Populus, which can integrate heterosis and ploidy vigor in an elite variety. However, the phenotypic divergence of unselected allotriploids with the same cross-combination remains poorly understood, and the contributions of ploidy, gender, and genotype effects on phenotypic variation are still unclear. In this study, wood and fiber traits, including basic density (BD), lignin content (LC), fiber length (FL), fiber width (FW), and fiber length/width (FL/W), were measured based on a 10-year-old clonal trial, including full-sib diploid and triploid hybrids of (Populus pseudo-simonii × P. nigra ‘Zheyin3#’) × P. × beijingensis, and contributions of ploidy, gender, and genotype effects on the variation of these traits, were disaggregated to enhance our understanding of triploid breeding. We found a significant phenotypic variation for all measured traits among genotypes. All the wood and fiber traits studied here underwent strong clonal responses with high repeatabilities (0.55–0.76). The Pearson’s correlation analyses based on the best linear unbiased predictors (BLUPs) revealed that BD was significantly positively correlated with FL , suggesting that BD could be improved together with FL during triploid breeding. The FL of the triploids was significantly larger than that of the diploids (p < 0.001), suggesting that ploidy strongly affected the variation of FL traits. The difference between females and males was not significant for any measured trait, implying that gender might not be a major factor for variation in these traits. Further analyses of variance components showed that genotype dominantly contributed to the variation of BD, LC, and FW traits  and ploidy contributed strongly to variation in FL and FL/W . The genetic coefficient of variation (CVG) of triploids for each trait was low, suggesting that it is necessary to produce many triploids for selection or to use different Populus species as parents. Our findings provide new insights into the genetic effects of ploidy, gender, and genotype on wood and fiber traits within a full-sib poplar family, enhancing the understanding of the triploid breeding program of Populus.Triploid breeding based on unilateral sexual polyploidization is an effective approach for genetic improvement of  Populus, characterized by fast growth, good wood properties, strong resistance, and wide adaptability, are widely distributed over the northern hemisphere , lignin content (LC), and fiber length (FL), directly affect pulp value and paper quality.The species in the genus misphere . They armisphere . As an iAcacia, allotriploid clones derived from crossing tetraploid Acacia mangium with diploid A. auriculiformis possessed a higher heartwood proportion and bark thickness than commercial diploid clones  × P. tomentosa, although the 2-year seedling height and ground diameter of 12 triploids were 33 and 38% larger than those of the diploids on average, respectively, several triploid genotypes exhibited lower growth performance than the diploids  than the males on average  and male parent P. × beijingensis  were analyzed in this study. The triploid hybrids were derived from crossing induced 2n eggs through colchicine-induced embryo sac chromosome doubling of ‘Zheyin3#’ with the BJY  mixture of acetic acid and hydrogen peroxide at 60°C for 24 h. After rinsing three times with distilled water, the specimens were vibrated to scatter fibers in test tubes. Then, the fibers were stained with safranin solution and observed under an Olympus BX51 microscope. The FL and FW were measured using an ocular micrometer. More than 200 fibers were measured for each specimen. FL/W was calculated based on the FL and FW values.For intra-ring analysis of fiber properties, four matchstick-sized wood specimens were excised from the above rectangular wood specimens at the seventh and eighth rings and then macerated in a 1:1  of the United States . The LC p < 0.05) to reveal the difference among genotypes. The repeatability (R2) of each measured trait was estimated using the repeatability function in the heritability R package (version 1.3) developed by G), and variance components of ploidy, gender, and genotype effects were estimated by the AFEchidna R package (version 1.54) developed by t-test were used to identify the statistical differences between diploid and triploid groups and between female and male groups nested in the two ploidy levels for all traits.Statistical analyses were performed in the R statistical environment . The res–3, with an average of 355.90 ± 5.57 kg m–3. The LC ranged between 25.68 and 30.33% with an average of 27.62 ± 0.24%. The FL ranged from 0.890 to 1.293 mm, with an average of 1.115 ± 0.029 mm. The FW ranged from 19.05 to 26.30 μm with an average of 22.97 ± 0.40 μm. The FL/W ranged from 41.55 to 61.53 with an average of 50.05 ± 1.29. The CVG of the traits in this family was 7, 4, 44, 7, and 10% for BD, LC, FL, FW, and FL/W, respectively. The CVG of BD, LC, FL, FW, and FL/W in the diploids was 7, 4, 6, 8, and 6%, respectively. The CVG of these traits in the triploids was 5, 3, 3, 6, and 6%, respectively.For the full-sib diploid and triploid hybrid poplar family, the BD ranged from 320.21 to 410.35 kg mOne-way ANOVA tests revealed significant differences among the genotypes in all measured traits , suggestR2) for all measured traits were estimated and are shown in R2 of the wood and fiber properties in the whole family ranged from 0.55 to 0.76, which suggests that the observed phenotypic variation of these traits in the full-sib family was strongly affected by genetic effects. BD had the highest estimated repeatability (R2 = 0.76) with 95% confidence intervals ranging from 0.57 to 0.89. FL/W had the lowest estimated repeatability (R2 = 0.55) with 95% confidence intervals ranging from 0.29 to 0.77.Repeatabilities (r) between traits in the whole family ranged from −0.69 to 0.77, and the BLUP correlation coefficients ranged from −0.68 to 0.56. BD was significantly negatively correlated with FW in both phenotypic  and BLUP  levels. There was a strongly positive phenotypic correlation between FL and FL/W , but their BLUP correlation was not significant. Although the phenotypic correlations between BD and FL/W and between FW and FL/W were not significant, their BLUP correlations reached a significant level.Pearson’s correlations of both phenotype and BLUPs among all measured wood and fiber traits were calculated and are presented in In the diploid group, BD was significantly negatively correlated with FW at both the phenotypic and BLUP levels. There were also significant negative correlations between BD and FW in diploid males. In diploid females, BD was significantly positively correlated with FL/W at both the phenotypic and BLUP levels. In the triploid group, both phenotypic and BLUP correlations between BD and FL/W and between FW and FL/W were significant, and the BD was significantly positively correlated with FL at the BLUP level. In triploid females, BD was significantly correlated with FL, FW, and FL/W. FL was strongly negatively correlated with FW and positively correlated with FL/W, and FW was strongly negatively correlated with FL/W. However, in triploid males, there was no significant correlation between the traits no matter at phenotypic or BLUP level.