Correlation analysis and Least Absolute Shrinkage and Selection Operator were performed to address the association between morphological diversification and metabolic perturbation. We demonstrate that grafting the same variety onto different rootstocks resulted in scion phenotypic heterogeneity and emphasized the productivity efficiency of M82 irrespective of the rootstock. Spectrophotometric analysis to test lipid oxidation showed largest variability of malondialdehyde (MDA) equivalents across the population, while the least responsive trait was the ratio of fruit fresh weight to total fresh weight (FFW/TFW). Generally, grafts showed greater values for the traits measured than SG-M82, except for branch number and wild race-originated rootstocks; the latter were associated with smaller scion growth parameters. Highly responsive and correlated metabolites were identified across the graft collection including malate, citrate, and aspartate, and their variance was partly related to rootstock origin. A group of six metabolites that consistently characterized exceptional graft response was observed, consisting of sorbose, galactose, sucrose, fructose, myo-inositol, and proline. The correlation analysis and predictive modelling, integrating phenotype- and leaf metabolite data, suggest a potential predictive relation between a set of leaf metabolites and yield-related traits.Grafting has been demonstrated to significantly enhance the salt tolerance of crops. However, breeding efforts to develop enhanced graft combinations are hindered by knowledge-gaps as to how rootstocks mediate scion-response to salt stress. We grafted the scion of cultivated M82 onto rootstocks of 254 tomato accessions and explored the morphological and metabolic responses of grafts under saline conditions (EC = 20 dS m Moderatic crops . Plants ic crops –7.Considering that soil salinity poses a significant threat to agriculture, improving salt tolerance of crops and identifying the biochemical and molecular basis of salt tolerance are high-priority goals of scientific research and agricultural practices , 8, 9. HGrafting establishes a vascular continuity in a natural or deliberate fusion of plant parts and results in the genetically composite organism functioning as a single plant . CurrentGrafting in tomato has been mostly investigated in small-scale experiments, indicating the morphological –30, physMetabolomics-assisted breeding has been proposed to accelerate breeding processes . The potIn this study, we explored the effect of a collection of 254 tomato rootstock accessions on the morphological and metabolic traits of cv. M82 plants under saline soil conditions. We then tested predictive models to link the metabolic alteration mediated by the rootstocks in the plant leaves with its yield-related traits.2 FCs of each graft against SG-M82 were calculated to visualize the effect of rootstocks on scion performance under saline (NaCl) irrigation; thus, positive and negative values show the increases and decrease over control, respectively  among seven morphological traits including FFW, FDW, PFW, PDW, TFW, TDW, and FN  between TFW and FFW resulted in relatively stable FFW/TFW across the entire population, as indicated by the lowest CV value for FFW/TFW  corroborated the stability of HI across the population with a considerably low CV ; however, it was only weakly correlated with TDW , consistent with previous findings [r = 0.82, p < 0.01) across the population. Within each rootstock origin, the correlations between PFW and FFW remained strong  between the wild and the domesticated rootstocks on PC1, explaining 37.7% of total variation  and SLC groups   . Three m2 transformation  distanced from other groups with the exception of the wild group on PC1, explaining 27.5% of the total variation  and six metabolites  (With a significance threshold at hronate) . The metTo capture the predictive power of a metabolite towards yield-associated traits, we used the LASSO method . By perfInstead of using all metabolites, LASSO performed variable selection to improve predicting accuracy . This enSolanum lycopersicum) was grafted onto rootstocks of 254 accessions, representing various groups across the domestication history of tomato, consisting of wild species (Wild), Solanum pimpinellifolium (SP), Solanum lycopersicum var. cerasiforme (SLC), S. lycopersicum L. var. lycopersicum (SLL), and few other accessions (Other). Among the different grafts, the genetic background of the rootstocks used is the sole factor influencing M82 scion growth and development under saline conditions.A population of grafted tomato plants was generated in which the unitary shoot from commercial variety M82 , while FFW/TFW was the most conserved trait in the collection. FFW/TFW and HI, which represent the ability of a plant to allocate assimilated photosynthates to the harvestable product , 61, werSolanum habrochaites- and S. pimpinellifolium-derived hybrids showed a reduction in fruit biomass, but two hybrids had opposite effects on plant biomass under optimal conditions. Our study shows that rootstock-mediated phenotypic diversification is expressed differently across the measured morphological traits under non-optimal conditions of growth  or significant correlation (q < 0.05) were obtained between traits related to plant growth and energy-related metabolites  [Plants under salt stress can accumulate compatible osmolytes such as proline, sucrose, and  process , 79, 80. process . We obse sorbose , indicat sorbose , which h(LeGPAT) .Using the 54 annotated metabolites and 14 morphological traits (including MDA content) in scions across the graft population, we calculated the overall variance, correlation distribution, and implemented model prediction to reveal associations between metabolites and morphological traits. The variance-based analysis indicated the existence of three major groups of grafts  differinr| >0.3, q < 0.05, S2).The correlation analysis identified significant relations between four yield-associated traits and six metabolites (|r = 0.68), HI (r = 0.51), and FFW (r = 0.49) (p < 0.05 (The association between plant phenotype and metabolism is complex due to the intricate system(s) of different regulatory levels . Various = 0.49) , validatp < 0.05 , revealiUsually, model prediction has been performed on a collection consisting of subpopulations, for instance, from different species , generatThe concern exists for the practical application of the metabolite contributed prediction of phenotypic traits, as these profiles capture a snapshot of the highly dynamic plant metabolism system(s) that change substantially over time and conditions . The higMoreover, instead of using unknown metabolic features as predictors , 101, weIn conclusion, we have shown the phenotypic diversity of scion and metabolic perturbation in leaves, which were both modulated by rootstocks in response to saline conditions. We found highly responsive trait (MDA) and intrinsic traits (FFW/TFW and HI) of M82 across the graft population. Leaf metabolites malate, citrate, and aspartate showed to be central in the response to salinity and in the rootstock-mediated energy repartitioning between plant growth and stress defense. The indirect connections between morphology traits and metabolite content were complemented and expanded with a predictive model LASSO, which emphasized the role of metabolites in phenotype modulation. Future studies should tackle the regulatory mechanisms underlying these associations. Our results could provide new insights for further research in grafting biology in relation to abiotic stress and set the basis for metabolic marker assisted selection of rootstock mediated tolerance to salinity.S. pimpinellifolium L. (SP), 36 S. lycopersicum var. cerasiforme (SLC), 122 S. lycopersicum L. var. lycopersicum (SLL), 34 accessions which are still in processing (Other), and 17 accessions of wild species (Wild) including Solanum chmielewskii, S. habrochaties, S. huavlasense, S. peruvianum, S. corneliomuelleri, and Solanum neorickii. The shoot of commercial S. lycopersicum cv. M82 was used as a scion and grafted onto the rootstocks of the 254 accessions. The self-grafted M82 (SG-M82) was used as the reference for comparison with other grafts. For grafting, 30 day-old tomato seedlings were used to establish tomato grafts; in total 980 grafted plants consisting of 3 to 4 biological samples for each accession.A total of 254 tomato accessions were collected  includin−1. NaCl concentration was gradually increased from 0 to 200 mM over a 9-day period to avoid osmotic shock . The amount of leachate was maintained at ~20% of the irrigation solution with the aim of avoiding salt accumulation in the sand. In addition, every 7 days, pots were washed using an automatic drip irrigation system with a 2-L solution (200 mM NaCl +0.5 g/L fertilizer) to avoid salt accumulation. On a regular basis, plants were irrigated with a 0.5–1.5 L, adjusted to the developmental stage, i.e. from vegetative to mature fruiting stage [The experiment was conducted in August–October 2017 in a plastic net house, located on the Sede Boqer campus of Ben-Gurion University, Israel . Twenty days after grafting, plants were transplanted into 10-L growth pots with washed sand. Following an 18-day adaptation period, all plants were subjected to irrigation with a saline solution , yielding a final EC of 20 dS mng stage , every m−1) was determined from the total weight of red and green fruits; mean fruit weight  was calculated as FFW/FN; plant fresh weight  was determined by weighing the aboveground vegetative tissue without fruits; total fresh weight  was calculated as the sum of FFW and PFW; fruit dry weight  and plant dry weight  were obtained by drying fruits and vegetative tissue, respectively, until reaching constant weight; total dry weight  was determined as the sum of FDW and PDW; the ratio of fruit fresh weight to total fresh weight (FFW/TFW) was calculated; harvest index (HI) was calculated as FDW to TDW.Thirteen morphological traits were measured on the aboveground tissue : plant h−1 FW). To avoid variation from different measurement periods, a reference sample (SG-M82) was mixed, equally aliquoted, and arranged into each batch of measurements. Thus, the levels of MDA are presented as fold-change over SG-M82  with a setup as described previously  was usedp-value. This resulted in 100 predictabilities from 100 predictions for each trait. A permutation test was performed to assess the statistical significance of the observed predictability from the null distribution. In the permutation, the shuffled morphological data were assigned to the samples for prediction, repeated 100 times. The null distribution, formed from 10 000 permutation tests, consisting of 100-times permutation tests for each prediction, was compared against the mean of predictabilities to calculate an empirical p-value for each trait, as suggested from previous research [The Least Absolute Shrinkage and Selection Operator (LASSO)  implemenresearch .To evaluate the variation of morphologies and metabolites across the grafted population, the fold-change (FC) was calculated as the mean of biological replicates of each graft divided by that of SG-M82 under the same saline condition for each morphological trait and metabolite.2-transformed fold-changes (log2 FCs) of morphological traits and metabolites were used. For correlation analysis between tomato grafts, the mean values of morphological traits and metabolic data were normalized to the median of the population for each trait and metabolite, followed by log2-transformation, respectively. All correlation analyses were performed using the corr.test function and “Pearson” algorithm provided in the “pysch” package [corrplot function in the “corrplot” package [hclust function built in the “factoextra” package [For morphology-morphology, metabolite-metabolite, and metabolite-morphology correlation analyses, log package . Visuali package  using th package .2 FCs using the Heatmap function within the “Complexheatmap” package [t.test, Wilcox.test, and multiple comparisons using Tukey’s HSD test were used in corresponding comparisons between grafts according to the data distribution.A clustered heatmap with annotations was created based on log package . Princip package . The dia package . Statist package  and JMP®2 FC, relative to SG-M82, normalized the distance of the values of different traits  from SG-M82. To measure the dispersal of traits of each graft around the value of SG-M82, we used the modified variance measure:Variance in probability theory and statistics, measures the distance that a set of numbers is spread around the average value. The logX) = Dispersal of traits or metabolites of graft X around control.Var as part of the project The Root of the Matter: roots knowledge center for leveraging modern agriculture. We thank Dani Zamir for kindly providing us tomato seeds for this experiment. We greatly thank Dr. Kelem Gushu, Dr. Maria Dolores, Dr. Moses Kwame, Dr. Noam Reshef, Yaara Zohar, Noga Sikron, Lina Zhao, Liron Summerfield, and Junyi He for their support in the field and lab.C.S., T.A., and M.A.A carried out the experiment, plant harvesting, and leaf sampling. C.S. and T.A. were responsible for MDA estimation and metabolic extraction. C.S. performed GC–MS analysis, interpreted data, and wrote the manuscript. S.B., N.L, and A.F. designed this experiment, supervised the work, and revised the manuscript.The data and materials supporting the conclusions of this study are included in supplementary information.The authors declare no competing interest.Horticulture Research online.Web_Material_uhac061Click here for additional data file.


