Phenotypic and genetic trends, population genetic parameters, and theheritability and breeding values of the fattening and slaughter traits – namelymuscularity score (MUS), final fattening weight (FFW), weight gain per dayof life (WGD), slaughter weight (SLW), carcass weight (CAR), dressingpercentage (DRP), bone–meat production per day of life (BMP), SEUROPconformation and fat coverage score , and meat percentage (MEP) – of1162 Hungarian Simmental bulls were evaluated with the progeny test databaseof the Association of Hungarian Simmental Breeders. Trends were calculated byweighted linear regression analysis, while the population genetic parametersand breeding values used the BLUP (best linear unbiased prediction) animal model and general linear model (GLM).According to the results, the lowest heritability value ( Due to the simultaneous selection of milk and meat production,the number of traits taken into account can definitely be more diverse than that in the case of specialized breeds. As a result of this,along with the genetic antagonism between milk and meat production traits, theextent and assessment of genetic progress achieved in some traits may differfrom what is usual for specialized breeds. In the case of dual-purposebreeds, such as the Hungarian Simmental, it can be considered to be a good result if,in addition to achieving considerable genetic progress in a dairy trait, wedo not experience a decrease in some beef traits.In the Hungarian Simmental breed, 12 traits  were marked to be part of its breeding purpose and to be improved upon. The mentioned traits were included in the selection index, whichis called the dual-purpose production index (DPI). During the formation ofthe selection index, the breeding values estimated for the given traits areconsidered as a starting point; these traits are determined from the data obtainedduring the progeny tests. Therefore, breeding bulls suitable for artificialinsemination take part in a progeny test, during which a breeding value isestimated for the production  and conformationcharacteristics.Consequently, progeny tests organized to assess the meat-producing abilitywere separated from other types of fattening or slaughter experiments, suchas breed comparison tests; however, the scope of the examined traits  can be very similar inthese two test systems. In the literature, we can find many studies whose results were obtainedunder experimental conditions, but the number of results based on theprogeny performance test is extremely small.There were a number of sources in the literature regarding the heritability offattening and slaughter characteristics of different breeds of cattle.According to Crews et al. (2003), the heritability . Numerous literature sources on thestudy of environmental factors that influence fattening and slaughter values havebeen published . The results of these sources have been presented indetail in our previous work  so they are notdetailed here.Very little novel data on the phenotypic and genetic trends in fattening andslaughter traits have been found in the literature .For the Simmental breed and the Simmental-type BavarianSpotted breed, Elzo et al. (1987) and Kögel et al. (1991), respectively, reported data, but these can beconsidered to be quite old. Potočnik et al. (2007) identified increasinggenetic trends in a number of traits related to milk production and in theconformation and slaughter traits in Slovenian Simmental herds. Kapš et al. (2000) did not show a trend-like change when evaluating the change in theweight gain per day of life of fattening bulls in the progeny test. Similarresults have been reported by Röhrmoser and Pichler (2002).to determine the effect of different factors on the fattening and slaughtertraitsto determine the heritability values of the mentioned traitsto determine the breeding values of sires based on the mentioned traitsto determine the direction of the phenotypic and genetic trend in thementioned traits over the past 20 yearsto determine the changes in the meat production of the dual-purposeHungarian Simmental breed as a result of the simultaneous selection aimed atimproving both milk and meat production.Since relatively little information is available in the literature on thesubject in question, the aim of our present study was2This research is based on the results obtained in our two previous studies. Therefore, there were manysimilarities in the structure of the database, as well as in themethodological parts, which we will not repeat in the present paper.2.1In this study, the results of purebred male progeny of 111 dual-purposeHungarian Simmental sires  were processed. A total of 1162 bullprogeny of the 111 sires from 1023 randomly selected dams were included inthe study. The bull progeny were fattened and then slaughtered according tothe progeny test protocol of the Association of Hungarian Simmental Breeders.The number of offspring per sire ranged from 5 to 31, with an average of10.5 offspring per sire. The bull progeny were born between 8 January 2001and 8 December 2019, and the slaughter took place between 13 May 2002 and 3 May 2021. This rather long period gave us the opportunity to evaluate thephenotypic and genetic trends.The youngest bull progeny in the test was slaughtered at 12 months of age, andthe oldest one was slaughtered at 27 months of age. The average slaughter age was 18.1 months. Of the 111 dual-purpose sires in the study, the oldest were born in1995, and the youngest were born in 2016; thus, we were able to calculate genetic trendsfor the period 1995–2016.A total of 10 fattening farms took part in the progeny-testing procedure.The procedure for organizing and conducting the progeny performance test andthe conditions for the keeping and feeding of fattening bulls were described inFüller et al. (2009) and so are not repeated here.Data collection was regularly monitored by the staff of the Association ofHungarian Simmental Breeders. The measurements  and datacollection were carried out using the same methods and at the same times interms of slaughter and cutting technology at the different slaughterhouses.The Kolmogorov–Smirnov test was used to check the normal distribution of thedatabase, and the Levene test was used to check the homogeneity of the variances.2.2The evaluated traits were as follows: muscularity score (MUS), finalfattening weight (FFW), weight gain per day of life (WGD), slaughter weight(SLW), carcass weight (CAR), dressing percentage (DRP), bone–meat productionper day of life (BMP), SEUROP conformation and fat coverage score ,and meat percentage (MEP). The method of calculation, the acronyms and theunits of measurement of the examined traits are summarized in Table 1.2.3Data were evaluated by a multivariate analysis of variance . In constructing the model, the sire of the fatteningbulls was taken into account randomly; the location of the fattening farm(the fattening farm from which the offspring went to the slaughterhouse) andthe year of birth of the bull progeny were considered as fixed effects. The slaughter age (SLA) of the bullprogeny was included in the model as a covariant. In the course of thestudy, the 10 traits mentioned above were handled separately from eachother, and model calculations (runs) were performed separately.The general form of the estimation models used (using the CAR as an example)is described as follows:For all traits, the significance of the above-mentioned effects was alsostudied, but in the present paper, only the results for the sire and thebirth year of the bull progeny are presented.Phenotypic correlation coefficients were determined between the studiedtraits.The data were prepared using Microsoft Excel 2003 and Word 2003. Theevaluation of the database was performed with the statistical softwarepackage SPSS 27.0 .2.4When calculating phenotypic trends, we started with the average results of bull progenyper year of birth. Linears were fitted to the annual mean of thestudied traits using one-way linear regression analysis. The evaluatedtraits were considered to be dependent variables, and the year of birth wasconsidered to be an independent variable. The general form of the one-way linearregression equation used was as follows:2.5The GLM model (ANOVA Type III) was completely identical to the modelpresented in Sect. 2.3; thus, we will not repeat it here.Using the BLUP models, two matrices were created. One of these was thedatabase matrix, and the other was the pedigree matrix. The pedigree matrixof relatives included pedigree data for full sibs, half sibs, sires anddams.The examined fix  factors for all models were the fattening farmand the year of birth of the bull progeny.The covariant was SLA in themodels.Calculation of the population genetic parameters took place in accordance with the work ofSzőke and Komlósi (2000) and Lengyel et al. (2004), with two models beingused, namely one GLM model and one BLUP (best linear unbiased prediction) animal model.the genetic variance  variance ; thus, it is not detailed here.For the GLM model, SPSS 27.0  software was used, and for the BLUP animal model, DFREML and MTDFREML  software were used.2.6The breeding value (BV) of the sires in the study was also estimated with two models basedon the evaluated traits.In case of the GLM method, the BV was determined as the difference between theaverage performance of the sire's progeny group and the average performanceof the entire population for all 10 traits:In case of BLUP model, the animal model communicated the values of BVdirectly. The reliability value  calculation, similarly to in the study ofNúnez-Dominguez et al. (1995).Breeding values were determined for all sires, but in our paper, these are only shown (in a tabular form) for the 10 sires with the most offspring.2.7The genetic trend of the examined traits – as in Ostler et al. (2005) –was determined from the average BV of sires born in the same year. Similarlyto our previous work , calculations were performed usingweighted one-way linear regression analysis. The mean BV was thedependent variable, the sire's year of birth was the independent variable, andand the number of offspring per sire was used for weighting.33.1The effects of the sire, fattening farm, birth year of the fattening bulls and ageof the bull progeny at slaughter on the 10 examined traits were shown in Table 2. The effect of the sire was found to be statistically significant. The two extreme values of FFWwere 579.0 kg (in 2017) and 759.2 kg (in 2008). Accordingly, we found asimilarly large difference in the CAR .In terms of WGD, 2009 was the worst , itappears that there was no close relationship between the studied traits,with a few exceptions. Such an exception was, for example, the negative,moderately strong correlation between SLA, WGD and BMP .When estimated with the GLM method, there was a difference of 260 g dThe genetic trends obtained by averaging the estimated BV of the sires bornin the same year in terms of 10 traits were summarized in Table 9. According to theresults, for one trait, the change in the mean value over time , a higher WDG , it can be concluded that the studied factors significantly influencedthe fattening and slaughter results of fattening bulls.The results of the phenotypic trend calculation fell short of ourpreliminary expectations. Earlier , we observed a smallincrease in the phenotypic trend of the fattening and slaughtercharacteristics of Hungarian Simmental bulls; the results of the presentstudy showed a more stagnant state. The present results were different fromthose of Füller et al. (2009), who also observed a slight upward trend.After the end of the tests, the meat of the fattened and slaughtered progenywas sold in the same way as their counterparts from normal fattening. As aresult, the market and market trends and demands could inevitably have aninfluence on the evolution of certain traits, especially in theperspective of the examined 20-year time interval. Based on our results, itseems that, due to the changing market conditions, the FFW of the maleprogeny has decreased somewhat in the recent period. Lower finishing weightresulted in lower SLW and lower CAR, the reduction of which was also reflected inthe phenotypic trends.The worst inherited trait , wecan conclude that the sire of fattening bulls (breeding bulls underclassification) can have a significant influence on the evaluated fatteningand slaughter characteristics. We found greater differences in the BV of thesires in the study of some traits  and smaller difference in the studyof other traits . Based on the results of our work, it can be repeatedly stated that, by choosing a suitable sire, it is possible to significantly improve the fattening and cutting performances even within one generation.In the case of most of the examined traits, the genetic trends clearlyshowed an improvement in terms of the quality and BV of the sires used for breeding.The highest determination coefficients were shown for the DRP .The genetic trend of FAT and BMP showed a minimal increase of a few grams per year,which was not reflected in the phenotypic trends. The evolution of these twotraits can be considered to be stagnant from both a phenotypic and a genetic pointof view.The phenotypic and genetic trends of the FFW were in opposite directions.Presumably due to market changes, the offspring were fattened to a higherfinal weight in some years and to a lower final weight during the 20-yearstudy period, which resulted in an overall decreasing phenotypic trend data available; thus, theexamination cannot cover them. However, the continuation of the work will bethe examination of the snp effects in order to make a genomic estimationeven more accurate in the future.5Individuals of the Simmental breed group are generally good meat producers,but their milk production is much weaker than that of dairy breeds. As aresult, the focus with the Hungarian Simmental breed was mainly on theimprovement of milk production capacity and secondary characteristics; thus, inthe case of meat production capacity, maintenance or even a slight improvement can be considered to be an acceptable result. The latter statement is fullysupported by the results of our present study.The meat-breeding value index represents 27 % of the dual-purposeproduction index mentioned in the introduction. The meat-breeding valueindex is currently made up of the breeding value of three parameters – theseare the bone–meat production, the prime meat percentage and the SEUROPconformation score .Based on our results, it seems that the simultaneous selection in which, inaddition to milk production, meat production is included in the dual-production index at a proportion of almost one-third causes neither asubstantial improvement nor a deterioration in the meat production of theHungarian Simmental breed.


