The inclusion of multiple traits and multiple environments within a partially separable factor analytic approach for genomic selection provides breeders with an informative framework to utilise genotype by environment by trait interaction for efficient selection.This paper develops a single-stage genomic selection (GS) approach which incorporates information on multiple traits and multiple environments within a partially separable factor analytic framework. The factor analytic linear mixed model is an effective method for analysing multi-environment trial (MET) datasets, but has not been extended to GS for multiple traits and multiple environments. The advantage of using all information is that breeders can utilise genotype by environment by trait interaction (GETI) to obtain more accurate predictions across correlated traits and environments. The partially separable factor analytic linear mixed model (SFA-LMM) developed in this paper is based on a three-way separable structure, which includes a factor analytic matrix between traits, a factor analytic matrix between environments and a genomic relationship matrix between genotypes. A diagonal matrix is then added to enable a different genotype by environment interaction (GEI) pattern for each trait and a different genotype by trait interaction (GTI) pattern for each environment. The results show that the SFA-LMM provides a better fit than separable approaches and a comparable fit to non-separable and partially separable approaches. The distinguishing feature of the SFA-LMM is that it will include fewer parameters than all other approaches as the number of genotypes, traits and environments increases. Lastly, a selection index is used to demonstrate simultaneous selection for overall performance and stability. This research represents an important continuation in the advancement of plant breeding analyses, particularly with the advent of high-throughput datasets involving a very large number of genotypes, traits and environments.The online version contains supplementary material available at 10.1007/s00122-023-04305-1. This paper develops a single-stage genomic selection (GS) approach which incorporates information on multiple traits and multiple environments within a partially separable factor analytic framework. The factor analytic linear mixed model of Smith et al.  is an efPlant breeders select genotypes with superior performance across a set of production environments for multiple traits of commercial importance. Traditionally, threshold selection has been used by setting thresholds for each trait separately, but this ignores the genetic correlations between traits and may exclude genotypes that could serve as potential parents . A more efficient approach is to use a selection index, which does consider the genetic correlations between traits and weights their importance based on the breeding objectives Bernardo . SelectiGenomic selection is a form of marker-assisted selection that can improve the rate of genetic gain in plant breeding programs , days to flowering (DTF) and plant height . The multi-trait MET dataset for 2017-18 is summarised in Tables A total of 291 genotypes were evaluated across 12 environments in the Murrumbidgee Valley and Murray Valley rice growing regions of Australia  genotypes. Markers were coded as −1, 0 or 1 according to a set of 14,800 single–nucleotide polymorphisms generated using DArTseq  effects with ns-vector of residuals and The linear mixed model for t Bailey . FurtherIt is assumed that:additive and non-additive GET effects:additive genetic variance matrix is given by non-additive genetic variance matrix is given by A non-separable modelTwo separable modelsTwo partially separable models.All variance models are summarised in Table Following Tolhurst et al. , the GETVanRaden . The nonnon-separable model is fitted directly to the individual trait by environment combinations in The mentclass2pt{minimk model, where k denotes the number of common factors. The NFAk model is given by:vk-vector of genotype scores and vps-vector of regression residuals which are specific to individual trait by environment combinations. This model is analogous to a multiple regression with k terms, where the loadings are latent covariates and the scores are regression slopes.Smith et al.  proposedFollowing Smith et al. , the loah et al.  for GS.separable models assume complete separability between traits and environments, such that The mentclass2pt{minimAppendix  and MontAppendix  are consAppendix .partially separable models maintain the appealing features of the separable models but fit a more general framework for GETI like the non-separable model. The two partially separable models below are obtained by fitting an additional variance for individual traits or individual traits and environments. This relaxes the assumption of separability and produces a structure that can no longer be represented by the Kronecker product of two matrices. The variance matrix for ua can therefore be rewritten as:Dte is a The mentclass2pt{minimvps-vector of regression residuals which are specific to individual traits. This model is analogous to a multiple regression with Smith et al.  proposedIt is assumed that h et al.  for GS.The partially separable model developed below extends the UFAvps-vector of regression residuals which are specific to individual trait by environment combinations. This model is analogous to a multiple regression across two dimensions with The separable variance matrix in Eq.  Lastly, it follows from Eq.  ASReml-R  and the unstructured factor analytic linear mixed model (UFA-LMM). The SFA-LMM is used to demonstrate all remaining methods below, but note that similar results can be obtained for the NFA-LMM and UFA-LMM.All models for the additive and non-additive GET effects were implemented within the linear mixed model in Eq.  