–3, 26.37–30.33%, 0.890–1.081 mm, 19.05–24.35 μm, and 41.18–50.69, respectively. These traits of the triploid genotypes ranged from 320.21 to 390.06 kg m–3, 25.68–28.29%, 1.089–1.293 mm, 20.88–26.30 μm, and 45.70–61.53, respectively. The Student’s t-test showed that there were no statistical differences between the diploid and triploid groups on average for BD, LC, and FW traits . However, the FL and FL/W traits of the triploid group were significantly larger than those of the diploid group on average , which suggests that the gender effect might not be a major factor for variation of the measured traits.The phenotypes of the BD, LC, FL, FW, and FL/W traits of the diploid genotypes ranged from 334.66 to 410.35 kg m average , which sVariance contributions of genotype, ploidy, and gender effects for the wood and fiber traits were analyzed and are presented in Populus is an important source of industrial raw timber in the northern hemisphere. In China, the lumber of Populus is widely used to produce pulpwood. The basic wood density, LC, and FL affect the pulp yield and paper quality . Genotype and ploidy were the main contributors to trait variation, suggesting that integration of heterosis and ploidy vigor is important for polyploid breeding. However, because the number of induced triploid hybrids is limited, and from a single parental combination, it is not known how generalizable these findings are.Polyploidization plays an important role in plant speciation and evolution and contributes to plant breeding . Accordiant size . In the Populus, but did not affect the serration number on the leaf margin and petiole length of the leaf. Triploid elite plants in white poplar hybrids showed better wood properties and fiber traits compared with diploid control , suggesting that BD could be improved together with FL during triploid breeding. In the triploids of P. tomentosa, a significantly positive genotypic correlation between BD and FL was also found with the correlation coefficient ranging from 0.41 to 0.98 at different sites , in our study, suggesting that the selection of FL trait might lead to the decrease of LC in triploid breeding.An understanding of the relationships between these wood properties is of value in the development of a breeding program for the pulp and paper industry. In our study, both phenotypic and BLUP correlations between the traits were analyzed. The results showed that the phenotypic correlations had similar changes with the BLUP correlations, though the significance levels in several trait pairs varied, which reflected on the difference between the phenotypic value and BLUP-adjusted genotypic value. BLUP, presented by nt sites . There wG for each measured trait was low, ranging from 4 to 11%, which indicates that the genetic variation is quite small for these wood traits. It means that changes from direct selection and correlated responses will be quite small for these traits in the family of our study. The CVG values in triploids were no larger than that of the diploids in this family. In the triploids of P. tomentosa, the CVG values of the wood and fiber traits were also very small, ranging from 1.4 to 6.4 at different sites , resulting in limited variations of the traits in progeny. In future, more species, such as P. simonii, P. nigra, and P. deltoides, could be used for parental selection with large combining ability, and triploid induction should be conducted using the parents.In this study, the CVnt sites . Therefoeveloped . The appeveloped , which sThe original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.JW conceived and designed the research. X-YH, JS, and Y-HZ collected wood disks and conducted laboratory experiments. JS and D-LL analyzed the data. L-JS managed the field trial. X-YH, D-LL, and JW wrote and revised the manuscript. All authors read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Previous studies have shown reduced enteric methane emissions (ME) and residual feed intake (RFI) through the application of genomic selection in ruminants. The objective of this study was to evaluate feeding behaviour traits as genetic indicators for ME and RFI in Australian Maternal Composite ewes using data from an automated feed intake facility. The feeding behaviour traits evaluated were the amount of time spent eating per day  and per visit , daily number of events (DNE), event feed intake  and eating rate . Genotypes and phenotypes of 445 ewes at three different ages  were used to estimate the heritability of ME, RFI, and the feeding behaviour traits using univariate genomic best linear unbiased prediction models. Multivariate models were used to estimate the correlations between these traits and within each trait at different ages. The response to selection was evaluated for ME and RFI with direct selection models and indirect models with ETE as an indicator trait, as this behaviour trait was a promising indicator based on heritability and genetic correlations. Heritabilities were between 0.12 and 0.18 for ME and RFI, and between 0.29 and 0.47 for the eating behaviour traits. In our data, selecting for more efficient animals (low RFI) would lead to higher methane emissions per day and per kg of dry matter intake. Selecting for more ETE also improves feed efficiency but results in more methane per day and per kg dry matter intake. Based on our results, ETE could be evaluated as an indicator trait for ME and RFI under an index approach that allows simultaneous selection for improvement in emissions and feed efficiency. Selecting for ETE may have a tremendous impact on the industry, as it may be easier and cheaper to obtain than feed intake and ME data. As the data were collected using individual feeding units, the findings on this research should be validated under grazing conditions. Two major challenges for the Australian sheep industry are to reduce enteric methane emissions and to improve feed efficiency.Feed efficiency in sheep is a trait of socio-economic importance. The efficient use of feed by animals is vital in providing animal protein to an expanding human population, especially on land that cannot be used to produce human food. Feed is the highest production cost in the sheep industry, and for that reason, improving feed efficiency could increase profitability. Residual feed intake (RFI) is an accepted measure of feed efficiency that estimates the difference between the actual and predicted daily dry matter intake (DMI) . RFI is The reduction of methane emissions from sheep production is an important goal. Australia is committed to reducing greenhouse gas emissions  and decrFeeding behaviour is the behaviour of animals related to access and consumption of food. These traits could be of interest as indicators for methane emission and residual feed intake. Feeding behaviour traits include eating time , number and size of eating events and eating rate. Feeding behaviour traits, methane emissions, and RFI have been shown to be under partial genetic control . PhenotyGenomic selection has generated permanent and cumulative genetic change over generations to reduce methane emissions in sheep  and has The accuracy of genomic breeding values partially depends on measuring the goal and indicator traits under environmental and management conditions that are as similar as possible to commercial production systems. In the case of the Australian Maternal Composite sheep, the commercial conditions include grazing pastures that vary in quantity and quality throughout the year. Under these conditions, the relationship between methane emissions, feed efficiency and feeding behaviour is expected to be more complex than under controlled environments. For instance, sheep under grazing conditions may show variation in their ability to walk to obtain enough food and water. However, measuring methane emissions, feed intake and feeding behaviour traits under grazing is cost-prohibitive and technically not possible at the moment. As an alternative, measuring these traits under controlled conditions is feasible and can help to elucidate the heritability of the traits and their correlations, in addition to developing genomic predictions. However, it is advisable to validate the genomic predictions under commercial conditions before implementing a genomic selection program.The objective of this study was to evaluate the feeding behaviour traits associated to the amount of time spent eating per day  and per visit , daily number of events (DNE), event feed intake  and eating rate  as genetic indicators for MeP, MeY, RMP, and RFI in Australian Maternal Composite ewes at post-weaning (PW), hogget, and adult ages using data from an automated feed intake facility.Data were available on 445 Australian Maternal Composite ewes, a popular composite breed in Australia . Of thesn = 165) and “15K” ], and medium-density [”50K” ] single nucleotide polymorphism (SNP) chips. SNP were excluded if the call rate per SNP [the proportion of SNP genotypes that have a GC  score above 0.6] was less than 0.9. Furthermore, if the average call rate per individual was less than 0.9, those animals were removed from the SNP data. After applying this quality control procedure, 9,325, 9,190, and 34,770 SNP remained for the 12K, 15K and 50K SNP chips, respectively. Subsequently, the imputation of sporadic missing genotypes within each SNP array was performed using FImpute (GRM) was constructed with the imputed genotypes of the 445 Australian Maternal Composite ewes using the function Gmatrix of the R