lipids in medium VLDL , TG in medium VLDL , serum TG , and TG in large VLDL .To better understand how genetic variation at the n (VLDL) . We founPCSK6 in GTEx v8 liver tissue  at the SORCS3 locus rs11599236 .We detected a nominal genome-wide significant signal with PAD and TC (bivariate 11599236 . This lo11599236  (Supplem11599236 . This siy tissue . The oppp = 1.9 × 10−9) at the HMBS locus rs1006195  demonstrated the same direction of effect with PAD, CAD, and smoking, indicating that increased HMBS expression is associated with increased PAD, CAD, and smoking risk. VPS11  demonstrated the opposite direction of effect in all tissue except skeletal muscle and the left ventricle of the heart, meaning increased VPS11 expression in skeletal muscle and left ventricle is associated with increased PAD, CAD, and smoking risk.We detected a trivariate GWAS signal with PAD, CAD, and smoking (trivariate s1006195 . This vas1006195 . This pl tissues . HMBS (βp = 7.8 × 10−9) at the VDAC2 locus rs7088974  is associated with decreased PAD and CAD .We also detected a nominal genome-wide significant signal with PAD and CAD (bivariate s7088974 . Variants7088974  (Supplems7088974 . Althougs7088974  (Supplems7088974 . This sil artery . The dirp = 3.4 × 10−11) at the LRCH1 locus rs9526214 .Finally, we detected a trivariate signal with PAD, CAD, and HDL-C   . This smne study . The senne study .PCSK6 also influences lipid metabolism, a known upstream cause of atherosclerotic progression. This is in agreement with the known role of PCSK6 in lipid metabolism, where it cleaves and inactivates endothelial lipase (EL) and lipoprotein lipase (LPL) (PCSK6 associated with decreased LDL-C (Our data suggest se (LPL) , which cse (LPL) . This fied LDL-C .It remains to be determined how changes in PCSK6 activity result in altered lipid metabolism and risk of PAD. It is possible that changes in PCSK6 activity alter lipid metabolism and therefore have downstream effects on PAD or altered PCSK6 activity may effect LDL-C and PAD separately through independent mechanisms. In all likelihood, it is a combination of LDL-C dependent and independent mechanisms that link PCSK6 to PAD.From a drug targeting standpoint, we would expect that inhibiting PCSK6 reduces LDL-C levels and PAD risk. There are several non-FDA approved, non-specific PCSK6 inhibitors that influence lipid metabolism: alpha1-antitrypsin Portland  , profuriSORCS3 locus between PAD and TC. Colocalization experiments support SORCS3 as the causal gene at this locus. SORCS3 is a type I transmembrane protein that is a member of the Vps10p receptor family  neurons leads to unchecked production of circulating AGRP (SORCS3 in the pituitary gland is related to high levels of TC and an increased overall risk for PAD, potentially through increased circulating AGRP diverting energy metabolism away from lipid sources.We identified a novel pleiotropic association at the r family . The posr family . A recendiposity . The proing AGRP . ARGP ising AGRP . One limWe acknowledge there are several limitations to this study. First, there is sample overlap between several of our single trait summary statistics files. The N-GWAMA method attempts to account for this, but if the correction for the overlap was insufficient this could inflate our false discovery rate. Second, in using the null Z-score resampling strategy to estimate the appropriate multiple testing correction, we assume that the correlation of the Z-scores across the entire genome that resulted from the N-GWAMA multi-trait GWAS are a reasonable approximation of the correlation between the multivariate null distribution of Z-scores. This assumption could be broken due to highly associated SNPs leading to an altered correlation matrix compared to the true null distribution and the Z-scores having an inflated variance from confounders that may not have been corrected for by LDSC. Finally, we selected nine atherosclerotic and cardiometabolic traits based on conventional relationships with atherosclerosis; however, there are likely multifactorial and multidirectional relationships within this group of traits. It is possible that some of the novel loci represent the interaction between traits instead of the intended representation of atherosclerosis as we have interpreted it. It is also possible that including other sets of cardiometabolic risk factors may identify additional novel loci.PCSK6 represents possible causal biology with known inhibitors that has large potential to be a therapeutic target for atherosclerosis. These results highlight the biological underpinnings of atherosclerosis and the potential to develop non-invasive medical treatments for atherosclerosis.We have shown that publicly available GWAS data can be leveraged to perform multi-trait scans with N-GWAMA methods to identify novel loci that unify atherosclerosis. In this study, 25 nominal genome-wide significant loci were associated jointly with PAD and other atherosclerotic traits. These loci may represent novel genetic etiologies of atherosclerosis. A total of 34 candidate causal genes were identified across 14 novel pleiotropic loci and among those,