It is important for the geneticist to know the heritability of these characteristics and their genetic relationships. Heritability estimates for growth traits and feed efficiency traits, as well as genetic correlations between these traits, were determined in this study using Japanese quails, which are acknowledged as model animals for poultry species. Heritability estimates were found to be high for body weight traits, moderate to high for the Gompertz growth curve parameters, and moderate for feed efficiency traits. Negative and moderate genetic correlations were estimated between feed conversion efficiency traits and body weight traits, but no high genetic correlations were estimated between feed conversion efficiency traits and the Gompertz growth model’s asymptotic weight parameter. As a result, it was concluded that the βThis study aimed to estimate heritabilities for weekly body weight traits, the Gompertz growth curve parameters, and feed efficiency characteristics, as well as genetic correlations among characteristics. A total of 700 Japanese quails with pedigree records were used in this study. Body weight and feed consumption were measured individually on a weekly basis. Using weekly body weight data, the growth model parameters were estimated for each bird using the Gompertz nonlinear regression model. Multi-trait variance-covariance matrices were obtained with Bayesian inference using the Gibbs sampler. While estimates of high heritability (0.59 to 0.61) were found for weekly body weight traits, estimates of moderate heritability (0.23 to 0.37) were determined for feed intake and feed conversion efficiency traits. The estimated heritabilities for the parameters of the Gompertz model and inflection point coordinates were moderate (0.37 to 0.47). While genetic correlations between feed intake and body weight characteristics were positive and moderate (0.28 to 0.49), the genetic correlations between feed conversion efficiency and body weight traits were positive and strong (0.52 to 0.83). It has been concluded that the moderate negative genetic relationship between feed conversion efficiency and body weight may constrain selection studies. Due to the weak genetic correlation between the asymptotic body weight parameter of the Gompertz model and the feed conversion efficiency, it is thought that the total genetic gain will be greater if the mature weight parameter is also used as a selection criterion in genetic improvement studies. Studies on the genetic improvement of poultry are typically conducted in two distinct phases. The first of these is to enhance the within-line selection of desired characteristics. In the first stage, additive genetic variation is utilized to increase related traits, and birds with the best breeding value for multiple characteristics are selected as the parents of the following generation. The second stage entails the crossing of improved lines. Multiple line crosses improve profitability when there is non-additive genetic variation for one or more traits contributing to the profit function, and consequently, commercial birds are the result of three- and four-way strain crosses ,2. WithiThere have been studies conducted to improve the meat and egg production of Japanese quail ,6,7,8. ACoturnix coturnix japonica) were used as animal material in the research. A total of 700 chicks obtained from 40 male and 120 female quails in a breeder flock in the Akdeniz University livestock facility, which have not been genetically improved before, constituted the animal material of this study. Each egg collected from the breeder flock for 10 days was numbered according to their parents. The collected eggs were kept at 14 °C and 70% relative humidity in a chiller cabinet. Before the eggs were placed in the incubator, gradual preheating was applied. In a commercial incubator, the eggs were separated according to their parents and exposed to a temperature of 37.5 °C and a relative humidity of 55% for the first 14 days. During the last three days of incubation, the eggs were exposed to a temperature of 37.2 °C and a relative humidity of 70% in parental-designed hatching baskets. The newly hatched chicks were kept until they were dry, after which the wing numbers were attached. Thus, the pedigree records  were generated, and weekly live weights and other measurements were taken by matching the pedigree records during the experiment. Quail chicks were housed in brooder cages (90 cm2/quail) for the first three weeks (each chick was raised in divided cages for a period of 21 days.); following sex determination in the third week, they were transferred to individual fattening cages (160 cm2/quail) where they remained until the age of 42 days. At 21 days of age, the presence  or absence  of speckled breast feathers was used to determine the gender of birds. A grower diet with 24% crude protein and 2900 kcal of metabolizable energy/kg/kg was employed for the first 21 days, followed by a fattening diet with 23% crude protein and 2800 kcal of metabolizable energy/kg. From hatching until the end of the trial, ad libitum feeding, water, and a 23 h per day lighting schedule were utilized.The experiment was conducted at the Department of Animal Science, Akdeniz University, Türkiye. The care and usage of birds complied with Turkish laws and regulations and was approved by the Ministry of Food, Agriculture, and Livestock (decision number 22875267-325.04.02-E.3751911) and Akdeniz University’s Animal Experiments Local Ethics Committee. Japanese quail . Birds were fasted for 4 h before weighing. Due to technical limitations, the feed intake was measured in small groups , and feed conversion efficiency was measured individually for a total of 700 chicks. Feed intake and FCE were determined weekly and recorded for 1, 2, 3, 4, 5, and 6 weeks. Using these data (feed intake and body weight gain), cumulative feed consumptions and cumulative feed conversion efficiencies for each bird were calculated for ages 5 and 6 weeks.t, In many previous studies, it has been reported that the Gompertz function is the most compatible growth model for the quail species ,18,19,20arameter . The GomThe descriptive statistics and Kolmogorov–Smirnov normality tests of the traits were obtained using the UNIVARIATE procedure of SAS 9.4 statistical software.β is a vector of fixed effects, and u is a vector of random genetic effects. X and Z are known design matrices relating phenotypic records to u, respectively. e is a vector of random errors. It is further specified that covariance matrix is equal to I represent the identity matrix and denotes genetic covariance matrix, while the matrix A is the numerator relationship matrix.The following linear mixed effects model for two traits (4) was used in the analysis:Finally, it is assumed that y and prior distributions of To perform Bayesian analysis, the likelihood function of β, which is a vector of fixed effects. The prior distribution assumed for k = 2 for a multivariate model. We can write the joint posterior density of all parameters asA non-informative prior was assumed for ribution .pβ∝consi and i and Bayesian analyses were carried out using the MCMCglmm package of R . A singl0, β1, β2, IPT, and IPW are presented in p < 0.05). At 5 and 6 weeks of age, the mean body weights of male quails were 181.88 g and 201.57 g, while the mean body weights of female quails were 191.78 g and 211.16 g, respectively. At 5 weeks of age, the average cumulative feed intake value for male quails was 431.45 g, whereas the average for female quails was 470.98 g. Similarly, the average cumulative feed intakes of male and female quails at 6 weeks of age were 595.88 g and 652.74 g, respectively. There were no statistical differences between the average cumulative feed conversion efficiencies of female and male quails at 5 and 6 weeks of age (both p > 0.05). At 6 weeks old, male and female quails had mean cumulative feed conversion ratios (1/FCE) of 2.46 and 2.43, respectively; at 6 weeks old, these averages had increased to 3.01 and 3.08, respectively.The descriptive statistics of BW5, BW6, FI5, FI6, FCE5, FCE6, β2) in the range of 0.9978 to 0.9999 were found. In terms of the distant asymptotic weight parameter (β0) of the Gompertz function, a statistically significant difference was found between male and female quails (p < 0.05). While the average β0 parameter of males was found to be 235.94 g, the mean value of the β0 parameter of females was estimated to be 260.01 g. Males and females did not differ statistically in terms of the scaling parameter and the instantaneous growth rate parameter of the Gompertz model (both p > 0.05). The mean values of β1 and β2 for male quails were estimated to be 3.29 and 0.068, while those for females were 3.32 and 0.071. Statistical differences were identified between genders in terms of inflection point coordinates of the Gompertz model (p > 0.05). The estimated age of inflection is younger for females (16.90 days) than for males (18.51 days). Likewise, the average inflection point weight of female quails (95.65) was found to be greater than that of males (86.81 g).1, β2, and IPW characteristics. Heritability estimates for FI5, FCE5, β0, and IPT traits were moderate to high (0.36–0.37). In addition, estimations of the heritability of FI6 and FCE6 characteristics  ranged from low to moderate.Summary statistics for the posterior distributions of heritability estimates of growth and feed efficiency traits are presented in 0/e and IPT = ln(β1)/β2), there are fixed relationships between parameter β0 and IPW and between IPT and parameters β1 and β2. Although the β0 parameter of the Gompertz model exhibited strong genetic and phenotypic relationships with BW5 and BW6 (0.52–0.82), no significant correlations were observed with feed efficiency characteristics. The phenotypic and genetic relationships between parameters β1 and β2 of the Gompertz model with body weight and feed efficiency characteristics were weak and statistically insignificant (ranging from −0.19 to 0.20).The average 5-week-old