R Butler . The parR. In particular, the top left elements in Rt are set to one, the upper right triangle in Constraints are required in the SFA-LMM to ensure unique solutions for ASReml-R obtains REML estimates of the key variance parameters in the SFA-LMM (distinguished by hats) and EBLUPs of the key random effects (distinguished by tildes). A more efficient computational approach for fitting the SFA-LMM is developed in the Supplementary Material. Lastly, note that the phenotypes for each trait were scaled to unit variance to assist convergence in ASReml-R and then transformed back to their original scale after estimation.ps trait by environment combinations. Similar measures are also obtained for the p environments and all s traits, respectively.Model selection in the SFA-LMM was achieved using a combination of formal and informal criteria. Formal selection was achieved using the Akaike Information Criterion (AIC) and informal selection was achieved using measures of variance explained by the common factors. The percentage of variance explained for the p environments and s traits.Interpretation from the SFA-LMM was achieved using the overall variance matrices between traits and between environments. The variance matrix between traits for the The selection tools of Smith and Cullis  were extv-vector of predicted scores, with v-vector of standardised overall performances, with The overall performance of all genotypes for the i within the same selection index as OPi is the topic of current research.Lastly, the stability of all genotypes for the This section presents the results from fitting the non-separable, separable and partially separable linear mixed models to the multi-trait MET dataset. The variance models for the additive GET effects are summarised in Table The analyses began by fitting four baseline linear mixed models which assume the additive GET effects are independent across different trait by environment combinations Table a. These resresresresresresThe single-trait single-environment analyses were also used to compare the four residual models in Table A series of NFA-LMMs were then fitted which include a non-separable model for the additive GET effects Table b. These A series of UFA-LMMs were then fitted which include a partially separable model for the additive GET effects with fewer variance parameters than the NFA-LMMs Table a. Two enA series of SFA-LMMs were then fitted which include a partially separable model for the additive GET effects with fewer variance parameters than the NFA-LMMs Table b. The SFmdiag, is a multi-trait extension of the main effects plus diagonal model. This model provides a poor fit and is restrictive since it assumes the same GEI pattern for each trait and the same GTI pattern for each environment. The separable model of Montesinos-López et al. . The SFA3-2 model does have more variance parameters than the UFA2 model for the current dataset since it includes a specific variance for individual traits and environments, rather than just traits (14 compared to 3). These results suggest that the SFA-LMM will provide a superior fit than the UFA-LMM with fewer variance parameters as the number of traits and environments increase. Further interpretation of the SFA3-2 model is presented below.Formal model selection criteria was used to compare all variance models for the additive GET effects in Table z et al. , mus, exo et al. , comp, wTable M and three Murrumbidgee Valley environments (− 0.30), with − 0.20 overall. Lastly, the correlations between DTF and PHT were again highest for 17MVM and the same three Murrumbidgee Valley environments (− 0.33), with − 0.31 overall.Table The heatmaps in Fig. Tables The selection tools were applied to the SFA3-2 model in terms of the additive GET effects. These tools provide breeders with measures of overall performance , that is 0.69 for G1, 0.35 for G2, − 0.56 for G3 and − 1.36 t/ha for G4. This measure reflects the expected YLD performance of G1-G4 in an average environment.The OPThe RMSDi plot in Fig. i and low RMSDi. Conversely, low yielding and unstable genotypes which have low OPi and high RMSDi occur at the bottom right (G4 and Topaz). The remaining highlighted genotypes are above average  or below average (G3 and Kyeema) yielding, with different stabilities. Similar interpretation can be made for DTF and PHT in Fig. Selection across all genotypes for YLD can then be achieved using the OPSimultaneous selection across all traits can be achieved using the selection index in Fig. This paper developed a single-stage GS approach which incorporates information on multiple traits and multiple environments within a partially separable factor analytic framework. The advantage of using both sources of information is that breeders can utilise GETI to obtain more accurate predictions across correlated traits and environments. The partially separable factor analytic linear mixed model (SFA-LMM) developed in this paper was motivated by the need for a more parsimonious alternative to the non-separable factor analytic linear mixed model of Smith et al.  and the multi-trait MET datasets. Many of the current approaches for GS, however, only include models for GEI in single traits or models for GTI in single environments, but very few have considered appropriate models for GETI across multiple traits and multiple environments. Recently, Montesinos-López et al.  indicates that the current breeding germplasm could be subject to selection for DTF without impacting YLD. For example, there is a need to develop high yielding cultivars for early and late flowering time, and the results suggest that both breeding targets could be pursued with the current germplasm. The negative correlation between YLD and PHT (− 0.20) indicates that shorter genotypes tend to be high yielding. This correlation reflects the breeding objectives where selection has primarily been for high yielding and short genotypes. It could also reflect the interaction between lodging and yield since taller genotypes in the south-eastern growing region are typically more prone to lodging at maturity . This demonstrates the immediate genetic gain that can be made in the Australian rice breeding program. The parallel coordinate plot in Fig. The selection index provides important information on the overall merit of test genotypes compared to the check cultivars. The check cultivars provide a baseline for the three traits under selection. For example, Langi is a high yielding and soft cooking long grain cultivar which is broadly adapted across the Australian rice growing area. Doongara is a semi-dwarf The SFA-LMM developed in this paper includes a partially separable factor analytic model for GETI, which exploits the appealing features of separable and non-separable models. The utility of the SFA-LMM was compared to non-separable, separable and partially separable models for GS. The results show that the SFA-LMM provides a better fit than the separable approaches and a comparable fit to the non-separable and partially separable approaches. In fact, all factor analytic linear mixed models provide a good fit since they include appropriate models for GETI. The distinguishing feature of the SFA-LMM is that it will include fewer parameters than all other approaches as the number of genotypes, traits and environments increases. This research represents an important continuation in the advancement of statistical analyses of plant breeding datasets, particularly with the advent of high throughput phenotypic data involving a very large number of traits and environments.Supplementary file 1 (pdf 77 KB)Supplementary file 2 (R 25 KB)Below is the link to the electronic supplementary material.


