Recently, crossbred animals have begun to be used as parents in the next generations of dairy and beef cattle systems, which has increased the interest in predicting the genetic merit of those animals. The primary objective of this study was to investigate three available methods for genomic prediction of crossbred animals. In the first two methods, SNP effects from within-breed evaluations are used by weighting them by the average breed proportions across the genome (BPM method) or by their breed-of-origin (BOM method). The third method differs from the BOM in that it estimates breed-specific SNP effects using purebred and crossbred data, considering the breed-of-origin of alleles (BOA method). For within-breed evaluations, and thus for BPM and BOM, 5948 Charolais, 6771 Limousin and 7552 Others (a combined population of other breeds) were used to estimate SNP effects separately within each breed. For the BOA, the purebreds' data were enhanced with data from ~ 4K, ~ 8K or ~ 18K crossbred animals. For each animal, its predictor of genetic merit (PGM) was estimated by considering the breed-specific SNP effects. Predictive ability and absence of bias were estimated for crossbreds and the Limousin and Charolais animals. Predictive ability was measured as the correlation between PGM and the adjusted phenotype, while the regression of the adjusted phenotype on PGM was estimated as a measure of bias.With BPM and BOM, the predictive abilities for crossbreds were 0.468 and 0.472, respectively, and with the BOA method, they ranged from 0.490 to 0.510. The performance of the BOA method improved as the number of crossbred animals in the reference increased and with the use of the correlated approach, in which the correlation of SNP effects across the genome of the different breeds was considered. The slopes of regression for PGM on adjusted phenotypes for crossbreds showed overdispersion of the genetic merits for all methods but this bias tended to be reduced by the use of the BOA method and by increasing the number of crossbred animals.For the estimation of the genetic merit of crossbred animals, the results from this study suggest that the BOA method that accommodates crossbred data can yield more accurate predictions than the methods that use SNP effects from separate within-breed evaluations.The online version contains supplementary material available at 10.1186/s12711-023-00806-1. The use of crossbred animals in livestock production has been adopted for many years in pigs and poultry, and currently, it is increasing in cattle to improve the profitability of the systems . AccordiGenomic prediction (GP) consists of the use of a large number of single nucleotide polymorphisms (SNPs) to predict the genetic merit of selection candidates  and has For prediction in crossbred animals, VanRaden et al.  suggesteThus, this study had two principal aims: (1) to investigate whether the accuracy of GP from a BOA method is higher than that from methods that combine SNP effects from within-breed evaluations (BPM and BOM) for crossbred animals and (2) to investigate the impact of the number of crossbred animals in the reference population when using the BOA method. The predictive ability and absence of bias of the three methods were tested using purebred and crossbred animals from the multi-breed Irish cattle population.https://www.icbf.com) national database. In the genomic prediction models, we used carcass weight (kg) and genomic data from 26,164 purebred and 47,818 crossbred animals born between 2000 and 2020 and slaughtered between 307 and 1277 days of age. In this study, an animal was defined as a purebred when the pedigree breed proportion was 32 out of 32 from a particular breed, which means that all its ancestors, five generations back, had to be registered as a purebred of the same breed; otherwise, it was considered a crossbred.The cattle data used in our study were sourced from the Irish Cattle Breeding Federation , while purebred animals were genotyped with 20 SNP arrays, of which 84% were of medium density (between 45 and 63K). The ICBF provided the genotypes imputed to a common reference panel for all breeds and phased them into two haplotypes using the FImpute software . In totaAnalysis using BOA information requires that the breed-of-origin of phased alleles in the crossbred animals is known. We estimated BOA for all the crossbred animals using the four breeds that contributed the most to the crossbred genotyped animals as parental breeds, i.e. Limousin, Charolais, Angus and Holstein, and then Others group that included the purebred animals of the five breeds that ranked after the previous four in terms of their contribution to the crossbreds, i.e. Friesian, Simmental, Hereford, Belgian Blue and Shorthorn. We used this grouping strategy because the number of purebred animals from these breeds was small, and their individual contribution to the crossbred animals was very low (~ 3%). The contribution of the other breeds to the crossbreds was so low that they were considered negligible.We used the AllOr software  to inferWe also included a pedigree file that contained 481,058 animals born from 1966 to 2020 , which were obtained by tracing back the genotyped crossbred animals in a full pedigree containing 11,850,818 animals. We used the DMU Trace program [AllOr detects BOA for each chromosome separately, thus requiring a split analysis by chromosome. Therefore, 290 analyses were performed (one per chromosome per set). On average, across the 10 sets, for the longest chromosome (3139 SNPs), it took ~ 102 h to detect BOA, and for the shortest chromosome (895 SNPs), it took ~ 28 h. Analyses required ~ 860 Mb and ~ 283 Mb of memory for the longest and the shortest chromosomes, respectively.The alleles with their assigned BOA were used to build the breed-specific genotype matrices required for the BOA method and for validation using the BOM (explained later). The alleles not assigned to Limousin, Charolais, Angus and Holstein, and Others were also defined as originating from the Others group.The contribution of Angus and Holstein alleles in the crossbred genotyped population was small, i.e. ~ 13% for each breed and only 2215 Angus and 1515 Holstein individuals had phenotype data in addition to genotypes. Therefore, keeping these as two separate breeds when estimating parameters in the models resulted in convergence issues, especially in the estimation of covariances between breeds in the BOA method. Thus, these breeds were included in the Others group, keeping three components for GP, the two main breeds: Limousin and Charolais, and Others.After estimating BOA, genomic breed proportions (GBP) were calculated for each crossbred animal by counting the alleles assigned to Limousin, Charolais, or the Others group.Before running the prediction models, the phenotypes of all animals in the population  were pre-corrected with the DMU software  using anUNCOR) or (3b) correlated (BOACOR), as shown in Table We compared three methods based on different data sources in the reference population and validation strategies used. In two methods (BPM and BOM), separate within-breed evaluations were performed to obtain SNP effects for each breed. Those effects were then used to obtain the PGM, either 1) based on GBP (BPM) or (2) based on breed-origin-of-alleles (BOM). The third method (BOA) considered information on crossbred animals to estimate SNP effects and combined those effects with the BOA estimates to obtain PGM. The BOA method was implemented assuming that the SNP effects of the different breeds are (3a) uncorrelated , we applied a Bayesian whole-genome regression method to estimate SNP effects:dfu-2dfu . Here, \The following model was used to jointly analyze all purebreds and crossbred animals, which is referred to as the BOA model :2\documementclass2pt{minimo et al. . In theiUNCOR).Finally, The k2df-2df . Here, \COR). In that case, a multivariate normal distribution was assigned for each vector of SNP effects: Priors were also assigned to estimate breed-specific SNP effects, but assuming that the SNP effects between the different breeds were correlated  algorithm with Gibbs sampling was run for 100,000 cycles to infer model parameters, and the first 20,000 cycles were taken as the burn-in period and thus discarded. Every 10th cycle of the remaining samples was saved, giving 8000 posterior samples for each parameter. The mean value of each parameter over the posterior samples was used as its estimate. In addition to the estimates of SNP effects, we also obtained the posterior distribution of ds Table . The anaReference and validation populations were defined similarly to those in Su et al.  and Liu For the reference set used in within-breed evaluations, we used all available purebred animals that were not allocated to the validation population: 5948 Charolais, 6771 Limousin and 7552 Others. For the reference sets used in the BOA method, we used the purebreds mentioned for within-breed evaluations and added an increasing number of crossbred animals. The selection of the crossbred animals to be included was based on a minimum number of animals in each contemporary group of the herd of slaughter: 15 animals per group (4423), 10 animals per group (8239) and five animals per group . A summary of the reference sets used in the BOA model is in Table The validation set consisted of 35,362 animals, of which 5893 were purebred  and 29,433 were crossbred. Crossbred animals were separated into four groups depending on the contribution of the two main breeds: a contribution of < 25% , After estimating the SNP effects using each reference population, PGM were estimated differently depending on the approach used. It should be noted that both BPM and BOM rely on the same set of SNP effect estimates obtained by fitting Eq.  to each The PGM from the BPM is obtained as follows :3\documeThe PGM from the BOM is obtained as follows :4\docume for Eq. , \documeThe PGM from the BOA is obtained as follows: for Eq. , \docume for Eq.  and \docWe tested the ability of each method to predict the performance of the validation animals. The predictive abilities of each method were calculated as the correlation between adjusted phenotypes (y*) and PGM for the validation animals. A nonparametric bootstrap procedure, with a size equal to that of the validation population, was obtained by random sampling with replacement from validation animals. We repeated the bootstrap procedure to obtain 10,000 bootstrap samples of the PGM of each scenario. The comparisons were performed using a two-tailed paired t-test of the 10,000 samples of each scenario. Bonferroni correction was applied to account for the multiple-test comparisons. The slopes of regression of adjusted phenotypes on PGM were calculated as a measure of bias for all methods. Slopes of ~ 1 imply no bias, while slopes greater than or less than 1 indicate under- or overdispersion of genetic merits, respectively.The AllOr software was able to assign most alleles to their breed-of-origin, i.e. on average, 97.03% (ranging from 69.99 to 99.88%) of the alleles were assigned a breed-of-origin. We observed no trend in the assignment between different chromosomes, but the extremities of all the chromosomes had the lowest assignment Fig. .Fig. 1AvCOR and BOAUNCOR did not differ significantly. However, within the BOA analyses, BOAUNCOR including ~ 4K crossbreds yielded the worst performance and the BOACOR including ~ 18K crossbreds yielded the best performance.The genomic prediction results are in Table COR in the 20K_P_18K_C scenario compared to those of BPM were equal to 14%, 43%, 35% and 37% for the groups with a contribution of the main breeds of < 25%, The advantage of BOA over the other two methods for crossbred animals tended to be larger as the proportion of the main breeds contained in the tested crossbred animals increased. The increases with the BOAThe regression slopes of adjusted phenotypes on PGM per scenario and method are in Table The correlations between