Duration of fertility (DF) is an important economic trait in poultry production because it has a strong effect on chick output. Various criteria or traits to assess DF on individual hens have been reported but they are affected by many nongenetic factors. Thus, a reliable definition and associated genetic parameters are needed. Because egg production is also vital in chicken breeding, knowledge of the relationship between DF and laying performance is needed for designing selection programs.We used five traits that consider both fertility and embryonic livability to delineate DF. Phenotypic and genetic analyses were completed for 2094 hens, with measurements of DF at 35 and 60 weeks of age and hatching egg production at 400 days of age (HEP400). The selection differentials for DF and HEP400 were evaluated.DF is largely independent of the number of oviposited eggs in the peak laying period but both egg production and DF naturally decline with age. The heritability of the five DF traits ranged from 0.11 to 0.13 at 35 weeks of age and increased slightly in the later laying period, ranging from 0.14 to 0.17 . Estimates of the genetic correlation for a given trait measured at the two ages were moderate (0.37–0.44), except for efficient duration. However, number of viable embryos depends strongly on egg production. Estimates of genetic correlations of fertility duration day (FDD) at both ages with HEP were weak. Selection for FDD improved DF but without a significant change in laying performance. Selection for increased HEP400 did not contribute to DF improvement.Although estimates of heritability of the five traits related to DF were low, selection to improve DF based on any one of them is possible. Among these, FDD is an effective selection criterion when the eggs are collected for approximately two weeks after insemination. The best selection procedure for DF improvement would involve multiple measurements at various ages. FDD is independent of laying performance and can be incorporated into a breeding program with egg production to improve reproductive efficiency. Chickens are the most ubiquitous of all domesticated animals worldwide. Chicken meat and eggs are not only important sources of high-quality protein for humans  but alsoFertility varies remarkably between breeds, strains, and individuals and is affected by both genetic and nongenetic factors. Except for sperm quality of the male, several physiological factors originating from the female can markedly influence egg fertilization . PreviouDistinct individual differences in measures of DF have been observed, and all of the DF variables vary in length from a few days to several weeks , 14, 15.However, several studies have revealed that genetic improvement of DF is accompanied by an increase in embryonic mortality , 12. TheAlthough previous experiments showed that genetic improvement of DF is possible, it has not yet been used on a large scale in commercial breeding programs. A major reason for this is that DF is not well defined, with values that depend on the definition of the trait , 22, 23 Egg production is also important for reproduction efficiency in poultry production. However, the genetic relationships between DF and laying performance remains to be established. Accurate phenotypic measurements and reliable genetic parameters are vital for choosing a selection criterion and enabling breeders to evaluate the potential impact that selection for DF will have. Thus, the purpose of our study was to determine which DF measurement, considering both fertility and embryonic survival, is preferred for use in chicken breeding programs. Our study also focused on estimating the genetic parameters of DF at various ages and ascertaining the genetic relationship between DF and laying performance.We used a yellow-feather chicken breed from Guangdong Wen’s Nanfang Poultry Breeding Co., Ltd. . This population is a purebred dam line and has been selected for laying performance over 25 years. This experiment included 2094 pedigreed hens from two consecutive hatches , which were separated by 12 days. The population of 2094 hens produced from 70 sires and 569 dams. All birds were placed individually in layered cages in one poultry facility and accurately identified by bar codes and cage numbers. The environmental conditions were controlled for ventilation, temperature (19 ~ 28 °C) and photoperiod (16 h light/8 h dark).Given that age has an adverse effect on the reproductive success of birds , 26, the6 viable spermatozoa per AI dose, which is higher than recommended in practice [The effect of sperm quality on DF has been clearly demonstrated by Brillard et al. . To circpractice  and shouFrom a physiological point of view, pedigreed eggs should be assessed for more than 18 days after insemination , 11, espAll eggs were classified as normal (settable eggs), cracked, soft-shell, double-yolk or misshapen. Since eggs stored more than eight days have reduced hatchability , settablHatchability was calculated as the ratio of the number of viable embryos to the number of eggs incubated. Infertile eggs and dead embryos were considered unhatched eggs. We used five traits to assess DF: (1) MD, which is defined as the number of days between AI and the final viable embryo; (2) ED, which is defined as the number of days between AI and the last viable embryo before the first unhatched egg ; MD and For each bird, hatching egg production (HEP) was recorded daily with a barcode scanner until 400 days of age. The age at first egg (AFE) and total HEP at 400 days of age (HEP400) were determined. Mortality was recorded daily. The hen-day laying rate was calculated as the ratio of the number of eggs produced per day to the number of birds in the population that day, expressed as a percentage. Dead hens (N = 53), which were excluded from the calculation of HEP400, were included in the analysis of DF and AFE.P value was less than 0.05.The DF traits measured at the two ages were viewed as different phenotypes in our study. Normality for all traits was assessed using the Shapiro–Wilk test  with thePedigree information from six generations was included in the genetic relationship matrix. Heritabilities and phenotypic and genetic correlations were estimated using the AI-REML of DMU software  combinedImproved criteria to select for DF were derived as the averages of the estimated breeding values (EBV) of a given trait at the two ages, after normalizing each EBV to have a zero mean and a variance of 1. All the hens were successively ranked by their average EBV for the various DF traits and the top 50 or 30% of individuals for each of the five traits were selected, resulting in five selected groups. For each group, the selection differential for hatchability was quantified by the difference of the population mean and the mean of the selected hen.To better understand the relationship between DF and laying performance, the population was separated into two equally-sized groups  based on their EBV of HEP during the observation period or the average EBV for DF. Statistical comparisons of DF variables were then performed between the highest and lowest HEP group with the Wilcoxon rank-sum test. In addition, statistical comparisons of HEP400 and AFE were then performed between the highest and lowest DF group with one-way analysis of variance.P < 0.01). The higher rate of laying at 35 weeks of age may explain the higher rate of decline in hatchability from days 9 to 15 for the younger flock. To prevent any undesirable effects of the number of eggs incubated on the DF analyses, hens that produced less than five eggs during collection period were excluded from the DF analyses. In total, 2044 and 1619 chickens were used in the calculation of the five DF traits at 35 and 60 weeks of age . However, ED was significantly higher at 60 weeks of age than at 35 weeks of age , which may be explained by the higher egg production rate of younger hens, resulting in a shorter period for the first unhatched egg after insemination.Descriptive statistics for all phenotypes are summarized in Table P < 0.001), although the distribution of EN was close to normal. Therefore, the DF traits were regarded as ordinal traits and generalized linear mixed models were used to estimate genetic parameters.None of the DF variables measured in our study exhibited a normal distribution  for each trait were considered as a group. Only 587 birds (56.1%) were present in the top group for all traits Fig. a. The toAs shown in Fig. We also evaluated the associations of each DF trait with HEP during the collection period. The phenotypic relationship of EN with the number of eggs incubated at 35 weeks of age was 0.70 Fig. a. A simiEstimates of genetic correlations of each DF trait with HEP are shown in Fig. P < 0.001). However, there was no obvious distinction in hatchability between the two groups at a younger age , especially for EN.To further explore the effect of the number of eggs incubated on DF, the breeder flock was separated into two equally-sized groups based on the EBV for HEP at the corresponding age. Comparisons of DF traits between the highest N = 1047) and lowest HEP EBV groups (N = 1047) are shown in Fig. 7 and lowAs noted above, FDD is one of the efficient indexes for characterizing DF. Thus, we chose FDD to further assess the relationship between DF and laying performance. Estimates of heritability for AFE and HEP400 were 0.31 and 0.22 Fig. , respectP < 0.05, χ2 test, Fig. P < 0.001). However, the hen-housed laying rate was similar for the highest FDD group and the lowest FDD group . Comparisons between the highest and lowest HEP400 groups are shown in Fig. P < 0.001). There was no obvious discrepancy in hatchability between the highest and lowest HEP400 groups at either age .To better understand the above result, all the hens were successively ranked by the average EBV for FDD or the EBV for