weight value for Japanese quails in this study is consistent with the findings of some researchers ,24,25,260 parameter (249.91 g) was in agreement with the mean values reported by Beiki et al. [0 parameter of the Gompertz function in Japanese quails were estimated in the range of 242–276 g by Karabağ et al. and Hyankova et al. [1) of the Gompertz model for the growth of Japanese quail was estimated by Akbaş and Yaylak [Estimates of the Gompertz growth curve were produced for all birds with high determination coefficients (0.9978–0.9999). This indicates that the Gompertz function effectively explained the growth curves of the observed data, in accordance with Akbaş and Oguz  and Narii et al.  and Narii et al. . Similara et al. ,33. The d Yaylak  and Narid Yaylak  to be 3.d Yaylak ,27,28 em0 parameter of the growth model [It has also been reported in previous studies that females have higher averages than males in terms of weekly body weights ,35, feedth model  and inflth model .The Bayesian approach replaces frequentist credible intervals with Bayesian credible intervals (BCI). The interpretation of a 95% BCI is that there is a 0.05 statistical probability that the parameter falls inside the specified interval. Additionally, if the posterior distribution is asymptotic, the highest probability density interval (HPD), the shortest possible interval containing 95% of the posterior mass, is selected by Waldmann and Ericsson . The oveThe heritability estimates for body weight at 5 and 6 weeks were 0.59 and 0.61, respectively . Numerou0 parameter. In addition, there are researchers who find the estimates of heritability for the β0 parameter to be both lower (0.17) [1 and β1 parameters of the Gompertz model in this study are consistent with the findings of Saghi and Saghi [0 parameter by number e, all the variance components are equal to the β0 parameter. Due to the nature of the Bayesian estimator (Gibbs sampler) utilized in this study, only small iteration-related differences between the variance component estimates of β0 and IPW occurred.In agreement with this study, ,18,31 for (0.17)  and highr (0.17) . The hignd Saghi  and Narind Saghi . Variousnd Saghi ,18,31 hand Saghi . Because0 parameter of the Gompertz growth model. Nevertheless, the genetic correlations (0.28 and 0.34) between β0 and FCE characteristics were relatively weaker than those between FCE and BW (ranging from 0.52 to 0.82).It has been reported by many researchers that there are positive and high genetic and phenotypic correlations between the live weight characteristics of close weeks in Japanese quails ,43,44,450 parameter of the Gompertz growth model and the FCE traits, it has been determined that the β0 parameter can be used as an alternative in case the additive genetic variance for live weight decreases in the following generations of selection.In this study, it was determined that the heritability estimates for feed conversion traits were moderate, but considering the genetic relationships with other yield traits, using the FCE trait as a selection criterion would be more beneficial than the FI trait. In addition, it has been determined that the strong and positive genetic relationships between FCE traits and BW traits can have a very beneficial effect in multi-trait selection studies. Finally, due to the moderate genetic correlation between the β


Eggshell speckle phenotype is an important trait in poultry production because they affect eggshell quality. However, the genetic architecture of speckled eggshells remains unclear. In this study, we determined the heritability of eggshell speckles and conducted a genome-wide association study (GWAS) on purebred Rhode Island Red (RIR) hens at 28 weeks to detect potential genomic loci and candidate genes associated with eggshell speckles.LOC423226, SPTBN5, EHD4, LOC77155, TYRO3, ITPKA, DLL4, PLA2G4B, PLA2G4EL5, and PLA2G4EL6 were considered the most promising genes associated with eggshell speckles that were implicated in immunoregulation, calcium transport, and phospholipid metabolism, while its function in laying hens requires further studies.The heritability of eggshell speckles was 0.35 at 28 weeks, and the speckle level is not related to other eggshell quality traits in terms of phenotypic correlation. We detected 311 SNPs  and 39 candidate genes associated with eggshell speckles. Based on the pathway analysis, the 39 candidate genes were mainly involved in alpha-linolenic acid metabolism, linoleic acid metabolism, ether lipid metabolism, GnRH signaling pathway, vascular smooth muscle contraction, and MAPK signaling pathway. Ultimately, ten genes, This study provides new insights into understanding the genetic basis of eggshell speckles and has practical application value for the genetic improvement of eggshell quality.The online version contains supplementary material available at 10.1186/s12864-023-09632-7. As a globally important poultry species, hens are widely farmed in China for egg production due to their high-quality protein and affordable price. Brown-shelled eggs are favoured by consumers in the Chinese market . After eThe speckle level can be recorded using a scoring method based on the severity of eggshell speckles artificially, as shown in Fig. The Rhode Island Red (RIR) hen, a pure line widely farmed in China, is also the representative of brown-shelled egg layers. The genetic parameters of speckled eggs in RIR chickens and the extent to which the speckle level is phenotypically and genetically related to eggshell quality remain unclear. Therefore, in the present study, we evaluated the genetic parameters of eggshell speckles and determined the phenotypic and genetic correlations between eggshell speckle levels and egg quality traits . A genome-wide association study (GWAS) was employed to detect candidate genes related to eggshell speckles, which may be valuable for the genetic improvement of egg quality.The eggshell quality and the speckle trait of the RIR hens are described in Table The phenotypic correlations between SL and eggshell quality are shown in Fig. A total of 53 individuals laying normal eggs with an average score of 1 and 54 individuals laying speckled eggs with an average score of 2.5 or higher were selected as the control and case groups, respectively. After a series of strict quality control procedures, 107 individuals and 7,589,928 SNPs were used in the subsequent GWAS analysis. The Manhattan and Quantile–Quantile (QQ) plots are shown in Fig. The chi-square test was used to compare the allele frequencies of the six identified significant SNPs in the control and case groups. The results showed that the allele frequencies of the six significant SNPs differed significantly between the two groups  explained 7.80% of the phenotypic variance. The 305 suggestively significantly associated SNPs were mainly distributed on chicken chromosomes 5, 20, and 23, and the SNPs were mainly located in introns and intergenic regions, upstream and downstream of gene, corresponding to 39 genes  which were located on chromosome 5. The SPTBN5 gene encodes the βV-spectrin protein in many tissues and is involved in maintaining the stability, structure, and shape of the cell membrane [SPTBN5 leads to intellectual disability, seizures, and developmental delays in humans [SPTBN5 are related to adaptation to cold conditions and contribute to parasitic resistance in cattle [EHD4) gene. EHD4 has been described as a critical regulator of endocytic transport of cell membranes [EHD4 is reportedly associated with endometritis in cows [EHD4 gene may participate in the regulation of T-cell-mediated immunity. Little is known about the function of the LOC423226 and LOC771552 genes in the formation of speckled eggs, and further investigation is needed.Although the heritability of eggshell speckles has been previously investigated and found to be moderate to low, the genomic architecture of the eggshell speckles remains unclear. GWAS is an effective way to identify candidate genes and SNPs, and has been widely used to analyse the genetic mechanisms of important economic traits in livestock –13. In tmembrane . A varian humans . Studiesn cattle , 18. In embranes . It has embranes , and theembranes . EHD4 isTYRO3, ITPKA, DLL4, and some genes involved in the phospholipid metabolism pathway. TYRO3 is part of a 3-member transmembrane receptor kinase receptor family. The encoded protein is involved in controlling cell survival and proliferation, immunoregulation, and phagocytosis [TYRO3 gene participates in the regulation of the immunity mechanism in the spleen of laying hens infected by bronchitis virus [ITPKA regulates inositol phosphate metabolism and calcium signaling by phosphorylation of second messenger inositol-1,4,5-trisphosphate to inositol-1,3,4,5-tetrakisphosphate [ITPKA has also been found differentially expressed in laying hens’ uterus and to be associated with calcium signalling pathway [DLL4 encodes a membrane-bound ligand and plays an important role in the control of endothelial cell proliferation, migration, and angiogenic sprouting [PLA2G4B, PLA2G4EL5, and PLA2G4EL6). Phospholipids are important components of biofilms and regulate fat metabolism. PLA2G4B encodes phospholipase A2, which can participate in membrane homeostasis by changing phospholipid composition [We also identified other candidate genes associated with speckled eggshell, including ocytosis –26. It his virus . ITPKA rhosphate . ITPKA h pathway . DLL4 enprouting , 31. In position . We specLOC423226, SPTBN5, EHD4, LOC77155, TYRO3, ITPKA, DLL4, PLA2G4B, PLA2G4EL5, and PLA2G4EL6 were considered the most promising genes associated with eggshell speckles that were implicated in immunoregulation, calcium transport, and phospholipid metabolism, while its function in laying hens requires further studies. This study provides a reference for further exploring the molecular mechanisms underlying egg speckling.In summary, we calculated the heritability of speckled eggs to be 0.35, indicating that speckling is a moderately heritable trait. A GWAS