During the last few years, the increasing evidence of dietary antioxidant compounds and reducing chronic diseases and the relationship between diet and health has promoted an important innovation within the baked product sector, aiming at healthier formulations. This study aims to develop a tool based on mathematical models to predict baked goods’ total antioxidant capacity (TAC). The high variability of antioxidant properties of flours based on the aspects related to the type of grain, varieties, proximal composition, and processing, among others, makes it very difficult to innovate on food product development without specific analysis. Total phenol content (TP), oxygen radical absorbance capacity (ORAC), and ferric-reducing antioxidant power assay (FRAP) were used as markers to determine antioxidant capacity. Three Bayesian-type models are proposed based on a double exponential parameterized curve that reflects the initial decrease and subsequent increase as a consequence of the observed processes of degradation and generation, respectively, of the antioxidant compounds. Once the values of the main parameters of each curve were determined, support vector machines (SVM) with an exponential kernel allowed us to predict the values of TAC, based on baking conditions (temperature and time), proteins, and fibers of each native grain.  Cereals and legumes are common staple foods. These products represent an important source of nutrients from the diet and have an irreplaceable role in nutrition . CerealsTraditionally, fruits and vegetables have been identified as major sources of dietary antioxidants, but grains have gained interest due to the proven inverse association between whole grain consumption and the risk of chronic diseases, such as cardiovascular diseases, diabetes, and cancer ,3,4,5, dDuring the last few years, the increasing awareness by consumers of the relationship between diet and health and the industrial innovation to satisfy consumer demands have driven the baked-product sector to work on more nutritional and healthier formulations. The market for natural antioxidants, with high demand not only in the food sector but with other applications such as cosmetics and plastic, is expected to grow from USD 321.4 million in 2022 to USD 409.7 million in 2032, at a CAGR of 2.4% .Our body is naturally equipped with antioxidant mechanisms to control damage from ROS and RNS (reactive oxygen or nitrogen species); however, dietary antioxidants from food or nutritional supplements are believed to contribute to oxidative balance . MoreoveFood bioactive molecules are characterized by their chemical diversity, which complicates the measurement of single-compound antioxidant capacity. The concept of total antioxidant capacity (TAC), including the synergic and redox interactions between the different molecules present in the food, was introduced to avoid these limitations. The appropriate use of TAC measurements provides support for the interpretation of complex phenomena and can be a tool for screening studies previous to in-depth research investigations .The broad types of chemical structures of antioxidant compounds in food complex matrices make necessary the use of more than one measurement to assess their antioxidant capacity. One of the main aspects of selecting antioxidant assays is the reaction mechanism. There are two main mechanisms, single electron transfer (SET) and hydrogen atom transfer (HAT). In SET antioxidant reactions, the free radical loses its condition by “pairing” its unpaired electron. In HAT reactions, the free radical is electronically stabilized through a mechanism that involves the direct transfer of a hydrogen atom .+•  and DPPH•  radical reduction methods can be found; both can be reported with Trolox  as standard. The ABTS+• assay is effective in estimating hydrophilic and lipophilic antioxidants, while the DPPH• assay has important limitations for lipophilic compounds. Although they are used widely in screening studies, both have limitations associated with oxygen, pH, or uncertain reaction kinetic endpoints, which can reduce their reproducibility. Also, both assays use synthetic radicals, which are not present in biological systems.Different assays can be used to estimate the total capacity of foods to scavenge reactive oxygen and nitrogen species and free radicals or to assess the content of reducing compounds. Among the most popular assays, ABTSOxygen radical absorbance capacity (ORAC) is another widely adopted method; in this case, although the radical initiator is a synthetic compound, the peroxyl radicals produced, against which antioxidant capacity is evaluated, are of relevance for food oxidation mechanisms . Lipid aThe reducing capacity is also important to assess the TAC; the ferric-reducing antioxidant power assay (FRAP) is based on the reduction of the 2,4,6-tripyridyl- -triazine (TPTZ)–Fe3+ to TPTZ-Fe2+ complex.Wholegrain cereals and pulses contain a wide range of bioactive components with antioxidant effects, such as proteins, dietary fiber (TDF), and phenolic compounds present in free and bound forms ,15,16, wDuring the last few years (2010–2022), more than 8000 articles have been published on the antioxidant capacity in the food area . HoweverOne strategy to overcome the difficulties of the variability in antioxidant capacity evaluation is relying on extensive databases , which cThe use of mathematical tools for assessing and predicting antioxidant properties of compounds can also be used as a strategy for food antioxidant estimation. QSAR models (quantitative structure-activity relationships) are used successfully to identify patterns in chemical structure data and correlate and predict the antioxidant capacity of compounds . HoweverSupport vector machine (SVM) methodology is extensively used in pattern recognition because of its capability to classify future unseen data and its good generalization performance . SVM alsFor this reason, the objective of this paper was to use the support vector machine (SVM) technique to develop a mathematical modeling tool based on proximal composition to predict total antioxidant capacity, as measured with three widely adopted methods integrating different aspects of antioxidant capacity assays . This work was intended to model the redox status of thermally-processed flours from different types of cereals and pulses and provide an estimation of TAC of baked cereal- and legume-based products to the food industry.A total of 136 varieties of 10 different grain types  were provided by the experimental agronomic program (PEA) of ITACyL or obtained from local markets. The varieties provided by PEA were cultivated and harvested according to uniform protocols, which reduce variability associated with agronomic aspects. The varieties and grains were the following:Wheat . Rye . Triticale , RGT/KADJAC, RGT/COPLAC Cerraton, Bondadoso Cerraton). Oats . Rice . Sorghum . Chickpea . Lentil . Soybean .Grain samples were reduced to uniform powder using a mill  fitted with a 0.5 mm screen, and then stored in sealed polyethylene/plastic bags in dark conditions to ensure stability until analysis.Afterward, wheat and rye flours were used as models for the evaluation of thermal processing (temperature and time) on the antioxidant capacity . Biscuit−1 dry matter (d.m.).Moisture content was measured gravimetrically by drying samples at 100 °C for 24 h. Total protein content was determined by the Dumas method (AOAC method 990.03) . A conveColor parameters lightness (L*), redness (a*), and yellowness (b*) were measured using a colorimeter  adjusted as D65 standard illuminate, 45/0 sensor, and 10° standard observer. The colorimeter was standardized using a light trap and a white calibration plate. Four measurements per sample were taken directly on the samples.v/v; acidified to pH = 2 with 0.1 M HCl) in an orbital shaker  using magnetic agitation. After centrifugation , the supernatant was collected and filtered (Filter lab paper n. 1249). The extraction was repeated three times. The combined methanol extracts were adjusted to 25 mL. All analyses were performed in duplicate. Extract aliquots were stored at −80 °C until further analysis.One gram of ground (mesh size 0.5 mm) sample was extracted with 8 mL of methanol: water .TP was measured using the Folin−Ciocalteu method as described by Slinkard and Singleton, with modifications . The abs−1 sample (d.m.).The procedure was based on a previously reported method by Ou et al. , with sl3.6H2O solution were prepared. The FRAP working solution was prepared by mixing the acetate buffer, TPTZ solution, and FeCl3.6H2O solution in a volume ratio of 10:1:1. The absorbance was measured at 593 nm with a microplate reader  using a calibration curve with FeSO4.7H2O (100–1000 µM). The results were expressed as mmol Fe equivalents (FeE) 100 g−1 sample (d.m.).The procedure was based on a previously reported method by Benzie and Strain , with slChemical composition and antioxidant capacity were shown using average and standard deviation, ANOVA one-way analyses were carried out to find differences between groups, and results were displayed in stacked bars and boxplot graphs. In addition, prior centerd and standardized data, principal component analysis (PCA), and distributed stochastic neighbor embedding (t-SNE) were used to visualize the chemical composition profiles of the grains, and Spearman Rank correlation coefficients were performed to elucidate the relationships among chemical variables and antioxidant biomarkers. All statistical analyses were performed with both R  and Python (Welcome to Python.org) software packages.2 was used as criteria for model applicability evaluation, using models from the scikit-learn packages from Python and lme4, nlme, and e1071 libraries from R. Finally, a local sensitivity analysis  was carried out to assess the predictor importance and identify their effect on the curve response.Modeling of the antioxidant capacity of the grains, using as output variables , was carried out considering as input variables the type of grain, proximal composition (protein and fiber), and baking temperature. Model development was performed with the RJAGS package (R Project). The maximization of RProximal analysis  was evaluated in all samples as it has been suggested that compositional variations can affect the antioxidant capacity of grains ,30 Figu.