package AGHmatrix . The pellets had 9-mm in diameter and their composition was 40% cereal straw, 20% cereal grain (barley or wheat), 15% legume grains , 15% oat hulls, 7% almond hulls, 1% lime, 1.5% bentonite, 0.5% gypsum. Samples of pellets were analysed using near-infrared spectroscopy. The methodological details on the pellets analysis are described by Five feed intake experiments were conducted between 2014 and 2016 . Feed inAnimals were spread across 10 pens each pen was equipped with two automated feeders and there was continuous access to water. After an adaptation period to the diet of 10–14 days, feed intake was measured for a total of 53 (±3), 42 (±3) and, 32 (±0) days for PW, hogget, and adult stages, respectively. The experiments were planned for a minimum of 42 days, but 2013-born adults and 2014-born hoggets were meadured for 32 and 39 days, respectively,  to fit a2), of which ∼80 m2 was free space for the sheep. No more than 24 ewes were included in each pen (3.33 m2 per sheep at maximum density), and there was a balanced distribution of sires and ewe live weights across pens. Although there were different numbers of sheep per feeder in each feeding period, the occupancy of the feeders in all periods was low enough to allow sheep to consume feed ad libitum, meaning that between animal interactions were unlikely to have affected eating rate substantially. Each pen had approximately the same number of animals in each experiment, but animals were not necessarily assigned to the same pen in different experiments at different ages.Feed intake was recorded automatically with a specific radio frequency identification tag each time an individual animal access the feed units (eating event). The automated feeders created and stored individual animal feeding events in addition to refilling of feed bins between feed events to 1,000 g—meaning that each animal had access to 1,000 g of feed at each visit. There was no limit on the number of feeder visits (eating events), or the time spent eating per visit  and, hence, the total feed consumption was not restricted. The automated feed system, operation and accuracy is described in detail by The design of the feeders made impossible for sheep to push each other out of the feeder because once an animal entered the feed race, an electromagnetic lock on the gate behind them was activated and kept lock while the animal was eating, and no other sheep was able to access the feeder or got close enough to the eating individual to push them out of the feeder. . Live wen = 5); PEN:EXP is the pen where animals were kept within each experiment nested into EXP (45 levels), AGE is the age in days at the start of the experiment, Pregnancy is days of pregnancy at the end of the experiment, and Pregnancy Scan is the number of lambs in pregnancy scan. RFI is the residual error of the equation and represents the intake after accounting for energy sinks. In this case, these sinks are the energy used for maintenance (MMWT) and growth (ADG) adjusted for EXP, PEN, and AGE.Residual Feed Intake was calculated using the following model:Feeding behaviour records were also obtained during feeding experiments using the automated feeding system . Sheep iMethane emissions were recorded twice during each feed intake experiment with the portable accumulation chamber (PAC) technique described by h2), and phenotypic (rp) and genetic correlations (rg) were obtained with the R package ASReml-R (Heritability (ASReml-R .Model 1 included in GRM (PC1 and PC2). PC1 and PC2 were included because it has been reported that genomic prediction accuracies in multi-breed sheep tends to be overestimated due to population structure, and this effect can be reduced by including principal components of the GRM as covariates  models of the formp matrix  calculatvariates . Model 1Model 1 in the form of bivariate GBLUP was used for estimating correlations between most of the traits, with some exceptions, where model 2 (as a bivariate GBLUP) was implemented. For example, model 2 was used to estimate the correlations of RMP and RFI with those traits used in their estimation, because RMP and RFI are the residuals of models that already include the same fixed effects of model 1 (except PC1 and PC2) as regressors. Model 2 was also used for instances when there were convergence issues for model 1, for example, between MeY and ER.Model 1 and model 2 were used for all traits replacing the mean with its multivariate equivalent.Phenotypic and genetic correlations were estimated between different stages of age. Multivariate GBLUPs were conducted to estimate genetic correlations between PW, hogget, and adult stages to determine whether the traits could be considered the same in all ages. model 1, and the bias as the regression slope of these adjusted phenotypes on GEBVs. The accuracy and bias of RMP and RFI were estimated as previously described, but the phenotypes were not adjusted for the fixed effects of model 1 because these effects were already included in the models used to obtain the phenotypes. This was repeated with every cohort as the validation set and averaged across cohorts.Genomic prediction accuracy and bias of the univariate models were estimated with a five-fold cross-validation, for this, animals were randomly assigned into five cohorts. One cohort was used as a validation set, and their phenotypic data were removed, while the remaining 4 cohorts were used to calculate the estimated breeding values (GEBVs). Except for RMP and RFI, the accuracy was estimated as the Pearson correlation between the GEBVs and phenotypes corrected for fixed effects using model 1.The response to selection on methane emissions and RFI with direct selection and indirect selection with eating time per eating event as indicator were estimated. Annual direct and indirect responses to selection on methane emission traits and RFI were predicted. The direct responses were calculated as:R2) of observed MeP regressed on predicted MeP was 0.63, and the R2 of the observed DMI regressed on the predicted DMI was 0.79  of MeP, MeY and RMP were 0.18 ± 0.05, 0.13 ± 0.04 and 0.13 ± 0.04, respectively, , DMI (5.6e-02) and RFI (p = 4.2e-04), and PC2 was significant for MeP (p = 5.8e-02) and MMWT (p = 3.1e-02)  .model 2 between PW and hogget, PW and adult, and hogget and adult were 0.84 ± 0.16, 0.76 ± 0.31, and 0.7 ± 0.20, respectively. Despite the large standard error of these genetic correlations, the high correlations suggest that DMI is genetically very similar at all ages. The phenotypic correlations for DMI were 0.32 ± 0.06, 0.30 ± 0.10, and 0.52 ± 0.08 between PW and hogget, PW and adult, and hogget and adult, respectively. The multivariate models between age stages for the rest of the traits did not converge, possibly due to the small number of animals in each stage.The genetic correlations for DMI obtained with rg = −0.79 ± 0.15; rp = −0.46 ± 0.03) . The genetic correlation of ETE with MeP was 0.44 ± 0.32 and with MeY was 0.63 ± 0.22 . The gen ± 0.03) . Further4 (−0.5 to −2.8% of MeP) with direct selection. Yearly decreases in MeY are projected between −0.2 and −1.4 L/kg of DMI (−0.4 to −3.0%) with direct selection, and between −0.1 and −0.7 indirectly by selecting on shorter ETE  with direct selection and between ∼−0.13 and −1.15 L (−0.5 to −1.0%) by indirectly selecting on shorter ETE . The annMaternal Composite sheep are a composite used for ewe replacement in Australia, which has been mainly selected for improving fertility and size traits . As theyThe number of eating events per hour throughout the day-cycle was relatively constant in some of the animals we used when they were kept in the same facilities and under the same conditions as our study . AdditioThere is potential to obtain ETE in large populations  with the use of wearable sensor devices for measuring aspects of feeding behaviour for sheep , althougThe heritabilities reported in this manuscript agree with the literature . The herThe accuracies of the breeding values for methane emissions and RFI are not high enough, for genomic prediction. These lower accuracies could be do to the relatively small reference population and to the fact that we used a multibreed population. It has been estimated that to achieve a predicted accuracy of around 0.7 in two genetically distant populations of dairy cattle for traits with heritabilities similar to those we reported