the SNP effects of the different breeds ranged from 0.49 to 0.86 Table , computeeen them .Table 5EIn this study, we investigated the use of three methods for predicting the genotypic merit of crossbred animals. On the one hand, the first two methods consider only purebred information (phenotypes and genotypes) to estimate SNP effects and combine those SNP solutions based either on breed proportions (BPM) or on the breed-of-origin of the genome regions (BOM). On the other hand, the third method relies not only on the purebred but also on the crossbred information and takes the breed-of-origin of the genome regions into account in the analysis (BOA method). In addition, for the last method, the impact of including different numbers of crossbred animals in the reference population was investigated.The BOM and BOA methods require that alleles are traced back to their breed-of-origin with high accuracy. The BOM method uses this information only to predict the genetic merit of crossbred animals but not to estimate the SNP effects, whereas the BOA method also uses it to integrate crossbred data in the reference population and thereby to estimate the SNP effects. Errors in the breed-of-origin assignments can affect the performance of both the BOM and BOA methods, but such errors may have a greater impact on the BOA method, as they also impact the accuracy in the estimation of the SNP effects. If the origins of alleles at certain loci are switched , then the estimates of breed-specific alleles will be affected by information from the opposite breed, resulting in a higher similarity, and therefore, in a higher correlation between them , when thVanRaden et al.  proposedA high percentage of the alleles were assigned to a breed-of-origin (i.e. only 3% were unassigned) even when the type of crosses varied in the population. The assignments did not differ much between chromosomes, but their extremities presented lower assignments than the middle regions. This occurs with haplotype-based assignment methods because the number of possible matching haplotypes decreases when the algorithm looks only in one direction, which occurs at the start and end of the chromosomes . For a rThe BOA method yielded a higher predictive ability for crossbred animals than the two BPM and BOM methods that are based on combining SNP solutions from separate within-breed evaluations, which indicates that SNP effects are estimated with higher accuracy when crossbred data are included in the reference population with the help of BOA. In addition, the predictive ability for crossbreds increased as the number of crossbred animals in the reference population increased. In our study, the predictive abilities of the methods that use SNP solutions from separate within-breed evaluations were not significantly different in most cases, while the BOM method proposed by Eiríksson et al.  outperfoIn addition to assessing the methods' ability to predict the genetic merits of crossbred animals, we also assessed their predictive ability in purebreds. Although there were no significant differences between the methods, BOA tended to yield higher predictive abilities for the purebred animals as the number of crossbreds in the reference population increased. When comparing the pure breeds, the predictive ability was higher for the Limousin than for the Charolais breed. One explanation is that the number of purebred animals to estimate SNP effects is larger for the Limousin than the Charolais breed (6771 vs 5948). Furthermore, when we used genomic relationship matrices within pure breeds, we observed that the genomic relationship between the reference and validation animals was higher in the Limousin than in the Charolais breed (results not shown), which could be due to the higher levels of artificial insemination used in this breed and thus implies that there were fewer effective sires in the Limousin population.Using the BOA method, the number of SNP effects to be estimated increases as the number of breeds included in the analysis increases, whereas the number of phenotypes may be constant. In this study, for the proof of concept, we applied a restriction on the breed proportions of animals, taking two dominating breeds (Charolais and Limousin) in the population as the main breeds of interest and the rest of the breeds as Others, implicitly assuming that those in the Others group have the same SNP effects. Such decisions are also needed even for relatively simpler approaches .The slopes of the BOA method were closer to 1 than those of BPM and BOM in most scenarios. The slope tended to be closer to 1 as the number of crossbred animals in the reference population increased. It has been previously reported that including crossbreds’ genomic information in prediction models reduces prediction bias . The sloWhen considering the methods based on breed-specific SNP effects, an important factor to consider is the level of genetic relationships among the breeds , 25, whiThe amount of genetic variation explained may vary between regions leading to heterogeneous variance patterns across the genome . AccountCOR and BOAUNCOR) including only purebred animals in the reference population. Some covariances did not fully converge for the BOACOR, and thus we do not present those results. The predictive ability of the BOAUNCOR using only purebred animals was 0.473 for crossbreds, 0.220 for Charolais and 0.287 for Limousin (results not given elsewhere), very similar to the results of BOM which also relies only on purebred data and assumes uncorrelated SNP effects.We ran additional BOA analyses . To assess this, an additional joint analysis was performed using a combined dataset of the reference population animals, with just one additive component in the model (i.e. ignoring the BOA). The same three groups of animals were used as for the BOA method, 20K_P + 4K_C, 20K_P + 8K_C and 20K_P + 18K_C. The description of this model and the results are presented in Additional file For the current implementation of the BOA method, all crossbred animals need to be genotyped. The single-step genomic BLUP , 34 faciOur results indicate that using phenotypes from crossbred and purebred animals can greatly enhance predictive ability over methods that rely on phenotypes from purebred animals only, even when ignoring BOA. Greater predictive abilities can be achieved by accounting for BOA. In addition, the predictive ability of the BOA method increases as the number of crossbred phenotypes included in the analysis increases. Therefore, this methodology could be a good alternative to predict genotypic values for crossbred selection candidates.Additional file 1. Model.Additional file 2: Table S1. Predictive abilitywith standard errorbetween brackets and differences in % of the BOAUNCOR minus the joint analysis. Table S2. Title: Average slope of regression for LPGV on adjusted phenotypes with standardbetween brackets and differences in % of the BOACOR minus the joint analysis.


Hoof structure and health are essential for the welfare and productivity of beef cattle. Therefore, we assessed the genetic and genomic background of foot score traits in American (US) and Australian (AU) Angus cattle and investigated the feasibility of performing genomic evaluations combining data for foot score traits recorded in US and AU Angus cattle. The traits evaluated were foot angle (FA) and claw set (CS). In total, 109,294 and ~ 1.12 million animals had phenotypic and genomic information, respectively. Four sets of analyses were performed: (1) genomic connectedness between US and AU Angus cattle populations and population structure, (2) estimation of genetic parameters, (3) single-step genomic prediction of breeding values, and (4) single-step genome-wide association studies for FA and CS.Bos taurus (BTA) chromosomes BTA1, BTA5, BTA11, BTA13, BTA19, BTA20, and BTA23. The candidate genes identified were primarily from growth factor gene families, including FGF12 and GDF5, which were previously associated with bone structure and repair.There was no clear genetic differentiation between US and AU Angus populations. Similar heritability estimates (FA: 0.22–0.24 and CS: 0.22–0.27) and moderate-to-high genetic correlations between US and AU foot scores (FA: 0.61 and CS: 0.76) were obtained. A joint-genomic prediction using data from both populations outperformed within-country genomic evaluations. A genomic prediction model considering US and AU datasets as a single population performed similarly to the scenario accounting for genotype-by-environment interactions , even though the genetic correlations between countries were lower than 0.80. Common significant genomic regions were observed between US and AU for FA and CS. Significant single nucleotide polymorphisms were identified on the This study presents comprehensive population structure and genetic and genomic analyses of foot scores in US and AU Angus cattle populations, which are essential for optimizing the implementation of genomic selection for improved foot scores in Angus cattle breeding programs. We have also identified candidate genes associated with foot scores in the largest Angus cattle populations in the world and made recommendations for genomic evaluations for improved foot score traits in the US and AU.The online version contains supplementary material available at 10.1186/s12711-023-00850-x. Lowly-to-moderately heritable traits  in livestock are substantially affected by environmental factors. Structural problems have been a concern in both beef and dairy cattle breeding schemes due to their impact on animal welfare, increased complexity in daily management , and economic losses due to costs of treatments and negative impact on production and fertility , 2. StruOver the past decades, advancements in reproductive technologies  have enabled higher-genetic-merit animals to produce more offspring under vastly different management systems and geographical regions . In thisAcross-country genetic evaluations in beef cattle have been implemented or are under investigation in various regions around the world –20. An iJoint genomic evaluations can be beneficial to beef cattle breeding programs as they provide an opportunity to increase the size of reference populations, especially for novel or lowly-heritable traits, and consequently increase the accuracy of GEBV without significant additional costs to producers –23. ConsBoth the American Angus Association  and Angus Australia  use similar scoring systems: FA  and CS . Both FA and CS are subjective measurements, and the scoring system is presented in Fig. www.angusaustralia.com.au/education/breeding-and-genetics/collection-guidelines-for-angus-research/collecting-mature-cow-structural-soundness-scores/ (accessed 27 July 2022). Foot score CG were formed by concatenating management factors, which differed between AAA and AAU. The CG from AAA was defined by the concatenation of sex, weaning and yearling information ; diet type , and herd and date when the foot scores were collected. The CG for AAU was formed by concatenating sex, weaning and yearling management groups, management group defined at the time of scoring the traits, and in which leg the score was collected . Foot scores from AAU animals are mainly recorded by trained technicians and two scores are available for each animal and metric: one for the front and on for the rear leg. However, only the worst scored leg in the AAU was used for this study to mimic the AAA dataset. More details about the recording performed by the AAA and AAU can be found at the AAA website  and www.or rear; ).The phenotypic datasets included data from purebred Angus cattle registered in the AAA (referred to as the US Angus population) and AAU (referred to as the AU Angus population). In total, 85,549 records  from the US population and 85,439 records  from the AU population were available for the analyses. The description of the phenotypic datasets prior to quality control is in Additional file biomaRt R package [The genomic dataset