was used to reveal the genetic architecture of speckled eggs. We detected 311 SNPs  and 39 candidate genes associated with eggshell speckles. Based on the pathway analysis, the 39 candidate genes were mainly involved in alpha-linolenic acid metabolism, linoleic acid metabolism, ether lipid metabolism, GnRH signaling pathway, vascular smooth muscle contraction, and MAPK signaling pathway. Ultimately, ten genes, All chicken were obtained from Beijing Zhongnongbangyang Layer Breeding Co., Ltd . This study was performed in accordance with the ethical and animal welfare guidelines approved by the ethics committee of China Agricultural University (permit number: AW11403202-1-32). All experimental protocols were performed according to the guidelines established by the Ministry of Science and Technology . We declare that this study was reported in accordance with ARRIVE guidelines.Salmonella pullorum and avian leukosis viruses.We used a population of purebred RIR hens from Beijing Zhongnong Model Laying Hens Breeding Co., LTD  as the experimental animals. The RIR chicken is a commercial brown-shell layer breed that has been selected for production performance over many years. This study included 1266 pedigreed hens aged 28 weeks. All birds were raised in individual cages under identical environmental and management conditions. The temperature of the housing was controlled at 20 ± 1 °C and the lighting program comprised a 16 / 8 h light–dark cycle. The experimental chickens had free access to drinking water and were fed at a fixed time every day. All birds were free of Eggs were collected from all hens for quality measurements. The measurement indices included EW, Eggshell colour (ESC), ESI, ESS, and SL. An electronic balance was used to determine EW. An eggshell colour tester , which works on the L* (brightness), a* (green-red), and b* (blue-yellow) colour model, was used to measure the ESC. The ESI value (length/breadth) was measured using Vernier callipers. An Eggshell Force Gauge  was used to assess the ESS. Finally, SL was determined using a previously published scoring method . A levelWe recorded the SL of each egg for the first five days. On the sixth day, individuals laying level 1 eggs were grouped into the control group, and those laying eggs at levels 3 and 4 were grouped into the case group. Finally, we selected 54 and 53 hens from the case and control groups, respectively. Blood samples were collected from the wing vein and stored in anticoagulant blood collection vessels at -20 °C for subsequent DNA extraction.Phenotypic and genetic correlations and heritabilities were estimated using ASReml software based on the pedigree information. Multiple trait model was used to estimate phenotypic and genetic correlations and heritabilities. The model used for each trait was as follows:where y is the vector of the daily speckle level; b is the vector of fixed effects; e is the vector of random residuals; and X and Z are the incidence matrices of the fixed and genetic effects, respectively.Genomic DNA was extracted from the blood using a TIANamp Blood DNA Kit . DNA concentration and purity were assessed using a NanoDrop 2000 spectrophotometer . The qualified DNA samples were sent to a commercial company for next-generation sequencing. All libraries were sequenced on the MGI DNBSEQ T7 platform with 150 bp paired-end reads. The sequence coverage of each sample was 10 ×.Low-quality reads and adapter sequences were removed using the fastp (version 0.20.1) software  with theGWAS of the eggshell speckle trait was performed using the univariate mixed linear model in the GEMMA software , with th− 7 , and the suggestive significance threshold was set as 1.17 × 10− 5 .where y represents the individual phenotypic value, namely either ‘0’ or ‘1’; W refers to the covariance matrix, the first column of which is the column vector 1, the second to the fourth columns are the fixed effects, the top three principal components respectively; 𝛼 denotes a vector comprising the intercept and corresponding coefficient; 𝑥 denotes a vector of the SNP genotypes; 𝛽 refers to the effects of the corresponding SNP; 𝑢 is a vector of random effects, obeying the covariance structure: u ~ N  normal distribution, which is suitable for the genetic relationship matrix K, Vg is the multiple gene additive variance; and 𝜀 is a vector of random residuals. The SimpleM package was used in this study to calculate the number of effective independent tests : 85,137 The R “qqman” package was used to draw Manhattan and QQ plots. The genomic inflation factor λ was calculated using the estlambda function of the GenABEL software package in R . For sighttps://www.ncbi.nlm.nih.gov/pubmed). KOBAS [http://kobas.cbi.pku.edu.cn/) for GO and KEEG analyses.LD analysis of significant SNPs was performed using the Solid Spine algorithm in Haploview software (version 4.2) . The snp). KOBAS  . Chi-square tests were performed using RStudio (version 1.3) to compare allele frequencies of four significant SNPs in the case and control groups. Haploview software (version 4.2) was used to draw LD plots.Below is the link to the electronic supplementary material.Supplementary Table S1. The allele frequencies of six significant SNPs in the case and control groupsSupplementary Table S3. Gene Ontology enrichment analysis of candidate genesSupplementary Table S2. Details of candidate SNPs associated with eggshell specklesSupplementary Table S4. KEGG pathways analysis of candidate genes


SORCS3) and chromosome 12  for coat colour and one SNP on chromosome 18  for the black middorsal stripe. In contrast, no SNPs were identified for skin colour. In selection signature analysis, 295 significant iHS genomic regions with a mean |iHS| score > 2.66, containing selection signatures encompassing 232 candidate genes were detected. In particular, 43 GO terms and one KEGG pathway were significantly enriched in the selected genes, which may contribute to the excellent environmental adaptability and characteristic trait formation during the domestication of YZD goats. In ROH detection, we identified 4446 ROH segments and 282 consensus ROH regions, among which nine common genes overlapped with those detected using the iHS method. Some known candidate genes for economic traits such as reproduction  and development and growth  were revealed by iHS and ROH detection. Overall, this study is limited by the small population size, which affects the results of GWAS to a certain extent. Nevertheless, our findings could provide the first overview of the genetic mechanism underlying these important traits and provide novel insights into the future conservation and utilisation of Chinese goat germplasm resources.This study aimed to reveal the potential genetic basis for litter size, coat colour, black middorsal stripe and skin colour by combining genome-wide association analysis (GWAS) and selection signature analysis and ROH detection within the Youzhou dark (YZD) goat population (n = 206) using the Illumina GoatSNP54 BeadChip. In the GWAS, we identified one SNP (snp54094-scaffold824-899720) on chromosome 11 for litter size, two SNPs on chromosome 26 (snp11508-scaffold142-1990450,  Litter size is a crucial index for estimating female goats’ reproductive performance and has aroused increasing interest from husbandry producers and breeders. Simultaneously, morphological characteristics have also increasingly attracted attention because they can be correlated with other economically relevant traits in goats and sheep. During the past decade, various efforts have been made to identify the genetic variations in litter size ,2, coat The YZD goat, one of the most famous Chinese indigenous goat breeds, has been primarily distributed in a mountainous and transportation-restricted region at an altitude of 800–1000 m in Youyang County, China, since approximately a hundred years ago. The long period of isolation in the environment shaped its distinctive character. The YZD goat has a small body size, with the adult male is merely 32–37 kg. Meanwhile, with the permeation of large meat goat breeds, raisers carried out excessive and disorderly crossbreeding, which reduced the YZD goat population and endangered it. In 2009, it was certified as a national livestock genetic resource. It is characterised by unique exterior pigmentation traits  Supple. After lIn the present study, we explored the putative candidate genes and SNPs related to litter size and three morphological traits  within the YZD goat population by combining a GWAS, selection signature analysis (iHS) and runs of homozygosity detection (ROH). Our study could provide novel insights into the molecular mechanisms underpinning these traits in Chinese native goat breeds, thereby contributing to their further utilisation.Blood samples were collected from 206 adult goats from the YZD goat breed conservation farm in Chongqing Province, Southwest China. For the litter size trait, only the data from female goats (n = 154) aged 1 to 4 years were analysed in the GWAS. Each trait was categorised as one or zero. The litter size trait was classified as one to two lambs n = 147) or more than two lambs (n = 7) based on the lambing record. We divided the three morphological traits according to presence or absence and included pictures for better understanding . The tra or more p values were used for the following association analyses. The population structure was investigated using ADMIXTURE 1.3 [p < 1.21 × 10−6 . Based on the Capra hircus reference genome GCA_000317765.1 (CHIR 1.0) in genome data viewer from the National Center for Biotechnology Information (NCBI), putative candidate gene(s) were identified within approximately 100 kb upstream and downstream of the most significant SNPs.We performed genome-wide association studies using the linear model (LM) implemented in Plink 1.07 . The corTURE 1.3 . The ggpp, respectively. Single-site iHS values were computed across the genome in the goat population. Then, the norm module of Selscan was applied to normalise the iHS score. SNPs with |iHS| scores