R2) with traditional methods above 0.46, shows that LiDAR technology is effective in characterizing the growth features of the grapevine. Heritabilities calculated with LiDAR-derived total canopy and pruning wood volumes were high, above 0.66, and stable between growing seasons. These variables provided genetic models explaining up to 47% of the phenotypic variance, which were better than models obtained with the exposed leaf area estimated from images and the destructive pruning weight measurements. Our results highlight the relevance of LiDAR-derived traits for characterizing genetically induced differences in grapevine growth and open new perspectives for high-throughput phenotyping of grapevines in the vineyard.The strong societal demand to reduce pesticide use and adaptation to climate change challenges the capacities of phenotyping new varieties in the vineyard. High-throughput phenotyping is a way to obtain meaningful and reliable information on hundreds of genotypes in a limited period. We evaluated traits related to growth in 209 genotypes from an interspecific grapevine biparental cross, between IJ119, a local genitor, and Divona, both in summer and in winter, using several methods: fresh pruning wood weight, exposed leaf area calculated from digital images, leaf chlorophyll concentration, and LiDAR-derived apparent volumes. Using high-density genetic information obtained by the genotyping by sequencing technology (GBS), we detected 6 regions of the grapevine genome [quantitative trait loci (QTL)] associated with the variations of the traits in the progeny. The detection of statistically significant QTLs, as well as correlations ( To meet the challenge of adaptation to climate change and the global demand for grapevine varieties resistant to diseases, the number of grapevine genotypes under evaluation is unprecedented and the need for high-throughput phenotyping methods continuously increases . SeveralEstimating grapevine growth-related traits is mandatory to appropriately describe the effects of training systems ,5, managGrapevine photosynthetic capability is primarily determined by leaf area. A reduced leaf-to-sink ratio can reduce the sugar concentration in berries at harvest ,15, delaSeveral methods were proposed to estimate leaf area for the grapevine , but theet al. [Multi-spectral and hyperspectral cameras onboard unmanned aerial vehicles (UAVs) and satellites enabled the estimation of vineyards' architectural parameters , leaf aret al.  developeet al. . At the et al. ,28 or biet al. . More reet al. ,31.High-throughput methods were also used to access parameters of responses to water shortage  or to deet al. [The use of LiDAR (light imaging detection and ranging) optical devices has progressively gained attraction in close-range phenotyping during the last decade . The woret al. , to provet al. ,9 or truet al. .The LiDAR technology has been already applied to describe genotype–phenotype relationships for canopy height response to temperature in wheat , to idenThe main goal of the present study was to evaluate the performance of phenotypic traits estimated from ground LiDAR sensors data to unravel phenotype–genotype associations in a biparental progeny. We estimated canopy volume in summer and wood volume in winter from LiDAR-based measurements. The estimation of winter wood volume from LiDAR sensors was compared with destructive measurements of pruning weight. Canopy volume at véraison estimated from LiDAR sensors was compared against estimates of exposed leaf area (ELA), derived from RGB images. We employed a SPAD-502 device to assess how genetic variations in leaf chlorophyll content could elucidate the growth potential of the studied genotypes, knowing the linear correlations between chlorophyll concentrations in the leaves, SPAD-502 measurements, and vine biomass production ,Quantitative trait loci (QTL) detection was carried out to identify genomic regions associated with the genetic variability of the destructive—pruning weight—and nondestructive observations of canopy and wood volume, ELA, and leaf chlorophyll content and, then, assess the ability of each variable to detect genetic factors associated to plant vigor.The experimental and technical design of the study is presented in Fig. Progeny from a cross between 2 grapevine interspecific hybrids, IJ119 (FRA038_PRO_HYB50001Col119) and Divona (VIVC 26503), was cultivated in the INRAE vineyard in Colmar, France. Both parents carry resistance genes to downy and powdery mildew.Vines were grafted on the Selektion Oppenheim 4 (SO 4) rootstock (VIVC 11473) in 2015, planted in the field in 2016, and trained with a double Guyot system on a vertical shoot positioning system at a planting density of 4,200 plants per ha. Plants were not irrigated. All the vines were trellised and trimmed on the same date to control the whole canopy architecture.Vitis vinifera cv. Chardonnay were planted according to a randomized complete 2-block design. Each genotype was represented by one elementary plot of 3 plants in each block. The control variety, Chardonnay, was represented by 6 elementary plots of 3 plants per block. Each block included finally 220 elementary plots .The plants were distributed in 11 rows, with 1.7-m spacing between rows and 1.4 m between plants. A set of 209 offspring of the IJ119 × Divona progeny, the parents, and the control variety https://www.fao.org/3/i3794en/I3794en.pdf) as “Bathyfluvic Calcaric Cambisol (Loamic)” with a loamy texture (>67%).The soil, deeper than 90 cm, is classified by the World Reference Base for soil resources (−1).In winter, after leaf (BBCH 97) and before the beginning of bud swelling (BBCH 01) , we measThe method used was already described [2 per plant, was then calculated according to Carbonneau [ELA = D × FC × S, where S is a constant calculated with the parameters of canopy geometry. S = 2.05 with our training system . D is the distance between plants (1.40 m), and FC is the estimate for foliage coverage. Images were taken on 2019 August 2, 2020 August 4, and 2021 July 22, around véraison time (BBCH 85).ELA, expressed in m−1. The start and stop of LiDAR acquisitions were automatically set at the beginning and the end of each of the 440 elementary plots using the positions recorded by the Real Time Kinematik (RTK) Global Positioning System (GPS). Data were obtained for the 2 sides of each row. To limit the effects of neighboring plants, LiDAR data acquisition was triggered only 25 cm after the GPS signal of entering an elementary plot.Point clouds of grapevine plants were obtained using the LiDAR technology. Two SICK LMS4000 LiDAR sensors were installed on a frame mounted on a tractor Fig. . The verSeveral campaigns were performed: on 2020 August 5, around véraison (BBCH85) (block 1 only), on 2021 February 9 (before pruning), on 2021 March 17 (after pruning), around véraison (BBCH 85) in 2021 (August 30), and before (February 8) and after (March 8) pruning in 2022. An example of point clouds collected from LiDAR sensors in summer and winter (before and after pruning) is given in Fig. www.cloudcompare.org) when necessary.Point clouds were generated from the GPS coordinates, the known distance between the GPS antenna and the 2 LiDAR sensors, and the sensor–target distance measured by the LiDAR, all contained in an HDF5 file for each elementary plot. Point clouds were then converted to a “.las” format and visualized with the CloudCompare software version 2.11.1 (LiDAR point clouds were then processed to remove the soil and background impacts as well as the overlapping area covered by the 2 LiDAR instruments. The 3D point cloud scenes were divided either into 10-mm voxels for canopy data and 5 mm for pruning wood data. This size was chosen to minimize the volumes of the files while keeping a resolution adapted to the size of the organs to characterize (leaves and shoots). The voxels containing at least one beam impact were then classified as “vine,” whereas those voxels not containing any impact were considered empty. The classified voxel scenes were then split into 3 equal zones corresponding, respectively, to the 3 vines present in each elementary plot . Values for the upper and lower LiDAR sensor were added to obtain a single value for each side of the row.The apparent volume for each plant for each side of the row was then computed as:iV is the apparent volume for plant i (expressed in L.plant−1), Niv, is the number of voxels classified as “vine” in the area occupied by plant i, and s is the voxel size, in L. Volumes were not calculated by plot because some elementary plots did not contain 3 adult plants. In the LiDAR acquisitions taken at véraison, the apparent volume corresponds to the total canopy volume (including leaves and woody parts).where The objective was to compare pruning weights to data obtained from LiDAR sensors. We consequently sought to estimate, using the LiDAR sensors, the volume of wood removed from the plants. As suggested ,45, the The total chlorophyll content of the leaves was assessed with a Konica-Minolta SPAD-502 Chlorophyll Meter . Three consecutive sets (one set by plant) of 5 measures were taken on 5 different leaves midway up the north face of the canopy. The 3 means of the 5 measures per plant were used for statistical analysis. Measures were performed on 2020 June 4 (BBCH65) and 2021 August 25 (BBCH85).H2 = σg2/(σg2 + σe2), where σg2 is the genetic variance and σe2 is the environmental variance. When data were obtained on 2 blocks (pruning weight and LiDAR data in February and March 2021), σg2 and σe2 were directly extracted from analyses of variance (ANOVAs). However, data from block 2 were not available in 2021 because this block was not protected against foliar diseases. To allow meaningful comparisons among all the datasets, we chose to calculate the heritabilities only on block 1 using the 6 control plots of Chardonnay planted across this block. If σt2 is the variance observed over all the genotypes from the progeny in block 1 and σe2 is the variance among the 6 plots of Chardonnay, we calculated broad-sense heritabilities as H2 = (σt2 − σe2)/σt2. If applicable, the best linear unbiased predictors were extracted from ANOVAs during the process.Statistical analyses were performed with R version 4.1.0 . HeritabGenomic DNA was extracted from young expanding leaves of 249 genotypes grown in the greenhouse using the Qiagen DNeasy 96 Plant Kit  as described by the supplier. Following quality control, DNA samples were analyzed with genotyping by sequencing (GBS), a method used to unravel single-nucleotide polymorphisms (SNPs) . DNA froV. vinifera “PN40024.v4” reference genome [The reads were aligned on the e genome  using BWe genome .2 test at P = 0.05) or not consistent with the genotype of the parents, were discarded.SNP calling was performed using the gstacks command of Stacks v2 pipeline . The outParental and average genetic maps were constructed using Lep-Map3 . The ParBefore QTL detection, redundant markers were removed using the findDupMarkers command in R/qtl.QTL detection was performed on the consensus map with the R/qtl software  using thR2 = 0.79), which is considered the reference methodology to measure this trait in the field. In the 2021/2022 growing season, the correlation is also high  that the ones observed between ELA and pruning weight (0.24 < R2 < 0.27). We have no convincing explanation for the better correlations between pruning weights between the 2 seasons (R2 = 0.56) than for apparent wood volumes (R2 = 0.48). A weak but significant opposition between the chlorophyll content and the ELA was observed in 2020 , comparable, in terms of genetic variance explained, to the apparent volume of pruning wood.r > 0.97, Spearman correlation test). After removing redundant markers, the genetic files used for QTL detection contained 1,582, 1,803, and 4,473 SNPs for the female, male, and consensus map, respectively. Detailed information is provided in Table Genetic information from 249 genotypes of the population was used for the construction of 2 parental maps and one consensus map, which all display 19 linkage groups and a high marker density of 0.1 cM between markers, on average. The consensus map, with a total length of 1,017.3 cM, contains 28,274 SNP. The