HEP400 and the highest and lowest 50% of birds for each of the traits were selected, resulting in four selected groups. The number of birds N = 564) that were in both the highest FDD group and the highest HEP400 group was significantly larger than those (N = 483) in both the highest FDD group and the lowest HEP400 group , while MD and ED were not strongly correlated with each other. A similar result was reported by Beaumont et al.  in egg-tIn commercial selection programs, chicken breeders must consider a wide array of traits that have some economic importance to achieve the genetic gains needed for commercial success. Persistence of egg production and DF are very important in chicken breeding because these traits are beneficial for chicken reproduction efficiency and are required in both grandparental and parental stocks. Knowledge of the relationship of DF with laying performance is important for optimization of selection programs. It is widely acknowledged that egg-type chickens have a higher laying intensity and persistence than meat-type chickens but the duration of fertility of meat-type chickens  is not sOur results suggest that DF traits that take both fertility and embryonic survival into account have a low heritability in the peak laying period and a slightly higher heritability in the later laying period. The best selection procedure to increase DF includes taking multiple measurements at various ages to improve the accuracy of selection. Genetic selection to improve DF based on MD or ED is possible but may not be efficient as it has a limited effect on DF improvement. With respect to EN, which depends mainly on the actual number of eggs incubated. Another appropriate trait to select for DF in chickens is FDD, which showed a higher selection differential than TD and is independent of laying performance. Thus, selection for FDD has a limited effect on laying performance. Thus, a breeding program including FDD and egg production in a selection index may be more efficient than other programs for commercial chicken production. Our study provides valuable insights into DF traits so that poultry breeders can make informed decisions regarding the optimization of breeding programs for profit maximization.


Robustness can refer to an animal’s ability to overcome perturbations. Intense selection for production traits in livestock has resulted in reduced robustness which has negative implications for livability as well as production. There is increasing emphasis on improving robustness through poultry breeding, which may involve identifying novel phenotypes that could be used in selection strategies. The hypothalamic–pituitary–adrenal (HPA) axis and associated hormones  participate in many metabolic processes that are related to robustness. Corticosterone can be measured non-invasively in feathers (FCORT) and reflects the average HPA axis activity over the feather growing period, however measurement is expensive and time consuming. Fault bars are visible feather deformities that may be related to HPA axis activity and may be a more feasible indicator trait. In this study, we estimated variance components for FCORT and fault bars in a population of purebred turkeys as well as their genetic and partial phenotypic correlations with other economically relevant traits including growth and efficiency, carcass yield, and meat quality. The estimated heritability for FCORT was 0.21 ± 0.07 and for the fault bar traits  estimates ranged from 0.09 to 0.24. The genetic correlation of FCORT with breast weight, breast meat yield, fillet weight, and ultimate pH were estimated at −0.34 ± 0.21, −0.45 ± 0.23, −0.33 ± 0.24, and 0.32 ± 0.24, respectively. The phenotypic correlations of FCORT with breast weight, breast meat yield, fillet weight, drum weight, and walking ability were −0.16, −0.23, −0.18, 0.17, and 0.21, respectively. Some fault bar traits showed similar genetic correlations with breast weight, breast meat yield, and walking ability but the magnitude was lower than those with FCORT. While the dataset is limited and results should be interpreted with caution, this study indicates that selection for traits related to HPA axis activity is possible in domestic turkeys. Further research should focus on investigating the association of these traits with other robustness-related traits and how to potentially implement these traits in turkey breeding. Robustness is a critical issue in poultry breeding because of its implications for profitability, health, and wellbeing2. Intense selection for production traits now has a well-established relationship with reduced robustness3. This trade-off between robustness and production is described in the “resource allocation theory” which states that the resources of an individual are limited, and allocation of these resources should optimize the individual’s fitness in its environment4. Breeding for increased production  is believed to compromise robustness and increase behavioural, physiological, and immunological disorders2. Traditionally, changes in housing and management are used to try and reduce these problems2, but strategies to improve poultry robustness through breeding have also been suggested. The most common strategies include reaction norm analyses5 or direct selection for robustness-related traits like disease resistance6 or leg health7.In livestock production, robustness can be defined as an animal’s ability to maintain production while being resilient to perturbations allowing for high production in many different environments9. The HPA axis is connected to robustness because of its pivotal role in regulating energy balance  and the neuroendocrine stress response11. In poultry, higher HPA axis activity has been demonstrated in genetic lines that are more resistant to disease/parasites12 and more resilient to environmental challenges13.Domestication and intense selection for production traits in livestock tends to result in reduced HPA axis activity, meaning lower levels of glucocorticoids are produced. Over the past few years, there has been a shift in how we interpret glucocorticoid levels in animals; specifically, challenging the assumption that low glucocorticoid levels indicate an animal that is healthy or doing well14. Low HPA axis activity has been associated with reduced disease and parasite resistance16, reduced early survival17, poorer adaptation to environmental challenges18, and poorer confirmation/leg soundness19. Therefore, it may be possible to improve robustness by selecting for increased HPA axis activity since individuals with higher HPA axis activity may be more resilient to the aforementioned challenges8.Another strategy to incorporate robustness into animal breeding is to investigate the genetics of the hypothalamic–pituitary–adrenal (HPA) axis20. In birds, the predominant glucocorticoid hormone is corticosterone (CORT)20 which can be measured in plasma offering an acute perspective on the circulating level of corticosterone. A more longitudinal and retrospective measure of HPA axis activity is feather corticosterone (FCORT)21 which benefits from minimally-invasive collection and represents a long period of time within a sample23. Another accessible and cost-effective phenotype might be the use of feather fault bars. Fault bars are visible feather deformities that run perpendicular to the rachis and are thought to be related to perturbations such as predators or nutritional deficiencies25. Although the development of these fault bars is typically attributed to acute perturbations, the incidence of fault bars has been shown to be positively correlated with FCORT27. There may be a genetic component that dictates the propensity for certain individuals to develop fault bars28.HPA axis activity is commonly quantified by measuring end-products such as glucocorticoid hormones30. Heritability of FCORT or fault bars has yet to be estimated; however, several estimates exist for plasma corticosterone in various domestic and wild bird species. Heritability of plasma corticosterone were estimated for bird species including turkeys31, laying hens32, quails33, zebra finches34, and owls35 (range: 0.05–0.30), suggesting that selection for this trait is possible. However, it should be noted that only one of these studies referred to turkeys and this study was conducted almost 50 years ago. The continued genetic selection over the past decade may have changed heritability of corticosterone, and to date, there are a lack of heritability estimates for fault bars in any avian species.Previously, we showed that FCORT levels differ between different turkey genetic lines which may indicate underlying genetic differences37. Due to the widespread influence of the HPA axis on many physiological processes, it is conceivable that FCORT and/or fault bars may have favourable or unfavourable relationships with traits such as yield, efficiency, and meat quality39. While unfavourable relationships between HPA axis activity and production have been well described in other species, there is less known about this relationship in domestic turkeys.While these novel phenotypes related to HPA axis activity may be a possibility for improving robustness, correlations with economically important traits must be considered40.The objective of the present study was to estimate heritability of FCORT and fault bars using data collected from 1131 male turkeys from three purebred genetic lines. Additionally, we described the genetic and phenotypic correlations of FCORT and fault bars with economically important traits in turkey production related to growth and efficiency, carcass yield, and meat quality. This study was conducted as part of a larger project to develop novel welfare phenotypes in domestic turkey breedingFeathers were collected from 1131 turkeys from three different genetic lines . Due to the observational design of the study, not all birds have