contained animals from multiple countries and various genotyping platforms or SNP panel densities. The genotypes were mapped to the bovine genome assembly UMD3.1. Genotype imputation was performed to a common 50K SNP panel, as implemented in the analytical pipeline of the AAA. Genotype imputation and quality control were done within each country. A quality control of the genomic data was performed at the SNP and animal levels, in which SNPs with a minor allele frequency higher than 0.05 and a call rate higher than 0.90 were kept for further analyses. Animals with a sample call rate higher than 0.80 were also kept. In total, 39,595 SNPs remained after quality control, and 1,145,751 genotyped animals remained for the genomic prediction analyses, containing 996,329 US, 131,951 AU, 17,403 Canadian, and 68 from other countries. Finally, the genomic coordinates were converted to the ARS-UCD1.2 bovine genome assembly , 28 usinOnly a subset of the genotyped population was used to obtain p-values for SNPs in the ssGWAS analyses because p-values depend on the prediction error variance of the SNP effect from the ssGBLUP results, and the full coefficient matrix needs to be inverted . To overThe pedigree of US and AU were merged based on a common identification number across countries, which was provided by both the AAA and AAU breeders’ associations. For the estimation of genetic parameters and ssGWAS, 122,471 and 119,160 animals for US and AU, respectively, were included in the pedigree. The pedigree file included up to four generations of ancestors from the animals with phenotypic information, including 10,945 sires in the US and 8885 in the AU pedigree dataset. In total, 973 sires had progeny in both US and AU datasets, accounting for 41%  and 25%  of animals in the US and AU pedigree files, respectively. Furthermore, 1383 dams with progeny shared between both countries were available, accounting for 4115 US and 2069 AU animals in the pedigree. Regarding the phenotypic data, 215 sires with progeny with phenotypic information in both countries were available, accounting for 27% of the animals with phenotypic records in the US dataset  and 13% of animals with phenotypic records in the AU dataset . For the genomic prediction analyses, the pedigree file was created by including up to four generations of ancestors from phenotyped animals (with and without genomic information) and relatives of genotyped animals without phenotypes, resulting in a total of 1,902,478 animals.2) between two alleles at different loci [2 values with the sign of the disequilibrium metric A, a and B, b; A at one locus, B at a different locus, and AB haplotype. Both metrics were calculated using the snp1101 software [The level of linkage disequilibrium (LD) was measured based on the squared correlation  analysis (PCA) was performed to investigate the genomic similarities between the US and AU Angus populations. The PC were estimated based on the genomic relationship matrix (VanRaden ) and usiVanRaden . The sofmentclass2pt{minim2 higher than 0.20 in a genomic window of 10 SNPs were filtered out. In total, 13,566 SNPs remained for the admixture analyses. The optimal K value was defined based on a fivefold cross-validation procedure and prior knowledge about the populations, in which the K values evaluated ranged from 1 to 10 , in which the datasets from each country were analyzed separately  and a covariance structure was considered between FA and CS [Eq.  descriptDescription (a) of Eq.  for a wientclass1pt{minimas in Eq. , and \dos in Eq. .2\documeSecond, a multi-country two-trait model (MC-TT) was analyzed [Eq.  descriptd in Eq. , \documes in Eq. . The othThird, a joint-country two-trait model was analyzed (JC-TT), in which the data for both FA and CS from the US and AU populations were combined as if they were a single population [Eq.  Descriptentclass1pt{minimas in Eq. , and \dos in Eq. . Age wasentclass1pt{minimaIdentifying the optimal scenarios for performing genomic prediction of breeding values for FA and CS in the US and AU Angus populations was a key goal of this study. The datasets used mimic the current genetic evaluation performed by AAA. A single-step GBLUP (ssGBLUP) approach was used for genomic predictions. A forward genomic evaluation was performed, in which animals born in 2020 for the US and 2019–2020 for AU were considered as the target  population. A simplification of the linear regression method  was usedls  was useThe genomic prediction scenarios changed depending on the phenotypic datasets included in the training population, statistical models, and estimates of genetic parameters. The scenarios tested are described in Table −11). The approximated theoretical  accuracies (by inversion of the coefficient matrix of the mixed model equations) were obtained from the accf90GS package [For these analyses, the blup90iod2OMP1 package , 37 was  package , 37, and package ).In the AC scenario (AC), data from Canadian Angus (part of the AAA database) were included for validation purposes. The whole dataset analyses based on the Legarra and Reverter  approach−6 and 10−4 for strong and moderate associations, respectively.Single-step genome-wide association studies (ssGWAS) were performed based on multi-country single-trait models (MC-ST). First, pedigree-based genetic parameters were obtained for the MC-ST model, and then the GEBV were estimated using those parameters. Subsequently in the ssGWAS step, SNP effects and p-values were calculated using the postGSf90 package from the blupf90+ family of programs , 41, 42.biomaRt R package [A genomic window of 100 kb on both sides of the significant SNPs was considered for identifying associated genes and performing functional analyses. Gene symbols were retrieved from Ensembl using the  package , 43. Fun package ) and met package ) availab package ; accesse package , 28.The LD decay for the US and AU Angus populations is shown in Fig. Figure mentclass2pt{minimFigure Table The genetic parameters for the MC analyses are in Additional file The heritability estimates of FA and CS when considering the JC-ST model were 0.24 0.01) and 0.25 (0.01), respectively. The heritability estimates for the JC-TT analyses were 0.24 for both FA and CS and the genetic correlation between both traits was 0.46  yielded higher accuracies than WC  were calculated, with high correlations (> 0.75) being observed across all scenarios  chromosomes BTA1, 5, 13, and 20  genomic prediction depends on the levels of LD and genetic links between the populations . The US www.angusaustralia.com.au/about/our-cattle/history-of-angus/; accessed 7 August 2022). Similarly, the first Angus animals were brought to the US in 1873, also directly from Scotland . In Fig. The first record of the Angus breed in Australia was in 1824, when animals were directly imported from Scotland  are positive, favorable, and moderately-to-highly genetically correlated  and CSAU . However, those differences could also be observed within each country. In a study conducted using the AU Angus dataset, depending on the trait being evaluated, reranking of sires was observed across states, such as, between Victoria and Queensland . A genetThe ultimate goal of this study was to identify the optimal statistical model to perform a multi-country genomic evaluation for foot score traits. A joint genomic evaluation across countries has many benefits, such as increasing the size of the genomic reference population, and making the published breeding values directly comparable across the countries involved, which facilitates the exchange of genetic material. Scenarios mimicking a within-country genomic evaluation were considered as a control and baseline comparison . Furthermore, two scenarios for a joint genomic prediction were tested including US and AU as two different populations  and US and AU as a single population (JC-ST or JC-TT). The last scenario could be more optimal because a single-scale breeding value would be available for both countries, more computationally feasible, and an additional encouragement to the standardization of the scoring protocols across countries. In national genomic evaluations for foot scores, FA and CS are currently fitted in two-trait models. If each country needs to be treated as a different population, a four-trait model  would be required.In summary, FA and CS benefited from a joint genomic evaluation. Including information from another country improved the accuracy of genomic breeding values from 0.34  to 0.58 (MC or JC) and from 0.35  to 0.56 (MC or JC) for FA and 0.44 (US) to 0.56 (MC) and from 0.48 (AU) to 0.58  or as a single population (JC) provided lowly biased results, less over/underestimated GEBV, and more accurate estimates Fig.  for bothAs predicted by formulas, traits with a lower heritability require larger reference populations to estimate the accuracy as a function of the previously mentioned factors . The acHayes et al.  reviewedUS = 0.58, FAAU = 0.55, CSUS = 0.59, and CSAU = 0.61) resulted in similar results compared to the analyses taking GxE into account , thus holding the same conclusions as those presented for an independent trait analysis for foot score.In general, within-country genomic evaluation when analyzing both FA and CS together outperformed the predictability of a model fitting FA and CS, separately Figs.  and 7, aFinally, we also performed an across-country genomic evaluation Figs.  and 7. AFew SNPs were significantly associated with foot scores, and they overlapped between the US and AU populations. In general, more significant regions were identified for the AU population, which may be justified by a slightly larger reference population , ATP13A5 (ATPase 13A5), HRASLS (HRAS like suppressor), and ENSBTAG00000055130. The fibroblast growth factor gene family is known to be linked to the biological regulation of, for example, articular cartilage [Relevant candidate genes that could be involved in the biological mechanisms underlying FA and CS were found on BTA1 Fig. . Four anartilage , 61, andartilage .C13H20orf194 gene on BTA13 [C13H20orf173  was identified as a candidate for both FATT.US and FATT.AU. GDF5 is also known as cartilage-derived morphogenetic protein-1, and studies in mice and humans have highlighted its importance for skeletal development and repair [www.mousephenotype.org; accessed 18 August 2022). Therefore, the regions identified in our study are relevant candidates to better understand the mechanisms underlying foot scores in both the US and AU Angus populations.On BTA13, the d repair –66. KnocGDF5), which has been validated, in animal models, as having a role in skeletal development.American (US) and Australian (AU) Angus cattle populations have similar genetic backgrounds and population structures. A joint genomic evaluation across both countries provided better predictivity ability compared to within-country evaluations. Although a genotype-by-environment interaction (GxE) was observed based on the genetic correlations between the US and AU foot scores, a single-trait model considering the US and AU populations as a single-population provided similar predictivity ability in comparison to modeling GxE. Considering these two populations as a single population for genomic evaluation provides advantages, such as easier implementation and a potential encouragement for the adoption of consistent measurement protocols across countries. Analyzing foot angle and claw set, separately or jointly (two-trait model), in a within-country evaluation had a significant impact on the performance of the genomic predictions. However, when both the US and AU datasets are analyzed together, no clear benefit was observed for the analysis of foot angle and claw set, separately or jointly. An across-country evaluation also indicated that the reference populations from one country can be used to predict breeding values in the other population but combining the data from both countries resulted in more accurate GEBV. Finally, foot scores have a polygenic