female map has 19,935 SNPs with a total genetic length of 1,110.1 cM, and the male map has 20,134 SNPs covering 1,177 cM. Genetic and physical distances of markers in the maps are highly correlated , apparent wood volumes, estimated with LiDAR data, and actual pruning wood weights were under the control of common QTLs on chromosomes 1, 5, and 18 Table .In 2022 (2021 growing season), the total percentages explained by the QTLs, for both apparent wood volume and pruning weight, were lower than in 2021, around 29%. Two QTLs for pruning weight, on chromosomes 1 and 18, were already detected in 2021, but this was only the case for the QTL on chromosome 1 for the apparent wood volume.Regardless of the methods used, only the QTL on chromosome 1 is stable, detected over 2 growing seasons, both in winter and in summer.Considering the objective of the study, the heritabilities of the traits and the detection of QTLs show that the use of LiDAR technology to estimate the volume of the grapevine canopy in summer and the volume of dormant tissue volume in winter is suitable for genetic studies and is more powerful than previously used methods.The reliability and relevance of LiDAR-derived apparent volumes for characterizing vine vigor will be ultimately validated if we can show that these variables are useful for analyzing variation in agronomic traits such as total biomass production, yield level, or whole canopy transpiration. At this stage, validation is based on correlations with other variables, heritabilities, and, finally, power for QTL detection.H2 ≈ 0.8) in both the 2020 and 2021 years. Similarly, the heritabilities of the apparent canopy volume at véraison are relatively high and consistent across years . These results show that both LiDAR-derived indicators are more relevant for characterizing genetic differences in vine growth than ELA and destructive pruning weight for which heritability was low (below 0.4) in at least one of the seasons. Although leaf chlorophyll content also has high heritability in both seasons , it is poorly correlated with ELA  in 2021, mainly due to a higher variance of Chardonnay data than in previous seasons. In contrast, LiDAR is an active device not very sensitive to natural lighting conditions. In addition, LiDAR processing requires only minimal parametrization—the voxel size and the number of hits to classify a voxel as canopy—to compute the apparent volumes, ensuring the repeatability of measurements, across dates and years.We cannot definitively conclude that LiDAR sensors are more relevant than images to characterize grapevine growth parameters. RGB images can also provide information on leaf area and yield  and, wheH2 = 0.33; Table H2 = 0.83), could be partially explained by differences in dry matter content. The dry matter content of the shoots may indeed vary according to the variety, environmental conditions, and the level of reserves in the plants [For dormant tissues, the heritability of apparent pruning volume obtained with LiDAR was higher than that of pruning weight. The low heritability of pruning weight in 2020 (e plants .R2 = 0.59 in 2020 and R2 = 0.46 in 2021) are lower than those reported by Siebers et al. [R2 > 0.92), Tagarakis et al. [R2 = 0.65, in 2010, R2 = 0.69 in 2011), or Moreno et al. [R2 = 0.75). These authors, however, established the relationships only for one variety, and the data presented by Tagarakis et al. [The correlations between LiDAR-estimated apparent wood volumes and pruning weights . Differences in QTL detection between winter and summer data were expected: genetic variations of internode length  can induOne of the main outcomes of our study is that LiDAR sensors are more powerful for deciphering the genetic determinism of grapevine growth parameters than reference methods. For the canopy, the LOD scores for apparent volumes were higher in both 2020 and 2021, but, more importantly, the total percentage of variance explained by the models varied between 21.8 and 27.0 % for ELA, in 2020 and 2021, respectively, whereas it reached 27.8% and 44.3% in 2020 and 2021, respectively, for apparent volumes calculated from point clouds (Table An important question is whether monitoring vine vigor in winter is necessary to describe genetic variations in vine growth. Pruning weight has traditionally been used as an indicator because it is easy to measure. However, the most important traits to consider in genetic studies are those that describe the ability of the canopy to synthesize sugars and those that are related to the light environment and microclimate around the grapes. Describing the canopy in summer using traditional methods, including digital pictures, is time consuming and was rarely performed on collections of genetic variants . With hiOur main objective was to validate the use of LiDAR sensors for high-throughput phenotyping of grapevine growth-related traits. The LiDAR-derived apparent volumes at véraison—total canopy volume—and in winter—pruning wood volume—showed high and more stable heritabilities than ELAs measured from images and destructive measurements of the pruning weight. They were further validated by powerful QTL detection. These results highlight the reliability of LiDAR-derived traits for characterizing genetic differences in grapevine growth, which can be used instead of traditional low-throughput methods. Our study also opens new perspectives for high-throughput phenotyping of grapevines in the vineyard to describe not only genetic variation but also the effects of environmental conditions, training systems, or management techniques.


Observational studies have suggested an association between obesity and iron deficiency anemia, but such studies are susceptible to reverse causation and residual confounding. Here we used Mendelian randomization to assess whether the association might be causal.Data on single-nucleotide polymorphisms that might be associated with various anthropometric indicators of obesity were extracted as instrumental variables from genome-wide association studies in the UK Biobank. Data on genetic variants in iron deficiency anemia were extracted from a genome-wide association study dataset within the Biobank. Heterogeneity in the data was assessed using inverse variance-weighted regression, Mendelian randomization Egger regression, and Cochran's Q statistic. Potential causality was assessed using inverse variance-weighted, Mendelian randomization Egger, weighted median, maximum likelihood and penalized weighted median methods. Outlier SNPs were identified using Mendelian randomization PRESSO analysis and “leave-one-out” analysis.P ≤ 0.001). Heterogeneity was minimal and no evidence of horizontal pleiotropy was found.Inverse variance-weighted regression associated iron deficiency anemia with body mass index, waist circumference, trunk fat mass, body fat mass, trunk fat percentage, and body fat percentage (all odds ratios 1.003–1.004, Our Mendelian randomization analysis suggests that obesity can cause iron deficiency anemia. Obesity is an energy metabolism disorder that results in excessive fat storage and that can lead to physical and psychological problems . The Worlower risk of iron deficiency anemia than normal-weight women (P > 0.05)  . This di > 0.05) , led us We turned to Mendelian randomization (MR) as a more robust method for inferring causality than conventional observational studies. The greater robustness is thought to arise from the fact that genotypes are defined at conception and are generally not associated with conventional confounders in observational studies , 19. MR We extracted data on SNPs and the following seven obesity-linked anthropometric traits from the UK Biobank, a prospective cohort study involving more than half a million people in the UK aged 40–69 years : body maData on obesity-linked anthropometric traits and SNPs in individuals were obtained from genome-wide association studies within the UK Biobank as described in P < 5 × 10−8). Only data from SNPs showing r2 < 0.001 for a window size = 10,000 kb were extracted in order to ensure absence of linkage disequilibrium, as shown in The abovementioned seven anthropometric traits were selected because they were the only ones for which the associated SNPs showed genome-wide significance  based on Cochran's Q statistic from inverse variance-weighted and MR-Egger regression analyses, in which case a random-effects regression model was used.Effect alleles were harmonized across the genome-wide association studies, and primary MR analysis was conducted using inverse variance-weighted regression. A fixed-effect regression model was used unless heterogeneity was significant (https://mrcieu.github.io/TwoSampleMR/) in R software .Since inverse variance-weighted regression is sensitive to invalid instrumental variables and pleiotropy , secondahttps://github.com/rondolab/MR-PRESSO) analyses (R2) in MR refers to the proportion of total variation in the exposure that is explained by the genetic instruments. R2 for each trait was derived from the original genome-wide association studies. We verified that the F-statistic, defined as mean β2/σ2 across all SNPs, was at least 10 for all the anthropometric traits in our analysis in order to minimize bias from weak genetic instruments , so random-effects regression model was used , which was conducted using a random-effects model because the P-value of the Q statistic was < 0.05 in MR-Egger and inverse variance-weighted analyses , and MR-PRESSO did not detect outliers . MR-Egger regression did not detect horizontal pleiotropy , and MR-PRESSO did not detect outliers , waist circumference , trunk fat mass , body fat mass , trunk fat percentage  and body fat percentage . These results were obtained in the absence of horizontal pleiotropy and outliers.Using a genetic approach, this study provides evidence that obesity can cause iron deficiency anemia, based on causal relationships between this type of anemia and six well-established anthropometric traits related to obesity: body mass index (IVW: OR = 1.003, 95% CI: 1.001–1.004, lower risk of iron deficiency anemia (via pathways unrelated to obesity (Our findings are consistent with numerous observational studies in various countries that reported associations between obesity and iron deficiency anemia –13. Howey anemia . We sugg obesity , 32. TheOur study justifies future research to clarify how obesity may trigger this condition . One posAdipose tissue releases various proinflammatory cytokines called adipokines, which have been linked to obesity-related comorbidities . ObesityOur findings should be interpreted with caution in light of several limitations. First, nearly all our data came from European populations, reflecting the Caucasian bias in genomic research on obesity and iron deficiency anemia. Our results should be verified and extended in other populations. Second, we cannot exclude that our analyses were confounded by intermediation effects. Third, we did not perform subgroup analyses according to sex or geographic area. These and other potential effect modifiers should be considered in future work.This two-sample MR study provides evidence of a potential causal association between obesity and iron deficiency anemia, which was robust to different analyses and rigorous pleiotropy testing. Among these anthropometric traits of obesity, people with a high body fat percentage may have a greater probability of developing iron deficiency anemia. This study can guide people to carry out scientific health management by reducing body fat percentage. Since dieting without exercise only loses muscle, not fat, people should lose weight through diet and exercise. In addition, increasing iron-rich foods is beneficial for people with iron deficiency anemia, regardless of weight. Further research should identify the molecules and pathways through which obesity can trigger iron deficiency anemia.The original contributions presented in the study are included in the article/Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.TW and GL designed this study and drafted the manuscript. ML, JX, XL, DS, ZC, QG, YW, CW, GX, TL, RW, and JZ collected and analyzed the data. YY and MY revised the manuscript. All authors have read and approved the final manuscript.