records for all growth and efficiency, carcass, and meat quality traits, and the number varies depending on the genetic line. A complete breakdown of the distribution of observations among the genetic lines can be found in Supplementary Table Across all lines, FCORT ranged from 0.14 to 4.16 pg/mg (N = 1131). The average ± standard deviation (SD) for FCORT across all individuals was 1.24 ± 0.55 pg/mg. The percentage of birds exhibiting fault bars (fault bar presence) was approximately 48% (N = 523) and the remaining 52% of birds did not exhibit any fault bars (N = 562). In birds that exhibited fault bars, the incidence of fault bars (FB incidence) ranged from 1 to 12 (mean ± SD = 1.9 ± 1.3). For FB severity, the majority of birds affected with fault bars received a score 2 , followed by score 4 , and lastly score 3 .41. The reported heritability estimates in this study are from the univariate models for each trait.Heritability estimates for the feather traits are presented in Table p = −0.11 to −0.14). Genetic correlations between FCORT and the fault bar traits varied from -0.07 to 0.39 with large standard errors. The other fault bar traits showed moderate to high partial phenotypic and genetic correlations among them. The phenotypic correlations ranged from 0.48 to 0.87 and the genetic correlations ranged from 0.68 to 1.00 (SE range: 0.17–0.63).Among the feather traits, FCORT had a significant partial phenotypic correlation with fault bar index  associated with stress and energy balance in domestic turkeys. The genetic and phenotypic relationships among these novel traits, production traits, carcass traits, and meat quality traits were evaluated to inform their potential for inclusion in breeding program.2 = 0.05)32, quails (h2 = 0.05–0.30)33, zebra finches (h2 = 0.04–0.27)34 after a restraint stressor was applied. More specific heritability estimates for turkey lines selected for their corticosterone response to cold stress (high vs. low response), were estimated to be between 0.14 ± 0.09 and 0.25 ± 0.4831. In general, stress-induced plasma corticosterone levels align better with FCORT compared to basal plasma corticosterone levels22. This may explain why these reported heritabilities aligned closely with the heritability estimate for FCORT in the present study (0.21 ± 0.07). This moderate heritability estimate is consistent with expectations based on our previous finding that FCORT concentrations were different between purebred turkey lines housed under identical conditions, indicating some contribution of genetics to this trait29. FCORT is believed to represent the average circulating level of corticosterone in the plasma over the period of feather growth, which could explain the similarity in heritability estimates between other studies for plasma corticosterone and FCORT in the present study34. In general, it is found that stress-induced plasma corticosterone levels align better with FCORT compared to basal plasma corticosterone levels22. Since the mentioned heritability estimates for plasma corticosterone were based on stress-induced levels (restraint or temperature), this may explain why they aligned closely with the heritability estimate for FCORT in the present study.Although there are no published studies of heritability estimates for FCORT, the results for this trait were similar to estimates from plasma corticosterone traits measured in laying hens , however, there was a significant influence of the nest of origin42. Since fault bars are believed to be caused by acute perturbations (resulting in stimulation of the HPA axis), we would expect that the incidence and severity of these bars would be positively phenotypically and genetically correlated to the FCORT concentration in the feather. Other studies of different bird species have demonstrated that FCORT is elevated in feathers that contain fault bars compared to those that do not  and that exposure to stressors increases the number of fault bars . Phenotypically, the strongest correlation was found between FCORT and fault bar index. This negative correlation suggests that higher FCORT is associated with fewer or less severe fault bars, contrary to expectations, although this correlation was still weak (-0.16). As discussed by Ganz et al.43 the keratin structure of the feather is what houses the corticosterone and so feathers with more keratin loss (from more severe fault bars) may actually have less measurable FCORT simply due to the keratin loss/breakage. However, other studies have found no phenotypic correlations between FCORT and fault bars44. Potentially, differences in how fault bars traits are assessed, scored and integrated  could have played a role.Before applying selection for novel traits in breeding programs, it is crucial to describe possible correlated responses13. This suggests that traditional lines are more robust or resilient than genetically selected lines, meaning that production potential is not/minimally compromised by perturbations 3. Faster growing lines of turkeys have been observed to be more sensitive to perturbations compared to smaller, slower-growing lines in response to e.g., transport stress45 or an E. coli challenge as indicated by greater body weight losses or mortality46. The trade-off between productivity and robustness is explained by resource allocation theory4. In brief, the energetic resources of an individual are limited, and selection for increased production allocates more resources for production traits , which leaves fewer resources for functional traits related to robustness or resilience47. Moreover, the primary role of glucocorticoids like corticosterone is to mobilize energy through protein catabolism38. Since high levels of protein catabolism would be detrimental to muscle growth and meat yield, selection for production will favor protein anabolism48. Therefore, it is logical that higher meat yields would be unfavorably correlated with FCORT. This relationship was also found in several experimental studies where elevated corticosterone levels result in decreased meat yield of poultry50 and in theunfavourable phenotypic correlations observed between FCORT and breast yield traits (−0.23 to −0.17) in the present study. Finally, there were significant positive partial phenotypic correlations between FCORT and drum weight and walking. This indicates that birds with higher FCORT have better walking ability and leg conformation, perhaps due to more proportioned/heavier drums, compared to birds with low FCORT. This may relate to selection for rapid growth and heavier breast weights resulting in structural problems in the legs and poor posture not suited for walking or standing12. However, these findings were not replicated in the genetic correlations and there is a need for further research with a larger sample size to test this hypothesis further.Moderate genetic correlations were found between FCORT and total breast weight, breast meat yield, and fillet weight (−0.33 to −0.45). This relationship may be due to the high selection pressure to increase meat yield in domestic turkeys and the integral role that the HPA axis plays in growth and metabolism. Modern, more productive, poultry lines tend to have lower HPA axis activity compared to traditional lines or wild relatives52. Glucocorticoids, like corticosterone, can mobilize energy by glycogenolysis i.e., breaking down muscle glycogen into glucose20. With high glucocorticoid levels and thus more glycogenolysis, there would be lower levels of glycogen available in the muscle before slaughter to be broken down into lactic acid. Consequently, meat pH would not decrease as much51 explaining the positive correlation between FCORT and ultimate pH. This relationship could be a desirable consequence of selection for higher FCORT as it may help reduce the prevalence quality defects such as pale, soft, and exudative (PSE) meat55 which is associated with low meat pH. The correlation between FCORT and ultimate pH may also be influenced by the negative relationship between FCORT and breast yield where larger breast yield is associated with decreased pH and water-holding capacity.There was also a positive genetic correlation between FCORT and ultimate pH. Ultimate pH is influenced by the amount of muscle glycogen which is broken down into lactic acid post-mortem and decreases the muscle pH37.Overall, this study presents initial heritability estimates and genetic and phenotypic correlation estimates for novel feather traits related to HPA axis activity. It should be reiterated that the sample size for this type of analysis was small (~ 1000 birds), and the results presented should be interpreted with caution. Moreover, the studied population included combined data from three purebred lines with line as a model effect to maximize the potential of our data and have more confidence in our estimates. When large enough sample sizes are available, future research should consider analyzing genetic line separately. In addition, structural equation models may be used to capture cause-and-effect mechanisms among the genetically correlated traits to improve our estimates and provide insights into how these estimates could be efficiently included in a turkey breeding program56 and so FCORT is an intermediate trait but what this intermediate optimum would be is yet to be determined. Additionally, before novel traits can be included it is important to understand the correlations with already existing traits59. This study is, to the best of our knowledge, the first to investigate these relationships among FCORT, fault bar traits, and economically important traits in turkeys providing initial indications of a relationship between FCORT and production traits that may indicate robustness. However, it is important to further elucidate the relationship between the HPA axis and robustness by evaluating more robustness traits such as survival