In mammals, the pigment molecule pheomelanin confers red and yellow color to hair, and the intensity of this coloration is caused by variation in the amount of pheomelanin. Domestic dogs exhibit a wide range of pheomelanin intensity, ranging from the white coat of the Samoyed to the deep red coat of the Irish Setter. While several genetic variants have been associated with specific coat intensity phenotypes in certain dog breeds, they do not explain the majority of phenotypic variation across breeds. In order to gain further insight into the extent of multigenicity and epistatic interactions underlying coat pheomelanin intensity in dogs, we leveraged a large dataset obtained via a direct-to-consumer canine genetic testing service. This consisted of genome-wide single nucleotide polymorphism (SNP) genotype data and owner-provided photos for 3,057 pheomelanic mixed breed and purebred dogs from 63 breeds and varieties spanning the full range of canine coat pheomelanin intensity. We first performed a genome-wide association study (GWAS) on 2,149 of these dogs to search for additional genetic variants that underlie intensity variation. GWAS identified five loci significantly associated with intensity, of which two (CFA15 29.8 Mb and CFA20 55.8 Mb) replicate previous findings and three  have not previously been reported. In order to assess the combined predictive power of these loci across dog breeds, we used our GWAS data set to fit a linear model, which explained over 70% of variation in coat pheomelanin intensity in an independent validation dataset of 908 dogs. These results introduce three novel pheomelanin intensity loci, and further demonstrate the multigenic nature of coat pheomelanin intensity determination in domestic dogs. For thousands of years, humans have selectively bred domestic dogs for desired physical and behavioral phenotypes, including a wide variety of coat colors and patterns , 2. For MC1R, known as the “E locus”) and agouti signaling protein  [MC1R gene inhibit the synthesis of eumelanin in hair follicle melanocytes, resulting in a solid “recessive red” coat containing only pheomelanin [ASIP variant (Ay), which produces “sable” coats with varying amounts of black/brown hairs concentrated around the dorsal midline, and pheomelanic hairs across the rest of the body [All canine coat colors and patterns result from varied expression of two pigment molecules: eumelanin, which is black or brown, and pheomelanin which is reddish-yellow. Most canids have coats containing a mixture of hairs expressing eumelanin, pheomelanin, or both, but many domestic dogs have coats in which only pheomelanin is expressed. These “pheomelanic” coats result from mutations in and around one of two genes that regulate switching between eumelanin and pheomelanin synthesis in hair follicle melanocytes: melanocortin 1 receptor ( locus”) . At leasomelanin , 17, 18.the body .The intensity of pheomelanic coloration varies widely across and within breeds that are fixed for recessive red or sable coats. For example, Irish Setters have consistently deep red coats, while Soft-coated Wheaten Terriers have coats that vary from cream to tan. Additionally, many breeds with solid white or cream coats have been shown to be recessive red, including Bichon Frisé, Samoyed, West Highland White terrier, and White German Shepherd , 19. OveMC1R gene that are highly predictive of extreme pheomelanin dilution in recessive red Siberian Huskies and Australian Cattle Dogs [MFSD12) that is associated with extreme pheomelanin dilution in a wide variety of breeds [KITLG) that was predictive of red intensity in Nova Scotia Duck Tolling Retriever and Poodle [Three recent studies have identified several genetic variants that are able to explain some coat pheomelanin intensity variation in certain breeds. The first study identified two variants in and upstream of the tle Dogs , but didf breeds . Howeverd Poodle , but notd Poodle ) and pheembarkvet.com/privacy-policy/).Participating dogs were part of the Embark Veterinary, Inc. customer base. Owners provided informed consent to use their dogs’ data in scientific research by agreeing the following statement: “I want this dog’s data to contribute to medical and scientific research”. Ethical approval was not required as non-invasive methods for genotype or phenotype