Boar taint (BT) is an off-odor and off-flavor affecting pork caused by the accumulation in the tissues of uncastrated male pigs of three main compounds: androstenone, skatole, and indole. The reduction in BT compound accumulation by genetic selection is a viable strategy to stop surgical castration of male piglets. Understanding the genetic relationships between BT compound concentration and carcass and meat quality is necessary to assess the potential effects of selecting against BT on other traits of interest. In pigs, selection is operated in purebred lines, but slaughter pigs are mostly crossbred. Therefore, before selecting against BT, it is crucial to evaluate the potential effects of such selection on commercial traits measured in crossbreds. The moderate heritability values estimated for BT compound concentrations confirmed that breeding strategies against BT can be successful. Results indicated that there is a correlation between BT compound concentrations measured in purebred pigs and carcass and ham quality traits of crossbreds. In particular, selection against BT might lead to a favorable increase in carcass backfat depth. However, such selection is also expected to negatively impact dry-cured ham quality through decreased ham fat thickness and increased proportion of unsaturated fatty acids in ham subcutaneous fat.Selecting pigs with reduced ability to accumulate boar taint (BT) compounds in their tissues is an alternative to male surgical castration. As the majority of slaughter pigs are crossbred, before selecting against BT in purebreds, it is essential to consider possible impacts on commercial traits in crossbreds. This study estimated the genetic correlations between BT compound levels measured in 1115 purebred pigs and carcass and ham quality traits collected in 26,577 crossbred Italian heavy pigs. Genetic correlations were estimated in bivariate Bayesian analyses including one BT trait and one production or ham quality trait at a time. Heritability of androstenone, skatole, and indole was 0.41, 0.49, and 0.37, respectively. A moderate negative correlation between skatole and carcass yield (−0.40), and between all BT compounds and backfat (from −0.26 to −0.55) was observed. Conversely, positive correlations (from 0.11 to 0.54) were found between skatole and ham fat thickness traits. Correlations between BT compounds and iodine number ranged from −0.07 (for androstenone) to −0.64 (for skatole), whereas those with PUFA ranged from −0.13 (for indole) to −0.33 (for skatole). Hence, reducing BT could decrease ham fat thickness and increase unsaturated fatty acids, with potential negative impacts on product quality. With the current and perspective future expansion of the ban on surgical castration in Europe, a gradual introduction of immunocastrated or entire male pigs is taking place ,2, resulGenetic selection to decrease BT is considered a safe and cost-effective strategy to stop surgical castration, with long-term cumulative effects . SelectiIn Italy, the pig breeding industry is largely oriented to the production of Protected Designation of Origin (PDO) dry-cured ham . On the To take advantage of both additive and nonadditive genetic effects, crossbreeding is widely adopted in pigs , and neaExisting estimates of the effects of BT selection strategies on production and meat quality traits are contradictory, and all the existing reports have focused on entire PB or CB pigs slaughtered at 100–110 kg BW ,20, and Data were collected from two locations: a nucleus farm, where purebred C21 boars are produced and boars selected as nucleus sires are mated to purebred C21 nucleus sows, and a sib testing station, where semen from C21 nucleus boars is used to inseminate crossbred sows to produce approximately 35 crossbred piglets per each nucleus boar. The hybrid dams originated from a cross involving boars of a synthetic line, derived from Large White and Pietrain breeds, and sows of a Large White line selected for maternal ability and prolificacy. Selection of C21 breeding candidates is based on estimates of genetic merit obtained from (i) their own phenotypes for growth performance, and (ii) phenotypic data on carcass and ham quality provided by their CB half-sibs raised under commercial conditions in the sib testing farm. A fat biopsy was collected from 1115 young boars of the PB C21 Goland sire line  from January 2020 to June 2022. Pigs were offspring of 27 nucleus boars and 226 nucleus sows. Pigs were reared at the same farm and fed ad libitum using 14.4% CP and 3.07 kcal ME/kg.Biopsy collection and quantification of AND, SKA, and IND were performed as described by Boschi et al. . BrieflyIndividual phenotypes on growth performance, as well as carcass and ham quality traits, were from 26,577 CB finishing pigs produced in the sib-testing program of the C21 Goland sire line . Pigs  were the progeny of 731 C21 Goland boars mated to 1885 CB sows. Crossbred piglets were born between February 1998 and July 2021. In the growth and finishing phase, pigs were managed under consistent conditions, as described by Rostellato et al. , and sla®, CSB-System AG, Geilenkirchen, Germany) and used to estimate carcass lean meat content according to EU guidelines [Final BW was adjusted to 270 days using individual linear regressions of BW on age estimated from 6 BW measures . Hot carcass weight was recorded online, while measures of carcass backfat and loin depth were estimated by image analysis of the left carcass side ; round shape ; marbling of the visible muscles of the thigh ; visible muscle color ; veining ; visible/near-infrared predicted ham weight loss  after 12 months of dry-curing, obtained by the method described by Bonfatti and Carnier [The following phenotypes of green ham  were also recorded, as detailed in Bonfatti et al. : visible Carnier .To normalize the distribution of AND, SKA, and IND content, variables were log-transformed before the statistical analysis. Additive genetic relationships among animals were computed on the basis of a minimum of six generations of known ancestors. Sires and dams of CB pigs were unrelated. The pedigree included 33,629 records from 1144 sires and 4585 dams.(Co)variance components for the investigated traits were estimated in a set of Bayesian bivariate analyses using the Markov chain Monte Carlo (MCMC) method of Gibbs sampling implemented in GIBBSF90 of the BLUPF90 family of programs . Each biFlat priors were assumed for the systematic effects and the covariance components . The priThe marginal posterior distribution of the genetic and environmental effects was estimated from a Gibbs chain of 1,000,000 samples of the parameters, where the initial 100,000 samples were discarded (burn-in), and a lag of 20 iterations was applied to reduce the autocorrelations between consecutive iterations. The convergence of the Markov Chain was tested using the algorithms of Raftery and Lewis .Numerical integration through the Gibbs sampler was used to estimate the marginal posterior densities of (co)variance components and genetic parameters. The posterior median was used as a point estimate of (co)variance components and parameters thereof. The lower and upper bounds of the highest 95% posterior probability density interval (HPD 95%) for (co)variance components and genetic parameters were obtained from the estimated marginal densities using POST-GIBBSF90.0) was calculated using the R software [01) was also calculated. Correlations were considered to be different from zero if P0 was >0.80, and they were considered to be relevant if P01 was >0.70.The probability of the estimated genetic correlation being >0 for positive estimates or, alternatively, being <0 for negative estimates (Psoftware . SimilarHeritability of a trait was defined asSummary statistics for the BT compound concentrations and CB traits are reported in Boar taint compound concentrations were moderately heritable : heritabg) among BT compound concentrations were all positive and ranged from moderate to high , indicating that selection strategies aiming at decreasing BT occurrence might efficiently reduce all BT compounds simultaneously. This can be ascribed to their common pathway; AND inhibits the synthesis of degradation enzymes for SKA and IND in the liver [The estimated genetic correlations , indicating that a reduction in SKA is expected to favorably affect this trait.Genetic correlations between BT compound concentrations and carcass and ham quality traits are reported in 01 equal to 0.80, 0.93, and 0.95, respectively. Consistently, the correlation between AND and ham marbling score was equal to −0.26 (P01 = 0.89). On the other hand, the genetic correlation between BT and carcass lean meat content was not different from zero. This indicates a negative genetic relationship between BT and fat accretion, but no association between BT and lean tissue accretion. In a genome-wide association study performed on the same pig line used in this study, genes related to adipogenesis have been reported to be associated to BT compound concentrations [Boar taint compound concentrations exhibited a negative genetic relationship with carcass backfat. Genetic correlations were −0.26, −0.55, and −0.44, for AND, SKA, and IND, respectively, with estimates of Ptrations .biceps femoris muscle. In addition, AND and SKA were also positively correlated to ham fat depth at the semimembranosus muscle . Hence, with a decrease in BT due to genetic selection, ham fat thickness is expected to decrease near both the biceps femoris and the semimembranosus muscle.Although the estimated correlations indicate that a reduction in BT compound concentration in PB by genetic selection would result in an increase in carcass backfat thickness in CB, an opposite effect is expected on ham subcutaneous fat thickness. Indeed, BT compounds exhibited a positive (SKA) or a negative/null (AND and IND) correlation with ham fat thickness score and fat thickness measured near the biceps femoris muscle (rg = 0.57) than near the semimembranosus muscle (rg = 0.20). Additionally, the genetic relationship between the depth of fat measured at the two muscles was low (rg = 0.29). In a previous study conducted on the same pig population [gastrocnemius, biceps femoris, semitendinosus, and semimembranosus muscles) was also low (rg = 0.28).According to the estimated genetic correlations between production and ham quality traits (data not reported in tables), carcass backfat thickness exhibited a stronger correlation with the subcutaneous fat thickness near the pulation , the genThese results seem to indicate that decreasing levels of BT compound concentrations are associated with a different distribution of the subcutaneous fat across anatomic regions. To the best of our knowledge, no studies have been conducted on the genetic relationships between ham fat deposition and BT compounds, but we might argue that lipogenesis/lipolysis occurs at different rates across body regions.semimembranosus muscle is significantly thinner than that near the biceps femoris. In particular, the anatomic region in proximity of the semimembranosus muscle, at which fat thickness was measured, is the region where fat depth is at its lowest. As ham subcutaneous fat thickness modulates salt penetration and water losses during dry-curing [biceps femoris and semimembranosus muscle and ham weight loss during dry-curing was −0.74 and −0.43, respectively (results not reported in tables). Even though a reduced fat thickness nearby the biceps femoris muscle is more desirable by consumers and may improve ham’s marketability [The