and confirmation as this is a complex concept and the number of traits in the current study is limited. Indeed, even glucocorticoid hormones like corticosterone are just one part of the HPA axis but there could be variation and opportunity for targeting other aspects of the system. For example, the responsiveness to adrenocorticotropic hormone (ACTH)31 or corticosterone carrying capacity that may vary between individuals and influence the biologically active “free” hormone fraction61.To be included in a breeding program, high quality phenotypes are needed that accurately reflect the heritable trait and can be reliably and cost-effectively measured in large numbers of animals. The heritability estimates for the novel HPA axis traits presented in this study suggests it is possible to select for FCORT and/or fault bars, however, there are several challenges that need to be addressed before their inclusion in breeding programs under industrial conditions. Most importantly, further investigation is needed to determine what our ‘ideal’ phenotype is for these novel traits. Glucocorticoid levels can be detrimental when they are too low and when they are too high From a practical point of view, measuring FCORT is time-consuming and relatively expensive and thus may not be feasible on a large scale. It might be more realistic from an industry perspective to use blood samples since these are collected routinely by breeding companies for genotyping, however, this measure is more sensitive to fluctuations and thus comes with its own challenges. Fault bars were included in this study as a non-invasive, inexpensive proxy that could be collected by human observers after initial training as similarly done for traits like walking score, breast conformation, or footpad dermatitis. Evaluating the presence versus absence of feather fault bars might be the most reliable and feasible of the fault bar traits since it does not require distinguishing between categories or spending time counting fault bars on each feather. These benefits would make FB fault bar presence the most logical proxy for FCORT but further validation, and potential alternative/automated methodologies for measurement, would be required. Although, based on the results of this study, the connection between FCORT and fault bars remains somewhat unclear with no genetic correlations between them which suggest fault bars would not be a good proxy. However, a more detailed understanding of fault bar development and different trait definitions would result in a different outcome.Finally, once these above-mentioned issues are resolved, uptake of these traits in industrial breeding programs relies on clear communication and cost–benefit analysis ensuring a balanced breeding goal considering the relationships with existing traits. There needs to be a clear communication of the costs and potential benefits throughout the lifecycle of the bird. This is especially important because of some of the unfavourable correlations between FCORT/fault bars and highly economically important traits like breast meat yield. It will be necessary to demonstrate that selection for i.e., FCORT will not reduce gains in breast traits, or that the benefits from selecting for FCORT  will outweigh any reduction to genetic progress in yield. Nevertheless, the results from this study represent an essential first step to identifying novel traits related to HPA axis activity that could potentially be used in turkey breeding strategies to improve robustness.62. Line A was a dam-line selected for body weight and reproductive traits. Line B was a dam line selected for reproductive traits. Line C was a sire line selected for growth, yield, and feed efficiency. During processing, birds were electrically stunned and exsanguinated. Birds were then scalded, defeathered, and eviscerated before moving to a water chiller (2 h) and kept refrigerated for 24 h. After this, birds were deboned, and further meat quality measurements were taken. All protocols complied with the Canadian Council on Animal Care (CCAC) guidelines and were approved by the University of Guelph Animal Care Committee (AUP 3782). The study was conducted in accordance with relevant guidelines and regulations as well as the ARRIVE guidelines63.Data were collected from male turkeys (N = 1131) from three genetic lines  between May and November 2019. Turkeys were raised under identical housing and management conditions and processed at a commercial poultry processing facility in Ontario, Canada29. A unique bird ID was recorded during feather collection to connect the feather with the other trait measurements. Feathers were rinsed briefly with ultrapure water to remove any debris that may obscure fault bars and/or influence FCORT, then allowed to dry overnight before being stored in paper envelopes until fault bar scoring and FCORT extraction.At the processing plant, a feather (primary 9) was taken from each bird to measure FCORT after stunning and before scalding. Primary 9 was chosen for ease of identification and was previously validated to measure FCORT in domesticated turkeys using an ELISA kit24 where 1 = no fault bars present, 2 = mild, 3 = moderate, 4 = severe . If fault bars were present, then fault bar incidence and severity were also recorded. Fault bar incidence was recorded as the number of fault bars present on each feather . Each feather was then scored on a 1–4 scale adapted from Sarasola and Jovani29. In brief, feather samples were minced with dissecting scissors and mechanically homogenized using a bead mill . Feather powder was weighed using an analytical balance . The powder was weighed into a test tube and 5 ml of methanol was added and sonicated in a water bath for 30 min. Samples were then moved to a shaking incubator at 50 °C for 12 h. After incubation, samples were vacuum filtered using Whatman #4 filter paper to separate the feather powder and methanol extract. During this process, the test tubes were rinsed with an additional 2 ml of methanol which was added to the extracted methanol . The methanol extract was evaporated using an evaporation plate under nitrogen gas at 40°. The extract residues were reconstituted in 0.5 ml of assay buffer immediately before being assayed. Samples were run in duplicate across 36 ELISA plates. The intra- and inter-assay coefficients of variation (CV) were 5.6% and 8.0%, respectively.Feather samples were processed and analyzed using an ELISA  as described in Leishman et al.64. Feed conversion ratio (FCR) was calculated as total feed intake divided by weight gain64 using a real-time automated system for recording individual feed intake65.Body weight was measured at 12 weeks of age (BW12) and two days before slaughter . Walking score (WS20) was evaluated by trained observers at 20 weeks of age on a 1–6 scale where a higher score indicated better walking ability and leg conformation41. For these select carcasses, fillets , tenders , thighs , and drums  were individually weighed, however a total breast meat weight  was estimated by adding the fillet and tender weight together. All remaining carcasses were processed separately, and only the BreastTotal was measured. Finally, breast meat yield  was calculated as BreastTotal divided BW20.Carcass component weights were recorded using a scale at 24 h post-mortem. A proportion of carcasses were randomly selected and cut up into the major components as part of another projectultimate Portable pH meter, Hanna Instruments, Woonsocket, RI, USA). Trichromatic coordinates  were measured on the skinless breast fillet using a colorimeter with D50 illumination .Meat quality measurements included pH, color, and physiochemical characteristics. At 24 h post-mortem, a pH measurement was taken from the cranial portion of the breast (pH66.At 24 h post-mortem (after weighing), two samples were taken from the breast fillet: a 13 ± 1 g sample to measure drip loss (DL) and a 200 g sample (approx. dimensions 7.5 × 7.5 × 4.5 cm) for measuring cooking loss (CL) and shear force (SF). Drip loss was calculated as the percent difference between the initial weight of the raw sample (13 ± 1 g) and final weight of the raw sample after refrigeration at 4 °C for 72 h. Cooking loss was calculated as the percent difference between the initial weight of the raw sample and final weight after cooking at 350 °C until an internal temperature of 72 °C was recorded using a thermocouple. The cooked samples were then refrigerated for 24 h. After allowing the samples to reach room temperature, SF was evaluated at six locations on the sample using a texture analyser  equipped with a Meullenet-Owens Razor Shear (MORS) bladey is the vector of observations sorted within animals; b is a vector including the fixed effects of hatch week-year (20 levels), age at slaughter (20–24 weeks), genetic line , and initial sample weight ; a is a vector of additive genetic effects , distributed as a ~ N, where A is the numerator relationship matrix including the inbreeding coefficients and R is the additive genetic variance–covariance matrix between the traits; e is the vector of residual effects which has a distribution of iy indicates a mixmi matrix corresponding to the traits that were present for animal i, and mi is the number of traits present for animal I; X and Z are incidence matrices for the fixed and random effects, respectively. The variance components were estimated using a restricted maximum likelihood method implemented in the BLUPF90 family of programs67.Univariate and bivariate linear animal models were used to estimate variance components as follows:P) coefficients were calculated using SAS  to account for the fixed effects included in the linear animal model shown above. Significant partial phenotypic correlations were determined based on the P value (P < 0.05).The partial phenotypic correlation (rSupplementary Information 1.Supplementary Information 2.