higher than 2.66 (the top 1%) were considered to have candidate selection signatures. The windows 50 kb upstream and downstream of candidate signatures were considered the selected regions to capture the putative genes. We then analysed the biological functions of the selected genes by Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment using the online tool DAVID 2021 (https://david.ncifcrf.gov/tools.jsp) (accessed on 1 December 2021). Human gene ontologies were used in this study, as the goat genome was not adequately annotated. In addition, the human genome is more annotated than other species, thus improving the probability of retrieving GO terms for the goat genome. Afterwards, we conducted a comprehensive review of the literature to annotate the functions of the identified genes.We employed the iHS method to detect strong footprints of recent positive selection within the YZD goat population. Beagle v3.3.2   was usedWe used PLINK v1.90  to detecp-value distribution (LRRTM4) for litter size (SORCS3) and one significant SNP on chromosome 12 (LOC102187779) for coat colour (white vs. brown) (TCF25) associated with the black middorsal stripe  b and onel stripe c. In conl stripe d.p < 0.05), as visualised in In total, 29,459 SNPs were obtained with estimated |iHS| scores . The scaWe identified a total of 4446 ROH segments from 205 YZD goats . For eacp < 2.2 × 10−16)  c.REM1, MAGI2, DNMT3B, ACSF3, FGGY, CTNNA1, P4HA2, GRM7 and CHL1. The majority of the nine common genes were related to reproduction, fat deposition, growth and development, meat quality, milk flavour and environmental adaptation.We found 282 consensus ROH regions and annotated 925 genes. It is worth noting that there were nine common genes revealed by both iHS and ROH analyses: LRRTM4. LRRTM4 is essential for excitatory synaptic transmission and was proven to be associated with human mental disease. Genome-wide analysis in Brazilian cattle breeds showed that this gene is involved in biological processes related to fertility [TSHR  [ANGPT4  [CENPF (early embryonic development in mice and bovines) [CHST1 (goat and sheep prolificacy) [PIBF1  [DACH1 and DIS3 (sheep litter size) [COL4A1 (sheep prolificacy) [PRKD1 (pig litter size) [DNMT3B (first-born litter size in goats) [Litter size is one of the most critical reproductive traits in goats and sheep, showing variation among breeds and remaining incompletely understood. Initially, no gene was found within the regions 100 kb upstream and downstream of the significant SNPs in this study. We then extended the region to 150 kb and found that the nearest gene was ertility . The seld goats) , ANGPT4 lopment) , CENPF (bovines) ,15, CHSTificacy) , PIBF1 (lopment) , DACH1 aer size) , COL4A1 ificacy) , PRKD1 (er size)  and DNMTn goats) .GDF9, BMP15 and BMPR1B. However, large-effect markers for litter size that can be used in a wide range, such as FecB in most sheep breeds, have not been found in goats thus far. This may be due to the low heritability of litter size traits in most domestic animals, genetic complexity and nutritional and environmental factors.Several candidate genes related to litter size or reproduction traits have been identified using data from resequencing or SNP chips. Numerous GWASs studies have reported some genetic markers associated with goat litter size and prolificacy, such as Coat colour is an economically important trait in domesticated goat breeds, particularly for wool/hair production. In fact, domestic goats are characterised by high variability of coat colour phenotypes due to human selection, so identifying the genetic loci affecting coat colour in goats has long been of interest. In YZD goats, most individuals generally possess white coat hair, while some have brown coat hair. The genetic mechanism underlying this coat colour segregation has not yet been fully elucidated.SORCS3 gene was proven to be related to the pathophysiology of destructive diseases of the nervous system, leading to the occurrence and development of brain diseases [SORCS3 gene is associated with temperament traits, energy balance, feed efficiency and backfat thickness in cattle, mice, broilers and Duroc pigs, respectively [SORCS3 gene may be associated with regulating melanocyte function, thereby participating in the coat colour determination in YZD goats. It is well established that coat colour variation is likely determined by the biochemical function, availability and distribution of eumelanin (black/brown) and pheomelanin (yellow/red) in mammals [ASIP and MC1R genes [MC1R and ASIP, other well-known genes have been widely confirmed to be associated with coat colour variation in various goat and sheep populations, such as TYRP1, MITF, KIT, RALY, KITLG and GLI3 [SORCS3 gene affects coat pigmentation in YZD goats remain to be addressed. In addition, the biological importance of uncharacterised LOC102187779 remains to be elucidated.The diseases . Studiesectively ,24,25,26 mammals , which aand GLI3 ,29,30. IIn addition to YZD goats, the black middorsal stripe phenotype can also be found in other domesticated and wild animals, such as Chinese hamsters, sugar gliders and slow lorises. Therefore, it is indispensable to elucidate the genetic basis of this special coat pattern.TCF25 and MC1R genes. Given this, we speculated that the regional differential expression of TCF25 might affect the subsequent melanocyte migration, leading to the black middorsal stripe in goats. Our results provide novel insight into the genetic factors underlying black middorsal stripe traits in animals. Nevertheless, the association between the black middorsal stripe and the TCF25 gene remains to be addressed further.T-cell transcription factors (TCFs) are essential regulators of Wnt-induced transcriptional activation. Interestingly, Wnt is an important upstream signal in melanogenesis . This fiA previous study in mice demonstrated that changes in skin colour depend on different processes that occur after neural crest differentiation but before mature hair growth . AdditioASIP gene by de novo sequencing of the YZD goat genome, which might be responsible for its lower expression in the hyperpigmented skin of YZD goats. Further investigations are ongoing in our laboratory to reveal the real genetic variation of dark skin colour in YZD goats.To our surprise, we did not detect any SNPs for skin colour by the GWAS approach in this study, given the apparent differences in the phenotype of skin colour. Some SNP markers specific to some local goat breeds could have been absent from the commercial chip (Illumina GoatSNP50 BeadChip) when the manufacturer designed it. However, our team recently identified a presumed structural variation (duplication) in the Detecting recent positive selection signatures in domesticated animals that have undergone both artificial and natural selection can contribute to the identification of beneficial mutations for economically important traits. In this study, we found that some GO terms can well mirrored the journey of domestication or indicated the formation of pigment phenotypes. For instance, the adenylate cyclase-modulating G-protein coupled receptor signalling pathway (GO:0007171) and cAMP-mediated signalling (GO:0019933) both participate in the regulation of melanogenesis and melanocyte biology, which may contribute to the pigmentation of hair and skin in YZD goats.SLITRK6, TNFRSF21, TENM3 and FAM161A,) [SLITRK6, SLC52A3, SLC26A4, MPZL2, LOXHD1 and GRM7) [TSHR, C2CD3 and NRG1) [Furthermore, the genes enriched in GO terms and genes corresponding to the highest iHS scores were reported to be associated with various properties, including visual impairment  ,45,46,47nd GRM7) ,50,51,52nd NRG1) , which pKIF3B, KIF17, KIAA0825 and TNFRSF21) [TNPO2, IFT80, UCP2, UCP3, GHRHR, SIM1, CCM2L, CTNNA3 and CTNNA1) [ACSF3 and FGGY) [TSHR and SUGCT) [CHL1) [GHRHR) [ACSF3) [GHRHR) [GLI2) [In addition to the genes related to early domestication, a panel of interesting genes associated with other economic traits was also revealed. These genes were previously reported in goats, sheep and other species, showing correlations with spermatogenesis and sperm motility (NFRSF21) ,54,55,56 CTNNA1) ,62,63,64nd FGGY) ,66, chicd SUGCT) , high-al) [CHL1) , milk co [GHRHR) , milk fl [ACSF3) , carcass [GHRHR)  and hair) [GLI2) . The iHSThis study detected some genomic loci and presumed genes related to agricultural economic characteristics by combining genomic approaches and could provide a reference for further conservation and utilisation of YZD goat germplasm resources and the knowledge of candidate genes can be incorporated into future genome selection strategies. For instance, the YZD goat is a kind of natural skin colour mutant that is rare in other mammals. Therefore, genetic analysis of its unique black skin could potentially provide a medical model to study human melanin-related diseases, such as skin melanopathy and mucosal melanosis. In addition, some consumers prefer dark-haired and/or dark-skinned livestock and poultry, such as black goats and black-bone silky chicken. Research on goat skin colour can help improve its competitiveness in the goat meat market. Moreover, the current small population limits the application of the genomic selection strategy, so identifying litter size-related candidate regions/genes may provide an opportunity to accelerate population expansion.Of course, this study did have some limitations. First, the GWAS was based on relatively small sample size, a common flaw in numerous previous studies on native goat breeds ,74. The In conclusion, this study preliminarily identified some significant SNPs and/or putative candidate genes related to litter size and three morphological traits in YZD goats using SNP chip genotyping data. The results provide valuable references for understanding the genetic basis of these traits and will facilitate further potential conservation and utilisation in YZD goat production. However, further research is needed to fully understand the genetic mechanisms of these traits in YZD goats.