subcutaneous fat at the y-curing , it playy-curing . In the tability , a decreThe BT compound exhibiting the largest correlations with both carcass and ham fat thickness was SKA, which is the compound that most influences BT perception . Hence, g = −0.26 to −0.10) and SKA (rg = −0.24 to −0.10) [Contrarily to our results, literature estimates of the genetic correlations between carcass backfat thickness and BT are consistently positive, indicating that selection to reduce BT would result in leaner carcasses. A positive relationship between AND and carcass fatness was also observed in a divergent selection experiment , where go −0.10) . When dio −0.10)  observedHowever, the large majority of previous studies estimated genetic correlations in entire male pigs of 100–110 kg BW belonging to lines selected for lean growth, with records on both BT compound concentrations and production traits. Furthermore, when BT and production traits were not recorded on the same animal, different animals of the same genetic background and sex were used to estimate the genetic correlations between traits. The current breeding goals of the pig line used in this study focus on the enhancement of growth performance, as well as ham and carcass quality traits. In particular, selective breeding aims at enhancing the quality of the green ham and its suitability for dry-curing, which largely depends on fat deposition. These breeding objectives, different from those of populations involved in previous studies, impact adipogenesis, which is known to be related, among others, to the hormonal status of the animal .ad libitum vs. restricted). The CB pigs used in our study had a lower ratio of lean-to-fat gain compared to pigs used in previous studies because they were slaughtered when their body accretion in the finishing phase was largely represented by fat deposition. As a biological relationship exists between steroid hormones and body fat deposition [In addition, in our study, genetic correlations were estimated between animals of different genetic background (PB vs. CB) and different sex , raised in different conditions , and fed using different feeding strategies , −0.64 (P0 = 1), and −0.54 (P0 = 1) for AND, SKA and IND, respectively. The genetic correlations with PUFA were −0.20 (P01 = 0.77) and −0.33 (P01 = 0.76) for AND and SKA, respectively, very similar to the estimates obtained for the correlations of the compounds with linoleic acid concentration. These estimates are consistent with the weak positive association (rg = 0.02–0.25) detected between the compounds and stearic acid content, and the moderately high relationship between the compounds and the MUFA-to-PUFA ratio (rg = 0.25–0.50).The estimated genetic correlations between BT compound levels and traits related to the concentration of PUFA in fat  were low and negative . In partFatty acid composition and iodine number are particularly important for ham production because they affect the overall quality of the final product, as well as fat firmness and texture . AlthougThe relationship between BT and fatty acid composition can potentially influence meat quality but, to date, specific studies on such relationship are scarce. Mörlein and Tholen  evaluateg = 0.01) and SKA (rg = 0.05), and positive for IND  (semimembranosus muscle.The estimated genetic correlations between BT compound concentrations and ham roundness were null for AND (r = 0.75) . Ham roug = 0.16, P0 = 0.85). The association between the levels of indolic compounds and color were slightly negative, but not statistically different from zero. Color score might be related to muscle pH and water holding capacity. As the desired color score is 0 , very close to the current average (−0.04), these correlations can be considered mildly unfavorable.Weak correlations between BT compounds and color were observed. Such correlations suggest that darker muscle color in CB hams is associated with high levels of AND in PB . Hence, a decrease in BT would be beneficial for the reduction in veining defect. In CB pigs, veining was weakly but negatively associated with fatness traits (data not reported in tables), and this might explain the association with IND.Veining is a visual defect of hams consisting in a visible subcutaneous venous lattice affecting the medial, or sometimes the entire, surface of the thigh. The estimated genetic correlations of BT traits with veining were not different from zero for AND and SKA, but a moderate positive correlation was observed between veining and IND . This indicates that selection against BT is not expected to have negative effects on ham weight loss during curing.Despite the unfavorable correlation between BT compound concentrations and ham fat thickness, the genetic correlation of BT compounds with ham weight loss was not different from 0 (rThe estimated heritability values for BT compound concentrations were moderate, confirming the opportunity of developing breeding programs to decrease BT. Estimating the genetic parameters for a large number of carcass and ham quality traits provided new insights into correlated effects affecting crossbred pigs and resulting from selection against BT operated in purebreds. Despite wide intervals obtained for some of the estimated correlations, our results indicate that a relationship between BT and carcass and ham quality traits exists. In particular, selection against BT could result in a favorable increase in carcass fat deposition. On the other hand, however, ham fat thickness is expected to be reduced, and the proportion of polyunsaturated fatty acids is expected to increase, with possible unfavorable repercussions on product quality.


Breeding target traits can be broadened to include nutritive value and plant breeder’s rights traits in perennial ryegrass by using in-field regression-based spectroscopy phenotyping and genomic selection.Perennial ryegrass breeding has focused on biomass yield, but expansion into a broader set of traits is needed to benefit livestock industries whilst also providing support for intellectual property protection of cultivars. Numerous breeding objectives can be targeted simultaneously with the development of sensor-based phenomics and genomic selection (GS). Of particular interest are nutritive value (NV), which has been difficult and expensive to measure using traditional phenotyping methods, resulting in limited genetic improvement to date, and traits required to obtain varietal protection, known as plant breeder’s rights (PBR) traits. In order to assess phenotyping requirements for NV improvement and potential for genetic improvement, in-field reflectance-based spectroscopy was assessed and GS evaluated in a single population for three key NV traits, captured across four timepoints. Using three prediction approaches, the possibility of targeting PBR traits using GS was evaluated for five traits recorded across three years of a breeding program. Prediction accuracy was generally low to moderate for NV traits and moderate to high for PBR traits, with heritability highly correlated with GS accuracy. NV did not show significant or consistent correlation between timepoints highlighting the need to incorporate seasonal NV into selection indexes and the value of being able to regularly monitor NV across seasons. This study has demonstrated the ability to implement GS for both NV and PBR traits in perennial ryegrass, facilitating the expansion of ryegrass breeding targets to agronomically relevant traits while ensuring necessary varietal protection is achieved. Lolium perenne L.) is an economically important pasture crop worldwide, particularly in northern Europe, New Zealand, and Australia. Yet genetic gain has been limited, with biomass yield achieving 0.25–0.6% improvement on average per year , primarily increasing genetic gain by shortening the breeding cycle . For PBR traits, individual samples from clonal row nurseries with phenotypic data used in commercial breeding and collected across three years for five PBR traits described by UPOV was used: heading date (HD), leaf width (LW), height (H), leaf curvature (LC) and growth habit (GH).The NV trial used a population derived from a single variety consisting of 480 plants grown at Hamilton, Victoria, Australia and had phenotypes measured for three key NV traits: CP, WSC and IVVDMD. These plants were part of a larger trial that has been previously described in Gebremedhin et al. , Smith eA number of PBR traits were phenotyped according to UPOV-described guidelines, with most characteristics visually classed from 1 to 9 except HD, which was recorded in days. For example, GH is scored from 1 to 9 for erect to prostrate growth, and LW is scored from very narrow to very broad using the 1 to 9 scale. Phenotypic data were collected for HD, LW and H in three years , LC in one year (2016) and GH in two years (2017 and 2018) across a total of 5,026 clonal row samples. Briefly, 1,650 plants were sampled in 2016, 1,738 in 2017 and 1,638 in 2018. Plants were grown as clonal rows comprising five transplanted clonal copies (per genotype) within each row at Christchurch, New Zealand. Other than within row, the clonal row screening nursery was unreplicated.For PBR samples RNASeq libraries were prepared from individual plants using the method described in Malmberg et al. , while aFor NV, a total of 485 samples from a single population were genotyped. Initially 135,950 SNPs were called, with 108,232 SNPs remaining after removing SNPs with alternative alleles other than those specified in the SNP list. After filtering across SNPs and samples for missing data 481 samples remained with 62,095 SNPs and average missing data rate of 16.9% prior to imputation. Missing data was imputed with an estimated accuracy of 93.5%.For PBR, a total of 5,026 clonal row samples were genotyped. Initially 197,137 SNPs were called, with 125,244 SNPs remaining after removing loci with alternative alleles other than those specified in the SNP list. After filtering across SNPs and samples for missing data 4,480 samples remained with 81,064 SNPs and average missing data rate of 26.2% prior to imputation. Missing data was imputed with an estimated accuracy of 93.7%.Narrow-sense heritabilities were calculated in the R package BGLR , u is the population mean, 1n is a vector of ones the same length, n, as the number of phenotypic values, K is a matrix of genotypes coded as the copy number of the alternative alleles , v is a vector of random SNP effects estimated from the reference population, where each SNP effect is where The BayesB model was also tested but found not to be significantly different (results not shown); therefore, in all analyses a BayesA model was fitted with a scaled-t density prior of marker effects. This was fitted with the default parameters used by the BLGR package. For computational convenience, BGLR samples marker effects from normal distributions where the variance of each SNP was sampled from an inverted Chi-squared distribution using default degrees of freedom and the scaling parameter defined by the BGLR package for trait heritability, with estimated heritabilities provided as described above. A total of 12,000 iterations with a burn-in of 2000 was used. Due to the relatively small scope of the NV study and using a single population, prediction accuracy was evaluated using a within-population k-fold approach, whereby each timepoint by phenotypic trait combination was evaluated by randomly masking 20% of samples at a time and averaged across the 5 iterations. Trace plots were manually investigated to ensure proper