collection were used . Dogs were never handled directly by researchers. Owners were given the opportunity to opt-out of the study at any time during data collection. The discovery and validation cohorts were selected from data available collected between October 2018 and June 2020. All published data have been de-identified of all Personal Information as detailed in Embark’s privacy policy  that encompass the range of coat pheomelanin intensity phenotypes in domestic dogs and obtained their hexadecimal values . We then used the Matplotlib  LinearSeTo assign coat color phenotypes to dogs, a single scientist visually evaluated owner-provided photos and assigned each dog to one of the six levels in the coat color scale or excluded it from further analysis. To account for red countershading—meaning darker red hair along the back, ears, and the tip of the tail in some breeds —all dogsAt this point, our sample contained an excess of purebred dogs from breeds that are fixed for cream coats compared to breeds that are fixed for red coats. In order to achieve a better balance between these two extremes, we used concordant owner-reported and genetically-determined breed assignments to identify an additional 197 genetically pheomelanic, purebred dogs with no owner-provided photo that belonged to breeds that are fixed for red coats . These dogs were assigned the most common six-point phenotype value in their breed across the rest of the sample. The dogs phenotyped in this manner consisted of 21 Brittanys, 2 Ibizan Hounds, 4 Irish Setters, 5 Irish Red and White Setters, 8 Redbone Coonhounds, 138 Rhodesian Ridgebacks, 16 Vizslas, and 3 Welsh Springer Spaniels  using the pandas DataFrame.sample method  and re-pPLINK 1.9  was usedWe grouped the 3,057 dogs according to their breed, subset each breed by six point phenotype value, and split each phenotype group randomly 70:30 into the discovery and validation datasets using the pandas DataFrame.sample method . As a reTo identify genomic regions associated with pheomelanin intensity variation, we encoded coat color as both a case-control (cream versus red) and quantitative trait  and applied a multivariate linear mixed model implemented in GEMMA v.0.98  to our dMC1R and RSPO2 genes on canine chromosome (CFA) 5 and CFA13, respectively (MC1R “e” [MC1R and/or have a high frequency of the “furnished” (“F”) allele at RSPO2 , and tiMC1R “e” , which rat RSPO2 , S1 Fileat RSPO2 . We discDue to the difficulty of obtaining appropriate hair samples for the thousands of dogs in our sample from individual owners, we were not able to experimentally measure the amount of pheomelanin in dogs’ hair coats (as done in ). BecausRaw whole genome paired-end short read sequencing datasets were downloaded as fastq files from the Sequence Read Archive  and aligTo determine which allele at each top GWAS marker was most likely the ancestral allele, we obtained genotypes at these markers across 54 publicly available wild canid whole genome sequencing datasets  from a previously published dataset available in the NCBI Sequence Read Archive (SRA) , 45. GenWe used the PLINK 1.9 —epistasid for each SNP as the difference between the observed and expected mean phenotype values in the heterozygote class. A positive value of d is consistent with the red-associated allele being at least partially dominant, and a negative value of d is consistent with the red-associated allele being at least partially recessive. We considered d to be statistically significant if the 95% confidence interval of the observed heterozygote mean phenotype did not include the additive heterozygote midpoint phenotype. All data used in this analysis are available in To evaluate the dominance relationship between the alleles at each of the top GWAS SNPs, we estimated predicted heterozygote phenotype values under complete additivity as the midpoint of the standardized six point phenotype values in the two homozygote classes . We thenUsing the linear_model module in the Python scikit-learn package version 0.21.3 , we fit 10 scale)  . The mos0 scale) , Table 12 between genotypes at each top GWAS variant and neighboring variants (i.e. linkage disequilibrium), are shown in S3 Fig in The locations of these markers relative to annotated canFam3.1 functional elements in the Ensembl Genes (v95) database , as wellTo the authors’ knowledge, the CFA2, 18, and 21 associations with coat pheomelanin intensity have not been previously reported. The top CFA2 variant, BICF2P1302896, falls within the second exon of the long intergenic non-coding RNA (lincRNA) ENSCAFG00000042716 at CFA2: 74,744,598–74,747,735 bp    that was previously associated with variation in coat pheomelanin intensity in Nova Scotia Duck Tolling Retrievers