−1   I. CerealProtein content and profile differ with species, variety, genotype, and environmental and fertilization conditions, among other factors ,33. The −1 in cereal, similar to previous results by other authors, which reported 1.9 g 100 g−1 for rice, 2.0 g 100 g−1 for wheat, 3.9 for g 100 g−1 corn, and 3.3 for g 100 g−1 sorghum [−1 and soy showed, as it also occurred in protein, the highest values ranging from 31 to 38 g 100 g−1. Oatmeal, chickpea, and soy were the grains where the effect of variety produced a higher dispersion of results in fat content.Fat was also evaluated in all the grains II, as va sorghum . Pulses −1, and the highest in soy, with values from 31.60 to 37 g 100 g−1; the results agreed with TDF reported for grains [Total dietary fiber (TDF) has been strongly related to food antioxidant capacity ,45. TDF,r grains . The conp ≤ 0.05) association with antioxidant activity [p ≤ 0.05) on L values; the darkest values were observed in sorghum; meanwhile, the highest lightness was observed in corn grains. The highest a* values (intensity of red) appeared in corn and sorghum samples, while the grains with lowest a* values (lowest intensity of red) were observed in lentil, rye, and soy. Significant differences (p ≤ 0.05) were observed when considering the b* parameter (blueness-yellowness). Positive b* values were observed in all samples indicating a yellow component in all the cases; the highest values were observed for corn, chickpea, and soy, and the lowest b* values (lowest yellowness) were obtained in rye, rice, and sorghum. The parameter a* was highly dispersed depending on the variety in the case of corn and sorghum; meanwhile, in b* value, corn and rice were the two grains that showed higher variability in yellowness as related to variety.The color of the grain samples was evaluated, and CIELAB parameters  content was evaluated in all the samples . The val−1), significantly higher than sorghum (90 mg GAE 100 g−1), which was the cereal with the highest TP content. The lowest TP values appeared in three of the cereals studied  and chickpea. The effect of variety on TP was more significant in the case of sorghum, lentil, pea, and oatmeal. In the case of triticale, rye, wheat, corn, and chickpea, the TP content showed a lower dispersion associated with variety.Lentil is a legume with a high level of phenol compounds and high antioxidant activity, as it has been described previously by different authors ,49. Soy −1 dry weight , which shows the highest prevalence in human biology . The ORAy weight I. The pu−1). Pea was the grain where higher reducing power was observed, 15–20-fold the values of the other grains analyzed. Lentil also showed high FRAP capacity after pea, presenting the highest differences due to variety. In contrast, a very low variability due to variety was observed in most of the grains.Reducing power was also evaluated in all the samples II; FRAP 2 0.47). The highest correlation of composition parameters with antioxidant markers was for fiber and ORAC (R2 0.49) and the lowest was for fat and ORAC (R2 0.23). According to the results, ORAC was a feature that explained that TAC was associated not only with total phenol but also with other compounds involved directly or indirectly with antioxidant activity, such as protein (peptides) and fiber.The degree of correlation between antioxidants , proximal parameters , and color parameters (L* a* and b*) was analyzed. . Among tA projection of the proximal composition  and type of grain on the principal component plane, with PC1 and PC2 on the X and Y axes, respectively, are presented in With the objective of finding nonlinear similarities in the antioxidant capacity between the different grains, t-distributed stochastic neighbor embedding (T-SNE) analysis was used, with the aim of reducing initial spatial dimensions and representing the observed data based on the three antioxidant parameters evaluated. Different perplexity levels were used to find the highest separation between samples, 20 being the level of perplexity where better stratification was obtained . The legBaked goods require a thermal process prior to consumption; the thermal treatment conditions (time and temperature) will determine the final antioxidant capacity of the product through the degradation of thermally unstable polyphenol compounds and formation of the novo antioxidants from the polymerization of Maillard reaction products ,54. BakeAfter this time, an increase in TP is observed in wheat biscuits baked at 220 °C and in rye biscuits at 180, 200, and 220 °C. At the end of the baking process (1500 s), wheat TP values were similar to or lower than the initial values, while significant increases after thermal processing were observed in the case of rye samples treated at 200 and 220 °C. Rye samples treated at 180 °C did not show significant variation after the baking process. TP methodology based on the Folin−Ciocalteu reagent can reflect not only phenolic content but also the presence of other antioxidants . Rye TP On the other hand, degradation of acrylamide may also occur in high-temperature treatments , while f−1) were significantly higher than the initial levels (1500–2000 µmol Eq Trolox 100 g−1). The gradual formation during the thermal process of high molecular weight melanoidins and binding of low molecular weight MRPs to melanoidin skeletons can contribute to the final antioxidant activity of the samples. These compounds react with the peroxyl radicals and are measured by the ORAC assay, as happened in the case of the Folin−Ciocalteu reagent in the TP method [Similar behavior of the antioxidant capacity measured against peroxyl radicals (ORAC) was observed compared to TP results II. The aP method .The ferric-reducing ability  of the rye was significantly higher than that of the wheat III. As ot’ represents time, ‘T’ temperature and Previous to model building, experimental data  were assθ, α1, α2, β1, β2), iterative optimization tools were used (R packages “nlme” and “nls”). Parameters β1 and β2 were fixed at 0.08 and 0.12, respectively, due to their low influence on curve shape. The number of hyperparameters was reduced to three , simplifying the solution search space and facilitating convergence in the search for an optimal solution (Equation (2)).As a first exploratory search of space of hyperparameters  was obtained , and traWith the use of the Bayesian hierarchical model, the parameters  of the double exponential curve were obtained; the best-fitting curves obtained are shown in 2 obtained for SVM model training can be found in Using as reference the optimal parameters  fitted with the Bayesian hierarchical model, a training procedure was carried out using as input data the compositional (protein and fiber), type of grain (wheat or rye) and temperature . Independent training steps using support vector regression (SVM) for each combination of parameters  and antioxidant value  were carried out. Datasets used for training in the different models are provided in Using the obtained SVM models, it is possible to predict the antioxidant capacity  at any given thermal processing temperature and time, protein, and fiber as proximal parameters, and wheat or rye as types of grain .Therefore, relying solely on the secondary models to determine the shape of the curve to be predicted, that is, using baking temperature, grain type, protein, and fiber as predictors, the fit shown below was achieved , where iTo estimate model stability, the variability of the predictions after different entry values was analyzed. Temperature and proximal composition  independent variations (maintaining the rest of variables fixed) were used as inputs for the model and compared with model outcome using available values of temperature and composition as reference. As can be observed, the model is more sensitive to temperature changes at advanced treatment times, with a linear relationship of temperature and curve slope, with lower variation at the initial stages of the thermal process; there was a higher variability of the final TP values in the case of rye products compared with wheat , revealiThe total antioxidant capacity profiles of a range of cereal and legume flours, as evaluated through three different methods , and their distribution variability related to proximal composition and color were reported. The distribution of the variability of the antioxidant data was significantly affected by the protein and fiber composition of the samples. The variability of legume flours showed in the generally higher values of antioxidant capacity compared to cereals, with special relevance to lentil flour. Reduced dimensionality techniques (PCA and T-SNE) applied to the data showed wheat and rye as two samples with homogeneous distribution and well separated.The behavior of the antioxidant capacity profiles of the baked products suffered a decrease over the first half of the treatment times, with a subsequent increase afterward. A predictive model for the estimation of the TAC of baked products, using Bayesian and SVM modelling tools and considering protein and fiber content, was developed. A double exponential parameterized curve that reflects the initial decrease and subsequent increase as a consequence of the processes of degradation and generation of the antioxidant compounds is proposed. An estimation of the antioxidant capacity of a baked product during its thermal process has been achieved; this model  is intenThe use of this type of tool may provide more accurate information to the agrifood industry, reducing the use of artificial antioxidants while enhancing naturally present antioxidant compounds. In this way, this research may serve to boost the development of innovative products relying on cleaner labels and antioxidant properties as potential claims for consumers.