Sorghum bicolor (L.) Moench] utilizes the cytoplasmic-nuclear male sterility (CMS) system for seed production and subsequently harnesses heterosis. Since the cost of developing and evaluating inbred and hybrid lines in the CMS system is costly and time-consuming, genomic prediction of parental lines and hybrids is based on genetic data genotype. We generated 602 hybrids by crossing two female (A) lines with 301 diverse and elite male (R) lines from the sorghum association panel and collected phenotypic data for agronomic traits over two years. We genotyped the inbred parents using whole genome resequencing and used 2,687,342 high quality  single nucleotide polymorphisms for genomic prediction. For grain yield, the experimental hybrids exhibited an average mid-parent heterosis of 40%. Genomic best linear unbiased prediction (GBLUP) for hybrid performance yielded an average prediction accuracy of 0.76–0.93 under the prediction scenario where both parental lines in validation sets were included in the training sets (T2). However, when only female tester was shared between training and validation sets (T1F), prediction accuracies declined by 12–90%, with plant height showing the greatest decline. Mean accuracies for predicting the general combining ability of male parents ranged from 0.33 to 0.62 for all traits. Our results showed hybrid performance for agronomic traits can be predicted with high accuracy, and optimizing genomic relationship is essential for optimal training population design for genomic selection in sorghum breeding.Hybrid breeding in sorghum [ Heterosis is the phenomenon by which the interest . Mid-parc values . The genulations . IMPH ar1 hybrid . Therefo1 hybrid .Sorghum bicolor (L.) Moench] is one of the self-pollinated crops that is bred as a hybrid crop, particularly in the United States (US) and Australia. Beginning in the 1950s, sorghum breeding in the US started utilizing the cytoplasmic-nuclear male sterility (CMS) system to produce hybrid cultivars  of R lines crossed to two female testers. The male lines used for generating single crosses are accessions included within the US sorghum association panel (SAP) that represent the genetic and phenotypic diversity of sorghum across the globe and could be used in allele mining and prebreeding (A sorghum hybrid diversity panel (SHDP) of 602 hybrids was generated by crossing 301 diverse male restorer (R) lines to two CMS female (A) testers: ATx642 (PI656029) and ATx2928 (PI629059) . The malHelicoverpa zea) and fall armyworm (Spodoptera frugiperda) infestation. Fields were watered using overhead irrigation only as needed to prevent drought stress.The hybrids and parental lines from the SHDP were planted along with the commercial check in a modified randomized complete block design with two replications in May 2019 and May 2020 at the Clemson University Pee Dee Research and Education Center  in Florence, SC. The modification was done by blocking each complete replication into smaller blocks based on previously observed maturity and height phenotypes for the male parent of the hybrids to avoid shading effect of taller lines on neighboring plants. There were a total of 40 blocks per replication and each block consisted of 30 individual plots. Each plot had two rows with a row length of 6.096 m and a row spacing of 0.762 m. The experimental hybrids, their parental lines and check hybrid variety (83P17) were randomized completely for each small block nested within each replication. Preplant fertilizer was blended based on soil sample results and per field recommendations for cereal grain production. In 2019, the fertilizer blend consisted of granular muriate of potash  at 300 kg haPhenotypic data were collected for days to anthesis (DTA) when about 50% of the primary panicles in the plot were at mid-anthesis. Plant height (PH) was measured from the base of the plant to the top of the representative panicle within the plot after physiological maturity. Plants were harvested when the majority of the plants in the plot reached post-physiological maturity (grain moisture below 18%) using a Wintersteiger Delta two row combine plot harvester. Harvest dates ranged from 16 to 18 September in 2019 and 17 to 25 September in 2020. Total harvest weight of the grain from each plot and the average moisture content of the harvested grain was used to calculate grain yield (GY) in bushels/acre and subsequently converted to ton per hectare  lines of the female parents were used to evaluate female parent performance. The following model was used to adjust for spatial variability within each experimental year:whereThe adjusted means (BLUPs) of wherewherevcftools   8) by redist function in R ..8). Suppd by GNP .For genomic GCA and genomic GCAe shared . For thee shared . Mean prGCAs of male lines were estimated from the genomic GCA model  using alThe ability to predict testcross performance in hybrids was evaluated for male general combining abilities (mGCA) using the GCA and GCAo models , Fig. 5.Hybrid sorghum breeding is based on a CMS system where male sterile females (A lines or seed parents) are crossed to restorer males (R lines or pollinator parents) to produce seed of commercial Following the wide range of diversity in the parental genotypes, the  to 20%) . The comSince the possible hybrid combinations even in a medium sized breeding program far exceeds the capacity to test them in actual hybrid crosses, one of the main motivation for the use of GP in hybrid breeding is to be able to estimate hybrid genetic value of those untested hybrid crosses  or only female parent (T1F) were shared between training and validation sets to understand the effect of parent testing on the accuracy of single cross predictions. While single-cross performance was predicted with high accuracy in T2 scenario, a strong decline (12–90%) in prediction accuracy was observed in T1F compared to T2. This is expected because the predictions from the model in T2 rely not only on kinship between male parents , as in the case of T1F, but also on information from other crosses in the training set that involves the male parent of predicted hybrid with the other female tester. Plant height showed the greatest decline in prediction accuracy in T1F, which could be due to very limited phenotypic variability among female lines for PH, and as a result, accuracy for PH could have been driven primarily by male parent. GP of sorghum hybrids using GBLUP model and cross-validation involving leave one parent out strategy (similar to our T1F) showed mean prediction accuracies (0.31–0.54) for grain yield that was similar to accuracy from our T1F prediction scenario (Prediction of testcross performance of inbred lines in hybrid combinations has been studied across multiple crop species ( studied . PredictThe phenotypic distribution of agronomic traits and consistent heterotic advantage of experimental Studies exploring the role of genetic diversity and genomic relationship on GP for trait phenotypic values as well as trait heterotic values are essential for optimization of training population for predicting hybrid performance. In silico simulation studies have highlighted strategies for application of rapid recurrent genomic selection combined with progeny testing to facilitate separation of heterotic groups, reduction in breeding cycle, and ultimately increase in long term genetic gain (jkac311_Supplementary_DataClick here for additional data file.


The explosion of available genomic data poses significant opportunities and challenges for genome‐wide association studies. Current approaches via linear mixed models (LMM) are straightforward but prevent flexible assumptions of an a priori genomic architecture, while Bayesian sparse LMMs (BSLMMs) allow this flexibility. Complex traits, such as specialized metabolites, are subject to various hierarchical effects, including gene regulation, enzyme efficiency, and the availability of reactants.Helianthus annuus individuals using multiple BSLMM and LMM frameworks.To identify alternative genetic architectures, we examined the genetic architecture underlying the carotenoid content of an association mapping panel of The LMMs of genome‐wide single‐nucleotide polymorphisms (SNPs) identified a single transcription factor responsible for the observed variations in the carotenoid content; however, a BSLMM of the SNPs with the bottom 1% of effect sizes from the results of the LMM identified multiple biologically relevant quantitative trait loci (QTLs) for carotenoid content external to the known (annotated) carotenoid pathway. A candidate pathway analysis (CPA) suggested a β‐carotene isomerase to be the enzyme with the highest impact on the observed carotenoid content within the carotenoid pathway.While traditional LMM approaches suggested a single unknown transcription factor associated with carotenoid content variation in sunflower petals, BSLMM proposed several QTLs with interpretable biological relevance to this trait. In addition, the CPA allowed for the dissection of the regulatory vs. biosynthetic genetic architectures underlying this metabolic trait. Genome‐wide association (GWA) research has exploded over the years since the first successful study was published in 2002 , popularized by Zhou and Stephens  with theBehind the Bayesian sparse linear mixed model (BSLMM) popularized in GEMMA , indicating the proportion of times a polymorphism's effect is estimated to have a nonzero effect during Markov chain Monte Carlo (MCMC) sampling. Within BSLMM, the PIP is often used similarly to P values; however, they do not have similar properties or caveats associated with multiple testing corrections , providing evidence for association with the phenotype comparable in interpretation to hypothesis testing in the LMM framework. In Bayesian approaches, however, thresholds that are not biologically relevant or important for the downstream use of results are unnecessary; for instance, in LMM frameworks, Comparisons between LMMs and BSLMMs demonstrate that the latter have increased genomic prediction accuracy and estimation of PVE , one QTL for lemon ray flower color (derived from carotenoids) was identified on chromosome 11 during an experimental cross of an oilseed maintainer and a confectionary sunflower  in the resultant offspring Gilmour, . HoweverH. annuus and the genomic locations of enzymes within the carotenoid pathway, allows for the interpretation of GWAS results within a biological context. In addition, disentangling associations within and outside the carotenoid pathway may lead to further insights.Biosynthetic pathways and metabolite variation are prime examples of systems that could be used to compare proposed genetic architectures because variation in the observed output of a metabolite or metabolite class is primarily due to variation in both enzymatic efficiency and regulatory control  is a specific case of GWAS used across the literature in a variety of styles that subsets genome‐wide SNPs to those found in collections of genes of interest, sets of transcription factors, or biochemical pathways  to determine whether we can leverage the low memory requirements of the LMM with the complex genetic architecture assessment of the BSLMM to provide a post‐GWA investigation technique that can be readily integrated into the many GWAS pipelines currently in use; (2) to assess the viability of CPA; and (3) to assess the ability of these methods to recapitulate previously identified QTLs in an association mapping panel of cultivated sunflower varieties.H. annuus with a minor allele frequency <5% and heterozygosity >10%, which we used for this analysis.We selected an existing association mapping panel of cultivated sunflower varieties known as the sunflower association mapping (SAM) panel .Phenotypic data were retrieved from the Dryad Digital Repository entry (D′ < 0.8), where the first four principal components were used as estimates of population structure , the highest 1% of β‐effect sizes (“1% effect size