and Poodles  downstream of a 6 kb copy number variant (CNV) near the KIT ligand gene (MFSD12) at CFA20: 55,856,000 bp  of at least one marker . Howeverd as the difference between the observed and expected mean standardized six point phenotype value for the heterozygote class (Methods) (For each of the top GWAS SNPs (which we refer to by their chromosome number in the remainder of this manuscript), we estimated the dominance effect Methods) .We found that the heterozygote mean phenotypes expected under additivity at the top CFA2 and 15 SNPs fell within the 95% confidence intervals of the observed heterozygote mean phenotypes, suggesting that these loci behave in a mostly additive manner. At the top CFA18, 20, and 21 SNPs, the mean heterozygote phenotypes were significantly higher than the additive expectations, suggesting that the red-associated alleles at these loci are at least partially dominant to the cream-associated alleles.When pairwise tests for epistatic interaction were applied to the top five GWAS variants, seven pairs of variants showed statistically significant interactions: CFA15 x CFA20, CFA18 x CFA20, CFA2 x CFA15, CFA18 x CFA21, CFA2 x CFA18, CFA2 x CFA21, and CFA15 x CFA21 .Two locus genotype and phenotype combinations for these variant pairs are shown in In agricultural, livestock, and canine genetics –53, a coFirst, we fit a model on normalized six point phenotype values that split the genotypes at all five loci into two variables each indicating whether or not they were heterozygous (“_1”), and whether or not they were homozygous for the red-associated allele (“_2”). The ratios of the model coefficients (β) for the _1 and _2 variables at each locus provided an additional evaluation of the dominance relationship between the two alleles: loci for which the _1 β was approximately half of the _2 β fit the assumption of additivity, whereas loci for which the _1 β was approximately zero were more consistent with the red-associated allele being recessive to the other allele, and loci for which the _1 and _2 βs were similar were more consistent with the red-associated allele being dominant to the other allele. Based on the β values for this model , we concNext, we fit five models with six point phenotype values as a function of genotype at each locus using its best dominance encoding in order to estimate the predictive power of each locus individually. This showed that the CFA2 and CFA20 loci each explained over 50% of the variance in six point phenotypes, while the CFA15, 18, and 21 loci each explained less than 10% of the variance .To quantitatively determine the best combination of dominance encodings in a multilocus model, we fit 31 models with each possible combination of the additive and most likely dominance encoding at all five loci. A model treating all five loci as completely additive was able to explain 73% of variation in the six point phenotype (adjusted R-squared = 0.730) . The dom−2) and was significantly more predictive than either the purely additive model (−9) or the model with the best fit dominance encoding and no epistasis (−6). We applied the reduced best fit predictive model to the 908 dogs in the validation sample and found that it was able to explain 72% (adjusted R-squared = 0.7211) of variation in coat pheomelanin intensity across all dogs , located approximately 82 kb downstream of ENSCAFG00000042716 at CFA2: 74,829,960–74,856,947. RUNX3 encodes a transcription factor that shows reduced expression in hair follicles in human premature hair greying and appears to regulate expression of several other genes that also show reduced expression in premature greying samples [RUNX3 is also known to be a regulator of hair shape determination during murine embryonic development [cis-regulatory module consisting of ENSCAFG00000042716, RUNX3, and possibly other unknown genic variants or functional genomic elements. Identifying the causal mutations underlying this association will require fine mapping of the locus, as well as molecular experiments to directly assess the functional impacts of any candidate mutations.The top CFA2 variant falls within a long intergenic non-coding RNA (lincRNA) with unknown functional significance in domestic dog. Many mammalian (including dog) lincRNAs are known to modulate the expression of nearby protein-coding genes via chanisms –57. The  samples . RUNX3 ielopment . We therTYR gene. This gene encodes the enzyme tyrosinase, which catalyzes the oxidation of l-dihydroxy-phenylalanine (DOPA) to DOPA quinone, a precursor of both eumelanin and pheomelanin. Mutations in and around TYR produce varying degrees of pheomelanin dilution in several mammalian species by decreasing the amount of pheomelanin produced in hair shaft