Eimeria spp. (EIM) in Nellore cattle that were raised under natural infestation and a prophylactic parasite control strategy. In our study, HR was defined as the slope coefficient of body weight (BW) when TICK, GIN, and EIM burdens were used as environmental gradients in random regression models. In total, 1712 animals were evaluated at five measurement events (ME) at an average age of 331, 385, 443, 498, and 555 days, which generated 7307 body weight (BW) records. Of the 1712 animals, 1075 genotyped animals were used in genome-wide association studies to identify genomic regions associated with HR.Host resilience (HR) to parasites can affect the performance of animals. Therefore, the aim of this study was to present a detailed investigation of the genetic mechanisms of HR to ticks (TICK), gastrointestinal nematodes (GIN), and Posterior means of the heritability estimates for BW ranged from 0.09 to 0.54 across parasites and ME. The single nucleotide polymorphism (SNP)-derived heritability for BW at each ME ranged from a low (0.09 at ME.331) to a moderate value (0.23 at ME.555). Those estimates show that genetic progress can be achieved for BW through selection. Both genetic and genomic associations between BW and HR to TICK, GIN, and EIM confirmed that parasite infestation impacted the performance of animals. Selection for BW under an environment with a controlled parasite burden is an alternative to improve both, BW and HR. There was no impact of age of measurement on the estimates of genetic variance for HR. Five quantitative trait loci (QTL) were associated with HR to EIM but none with HR to TICK and to GIN. These QTL contain genes that were previously shown to be associated with the production of antibody modulators and chemokines that are released in the intestinal epithelium.Eimeria ssp. Although we verified that sufficient genetic variation existed for HR, we did not find any genes associated with mechanisms that could justify the expression of HR to TICK and GIN.Selection for BW under natural infestation and controlled parasite burden, via prophylactic parasite control, contributes to the identification of animals that are resilient to nematodes and The online version contains supplementary material available at 10.1186/s12711-023-00844-9. Eimeria spp. (EIM) are endemic in tropical countries and responsible for several economic and productivity losses in cattle production systems [Ecto- and endoparasites such as ticks (TICK), gastrointestinal nematodes (GIN), and  systems . ParasitIn the literature, different phenotypes are used to describe response to disease, among which we would like to highlight resistance, tolerance and resilience . ResistaHR can be estimated as a continuous trait using reaction norm models of the host’s performance on environmental stress factors . Under tFasciola hepatica in Irish cattle [Reaction norm models have been used to estimate HR to h cattle  and resih cattle . Furtherh cattle  showed tWe used the data on Nellore bulls that were born between 2010 and 2016 and raised on the Mundo Novo commercial farm, which is located in Uberaba, Minas Gerais state, Brazil . The Ethics and Animal Experimentation Committee of the Universidade Federal de Minas Gerais approved the experiment and data collection (Protocol 255/2010). Detailed description about the farm and the herd are in Passafaro et al. .The bulls were raised on pasture, which comprised mainly (> 80%) grass of the Uruchloa genus, with a stocking rate of approximately 0.98 animal unit per hectare. Animals had free access to mineral supplementation and clean water throughout the year. After weaning (210 days old on average), the males were evaluated in performance tests that lasted 294 days, and included 70 days of adaptation, to minimize potential nutritional and social stress, and 224 days of evaluation , at the end of the adaptation period (day 70) and at four intervals of 56 days until the end of the test  were estimated by the number of eggs or oocysts per g of faeces, according to the modified McMaster technique [The tick counts used in the present study were obtained at each ME by counting the engorged female ticks, with a length size > 4.5 mm, on the right side of each animal . The eggechnique .Faecal samples were collected directly from the animals’ rectum using properly identified and lubricated plastic bags. They were then cooled and transferred into chilled coolers in the laboratory. To perform the counts, we diluted 2 g of faeces with 28 mL of water, prepared 2-mL aliquots of this mixture and mixed each aliquot with 2 mL of saturated Sheater’s solution . Then, a McMaster chamber was filled with 0.15 mL of the final solution to perform the counts of eggs and oocysts. Thus, in this study, tick, egg, and oocyst counts are the real counts observed on the right side of each animal or in the McMaster chamber.Cohorts were defined by the combination of contemporary group  and ME and only cohorts with more than five individuals were considered, thus, 7307 body weight records and parasite counts on 1712 animals were evaluated in this study  at the beginning of the performance tests (day 1 of the adaptation period). Approximately 65% of the bulls were dewormed. The choice of animals that received treatment was based on contemporary groups in such a way that all the animals that belonged to randomly chosen contemporary groups were dewormed.Blood samples were collected with sterilized syringes into 3.5-mL vacuum tubes containing 9NC coagulation sodium citrate 3.2%, to prevent blood from clotting and maintain DNA integrity. Blood samples were frozen and transferred into chilled coolers in the laboratory and stored in freezers at − 20 °C. In total, 1230 blood samples were selected for genotyping with a low-density DNA array, i.e. the Z-chip v2  mapped to the ARS-UCD1.2 bovine genome assembly). Most of the genotyped bulls were from the performance tests with more than 20 animals per group, as described above, and each animal had data for the three parasites for at least four ME.The quality control of DNA samples and markers was carried out using the SNP & Variation Suite v8.8.3 software . AllelesWe used single-trait linear random regression models (STM) where BW at each ME was considered as a dependent variable (trait) and the median count of the parasites for each cohort Table  as an inThe 15 STM were implemented using the Bayesian inference methodology and can be described as:The additive genetic variances for BW for each observed environmental burden were estimated by the product P-value related to each SNP solution was generated. For the GWAS, we used genotypes from animals for which their samples passed quality control (1075 samples). For all these animals, both BW records and estimated breeding values (EBV) for HR were available. The GWAS model used for BW can be described as:The 20 (4 traits × 5 ME) genome-wide association studies (GWAS) were carried out for HR to the three parasites and for BW and five ME using the SNP & Variation Suite v8.8.3 software . A mixede v8.8.3  uses a rEstimated breeding values estimated for HR to each parasite at each ME using STM were considered as the pseudo-phenotypes of HR for GWAS with no additional fixed effect. Note that using EBV as phenotypes may lead to double-counting of information and heterogeneous residuals. Given that all animals in the analysis had own phenotypes, it is expected that they had reasonably similar EBV, and thus homogeneous residuals, and that the double-counting of information was limited. Thus, the model used for GWAS for HR can be described as:VanRaden . We applVanRaden . It is nVanRaden , thus weVanRaden .It is important to highlight that, in the present study, the heritability for BW was estimated based on both the GRM and the pedigree-based relationship matrix, thus for BW we present both the conventional heritability and the SNP-derived heritability estimates.The pairwise SNP correlations of BW with HR to TICK, HR to GIN, and HR to EIM for each ME were computed by the Pearson correlation between the SNP effects of each one of the traits, as proposed by Fortes et al. , and wilP-value for each marker were calculated by combining the SNP P-value, direction of the effect, and sample size generated by the previous GWAS. The meta-analysis was performed for markers that had solutions estimated in at least two studies (i.e. for at least two ME) and no genomic control was performed during the meta-analyses. Then, we used a Bonferroni correction to define the two groups of SNPs: those that were significantly associated with a P-value < 2.31 × 10−6 and those that were suggestively associated with a P-value < 10−4 [The sample-size-based approach described by Willer et al.  was usede < 10−4 , 23.P-value for each chromosome (Chr); second, we searched for significant SNPs within 0.5-Mbp regions up and downstream of the peak SNP. If we identified other significative SNPs within this interval, the boundaries of the QTL were expanded to include the SNP and another 0.5-Mbp region (up and downstream) was investigated. The process was repeated until there was no more significant SNPs in these 0.5-Mbp windows. Finally, a new peak SNP was called if there was a significant SNP on the same Chr but outside of the boundaries of the first QTL. The process was repeated for each Chr until no more peak SNPs could be identified.Based on the results of the meta-analysis, we defined quantitative trait loci (QTL) associated with each trait. The QTL boundaries were defined as follows: first, we identified an initial peak, i.e. the SNP with the lowest P-value < 10−4) that were in high