SNP set”), and the union of both SNP data sets (“Combination SNP set”). In combination, we attempted to incorporate the multi‐locus models FarmCPU and BLINK; however, the memory requirements quickly exhausted a Mac M1 CPU processor with 16 GB RAM and eight cores. We therefore performed a subsequent analysis of the SNP subsets using FarmCPU and BLINK.We performed all analyses using the GWASvsCPAS pipeline Dowell, , with alWe used the following methods to analyze the genome‐wide SNP data set and SNP subsets taken from our LMM results. For kinship and population structure, we used the same estimates as in our LMM  database to curate a list of enzymes in the carotenoid biosynthetic pathway present in cultivated s Figure   with the carotenoid content, with a relative effect size of 16%. This SNP is centered in a 147.263‐kbp region on chromosome 11 Figure . Forty‐sPHOTOSYSTEM 1 ASSEMBLY 3 [PSA3]; Ha412HOChr15g0707801) and a putative monodehydroascorbate reductase gene (Ha412HOChr15g0707991)  skewed toward zero Appendix , indicat) Figure , which i) Figure . This reP values SNP set” , and the “1% effect size SNP” and “Combination SNP” sets, which explained over half of the PVE . This specific SNP (Ha412HOChr15:32353233) may contribute to a missense amino acid change from lysine to arginine, while any single insertion or deletion at this locus may produce a premature stop or new start codon. The resequencing depth of the population ranged from 5–25×  was skewed right Appendix , indicats Figure . Two of s Figure . The othH. annuus.It is a frequent practice to leverage SNP data in an LMM framework to explore the genetic architecture of traits and prioritize SNPs for functional analyses across plants, animals, and microbes; however, there has been little critical evaluation of alternative methods to the LMM that focus on the potentially conflicting genetic architectures that alternative methods may propose. Here, we compare the genetic architectures proposed by GWA and CPA in Bayesian sparse and traditional LMM frameworks, supply a post‐GWA examination approach using BSLMMs, and explore the architectures proposed for petal carotenoid content based on known biosynthetic enzymes in cultivated H. annuus  Anashch.  may arise from several combinations of unique compounds, obscuring the detection of within‐pathway control. Alternatively, the BSLMMs generated more nuance in describing the variation within and among biosynthetic enzymes, in that all biosynthetic enzymes had a nonzero effect on the observed carotenoid content. The nonzero effects across the SNPs within genes encoding biosynthetic enzymes indicate that these variants may function in a combinatory fashion to produce similar amounts of total carotenoids. Among the carotenoid pathway enzyme genes, a β‐carotene isomerase (Ha412HOChr15g0697271) had the largest effect on observed petal carotenoid content. Future work leveraging CPA should examine the relationship between compound class totals, individual compounds, and the impact of detecting associated SNPs, particularly emphasizing the influences of pathway structure and reaction types involved.While originally considered secondary or unnecessary for growth or reproduction, specialized metabolic pathways are strongly integrated with primary metabolism, with many nexus points between primary and specialized metabolisms across various biosynthetic pathways. In the carotenoid pathway, core photosynthetic pigments classically considered primary metabolites mediate abiotic stress responses  and are closely integrated to the production of key compounds more commonly considered secondary for their roles in biotic interactions .Helianthus L., Todesco et al.  Heynh. . In plants, monodehydroascorbate reductase forms a core component of the glutathione‐ascorbate cycle, one of the major antioxidant systems in plants  and A. thaliana result in the loss of PSI formation, even though there is no impact on PSI subunit biosynthesis  identified in our CPA shows similar constitutive expression patterns in the petals, mature leaves, and roots  Benth.  the production of carotenoids and carlactones are linked, (2) β‐carotene isomerase is constitutively expressed across several organs, and (3) there are few copies of β‐carotene isomerase genes, enzymatic variation may contribute to biologically relevant variations in the products of this pathway. β‐carotene isomerases link the production of β‐carotene to the production of strigolactone‐like compounds (carlactone) in roots, which is essential for plant mycorrhizal association and parasitic plant interactions  enzymes or more directly by inhibiting phytoene desaturase or diterpene biosynthesis  or shared explained variance with regulatory elements. When using only LMM GWA approaches, we can bias ourselves toward identifying singular large‐effect loci that represent an oligogenic architecture. For a metabolite, large‐effect loci may include cis‐ or trans‐regulatory elements affecting multiple portions of a pathway or controlling aspects outside the pathway of interest with linked functionality (Rowe et al., In traditional plant growth–defense theory, energetic tradeoffs are often estimated at the level of the whole organism or the level of constituent organs; however, tradeoffs may occur at much finer scales and be masked by larger tradeoffs elsewhere (van Noordwijk and de Jong, cis‐ and trans‐regulatory elements changing the enzyme abundance and that of the resulting precursors or products. In addition, when considering multiple metabolites within a class, the correlation among observed metabolites can be explained by the presence or absence of enzymes and the shared physiochemical properties of the observed metabolites (Dowell and Mason, While large‐effect regulatory loci may be useful for initial breeding purposes, this does not mean that genetic variation within the enzymes of a pathway does not contribute to overall variation. By taking a pathway‐centric view, we can narrow our search parameters to identify the most likely candidate enzyme variants within a pathway that should be subjected to a functional assessment, such as the β‐carotene isomerase CPA implicated by our BSLMM of carotenoid pathway SNPs. In addition, the genetic architecture of a metabolic profile, or the production of a specific metabolite, may be inherently polygenic due to the many biosynthetic steps involved; for example, to observe a given metabolite, all enzymes must be able to complete their reactions. Secondarily, variation in any enzyme may alter the observed amount of the metabolite, or net changes may produce the same amount of the metabolite. In this small example, one still must compound the complexity with H. annuus, our analyses complementarily recapitulated previously identified QTLs from association mapping and experimental crosses. Furthermore, CPA supplies novel insights into carotenoid content not observed among genome‐wide SNPs. Finally, as a post‐GWA investigation technique, BSLMM provided two new candidate QTLs for functional analyses, expanding potential breeding targets to improve cultivated H. annuus.In conclusion, common GWA methods can propose alternative architectures. In the case of petal carotenoid content in cultivated J.A.D. and C.M.M. designed the study. J.A.D. assembled the phenotypic and genetic data, implemented the analyses, and interpreted the results. J.A.D. wrote the manuscript with input from C.M.M. Both authors approved the final version of the manuscript.Appendix S1. Gene lists for the linkage groups associated with carotenoid content in cultivated Helianthus annuus.Click here for additional data file.Appendix S2. Data analysis workflow and hyperparameter sampling results for all Bayesian sparse linear mixed and multilocus models.Click here for additional data file.


Saprolegnia oomycete infection causes serious economic losses and reduces fish health in aquaculture. Genomic selection based on thousands of DNA markers is a powerful tool to improve fish traits in selective breeding programs. Our goal was to develop a single nucleotide polymorphism (SNP) marker panel and to test its use in genomic selection for improved survival against Saprolegnia infection in European whitefish Coregonus lavaretus, the second most important farmed fish species in Finland. We used a double digest restriction site associated DNA (ddRAD) genotyping by sequencing method to produce a SNP panel, and we tested it analyzing data from a cohort of 1,335 fish, which were measured at different times for mortality to Saprolegnia oomycete infection and weight traits. We calculated the genetic relationship matrix (GRM) from the genome-wide genetic data, integrating it in multivariate mixed models used for the estimation of variance components and genomic breeding values (GEBVs), and to carry out Genome-Wide Association Studies for the presence of quantitative trait loci (QTL) affecting the phenotypes in analysis. We identified one major QTL on chromosome 6 affecting mortality to Saprolegnia infection, explaining 7.7% to 51.3% of genetic variance, and a QTL for weight on chromosome 4, explaining 1.8% to 5.4% of genetic variance. Heritability for mortality was 0.20 to 0.43 on the liability scale, and heritability for weight was 0.44 to 0.53. The QTL for mortality showed an additive allelic effect. We tested whether integrating the QTL for mortality as a fixed factor, together with a new GRM calculated excluding the QTL from the genetic data, would improve the accuracy estimation of GEBVs. This test was done through a cross-validation approach, which indicated that the inclusion of the QTL increased the mean accuracy of the GEBVs by 0.28 points, from 0.33 to 0.61, relative to the use of full GRM only. The area under the curve of the receiver–operator curve for mortality increased from 0.58 to 0.67 when the QTL was included in the model. The inclusion of the QTL as a fixed effect in the model increased the correlation between the GEBVs of early mortality with the late mortality, compared to a model that did not include the QTL. These results validate the usability of the produced SNP panel for genomic selection in European whitefish and highlight the opportunity for modeling QTLs in genomic evaluation of mortality due to Saprolegnia infection. Saprolegnia infection, a disease-causing serious economic losses in aquaculture. We identify a strong QTL affecting survival, and we show that including the QTL in an enhanced BLUP approach improves the estimation of breeding values.We demonstrate the successful use of Next Generation Sequencing for genomic selection in aquaculture, testing survival to  MAS; QTL) of quantitative traits in a breeding value evaluation has been in fact largely abandoned in practice  to identify a novel panel of single nucleotide polymorphisms (SNPs) for genomic selection for commercial traits in whitefish, an aquaculture species that currently lacks commercial/off-the-shelf genetic marker chips that can be used for selection. We developed a SNP panel for European whitefish (Coregonus lavaretus L.) through double digest restriction site associated DNA (ddRAD) sequencing, using the recently published reference genome  and whether the explicit modeling of any major QTL would be useful in genomic selection-based breeding programs.Our aim in this work was manyfold. First, we aimed to test the feasibility of using next generation sequencing , whose data we present here. Mating was carried out for 1 wk, and the eggs from all mating were pooled after the fertilization. The eggs were incubated in a single large incubator. At eyed stage, the eggs were placed in a single indoor tank in which the hatching occurred. These fish are the safe reserve of the main breeding program stock in which the families are typically incubated separately, reared separately in family tanks, individually tagged without a need for genotyping and only then pooled together  and a fin clip was sampled for genotyping, and the fish were then moved to an outdoor tank