The Network for Pancreatic Organ donors with Diabetes (nPOD) is the largest biorepository of human pancreata and associated immune organs from donors with type 1 diabetes (T1D), maturity-onset diabetes of the young (MODY), cystic fibrosis-related diabetes (CFRD), type 2 diabetes (T2D), gestational diabetes, islet autoantibody positivity (AAb+), and without diabetes. nPOD recovers, processes, analyzes, and distributes high-quality biospecimens, collected using optimized standard operating procedures, and associated de-identified data/metadata to researchers around the world. Herein describes the release of high-parameter genotyping data from this collection. 372 donors were genotyped using a custom precision medicine single nucleotide polymorphism (SNP) microarray. Data were technically validated using published algorithms to evaluate donor relatedness, ancestry, imputed HLA, and T1D genetic risk score. Additionally, 207 donors were assessed for rare known and novel coding region variants via whole exome sequencing (WES). These data are publicly-available to enable genotype-specific sample requests and the study of novel genotype:phenotype associations, aiding in the mission of nPOD to enhance understanding of diabetes pathogenesis to promote the development of novel therapies. Genome-wide association studies (GWAS) have identified population-level risk loci  >1%), dominated by Human Leukocyte Antigen (HLA) class II and insulin, and accompanied by 77 additional regions, which in total cover over 3,600 predicted causal moderate effect size variants (odds ratio (OR) <2) associated with genes thought to impact leukocyte and pancreatic β-cell function3. While a combination of many such population-level variants may contribute to the development of “classical” T1D3 and latent autoimmune diabetes in adults (LADA)4, we are beginning to appreciate that rare (MAF ≤1%) larger effect size (OR ≥2) variants may explain the “missing heritability” of autoimmune diabetes5 -, and cytotoxic T-lymphocyte protein 4 (CTLA4)-associated diabetes6, in addition to non-autoimmune forms of diabetes such as maturity-onset diabetes of the young (MODY) and cystic fibrosis-related diabetes (CFRD)7.Genetic predisposition to risk for or protection from type 1 diabetes (T1D) is highly polygenic, with the total possible set of disease-associated variants yet to be fully definedes5 Fig. . In supp8, founded in 2007, has become the largest biorepository of human pancreata, pancreatic lymph nodes, and spleen from organ donors with T1D, MODY, CFRD, T2D, gestational diabetes, non-diabetic islet autoantibody positive (AAb+) donors, and non-diabetic autoantibody-negative (AAb-) control donors9. nPOD provides worldwide distribution of biospecimens to researchers working to elucidate T1D pathogenesis in order to promote the development of new strategies for prevention and treatment. To date (September 2022), nPOD has supplied biosamples to > 280 independent research projects studying β-cell physiology, β-cell differentiation, immunology, T1D biomarkers, technology development, T1D pathology, and diabetes etiology . A major goal of nPOD, in addition to biosample distribution, is the sharing of de-identified donor data from multiple core laboratories to facilitate discovery efforts. The nPOD Data Portal provides approved investigators with access to donor clinical and demographic information, serum HbA1c and C-peptide levels, islet AAb status 10, pancreas weights, and histopathology reviews11 . Whole slide scans from hematoxylin and eosin-stained (H&E) sections are available for online viewing via the Online Pathology portal  for access to cross-sectional pancreas morphology as well as multiplex immunohistochemistry (IHC)-stained sections for insulin, glucagon, somatostatin, and pancreatic polypeptide (PP) to visualize endocrine β-, α-, δ-, and PP cells, respectively. Multiplex IHC staining panels are also available for markers including, but not limited to, Ki67, CD3, insulin, and/or glucagon for quantification of cell proliferation and immune cell infiltration in pancreatic endocrine and exocrine compartments. Histopathology reports summarizing blinded assessment of H&E- and IHC-stained sections from pancreas and other available organs are provided to detail islet parameters and major abnormalities. In terms of genetic data, the standard operating procedures (SOPs) for nPOD donors were previously limited to the collection of high-resolution four-digit HLA genotypes12. The availability of additional high-parameter genotyping data has therefore been a high priority that is now realized with the data release described herein.The Network for Pancreatic Organ donors with Diabetes (nPOD)13 and 2) the University of California San Francisco (UCSF) standardized whole exome sequencing (WES) pipeline14. Specifically, nPOD cases (N = 372)— comprised of AAb- no diabetes controls (N = 147), AAb + without T1D (N = 26), T1D (N = 111), T1D medalists16 (N = 2), T1D recipients of pancreas transplant (N = 5), type 2 diabetes , gastric bypass (N = 2), gestational diabetes (N = 4), monogenic diabetes (N = 4), cystic fibrosis , other diabetes (N = 12), other no diabetes (N = 12), and pregnant without diabetes (N = 4)— were genotyped using the UFDIchip13 custom AxiomTM array 22 per donor.Our approach for characterizing nPOD donor genetics was twofold: donors were genotyped with 1) the University of Florida Diabetes Institute (UFDI) custom single nucleotide polymorphism (SNP) microarray (UFDIchip)ray Fig. . All nPO 14 Fig. . Data em25. Importantly, beyond explaining diabetes heterogeneity, the findings facilitated by these data are expected to inform precision medicine strategies for prevention or suspension of the pathogenesis of T1D as well as other forms of diabetes.These genotyping data have been generated and made accessible to enable genotype-selected sample requests and the study of novel genotype:phenotype associations by the international community of nPOD investigators. We anticipate that the diversity of nPOD donor genetics may be partly responsible for inter-donor heterogeneity observed in islet health, insulitis composition, age at T1D onset, islet AAb status, and other endotype-related characteristicshttp://www.jdrfnpod.org//for-partners/npod-partners/, accessed October 15, 2021) in accordance with federal guidelines, then processed by the nPOD Organ Processing and Pathology Core (OPPC) according to University of Florida (UF) Institutional Review Board (IRB) approved protocol IRB201600029, as previously described11. Studies conducted using organ donor tissue samples from the nPOD biobank are classified as minimum risk research, as study participants are no longer living. However, informed consent for research participation is obtained from family members via both written and verbal communication prior to organ donation, with the consent processes undertaken by qualified personnel affiliated with the U.S. OPO network. All subject information is de-identified in accordance with HIPAA regulations. For each donor, clinical and demographic information, were obtained via medical chart review and OPO-conducted interview with the donor’s family. High-resolution