for methane emission traits and RFI (between 0.13 and 0.18), it is necessary to include close to 10,000 animals of each of these two population  . This scrg = 0.99) (rp = 0.21) reported by The genetic correlations for DMI between different ages were similar to those  = 0.99) . DominikThe responses to selection using the GEBVs obtained with the data we used in this research are encouraging and agree with the reported literature. ad libitum intakes may favour shorter retention times of feed particles in the rumen, diminishing rumination, which is the primary digestion process involved in particle size reduction and digestion of cell walls.In our study, the genetic correlations of RFI with MeY and RMP were negative. The negative correlation between RFI and MeY indicates that more efficient animals (low RFI) tend to emit more methane per kg of DMI. The negative correlation between RFI and RMP suggests that more efficient animals tend to emit more methane after adjusting for methane emissions expected to occur due to DMI, MMWT and ADG. This is the first paper that reports the genetic correlations between methane emissions and RFI in sheep fed pellets with high NDF content. Nutrition studies indicate that selecting low methane emitting sheep and cattle may result in reduced efficiency of cell wall digestion (that is NDF) , which cThe underlying biology for variation in methane emissions and feed intake in ruminants is still unknown because there are many interacting biological variables . It is kLonger eating time could imply more chewing and salivation whilst consuming feed, which increases feed digestibility. The bicarbonate and phosphate present in saliva maintain an optimum ruminal pH for microbial digestion through processes such as the bicarbonate-dependent absorption of volatile fatty acids . SalivatThe negative correlation observed between RFI and MeY may be due to the high correlation of RFI with DMI which is in the denominator of MeY, and this implies an inverse relationship between MeY and RFI. Supporting this idea, the genetic correlation between DMI and MeY was also negative in our study. Another possible explanation for the negative correlation between RFI and MeY that is mentioned in gRFI-MeP = 0.76; rgRFI-MeY = 0.46). Feed particle size could influence the correlations between emissions, RFI, and ET. Longer ET may have reduced the particle size of the pellets, leading to feed efficiency improvement, and increasing exposure to digestion and therefore producing more methane. The presentation of pellets in a trough to sheep for consumption is different to that of intact forages where the ability of sheep to consume feed and the amount of time spent grazing is strongly influenced by the quality, height and density of the forage and the bite size and bite rate that can be achieved  with the use of devices that monitor feeding. Those devices are a promising technology for the sheep industry  that is The correlations between ETD and RFI should be evaluated in a larger reference population. A larger population is also needed to accurately estimate the genetic correlations of RMP with ETD and ETE. These unknown genetic correlations would help to elucidate the effect that selecting for eating time would have on emissions and feed efficiency. Studies at pasture are required to confirm our results and their application in extensive grazing systems such as those practice in Australia. If the correlations found here are confirmed, ETE could be used as an indicator trait for methane emissions and RFI, but an index approach would be required to simultaneously select for benefits in emissions, RFI, and related traits. Combinations of indicator traits appear to provide the most accurate estimates of methane emissions , and basThis study presented results on the genetic basis of methane emissions, feed intake, eating time, and related traits in sheep. As expected, prediction accuracies tend to increase proportionally to the heritability of the trait. Expansion of training sets and recording the traits under grazing conditions is required to achieve accurate predictions before these traits can be included in breeding programs for sheep kept on pasture.In our data, selecting for more efficient animals (low RFI) would lead to higher methane emissions per day and per kg of dry matter intake. In the same way, selecting for lower emitters per day or kg of dry matter intake would lead to less efficient animals (high RFI). Selecting for longer eating time per eating event would improve feed efficiency but would also increase the methane emissions per day and per kg of dry matter intake. Therefore, an index approach would be needed to select simultaneously for benefits in both methane emissions and feed efficiency, either by direct selection or by indirect selection using eating time per eating event as indicator trait. Further analyses in larger reference population and in grazing systems are advised to confirm the association between eating time, methane emissions, and RFI. If these associations are confirmed, selecting for eating time traits could have a tremendous impact on the sheep industry.


White matter tracts form the structural basis of large-scale brain networks. We applied brain-wide tractography to diffusion images from 30,810 adults (U.K. Biobank) and found significant heritability for 90 node-level and 851 edge-level network connectivity measures. Multivariate genome-wide association analyses identified 325 genetic loci, of which 80% had not been previously associated with brain metrics. Enrichment analyses implicated neurodevelopmental processes including neurogenesis, neural differentiation, neural migration, neural projection guidance, and axon development, as well as prenatal brain expression especially in stem cells, astrocytes, microglia, and neurons. The multivariate association profiles implicated 31 loci in connectivity between core regions of the left-hemisphere language network. Polygenic scores for psychiatric, neurological, and behavioral traits also showed significant multivariate associations with structural connectivity, each implicating distinct sets of brain regions with trait-relevant functional profiles. This large-scale mapping study revealed common genetic contributions to variation in the structural connectome of the human brain. Variability in white matter connections of the human brain is associated with hundreds of newly discovered genomic loci. Cognitive functions and behaviors are supported by dynamic interactions of neural signals within large-scale brain networks (Diffusion tensor imaging (DTI) enables in vivo noninvasive study of white matter in the brain  have reported that a substantial proportion of interindividual variability in white matter microstructural measures can be explained by common genetic variants, with single-nucleotide polymorphism (SNP)–based heritabilities ranging from 22 to 66%  likely relates to its global role in information transfer within multiple subnetworks, whereas individual connections between specific pairs of regions are more locally restricted measures. We anticipated that genetic influences on node-level and edge-level network measures might therefore be partly distinct, where some genetic effects are more relevant at larger scales whereas others could affect relatively specific circuit components.Our genetic analyses included SNP-based heritability estimation, multivariate GWAS (mvGWAS), and biological annotation of associated loci. Then, to illustrate how multivariate gene-brain associations arose in the data and how the brain-wide mvGWAS results could be queried in relation to any specific brain network of interest, we used the results to identify genomic loci that are associated with structural connections between core language–related regions of the left hemisphere. Various aspects of language function—especially related to language production—show strong hemispheric lateralization, with roughly 85% of people having left-hemisphere dominance  and genetic data after quality control, we performed deterministic fiber tractography . The latter observation is consistent with previous studies showing that subcortical regions connect widely with the cerebral cortex, to generate reciprocal cortical-subcortical interactions that together support many cognitive functions (h2) for each network measure, that is, the extent to which