genetic architecture and several important candidate genes were identified in this study. For both foot angle and claw set, BTA1 showed shared genomic regions, including a gene from the fibroblast growth factor gene family, which has been widely reported to affect cartilage repair. Another growth factor gene was identified on BTA13  and Australian (AU) Angus populations. The dataset provided presents the number of records, frequency of foot angle and claw set scores, number of animals, number of herds, number of contemporary groups, and date of birth of the animals. Table S2. Variance explained by the first ten principal components using different datasets to create the genomic relationship matrix. Variance explained by the 10 principal components using the four data subsets. Table S3. Estimates of the heritability for foot angle (FA) and claw set (CS) using the American (AU) and Australian (AU) Angus datasets analyzed with a single-trait model within each country dataset. Table S4. Genetic parameters  for foot angle (FA) and claw set (CS) based on multi-country two-trait models (MC-TT) between the American (AU) and Australian (AU) Angus populations. Table S5. Genetic parameters  for foot angle (FA) or claw set (CS) using genomic information based on a multi-country single-trait model (MC-ST) between American (US) and Australian (AU) Angus populations. Table S6. Genetic parameters for foot angle (FA) and claw set (CS) based on joint-country two-trait model (JC-TT). Table S7. Accuracy, bias, and dispersion of the genomic prediction: within-country two-trait (WC) and across-country two-trait (AC) model using Australian Angus (AU) as the estimation set. Predictive ability from a forward validation using the linear regression method, including bias, dispersion, and accuracy for a forward validation in the AU animals (2019–2020) and across-country evaluation.Table S8. Accuracy, bias, and dispersion of the genomic prediction: within-country two-trait (JC) and across-country two-trait (AC) model using American Angus (US) as the estimation set. Predictive ability from a forward validation using the linear regression method, including bias, dispersion, and accuracy for a forward validation in the US animals (2020) and across-country evaluation. Table S9. Accuracy, bias, and dispersion of the genomic prediction based on multi-country two-trait models (MC-TT). Predictive ability from a forward validation using the linear regression method, including bias, dispersion, and accuracy for a forward validation. Table S10. Accuracy, bias, and dispersion of the genomic prediction based on joint-country two-trait models (JC-TT). Predictive ability from a forward validation using the linear regression method, including bias, dispersion, and accuracy for a forward validation. Table S11. Accuracy, bias, and dispersion of the single-trait (ST) genomic prediction scenarios within-country (WC-ST), multi-country (MC-ST), and joint countries (JC-ST). Predictive ability from a forward validation using the linear regression method, including bias, dispersion, and accuracy in scenarios using genomic (ssGBLUP) or not (BLUP). Table S12. Predictive ability for all genomic prediction scenarios for the Australian Angus target population (AU) presented by validation year: for single-trait within-country (WC-ST), multi-country (MC-ST), and joint-country (JC-ST) models. Predictive ability from a forward validation using the linear regression method, including bias, dispersion, and accuracy in scenarios using genomic (ssGBLUP). Results are presented for each birth year validation group in the AU population. Table S13. Average individual accuracy for the genomic prediction scenarios for foot angle based on single-trait models (-ST). Table S14. Average individual accuracy for the genomic prediction scenarios for claw set based on single-trait models (-ST). Table S15. Pearson and Spearman correlations of genomic estimated breeding values (GEBV) of proven sires (with more than 50 progeny with phenotypic records and raised in either one of the countries) across scenarios for foot angle based on single-trait within-country (WC-ST), multi-country (MC-ST), and joint countries (JC-ST) models. Table S16. Pearson and Spearman correlations of genomic estimated breeding values (GEBV) of proven sires (with more than 50 progeny with phenotypic records and raised in either one of the countries) across scenarios for claw set based on single-trait within-country (WC-ST), multi-country (MC-ST), and joint countries (JC-ST) models. Table S17. Significant SNPs associated with foot angle, and the genes located within a 100-kb genomic region down- and up-stream based on multi-country single-trait models (MC-ST). The list of genes name, gene Ensembl identification, gene position, the identification of the significant SNP, the population in which the SNPs were identified (US or AU), and the log . Table S18. Gene ontology terms and pathways in which the annotated genes for foot angle are involved based on multi-country single-trait models (MC-ST). Table S19. Significant SNPs associated with claw set, and the genes located within a 100-kb genomic region down- and up-stream based on multi-country single-trait models (MC-ST). The list of genes name, gene Ensembl identification, gene position, the identification of the significant SNP, the population in which the SNP were identified (US or AU), and the log . Table S20. Gene ontology terms and pathways in which the annotated genes for claw set are involved based on a multi-country single-trait model (MC-ST). Table S21. Genetic parameters  between dataset collected by technicians and farmers in the Australian Angus dataset: two-recorder-trait considering the covariance between FA and CS equal to zero (-ST). Additional file 2: Figure S1. Age distribution of the US (green) and AU (red) Angus populations. Each dot represents an animal. Figure S2. Cross-validation error for different K values in the admixture analyses. Figure S3. Admixture analysis for the US and AU populations (K = 2 and K = 6). Figure S4. QQ-plot of p-values for the genome-wide association for foot angle and claw set. Four sets of plots. A. Foot angle for American (US) Angus population; B. Foot angle for Australian (AU) Angus population; C. Claw set for American (US) Angus population; D. Claw set for Australian (AU) Angus population. Lambda is an inflation metric; lambda statistic should be close to 1 if the SNPs fall within the expected range of significance or greater than one if the observed p-values are more significant than expected. Figure S5. Genetic trends of foot angle and claw set for US and AU as an independent country. The results come from a single-trait single-country analysis. Furthermore, in this plot only sires were used , and years with at least 100 individuals. Figure S6. Genetic trend of foot angle (FA) and claw set (CS) for the US and AU, separately. The results come from a single-trait single-country analysis, in which 51,146 genotyped animals were used for the US and 27,041 for the AU dataset . Furthermore, in this plot only sires were used , and years with at least 100 individuals.


RUNX2, IL11, GDF5, CCDC91, NOG, and ROR2). Overall, this study describes genetics of DISH and implicates the role of overactive osteogenesis as a key driver of the pathology.Diffuse idiopathic skeletal hyperostosis (DISH) is a condition where adjacent vertebrae become fused through formation of osteophytes. The genetic and epidemiological etiology of this condition is not well understood. Here, we implemented a machine learning algorithm to assess the prevalence and severity of the pathology in ~40,000 lateral DXA scans in the UK Biobank Imaging cohort. We find that DISH is highly prevalent, above the age of 45, ~20% of men and ~8% of women having multiple osteophytes. Surprisingly, we find strong phenotypic and genetic association of DISH with increased bone mineral density and content throughout the entire skeletal system. Genetic association analysis identified ten loci associated with DISH, including multiple genes involved in bone remodeling ( The genetic contribution to risk of Diffuse Idiopathic Skeletal Hyperostosis has been unclear. Here, the authors find genetic variation associated with Diffuse Idiopathic Skeletal Hyperostosis, finding phenotypic and genetic association with increased bone mass throughout the skeletal system. It is characterized as a non-inflammatory spondyloarthropathy manifesting in ectopic calcification of spinal tissues. Unlike many types of arthritis, DISH occurs more often in men than women, affecting about 1.5× the number of men than women2. Yet, despite its high prevalence, health professionals are often unfamiliar with DISH, and it is underdiagnosed and often misdiagnosed3.Musculoskeletal conditions are the largest contributor to years lived with disability worldwide, often due to reduced mobility with age. Within musculoskeletal conditions, DISH is the second most common form of arthritis after osteoarthritis occurring in ~23% of men and ~13% of women over age 504. As DISH is often found to be comorbid with metabolic disorders, it is possible that DISH is at least partly driven by a shared biochemical pathway with metabolic disorders such as hyperlipidemia and hyperglycemia. Various forms of osteoarthritis are also found in DISH patients, and it is possible that the disease is related to bone restructuring and cartilage loss. Drawing an analogy to Ankylosing Spondylitis (AS)—an inflammatory spine calcification condition, it is possible that inflammation may play a role in DISH5. Currently, there are no disease-modifying treatments available to patients, and can only be treated through surgical resection of osteophytes.The disease is considered to be idiopathic with unknown etiology6. It defines DISH as flowing calcifications and/or ossifications along the anterolateral aspect of four contiguous vertebral segments, without loss of IVD height in the absence of bony ankylosis of facet joints . However, it is increasingly recognized that the ossification observed in DISH progresses continuously but slowly over time leading to the development of continuous quantitative measures of DISH severity8.In the clinic, DISH is most commonly observed as an incidental finding in radiological scans in non-symptomatic individuals. The radiological diagnosis relies heavily on the severity of ossification, and a number of diagnostic criteria have been established to determine if the severity is sufficiently high to constitute a unique diagnosis. The most commonly used diagnostic criteria for DISH was first developed by Resnick and Niwayama in 197610. In advanced cases, other presentations include thoracic spine pain, dysphagia, and compression of the spinal cord and nerve roots12. DISH is also associated with an increased risk of spinal fractures13 and postsurgical heterotopic ossifications3. Furthermore, DISH patients have an increased history of upper extremity pain, and more extremity and spinal stiffness compared to healthy control patients. Specifically, both bending ability and grip strength are reduced in DISH patients compared to other back pain patients10.Symptomatic DISH often manifests in back pain and stiffness. The pathology is diagnosed fairly late, at the point when physical impairment is so severe as to limit the motion of the thoracic spine14, making it difficult to ascertain the relative contribution of this polymorphism to each disorder. Epidemiologically, it is commonly comorbid with numerous disorders including metabolic syndrome, cardiovascular disease, diabetes mellitus, and hypertension15, but to what extent the genetic risks are shared is also unknown.There have been no prior GWAS studies for DISH, and to what extent genetics contributes to the development of DISH is not known. A single nucleotide polymorphism in the COL6A1 gene has been correlated to the presentation of DISH, although only in a Japanese sub-population which also has a strong prevalence of