convergence of the algorithm. Genomic prediction accuracy was calculated as the correlation between GEBVs and observed phenotype.Estimation of narrow-sense heritabilities and genomic prediction was performed with the same parameters as described for the NV data set. Estimated heritabilities were used as a prior for genomic prediction in all scenarios, with the exception of HD prediction using a reference population composed of synthetic varieties, where an expected heritability of 0.85 was used  within-population (population defined as the year) k-fold approach, whereby each year by phenotypic trait combination was evaluated by randomly masking 20% of samples, (2) forward prediction where the samples from previous years was used to predict into the next year, and (3) where a reference population composed of synthetic varieties from a breeding program  of the correlations between timepoints were significant  delivered moderate-to-high GS accuracy in most traits Table . An exceA set of 697 synthetic varieties from a breeding program with genotypes in the form of allele frequencies were used as the reference population to predict HD across all three years in individual clonal row samples. Similar to forward prediction, GS accuracy was reduced compared to within-population k-fold evaluation but remained moderate to high Table .In order to evaluate the application of the data in a breeding program to generate varieties that fit into the DUS categories, a set of selection windows were evaluated. The GEBV selection windows were based on the observed phenotypes in each data set, ranging from 0–89 for HD, 2–9 for LW, 1–9 for H, 4–8 for LC and 2–9 for GH. Using a smaller selection window marginally reduced the resulting mean phenotype range of simulated 4-parent groups Table , but notRapid methodological advancements enable a more comprehensive and sophisticated approach to breeding in ryegrass and will be essential in delivering an ongoing sustainable industry through superior outcomes and products. Routine use of modern technologies that have been developed and established in other species will be essential. Breeding programs have the potential to increase genetic gain threefold or more by implementing GS over traditional approaches  interactions on GS performance. Methods to minimise variation in sampling conditions should be considered, such as normalising data by including replicate samples across all testing years and incorporating GxE effects into the prediction model; however, prediction of average performance of PBR traits rather than performance under extreme environments is likely to be of greater value to breeding programs for the establishment of DUS.Using a reference population composed of synthetic varieties from a breeding program to predict HD resulted in reduced prediction accuracies compared to within-population evaluation but remained moderate to high and comparable to forward prediction within clonal rows. Notably, the synthetic varieties reference population and the clonal row samples used in this study are derived from the same breeding program, and there is likely some genetic relationship between the two populations. This is beneficial for the application of GS within breeding programs, as it takes advantage of genetic relationships to improve persistency and accuracy of GS while leveraging existing resources, rather than developing large and relevant reference populations anew (Pembleton et al. Assessment of the use of GEBVs to select 4-parent groups from individual clonal row samples showed that the resulting mean phenotype range of simulated 4-parent groups did not change significantly with GEBV selection window. This may be attributable to the inflated prediction values observed, as GEBVs are higher compared to phenotypes such that while it appears there is a large difference in phenotype between samples based on GEBVs, this difference is not as pronounced in reality. The degree of variation, which is acceptable within a 4-parent group, will depend on the trait, and a possible strategy to mitigate this is to expand the groupings to include 5 or 6 parent plants based on GEBV and then use visual inspection to remove any plants which are obviously different in appearance. Although the creation of DUS populations could be improved through modelling of expected population distribution, GEBVs can be used to select 4-parent groups and achieve similar results to currently used phenotypic selection.Sensor-based phenomics are suitable for high-throughput phenotyping of NV traits in a GS context where screening of large diverse reference populations is required. The low-to-moderate accuracies achieved in this study are comparable to other studies and is expected to be improved through the expansion of reference populations, both for sensor-based phenotyping and for GS. Further consideration into which traits should be measured is essential, although it is clear that a seasonal approach is required as well as selection of individuals rather than populations for genetic improvement. Given the broad applicability of GS models to both complex and simple genetic traits, they are well suited to selection of PBR traits as demonstrated in this study. Furthermore, this study has shown that GEBVs can replace true phenotypes for selection of 4-parent groups for the generation of synthetic populations. Ultimately, PBR traits should base this selection on modelling the expected trait distribution in resulting synthetic populations. Nonetheless, applying restriction windows on GEBVs will achieve comparable results to the current phenotypic selection strategy typically employed by breeding programs. Modern technologies and breeding methodologies need to be fully exploited to further ryegrass breeding. This needs to begin with investing in the development of reference populations and prediction equations of numerous relevant traits to position strategically for the future.


Aquilegia viridiflora complex as a model system to examine genomic differentiation and corresponding phenotypic variations along geographic gradients. Our phenotypic analyses of 20 populations from northwest to northeast China identified two phenotypic groups along the geographic cline. All examined traits are distinct from each other, although a few intermediate individuals occur in their contacting regions. We further sequenced the genomes of representative individuals of each population. However, four distinct genetic lineages were detected based on nuclear genomes. In particular, we recovered numerous genetic hybrids in the contact regions of four lineages. Gene flow is widespread and continuous between four lineages but much higher between contacting lineages than geographically isolated lineages. Gene flow and natural selection might result in inconsistency between heredity and phenotype. Moreover, many genes with fast lineage-specific mutations were identified to be involved in local adaptation. Our results suggest that both geographic isolation and local selection exerted by the environment and pollinators may together create geographic distributions of phenotypic variations as well as the underlying genomic divergences in numerous lineages.How species diverge into different lineages is a central issue in evolutionary biology. Despite the increasing evidence indicating that such divergences do not need geographic isolation, the correlation between lineage divergence and the adaptive ecological divergence of phenotype corresponding to distribution is still unknown. In addition, gene flow has been widely detected during and through such diverging processes. We used one widely distributed  Aquilegia formosa and Aquilegia pubescens, the adaptive ecological divergence of floral characteristics mediated by pollinators likely contributes toreproductive isolation between populations , outgroup}, {, outgroup} and {, outgroup} showed a significant deviation of D-statistics from zero , indicating that gene flow exists between lineages EL and CN, CN and NE, and CN and EL. Interestingly, no gene flow was detected between the NW lineage and other lineages   . This reFST was calculated across different lineages  to infer population genetic differentiation. At the overall level of the genome, the FST values had the highest value between EL and NW, which indicated that the A. viridiflora complex had a moderate level of differentiation. Lineage NW was the most differentiated from the other lineages, possibly due to the lack of recent gene flow. This pattern was also obvious from the FST and DXY values calculated for the four lineages using 10-kb windows across genomes, which were consistent with FST at the overall level of the genome (r2). When r2 = .1, the decay distances of NE, EL, CN, and NW were 10, 37, 44 and 6.9 kb, respectively  of the NE, EL, CN, and NW lineages was calculated throughout the genome for each 50 kb with a 10-kb step size. Among the four lineages, NW showed the highest nucleotide diversity and EL showed the lowest nucleotide polymorphism . FST wase genome . We compectively . The rapA. viridiflora complex. The mean FST for the four lineages was 0.15019 (95% HPD 0.14949 - 0.15074), and the overall QST of the six traits was higher than the mean FST, including corolla diameter, petal length, spur length, pistil length, inflorescence number, and leaf area, indicating that these traits was driven by local adaptation  (Common garden experiments were conducted to investigate the phenotypic divergence evidence for genome differentiation and adaptation of the aptation . The higificant) . Interesificant) . EL showificant) .q-value between the CN and NW lineages based on the Bayesian method applied in BayeScan , and we then integrated the candidate genes with organ development, reproductive isolation, and biotic and abiotic stress responses. Among these genes, several  are invMAPKKK21  and SCL121 . After the removal of other Aquilegia species, we identified 496 715 SNPs with the ancestral state.High-quality clean reads of 80 individuals were mapped to the reference genome of t et al.  using BWt et al. . As the  related , the samur study , 42. SAMur study . The Hapur study . To imprAquilegia species with the A. viridiflora complex using IQ-TREE multicore version 1.6.12  and illustrated in iTOL (http://itol.embl.de). The chloroplast genomes of P. microphylla and S. adoxoides were also regarded as outgroups [To avoid the effect of sequence redundancy when inferring the phylogeny of utgroups .A. viridiflora complex,  672 439 highquality SNPs for downstream analysis were obtained from the remaining populations of the A. viridiflora complex after removing other Aquilegia species. We used a phylogenetic network by the NeighborNet algorithm in the software SplitsTree [Aquilegia species, 672 439 highquality SNPs for downstream analysis were obtained from the remaining populations of the A. viridiflora complex.To infer the lineage divergence and explore the role of gene flow in the speciation of the litsTree  with 100litsTree  to inferlitsTree . Among tA. viridiflora complex using SplitsTree. The haplotype network of chloroplast genome analysis was performed using the median-joining network in PopART v.1.7 with default parameters [In addition, SNPs in the chloroplast genome were also used to infer the phylogeny of the rameters .A. viridiflora complex was inferred by applying the ABBA-BABA test and Treemix v.1.1 based on the same method as above. The Python script easySFS was used to calculate the joint site frequency spectrum (SFS) for demographic analysis (https://github.com/isaacovercast/easySFS). We