variance in each connectivity measure was explained by common genetic variants across the autosomes . All of the 90 node-level (region-based) connectivity measures were significantly heritable (Bonferroni-corrected P < 0.05 after testing 90 measures), ranging from 7.8 to 29.5% , with the superior temporal cortex in the left hemisphere being the highest .The GCTA  software , ranging from 4.6 to 29.5% . Eleven edges had h2 > 20%, primarily for connections linking frontal regions  and supplementary motor and occipital cortex . The mean h2 was 9.9% for 351 edges within the left hemisphere, 10.0% for 333 edges within the right hemisphere, and 8.1% for 167 interhemispheric edges  showed significant heritability (Bonferroni-corrected r = 0.67 across 90 node-level connectivities and r = 0.60 across 851 heritable edge-level connectivities (fig. S4). This is consistent with the fact that measurement error is assigned to the “environmental”  component of trait variance in heritability analysis, so that less reliable measures tend to be less heritable. Regardless, we reasoned that all significantly heritable network measures had enough reliably measured variation to contribute to subsequent mvGWAS analysis. This is because detecting heritability relies on trait similarity being increased in pairs of individuals with higher genetic similarity and therefore necessitates at least a small proportion of trait variance being reliably measured across and within individuals, while single genetic loci are only expected to explain very small amounts of that heritable variance. The heritability data can be visualized interactively in a dynamic Web-based interface .Reliability of heritable network measures was assessed using data from 1005 of the 30,810 individuals who had undergone brain scans on two separate occasions : Intraclass correlation coefficients (ICCs) based on the same processing pipeline applied to data from the first and second scanning visits had a median of 0.83  for the 90 heritable node-level measures and a median of 0.66  for the 851 heritable edge-level measures (fig. S4 and tables S7 and S8). Measurement reliability was positively correlated with heritability: P = 2.5 × 10−8 significance level , we identified 140 lead SNPs in 117 distinct genomic loci associated with node-level connectivities  software , the fact that the large majority of lead SNPs were detected for either node-level or edge-level connectivity, but not both, supports the importance of performing genetic association analyses at these different network levels.z score derived from each metric’s univariate association with that SNP . In the node-level mvGWAS, regions with the greatest magnitude z scores considered across all lead SNPs were the bilateral putamen , left pallidum (mean |z| = 2.02), bilateral middle frontal cortex , and middle cingulate cortex . For example, the left putamen, which had the highest overall contribution across lead SNPs (mean |z| = 2.05), was especially strongly associated with rs12146713 on 12q23.3 (z = −10.48), rs72748148 on 9q31.3 (z = 7.35), rs798528 on 7p22.3 (z = −6.74), rs7935166 on 11p11.2 (z = 6.22), and rs3795503 on 1q25.3 .For each lead SNP, MOSTest indicated the contribution of each brain metric to its multivariate association, by reporting a z scores considered across all lead SNPs mainly connected the precuneus, calcarine, middle temporal, and pre- and postcentral cortex (table S14 and fig. S9). The edge linking the left and right precuneus had the greatest contribution across lead SNPs (mean |z| = 1.60) and was especially associated with the variants rs946711 on 10p12.31 (z = −5.58) and 3:190646282_TA_T on 3q28 (z = −5.53).In the mvGWAS of edge-level connectivity, edges that showed high magnitude Together, our node-level connectivity mvGWAS and edge-level connectivity mvGWAS identified 325 lead SNPs, of which only 101 were previously associated with at least one trait in the NHGRI-EBI GWAS catalog (tables S9 and S10)  is intronic to CD82, which has been reported to promote oligodendrocyte differentiation and myelination of white matter  affects the expression of its surrounding gene FOXO3, a core element of the TLR/AKT/FoxO3 pathway that is important for repairing white matter injury mediated by oligodendrocyte progenitor cell differentiation  is an eQTL  is an eQTL of DCLK2 that is important for axon growth cone formation and neural migration  is a missense coding variant in the gene SLC39A8 that showed a high combined annotation-dependent depletion score of 23.1, which indicates that this SNP is deleterious (its frequency was 7.01%). The same SNP has been associated with white matter microstructure integrity . One hundred thirty-five of 211 lead SNPs had at least one eQTL annotation or chromatin interaction. For example, rs13084442 on 3q26.31  (table S18 and fig. S11), 237 of which overlapped with those annotated by at least one of the three strategies used above . The gene-based P values were then used as input to perform gene-set enrichment analysis, in relation to 15,488 previously defined functional sets within the MSigDB database , “go_neuron_differentiation” , and “go_cell_morphogenesis_involved_in_neuron_differentiation” .We used MAGMA  , 444 of which overlapped with genes mapped through physical location, eQTL annotation, or chromatin interaction (table S20 and fig. S11). Seventy-two gene sets were significant after Bonferroni correction for 15,488 sets , “go_neuron_projection_guidance” , and “go_axon_development” .For edge-level connectivities, we identified 561 genes with significant gene-based association  to late  prenatal stages or from 9  to 26  postconceptional weeks  to late  prenatal stages or from 9  to 24  postconceptional weeks  (P = 4.34 × 10−5) and edge-level connectivity   and stem cells  at 10 gestational weeks of age (GW), astrocytes at 19 GW  and 26 GW , and GABAergic neurons at 26 GW  (P = 5.07 × 10−4) and stem cells  at 10 GW, neurons at 16 GW , and astrocytes  and GABAergic neurons at 26 GW . For example, rs12636275 on 3p11.1 is located within an intron of EPHA3, a gene that encodes an ephrin receptor subunit that regulates the formation of axon projection maps  involved in chromosome maintenance and the cell cycle : 16 correlations were positive, with the highest between polygenic scores for schizophrenia and bipolar disorder  and between attention-deficit/hyperactivity disorder and autism , while 2 were negative, between polygenic scores for amyotrophic lateral sclerosis and bipolar disorder  and amyotrophic lateral sclerosis and autism .For each of the 30,810 individuals in our study sample, we calculated polygenic scores . Similar observations were made for polygenic risk for amyotrophic lateral sclerosis, where 74 of 90 regions showed positive loadings .For example, the mask for schizophrenia polygenic risk comprised 32 regions showing the strongest associations with white matter connectivity, distributed in the bilateral temporal, dorsoventral, and posterior cingulate cortex  active in the prenatal developing brain; (ii) up-regulated in stem cells, astrocytes, microglia, and neurons of the embryonic and fetal brain; and (iii) involved in neurodevelopmental processes including neural migration, neural projection guidance, and axon development. A likely neurodevelopmental origin of much interindividual variation of adult white matter connectivity is consistent with findings from large-scale imaging genetic studies of other aspects of brain structural and functional variation . An alternative approach, probabilistic tractography, is generally more demanding in terms of run time and storage requirements but can have advantages, especially as it permits modeling of multiple tract orientations per voxel and therefore allows for crossing fibers  atlas (This study had some limitations: (i) We maximized our statistical power for GWAS using the available data as one large discovery sample, but this did not permit a discovery-replication design. Nonetheless, ultimately, the total combined analysis in the largest available sample is the most representative of the available evidence for association. As mentioned earlier in this section, the various enrichment analyses indicated biological validity of the GWAS findings. It has been argued that discovery-replication designs have less utility in the current era of Biobank-scale genetic studies than they used to and