the related spine calcification disorder OPLLThe availability of large-scale DXA imaging in the UK Biobank with corresponding genetics and biochemistry data for all individuals provides a unique opportunity to uncover risk factors and genetic drivers of the disease. As DISH is severely underdiagnosed, we first trained a machine learning model to automatically detect osteophytes in lateral DXA scans. We then scored the severity of pathology and carried out both phenotypic and genetic characterization of pathology across ~40,000 participants. We show that pathology is severely underdiagnosed and highly prevalent. We also find that increased bone mineral density across the whole skeletal system was among the strongest predictors of DISH and that several musculoskeletal traits share a common genetic architecture with DISH. We further carry out GWAS, fine map and functionally annotate potential loci involved in the formation of DISH. Overall, our analysis puts forward a hypothesis that overactive osteogenesis plays a key role during the development of DISH. We further support this hypothesis using Mendelian Randomization (MR) technique to test for a causal association between gene expression and the development of DISH (outcome).7. The baseline characteristics of the cohort are listed in Table 16. To detect osteophyte formation in large imaging datasets, we automated the manual annotation of the DISH flow score by developing an object-detection and multi-category classification machine learning (ML) pipeline.In our analysis we quantify the extent of osteophyte bridge formation across all visible vertebrae in the ~40,000 UK Biobank lateral-view DXA scans using a continuous measure of DISH severity (flow score) developed by Kuperus et al.In the first step, the ML model detects the anterior side of each intervertebral disc space. The fourteen highest-confidence regions between C1 and L4 were used to define sub-images that were individually fed to an image-classification ML model, with categories corresponding to flow scores of zero (absence) to three (completely fused), see Fig. p value = 8.5e−42, Supplementary Fig. github.com/calico/DISH.To create an independent validation dataset to evaluate the performance of the ML model, three annotators manually inspected 300 DXA scans at each intervertebral disc region and carried out aggregate flow score calculation. There was strong inter-curator agreement (Correlation > 0.8). The machine learning pipeline performed well Fig. , matchin6. DISH flow scores are a more continuous measure of DISH severity and do not place such restrictions on the scoring scheme. Consequently, we assessed how our ML-generated DISH flow scores correspond to the DISH diagnostic events as judged by two independent expert radiologists.A number of related skeletal disorders are also known to form osteophytes and fusions between vertebrae (such as Ankylosing spondylitis and Spondylosis). These conditions are specifically differentiated from DISH based on the criteria that osteophytes in DISH are not accompanied by a reduction of intervertebral disc spacing and a threshold of three complete osteophyte fusion events of three adjacent vertebraeTo carry out this analysis, we randomly selected 20 representative images from different ML score bins representing varying levels of DISH severity. Experts were blinded to the ML scores, and were asked to evaluate the presence/absence of DISH based on Resnick criteria. If loss of vertebral spacing was observed, radiologists were asked to further evaluate if osteophyte formation might be due to related musculoskeletal disorders , and were able to estimate the prevalence of pathology in the cohort Fig. . We esti014+, andBased on these statistics we estimate that ~65,000 participants  in UK Biobank have a moderate form of DISH, and ~34,900 participants  would meet Resnick criteria (see Methods). Yet, DISH remains significantly underdiagnosed, with only 43 EHR diagnostic records in the whole UK Biobank , and have increased regular usage of non-steroidal anti-inflammatory drugs (NSAIDs) . However, since the use of pain medications can be attributed to multiple underlying conditions, whether this is specifically due to DISH or other comorbidities that coexist with DISH is hard to determine.We used data from pain questionnaires and diagnosed pre-existing conditions during the imaging visit to assess the contribution of DISH to these pathologies , obesity (OR ~ 1.1), mononeuropathies of the upper limb (OR ~ 1.1), and other forms of osteoarthritis (OR ~ 1.1) , we found that DISH was strongly genetically correlated with numerous BMD measures, including spine BMD (LD-score correlation 0.286), and spine BMC (LD Score correlation 0.378). Most importantly, DISH was also significantly genetically correlated with BMD measurements in distal parts of the skeleton including ribs BMD (~0.17), skull BMD(~0.14), and arms BMD (~0.10) Fig. . The str22, GARFIELD23, and DEPICT24. Here we did not find significant enrichment of musculoskeletal phenotypes largely due to the underrepresentation of musculoskeletal expression and chromatin mark data in public datasets used by these tools. We carried out manual annotation of genes in proximity to GWAS signals using published literature. Summary of our findings is reported below and Table To get a better understanding of molecular mechanisms that may underlie genetic associations, we carried out GWAS-based gene set enrichment using three different algorithms LDSCTo gain a better understanding of the common molecular mechanisms that underlie the genetic risk of DISH and related musculoskeletal phenotypes, we carried out fine mapping and colocalization analysis.−8 . Here we found 19 independent risk signals containing 340 potential causal variants (posterior inclusion probability > 1%) within the ten loci below the genome-wide significance threshold of 5 × 10RUNX2/SUPT3H, POLD3/CHRDL2, IL11, UQCC1/GDF5, PIK3R1), suggesting that expression of these genes might influence the development of DISH. A critical limitation of GTEx eQTL colocalization analysis for musculoskeletal traits is that GTEx does not contain eQTL for bone measurements, and observed signals must be originating from other tissues. In the UK Biobank colocalization analysis, we observed the colocalization of multiple DISH fine mapped SNPs with the genetics of bone traits and osteoarthritis. The overall direction of genetic colocalization and phenotypic correlations are consistent, with exception of IL11 and POLD3/CHRL2 variants, where colocalization is anti-correlated with osteoarthritis, while phenotypically we see a positive correlation between DISH and OA. Below we describe colocalization results for each locus in more detail.Second, we performed colocalization analysis with UK Biobank phenotypes and GTEx tissue eQTL to identify traits that likely share the same underlying causal variants. A summary of colocalization results is shown in Fig. SUPT3H and one of the variants overlaps with the master transcription factor involved in osteogenesis RUNX2  and bone area for different parts of the body Fig. . In addiUQCC1 & GDF5) and a pseudogene (RPL36P4). GDF5 is a pleiotropic bone morphogenetic protein involved in joint and bone repair and development28. Alleles that reduce GDF5 expression were implicated in several processes  and Chordin-like protein 2 (CHRDL2). CHRDL2 is expressed in chondrocytes within joint cartilage and connective tissues and is associated with multiple musculoskeletal traits such as height, bone area, and osteoarthritis  to prevent BMP signaling and bone formation44. Consistent with its function in bone homeostasis, the genetic signal for DISH at this locus colocalizes with spine bone traits.The fifth locus on chromosome 11 is close to DNA polymerase 3 (PIK3R1) gene. PIK3R1 is involved in osteoblast differentiation through the phosphoinositide signaling cascade43. The role in DISH is likely to be through increased osteogenic potential. Consistent with this hypothesis, PIK3R1 is also associated with other musculoskeletal traits such as height  linked to gene expression (mediator) are tested for association with outcome (DISH). If a genetically linked variation in gene expression is associated with the outcome, it is possible to conclude that the gene is causally linked to the outcome (under some assumptions see Methods).PIK3R1, which didn’t have eQTL data, can be found in Table This methodology is only possible if eQTL for genes can be established. In our analysis, we carried out a two-sample Mendelian Randomization (MR) analysis using eQTL data from GTEx v7 across 44 tissues. One of the limitations of the analysis is that bone phenotypes are poorly measured in public databases, and generally bone and intervertebral expression data are not available. Thus we have to rely on eQTLs from other tissues. When multiple independent eQTLs were observed for the same gene in a single tissue within GTEx, we evaluated the causal effect of a gene on DISH using inverse variance weighted (IVW) analysis. For the genes with single independent eQTL in a tissue we used the Wald ratio test. A summary of our findings for all genes analyzed, with exception of CCDC91  and UQCC1 (most significant in transformed fibroblast cells) were significantly associated with increased DISH. In the Wald ratio test analysis, expression of RUNX2 (substantia niagra of the brain), IL11 (lung), GDF5 , and NOG  play a causal role in DISH with the effect sizes shown in Table CCDC91  or UQCC1 . Overall, MR analysis supports the hypothesis that expression difference in six genes  is causally linked to the development of DISH.We find that most GWAS had evidence of causal involvement in the development of DISH. In the IVW analysis, expression of 46, but to our knowledge, this is the most comprehensive analysis of the genetic and epidemiological characterization of DISH in the general population.Despite the high prevalence of DISH in the elderly population, it remains a poorly characterized and underdiagnosed disease. As a consequence, the genetic and environmental risk factors that lead to the development of this pathology are not well known, and the long-term consequences of DISH pathology on health outcomes have not been systematically analyzed. There have been a number of smaller studies that looked at the prevalence of DISHUsing a machine learning approach we predicted DISH flow scores for ~ 40,000 participants in UK Biobank. The algorithm automatically detects pairs of vertebral bodies and quantitates the severity of osteophyte formation between them. In our evaluation, the algorithm performed well, matching human annotators with correlation >0.8. We further showed that the DISH flow score predicted by the algorithm is capable of capturing established Resnick DISH diagnostic criteria annotated by expert radiologists.We find that the prevalence of DISH is strongly associated with age and male gender, with approximately one in eight individuals having multiple osteophyte bridges. Despite the high prevalence, DISH is severely underdiagnosed, with only 43 recorded diagnoses in the UK Biobank EHR. Based on the results of machine learning, we estimate that around ~34,900 UKBB participants would meet the radiological Resnick criteria for DISH diagnosis.47. While further research is needed to provide a definitive answer, association with pain in the neck and shoulders in comparison to lower back pain, suggests that DISH may be contributing to the pain symptoms.The presence of spine osteophytes is significantly associated with neck and shoulder pain and the use of non-steroidal anti-inflammatory drugs (NSAIDs). However, as DISH is also associated with the incidence of osteoarthritis, and it is not clear if NSAID usage is due to comorbidities or DISH itself. For example, the formation of osteophytes in the spine might be correlated with the formation of osteophytes in the hips as observed in hip OA, thus increasing the usage of NSAID pain medicationsMost notably