melanosomes [TYR mutations might also produce pheomelanin dilution in dogs [TYR variants can modulate coat pheomelanin intensity in dogs was finally supported when a recent study identified a missense mutation in the TYR gene as causal for a unique temperature-dependent pigment dilution phenotype (acromelanism) in a single dog [TYR variants and non temperature-dependent coat pheomelanin intensity variation, although fine mapping and functional validation will be required to definitively identify a causal variant. In multiple species, some of the genes located nearby TYR on CFA21  areSLC26A4 gene has no clear connection to hair coat pigmentation in mammals, it does play a role in a variety of hearing impairment phenotypes in human and inner ear abnormalities in mouse, including hyperpigmentation in the stria vascularis [MITF) [PMEL  [KITLG cause hearing loss in humans [The connection between coat pheomelanin intensity and the gene tagged by the top CFA18 association is less apparent. The A to G substitution at this variant results in an amino acid substitution from isoleucine to methionine in the solute carrier family 26 member 4 (SLC26A4) protein. Based on computational modeling (Sorting Intolerant from Tolerant (SIFT) score = 0.03), this substitution is predicted to be somewhat deleterious . Howeverscularis  and degescularis . There is [MITF)  and PMELas SILV)  genes, was SILV) , 68. Addn humans . Due to KITLG gene on CFA15 encodes a ligand that binds to the c-Kit protein on the surface of melanocytes, triggering the Ras/MAPK signaling pathway and stimulating melanocyte proliferation and melanogenesis [KITLG coding sequence, indicating that its likely affecting pheomelanin intensity by modulating KITLG expression. As noted in the study that first reported this association [KITLG have been associated with both pheomelanin and eumelanin dilution in several mammalian species [SLC26A4 gene on CFA18 encodes a transmembrane ion transporter that is highly expressed on the apical surfaces of epithelial cells in the inner ear [SLC26A4 have been associated with abnormal melanin deposition and hair cell degeneration in the inner ear. Unfortunately, little is known about the role that SLC26A4 plays in these phenotypes. It is also possible that our GWAS signal on CFA18 is actually driven by some other nearby gene that happens to be in high linkage disequilibrium with our top CFA18 variant in this study sample. The MFSD12 gene on CFA20 encodes a transmembrane solute transporter that localizes to melanocyte lysosomes and/or late endosomes in mice [MFSD12 influences hair pigmentation is still not well understood, but it has been suggested that it might regulate melanosome autophagy [MFSD12 cream-associated variant masks the effect of the KITLG red-associated variant by causing abnormal degradation of melanosomes downstream of pro-melanogenic signaling by KITLG.We also found significant evidence for epistatic interactions between the CFA20 locus and both the CFA15 and CFA18 loci. In fact, based on the PRE values in our linear regression analysis, the effect of the CFA15 x CFA20 interaction is greater than the effect of the top CFA15 variant . Based oogenesis –72. The ociation , this as species , 73–77. nner ear , thyroidnner ear , and kidnner ear  in human in mice . The molutophagy . If thisMC1R gene, which underlie cream coats in Siberian Huskies and Australian Cattle Dogs [A multigenic predictive model using genotypes at the most strongly associated single-nucleotide genetic markers on CFA2, 15, 18, 20, and 21, plus two interaction terms, was able to explain over 70% of the phenotypic variation across both the GWAS cohort and an independent validation cohort containing individuals from over 60 breeds as well as mixed breed dogs. This represents a gain of approximately 20% variance explained compared to a model using only the two previously discovered loci . BecauseTaken together, these results demonstrate that coat pheomelanin intensity in the domestic dog is a multigenic trait both across and within breeds, and that some loci controlling this trait likely interact via unknown biological pathways. Further fine mapping and experimental investigation will be required to validate the three novel associations, to characterize the roles these and other genetic loci play in pigmentation in dogs and other species, and to determine whether any mutations associated with coat pheomelanin intensity variation also exhibit pleiotropic effects on canine health, such as deafness.S1 Appendix(PDF)Click here for additional data file.S1 Table(TSV)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(TSV)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S1 File(TXT)Click here for additional data file.