that other forms of validation such as biological enrichment should be given increased weight in interpretation , and all of their procedures were performed in accordance with the World Medical Association guidelines , followed by removing multiallelic variants that cannot be handled by many programs used in genetic-related analyses. This pipeline lastly yielded 9,803,735 biallelic variants.We downloaded imputed SNP and insertion/deletion genotype data from the U.K. Biobank . QCTOOL (v.2.0.6) and PLINK v2.0 , genotype measurement batch (data field: 22000), and sex (data field: 31). Residuals were then further regressed on the first 10 genetic principal components that capture population genetic diversity (U.K. Biobank field: 22009) , nonlinear age [i.e., (age-mean_age)P > 1 × 10−7, using GCTA (version 1.93.0beta) .For our main analysis of the 30,810 individuals (above), we used data from the first scanning visit at a U.K. Biobank assessment center. One thousand five of these individuals had also undergone brain scans  on a subsequent, separate occasion, from 733 to 974 days after their first scan. To examine the reliability of significantly heritable brain network measures we re-ran deterministic tractography on the “second scan” data from these 1005 individuals, with the same set of parameters and quality filters as the primary analysis, and recomputed the same edge-wise connectivity metrics as in the primary analysis. Each edge measure was then linearly adjusted for the same covariates as the main analysis, and rank-based inverse normalization was applied as in the main analysis. The adjusted, normalized values from the first and second scans were then used to compute the ICC for each heritable network measure, to evaluate reliability . In this framework, the signs (positive or negative) of univariate z scores indicate the corresponding directions of effects , whereas multivariate z scores are always positive.A total of 9,803,735 biallelic variants were used for mvGWAS analysis, spanning all autosomes and chromosome X. The sample size for mvGWAS was 30,810 . We applied the MOSTest toolbox , and independent “lead SNPs” were also defined among them as having low LD (r2 < 0.1) with any others. If LD blocks of significant SNPs were located within 250 kb of each other, then they were merged into one genomic locus. Therefore, some genomic loci could include one or more independent lead SNPs. The major histocompatibility complex region on chromosome 6 was excluded from this process by default, because of its especially complex and long-range LD structure. Functional annotation and SNP-to-gene mapping were carried out in FUMA according to previously published criteria (We used FUMA (version v1.4.0) . To determine significance in this context, a threshold z score with an unsigned magnitude of >3.56 was applied, corresponding to a P value of 2.37 × 10−4 . To determine which structural connectivity measures contributed most to the multivariate associations as considered across lead SNPs, we summed the unsigned univariate z scores separately for each measure across all lead SNPs .For each SNP, MOSTest derives a P < 0.025/20,146), separately for each mvGWAS.MAGMA (v1.08) , separately for each mvGWAS.MAGMA (v1.08), with default settings as implemented in FUMA, was used to examine the enrichment of association for predefined gene sets. This process tests whether gene-based z scores were positively associated with higher expression levels in certain cell types, based on single-cell RNA sequencing data from the developing human prefrontal cortex (GSE104276)  to examine whether lower gene-based association The Brainspan study originally collected and assigned human brain postmortem tissue samples to 1 of 31 developmental/life-span stages          and visual inspection of their distributions to confirm normality. The adjusted and normalized polygenic scores were used as input for subsequent analyses.Polygenic scores were linearly adjusted for the effects of age, nonlinear age [i.e., (age-mean_age)Separately, for polygenic scores for each disorder or behavioral trait, canonical correlation analysis across 30,810 participants (“canoncorr” function in MATLAB) was used to test multivariate association with the 90 heritable node-level connectivity measures . This multivariate analysis identified a linear combination of the 90 node-level connectivity measures  that maximally correlated with the polygenic score for a particular disorder or behavioral trait across participants. Separately, for the polygenic score of each disorder or behavioral trait, the cross-participant Pearson correlation between each node-level connectivity and the canonical variable was used as a loading, reflecting the extent and direction of the contribution that a node-level connectivity made to a particular multivariate association. We also assessed the pairwise correlations across individuals between adjusted and normalized polygenic scores for the different disorders and behavioral traits.APOE locus is known to have a substantial effect on the risk of Alzheimer’s disease, we also recalculated polygenic scores for this disease after excluding a region from Chr19:45,116,911 to Chr19:46,318,605 (GRCh37) (As the http://neurosynth.org), a platform for large-scale synthesis of functional MRI data , and one from each pair of virtually duplicated terms (such as “Words” and “Word”). This method does not use inferential statistical testing but rather ranks terms based on the correlations between their activation maps and that of the input mask.From each separate canonical correlation analysis of polygenic scores and node-level connectivity, we identified the regions showing loadings of >0.2 or <−0.2, which were then used to define a single mask in standard brain space  . Each mask was analyzed using the “decoder” function of the Neurosynth database (


Solanum lycopersicum L.) accessions, including diverse varietal types , were profiled over two seasons for the main chemical and biochemical fruit traits. A medium–high level of heritability was found for all traits ranging from 0.52 for soluble solids to 0.99 for fruit weight. The average content of ascorbic acid was ~31 mg 100 g−1 of fw in both seasons, while the greatest variability was found for carotenoids with peaks of 245.65 μg g−1 of fw for total lycopene and 32.29 μg g−1 of fw for β-carotene. Dissection of genotypic (G) and seasonal (Y) factors highlighted genotype as the main source of variation for all traits. No significant effect of Y and G × Y was found for ascorbic acid and fruit weight, respectively, whereas a high influence of Y was found on the variation of lycopene. Molecular fingerprinting was performed using the 10K SolCAP array, yielding a total of 7,591 SNPs. Population structure, phylogenetic relationships, and principal components analysis highlighted a differentiation of plum and cherry genotypes with respect to the beefsteak and globe types. These results were confirmed by multivariate analysis of phenotypic traits, shedding light on how breeding and selection focused on fruit characteristics have influenced the genetic and phenotypic makeup of heirlooms. Marker–trait association showed 11 significantly associated loci for β-carotene and fruit weight. For β-carotene, a single variant on chromosome 8 was found at 12 kb to CCD8, a cleavage dioxygenase playing a key role in the biosynthesis of apocarotenoids. For fruit weight, a single association was located at less than 3 Mbp from SLSUN31 and fw11.3, two candidates involved in the increasing of fruit mass. These results highlight the potentiality of heirlooms for genetic improvement and candidate gene identification.A revival of interest in traditional varieties reflects the change in consumer preferences and the greater awareness of the quality of locally grown products. As ancient cultivars, heirlooms have been selected for decades in specific habitats and represent nowadays potential germplasm sources to consider for breeding high-quality products and cultivation in sustainable agriculture. In this study, 60 heirloom tomato ( A dietaQuality improvement is among the major targets to achieve in tomato breeding programs following the increase of the standards required in marketplaces over the past years . ProgresS. lycopersicum, S. lycopersicum var. cerasiforme, and S. pimpinellifolium L. was designed using 10,000 probes