we observed a strong association of DISH with bone mineral content and bone mineral density across the entire skeletal system. We carried out multivariate analysis after identifying a set of variables that jointly predict DISH and found that association remains significant with an effect size similar to age, indicating that high BMC/BMC is an independent risk factor for DISH. This observation raises a possibility that processes that drive the formation of higher bone density, might also be driving the formation of DISH. We considered the possibility that BMD/BMC association might be a confounded presence of osteophytes in the regions of the skeleton that are known to form osteophytes. To investigate this further, we examined if association persists in regions of the skeleton that are not known to form osteophytes , and found that association persists in these regions.RUNX2, BMP signaling , PI3K pathways (PIK3R1), and Wnt-signaling (ROR2), and IL11  with OPLL - a related spine ossification pathology more commonly observed in Asian populations36. RUNX2 haploinsufficiency is sufficient to rescue OPLL in a mouse model providing further evidence that RUNX2 may also play a role in the development of DISH48. The overlap of the genetic risk factors for DISH and OPLL indicates that the two pathologies at least in part share a common genetic etiology. Of note, we did not see an overlap with genetic risk factors of Ankylosing Spondylitis, an inflammatory condition that also results in vertebral fusions, indicating that the two pathologies have distinct genetic etiology.In an analysis of related conditions, we also see a significant overlap in GWAS loci , but not with the bone area . The contribution of osteophytes to the total bone area and BMC is relatively small, indicating that DISH must be occurring through a significant increase in bone content without appreciable changes to bone area. As an additional line of evidence, we asked if the genetic correlation between DISH and Spine BMD/BMC persists if we stratified cohorts into two groups with and without observed DISH. Genetic correlation between these traits remained significant in both groups with approximately the same correlation (rg = 0.27 and rg = 0.25 respectively). The only plausible explanation for the genetic correlation is that traits share a common genetic architecture beyond observed osteophyte formation.Third, we observe significant genetic correlations between DISH and BMD/BMC across the entire skeletal system, but not with the bone mineral area (BMA). In the spine regions, the genetic association with DISH was significant for BMD/BMC , some residual participation bias might impact our results. However, this selection bias is unlikely to lead to large correlations between different bone traits and DISH.We have tried to assess whether osteogenesis pathways are implicated in DISH using gene and tissue enrichment tools. However, due to the poor representation of bone cell types in public datasets, none of these methods showed osteogenesis as a significantly enriched pathway for either the Spine bone mineral traits or DISH .Overall, we have carried out the largest analysis of DISH to-date and show that prevalence of DISH is substantially underappreciated in the general population. We have confirmed many of the previous epidemiological associations with pain and other comorbidities, and characterize the genetics of this previously considered idiopathic condition. Through genetic analysis, we put forward a hypothesis that overactive osteogenesis is playing a key role in the development of pathology. If this hypothesis holds, it would suggest that interventions that restore bone formation imbalance may offer a plausible path to treating this condition.50. The UK Biobank aims to scan 100,000 of these participants by the end of 2023 with various imaging modalities51; the current analysis includes 41,233 participants for whom lumbar spine DXA imaging scans  were collected for body composition and bone mineral density assessments (April 2014–September 2019). At the imaging assessment visit, information was collected on a range of demographic and lifestyle factors, including ethnicity, education, occupation, alcohol consumption, smoking status, socioeconomic status, and physical activity. Various measurements were also taken, including height, weight, waist and hip circumferences, and blood pressure. All study participants provided informed consent and the North West Multi-centre Research Ethics Committee approved the study. Further details about the procedural characteristics of the imaging data have been published online .In 2006 through 2010, 503,000 adults (aged 40–70 years) were recruited from the general population in the United Kingdom into a prospective cohort studyThe pipeline described here scores the extent of hyperostosis in the intervertebral disks that can be observed in a lateral dual-energy X-ray absorptiometry (DXA) scan image of a human torso. As described by Kuperus et al. (2018), such hyperostosis can create bridges between vertebrae that limit flexibility and ultimately contribute to diffuse idiopathic skeletal hyperostosis (DISH).github.com/calico/DISH.The analysis occurs in three steps: 1) identification of anterior intervertebral junctions; 2) scoring the hyperostosis of each intervertebral junction; and (3) summing the bridge scores across the spine Fig. . Details identifi scoring 52. A set of 160 images was annotated, which included 2271 boxes drawn around vertebral junctions. The annotated images were separated into training and test sets of 100 and 60 images, respectively. The performance of the object detector was evaluated in the 60-image test set using intersection-over-union (IoU) for the 14 top-scoring predicted boxes vs all of the annotated boxes, allowing each predicted box’s intersection to only be counted for its most-overlapping annotated counterpart. The average IoU across the 60 test images was 68.9% (SD 5.9%).The first step in the analysis of DISH was the identification of the anterior portions of the intervertebral gaps along the entire spine. These are the loci where DISH-relevant bridges can form that are visible in lateral DXA images. An object-detection model was applied to this task. It was trained by transfer learning from the MobileNet ssd_mobilnet_v1 model7. Four categories were established corresponding to the severity of hyperostosis from no visible osteophytes (category 0) to complete fusion (category 4). The image classification model was trained to classify the severity of hyperostosis across 9702 bridge images (693 DXA images) and tested on randomly selected 2800 bridge images (200 DXA scans). The training was performed using transfer learning from the efficientnet/b1 model. Cohen’s kappa value for the final model was 0.405.For each intervertebral junction, a numeric score was to be assigned according to the criteria described in Fig. The final output value of the model evaluates overall DISH-like hyperostosis across the spine. Final evaluation was performed using a hold-out set of 200 DXA images that were scored by three independent raters. Raters used the same bridge-score scheme described above. For each DXA image, those numeric scores were summed to produce the final DISH score. The evaluation was performed using the mean rater score for each DXA image. Results for the comparison between manual annotators and the ML pipeline are shown in Supplementary Fig. The prevalence of DISH in the imaging cohort was estimated using the distribution of scores in different bins and the probability of being diagnosed with DISH by expert radiologists within each bin Fig. . The prehttps://biobank.ndph.ox.ac.uk/showcase/showcase/docs/impute_ukb_v1.pdf), after excluding SNPs with minor allele frequency <1% and poor imputation quality . We removed participants who were not of European descent, exhibited sex chromosome aneuploidy, heterozygosity outliers, or genotype call rate outliers. Additionally, we removed variants with genotype missingness > 10% or that deviated meaningfully from Hardy-Weinberg equilibrium in a European ancestry cohort . In total, we considered 9,633,695 SNPs and 33,413 individuals for genetic analysis.We used the UK Biobank imputed genotypes , sex, age, age2, age × sex, and recruitment center as fixed effect covariates as recommended in the BOLT-LMM53. The GWAS was performed against standardized DISH scores per participant. We verified that the test statistics showed no inflation compared to the expectation using the genomic control lambda coefficient (1.10) and the intercept  of linkage disequilibrium (LD) score regression (LDSC)54.To conduct the genetic association study, we used BOLT-LMM21. As a result, adding sex as a covariate in the GWAS might lead to collider bias that leads to significant results in GWAS on some autosomal variants that differ in frequency between the two sexes due to sex-differential participation bias. To address this issue, we have implemented a sensitivity analysis to assess the impact of sex-biased participation on our DISH and BMD GWAS. We have applied the same BOLT-LMM procedure as before but performed sex-stratified association tests using the UK Biobank data  and estimated genetic correlation and heritability using the default parameters and the –rg command and –h2 command respectively . We pre-harmonized the polarization of alleles to match the reference file w_hm3.snplist.We estimated the heritability and genetic correlation between traits using LD score correlation (28). We accessed the repository at 55, considering all variants that passed quality control measures and were within 500 kb of the locus index variant. As a reference panel for LD calculations, we used genotypes from 5000 UK Biobank participants that were randomly selected after filtering for unrelated, European participants. We excluded the major histocompatibility complex (MHC) region due to the complexity of LD structure at this locus . For each locus, we considered variants with locus-wide evidence of association  to be conditionally independent while the genome-wide association threshold to define a locus was set at p value < 5 × 10−8.We performed approximate conditional analysis using GCTAGC) was 1.10 (LD score regression intercept = 1.004), indicating that there was a low possibility of false positive associations resulting from population stratification.The genomic inflation factor 2, where σ is the standard deviation of the phenotype estimated using the variance of coefficients (Var(βj)), minor allele frequency (fj), and sample size (nj):For each distinct signal, we calculated credible sets with 95% probability of containing at least one variant with a true effect size not equal to zero. We first computed the natural log approximate Bayes factor2 is the coefficient of the regression, estimating σ such that Here, σj, that the j th variant is driving the association, given l variants in the region, by:p valuemarginal < 5 × 10−8 to identify loci for fine-mapping.We calculated the posterior probability, πThe 95% credible set for each signal was then constructed by (i) ranking all variants according to their Bayes factor and (ii) including ranked variants until their cumulative posterior probability of driving the association attained or exceeded 0.99.57 using default priors and all variants within 500 kb of the index variant. As performed by57, we considered two genetic signals to have strong evidence of colocalization if PP3 + PP4 ≥ 0.99 and PP4/PP3 ≥ 5 and suggestive evidence of colocalization if PP3 + PP4 ≥ 0.8 and PP4/PP3 ≥ 3. For gene expression colocalizations, we used summary statistics from GTEx v758. For disease and quantitative trait colocalizations, we used UK Biobank summary statistics of ICD10 codes, normalized quantitative traits . For analysis we selected UK Biobank phenotypes where the minimum p value within the ±500 kb region around the locus tag SNP was <5 × 10−8.We performed colocalization analysis using the coloc method59. In