linkage disequilibrium (LD) with the peak or another significant SNP in the QTL were considered. The LD between SNPs was evaluated by the D prime (D’) value that was estimated using the expectation–maximization method by pairwise analysis in the SNP & Variation Suite v8.8.3 [Moreover, only regions with at least four significant or suggestive SNPs were considered as QTL . In adde v8.8.3 . SNPs wehttps://www.ncbi.nlm.nih.gov/assembly/GCA_002263795.2) with the GALLO package [We searched for genes located within the QTL boundaries using the ARS-UCD1.2 bovine genome assembly . The output of GUILDify is a trained list of genes that are ranked according to the interaction network. The first 100 genes were used as the trained gene list for each studied trait.Candidate gene prioritization analysis was conducted using the ToppGene Suite  and consP-value cut-off for each training parameter was 0.05 with a false discovery rate correction. After this step, a representative profile of the trained gene list was obtained.For functional enrichment analysis, the trained gene list was compared with random sets of genes in the genome to search for any functional category or parameter that was overrepresented in our trained list compared with the background. We used Gene Ontology , Human phenotype, Mouse phenotype, Pathway, PubMed, Transcription factor binding site, Co-expression, and Disease as training databases. The P-values are described in Chen et al. [P-value for each one of the functional categories.In the second step, a similarity score was generated for each gene in our list of candidate genes. This score is created by functional annotation of the candidate gene followed by a comparison of its function to each enriched term that is learned in the training step. The similarity score calculation and the associated n et al. . In summn et al. , and Pean et al. . At the P-value that will be used to determine if each gene was prioritized or not. For this computation, we need to assume that the P-values come from independent tests, thus the Fisher’s inverse chi-square method was applied to combine the P-values from multiple annotations into an overall P-value [P-value ≤ 0.05. For the candidate gene prioritization analysis, we used the default setting in the ToppGene Suite that has a background gene set from the genome for computing the P-value with 5000 coding genes and two features to be considered for prioritization.A final test is carried out to compute an overall  P-value . The priIn general, the highest posterior density interval with 90% of samples (HPD90) related to the posterior mean of the intercept and slope variances were wide, indicating no difference between genetic parameters estimated across ME. However, residual variances of BW estimated at ME.331 were smaller than those at ME.555  for BW Table  at each The SNP-derived heritability estimates for HR to TICK, GIN, and EIM at each ME were computed through GWAS when the slope solutions (genetic effects) were considered as the HR phenotype. As expected, the magnitude of these estimates was large Table , rangingWhile in some cases, the genetic correlations between intercept and HR did not differ from zero , the pairwise SNP correlations indicate that there is some genetic association between BW and HR. In general, the pairwise SNP correlations of BW with HR to GIN and HR to EIM were zero, or favourable Fig. . HoweverThe genes that were associated with HR were searched within the QTL that were built from the significant and suggestive SNPs obtained by the meta-analysis GWAS Fig. . The metApart from the presence of some significant isolated SNPs, we detected no QTL, i.e. no genomic regions, that were associated with HR to TICK and HR to GIN. Five QTL located on Chr 4, 6, 7, 12, 13 were associated with HR to EIM  as selection criteria . IDEX isThe unfavourable correlations between BW and HR to TICK were stronger for younger (ME.331) than older animals (ME.550). This result might be partially explained by the effect of age on immune response mechanisms –53. MoreThe methods, which were used here to search for functional candidate genes only in the QTL regions, filtered out the search for genes associated with HR to TICK and HR to GIN. In spite of the absence of significant QTL, some SNPs were significantly associated with these traits. For research purposes and with the main objective of avoiding the discussion of spurious associations from the GWAS, we believe that adding the QTL definition based on the presence of multiple significant and suggestive SNPs at a given region, is a good quality control to select for true associations. However, we do acknowledge that the population with available phenotypes was small, and that regions with significant SNPs associated with HR to TICK and HR to GIN might be within important QTL that were not identified because of the limitations due to the size of our database. Thus, future studies might bring other interesting insights on HR.CXCL9, CXCL10, and CXCL11, which were related to HR to EIM. The transcripts of these genes are proinflammatory chemokines that are released from the intestinal epithelium [CXCL9 and CLCX11 transcripts were found to be increased in the gut tissue of susceptible mice that were artificially infected with Trichuris muris, but the up-regulation of these genes has not been verified in artificially-infected resistant mice [CXCL10 gene resulted in a significant reduction in worm burden and increased rate of epithelial cell turnover in infected susceptible mice [CXCL9 gene plays an important role in antimicrobial defence by protecting the gut of artificially infected mice from the invasion by the bacteria Citrobacter rodentium and restoring the damaged tissue. These studies in mice suggest a possible mechanism underpinning the association of CXCL9, CXCL10, and CXCL11 with HR. The immune responses mediated by chemokines is probably an important mechanism for HR to all intestine parasites, including protozoans. However, further studies in cattle are necessary to confirm the role that these genes might play in HR.We identified several genes in the chemokine pathways, such as ithelium . The levant mice . Furtherble mice . Cliffe ble mice  demonstrIRS2, LIG4, and TNFSF13B genes were associated to HR to EIM in our study. These genes were also associated with human susceptibility to the nematode Ascaris lumbricoides, an endemic disease in tropical areas [TNFSF13B gene plays an important role in the class-switch recombination process and in the proliferation of B cells by rearranging its DNA sequence to switch their expression from one class of immunoglobulin, such as IgM, to an immunoglobulin heavy-chain constant region, which results in antibodies with different effector functions [TNFSF13B in the intestinal tissue of chickens that were orally infected with Eimeria acervulina increased after coccidiosis infection and led to a high antibody response [The modulators of antibody-mediated immune response, i.e. the al areas . The TNFunctions . Moreoveresponse . TherefoEimeria ssp. and that are expected to perform better under challenging environments . Chemokine pathways and intestinal epithelial cells are important for HR to gastrointestinal parasites and further studies focused on the expression of the candidate genes discovered in this study might help to better understand the HR mechanisms to different parasites.Selection under natural infestation and controlled parasite burden, via prophylactic parasite control, contributes to identify animals that are resilient to nematodes and Additional file 1: Table S1. Number of repeated measurements per animal. Table S2. Keywords used to construct the trained list of genes for body weight (BW) and host tolerance to ticks (HT.TICK), gastrointestinal nematodes (HT.GIN) and Eimeria spp. (HT.EIM). Table S3. Summary statistics of genes submitted to candidate gene prioritization analysis for host tolerance to Eimeria spp.Additional file 2: Figure S1. Distributions of body weight information (BW-a), ticks (TICK-b), gastrointestinal nematodes eggs (GIN-c), and Eimeria spp. oocysts (EIM-d) counts at each measurement event (ME). 331, 385, 443, 498, and 555 represent the mean ages of the animals at each ME, respectively.Additional file 3. Additional information on the methods used to process the statistical analysis [analysis –65.Additional file 4: Figure S2. Manhattan plots for the genome-wide association studies for host tolerance to ticks evaluated at different measurement events (ME). 331, 385, 443, 498, and 555 are the mean ages (in days) of the animals at each ME. The dotted line (y = 5.64) indicates the threshold for statistical significance. The dashed line (y = 4.00) indicates the threshold for suggestive evidence of association.Additional file 5: Figure S3. Manhattan plots for the genome-wide association studies for host tolerance to gastrointestinal nematodes evaluated at different measurement events (ME). 331, 385, 443, 498, and 555 are the mean ages (in days) of the animals at each ME. The dotted line (y = 5.64) indicates the threshold for statistical significance. The dashed line (y = 4.00) indicates the threshold for suggestive evidence of association.Additional file 6: Figure S4. Manhattan plots for the genome-wide association studies for host tolerance to Eimeria spp. evaluated at different measurement events (ME). 331, 385, 443, 498, and 555 are the mean ages (in days) of the animals at each ME. The dotted line (y = 5.64) indicates the threshold for statistical significance. The dashed line (y = 4.00) indicates the threshold for suggestive evidence of association.