leading to a total of 8,784 SNPs of which 7,720 are of high quality, thus being appropriate for genomic scale analysis of germplasm collections representing different market classes  determine the variable level of main carotenoids, ascorbic acid, and chemical traits and estimate the genotypic and environmental effects underlying the variation of traits; b) investigate the population structure and phylogenetic relationships using the available 10K SNP array in order to corroborate metabolic and genomic profiles of the collection; and c) implement an early marker–trait association to identify potential candidates underlying the quality-related traits. The results of the study will be of interest for the enhancement of heirloom tomato varieties by promoting their use in local markets and sustainable agriculture and as a source of valuable quality-related traits for genetic improvement programs.22.1S. lycopersicum) accessions, retrieved from the collection available at the genebank of the Research Centre for Vegetable and Ornamental Crops  or provided by farmers’ associations. The genotypes’ panel was composed of diverse heirloom varietal types including beefsteak (11), cherry (16), globe (8), oxheart (3), and plum (22)   and 2020 (Y2)  in the experimental farm of CREA  following a randomized block design with three replicates and five plants/replicates. Plants were grown in single rows adopting distances of 1.50 m between the rows and 0.5 m apart on the rows. Black mulch was used to cover the rows. Cultivation was managed according to standard agronomic practices: plants were irrigated throughout the entire cultivation period using a drip irrigation system, and fertilization was provided through irrigation water. The transplant occurred in May and harvesting in August. At the maturity stage, a bulk of 10 fruits from each accession/replicate, for a total of 30 fruits/accession, was harvested, weighted , and subjected to chemical analysis . For the biochemical analysis, three replicates consisting of eight fruits for each accession were collected, pooled frozen in liquid nitrogen, and then stored at −80°C until analysis.2.2The chemical traits were as follows: titratable acidity (TA) and pH (pH) were measured on a 10% (w/v) aqueous tomato extract and NaOH 0.1 M as a titrating reagent using a pH-Matic 23 analyzer titroprocessor equipped with a pH electrode including a temperature sensor (model 5011T)  and soluble solids content (SSC) was measured on 0.5 ml of a tomato liquid extract using a digital refractometer . TA was expressed as g citric acid/L juice, and SSC was expressed as Brix degree.2.3g for 15 min. The extraction was repeated twice on the pellet; the supernatant was collected each time and finally made to 10 ml with the extracting solvent. The extracts were then filtered using 0.2-μm polytetrafluoroethylene (PTFE) filters. A final volume of 5 μl of the sample was injected on a Kinetex  column . The mobile phase constituted 0.02 mol L–1 of H3PO4 aqueous solution at a flow rate of 0.35 ml min–1. Quantification of AsA was performed at 254 nm using a calibration curve of an authentic chemical standard of ascorbic acid from Sigma-Aldrich . The AsA content was adjusted as mg 100 g−1 of fw (fresh weight).Ascorbic acid (AsA) was determined using an Ultimate 3000 UPLC system . Aqueous extracts (1 g of fruit plus 3 ml of 6% metaphosphoric acid in distilled water) were homogenized for 30 s using an Ultra-Turrax  and then centrifuged at 1,975×2.4trans-lycopene, and cis- lycopene were analyzed by reversed-phase high-performance liquid chromatography (RP-HPLC) using a Waters E-Alliance HPLC system constituted by a separations module with a quaternary pump, an autosampler, and a photodiode array detector ; data were acquired and analyzed with Empower software (Waters). Five grams of fruit pericarp was used. A reversed-phase analytical polymeric C30 column  was used for chromatographic separations. The separation parameters were a flow rate of 0.8 ml min−1 and 0.005 AUFS . Solvents used for sample preparation and extractions were of analytical grade, while those for HPLC analysis  were of HPLC grade; all were obtained from Merck . Trans-carotenoid standards (lycopene and β-carotene) used in the HPLC analyses were purchased from Sigma Chemical Co. . Results were expressed as μg g−1 of fw.β-Carotene, 2.5DNA was isolated by collecting six young leaves (100 mg) from each accession. Two leaves were considered for each accession/replicate. Once harvested, the tissue was lyophilized in microcentrifuge 2-ml tubes and then disrupted to a fine powder using a Tissue Lyser II  at 30 strokes per second for 30 s. Nucleic acid isolation was then performed using the PureLink™ Genomic Plant DNA Purification Kit . DNA concentration and quality were measured by absorbance at 260 and 280 nm, respectively, using both UV–Vis spectrophotometer  and Qubit 2.0 Fluorometer based on Qubit dsDNA HS Assay . The DNA solution was then diluted to a working concentration with distilled water. Genotyping was performed using the Illumina 10K SolCAP Tomato Infinium array  yielding a total of 7,720 SNPs . SNP cal2.6P< 0.05). The coefficient of variation (CV) as a percentage was expressed as the ratio of the standard deviation to the mean value multiplied by 100. Broad-sense heritability (H2) was estimated according to the formula:All traits were subjected to analysis of variance (ANOVA) using the generalized linear model (GLM) implemented in IBM SPSS Statistics, version 25.0  to determine the overall differences within the studied accessions. A two-factorial linear model was used to determine the effects of genotype (G), year (Y), and their interaction (G × Y) on trait performance. Mean square values (MS) were used to estimate the magnitude of the observed effect, while the total sum of squares in percentage (TSS%) was calculated by dividing the TSS of the effect by the total TSS. Means were compared using Tukey’s honestly significant difference test  were used. Runs were done using 50,000 burn-in cycles followed by 100,000 MCMC iterations, with the number of subpopulations (K) ranging between 1 and 10 with five independent runs for each K. The most probable numbers of subpopulations were determined according to Evanno’s method using Structure Harvester , and the biplot was drawn using the ggplot2 R package  and the kinship (K matrix) estimated using the identity by state (IBS) for accounting relationships among individuals. Phenotypic data from the two independent experiments were implemented. The significance threshold for marker–trait association was determined after Bonferroni multiple test correction with genome-wide α = 0.05. All analyses were performed in Tassel v 5.2.82 (Q–Q) plots for GWAS results were produced using the R package CMplot. Significant association signals were checked for their physical position on the S. lycopersicum SL4.0 genome. Underlying genes and their functions were determined considering the tomato ITAG release 4.1.Association analysis was performed using four models: i) the simple regression model , ii) the general linear model incorporating the population structure Q matrix (GLM-Q), iii) the general linear model incorporating principal components as a covariate (GLM-PCA), and iv) the mixed linear model (MLM) incorporating the population structure covariates (v 5.2.82 . To confv 5.2.82  were per33.1P > 0.001) among the heirloom tomato varieties for all traits when pooled data from the two seasons were analyzed  was found for all the traits. Partitioning of TSS% in the other components affecting the variation highlighted a generally smaller influence of Y (average 4.76%) and G × Y (average 16.57%) compared with G on the analyzed parameters. Fruit weight showed the strongest effect of genotype, being the only trait exhibiting no significant effect for G × Y. Ascorbic acid was instead slightly influenced by seasonal factors, thus showing no significance for the Y effect. Among bioactive compounds, β-carotene had the highest genotypic effect (81.24%), whereas a different degree of variability was found for the rest. Trans-lycopene showed a higher seasonal effect than cis-lycopene. For the latter, cis-9 lycopene was more strongly affected by genotype than the other two isomers.The results of the combined analysis of variance for fruit weight and chemical and biochemical traits across the two seasons are