MR, we use genetic variants as a proxy for risk factors to minimize confounding and reverse causation in observational data. Genetic variants are randomly assigned when passed from parents to offspring and therefore minimize the effect of confounders and avoid reverse causation. However, little is known about the genetic component or the causal genes and pathways driving DISH.The observational studies that are impaired by confounding or reverse causation, it is hard to assess whether the risk factors are causal or correlated with DISH. Mendelian randomization (MR) provides a way of using genetics to test for causal driving factors for a phenotype of interestthe chosen genetic variants are associated with the exposure of interest,they are not associated with any confounders, and60.they are not associated with the outcome via any pathway other than through the exposure of interest There are three important assumptions in MR analyses:58. To identify independent SNP instruments for each exposure, GWAS-significant SNPs (P < 5 × 10−8) for each risk factor were pruned . As a reference panel for LD calculations, we used genotypes from 5,000 UK Biobank participants that were randomly selected after filtering for unrelated European participants. We then estimated the causal effect of the risk factor on the disease trait according to the MR paradigm. The Wald ratio method was used when only one independent variant was associated with expression of the gene in GTEx while IVW was used to test for causal effects in the presence of multiple independent eQTLs for a gene. We applied Bonferroni correction for all the gene-based tests with an FDR cutoff 0.1 to be considered significant. This study was not preregistered.We performed two-sample Mendelian randomization analyses using the Inverse Variance Weighted (IVW) or Wald ratio methods included in the R package twoSampleMR. GTEx v7 summary statistics were used for the effect of variants on gene expression of different genes62. The weighted median and mode estimates are less sensitive to the effect of pleiotropic variants as this method assumes that the estimates from pleiotropic variants would be outliers. On the other hand, the MR-Egger approach performs a weighted linear regression of the marginal effect of each SNP to the outcome on the marginal effect of each SNP to the exposure. In this test, the analysis of the regression intercept detects an overall directional pleiotropic contribution of weak instrumental SNPs on the risk estimate63.As a sensitivity analysis, we also ran the MR analysis using MR-Egger regression, weighted median-based, and weighted mode-based tests and tested whether they predicted causal effects of similar direction between exposure and outcome. The weighted median (mode) estimator is the median (mode) of a distribution in which Wald ratio estimates have been ordered and represent percentiles of this distributionhttps://biobank.ctsu.ox.ac.uk/crystal/crystal/docs/Consent.pdf). UK Biobank data were accessed under the approval of UK Biobank within project 18448. The study was conducted following the principles of the declaration of Helsinki and all participants gave prior written informed consent. All data used in this study were anonymized before its use.The UK Biobank project was approved by the National Research Ethics Service Committee North West-Haydock (REC reference: 11/NW/0382). An electronic signed consent was obtained from the participants (more information on UK Biobank participant consent can be found at: Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Reporting Summary


P < 8.33 × 10−9) with the six fornix dMRI traits. Geminin coiled-coil domain containing (GMNC) and NUAK family SNF1-like kinase 1 (NUAK1) gene were highlighted, which were found in UKB and replicated in ABCD. The heritability of the six traits ranged from 10% to 27%. Gene mapping strategies identified 213 genes, where 11 were supported by all of four methods. Gene-based analyses revealed pathways relating to cell development and differentiation, with astrocytes found to be significantly enriched. Pleiotropy analyses with eight neurological and psychiatric disorders revealed shared variants, especially with schizophrenia under the conjFDR threshold of 0.05. These findings advance our understanding of the complex genetic architectures of fornix and their relevance in neurological and psychiatric disorders.The fornix is a white matter bundle located in the center of the hippocampaldiencephalic limbic circuit that controls memory and executive functions, yet its genetic architectures and involvement in brain disorders remain largely unknown. We carried out a genome-wide association analysis of 30,832 UK Biobank individuals of the six fornix diffusion magnetic resonance imaging (dMRI) traits. The post-GWAS analysis allowed us to identify causal genetic variants in phenotypes at the single nucleotide polymorphisms (SNP), locus, and gene levels, as well as genetic overlap with brain health-related traits. We further generalized our GWAS in adolescent brain cognitive development (ABCD) cohort. The GWAS identified 63 independent significant variants within 20 genomic loci associated ( The fornix is a C-shaped white matter bundle located in the center of the hippocampaldiencephalic limbic circuit . It is tParameters from diffusion MRI (dMRI) techniques, such as DTI and neurite orientation dispersion and density imaging (NODDI) can lead to a better understanding of the fornix microstructure. DTI permits in vivo assessment of neural microstructure by utilizing the diffusion properties of water in constrained compartments and can provide a measure of the coherence of neuronal fibers . The twoA comprehensive knowledge of the forniceal white matter microstructure could serve to inform clinicians of cognitive subpathways and corresponding memory deficits that arise from neurodevelopmental and neurodegenerative diseases. However, the genetic underpinnings of the fornix region remain underexplored. Here, we aimed to: 1) illuminate the genetic architecture of the fornix white matter microstructure using three DTI models  and three NODDI models  by performing the first and largest genome-wide association study (GWAS) to date; (2) and investigate its involvement in common brain disorders, especially those reported in previous observational studies. The analytical workflow of this study is shown in Fig.  illuminahttp://www.ukbiobank.ac.uk/) [We used the brain dMRI data from 41,984 genotyped individuals from the UK Biobank (UKB) (.ac.uk/)  under ac.ac.uk/) . The ABCP < 1 × 10–06, or duplicated, and further filtered out individuals with abnormal sex chromosome, putative sex chromosome aneuploidies, heterozygosity rate outliers, having more than 10 putative third-degree relatives, or missing genotype rate ≥ 5% using PLINK [Our analytic sample was restricted to white British individuals whose data were used in calculating principal components (PCs). We applied standard quality control (QC) procedures to the UKB v3 imputed genetic data by removing SNPs with call rate < 0.95, imputation quality score < 0.5, a minor allele frequency (MAF) < 0.01, failing the Hardy–Weinberg equilibrium tests at ng PLINK . For ABC2, sex, scanning site, intracranial volume (ICV), and the first 10 genetic PCs. We first performed univariate GWAS of the individual traits, then the resulting residuals for the 6 traits were jointly fed into the multivariate omnibus statistical test (MOSTest) analysis [GWAS was run via PLINK 1.9  adjustinanalysis . MOSTestanalysis , yieldinhttp://fuma.ctglab.nl/). Allele linkage disequilibrium (LD) correlations were computed from the European panel of the 1000 Genomes phase 3 data. Independent significant SNPs were identified by the statistical threshold (5 × 10–08/6 = 8.33 × 10–09) and independency (r2 ≤ 0.6) [r2 < 0.1 [r2 ≥ 0.6) with one of the independent significant SNPs in the genetic loci [Genomic risk loci were identified using the Functional Mapping and Annotation (FUMA) of the GWAS SNP2GENE online platform  (version2 ≤ 0.6) . Lead var2 < 0.1 . Candidatic loci . Genomictic loci .The NHGRI-EBI GWAS catalog  was subsGC), LDSC intercepts, and LDSC ratios for each GWAS were calculated. We used precomputed LD scores calculated by 1000 Genomes European data. In addition, we calculated the pairwise genetic correlation estimates between the six fornix phenotypes using LDSC v1.0.1.SNP-based heritability analyses were conducted using linkage disequilibrium score (LDSC) regression . HeritabFUMA  annotateP = 0.05/18,879 genes = 2.65 × 10–06. In addition, we performed a gene-set analysis using the g:Cocoa (compact comparison of annotations) function in g:Profiler web server for curated gene sets and Gene Ontology (GO) terms.Genome-wide gene-based association analysis (GWGAS) was performed using GWAS summary statistics as input into multimarker analysis of genomic annotation (MAGMA) (v1.08)  with defP values < 0.05 were considered significant.To assess whether genes are disproportionately expressed in certain cell types, we investigated associations with several gene expression profiles using MAGMA’s gene expression analysis. We used the CELL TYPE function in FUMA to test whether fornix-linked genes were associated with differential expression levels across different cell types. FDR-corrected rg) between fornix traits and 22 DTI phenotypes reported by Zhao et al.’s GWAS [P values survived the FDR correction (P < 0.05) were considered significant.We used LDSC  to estim.’s GWAS . For the.’s GWAS , who rep.’s GWAS . GeneticTo further examine the genetic overlap between fornix phenotypes and 10 brain health-related traits, GWAS summary statistics for cognitive traits  and for subsets of SNPs with significance levels in another phenotype  below the indicated cutoffs . The enrichment is visualized as successive leftward deflections from the null distribution in conditional Quantile–Quantile (Q–Q) plots. All P values were corrected for inflation using a genomic inflation control procedure, considering that the empirical null distribution in GWASs is affected by global variance inflation [P values for that SNP are lower than the observed P values in both the primary and secondary phenotypes. Regions of complex LD patterns, such as MHC (chr 6: 25119106–33854733) and 8p23.1 (chr 8: 7242715–12483982) regions, apolipoprotein E (APOE) for AD, and microtubule-associated protein tau (MAPT) for Parkinson’s disease (PD) were excluded before performing the analysis. The FDR significance cutoffs were 0.01 for condFDR and 0.05 for conjFDR, in line with prior studies [Further cFDR (condFDR and conjFDR) methods using MATLAB R2018b and Python 3.7.7 were employed to investigate the shared loci between fornix phenotypes and the eight brain disorders. Using the associations between genetic variants and the secondary phenotype, the condFDR analysis re-ranked test statistics and recalculated the associations between these variants and the primary phenotype, thus prioritizing variants for follow-up analyses . We plotnflation . We nextnflation  method,  studies .2, sex, imaging site, ICV, and the first 10 genetic PCs. The location of the fornix region in the human brain and the fiber bundles and their partitions were depicted in Fig. The GWAS made use of data from 30,832 UKB brain imaged samples  and a total of 63 independent significant SNPs, tagging 20 independent genomic loci , in which only the data on FA and MD were available. We found that 7 out of the 20 independent significant SNPs for FA discovery GWAS had the same effect direction in the generalization, and 14 of 25 for MD GWAS  and NUAK family SNF1-like kinase 1 (NUAK1) gene were found in UKB  and replicated in ABCD .The fornix-associated significant SNPs of the UKB sample were further evaluated in the generalization GWAS of the ABCD cohort  were detected  using g:Profiler web server , P = 0.012”, and “cellular developmental process (GO:0048869), P = 0.014” pathways.Functional enrichment analysis