four-digit HLA typing was performed by Next Generation Sequencing (NGS) as previously described12 at the Barbara Davis Center for Childhood Diabetes HLA Core . nPOD donors were categorized by diabetes type, verified by UF endocrinologist review of the de-identified terminal medical records , donor metadata , and additional data . Unique research resource identifiers (RRIDs) were assigned to each organ donor, in order to facilitate the provenance and reproducibility of results26.Transplant-quality organs, including pancreas and up to 13 other tissues, were recovered from cadaveric organ donors by United States (U.S.) organ procurement organizations .Phenotype_data.txt27) were genotyped at 985,971 unique loci on a custom SNP array termed the UFDIchip13 , to which all content from the ImmunoChip v228 was added, as well as all previously reported credible T1D risk variants3 . The array also includes dense coverage of the highly polymorphic HLA region, which allows for accurate imputation of HLA haplotypes to 4-digit resolution.372 nPOD donors  from all cases are stored in the database of Genotypes and Phenotypes (dbGaP)27. Subsequent analyses included relatedness estimation using KING30, genetic ancestry imputation using ADMIXTURE18, HLA imputation using AxiomTM HLA Analysis Software19, imputation to 300 M SNPs and indels using the Trans-Omics for Precision Medicine (TOPMed) reference cohort with the Michigan Imputation Server31, and calculation of a T1D GRS22.UFDIchip plates were processed on an Affymetrix GeneTitan instrument with external sample handling on a BioMek FX dual arm robotic workstation. Axiom™ Analysis Suite software  was used to process raw CEL file data to plink text files. The software includes quality control (QC) procedures at the sample, plate, and SNP levels. These QC threshold parameters were set to Axiom™ Analysis Suite default stringency (“Best Practices Workflow” using “Human.legacy.v5” settings). Under these settings, samples were included in analysis if dish QC (DQC) ≥ 0.82 and if QC call rate ≥ 97%. Plates were considered acceptable for analysis if average QC call rate ≥ 98.5% for passing samples. Best probe set was identified per SNP, with the SNP call rate threshold set to 95%. A screen for discordance from reported sex via X chromosome heterozygosity was then performed using plink v1.930 (v2.1.2) software.DNA from 24 nPOD donors were run in duplicate on the UFDIchip. SNP call rates were compared between technical replicates using Bland-Altman analysis. Reproducibility of genotype calls between technical replicates were evaluated by kinship coefficient using KING32 with known family relationships were merged and analyzed for genetic relatedness using KING30 (v2.1.2) software. The integrated relationship inference command was used to infer up to third-degree relatives. Relationships between nPOD case pairs and between 1000 Genomes pairs were represented by plotting estimated kinship coefficients. Kinship coefficients of unrelated 1000 Genomes pairs were randomly downsampled to the number of nPOD subject pairs to allow for data visualization.Genotyping data from the nPOD cohort with unknown and from the 1000 Genomes phase 3 cohort32 were filtered for SNPs that overlap with the UFDIchip array using plink v1.929. The data were pruned for linkage disequilibrium (LD) by removing SNPs with R2 > 0.1, screening within a 50 SNP block and proceeding by steps of 10 SNPs. This yielded 1000 Genomes genotypes for 320,005 SNPs, which were used to run an unsupervised analysis using ADMIXTURE software18 (v1.3.0) with k set to five populations. Each of the five groups represented a unique continental population from 1000 Genomes and as such, were assigned: 1) African (AFR), 2) Admixed American (AMR), 3) East Asian (EAS), 4) European (EUR), and 5) South Asian (SAS)32. The 372 nPOD cases were then projected onto the reference population to estimate ancestry proportions. Dimensionality reduction of the resulting Q-values (ancestry proportions) was performed using principal component analysis (PCA) to enable visualization.Data from unrelated subjects from the 1000 Genomes phase 3 cohortTM HLA Analysis Software (v1.2.0.38)19 was used to impute 2-digit and 4-digit HLA genotypes, along with probability scores for the imputed calls. Concordance with nPOD HLA typing results8 was assessed at HLA-A, HLA-DRB1, HLA-DQA1, and HLA-DQB1. The typed result was considered ground truth when the imputed result was discordant.AxiomAcc(L)] was calculated as previously reported33, substituting the dosage for the probability score that is provided by AxiomTM HLA Analysis Software19:Pi is the probability for imputed alleles A1i,L and A2i,L for donor i at locus L. Imputed alleles were considered concordant when they were included in the donor’s set of typed alleles at locus L, and discordant when they were not in the set of typed alleles at locus L. For discordant alleles, Pi was set to 0. The summation of probabilities for the total number of donors assessed, n, was then divided by the total number of alleles tested, 2n. The accuracy score ranges from 0, for no concordant calls, to 1, for complete concordance with probabilities of 1 for all alleles.Imputation accuracy for each of these loci  was calculated as:Pj is the probability for imputed alleles A1j,S and A2j,S at each HLA locus j of donor S. Concordance was determined as described above, and Pj was set to 0 for discordant alleles. The total number of loci tested, n, was 4 per donor . The accuracy score ranges from 0, for no concordant calls, to 1, for complete concordance with probabilities of 1 for both alleles at each locus for donor S.Donor-level imputation accuracy  (median [interquartile range (IQR)]). All nPOD samples were assessed for concordance between reported and imputed sex according to level of X chromosome heterozygosity using plink v1.929. For all nPOD cases, imputed sex matched reported sex. Thus, all 372 nPOD samples passed basic QC measures. Additionally, 24 nPOD samples were run in technical replicate to assess assay reproducibility. Call rates between the technical replicates differed minimally, with 0.087 ± 0.640% bias . All array results were subjected to basic QC analyses that assessed donor-level DQC; donor-, plate-, and SNP-level call rate; and sex concordance. Donor DQC or call rate failures were re-processed with freshly extracted DNA when necessary. nPOD samples were batch-processed with data from living donors30 found that all of these donor pairs were inferred to be unrelated (>third-degree relatives). For comparison, we also assessed the relatedness of 2,504 1000 Genomes phase 3 cohort32 subjects. While this set was designed to consist of unrelated individuals, it unintentionally included a few first-, second-, and third-degree relatives32. When relatedness between nPOD donor pairs was compared to relatedness between 1000 Genomes32 subject pairs, nPOD donor pairs showed significantly smaller kinship coefficients than inferred parent-offspring (PO), full sibling (FS), 2nd degree relative, and 3rd degree relative pairs from 1000 Genomes  analysis methods with these genetic data.Next, relatedness between donors was assessed. Due to the nature of donor organ procurement, it is highly improbable, although not impossible, that nPOD donors may be related. A relatedness analysis of the 372 nPOD donors  using KING softwaremes Fig. , suggest 32 Fig. , potenti32 to build a reference model for genetic ancestry using ADMIXTURE software18 32 clustered to occupy distinct PC space , as well as the subcontinental populations, Americans of African ancestry in SW USA (ASW) and African Caribbeans in Barbados , which is consistent with other U.S.