also calculated the likelihood of different demographic scenarios in fastsimcoal2 software [A. viridiflora complex. A neutral mutation rate of 10−8 per site per generation and a generation time of 1 year were used to estimate the effective population size, divergence times, and migration rates [Hybrid individuals were removed due to the levels of heterozygosity, and the remaining individuals were used for subsequent analysis. The gene flow between each lineage in the software  using thsoftware  was usedon rates .FST) and nucleotide divergence (DXY) between each of the four groups were calculated by PIXY [Nucleotide diversity (π) was calculated for the four groups using a 100-kb non-overlapping sliding window by using VCFtools . Additio by PIXY  in 100-k by PIXY  softwareST) and FST were be compared to explore the effect of adaptation on 11 quantitative traits, so we used the single-phenotype QST–FST test with the R package QstFstComp [QST > FST, the differentiation of traits is the major effect of divergent selection and shows local adaptation. We used the half-sib dam model and 10 000 resampling steps for each QST–FST analysis. Taking into account the phenotypic and genetic variation of the A. viridiflora complex, we compared the levels of FST between the CN and NW lineages to identify candidate loci under natural selection from 143 318 SNPs by BayeScan v.2.1 [q value <.05 were considered potentially selected loci. Candidate SNPs were mapped to the corresponding genes, and Gene Ontology (GO) functional enrichment analysis was performed by the clusterProfiler package [P values of Fisher’s exact test and correction for multiple testing was performed using the Benjamini–Hochberg (BH) method based on the A. coerulea gene annotation information. GO terms with a BH correction false discovery rate (FDR) value < 0.05 were considered to be significantly enriched.The divergence in quantitative traits (QtFstComp . If QST an v.2.1  softwarean v.2.1  was used package  in R to http://www.worldclim.org/). Simultaneously, we recorded the GPS information of the sampling locations and downloaded the GPS information for A. viridiflora from the Chinese Virtual Herbarium  and the Global Biodiversity Information Facility . ArcMap v.10.4 was used to limit the spatial extent according to the buffer radius (5 km) around each occurrence record. We used |r| < .8 (Pearson correlation coefficient) as a cutoff to remove highly correlated variables. The seven retained current bioclimatic factors  were used for subsequent analysis.Temperature and precipitation are considered the driving factors that affect the population growth rate and limit the distribution of species . TherefoFST/(1 − FST) [Isolation by environment (IBE) and isolation by distance (IBD) should take the spatial dependence into account in the data. We therefore ran a partial Mantel test using 999 permutations with significance determined to explore the effects of environmental differences and geographic distance on genetic distance in the R package vegan . Environ1 − FST) . The gen1 − FST)  to avoid1 − FST) , 70. As A. viridiflora complex in R. For the same reason as selection analysis, we also used the CN and NW lineages to identify the loci related to the seven retained current bioclimatic factors. Bayenv2 [Redundancy analysis (RDA) was used to assess the impact of current bioclimatic factors on the genomic composition of the  Bayenv2  was usedA. viridiflora complex in these regions, we sampled and recorded pollinator insects with haphazard selection of seven patches (ranging from ~20 to 30 flowers in each patch) at different days and times of day. On the first day of field observation, pollinator insects of the A. viridiflora complex visited flowers very rarely after 5 p.m. Therefore, we decided to restrict field observation to 8 a.m. to 5 p.m. for each study day and the field observations lasted for at least 3 full days with good climate conditions. We recorded the pollinator insect species, the duration of visitation, and the number of flowers visited by the pollinator. Then, the pollinator visitation rates were calculated as the duration of visitation dividing the number of flowers visited by the pollinator. We used clean entomological net in different sampling sites to prevent cross-pollution between samples from each site. After collection, each insect was stored separately in a tidy tube that was filled with 75% ethanol.To compare the divergence of pollinator insects of the CN and NW lineages, we selected the YM and HD populations for field observations of insects. During April and May of 2021, which is the peak flowering time for et al. [We used DNA barcoding basing on mitochondrial cytochrome oxidase subunit I (COI) to identify tentatively the pollinator insects. The thorax or leg of insects was used for DNA extraction. The primer pair LCO1490 (5′-TTGATTTTTTGGTCA TCCAGAAGT-3′) – HCO2198 (5′-TCCАATGСAСТAATСTGCСAТATTA-3′) was used to amplify DNA fragments. The protocols of laboratory and data processes were adopted as described in Galimberti et al. . NucleotWe are grateful to Jianquan Liu for suggestions and comments to improve the manuscript; we also thank reviewers for their comments on the manuscript; Mingzhou Sun and Shu Wang for collecting the plant materials; and Peng Peng for providing photographs. The research was supported by the National Natural Science Foundation of China (32070244) and Fundamental Research Funds for the Central Universities.X.H.X. and W.H.Y. designed the study and evaluated the results; W.H.Y. and Z.W. collected the materials; Z.W., Z.T.J., F.X.X., and L.M.Y participated in data analysis; Z.W. and W.H.Y. prepared the manuscript; all authors read and approved the final manuscript.https://doi.org/10.5061/dryad.cnp5hqc8g).Raw sequence data are available from the National Center for Biotechnology Information’s (NCBI) Sequence Read Archive (SRA) under the submission PRJNA666554. Data are available from the Dryad Digital Repository (Horticulture Research online.Web_Material_uhad041Click here for additional data file.


The prediction accuracy with the single trait was statistically similar (p ≥ 0.05) to the multi-trait accuracy, possibly due to the low correlation of severity between the diseases.Genomic prediction combines molecular and phenotypic data in a training population to predict the breeding values of individuals that have only been genotyped. The use of genomic information in breeding programs helps to increase the frequency of favorable alleles in the populations of interest. This study evaluated the performance of BLUP (Best Linear Unbiased Prediction) in predicting resistance to tan spot, spot blotch and Septoria nodorum blotch in synthetic hexaploid wheat. BLUP was implemented in single-trait and multi-trait models with three variations: (1) the pedigree relationship matrix (A-BLUP), (2) the genomic relationship matrix (G-BLUP), and (3) a combination of the two matrices (A+G BLUP). In all three diseases, the A-BLUP model had a lower performance, and the G-BLUP and A+G BLUP were statistically similar ( Breeding for resistance to diseases is a central focus in plant breeding programs, since any successful variety must have the complete package of a high grain yield, resistance to diseases, desirable agronomic characteristics, and end-use quality .Pyrenophora tritici-repentis (Died.) Drechsler causes tan spot and occurs frequently in Canada, the USA, Australia and South Africa. Spot blotch is caused by Bipolaris sorokiniana Shoemaker and causes intense damage in the wheat-growing areas of South Asia, Latin America and southern Africa [Stagonospora nodorum Berkeley is the causal agent of the Septoria nodorum blotch, which is of economic importance in Australia, the USA, Western Europe and southern Brazil [Tan spot, spot blotch and Septoria nodorum blotch are leaf-spotting diseases in wheat with global importance. The necrotrophic fungus n Africa . The filn Brazil ,3.Conventional plant breeding has created wheat lines with major disease resistance genes , and minor Quantitative Trait Loci (QTL) are usually neglected, which restricts the diversity of resistance genes in new varieties, and a substantial part of the genetic variance provided by the minor-effect loci is lost ,5.A promising approach in plant breeding for disease resistance is genomic selection, which can select both major and minor QTLs that synergistically confer more durable resistance. When the major QTLs are rendered ineffective, the quantitative resistance (provided by minor QTLs) will still provide a good level of protection against a pathogen population boom .Genomic selection uses a “training population”  to train a model that predicts the breeding values of individuals in a selection population that have not been phenotyped .Genomic prediction can be applied to single traits and multiple traits. Single-trait models predict the value of a single phenotype in a test data set, whereas multi-trait models predict two or more traits simultaneously. In general, multi-trait models represent complex relations between traits more efficiently since they use correlations between genotypes and traits .The accuracy of genomic prediction is affected by several genetic factors, including marker density, training set size, the relationship between the training population and test population, population structure, the heritability and genetic architecture of target traits, and linkage disequilibrium between markers and causal variants. In addition to the above genetic factors, the statistical method is another important factor affecting the predictive ability .Triticum turgidum L.; 2n = 4x = 28, AABB) and wild goat grass , the F1 triploid of which is added colchicine to induce the duplication of chromosomes [Synthetic hexaploid wheats (SHWs) provide an option in the search for genetic resistance to diseases. This wheat is generated by hybridizing modern durum wheat  the pedigree relationship matrix (A-BLUP), (2) the genomic relationship matrix (G-BLUP) and (3) the combination of these two matrices (A+G BLUP). The models were implemented as both single-trait and multi-trait models to predict resistance to tan spot, spot blotch and Septoria nodorum blotch.2) was relatively high, between 0.84 and 0.89, while the genomic heritability estimates ranged from 0.54 to 0.68 (The Best Linear Unbiased Estimator (BLUE) for severity had a mean of 1.63 for tan spot, 1.64 for spot blotch and 1.61 for Septoria nodorum blotch. The minimum values were similar for the three diseases, around 0.95, whereas the maximum values were 3.47, 4.08 and 3.67 for tan spot, spot blotch and Septoria nodorum blotch, respectively. For the three diseases, the standard error of the mean was low, and heritability (H to 0.68 . Some cu to 0.68 .p < 0.01), were low: r = 0.27 between Septoria nodorum blotch and spot blotch, r = 0.39 between tan spot and spot blotch and a value between Septoria nodorum blotch and tan spot (r = 0.46) that was considered moderate.The values of the correlations, although positive and significant (Genotyping via sequencing produced 6548 single-nucleotide polymorphisms (SNPs), of which 1616 (24.6%) belonged to sub-genome A, 1653 (25.2%) to sub-genome B and 3279 (50%) to sub-genome D. After purifying and imputing the markers, 4053 SNPs remained useful.The most accurately predicted disease was tan spot in both the single-trait and multi-trait models. The prediction accuracy fluctuated between 0.67 (single-trait G-BLUP and A+G BLUP models) and 0.39 (multi-trait A-BLUP model) . The meap < 0.01) from