given in 3.2Marked divergences were found among the considered varietal types for fruit weight. Beefsteak and oxheart types showed the highest average fruit weight . All accessions exhibited a pH<5, being on average 4.49 and 4.39 during Y1 and Y2, respectively. Only ‘San Marzano lungo’ (plum) showed values of 5.05 during the first season  than in the second one (CV = 14.01%). A different trend was instead observed within each category: plum and oxheart types showed average higher AsA levels during the second season; contrariwise, beefsteak, cherry, and globe types showed lower levels (−1 of fw) during the first season  in plum types  and the negative of the second one (PC2), respectively, whereas globe types were distributed on both PC1 and PC2 excluding both the positive and negative sections of the first and second axes. The first component, which explained 43.04% of the total variance, was positively correlated with carotenoids, fruit weight, and pH and negatively correlated with ascorbic acid, acidity, and soluble solids. The second component, which explained 18.02% of the total variance, was positively correlated to all traits except pH and fruit weight. Cis-lycopene and acidity were the main factors discriminating the genotypes under study accounting for 17.32% and 33.15% of the total variation of the first and second components, respectively. The projection of the accessions on the two-dimensional PCA graph highlighted the differentiation of cherry and globe types which were mostly distributed in the negative axis of the first component, plum accessions which were mostly placed in the negative axis of the second component, and oxheart and beefsteak cultivars which were mainly distributed in the positive loadings of the first component. Based on PCA, it was possible to discriminate the cultivar groups according to specific features: cherry cultivars showing small fruits and a higher content of soluble solids and vitamin C and beefsteak and oxheart exhibiting greater fruit weight of the collection. The Pearson correlation matrix (P< 0.05) for the two growing seasons is reported in trans- and cis-lycopene were negatively correlated with soluble solids, and cis-lycopene was negatively correlated with acidity, whereas a significant positive correlation was found between acidity and soluble solids. All correlations among the different trait categories showed a coefficient<0.4.For the 11 analyzed traits, the PCA in the first two dimensions revealed 61.05% of the total variance  and the lowest on chromosome 12 (n = 383)  >0.99. The high coefficient membership for the accessions of cluster 1 was retained at K = 4 and K = 6, whereas a different degree of admixture was found for the accessions of cluster 2, particularly for ‘Small red,’ ‘Gold cherry,’ and ‘Fiaschello.’Based on STRUCTURE analysis  = 9.03] falling within a pto-interacting protein 1-like was found at the bottom of chromosome 8 (Snp_64706). A Small nuclear ribonucleoprotein located at the bottom of chromosome 6  was at 196 kb distance from the second peak detected. In the same region  = 8.17] and the remaining two  at less than 1 Mb from a CASP-like protein and a DUF674 domain-containing protein. Another cluster on chromosome 8  was found at 53-54 Mbp position at 12.40 kb distance from a Carotenoid Cleavage Dioxygenase 8 and at 1.90 kb distance from a Cytochrome b-c1 complex subunit 7. Another peak at the bottom of chromosome 7  was found at 3.29 kb from a glycotransferase, whereas the remaining associations at the bottom of chromosomes 9  and 5  were not associated to any known protein. For fruit weight, only the SNP SL1_00sc6004_2094360_Snp_44897 [−log10(P) = 5.23] was found in intergenic regions at 2.41 kb from a Receptor-like serine/threonine-protein kinase NCRK on chromosome 11. The peak was not significantly associated using the CMLM model implemented in GAPIT.Associations between SNP alleles and traits were acquired based on different models. Significant associations were found in both GLM and MLM models only for fruit weight and β-carotene. The generalized linear models highlighted several spurious associations, as suggested by a comparison with probabilities predicted from a theoretical uniform distribution of 4Intensive farming, urbanization, and marketing have impacted the diversity of agricultural systems, increasing the genetic erosion of crops. Globally, few plant species are dominating the agricultural scenario of which the production is based on a limited number of reference varieties . During 4.1Trans-, cis-13 lycopene, and soluble solids content were instead majorly affected by seasonal factors. During the second season, a substantial increase of trans-lycopene up to +70% with respect to the previous one, as well as a decrease of soluble solids and cis-lycopene for 14.44% and 37.35%, respectively, was found. The accessions were grown in the same site, and only a slight variation in terms of temperature was found in the two seasons as well as an increase of 1% of humidity and 0.3 mm of rain/precipitation in the second year. It has been reported that water shortage could have impact on soluble solid ; furthermore, for many maturing yellow accessions, no trace of the cis-isomer was found. It has been reported that the cis form of lycopene is better adsorbed by the organism compared with the trans one due to the shorter length of the molecule, less tendency to aggregate, and higher solubility . This gene has been reported to play a role in the biosynthesis of apocarotenoids being involved in the conversion of all-trans-β-carotene to strigolactones (Marker–trait association allowed the detection of interesting candidates underpinning β-carotene and fruit weight. Although the two genic lactones .Solyc11g069590.2.1 falls at 1 Mbp distance from SLSUN31, a SUN-LIKE family member reported to be involved in the increase of fruit weight (fw11.3, a major QTL controlling the variation of cell size responsible for the increment of fruit mass. However, this association, although confirmed in both general and mixed linear models, was under the significant threshold when implementing CMLM in GAPIT, thus suggesting further investigations toward the validation of Solyc11g069590.2.1.For fruit weight, the peak near t weight , and at These results shed light on the potentiality of the diversity enclosed in the heirlooms for gene candidate identification. It must be recognized that the number of individuals used for association analysis is low, although examples are reported with a similar or even a lower sample size .The genotyping strategy used allows for the implementation of genomic data with more individuals, building a broader collection for future genome-wide association mapping studies.5The increased interest in traditional food staples led to the resurgence of locally grown varieties with distinct nutrition and sensory traits. In this frame, heirlooms are increasingly popular on the market, thanks to their intriguing diversity. In this work, a tomato heirloom collection has been profiled through genomic and metabolic approaches determining qualitative characteristics and genetic diversity and investigating the genetic and environmental factors underlying trait variation. The comprehensive approach used has demonstrated how the set studied provides a broad source of quality-related characteristics. We also highlighted the potentiality of heirloom tomato varieties for gene discovery by generating molecular data through the tomato genotyping array. This strategy allows for the implementation of the collection with new accessions and/or data from previous studies. Overall, the study provides knowledge to determine the importance of heirlooms for their direct consumption or to promote their sustainable use in safeguarding biodiversity and conservation programs and for breeding high-quality products.https://figshare.com/articles/dataset/SNP_Data_60_heirlooms_xlsx/21231260.The original contributions presented in the study are publicly available. These data can be found here: PT conceived the project and coordinated the experiments. PT and GF designed the experimental layout. GF and AD’A performed the metabolic characterization. PT performed the genomic analysis. PT and GF collected and curated the data. PT analyzed the data and drafted the manuscript. All authors critically revised the manuscript and approved the submitted version.