-based admixture studies55. Notably, racial identity is complex and the method of estimating proportions of continental genetic ancestries may not adequately reflect genetic diversity56. With this limitation in mind, these analyses accomplish the aims of: 1) ADMIXTURE model validation using UFDIchip array data and 2) qualification of the genetic ancestry results as an alternative or additional covariate to reported race for users of this resource (UFDIchip_admixture.xlsx27)57.To further validate the UFDIchip data, we used the 1000 Genomes phase 3 cohorte18 Fig. , projecte18 Fig. , and comace Fig. . This suace Fig. . Donors HLA-A, HLA-DRB1, HLA-DQA1, and HLA-DQB18 to identify donors with genotypes that are associated with T1D risk or protection36. This enables an extra level of QC and validation of the UFDIchip array data by comparing typed to genetically imputed HLA genotypes. Imputation accuracy at each locus, Acc(L), was calculated assuming typed results were correct if discordant with imputed results. Overall, Acc(L) was >0.93 for low-resolution HLA (2-digit) and >0.90 for high-resolution HLA (4-digit) for the four loci tested (UFDIchip_HLA_imputation_accuracy.xlsx27).The nPOD cohort was HLA typed using next generation sequencing (NGS) at 36  or protective  genotypesAcc(S), for 2-digit HLA at the four typed loci. Acc(S) was 0.984 [0.946–0.998] (median [IQR]), indicating high performance of the imputation methodology per sample  had significantly higher GRS1 values than White No Diabetes donors . Note that these scores differ from those provided in Kaddis, et al.53, due to updating the reference cohort for imputation from the Haplotype Reference Consortium (HRC)61 cohort, with predominantly European ancestry, to the TOPMed31 reference, with diverse genetic ancestry.Polygenic risk scores summarize genetic risk for T1D as a continuous value by aggregating estimated risk at HLA and non-HLA loci207 nPOD donors were also queried for rare variants using WES and associated data are accessible on dbGaP (see Data Records). Standard QC measures were performed to minimize adapter contamination, low-quality reads, error rate, and sequencing bias. To further validate data quality, we measured concordance between genotype calls from the UFDIchip and WES (N = 167 donors). Indeed, 27,852 autosomal biallelic variants with at least one minor allele count (MAC) in the WES data showed a concordance of 98.8% [92.2%–99.7%] (median [IQR]) with UFDIchip calls.KCNJ11, LMNA, HNF1A, GLIS3, INSR, and GATA6 using a custom-designed NGS panel that included 140 genes implicated in monogenic diabetes62. These genetic variants were validated with WES  or MODY and neonatal diabetes 66 were observed in 141 nPOD cases with T1D  and deleterious . A suggested workflow for evaluating genetic variants for potential clinical significance is shown in Fig. There are several databases and tools available to help with the identification and interpretation of genetic variants. For example, the frequency of a variant in the general population can be estimated using the gnomAD, which contains data from 140,000 + exomes and genomes from unique, unrelated individuals spanning six global ancestriesore Fig. , Table 5The associated data are openly available with unrestricted access.


This study evaluated the phenotypic plasticity and RSA responses to a range of [P]ext in 2 sorghum genotypes. The results showed that both genotypes responded to [P]ext but with significant variations in about 80% of the RSA traits analyzed. Aboveground biomass and most RSA traits increased with increasing [P]ext. Plasticity was both genotype- and trait-dependent. For most RSA traits, the white sorghum genotype showed significantly higher plasticity than the red genotype, with the former having about 28.4% higher total plasticity than the former. RSA traits, such as convex area, surface area, total root length, and length diameter ranges, showed sizeable genetic variability. Root biomass had a high degree of plasticity, but root number and angle traits were the leading contributors to variation. The results suggested 2 root trait spectra: root exploration and developmental spectrum, and there was an indication of potential trade-offs among groups of root traits. It is concluded that RSA traits in sorghum contribute to variability and plasticity in response to [P]ext. Given that there might be trade-offs among sorghum root traits, it would be instructive to determine the fundamental constraints underlying these trade-offs.Due to roots’ physical and physiological roles in crop productivity, interest in root system architecture (RSA) and plasticity in responses to abiotic stresses is growing. Sorghum is significant for the food security of millions of people. Phosphorus deficiency is an important limitation of sorghum productivity. There is little information on the RSA-based responses of sorghum to variations in external P supply ([P] Roots’ biophysical and physiological functions make them critical for understanding and improving crop productivity and adaptation or resilience to abiotic stresses. Root system architecture (RSA), the spatial arrangement of the root system, mediates root–environment interactions. The plasticity and responses of root systems to environmental conditions, such as nutrients, water, and pH, have been reported –3. PlastSorghum bicolor L. Moench) is an essential cereal grown globally as a grain, forage, syrup, and energy crop. It is the fifth most important staple food and fodder cereal  P fertilizers were used, and no additional or basal fertilizer was applied. Recommended agronomic crop production practices were followed after germination.Between April and August 2020, we established 2 fields under rain-fed conditions. Cassava was the preceding crop, but the fields had been left fallow for a year. The 2 fields were planted 1 month apart and spatially separated by a 100-m fallow. Each trial was set up in a randomized complete block design (RCBD) with 2 blocks and 3 replicated plots. The experimental variables were 2 sorghum genotypes and 6 levels of P supply. The P application rates were chosen to be as close as possible to the fertilizer recommendation rates for sorghum for the present soil. A range of 10.9 to 21.4 mg kgnt soils . If the nt soils . Sorghumina Faso . AccordiExcavation of root crowns followed the processes described by York  for maizTwo data types were extracted from the excavated roots: manual measurements taken directly on the roots and data determined from the root images using image analysis software. Two shoot traits (shoot fresh and dry weight) and 14 root traits were extracted for the manual measures. Biomass traits