Phaseolus vulgaris L.) in China. Genomic prediction (GP) is a breeding method that uses whole-genome molecular markers to calculate the genomic estimated breeding value (GEBV) of candidate materials and select breeding materials. This study aimed to use genomic prediction to evaluate 15 traits in a collection of 628 common bean lines (including 484 landraces and 144 breeding lines) to determine a common bean GP model. The GP model constructed by landraces showed a moderate to high predictive ability (ranging from 0.59–0.88). Using all landraces as a training set, the predictive ability of the GP model for most traits was higher than that using the landraces from each of two subgene pools, respectively. Randomly selecting breeding lines as additional training sets together with landrace training sets to predict the remaining breeding lines resulted in a higher predictive ability based on principal components analysis. This study constructed a widely applicable GP model of the common bean based on the population structure, and encouraged the development of GP models to quickly aggregate excellent traits and accelerate utilization of germplasm resources.Due to insufficient identification and in-depth investigation of existing common bean germplasm resources, it is difficult for breeders to utilize these valuable genetic resources. This situation limits the breeding and industrial development of the common bean ( Phaseolus vulgaris L.) is an edible legume with the widest and largest cultivation area in the world [The common bean   has beenIn this study, we optimized the model’s precision to predict breeding lines by exploring the composition of the training set in the GP of the common bean . FifteenFifteen traits were used to evaluate the model’s predictive ability among 484 common bean landraces from the germplasm collection at the Chinese Academy of Agriculture Sciences (CAAS). We performed cross-validation using the ridge regression best linear unbiased prediction (RR-BLUP) model for landraces from two common bean gene pools, respectively. Multiple gradients of population size and number of SNPs were designed to examine the predictive ability of the combined landraces for multiple traits. This study found that the predictive ability of the landraces from the two gene pools differed greatly in different traits (0.35–0.82) , and thaWe designed a pattern of landrace predicting breeding lines based on two gene pools (An and M) of the common bean. The ability of landraces to predict breeding lines ranged from 0.54 to 0.82, except for two traits that were more affected by photoperiod, DF, and DM  . A few dscenario 1 investigated the impact of adding breeding lines as the training set on the predictive ability of the two gene pools. In the prediction using 50% Andean gene pool breeding lines (A2) as the testing set, the predictive ability of the model was improved in all traits to varying degrees compared to when landraces and 50% breeding lines (A1 or A1 + M1) were used as the training set. Adding 50% breeding lines (M1 or A1 + M1) to the prediction using 50% Mesoamerican gene pool breeding lines (M2) as the testing set also improved the model’s precision. However, the addition of breeding lines was more stable for improving the Andean model’s precision  extracted from the subgene pool as the additional training set had a more stable and improved predictive ability of the model, especially for the traits PL (+0.12) and PW (+0.25)  into components explaining most of the genetic variations. The first three axes of PCA explained 63.23% of the variability in the entire collection. The two gene pools of the common bean, Andean and Mesoamerican, were undoubtedly distinguished as two clusters . Based orecision . Then, s (+0.25) .Landraces are accessions with rich genetic diversity and are widely used in the genomic prediction of crop germplasms. Moderate to high prediction abilities were obtained for accessions such as wheat ,31, maizscenario 1 indicated that we artificially increased the relevance between the training sets and the testing sets by adding a part of the breeding lines as the additional training sets. The GP model predicted the remaining breeding lines more accurately. In the Andean gene pool, compared to G1, the prediction abilities of G2 and G3 were improved, especially in GW  and SW . In the Mesoamerican gene pool, the prediction abilities of G5 and G6 were improved for traits such as DF  and SNN  compared to that of G4  collection determined and evaluated by Wu et al. , comprishttps://github.com/vcftools/vcftools, accessed 2 March 2021), the sample function in R version 4.0.2 (https://www.r-project.org/) was used to sample 11 chromosomes according to the proportion of SNPs, and 9781 SNP markers were obtained.Genotyping data for 4.8 million SNPs distributed over all 11 chromosomes published in an article by Wu et al.  were usePhenotypic data for 15 traits in the common bean dataset from the published article by Wu et al.  were anarrBLUP”. The rrBLUP package [N × 1 vector of observed phenotypes (N is the size of the training set), N × M genotypic matrix for the random effects M is the number of SNPs), and The genomic estimated breeding values (GEBVs) were calculated using the ridge regression best linear unbiased prediction (RR-BLUP) of the GP model. The model assumes that the effects of all SNPs have a common variance, and a stable predictive ability could be obtained in most traits , which is implemented in the R package “ package  can solvr) between the estimated and observed values in the testing set [r-value is to 1, the higher the model’s predictive ability. The figures and tables in the article were generated using the R package ggplot2  and Microsoft Excel 2019.In this study, we conducted three GP analyses based on this model. (1) Seven different population sizes  and eleven different numbers of SNPs  were used in a ten-fold cross-validation with 100 replications for 15 agronomic traits in 484 common bean landraces. (2) Combining gene pool prediction: landraces (An + M) were used as the training set to predict breeding lines (An) and breeding lines (M). Assigning gene pool prediction: landraces (An) were used as the training set to predict breeding lines (An), and landraces (M) were used as the training set to predict breeding lines (M). (3) Two scenarios were designed: adding 50% breeding lines in different gene pools as additional training sets to predict the remaining 50% of breeding lines in each gene pool, selecting by gene pool; or randomly selecting the added breeding lines to predict the remaining individuals. The predictive ability was calculated using the mean of Pearson’s correlation coefficient (ting set . The cloThis study used existing common bean germplasm resources to perform genomic prediction in multiple traits. The research showed the following. (1) The number of markers and the size of the training population required to build a GP model for different traits of the common bean differ according to their heritability levels. For traits with high heritability, the number of markers required by the GP model and the size of the training population was smaller than those with low heritability. (2) Using the common bean as an example, the construction of the GP model for germplasm resource prediction using landraces and the optimization of the training population were analyzed. The main result was that adding a part of the breeding lines as an additional training set could improve the predictive ability of the GP model built by landraces on the remaining breeding lines. By analyzing the population structure of all accessions, the training population was optimized and the traits were predicted more accurately. This investigation provides an argument for mining and analyzing germplasm resources through GP for crops with a population structure similar to that of the common bean and provides a reference for germplasm selection for breeders.


With the continued deluge of results from genome-wide association and functional genomic studies, it has become increasingly imperative to quickly combine and visualize different layers of genetic and genomic data within a given locus to facilitate exploratory and integrative data analyses. While several tools have been developed to visualize locus-level genetic results, the limited speed, scalability and flexibility of current approaches remain a significant bottleneck. Here, we present a Julia package for high-performance genetics and genomics-related data visualization that enables fast, simultaneous plotting of hundreds of association results along with multiple relevant genomic annotations. Leveraging the powerful plotting and layout utilities from Makie.jl facilitates the customization and extensibility of every component of a plot, enabling generation of publication-ready figures.https://github.com/mmkim1210/GeneticsMakie.jl). The GitHub repository contains installation instructions as well as examples and documentation for built-in functions.The GeneticsMakie.jl package is open source and distributed under the MIT license via GitHub (Bioinformatics online. The last decade has seen an exponential increase in the volume of large-scale genetic association results such as those from genome-wide association studies (GWAS) and phenome-wide association studies. The rapid advancements in high-throughput sequencing technologies have further led to an increase in the volume and diversity of molecular genomic readouts, such as 3D genome contacts and chromatin immunoprecipitation sequencing (ChIP-seq). As these efforts continue to scale, it is becoming increasingly critical to develop efficient ways for simultaneous visualization and integration of multiple such datasets to develop an intuitive understanding of potential underlying biological relationships.Several tools have been developed to visualize genetic association results within a specific locus along with corresponding genomic annotations, exemplified by the original ‘LocusZoom’ style plots  genes so that one can iterate through either GWAS loci or their cognate genes. To color SNPs by LD with a designated SNP of interest (e.g. index or sentinel SNP), any custom reference panel can be loaded using SnpArrays.jl package (https://github.com/mmkim1210/GeneticsMakieExamples.Here, we present the Julia package GeneticsMakie.jl, which builds upon and extends Makie.jl’s plotting tools to generate publication-quality figures visualizing multiple genetic and genomic data modalities on different layers, as shown for the KR locus . To furtTo further showcase the power of GeneticsMakie.jl, we share phenome-wide LocusZoom plots for 239 GWAS loci for schizophrenia (In summary, GeneticsMakie.jl allows scalable and flexible visual display of high-dimensional genetic and genomic data within the Julia ecosystem. It produces high-quality, publication-ready figures by default. As the volume and diversity of molecular readouts as well as genetic association results continue to increase, trans-phenotype, trans-tissue, trans-cell-type, trans-ethnic and trans-omic analyses that consider multiple layers of data will become ever more important. We envision GeneticsMakie.jl facilitating these types of multivariate analyses by enabling fast and seamless data visualization within the larger Julia data science and OpenMendel ecosystems (btac786_Supplementary_DataClick here for additional data file.