the G-BLUP and A+G BLUP models  had a lower performance than the other two, in both the single=trait and multi-trait cases, and at the same time, this model was different (P models .p ≥ 0.05) , and the one with the highest performance was the A+G BLUP multi-trait model (r = 0.45 and MSE = 0.225), which represents an advantage of 66.6% in terms of prediction accuracy . The sin ≥ 0.05) .The accuracy for Septoria nodorum blotch fluctuated between 0.40 (single-trait A-BLUP) and 0.55 (multi-trait G-BLUP and A+G BLUP) . CompariWhen evaluating different marker densities, it was found that the prediction accuracy increased with a higher marker density, although the increase in accuracy became marginal beyond 4000 markers. A similar response was observed for all three diseases. The mean accuracy ranged from 0.87 to 0.90 for tan spot, 0.79 to 0.84 for spot blotch and from 0.86 to 0.91 for Septoria nodorum blotch .SHWs are a source of genetic diversity that can be used in breeding programs for diverse traits. This study found a high proportion (50%) of SNPs in the sub-genome D of SHW, which contrasts with the results obtained by Phuke et al. , who fouThe diversity of sub-genome D and the high resistance to these three diseases indicate that SHWs are useful parents for developing varieties. However, SHWs usually house unfavorable alleles; therefore, complementary strategies must be followed in breeding programs, such as backcrossing with adapted elite lines .This study obtained a prediction accuracy ranging from 0.39 to 0.67 for this disease; these values are higher than those obtained by Semagn et al. , who obtAnother study in which predictions were carried out for tan spot in bread wheat, both in seedling and in adult plants, obtained accuracies of up to 0.77 for seedlings and 0.57 for adult plants. The best model included information on the markers and pedigree . In thisIn this case, a maximum accuracy of 0.45 was obtained using the A+G BLUP model, which is lower than that reported by Juliana et al.  for breaRegarding the predictions for this disease, Juliana et al.  obtainedThe good performance of the G-BLUP and A+G BLUP models in this study can partially be explained by the fact that the genomic information helps to predict the Mendelian sampling, and it has the ability to use the information of close and distant relatives  .p ≥ 0.05), and similar results were reported by Crossa et al. [No differences were found in the prediction accuracies of the G-BLUP and A+G BLUP models  Wide-Cross Program, were used. These genotypes were selected from a group of 1524 lines with favorable agronomic characteristics and resistance to several diseases.The three diseases were evaluated in the seedling stage (when the blade of the second leaf was fully expanded). The experiments were carried out independently for each disease between 2018 and 2019 in greenhouses at the experimental station located in El Batán, Edo. de Mexico, Mexico .The experiments were organized in a randomized complete block design, with 12 replicates for tan spot and Septoria nodorum blotch and six for spot blotch. Four seedlings were used for every repetition, all planted in plastic trays. Fungal isolates that produce ToxA were used for the three diseases . The inoThe severity of the diseases was evaluated seven days after inoculation, according to the Lamari and Bernier ordinal scale, ranging from 1 to 5 . Based oThe genomic DNA was taken from 10-day-old seedlings using the cetyltrimethyl-ammonium bromide method in a 96-well plate format .The DArT genotyping, based on sequencing, followed the DArTseq™ methods . DNA was digested with the restriction enzymes PstI (5′-CTGCA|G-3′) and HpaII (5′-CCG|G-3′) and barcode adaptors were linked to individual samples.The libraries were sequenced using Illumina Novaseq 6000  in the lab of the Servicio de Análisis Genético para la Agricultura  in CIMMYT, Mexico. SNP calling was carried out with the analytical pipeline patented by DArT P/L .The SNP marker matrix was converted into the 1, 0, −1 format, where it is 1 if the individual is homozygous for the most frequent allele, 0 if it is heterozygous and −1 if it is homozygous for the least frequent allele.The SNP markers were purified when eliminating markers with more than 40% of the data missing and with a minor allele frequency below 5%. The missing data were imputed using the expectation–maximization algorithm.2) using the software META-R version 6.0 [With the data for the severity of each disease, the BLUE was calculated along with the broad-sense heritability  model, in the single-trait and multi-trait cases, with three variants: (1) using the pedigree relationship matrix (A-BLUP), (2) using the genomic relationship matrix (G-BLUP) and (3) combining the two matrices (A+G BLUP).jth wheat line, jth line derived from the calculation of the additive relationship matrix of the pedigree (coefficient of parentage A), and The simple main effects model A-BLUP  was implemented as follows:Other assumptions of the model are that the vector of random effects jth wheat line. This approach is implemented via the regression of the markers SNP jth line in the mth marker, and mth marker with the assumption that G-BLUP is similar to Model 1, except for the fact that random effect G is the genomic relationship matrix and Vector  line Lj .G is a matrix of genomic relations given by ith individual that is counted in G converge to twice the coefficient of parentage (or coancestry) between lines (A).The model A+G BLUP is constructed when joining pedigree Model 1 and genomic Model 2 as:i = 1, 2, 3, j = 1, …, n is the response of the ith disease in the jth wheat line; The multi-trait models were implemented according to Pérez and de los Campos :(4)y1y2ynxnI is the identity matrix and R is the residual variance–covariance matrix.It is assumed that The models were executed in the BGLR package version 1.1.0 in R  with 10,A 50-fold cross-validation was performed. In each fold, the data were randomly divided into two subsets, one with 70% of the genotypes (training set) and the other with 30% (testing set). In each fold of cross-validation, the mean square of the error (MSE) and the prediction accuracy were calculated, the latter understood as the Pearson coefficient correlation between the predicted and the real breeding values.p-values were fitted using the Games–Howell procedure with the ggstatsplot package version 0.11.1 in R [The statistical differences in prediction accuracy between the models were based on Welch’s F test. The 1.1 in R .To determine the impact of marker density on the prediction accuracy, random distributed marker subsets of varied sizes  were selected for comparison with the full marker collection. The accuracy for each marker density was calculated using the average of the 50-fold cross validation.SHW is confirmed to be a valuable genetic resource that can be used to improve genetic resistance to foliar diseases, particularly the three diseases targeted in this study. Models that include genomic information have a higher prediction accuracy than the traditional model, which only includes the pedigree. To improve the prediction accuracy of multi-trait models for resistance to tan spot, spot blotch and Septoria nodorum blotch, other traits must be incorporated with the severity of the diseases in question.


Oxytocin is one of the most important hormones in birth and lactation. Its importance lies in the fact that it is also involved in various bodily functions around parturition and establishing maternal behavior. We aimed to analyze all the facts involving oxytocin regulation and participation in different peripartum situations in domestic mammals. In addition, the authors examined the impact of altering normal plasma oxytocin values on maternal behavior and care, productive parameters, and imprinting.Oxytocin (OXT) is one of the essential hormones in the birth process; however, estradiol, prolactin, cortisol, relaxin, connexin, and prostaglandin are also present. In addition to parturition, the functions in which OXT is also involved in mammals include the induction of maternal behavior, including imprinting and maternal care, social cognition, and affiliative behavior, which can affect allo-parental care. The present article aimed to analyze the role of OXT and the neurophysiologic regulation of this hormone during parturition, how it can promote or impair maternal behavior and bonding, and its importance in lactation in domestic animals. Oxytocin (OXT) is a uterotonic hormone in the endocrine response during parturition . It is sCanis familiaris) [Pan troglodytes) [In mammals, several species have been evaluated to study the association between OXT concentration and maternal care , social iliaris)  and chimlodytes) ,18.The degree of interaction between parturition and maternal attachment to OXT concentrations is suggested to be related to a high density of oxytocinergic neurons in the brain, which would have a beneficial effect on the behavior of the mother in facilitating maternal recognition, protection of the offspring, and even habituation to the environment ,20,21. HThe present article aimed to analyze the role of OXT and the neurophysiologic regulation of this hormone during parturition, how it can promote or impair maternal behavior and bonding, and its importance in lactation on domestic animals, including pets and livestock.OXT is a cyclic neuropeptide of nine amino acids whose chemical structure contains a cyclic disulfide essential for its biological effect . It has From the paraventricular nucleus (PVN), where OXT is synthesized, OXT is transported to the neurohypophysis as a precursor molecule in oxytocin-neurophysin. It is later catalyzed to its active form with the activity of the enzyme monooxygenase peptidyl glycine α-amidating .OXT synthesis does not only occur at the hypothalamic level, since its synthesis has also been recorded, to a lesser extent, in the corpus luteum, ovary , and tesAdditionally, high levels of OXT mRNA receptors were observed in the vena cava, vein, and pulmonary artery, indicating specific sites of action. These findings suggest the role of OXT in regulating vasomotor tone due to its natriuretic properties. In the heart, Jankowski et al.  studied OXT secretion also occurs at the brain level in the dendrites of magnocellular neurons. These neurons express a more significant amount of protein products of the immediate early c-fos gene. This effect was reported by Douglas et al. , who useConcerning this, Ochedalski et al.  determinThe above mentioned studies show the complex functions and interactions of OXT in the neurobiology of the organism. Brunton  points oIn addition, a study on the interaction between OXT and nitric oxide (NO) has shown that the increase in NO can lead to a decrease in c-Fos expression and, consequently, inhibit OXT secretion . SrisawaTherefore, since the chemical structure of OXT is similar to AVP, it is possible to understand its participation in cardiovascular control processes and stress-related events such as parturition, known as a eustress process ,25. LikeIn all mammals, the perinatal period is characterized by various hormonal changes, including increases in plasma E2, prolactin (PRL), and cortisol, as well as activation of the oxytocinergic system at parturition ,48. ThisThere are important interrelationships between OXT and prostaglandin (PGs). OXT has been shown to stimulate PG release in many animals, primarily in the uterine epithelium/decidua. The effects of OXT are mediated by the presence of the tissue-specific OXT