to initiate the experiment. The tank was an concreate, flow through tank of size of 63 mrolegnia .3” and body height to body length ratio “Height/Length3”. Height/Length3 is one of the selected traits in the breeding program, because an elongated shape is preferred  was done on the NextSeq 550 (Illumina).GBS) pipeline was implemented in Snakemake  with one index mismatch allowed. For each sample individually, the paired-end fragments were aligned to the reference genome  using Bonakemake  and is anakemake . This pinakemake .The raw ddRAD genotyping produced 65,318 SNPs. DMU variances of fish traits were estimated using the Average Information Restricted Maximum Likelihood module of U DMUAI; . Heritabion bias . Correlayij is the phenotype of a trait j for the ith individual, µj is the mean value of a trait j, aij is the random genetic effect explained by the realized genomic relationship matrix between genotyped individuals (genetic relationship matrix [GRM]), and eij the random residual error. The GRM was calculated using htginv module of the MiX99 software package . The genetic correlation was:with jk and covjk the genetic correlation and covariance between traitj and traitk, and σ2gj and σ2gk the genetic variances for traitj and traitk. The phenotypic correlations were calculated accordingly. Phenotypic correlations were tested for significance using a Pearson’s correlation test. Genetic correlations were considered significant when the interval corjk ± 1.96 × SEjk did not include 0.with corSimilar to our analysis here, linear multitrait models are routinely used for binary traits in practical commercial breeding value evaluations. Both heritability estimates and selection accuracies from linear models were transformed to account for the binary nature of the traits (see details below). Genetic correlations of binary traits estimated using linear models are expected to be unbiased, whereas phenotypic correlations are biased downwards .h2 on the observed scale to the heritability on the underlying liability scale. Phenotypic and residual correlations of Mortality3 and Mortality4 cannot be estimated because of the limited number of samples in common between the two phenotypes.For mortality traits, the formula of GWAS), using the leave one chromosome out approach described by 2 was always included, was used:The statistical software DMU was used for a genome-wide association study  for genome-wide significance, and as 0.05/(mean number of SNPs per chromosome) for chromosome-wide significance.in which The percentage of genetic variance explained by a SNP was calculated following , as:b is the fixed regression slope from Model 2, p is the frequency of the major allele, σ2G is the total genetic variance explained by the full GRM (from Model 1).in which We tested the effect of the presence of a QTL for mortality by specifically modeling the fixed effect of SNP LR664349.1_1710215  along with the remaining GRM effects in the successive analyses.3, genomic breeding values were estimated in two ways, either excluding or including the top QTL SNP as a fixed effect. We focused only on Weight2, Weight3, and Mortality3 to illustrate the modeling approaches. GEBVs were estimated to solve the mix models with MiX99 variance components needed as input were estimated using the same models in DMU. The models used were the bivariate models, having Weight2 always as one of the traits in the analysis.in which QTL3 are shown for both GBLUP Model 1 and GBLUP+QTL Model 3, and for body weights, only Model 1 is given because the QTL used in Model 3 was not QTL affecting any weight trait.The results of Mortality3, Weight2, and Weight3 was validated in two different ways. Both validation steps were run both on GBLUP model and on the GBLUP+QTL model that included the genotype of the top SNP as a fixed effect.The predictive ability of the GEBVs of Mortality3 we also calculated the area under the curve (AUC) of the corresponding receiver–operator curve, that is, we assessed the classification performance of the GEBV in predicting the actual mortality phenotypes. For a completely random performance (no classification power) the AUC is 0.5, whereas a perfect classification corresponds to a value of 1. For cross-validation and calculation of AUCs, we ran 1,000 resampling steps, and the means and standard deviation are presented.First, a cross-validation approach was used to randomly mask 20% of phenotypes of a trait, and then GEBVs of all fish were estimated. Then, for the masked individuals only, the correlation of their GEBVs to their actual phenotypes was calculated. Accuracy was calculated as the Pearson correlation coefficient between the GEBVs and the observed phenotypic values divided by the square root of the heritability of the trait on the observed scale . For MorWhen sampling individual fish randomly into the two groups, the reference group and the validation group maintain their close relationship. This is a typical setup when applying genomic selection in commercial aquaculture breeding programs in which all the families are held in the nucleus and their sibs are recorded for hard-to-record traits such as disease resistance, carcass traits, or product quality. Such close relatedness is used to improve the power of genomic selection in commercial breeding, and our estimates of selection accuracy are expected to be higher than if the reference group and the validation group were less related .3 to predict the phenotype of Mortality4, using all data and the correlation between the GEBVs and phenotypes.Secondly, we tested the power of the GEBVs of Mortality2 and Weight3  and Mortalitytot (0.99). The correlation between Mortality4 and Mortalitytot was estimated as 1 with no standard error estimation. These high positive correlations imply that mortality has similar genetic determination across ages.Genetic correlations broadly followed the phenotypic correlations , but all/Length3 . Weight32 showed two genome-wide significant peaks: one, significant, on chromosome 4, and a second on chromosome 20 , explaining 7.65% to 15.82% of the genetic variance  is −0.18. When the top SNP for Mortality3 is fitted as a categorical fixed effect, the result does not indicate any signs of dominance .As a comparison, for WeightThe cross-validation accuracies showed large variation, especially in mortality traits . For bin3 for Mortality4, we observe a positive correlation between Mortality3 GEBV with Mortality4 phenotype, of 0.18 for the GBLUP model, and of 0.22 for the GBLUP+QTL model , and h2 on the underlying liability scale showed a substantial increase (0.20 to 0.43), suggesting that the genetic component for the mortality traits is actually bigger than what can be directly observed. These estimates are clearly higher than the h2 estimates of general mortality whose causes are unknown , the QTL on chromosome 6 overlays six different putative genes . BlastinSaprolegnia is nearly identical at all time points. Thus, our data suggest that using an early mortality to Saprolegnia measure would provide a robust proxy for all mortality to Saprolegnia at all times during the breeding program. Mortality data collected before the maturity will allow to estimate GEBVs and make selection well in advance before the fish are spawning.One of the important considerations stemming from our work is the importance of choosing the most informative time-period to collect mortality data in a breeding program. Because the number of fish surviving over time decreases over time, our ability to calculate precise GEBVs for all traits, not just mortality, decreases over time, as the sample size shrinks. In our data, we observed a strong genetic correlation between the mortality traits over time , and thaImportantly, while the genetic correlation between size-based traits and mortality traits is low with large standard errors, the phenotypic correlations are either not significant, or positive and significant, suggesting that phenotypically larger fish at any time point are more likely to have died by the time the next mortality census is carried out. The low genetic correlations mean that both weight and mortality can be improved simultaneously, yet care should be taken to track potentially correlated genetic changes in multiple traits.2 as a trait in all (multivariate) analyses.In addition, the phenotypic correlation between weight and survival indicates that the phenotypically the smallest fish tend to die during growth, creating a selection bias in the data. When such selection bias occurs, it needs to be taken into account by including the pre-selected trait(s) recorded from all the individuals in a multivariate animal model . Hence, Heritability for body weight was moderate (0.44 to 0.53) and of similar magnitude to what has been observed previously for this species , yet theOur results highlight that, irrespective of any other consideration, the benefit of testing for the presence of QTLs before estimating genomic breeding values is worth the extra effort in breeding settings. The inclusion of the QTL in genomic breeding value evaluation increased the accuracy of GEBVs, with a clear change in GEBV distribution pattern matching the actual QTL genotype. Estimation of GEBVs based on genotype data would require only a minimum amount of extra computational effort to test for QTLs, because the required phenotypic data would need to be ascertained in any case, with obvious benefits in terms of potentially increased selection accuracy due to the more detailed modeling of the mode of inheritance.The assumption of genomic evaluation with a genomic relationship matrix (GBLUP) is that SNP effects are small and normally distributed, but these assumptions do not necessarily hold in practice. When these assumptions do not hold, other sophisticated statistical approaches have been developed. Examples of these approaches are Bayesian methods that can model non-normal distribution of SNP effects , weighteSaprolegnia resistance here, the use of GBLUP+QTL is currently in fact an appealing approach. For instance, we validated the GEBV prediction improvement using a cross-validation approach. We observed an average of 0.33 in the accuracy for mortality calculated using the GBLUP model, but the accuracy increased to a mean of 0.61 for the GBLUP+QTL model, almost a doubling of the predictive accuracy. Additionally, when we used the AUC approach, to account for the fact that mortality is binary trait, we observed that the mean accuracy for the GBLUP+QTL (0.67) is not only higher than the mean accuracy for the GBLUP (0.59), but it is very close to the maximum theoretical accuracy we can expect for this dataset, which is 0.7 (3 GEBVs estimated with the GBLUP and GBLUP+QTL models to predict the Mortality4 phenotype improves from 0.46 in the GBLUP model to 0.61 for the GBLUP+QTL. All these observations indicate that the GBLUP+QTL improves our predictive ability compared to the simpler GBLUP model. Of course, these results are currently limited to the present subset of data, and the effect of the QTL estimated in a single fish group. The results would have to be validated over different generations of the breeding program to assess the overall, general effect of the GEBVs and QTL on survival to Saprolegnia.For strongly oligogenic traits, such as for h is 0.7 . FurtherSaprolegnia, a common disease affecting this and many other fish species in aquaculture, indicating that there are opportunities to use genomic selection to improve resistance to Saprolegnia.Overall, our results indicate the value of using NGS techniques for genomic selection on European whitefish aquaculture, and highlight the benefit that genome-wide markers offer, not just for genomic selection, but for exploring the genetic architecture of traits under selection, and to increase the precision of the estimation of GEBVs. Additionally, we identified the presence of a major QTL for the resistance to skad333_suppl_Supplementary_Table_S1Click here for additional data file.