Challenged by the sugarcane genome’s complexity and the sucrose yield’s quantitative nature, phenotypic selection is still the most commonly used approach for high-sucrose yield sugarcane breeding. In this study, a hybrid F1 population containing 135 hybrids was constructed and evaluated for 11 traits (sucrose yield (SY) and its related traits) in a randomized complete-block design during two consecutive growing seasons. The results revealed that all the traits exhibited distinct variation, with the coefficient of variation (CV) ranging from 0.09 to 0.35, the Shannon-Wiener diversity index (H′) ranging between 2.64 and 2.98, and the broad-sense heritability ranging from 0.75 to 0.84. Correlation analysis revealed complex correlations between the traits, with 30 trait pairs being significantly correlated. Eight traits, including stalk number (SN), stalk diameter (SD), internode length (IL), stalk height (SH), stalk weight (SW), Brix (B), sucrose content (SC), and yield (Y), were significantly positively correlated with sucrose yield (SY). Cluster analysis based on the 11 traits divided the 135 F1 hybrids into three groups, with 55 hybrids in Group I, 69 hybrids in Group II, and 11 hybrids in Group III. The principal component analysis indicated that the values of the first four major components’ vectors were greater than 1 and the cumulative contribution rate reached 80.93%. Based on the main component values of all samples, 24 F1 genotypes had greater values than the high-yielding parent ‘ROC22’ and were selected for the next breeding stage. A rapid sucrose yield estimation equation was established using four easily measured sucrose yield-related traits through multivariable linear stepwise regression. The model was subsequently confirmed using 26 sugarcane cultivars and 24 F1 hybrids. This study concludes that the sugarcane F1 population holds great genetic diversity in sucrose yield-related traits. The sucrose yield estimation model, Sugarcane is the major sugar-producing crop worldwide, and hybrid F It accounts for 85–90% sugar production in China and is mainly distributed in Guangxi, Yunnan, and Guangdong provinces ,2. Until.99 t/ha . With Ch.99 t/ha . To meetSaccharum officinarum and S. spontaneum and may even contain chromosome from other related genera, such as Erianthus arundinaceus, S. robustum, et al. [However, the complex sugarcane genome, the laborious breeding technologies, and large number of phenotypic traits considerably restrict the efficiency of genetic improvement ,7. Moder, et al. ,9,10,11., et al. ,13,14. B, et al. ,16,17,181 progenies; (ii) selection of the individuals with desirable traits from the seedling nursery; (iii) selection of excellent breeding lines; (iv) line evaluation over several harvests in multi-environment trials; (v) a variety of regional tests. The breeding process is expensive and laborious, and 12 years are approximately required to obtain one or a few cultivars [1 progenies is about 0.6–1 million seedlings each year in China, which further increase the difficulty and the cost of sugarcane breeding.In the conventional sugarcane breeding program, a sugarcane cultivar is released after the following stages: (i) crossing between elite parental accessions to generate hundreds of thousands of Fultivars ,19,20,211 population and select the desirable individuals [Systematic phenotypic characterization is still essential in sugarcane genetic improvement . Phenotyividuals . If the 1 population and systematically investigated the phenotypes of 11 sucrose yield-related traits in hybrid F1 populations derived from the elite parents ‘ROC22’ and ‘Yacheng 9446’ in 2020 to 2021. Diversity, correlation, cluster, and principal component analysis were conducted to evaluate the F1 population’s phenotypic traits and understand their genetic control. Multivariable linear stepwise regression analysis was applied to mine key sucrose yield composition traits and establish a rapid sugar yield estimation model to access the breeding process. This study lays the foundation for understanding the genetic characteristics of sucrose yield composition traits and thus aiding the breeding of high-yield sugarcane varieties.In this study, we constructed an F1 hybrids were detected. Finally, after removing 36 seedlings that died of disease, we constructed an F1 population with 135 progenies, and this population was used for the genetic diversity analysis.Primer combinations were used to genotype the parental lines and identify the hybrid plants. In this study, 3 SSR markers   were det1 population. On the other hand, the traits’ stalk diameter (SD), internode length (IL), Brix (B), and sucrose content (SC) exhibited a relatively small coefficient of variation (CV < 0.15), with Brix (B) having the minimum coefficient of variation (0.09), indicating that these traits were relatively stable in the population. The skewness and the kurtosis ranged from −0.09 to 1.86 and from −0.20 to 5.57, respectively. The Shannon-Wiener diversity index (H′) of the traits evaluated ranged between 2.64 and 2.98, indicating abundant phenotypic diversity. The broad-sense heritability of the traits ranged from 0.75 to 0.84. The high heritability indicated that genetic factors played a predominant role in determining trait variation. According to the variance analysis, the environment also significantly influenced the traits (p < 0.001). The trait frequency distribution was partial or bimodal, as shown in The trait name, abbreviations, units, and descriptive statistics of the 11 traits are shown in p < 0.001, 7 pairs were significant at p < 0.01, and 6 pairs were significant at p < 0.05 , corresponding to 17.8% of the F1 population, with 13 genotypes in Group I and 11 in Group II , stalk height (SH), stalk diameter (SD), stalk number (SN), internode length (IL), stalk weight (SW), Brix (B), stalk weight (SW), and sucrose content (SC), were used as independent variables. Linear regression analysis indicated that sucrose yield (SY) was related to stalk number (SN) (p < 0.001), Brix (B) (or sucrose content (SC), p < 0.001), and stalk weight (SW) (p < 0.05) (2 at 0.9328 (p < 0.001)  was used as the dependent variable, and its significant correlated traits ( < 0.05) . Subsequt 0.9328 . Conside< 0.001) , and mulf 0.9301 . The sucp < 0.05), and their mean values were not significantly different (p < 0.05) (p < 0.05), which may be due to a lot of outliers being recorded for this cultivar that affected the prediction. Further, the sucrose yield (Y) of the 24 F1 genotypes  were estimated using the seedling data, and the estimated sucrose yield distribution trends showed a good consistency with the measured mean yield of 2020 and 2021, with correlation coefficients of 0.9783 , stalk diameter (SD), stalk number (SN), and Brix (B). The estimated sucrose yield exhibited an extremely significant correlation with the measured sucrose yield. The correlation coefficients of the 14 cultivars were 0.9440, 0.9746, and 0.9594 from 2018 to 2020, respectively. Moreover, the correlation coefficients of the 12 cultivars were 0.9806, 0.8772, and 0.8454, respectively. The one-way ANOVA analysis indicated that the estimated and measured sucrose yield variance were not significantly different ( < 0.05) . In addi < 0.05) . The meaf 0.9783 . All theThe combination of the genotype and environmental interactions determines the phenotype. The phenotype is an important manifestation of genetic variation and can directly indicate functional gene diversity ,26. In s1 hybrids were evaluated and statistically analyzed. The Shannon–Wiener index of the traits ranged from 2.64 to 2.98, with an average value of 2.89, indicating that the trait diversity was high after the cross of ‘Yacheng 9446’ and ‘ROC22’ and may generate phenotypically overdominant to the parents’ progeny. The coefficient of variation could reflect a trait’s degree of dispersion to a certain extent [1 population, implying that the F1 population parental lines had a good agronomic trait breeding potential. The broad-sense heritability of the 11 traits ranged from 0.75 to 0.84, indicating that they were controlled mainly by genetic factors, consistent with previous research using F1 populations and a core sugarcane diversity panel of WCSRG [1 population, the broad-sense heritability of stalk number (SN), stalk diameter (SD), stalk height (SH) and Brix (B) were 0.81, 0.91, 0.83, and 0.81, respectively [In this study, 11 traits in 135 Fn extent ,34. Usuaof WCSRG ,15,18. Fectively . Howeverectively , which m1 hybrids into three groups: (1) higher stalk (SH), wider diameter (SD), longer internode (IL), higher stalk weight (SW), yield (Y), and sucrose yield (SY); (2) higher effective tillering ratio (ETR), greater stalk number (SN), and higher sugar (B and SC); (3) higher tillering ratio (TR). This analysis can aid the selection of elite individual F1 hybrids and intermediate materials to support the sugarcane breeding programs. The principal component analysis is a comprehensive evaluation method used in the phenotypic evaluation and classifications of rice [1 population into three groups similarly. By calculating the comprehensive PC values (F) of the F1 hybrids and their high-yield parent ‘ROC22’, 24 hybrids with greater F values were selected to proceed to the next breeding stage or be used as intermediate materials.Phenotype clustering can directly reflect the genetic background and be effectively used for a preliminary genetic resource assessment . The clu of rice , cotton  of rice , peanut  of rice , et al. The sugarcane genome complexity and the quantitative nature of sugar- and yield-related traits are challenging for higher breeding-mediated gains for this crop ,18. Corr2 of 0.9301. The Brix (B) was selected in the model rather than the sucrose content (SC) due to the convenience of phenotyping. Then, the equation was evaluated by comparing the estimated and measured sucrose yield of 26 new sugarcane cultivars with different genetic background and suitable for different ecological regions in China and sucrose yield distribution trends of 24 F1 genotypes. The model could estimate the sugar yield using the stalk height (SH), stalk diameter (SD), stalk number (SN), and Brix (B) values, and could be used at the seedling stage and could be further verified in other stages of sugarcane breeding. In brief, the model can provide a valuable reference for reducing sugar yield evaluation difficulty, aiding for the selection of elite F1 lines and reducing time and cost constraints during sugarcane breeding. To accelerate crop genetic improvement and yield prediction, many efforts have been made. In the main food crop , genomic selection and phenomic slection have been widely used in breeding ,41, whic1 population was derived from two elite sugarcane resources, Yacheng 9446 and Roc22, which were planted and crossbred at the Hainan sugarcane breeding station  in the year 2018. In 2019, the F1 seeds were grown into seedlings and planted with the row space of 1.1 m and plant space of 0.33 m in the experimental field of the South subtropical crops research institute . The heterozygosity in hybrid F1 plants was confirmed using three differentially amplified SSR markers in the parents we developed previously , effective tillering ratio (ETR), stalk number (SN), stalk diameter (SD), internode length (IL), stalk height (SH), Brix (B), stalk weight (SW), sucrose content (SC), sugarcane yield (Y), and sucrose yield (SY) of the Fi et al. . In briei et al. . Yield  was carried out using the prcomp function. The 11 traits of the F1 population and the parental line ROC22 were normalized to form a correlation matrix. The correlation matrix was used to determine the eigenvalues and relative contribution rates, and the factor values of the principal components (PC) of each genotype were calculated. All the Fe ggplot . p < 0.001) were used as independent variables, and sugar yield was used as the dependent variable. Multivariate linear stepwise regression analysis was performed using the R package MASS to mine the sugar yield composition traits. The sugar yield selection equation was established using the easily quantifiable traits in the field. The sugar yield selection equation was validated using phenotypic data of 26 sugarcane varieties and 24 F1 genotypes with greater sucrose yield potential than the high-yield parent ‘ROC22’.Sugar yield-related traits were selected based on the correlations between the 11 traits. Traits highly significantly correlated with yield or sugar yield , suggesting the genetic complexity underlying sugarcane high sucrose yield production. Based on the main component values of all samples, 24 F1 genotypes had greater values than the high-yield parent ‘ROC22’ and were selected to proceed to the next breeding stage. The rapid sucrose yield estimation equation, The sugarcane hybrid F