O-glycoside, has a stronger UGT2B7 binding effect than its metabolites. After testing a larger number of flavonoids with different aglycones, their aglycones, and metabolites, we demonstrated that typical dietary flavone O-glycosides generally have high binding affinities towards UGT2B7 protein, but the flavone C-glycosides and the phenolic acid metabolites of flavones had no significant effect on this. With the disposition of 4-methylumbelliferone examined by HPLC assay, we determined that 10 μM rutin and nicotifiorin could significantly inhibit the activity of recombinant UGT2B7 protein, which is stronger than isovitexin, vitexin, 3-hydroxyphenylacetic acid and 3,4-dihydroxyphenylacetic acid. In addition, in vitro experiments showed that in normal and doxorubicin-induced lipid composition, both flavone O-glycosides rutin and flavone C-glycosides isovitexin at 10 μM had no significant effect on the expression of UGT1A1, UGT2B4, UGT2B7, and UGT2B15 genes for 24 h exposure. The obtained results enrich the regulatory properties of dietary flavone glycosides, aglycones, and metabolites towards the catalysis of UGTs and will contribute to the establishment of a precise nutritional intervention system based on lipid bilayers and theories of nutrients on endoplasmic reticulum and mitochondria communication.Flavone glycosides, their aglycones, and metabolites are the major phytochemicals in dietary intake. However, there are still many unknowns about the cellular utilization and active sites of these natural products. Uridine diphosphate glucuronosyltransferases (UGTs) in the endoplasmic reticulum have gene polymorphism distribution in the population and widely mediate the absorption and metabolism of endogenous and exogenous compounds by catalyzing the covalent addition of glucuronic acid and various lipophilic chemicals. Firstly, we found that rutin, a typical flavone  Flavone glycosides, their aglycones, and metabolites are commonly in foods of plant origin, major phytochemicals, and necessary for disaster risk reduction ,2. IntakO-glycosides and flavone C-glycosides. Although there have been studies comparing the differences in the performance of flavone O-glycides and flavone C-glycides in pyrolysis and enzymolysis [Compared to other types, flavonoid glycosides are the main form of human intake from plants. According to the types of glycosidic bonds, flavones can be divided into flavone ymolysis , as wellymolysis ,9, the mMetabolism of the endogenous and exogenous compounds in human cells is mainly divided into two stages: Phase I metabolic reaction mainly involves redox reaction, and it hydrolyzes non-polar fat-soluble macromolecular substances into polar water-soluble small molecular metabolites. Phase II metabolic reaction combines the metabolites of phase I with endogenous small molecular organic substances for further utilization by the human body, which affects the utilization of nutrients and the outflow of toxins in human cells ,11,12. PUridine diphosphate glucuronosyltransferase (UGT) is a family of phase II binding enzymes that are widely distributed in various organs of the human body. It catalyzes the glucuronidation process and the covalent addition of glucuronic acid to generate a wide range of lipophilic chemicals, which is more conducive to the participation of substances in cell metabolism ,19. UGT 4-methylumbelliferone (4-MU) has been widely used as a non-specific substrate of UGT to determine the effect of chemicals on UGT activity ,33,34,35Rutin, nicotifiorin, isovitexin, vitexin, HPAA, DHPAA, aucubin, and asperuloside were purchased from Yuanye Co. , with purity above 98% (HPLC). 4-MU, 4-MUG, 7-hydroxycoumarin, uridine-50-diphosphoglucuronic acid (UDPGA), and doxycycline (DOX) were purchased from Solarbio Co. , with purity above 98% (HPLC). Recombined human UGT2B7 protein was obtained from IPHASE. All other commercially available reagents were of HPLC grade or the highest grade.https://www.genecards.org/, accessed on 6 June 2022). Human biological pathway unification was completed by PathCards . The diverse clinical and genetic annotation analysis of the human disease compendium was conducted by MalaCards . Protein–protein interaction networks and the functional enrichment analysis were obtained using the STRING database .The chromosomal localization and tissue specificity analysis were obtained by GeneCards  was downloaded from the PDB database. Polar hydrogen atoms were added to the UGT isoform, non-polar hydrogen atoms were merged, and the UGT protein structural ends were repaired by terminal treatment. SDF files of phytochemical structures were downloaded from PubChem. Ligplot was used for the protein-fixed ligand-flexible docking calculations. With docking calculation, the ligands were calculated and ranked according to the total score, crash score, and polar energy terms.2, 50 mM Tris HCl buffer (pH 7.4), and 1 mM 4-MU in the absence or presence of 1 μM–1 mM test substances in DMSO [g for 10 min at 4 °C. The products in the supernatant were measured by high-performance liquid chromatography . The mobile phase consisted of acetonitrile (A) and 0.5% formic acid (B). All experiments were performed at least three times.According to the chromatogram of 4-MU, a nonselective substrate of UGT2B7, with different concentration gradients (0–1 mM), the standard curve of 4-MU under 316 nm irradiation in C18 HPLC column  at a flow rate of 1 mL/min was obtained ,33,34,35 in DMSO . After a2 and 95% saturated atmospheric humidity. At approximately 70% confluence, the HKC cells were treated with 1 μM DOX, 10 μM rutin, isovitexin, and 1 μM DOX with these 50 μM phytochemicals for 24 h. Cells were transferred to ice before subsequent treatment.The human renal proximal tubular cell line (HKC) was purchased from the China Infrastructure of Cell Line Resource. Cell culture method according to previous studies , the culThe total RNA of cells was isolated using Trizol reagent  and reverse transcribed into cDNA using the modified fastquant RT Kit  method , followet-test. Differences were considered to be significant for p-values less than 0.05.The experimental data were shown as the mean value plus the standard deviation of three parallel experiments. Statistical analysis was carried out using GraphPad Prism 8.0. Multiple groups were subjected to a one-way analysis of variance (ANOVA), and the means were compared using a two-tailed unpaired Student’s UGT2B7 gene was widely expressed in metabolic organs , 4-hydroxy-3-methoxyphenylacetic acid (HVA), 3,4-dihydroxyphenylacetic acid (DHPAA), and 3-hydroxyphenylacetic acid (HPAA) [C-terminal domain sequences from human UGT2B7 [The molecular docking of plant chemical ligands with UGT2B7 protein motifs (PDB ID: 2O6L) was used to explore the interaction and combination patterns between dietary flavone glycosides, aglycones, or their metabolites with UGT2B7. UGT2B7 is a nuclear gene located on human chromosome 4 A. Initiad (HPAA) ,41 , and catechol-O-methyltransferase (COMT) . Among tn extent . Throughte drugs ; the risy system . The genO-glycosides and flavone C-glycosides treatment did not affect the expression of UGT at the transcriptional level. However, 50 μM rutin and isovitexin showed a disturbing inhibition of UGT, suggesting a potential risk of lipid metabolism regulation. DOX-induced lipid composition is based on UGT inhibition and the over-production of peroxynitrite that induces endoplasmic reticulum disorder and mitochondrial damage [O-glycosides inhibited UGT activity, it did not further reduce DOX-mediated UGT expression.To further compare the effects on metabolism in living cells, we focused on the correlation between the dose effect of flavone glycosides and the expression level of UGT enzyme in a human renal tubular epithelial cell line, where there exists large endoplasmic reticulum membranous surfaces, an active platform of chemicals exchange and also an important model to study the ER homeostasis and inflammation . The resl damage ,54,55. TO-glycosides and flavone C-glycosides components [O-glycosides and UGT2B7 [C-glycosides and UGT2B7. However, the sites and applicability of these components in cell metabolism have not been well clarified. The upstream interaction sites of UGT in human cells are the phase I metabolic enzymes cytochrome P450 (CYP) [In recent years, a large number of foods and supplements with the lipid bilayer and mitochondrial health regulatory effects to reduce the risk of disease have been reported, including flavone mponents . Currentd UGT2B7 , but we 50 (CYP) ,59, whic50 (CYP) , catalyz50 (CYP) . Notably50 (CYP) ,63,64,65O-glycosides generally have a strong binding with UGT2B7 protein, but the flavone C-glycosides, their aglycones, and metabolites have no significant effect on this, which we summarized in O-glycosides rutin and flavone C-glycosides isovitexin had no significant effect on the expression of UGT1A1, UGT2B4, UGT2B7, and UGT2B15 genes in renal tubular epithelial cells. This study focused on the bioavailability of flavones and found that UGT2B7-mediated glucuronidation of flavone O-glycosides was generally stronger than that of flavone C-glycosides and phenolic acids. This study enriches the regulatory properties of dietary flavone glycosides, aglycones, and metabolites on the catalysis of UGTs and will contribute to the establishment of precise nutritional intervention systems based on lipid bilayers and theories of nutrient utilization on endoplasmic reticulum and mitochondria.In conclusion, our study shows that typical dietary flavone