identified 9 GO sets significantly associated with fornix traits  was 23% for FA, 26% for MD, 13% for MO, 10% for OD, 17% for ICVF, and 27% for ISOVF, illustrating themselves as genetically determined traits , the body of corpus callosum , genu of corpus callosum , anterior corona radiata , fornix (cres)/stria terminalis  and cingulum , and reverse correlation with the posterior limb of the internal capsule  for fornix FA were identified.We used the GWAS results to estimate the genetic overlap with brain DTI and volumetric traits via LDSC . A totalrg = −0.24, PFDR = 0.025 for FA; rg = 0.21, PFDR = 0.029 for MD; rg = −0.28, PFDR = 0.023 for ICVF; rg = 0.20, PFDR = 0.042 for ISOVF) and cuneus , the thickness of superior temporal , and parahippocampal , and thalamus . Four traits were related to WMH, amongst which were FA , MD , ICVF , and ISOVF .LDSC regression  was usedWe leveraged the genetic overlap to discover more genetic underpinnings of fornix white matter microstructure by employing condFDR statistics with eight disorders: AD, anxiety disorders, BD, major depressive disorder (MDD), SCZ, stroke, epilepsy, and MS Table S. The conGMNC and NUAK1 were found to be closely related to the fornix. Bioinformatics analyses revealed the enrichment of biological pathways of cell development and differentiation, as well as astrocyte-specific functional enrichments. Findings also highlighted the importance of fornix in brain disorders, especially in SCZ. Together, these results shed light on the genetic architecture of fornix, its biological functions, and the possibly important roles in common brain disorders.Using brain dMRI data from 30,832 UKB white British participants, this is the first large-scale GWA analysis of fornix white matter microstructure, identifying 20 independent genetic loci across 14 chromosomes. GMNC and NUAK1 gene, which were found in UKB and replicated in ABCD. GMNC has been identified as an essential regulator of axon guidance and ephrin signaling and is also involved in neuronal plasticity and regulation of gene expression [GMNC was further identified as loci for cerebrospinal fluid (CSF) phosphorylated Tau levels [NUAK1, one of the AMP-activated protein kinase (AMPK)-related kinases [NUAK1 to be a novel therapeutic entry point for tauopathies [The fornix is the predominant bundle of efferent fibers connecting the hippocampus to other brain structures , 48 and pression , 54. Recu levels –58, and u levels . NUAK1,  kinases , was repopathies .LPAR1, which was reported to participate in regulating cell proliferation, migration, survival, and apoptosis, and could cause neurodevelopmental disorders and neuropsychiatric diseases [GNA12 was reported to play a key role in the genetic architecture underlying normal gray matter density variation in frontal and parietal regions [TIE1 could control angiopoietin function in vascular remodeling and inflammation [STAG1 could notably attenuate astrocyte dysfunction and depressive-like behaviors in mice models [LLT10  [SKIDA1 [CASC10 could promote cisplatin resistance in high-grade serous ovarian cancer [CDC20 in a variety of human malignancies was reported [We employed many strategies to annotate SNPs to genes, and then genes to functions. As is known to all, genetic variants associated with common diseases are usually located in noncoding parts of the human genome . Positiodiseases . A list  regions  and was  regions . TIE1 coammation . Overexpe models . The funas AF10)  and SKID [SKIDA1  are commn cancer . The oncreported . WhetherUsing the LDSC regression method, we revealed significant genetic correlations between fornix and distinct cortical measures , and subcortical measures . These above-mentioned brain regions are significantly associated with cognitive and memory functions. The fornix and parahippocampal-cingulum are two prominent limbic white matter tracts that connect the medial temporal lobe structures to other memory-related brain structures . Jang etSignificant genetic correlations between fornix white matter with reaction time  were revealed. DTI studies suggest that forniceal measures correlate with episodic memory during brain development and aging . A relatDWI has some known limitations. First, it neither can reveal the direction of information flow nor can distinguish between the different fibers that constitute a pathway  . Second,In summary, the current study provides new insights into the genetic architecture of fornix white matter by identifying the significant genetic loci through GWAS, the functional annotation for biological processes, analyzing genetic overlap with other traits, and showing evidence for involvement in common brain disorders. Taken together, these results advanced our understanding of the genetic architecture of the fornix and shed light on further research into the neurobiological basis of its anatomy and associations with brain disorders.Supplemenatry FigureSupplementary Table


Date palm is an economically important fruit crop in the arid and semi-arid regions of the world. The knowledge of genetic variation, genotype-by-traits comparison, as well as the relationship among several phenological traits is effective for developing breeding populations by choosing the best allelic combinations and employing selection approaches. Information regarding the genetic variability and inter-relationships among fruit characteristics is facilitating the plant breeders to efficiently exploit the date palm germplasm for the introduction of elite genotypes. The present study was conducted to examine genetic variation among different date palm cultivars -collected from two different research stations in Pakistan as well as the relationship among their phenological and biochemical characteristics. Biplot analysis revealed that the cultivars ‘Dhakki’, ‘Chohara’, and ‘Kupra’ possessed the highest fruit and pulp weights. Moreover, the fruits of the cultivars ‘Champa Kali’ and ‘Shakri’ had the maximum TSS  and total sugar content. Similar variations were observed in the mean values of the studied phenological and biochemical characteristics as in the biplot analysis. Cluster analysis on the basis of phenological and biochemical characteristics divided 50 cultivars into seven clusters, showing differences in the studied characters. A correlation matrix revealed that pulp weight had a strong relationship with fruit weight, length, and diameter. A strong correlation also existed among antioxidant capacity, antioxidant activity, and total phenolic content. These results demonstrated a huge diversity in phenological and biochemical characteristics of date palm cultivars as well as a correlation among several characteristics of the studied germplasm, which can be exploited in future breeding programs. Phoenix dactylifera L.) belongs to the family Arecaceae . Geographical and meteorological data from Bahawalpur and Jhang locations are presented in A set of fifty cultivars of date palm was selected from two different research stations in Punjab, Pakistan  was calculated from the juice, by the method described by -1) and antioxidant activity (%) of fruits were estimated as described earlier (-1) in fruits as described by For biochemical analysis, 40 g of fruit samples were ground in 60 ml of distilled water using a dilution factor. The total soluble solids (°Brix) in the fruit juice were estimated using a hand refractometer. Fruit acidity (%) was determined by using a method described previously . Vitamin earlier . ApproxiThe phenological and biochemical data were analyzed using the statistical software, Statistix 8.1 , with three replications for each cultivar. The treatment means were separated using a least significant difference (LSD) test at a 5% probability level. Correlation matrixes were constructed using R statistical software. Biplot analyses were made using XLSTAT, 2023 and dendrograms  were constructed using Minitab.The ‘Chohara’ cultivar showed the highest fruit weight (19.80 g) and pulp weight (18.26 g). ‘Eedel Shah’ had the largest fruit length (48.84 mm) and ‘Kupra’ exhibited the largest fruit diameter (30.37 mm). ‘Dedhi’ showed the highest stone weight (1.74 g). ‘Gajar’ had the largest stone length (31.10 mm) and the largest stone diameter was observed in ‘Kupra’ (30.37 mm) as shown in The mean values also confirmed the best-performing cultivars with 18.83°Brix of TSS and 14.36 mg/100 ml of vitamin C content for ‘Champa Kali’, and 18.75°Brix of TSS and 68.81% of total sugar content in ‘Shakri’  showed the maximum variability among the studied traits of date palm cultivars. However, others showed negligible contribution to the variability of date palm cultivars (Biplot was developed based on the first two components and both components shared a maximum variability of 57.82%. The biplot analysis for phenological biochemical traits revealed that the date palm cultivars, i.e., ‘Dhakki’, ‘Zahidi’, ‘Kantar’, ‘Zarin’, ‘Begum Jangi’, ‘Karbalaen’, ‘Wahn Wali’, ‘Haleeni’, ‘Sundri’, ‘Pathri’, ‘Dedhi’, ‘Kohraba’, and ‘Chohara’, were at the vertex of a polygon. Among these, some genotypes, i.e., ‘Dhakki’, ‘Zahidi’, ‘Kantar’, ‘Dedhi’, ‘Kohraba’, and ‘Chohara’, were found to be more diverse because these were found to be away from the center of the trait vectors. Moreover, genotypes grouped into quadrants I and II showed good performance for the phenological and biochemical traits as compared to quadrants III and IV (A dendrogram based on the phenological traits divided the date palm cultivars into seven major clusters (The correlation matrix for phenological traits revealed that fruit weight had a significant association with pulp weight, and fruit length and diameter, while it had a non-significant association with stone weight, length, and diameter. However, stone weight did not show any association with pulp weight, and stone length and diameter (Date palm fruit is considered a good source of carbohydrates, protein, minerals, vitamins, and dietary fiber . GeneticA multivariate approach has been found very effective for the identification of diverse germplasm with desired traits . Mean peCluster analysis could be effective for the identification of high yielding genotypes . In thisA correlation matrix provides a symmetrical association among a large number of traits . A correDate palm has diverse germplasm, hence the collection and preservation of this germplasm is a pre-requisite for future crop improvement programs. The characterization of this germplasm is important particularly when evaluated traits are directly related to fruit quality and crop yield. Thus, accurate information on phenological and biochemical traits provides a better guideline for diverse parent selection in breeding programs for the production of ideal products focused on producer and consumer demands. This study demonstrated that large variations exist in the phenological and biochemical traits of date palm cultivars that can be utilized for various crop improvement purposes. Conclusively, the results of the current study might be suitable to manage germplasm collection and helpful for choosing parents in future breeding programs for date palm genotypes.Fruit yield and quality traits are still gaining more attention in the breeding of fruit crops. It has been recommended that phenological and biochemical traits are major points of concern for the identification of higher-yielding elite genotypes. Moreover, mean values, coefficients of variation, biplot analysis, cluster analysis, and trait association are alternative tools for providing the appropriate information for the evaluation of genetic diversity which can be further utilized during diverse parent selection involving new commercial and high-yielding genotypes.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.RA, HMA and MAA: Conceptualization, Literature review, Writing major and original draft. AL, WFAM and SE: Literature survey, Writing review and editing, Figure designing. All authors contributed to the article and approved the submitted version.