Zea mays L.) yield and limiting mechanized harvesting. Developing maize varieties with high stalk lodging resistance requires exploring the genetic basis of lodging resistance-associated agronomic traits. Stalk strength is an important indicator to evaluate maize lodging and can be evaluated by measuring stalk rind penetrometer resistance (RPR) and stalk buckling strength (SBS). Along with morphological traits of the stalk for the third internodes length (TIL), fourth internode length (FIL), third internode diameter (TID), and the fourth internode diameter (FID) traits are associated with stalk lodging resistance.Stalk lodging is one of the main factors affecting maize  markers was used for genome-wide association study (GWAS) for six stalk lodging resistance-related traits. The heritability of all traits ranged from 0.59 to 0.72 in the association mapping panel. A total of 85 significant SNPs were identified for the association mapping panel using best linear unbiased prediction (BLUP) values of all traits. Additionally, five candidate genes were associated with stalk strength traits, which were either directly or indirectly associated with cell wall components.These findings contribute to our understanding of the genetic basis of maize stalk lodging and provide valuable theoretical guidance for lodging resistance in maize breeding in the future.The online version contains supplementary material available at 10.1186/s12863-022-01091-5. Zea mays L.) plays an important role in food security, feed provision, and fuel resources. Nevertheless, stalk lodging can lead to 5–20% maize yield loss annually worldwide [Maize  identify associated loci for RPR, SBS, and morphological traits of the stalk of maize; (2) ascertain stable SNPs and predict potential candidate genes in these regions; (3) dissect the genetic architecture of stalk lodging resistance-related traits.The phenotypes of all lodging resistance-related traits in the association mapping panel are shown in Table h2B) of all traits in low and high plant densities ranged from 0.59 to 0.72 and 0.61 to 0.71, respectively  for six traits ranged from 13.30 to 21.13% and from 10.10 to 21.01% at low and high plant densities, respectively  based on the LD decay distance. A total of 346 candidate genes with gene descriptions were found  in this study. In addition, the SNP Chr1_272576164 (272 Mb) was detected association with SBS in this study also have same physical position with Liu et al. study. The remaining SNPs in this study were first reported to be associated with lodging resistance-related traits in maize.Hu et al.  detectedThe SNP repeatedly detected in multiple environments is generally considered a stable SNP. Stably expressed SNPs detected in this study, five co-localized SNPs  were simultaneously identified under two plant densities. These stable SNPs were insensitive to the external environment and were hence considered to be important loci for the improvement of stalk lodging traits, as such, they can provide references for further gene cloning. Meanwhile, some specific SNPs were detected at high or low plant densities, respectively, which may be environmentally-specific loci requiring further genetic mapping.From the comparison, we found some co-located locus in different densities in the same environment, but extremely few stable sites in different environments. The reason we detected less consistent loci in different environments may be because stalk strength trait itself is a relatively complex quantitative trait and is greatly affected by the environment. In addition, we found that the heritability of these traits is relatively low. This reason was further confirmed. From the results of the phenotypic correlation analysis, the correlation coefficient of both TID and FID was as high as 0.97 at both densities. Similarly, we located three SNPs  associated with both TID and FID at both densities, this confirms the views of previous, phenotypic correlations between quantitative traits may derive from the correlation between QTL controlling them . HoweverGRMZM2G074792, which is located in Chr6_158343036 of RPR, encodes xyloglucan glycosyltransferase and related to plant cell wall cellulose synthesis, which is the major source of cellulose-harbours enzyme [GRMZM2G300412, encoded for UDP-glucuronic acid decarboxylase, was located in Chr1_272576164 of SBS, involving in metabolic pathways and amino sugar and nucleotide sugar metabolism. GRMZM2G072526 was located in Chr7_160255239 and Chr7_160255241, controlling SBS, whose encoded glucan endo-1,3-beta-glucosidase is mainly involved in carbohydrate metabolism, it is associated with cell wall synthesis, which may be related to maize lodging. Previous studies demonstrated that UDP-glucuronic acid decarboxylase was a key enzyme in the synthesis of UDP-xylose for the formation of xylans during cell wall biosynthesis [GRMZM2G111344, was located in Chr5_15958677 of TIL, encoding for UDP-glycosyltransferase (UGT), involved in flavonoid biosynthesis and biosynthesis of secondary metabolites. According to previous studies, UGT was the key precursors of cell wall carbohydrates [GRMZM2G007899 and GRMZM2G311059, were located in Chr10_139852648 of TIL, showed high expression of MYB transcription factor had increased ectopic lignin and the xylem vessels were regular and open, are related transcriptional activators of the lignin biosynthetic pathway during secondary cell wall formation in Arabidopsis [GRMZM2G021051 was located in Chr2_233691559 of FIL, whose the homologous with shortened basal internodes, is a new rice lodging-resistance gene and encodes a gibberellin (GA) 2-oxidase and can control the elongation of internodes at the base of the stem by regulating the activity of the GA [GRMZM2G408462, which is located in Chr3_212705423 of FID, encoded for WRKY transcription factor, whose directly regulate expression of the major monolignol biosynthetic genes and genetic modification of genes involved in lignin biosynthesis [We identified 346 candidate genes in total located around common loci for stalk lodging resistance-related traits, which are involved in a variety of biochemical metabolic pathways. Based on the information of the gene model on MaizeGDB Table S. Notablys enzyme . GRMZM2Gynthesis . GRMZM2Ghydrates . These dbidopsis . In riceynthesis . AlthougIn this study, we identified 6, 3, 10, 8, 8, 7 SNPs associated with RPR, SBS, TIL, TID, FIL, FID at low plant density and 5, 9, 7, 5, 7, 6 SNPs associated with RPR, SBS, TIL, TID, FIL, FID at high plant density, respectively, via GWAS. Most markers were located within or close to QTLs identified in previous studies. We were particularly interested in the seven potential candidate genes that were predicted based on functional annotations, but further investigation is needed for verification of this hypothesis. These findings shed light on the genetic basis of six stalk lodging resistance related-traits, and candidate genes could be used for further positional cloning.A total of 248 diverse maize inbred lines were used to form an association mapping panel. All lines were grown according to the split-plot set two densities, two replicates for each density, and a low density of 75,000 plants/ha and a high density of 105,000 plants/ha. The work was performed at the Experimental Station of Hebei Agricultural University in Baoding and Shijiazhuang in 2015 and 2016. For each replicate, each line was grown in a 3-m long single-row plot, with a 0.6-m inter-row spacing. All of the plant materials used in our study were derived from the China Agricultural University and National Maize Improvement Center of China.2 and N, respectively.Based on previous studies on stalk lodging resistance in maize, we decided to measure morphological traits and stalk strength during one week after grain filling . Five reh2B) was calculated according to Knapp et al. [The mean value of each inbred line for each trait was used for descriptive statistical analysis. Analysis of variance (ANOVA) was carried out with SPSS19.0 for related traits of stalk lodging resistance under two plant densities in the association mapping panel. Broad-sense heritability  of the phenotypic values of each line was calculated across all environments using the R package “lme4” . The BLU2 = 0.1) based on previous study [The GBS method was used to genotype the 248 inbred lines of the association panel . First, us study , 53. TheGWAS data was analyzed with the mixed linear model (MLM) using the “GAPIT” package in R. The SNP markers of six stalk lodging resistance related-traits in the association mapping panel together with the Q and K matrix were used as covariates to decrease spurious association and detect marker loci combining with target traits. The GWAS analysis is performed with a Bonferroni correction, however this was found to be too strict for less significant trait associations. Therefore, we reduced the significance threshold to–log10 (P) ≥ 4 for all traits .http://www.maizegdb.org/) and NCBI website (https://www.ncbi.nlm.nih.gov/). Gene ontology (GO) enrichment analysis was performed using the Gene ontology website (http://www.geneontology.org/). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using the KOBAS version 3.0 (http://kobas.cbi.pku.edu.cn/kobas3/?t=1) [The candidate gene analysis was based on the maize inbred line B73 reference genome version v2 (centering on the marker site and extending 120 kb upstream and downstream) and searching for the information and functions of the candidate genes on the MaizeGDB genome browser (s3/?t=1) .Additional file 1: Supplementary Table S1. List of genes within the 120 kb upstream and downstream extension of significant SNPs identified via GWAS. Supplementary Table S2. SNPs detected for lodging resistance-related traits using BLUP value in the association mapping panel. Supplementary Table S3. List of genes within the 120 kb upstream and downstream extension of significant SNPs identified via GWAS.