Among these traits, loin muscle depth (LMD) is an important one as it impacts the lean meat content. In this study, we integrated cis-regulatory elements and genome-wide association studies (GWAS) to identify candidate genes and genetic variants regulating LMD.The lack of integrated analysis of genome-wide association studies (GWAS) and 3D epigenomics restricts a deep understanding of the genetic mechanisms of meat-related traits. With the application of techniques as ChIP-seq and Hi-C, the annotations of BMP2 gene was identified as a candidate gene for LMD based on the integrated results of GWAS, Hi-C meta-analysis, and cis-regulatory element data. The identified QTL region was further verified through target region sequencing. Furthermore, through using dual-luciferase assays and electrophoretic mobility shift assays (EMSA), two SNPs, including SNP rs321846600, located in the enhancer region, and SNP rs1111440035, located in the promoter region, were identified as candidate SNPs that may be functionally related to the LMD.Five single nucleotide polymorphisms (SNPs) located on porcine chromosome 17 were significantly associated with LMD in Yorkshire pigs. A 10 kb quantitative trait locus (QTL) was identified as a candidate functional genomic region through the integration of linkage disequilibrium and linkage analysis (LDLA) and high-throughput chromosome conformation capture (Hi-C) analysis. The cis-regulatory elements, the BMP2 gene was identified as an important candidate gene regulating variation in LMD. The SNPs rs321846600 and rs1111440035 were identified as candidate SNPs that are functionally related to the LMD of Yorkshire pigs. Our results shed light on the advantages of integrating GWAS with 3D epigenomics in identifying candidate genes for quantitative traits. This study is a pioneering work for the identification of candidate genes and related genetic variants regulating one key production trait (LMD) in pigs by integrating genome-wide association studies and 3D epigenomics.Based on the results of GWAS, Hi-C, and  BMP2 gene as a major candidate gene, and SNP rs1111440035 and rs321846600 were identified as likely candidate mutations affecting the LMD. Our study is unique in its attempt to identify candidate genetic variants by integrating GWAS and 3D epigenomics in pigs.The loin muscle depth (LMD) is positively correlated with lean meat content and can therefore be used to estimate the lean meat content of pigs. The loin muscle depth is a quantitative trait that is influenced by whole-genome distributed polygenes. To identify the possible candidate genes and mutations impacting the LMD, we used genome-wide association study (GWAS), which is commonly used to identify candidate genomic regions associated with complex growth traits, to mapping the genomic regions. As over 90% of the identified variants have been localized to non-coding regions of the pig genome and their functions in phenotype regulation are poorly understood, we further combined 3D epigenomics to understand the mechanism of genetic variants regulating LMD. In combination with different types of data and analyses, we identified a 10 kb quantitative trait locus (QTL) on chromosome 17 that was significantly associated with LMD. We identified the  Lean pigs provide the majority of pork for the consumer market, and lean meat content is a critical breeding goal for the pig industry ,2. The lhttps://www.animalgenome.org/cgi-bin/QTLdb/SS/index)  = 5.93).The genome-wide association study (GWAS) was performed on 2,733 pigs with both phenotypic and genotypic information by using a mixed linear model-based association analysis (MLMA) in software rMVP . The foln (2008) ; σu2 is https://www.animalgenome.org/cgi-bin/QTLdb/SS/index).Identification of linkage disequilibrium (LD) blocks was performed in the chromosomal regions containing the identified significantly associated SNPs by software Haploview . The QTLHaplotypes were constructed in the LMD-associated region on SSC17 through the Linkage disequilibrium and linkage analysis (LDLA) method . The LDLThe topologically associated domains (TAD), sub-domains, Hi-C contact matrix data, significant H3K27ac peaks, and enhancer-gene pairs were all from our previous study based on the Sscrofa11.1 pig genome . Hi-C daY = 1μ+genotype+e, where Y is the response vector of the LMD, μ is the mean of LMD, genotype contains three different levels of genotypes . The genotype was considered a fixed effect in the model, and e represents a vector of residual errors. For LMD, the least square means of different genotypes  were compared through Least Significant Difference (LSD) in R. An SNP with a p -value< 0.05 was considered to be significantly associated with LMD.To further identify important functional variants, the genomic region between 15.51 Mb and 16.31 Mb on SSC17 was deeply sequenced (>20X) by using target region sequencing technology on 732 randomly selected individuals across the entire population. Target region sequencing libraries were created from isolated DNA according to the manufacturer’s instructions. The high-quality libraries were sequenced using the Illumina HiSeq3000 platform, which generated paired-end sequencing data. Quality control of paired-end sequencing reads was conducted with Trimmomatic, followed by alignment to the pig reference genome using BWA. The variants were identified using GATK software according to specific criteria . Subsequently, SNPs located between 15.51 Mb and 16.31 Mb on SSC17 and distributed within the region of significant H3K27ac peaks were selected for trait association analysis with the corrected LMD phenotypes across the 732 randomly-selected individuals. The association analysis between an individual SNP and LMD was carried out using a generalized linear model using R software. This model was BMP2, which impact transcription factor binding sites (transcription start site (TSS) was considered the promoter of the candidate genes.For the SNPs that were identified to be significantly associated with LMD through the aforementioned steps, JASPAR  was usedng sites , we cond2. PK15 cells were plated into 48-well plates and co-transfected with reporter vectors and pRL-TK renilla luciferase control vector  using Lipofectamine 2000 . The transfected cells were incubated for 24 hours prior to lysis, which were lysed by using the Dual-Luciferase Reporter Assay System . For each construct, the reporter assay was repeated at least three times independently, and the results from a single representative experiment are presented in this study. Statistical analysis was conducted using a Student’s t-test, and a p-value lower than 0.05 was considered significant.Genomic DNA was extracted from the ears of Yorkshire pigs. The promoter and enhancer regions of candidate genes were amplified from genomic DNA through PCR using Phanta Max Super-Fidelity DNA Polymerase . The PCR product was subsequently cloned into the pGL3- basic  and pGL3-promoter vectors  upstream of the luciferase gene for promoter and enhancer assays, respectively, using restriction enzymes NcoI  and KpnI . Dual-luciferase assays were conducted in PK15 (Porcine Kidney 15) cells which were cultured in a 37°C incubator with 5% COPK15 nuclear extracts were prepared using NE-PER Nuclear and Cytoplasmic Extraction Reagents  and quantified using the BCA method . Single-stranded and reverse-complement DNA probes were synthesized with or without a 5′-end biotin label. For the candidate SNPs, probes were designed as follows: for SNP rs1111440035 , the M4-L-CC probe forward strand was 5′-CCCACCCGAACGACCTCGGGGCGA-3′; M4-H-GG probe forward strand was 5′-CCCACCCGAAGGACCTCGGGGCGA-3′; for SNP rs80791204 , the M5-H-GG probe forward strand was 5′-AGGGAGAATAACTTGGGCTCCTCACTTCGCG-3′; and the M5-L-CC probe forward strand was 5′-AGGGAGAATAACTTGCGCTCCTCACTTCGCG-3′; for SNP rs321846600 (in the enhancer), the TT probe forward strand was 5′-GACAACCAGATCCATCTGGGCACCAGTC-3′; and the CC probe forward strand was 5′-GACAACCAGATCCACCTGGGCACCAGTC-3′.2O; for the experiment groups (lanes 2 and 5), 1 μL (2 μg) of nuclear protein, 1 μL of 10× binding buffer, 0.5 μL of 1 μg/ μL poly(dI-dC), and 6.5 μL of ddH2O; for the competition groups , 1 μL (2 μg) of nuclear protein, 1 μL of 10× binding buffer, 0.5 μL of 1 μg/ μL poly(dI-dC), 2 μL of 1 pmol/μL unbiotin-labeled probes, and 4.5 μL of ddH2O. Binding reactions were incubated at room temperature for 20 minutes, followed by an additional incubation at room temperature for 30 minutes after the addition of biotin-labeled probes. Then, 2.5 μL of 5× Loading Buffer was added to each 10μL reaction. To run the 6% polyacrylamide gel, the voltage was set to 100 V, and samples were electrophoresed until the bromophenol blue dye migrated approximately 3/4 through the length of the gel in 0.5× TBE buffer. We transferred the bands at 380 mA (~100V) for 60 minutes. Finally, the biotin-labeled DNA was detected using chemiluminescence.An EMSA assay was performed using a Light Shift Chemiluminescent EMSA Kit  according to the product manuals. We prepared and performed the binding reactions as follows: for the negative groups (lanes 1 and 4), 1 μL of 10× binding buffer, 0.5 μL of 1 μg/ μL poly(dI-dC), and 6.5 μL of ddHIn this study, we obtained 16,533 recordings of LMD in Yorkshire pigs. Descriptive statistics of the LMD recordings are listed in A linear mixed model analysis was applied to perform the GWAS analysis of the LMD trait. The significance threshold was calculated as the cut-off after the Bonferroni correction. In total, 5 SNPs reached the significance threshold of 5.93 (−log10(0.05/42772) = 5.93) . All sigcis-regulatory elements within the same TADs were significantly stronger than those spanning the two nearest adjacent TADs  in order to understand this paper. Also see wording comment below.284-308: As mentioned above, these results are not integrated clearly with the results from the KASP genotyping. For example, why wasn’t there any overlap between the two sets of identified SNPs? More generally, the results of the two approaches should be linked as they are addressing the same aim.353-362: This paragraph needs more details on how to interpret these results, in particular for rs1111440035 or rs80791204. For example, “no additional shifted bands were detected” (359-360), should be following by “suggesting that these alleles did not differ in binding affinity” (if that is correct?). I could not understand the results for rs1111440035 (357-360).Discussion371-389: As mentioned above, this section needs editing to give a more logical explanation of the study design and interpretation of results.Grammar/wording76: change “variations” to “variation” (2 places); change “these” to “this”102: reword, e.g. “SNPs were mapped to the Sscrofa11.1 pig genome assembly.”108: reword, the ssGBLUP analysis incorporates both genetic and non-genetic effects.150: add “A” before “likelihood ratio test”170-171: needs rewording (I don’t understand what is meant here by “meanwhile distributed”)173: change to “R software” or “R”180: change to “…promoter and enhancer regions”182: change “predict” to “identify”183: change to “affect a transcription factor binding site”185-188: This section needs rewording--be more precise about their proximity of the two SNPs--I don’t follow why 5 fragments were cloned--be more precise about “Similar experiments…”--change “executed” to “carried out”190: should this be “from genomic DNA”?192: change “assay” to “assays”194: change “cell” to “cells”217-229: this section requires editing to correct grammar (e.g. verb tenses) and wording234-236: change “standard errors” to “standard error” (3 places)238: change to “A linear mixed model analysis was applied…”245: need to reword “insulation of interactions” (I don’t have any idea what this means)250: change to “in the same TAD region”256: add “the” before “above-mentioned”265-2666: change “performed the linkage disequilibrium (LD) analysis” to “investigated linkage disequilibrium (LD) patterns”274: change “vital” to another word (“compelling”?)287-288: change to “Based on whole genome sequencing data for 60 Yorkshire pigs”288: remove “the”289: change to “the pig genome”290: should mention this is on SSC17298-308: this section requires editing to correct various grammar and wording problems320: change to “supporting the hypothesis that all 6 of these SNPs can function as enhancers”322: change to “in the enhancer region”323: change “rest” to “remaining”340: change “affecting” to “affect”344: change “differential” to “different”357: add “the” before “T>C”359: change “probe” to “probes”360-362: change to “…this suggests that SNPs rs321846600 and rs1111440035 influence transcription factor binding and affect…”392: remove “the”392-393: change to “four blocks are found in the LMB-associated region”396: typo (“backfat”)397: do you mean “the most significant SNP”?398-399: reword sentence; does “this region” refer to that reported in Reference 44?413: change to “This evidence supports the hypothesis that …”415-427: this section requires editing to correct grammar and improve wording**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: YesNo: I presume that the journal will check that the data is made available if the paper is accepted. Currently the data is not available.Reviewer #3: **********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  Provide a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript.archive. If your image is from someone other than yourself, please ensure that the artist has read and agreed to the terms and conditions of the Creative Commons Attribution License. Note: we cannot publish copyrighted images.2) Upload a Striking Image with a corresponding caption to accompany your manuscript if one is available (either a new image or an existing one from within your manuscript). If this image is judged to be suitable, it may be featured on our website. Images should ideally be high resolution, eye-catching, single panel square images. 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Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.Similarity Check, powered by iThenticate, into its journal-wide submission system in order to screen submitted content for originality before publication. Each PLOS journal undertakes screening on a proportion of submitted articles. You will be contacted if needed following the screening process.PLOS has incorporated To resubmit, you will need to go to the link below and 'Revise Submission' in the 'Submissions Needing Revision' folder.Please let us know if you have any questions while making these revisions.Yours sincerely,Martien Groenen, PhDAcademic EditorPLOS GeneticsGregory BarshEditor-in-ChiefPLOS GeneticsAs reviewer 1 points out, you still oversell your data as providing conclusive evidence that the causal variants are located within a 10 kb region by using epigenetic data. Please tone down this statement and address the remaining comments made by the two reviewers.Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: This paper reports convincing evidence for a major QTL on pig chromosome and report functional characterization of candidate SNPs in an enhancer and the promotor of the BMP2 gene. The data are good. However, the remaining problem is that the authors still oversell their data as providing conclusive evidence that the causal variants are located within a 10 kb region by using epigenetic data. The problem here is that the QTL region is large 800 Mb and functional data can neither prove or exclude the importance of a sequence variant for a genotype-phenotype relationship, this can only be achieved by genetic analysis. For instance, a functional assay such as a luciferase assay used here can show that a sequence variant affect function but it does not prove that this effect is important for the genotype-phenotype relationship. Further, functional assay may fail to reveal a significant effect of a sequence variant because the assay does not replicate the conditions when the sequence variant is important. For instance, in this study the authors use a kidney cell line to study the possible effect of sequence variants affecting muscle development (LMD). In conclusion, I think the paper is fine if the authors correct some remaining issues described below and are more realistic what they can and cannot conclude based on these data. Hopefully, my comments below are useful.Specific commentsL302- (Narrowing down the QTL region). I am still not convinced that these data are sufficient to narrow down the QTL region from about 800 kb to about 10 kb. The reason is that the authors have used a sparse SNP panel. There could be other sequence variants within the 800 kb region with equally strong association to phenotype as those within LD block 2. For instance, Figure 3 shows that the SNP INRA0052824 that is located about 800 kb from the top SNP still shows D’=1 (or very close to 1) to the top SNP in the 10 kb region.Figure 4d. This figure illustrates very well that it is impossible to identify a 10 kb interval as the sole genomic region harboring causal mutations for the LMD QTL because the strong association with phenotype is over a broad region and may reflect a haplotype effect. To make this figure coherent I suggest that the authors mark their favorite region 15.65-15.75 in Fig. 4d. As far as I can see it is not at all the most strongly associated region in 4d. The authors should also explain in the legend that this plot tests for genetic association to the LMD phenotype.Figure 5. The text referring to these experiments is a bit confusing. The authors clone 800-bp genomic regions harboring 5 candidate SNPs. The problem is apparently that these 800 bp fragments may contain other SNPs in addition to the candidate SNP. This becomes apparent in Fig. 5d when the result first is highly significant but then the authors make the specific T to G change in the T construct and then there is no significant difference. The authors conclusion on line 364-365 “that SNP 15684170 is not an important site for enhancer activity” is wrong. This is still possible but the important conclusion is that the highly significant difference between the T and G construct must be caused by another SNP in this interval, perhaps that is a candidate causal sequence variant? The authors end this section by concluding that SNP rs321846600 is the best candidate SNP. However, it is not clear if the 800 bp fragment also carries other SNPs that may be important. The authors need to sequence the four 800 bp fragments and declare which sequence differences each region contains in a Supplementary Table. If the fragment with SNP rs321846600 contains multiple sequence variants they need to mutate the candidate SNP to prove that this is underlying the difference in luciferase activity between constructs.L432-445. I still find the authors arguments here problematic. They are mixing up genetic significance and functional significance. Epigenetic data cannot be used to narrow down a QTL region. It can be used to test the functional significance of SNPs within a QTL region. In this paper the authors have decided to focus on a 10 kb region but they have not excluded that sequence variants outside this 10 kb region but within the 800 kb region contribute to the QTL effect or even is more important than the possible effect of the variants within the 10 kb region. This paragraph needs to be modified to not be misleading to the field. In particular the sentence (The significant SNPs ..) on line 440-441 needs to be followed by something like this: “However, this does not exclude the possibility that sequence variants outside this region contribute to the LMD QTL effect in the genomic region 15.51-16.31 on SSC17.”L468-471 There is a conflict between these two studies. I recommend that “demonstrated” is replaced with “suggested”Minor commentsL32: delete “the”L34: replace “the key” with “candidate”L45-46: change to “candidate SNPs that may be functionally related to”L51: change “major” to “candidate”L66. Change “loci” to “locus”L69. Change “the major” with “candidate”L277: Change “were” to “are”L346: Since these are predictions, I think this sentence is too strong and should read “Results suggested that these five SNPs may all disturb…”L357 and 359: delete “different carried”L396: change “supports the fact” with “shows”L397: change “conservative” to “conserved”Figure 5 and 6. Please add a few words to the legend and explain that PK15 is a porcine kidney cell line so that readers don’t have to search for this info in the M&MFigure 5e: It is unclear what WT and MUT refer to here. It would be much better to use the nucleotides instead alleles T and C. That would make it much easier to compare the results in Fig. 5c and 5e. It may also be better to change WT and MUT to H and L in Fig. 7 because it is not clear what is WT and MUT given that these are polymorphisms segregating in pig populations.Line 415: Add “using a 50k SNP chip” after “pigs” in order to explain that a sparse SNP panel has been used at this step.Reviewer #3: The authors have made substantial changes to the manuscript and addressed most of my concerns from the previous review. I think it will be suitable for publication following minor changes and editing, as described below. The authors state that they have had assistance from professional editors and/or native English speakers, however, the manuscript would benefit from further editing. There are many places where the language/grammar is problematic.Specific commentsI am not convinced that including F_ST between American Yorkshire and American Landrace breeds is a good addition to the paper. In my view, this weakens what is otherwise a rigorous study. There is no reason to expect that these two breeds will be differentiated in this region of the genome, which has been identified using GWAS within a population of Yorkshire pigs. Or at least, the authors do not provide any justification for this analysis. In terms of the Results, they show a small peak overlapping the region identified by the GWAS but this may be coincidental. There is a higher peak in the 3’ direction . As they only looked at F_ST in a small part of the genome, there is no way to tell how extreme these peaks are on the genome-wide scale. I would recommend removing this section of the paper.Lines 278-293: I appreciate the addition of details from the previous study to which the authors refer. However, I found this section somewhat hard to follow. Are the authors saying that the Hi-C data helped them narrow down the region identified by GWAS (line 282)? I don’t see how this is the case if the TAD region is actually larger than the GWAS region.