included root fresh weight (RFW) and root dry weight. Dry biomass was obtained by oven-drying fresh biomass at 70 °C for 72 h. The root traits included the number of whorls (NoW), number of brace roots (NBR), brace root length, branching density of brace roots (BDBr), first arm brace root angle from first and second whorls in relation to horizontal (BA1a/b), and second arm brace root angle from first and second whorls in regard to horizontal (BA2a/b). Other root traits extracted manually were the number of crown roots, crown loot length, branching density of crown roots, and angle of crown roots. The branching density of each root type was calculated within 3 to 5 cm and 1 cm of the root’s origin, and we assumed that the branching density would remain constant over the rest of the root’s axis. The root angles were measured with the “shovelomics scoreboard” developed by Trachsel et al. .We used the “Whole Root” mode in the Rhizo Vision Explorer, version 2.0.3 , to analx), standard deviation (σ), range, and CoV. For further analysis, we maintained traits with CoV values ≥ 30% [H2) across the experiments based on variance components for the remaining traits, employing residual maximum likelihood (REML) procedures. We categorized all factors as random in the REML analyses so that the proportional contribution of genotype to overall variation in traits could be determined [yijkl represents the observation from the thijkl genotype, P supply rate, experimental run, and block; μ is the overall mean; ig is the effect of the thi genotype; jp is the effect of the thj P rate; gpij is the interactive effect of the thi genotype with the thj P supply rate; prjk is the interactive effect of the thj P supply rate with the thk experimental run; gprijk is the interactive effect of the thi genotype with the thj P supply rate and the thk experimental run; prbjkl is the interactive effect of the thj P supply rate, the thk experimental run, and the thl block; gprbijkl is the interactive effect of the thi genotype with the thj P supply rate, the thk experimental run, and the thl block; and εijkl is the experimental error.We employed various processes to progressively reduce the dimensions of the 51 traits measured in this study. We initially eliminated features based on the magnitude of the coefficient of variation (CoV) or differences between genotypes . First, es ≥ 30% . Subseques ≥ 30% . We thentermined . The ranH2 as the quotient of the genotypic variance and the total phenotypic variance for the trait (gσ2/pσ2) [r is the number of replicates, n is the number of trials, and σg2 × t is the genotype× trial variance.We estimated σg2/σp2) . We emplσg2/σp2) , to estiWe used multiple factor analysis (MFA), a multivariate ordination procedure, to identify significant root trait groupings accounting for most variation among the genotypes and provide an integrated picture of the root traits and the relationships between trait groupings. Thus, the MFA tested if root trait groupings covaried. Generally, MFA searches for similarities in datasets by doing a principal component analysis (PCA) on each data. The data are normalized as the quotient of its elements and the square root of the first eigenvalue from its PCA. In the second step of the MFA, the normalized datasets are combined to form a distinctive matrix on which a global PCA is performed. Individual datasets are projected onto a global analysis to uncover communalities and differences . Unlike x), RDPIx) by the relative phenotypic distances across replicates (ijd→’j’i) of the same genotype under varied P supplies. For each genotype, an RDPI ranging from 0 to 1 was calculated as follows:n is the total number of distances. Adapting the procedure of Lak et al. [Total, considering relative phenotypic distances between all P levels; (b) RDPIP15 vs. P0 (P15), weighing relative phenotypic distances between traits under 15 and 0 kg P/ha only; (c) RDPIP30 vs. P0 (P30), considering relative phenotypic distances between root traits under 30 and 0 kg P/ha only; (d) RDPIP45 vs. P0 (P45), weighing relative phenotypic distances between root features under 45 and 0 kg P/ha only; (e) RDPIP60 vs. P0 (P60), considering relative phenotypic distances between traits under 60 and 0 kg P/ha only; and (f) RDPIP75 vs. P0 (P75), weighing relative phenotypic distances between root features under 75 and 0 kg P/ha only.We employed the relative distance plasticity index method (RDPI) proposed by Valladares et al.  to deterk et al. , we compWe performed REML and ANOVA analyses with GenStat . We used the FactoMineR package in the R software, the Language and Environment for Statistical Computing ,46 for tWe obtained descriptive statistics for 51 traits consisting of 2 shoot traits and 49 root traits . There wP ≤ 0.001 or P ≤ 0.05) for approximately 80% of the traits evaluated  among the P fertilizer rates in 82% of the traits evaluated  were derived from variance components for 30 traits  to 1.0 (convex area) (H2 was large (≥0.60) for 8 traits, including volume, volume diameter—range 3, number of root tips, surface area, and fresh root weight ( H2 estimates were intermediate (0.25 to 0.59) for 15 traits, including average hole size, range 1 of root length diameter, volume diameter, projected area diameter, and surface area diameter  for traits such as numbers of aboveground whorls and brace roots, branching of brace roots, perimeter, and network area  . The H2  weight  . The H2 iameter  . The H2 ork area .r = 0.12 to 0.99; P < 0.001) with several traits, including perimeter, network area, number of root tips, projected area diameters, surface area diameters, and root length diameters. Fresh root biomass was positively but moderately or weakly correlated  with several traits, including total root length, perimeter, network and convex areas, hole size, and various diameter ranges. There were also some negative but significant correlations  with traits such as total root length and maximum width. Little correlation was found between the number of aboveground whorls or the root insertion angle traits  and other traits , all the trait groupings contributed more significant portions to the total distance than amounts contributed by root angle and root biomass groups  to moderate (0.3 to 0.5), ranging from 0.08 ± 0.004 (BA2a) to 0.433 ± 0.020 (RFW) for the red sorghum and from 0.092 ± 0.004 (BA2a) to 0.394 ± 0.018 (RFW) for the white sorghum. All but one trait (RFW) recorded low indices for RDPITotal  of the white sorghum was significantly (P < 0.05) greater than those for the red sorghum  than red sorghum’s. The RDPI for RDPIP15 vs. P0 ranged from 0.10 ± 0.01 (BA1b) to 0.422 ± 0.03 (RFW) for the red sorghum and from 0.131 ± 0.001 (MW) to 0.427 ± 0.02 (RFW) for the white sorghum  and 0.23 and 0.19 (RDPIP60 vs. P0), respectively. On average, there was no significant difference between the RDPIs of the white and red sorghum for comparing RDPIP75 and P0  showed that plasticity depended on the genotype and measured trait Fig. A. RDPITo0.3 to 0., ranging0.3 to 0., ranging0.3 to 0., ranging0.3 to 0., ranging0.3 to 0., ranging0.3 to 0., ranging0.3 to 0., ranging0.3 to 0., ranging0.3 to 0., ranging0.3 to 0., ranging0.3 