receptor, causing myometrium contraction and synthesis of PG in the decidua . HoweverThe OXTR increase during pregnancy in the endometrium and myometrium has been reported in various species, e.g., in humans , rats 6, sheep , bovinesp = 0.005) with no correlation to plasma E2. In contrast, the myometrial receptor concentrations showed no correlation with plasma P4 but an inverse correlation with plasma E2 (p = 0.004). Similarly, in a study by Ou et al. [In a study by Fuchs et al. , the endu et al. , the autu et al.  in ewes,u et al. .The clearest example of the participation of different hormones during parturition is the relationship between estrogen and OXT. Masoudi et al.  evaluateNevertheless, mRNA expression for OXT increased during the last week of gestation. These results demonstrated that the estrogen peak during labor would not be related to the activity of OXT on the myometrium. Fuchs et al.  maintainSome stimuli are involved in the release of OXT; the first is the Ferguson reflex, which consists of the pressure of a puppy’s head on the cervix during parturition , cervicop < 0.001) [s = 0.28) and piglet colostrum intake, potentially improving their immune system and survival. For example, in sows, low levels of OXT can increase the duration of farrowing. According to Alonso-Spilsbury et al. [It is also important to mention that levels of OXT could influence the interval of expulsion or the total parturition time, as reported in sows and bitches. In sows, low levels of OXT depend on environmental factors influencing the farrowing phase, such as being confined in crates 38.1 ± 24.6 pg/mL) rather than pens (77.6 ± 47.6 ng/mL), having a direct effect on farrowing duration (< 0.001) . This wa< 0.001)  when proy et al. , OXT is y et al. . Similary et al.  observed4.6 pg/mLy et al. ,76, or mMaternal care is a series of species-specific behavioral patterns to ensure the offspring’s survival . They inOXT is involved in the different phases of parturition and is considered an essential hormone to facilitate maternal bonds . In seveAlthough OXT is considered essential for maternal care, which includes nest-building, reluctance to leave the nest, genital and overall licking of the newborn, nursing, and direct contact with the litter  in mammaFor example, licking, nursing, and low-pitched bleats are rapidly observed (during the first minutes post-parturition) in ewes and goats. These behaviors are part of the maternal responsiveness to the hormonal cascade started by vaginocervical stimulation, OXT secretion, and activation of cerebral structures such as the main olfactory system (MOB), MPOA, and PVN . Likewisp = 0.002), PRL decreases (p = 0.005), and no change in somatostatin (p = 0.65) were reported during nursing [p < 0.01), as well as longer times in lateral recumbency after farrowing (a behavior that promotes suckling from the piglets) [Apart from OXT, PRL and somatostatin have also been studied as markers of maternal behavior in sows and ewes. In sows, blood OXT increases ( nursing . Further nursing . As for piglets) . In ewespiglets) .p < 0.05), indicating that OXT concentration has a secondary role in developing maternal traits in these species [In contrast to livestock, in non-domestic species such as prairie voles, increased plasma OXT levels were obtained in reproductively naïve male individuals after 10 min of pup exposure, activating oxytocinergic neurons in the paraventricular nucleus (PVN) of the hypothalamus . A high  species . Its rol species . Therefo species , the impSince OXT is essential to establish positive interactions with newborns, alterations or decreasing levels of this hormone have been associated with maternal aggression and cases of cannibalism. In a study made in Kangal dogs, cannibalism has been associated with low concentrations of plasma OXT (3.58 ± 0.43 pg/mL), as well as cholesterol (125.50 ± 8.6 mg/dL), high-density lipoproteins (52.00 ± 9.34 mg/dL) and low-density lipoproteins in serum (30.45 ± 3.56 mg/dL) . NegativAccording to the species in question, the results can be variable regarding the function of the OXT. Although OXT and other biomarkers such as PRL, AVP, or cortisone are used to study maternal behavior, some limitations exist , as Ogi Bonding is a process by which the dam and the offspring establish a social preference ,111. It Maternal recognition occurs in precocial and altricial animals; however, the level of autonomy at birth and their early learning ability determines the type of bond in domestic animals, wildlife, and companion animals ,120,121.OXT participates during dam–young bonding and simultaneously acts peripherally and centrally with other hormones from pregnancy to postpartum ,125. ForThe so-called sensitive period, which can last a few hours or days depending on the species, is the interval in which both the female and the offspring are more receptive to selective recognition ,126. In For mammals, olfactory recognition is one of the leading systems to establish the maternal bond, due to the interaction between the central olfactory system and OXT release by the anterior pituitary . OXT andIn this sense, Kojima et al.  observedA neuro-hormonal reflex produces milk ejection: this comprises an ascending neuronal pathway from the nipples to the hypothalamus and a descending vascular link that transports pituitary hormones, especially OXT, to the mammary gland (MG) . Milk ejOXT plays an essential role in milk ejection, and mice without OXT could not breastfeed their pups ,142. LikBos taurus with or without OXT administration. In non-lactating animals, cortisol concentrations were higher (p = 0.02), but the authors concluded that OXT administration alone could not reduce cortisol levels. In contrast, Cook [Although OXT is the main hormone associated with milk ejection, hormones such as PRL, cortisol, and the growth hormone, among others, also participate in this process ,147. In st, Cook  indicateFor species whose zootechnical purpose is milk production, milk ejection and flow begin with a pronounced increase in plasmatic OXT before milking, highlighting the large reservoir available for milk storage in the udder cistern . StudiesAn essential element during lactation is reducing stressors to achieve adequate milk ejection ,68,70. Tp < 0.05) when comparing Holstein Friesian cows without/with brushing and without/with stimulation by vibration . However, the amount of residual milk was not affected by treatment and stayed within a normal range (14.9–15.8%) [In addition to providing a comfortable environment, an adequate lactation diet during gestation and after parturition can also activate OXT neurons, as reported in Holstein cows consuming 0.67 kg of dry matter fiber per day . At the 9–15.8%) . Exposur9–15.8%) .On the other hand, exogenous administration of OXT in animal production is a common therapeutic practice to treat uterine inertia, placental retention and incomplete abortion, prevent bleeding after parturition, facilitate milk ejection and reduce milking times ,165,166.p < 0.05), more artificial insemination (AI) per conception was required, and the presentation of fetal losses increased in comparison to animals not receiving OXT [p = 0.02) than those registered in control groups . In the case of ewes, after cervical insemination in 300 animals, a practice where OXT is used to dilate the cervix, lambing rates decreased from 42–69% to 10–52%. However, in these animals, litter size was not affected by OXT treatment [Some of the reported adverse effects are changes in the physicochemical analysis of the milk and impacts on the reproductive health of females. For example, the effect of long-term administration of OXT on pregnancy rates of 23 Nili Ravi buffaloes was studied by Murtaza et al. . The aniving OXT . A similving OXT . After Areatment . Contrarreatment , it is ip < 0.05), protein (p < 0.01), energy (p < 0.05), and even higher IgA concentrations [Concerning the productive affectations in Sahiwal cattle, when groups that did not receive exogenous OXT (MG) and those with 20 IU were compared, a reduction in the percentages of fat (4.49 ± 0.11 G1 vs. 4.49 ± 0.11 G2), lactose (4.84 ± 0.04 G1 vs. 4.37 ± 0.22 G2), protein (3.75 ± 0.08 G1 vs. 3.49 ± 0.15), total solids (13.84 ± 0.20 G1 vs. 13.84 ± 0.20 G2) and non-fat solids (9.35 ± 0.11 G1 vs. 8.73 ± 0.34 G2) was found in the OXT group. In addition, it was found that the application of OXT considerably influenced the mineral profile in milk, causing negative variations in the manufacture of dairy products and by-products . Morgan trations .According to these results, the effect of OXT on milk composition, milk yield, and reproductive health of females depends on the lactation phase, parity, species, pregnancy periods, dosages, and administration routes. Therefore, these elements must be considered when assessing the advantages and disadvantages of OXT.Furthermore, if circulating OXT levels are low because the mother is in threatening or challenging environments, insufficient milk ejection results, causing inconsistencies and resulting in incomplete emptying of alveolar and cisternal fractions of MG, harming the productive levels, the health status of the MG , and the survival of the offspring ,151. ForThe possible perspectives of the study concerning OXT could be aimed at assessing the synthesis sites of OXT in tissues other than the myometrium. For example, this hormone can be synthesized in vascular tissue, where it may have a local effect, allowing regulation of the vasomotor response. However, it is still unclear if this could affect the myometrium or if it can alter the behavioral response in animals.The investigations of OXT in different anatomical regions and compounds are another research field. Recent studies in rabbits have shown that OXT antagonist administration does not impair maternal behavior ; howeverRegarding the exogenous management of OXT, although it is used to reduce expulsion times, improve milk yield and ejection, and reduce newborn mortality ,48,49, iThe influence of OXT in species where allomaternal care is present could help to understand the importance of this hormone in both domestic animal behavior and physiology.Parturition in mammals is an event coordinated mainly by OXT and its positive feedback loop to maintain constant and synchronous contractions. Although OXT is essential for the onset of parturition, other hormones such as E2, P4, and PG influence the onset of parturition and myometrial contractions.Immediately after parturition, maternal attachment and behaviors aimed at caring, nursing, and protecting the newborn are highly influenced by increases in serum, milk, and salivary OXT. In several domestic species, low levels of OXT are associated with impaired maternal fitness and delayed or deficient dam–young bonding. The neuroendocrinal management of maternal traits is a relevant topic because it is not only associated with behaviors towards the newborn but also with altruistic care for non-filial offspring, known as allo-nursing, and because neonatal survival is strictly dependent on maternal behavior.Likewise, the role of OXT in lactation and milk ejection involves the recognition of sensorial stimulus that promotes OXT release and its action on myoepithelial cells located in the alveolus. Due to its importance, exogenous OXT is common in dairy production systems. However, before continuing with its administration, the potential adverse effects must be considered. Therefore, OXT is one of the main hormones influencing physiological and behavioral traits in parturient domestic animals.