--line 279: change “isolate” to another word (“limit”?)--need to define “sequesters”--line 286 (and elsewhere in the paper): need to explain in the text what is meant by “interact” and “interactions” in this context--lines 291-293: please reword to clarify what is meant by “… SNPs are closely related to each other” and “… their target genes were expected in this TAD region”Figure 4d. The legend for this plot needs to be changed to explain that this shows results from an association analysis .Line 348-365: Change “carried” to a different word (“cloned”?)Line 389: Change “NR2C1 cannot binding” (I don’t know what this means)Line 397: Change “conservative” to “conserved”Line 409: Change to either “do not differ” or “are not different”Line 422: Remove “newly developed”Line 426: Change “verify” to “evaluate”Line 430: Change “provides” to “identifies”Line 472: Change to “…increased BMP2 enhancer activity…”Line 478: Should this be “have previously been reported”?**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #3: Yes**********what does this mean?). 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Oryctolagus cuniculus) and has specific economic importance in the Rex rabbit industry. Coat color varies considerably among different populations of rabbits, and several causal genes for this variation have been thoroughly studied. Nevertheless, the candidate genes affecting coat color variation in Chinese Rex rabbits remained to be investigated. In this study, we collected blood samples from 250 Chinese Rex rabbits with six different coat colors. We performed genome sequencing using a restriction site-associated DNA sequencing approach. A total of 91,546 single nucleotide polymorphisms (SNPs), evenly distributed among 21 autosomes, were identified. Genome-wide association studies (GWAS) were performed using a mixed linear model, in which the individual polygenic effect was fitted as a random effect. We detected a total of 24 significant SNPs that were located within a genomic region on chromosome 4 (OCU4). After re-fitting the most significant SNP  as a covariate, another near-significant SNP  was still present. Hence, we conclude that the 2.1-Mb genomic region located between these two significant SNPs is significantly associated with coat color in Chinese Rex rabbits. The well-studied coat-color-associated agouti signaling protein (ASIP) gene is located within this region. Furthermore, low genetic differentiation was also observed among the six coat color varieties. In conclusion, our results confirmed that ASIP is a putative causal gene affecting coat color variation in Chinese Rex rabbits.Coat color is an important phenotypic characteristic of the domestic rabbit ( Oryctolagus cuniculus) are among the most recently domesticated species, although the exact domestication date of the species remains controversial when examined on the basis of archeological records and genetic evidence  is the first gene to have been thoroughly studied, and several causal mutations have been successfully identified as affecting coat color (MC1R in the melanin synthesis pathway (ASIP) gene has been reported to be responsible for the non-agouti black coat color in rabbits (TYRP1) gene is associated with brown coat color in rabbits (POU2F1) affects fur color formation in Rex rabbits (TYR) gene has been studied in domestic and wild European rabbits; this work has confirmed the effects of missense mutations on coat colors  gene and identified a frameshift mutation associated with the dilute coat color.Coat color in mammals is determined by the relative amounts of eumelanin and phaeomelanin in melanocytes, and many studies have been conducted to identify coat-color-associated genes and causal mutations in domestic animals during the past two decades . In domeat color , 11. As  pathway , a prema rabbits . Additio rabbits . Based o rabbits . The vart colors . The gent colors . In addii et al.  successfDue to the wide application of high-throughput sequencing technologies, large numbers of genome-wide variants can now be discovered and genotyped at an affordable cost . Among tVenous blood was collected from the marginal ear veins of 250 Rex rabbits raised at the Research Farm of Sichuan Academy of Grassland Sciences. The rabbits consisted of six coat color varieties: 40 White Rex (WT), 42 Californian Rex (CL), 42 Black Rex (BL), 42 Chinchilla Rex (CC), 42 Dark Chinchilla Rex (DC), and 42 Light Chinchilla Rex (LC). Among these varieties, WT, CL, BL, and CC exhibit different coat colors, and there are two varieties of CC with a darker (DC) and lighter (LC) coat color, respectively . There wEcoRI  was successfully used to digest genomic DNA (~1 μg per sample used). Sequencing libraries were constructed according to the recommended pipeline  for more than 50% of the total length. If either member of the paired reads was marked as low quality, both pairs were discarded. After these steps, we obtained clean reads and subjected them to the following analyses.After the initial sequencing images were converted into sequence files in the FASTQ format using a standard pipeline, we first investigated the Q package , low-qua package : (1) reap < 10−8). Finally, we extracted biallelic SNPs and generated a clean set of SNPs. The missing genotypes were further imputed using the Beagle software package v5.4 with default parameters (All clean reads were mapped to a rabbit reference genome (UM_NZW_1.0) using the BWA software with default parameters . Subsequrameters .ST, and intra-variety Wright’s FIS  and then subjected to the construction of a phylogenetic tree according to the neighbor-joining method , introns , 1 kb upstream/downstream regions of genes , and intergenic regions .We obtained 208.51 Gb raw paired-end reads (approximately 1.5 billion reads) across all the sequenced samples, from which 208.48 Gb clean reads (0.83 Gb per sample) were generated after the quality control steps. On average, 98.9% of the clean reads were successfully aligned against the reference genome. A total of 5,162,522 raw SNPs were generated on 21 autosomes, and we finally obtained 91,546 high-quality biallelic SNPs according to our custom filtering process. These SNPs were distributed across the whole genome, and an average of 42.3 SNPs per Mb genomic region was comparably observed among all autosomes . The meaAmong all clean SNPs, the distribution density of nucleotide diversity exhibited a single peak close to 0.5, and a similar pattern was also observed for PIC . The medST = 0.0962), and between the Chinchilla Rex and the Dark Chinchilla Rex (FST = −0.0002), respectively (IS) ranged from −0.1221 in the Californian Rex to −0.0522 in the Black Rex. According to the phylogenetic tree for all samples  as a covariate, the association signal within this region noticeably decreased, but it still almost reached the threshold for significance . The allelic frequencies of the two SNPs within each population are shown in The association analysis results are shown in ASIP gene, which is significantly associated with agouti coat color in rabbits, was located 150 kb upstream of the most significant SNP . However, the second-most independently significant SNP  was located at a large distance, 1.7 Mb upstream of the ASIP gene. Besides ASIP, no other known coat-color-associated gene was found within this candidate genomic region.We further investigated the annotated genes within this candidate genomic region , identifying 51 positional candidate genes in total. Among these genes, the well-studied de novo through implementation high-throughput surveys, such as GWAS, for economically important traits and for the investigation of population genetic structures. In this study, we collected six coat color varieties of Rex rabbits raised in China and employed a high-throughput approach to successfully identify genome-wide and evenly distributed SNPs.Coat color is an important phenotypic characteristic in domestic animals and has been directly subjected to artificial selection . It has ASIP, which has been shown in previous studies to be significantly associated with coat color , possible roles for these SNPs in regulating gene expression cannot be excluded. Another possibility is that the two candidate SNPs are closely linked to the potential causal variant(s).Coat color in mammals is generally considered to be a qualitative trait, although the phenotypic variations are genetically determined by polygenes. Therefore, the genome-wide scanning approach has been increasingly widely used to reveal coat-color-associated candidate genes and causal mutations. For example, Li et al.  genotypeat color , is alsoat color  found th rabbits . In the omelanin . HoweverST values were significantly higher than our estimates in this study; this may suggest that genetic differentiation among different populations of Rex rabbits is relatively low in comparison with other indigenous breeds. In accordance with this possibility, less inter-variety genetic differentiation was observed, with lower Fst values, than in former reports  can be found in the article/The animal study was approved by the Animal Care and Use Committee of Sichuan Academy of Grassland Sciences (YTS20B03). The study was conducted in accordance with the local legislation and institutional requirements.KZ, HL, and S-YC: conceptualization. KZ and GW: formal analysis. LW, BW, XF, NL, ZY, WJ, and XG: resources. KZ: writing—original draft preparation. HL and S-YC: writing—review and editing. All authors contributed to the article and approved the submitted version.This work was financially supported by the Earmarked Fund for the China Agriculture Research System (CARS-43-A-3) and Science and Technology Department of Sichuan Province (2021YFYZ0033 and 2021YFYZ0009).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.