to 0., rangingUnderstanding sorghum RSA traits and plastic responses to variations in soil P can be instrumental to the selection, breeding, and improved productivity of efficient genotypes. In this study, we investigated variations in root traits in 2 sorghum genotypes. The study sought to answer the following questions: (a) Which root traits of sorghum adapt under soil P limitation? (b) What is the extent of plasticity in root traits in response to P supply? (c) Does the extent of root trait plasticity vary under different rates of P supply? (d) What is the most critical root trait spectrum that contributes to a genotypic variation on sorghum? No root system genetic data for the 2 species used in this study are available. Still, root trait values revealed significant variation between the genotypes, suggesting potential genetic differences. The white genotype showed superiority in many traits, suggesting a possible systematic pattern that could hierarchize the genotypes, but this hierarchy cannot be immediately explained. The 49 root traits investigated form valuable descriptors of the RSA and its variability and can thus summarize roots’ developmental processes and characteristics .Lactuca genotypes. Given that the extent of genetic variability for a given trait signifies its improvement, the observation here suggests that there are divergent prospects for improving the features measured in this study. Root system traits that showed sizeable genetic variabilities, such as convex area, surface area, total root length, volume, and root length diameter ranges, could be easy targets for improvements through breeding. Conversely, traits that showed low genetic variabilities, such as BDBr, average root orientation, median diameter, and medium and shallow angle frequency, might be intractable when breeding for improvement to adapt to environmental conditions [Significant genetic variations were observed for the majority of the traits. Still, the magnitude of variations differed across the root traits, similar to the findings of Beroueg et al.  for Lactnditions . The varBrassica rapa) seedlings (5.8% to 83.2%) [CoVs for the measured root traits varied considerably , and theo 83.2%) . The exto 83.2%) . The faco 83.2%)  observedo 83.2%)  argued to 83.2%) . Thus, to 83.2%)  for fielo 83.2%) . TherefoIn the present study, the shoot biomass of the sorghum genotypes increased to an asymptote with increasing P addition to the soil Fig. A. This dThe capacity to adjust root mass ratio favoring root growth is expressed most effectively by species that have evolved in fertile soils . PerhapsHere, RDPI for most traits in the white sorghum was highly significant than those in the red sorghum Fig. A. Still,We found a high degree of plasticity in root biomass, while the morphological root traits studied presented relatively minor plasticities Fig. . This ob−1 on one side and 15, 30, 45, 60, and 70 kg P ha−1 on the other side (Figs. Furthermore, we hypothesized that the extent of root trait plasticity would depend on the gradient of P availability. Our study presented limited evidence for a difference in root trait plasticity based on the gradient of P availability or the magnitude of the P deprivation. Generally, the RDPI for both genotypes showed a consistent trend for the comparisons between 0 kg P halant roots have developed several adaptations to help them establish, grow, and function in soils. The many adaptations may have resulted in an inherent dimensionality crucial to the root system’s performance. These dimensionalities might be analogous to the simple building blocks used in describing the RSA, including root geometry, morphology, topology, and growth dynamics . Some ofHere, the MFA produced 2 independent axes Figs. B to 3D, Even so, it was also interesting that all the trait groups on the first dimension or spectrum, including attributes for root length, area, volume, and diameter, were automated features that have been more mathematical . On the Here, root number, angle, diameter, and width measures met the cutoff in the 2 significant dimensions and are therefore considered essential in contributing to the overall variation in this dataset and warrant further investigations Figs. A and 4B.In MFA and PCA, variables that do not correlate with any PC typically present minimal contribution to dataset variations and might be removed to simplify the overall analysis. The top contributing variables to the first few PCs provide insights into which variables underlie variations in the dataset and may help with trait selection for downstream analyses. Adu et al.  employedNonetheless, selecting variables based on their contribution to PCs requires prudence. A single variable having a more significant size effect and accounting for most of the variance along the first PC might have a high loading and drive the dimension. Furthermore, the signal might be created by a few more essential factors, and a variable could have a higher weight on numerous components. As a result, considering the purpose before picking a variable based on this form of multivariate analysis may be more enlightening. If the goal is to choose features from a multivariate dataset with an immediate outcome, approaches that yield a more interpretable variable importance measure may be preferable.Sorghum remains a critical cereal crop for food security as it has a high potential to feed millions of people. While there is an urgent need to understand RSA and root system plasticity to environmental conditions to support crop improvement, little information exists on the RSA and phenotypic plasticity of sorghum in relation to soil P supply. The functional interpretation of root trait plasticity and variability in sorghum would require the knowledge of several root system traits and their role in the plant’s performance. Here, the RSA and phenotypic plasticity of 2 sorghum genotypes were studied to identify traits that contribute to responses to a gradient of soil P. Root system traits were found to contribute to variability and plasticity in response to external P supply. Both genotypes showed a response to external P application, but significant variations were observed between the 2 genotypes with respect to a majority of the root system traits evaluated. The white sorghum seemed better adaptable to a low-P environment as it showed exploitative plasticity. Root system traits such as convex area, surface area, total root length, volume, and root length diameter ranges showed potential for selection for improvements of the genotypes studied. Extensive root biomass seemed important for adapting to low soil P conditions. However, root length seemed to respond in proportion to P supply, showing a fairly balanced partitioning of resources between mass and length. This appears to be unique but requires further studies for validation. Selection of sorghum genotypes for breeding improvements and P-use efficiency can be based on RSA and plasticity of root system traits to P supply. Further studies on sorghum RSA and plasticity in different soil types are recommended to help elucidate the root system traits that contribute most to responses to P supply.


This study aims to identify trends and hot topics in breeding value to support researchers in finding new directions for future research in thatarea. The data of this study consist of 7072 academic studies on breeding value in the Web of Science database. Network visualizations and in-depthbibliometric analysis were performed on cited references, authors, countries, institutions, journals, and keywords through CiteSpace. VanRaden (2008) is the most cited work and has an essential place in the field. The most prolific writer is Ignacy Misztal. While the most productivecountry in breeding value studies is the United States, the People's Republic of China is an influential country that has experienced a strongcitation burst in the last 3 years. The National Institute for Agricultural Research and Wageningen University are important institutions that playa critical role in connecting other institutions. Also, these two institutions have the highest centrality values. “Genomic prediction” is theoutstanding sub-study field in the active clusters appearing in the analysis results. We have summarized the literature on breeding value, includingpublication information, country, institution, author, and journal. We can say that hot topics today are “genome-wide association”, “feedefficiency”, and “genomic prediction”. While the studies conducted in the past years have focused on economic value and accuracy, the studiesconducted in recent years have started to be studies that consider technological developments and changing world conditions such as global warmingand carbon emission. The deviation of the progeny produced by a particular progenitor from the mean of the reference population isthe breeding value . Falconer and Mackay (1983) defined an individual's breeding value not only in terms of the averageperformance of a reference population, but also in terms of the value of alleles it could pass from each ancestor to its progeny . It is generally accepted that breeding value is primarily influenced by additive genetic effects, which are the effects of genes passed downfrom parents to offspring and contribute to the individual's genetic makeup. However, other genetic effects such as dominance effects, epistaticeffects, and gene–environment interactions can also contribute to the variation in breeding values observed within a population. An accurateestimation of a selection candidate's breeding value in a breeding program is crucial. It indicates the selected candidate's ability to breed superiorprogeny. It is clear that there will be a greater need to increase the efficiency of animal product production. The primary purpose of livestockfarming is to produce goods and services for humanity's benefit using the feed materials of natural resources such as animal genotypes and theenvironmental conditions to which these genotypes are exposed. From this point of view, the size of the benefit or economic benefit from animalsdepends on that animal's genetic capacity. However, studying animals with the best genetic capacity for animal products may not always be possible. Atthis point, animal breeding plays a critical role in increasing the average genetic ability of the current population. The estimated breeding valuecan make it possible to rank the animals according to their predicted genetic potential, resulting in more accurate selection and faster geneticprogression across generations.A fundamental principle of animal breeding is to select animals as parents, which will improve the genetic level of the next generation. For quantitative traits for which genotypes can not be observed, the breeding value may measure the phenotypic value influenced by both the genotype and the environment . Animal breeding aims not to improve individual animals genetically but to improve animal populations. In animalbreeding, the first condition of establishing a selection program is to know the genetic characteristics of the traits of interest .Early prediction methods in breeding were based either on progeny testing or phenotypes measured in selection candidates. These methods were thenexpanded to selection indices which used various information sources, such as measurements of the same phenotype collected from relatives andcombinations thereof and secondary traits measured in the same individual . In the 1950s, C. R. Henderson furtherexpanded the methodology, developing mixed models containing fixed and random effects. These methods have been used to identify the animals with thebest genetic potential and, therefore, the highest value for breeding , using information about phenotypes determined not only by thegenetic potential but also by environmental influences. Henderson proposed the best linear unbiased predictor (BLUP) in 1984 to calculate estimatedbreeding values based on these phenotypic data.Defining breeding goals is one of the critical challenges of a breeding organization. While breeding goals determine where to go, breeding programsdescribe how to get there . Key in genetic improvement programs is the estimation of breeding values (EBVs). Breeding value, which canbe defined as the genetic values of an individual specified by the progeny, may be based on individual characteristics or selection index. Supposethere is only a single record on one animal and no information on their relatives. In that case, the EBV is the heritability multiplied by thedifference between the individual observation and the population mean .The principal objective of animal breeding is to generate genetic progress to breeding aims at changing populations in a desired, positive direction. Therefore, an important task of animal breeding research should be to enable genetic advancement.There is a growing body of literature that recognizes the importance of molecular markers (such as single-nucleotide polymorphism – SNP) for genomicprediction of breeding values . Genomic prediction of phenotypes and breeding values is an increasingly important area inanimal science . For example, over the past two decades, major advances in genomic prediction have doubledthe dairy cattle improvement due to reducing generation intervals .The bibliographic analysis can explore the academic field of knowledge and understand which questions researchers are trying to answer and whatmethods they have developed for this purpose . This method involves using a defined set of metrics to assess published research output,impact, and trends. In other words, bibliometric analysis is used to qualitatively and quantitatively analyze the effects of journals, institutions,research groups, individual researchers, or countries .Bibliometric studies summarize and analyze the current situation's changes, and research hotspots in breeding value studies are required to make anideal research plan . This study's results will help researchers see the development of breeding value literature, understandits course, and conduct better-planned research. This study is aimed to provide benefits in terms of seeing the progress of studies in this field andbetter evaluating the question marks for the future.22.1https://citespace.podia.com/, last access: 7 December 2022). Visualization maps used for bibliometric analysis in CiteSpaceconsist of nodes and links. While nodes represent analytical items such as author, journal, reference, and keyword, the node size shows the totalco-occurrence frequency of an item, the node's thickness, and the ring's color indicates the co-occurrence time periods of this item . Colored lines indicate connections between different nodes, describing collaboration and co-occurrence or co-citations. The thickness of a linebetween other nodes indicates the frequency of citations together. In contrast, the color of the lines indicates the first year of co-citationrelationships between these nodes . We used three structural measures to evaluate the network's structural quality: modularityBibliometric analysis was applied to investigate the origin, development, and evolution of the breeding value field, as well as the current status andpossible trend . This bibliometric study used CiteSpace, one of the most popular software tools to analyzeco-citation networks  database, apublisher-independent platform that provides comprehensive citation information for various academic disciplines . Web of Science “core collection” was preferred rather than all databases to obtain full records of influential papers and eliminate the subordinate papers. Web of Science corecollection (update: 4 January 2022) data, consisting of 7072 publications, had been downloaded. Search criteria were selected using the following search terms: topic – animal breeding or breeding value or selection index or selection intensity or breeding efficiency or breeding program; categories – agriculture, dairy, and animal science; document type – article or review article or proceeding paper; and time span – 2000–2021. According tothe determined criteria, 7072 papers' data were reached and recorded appropriately. The recorded data were converted into Excel data and werechecked. It was then converted to a data file format suitable for CiteSpace, saved, and analyzed. The analysis adopted the 33.1A total of 7072 publications on breeding value have been published, and the detail of the annual publication is shown in Fig. 1. As shown in Fig. 1,the sum of times cited per year was just 23 in 2000. Since then, the number of citations per year has increased faster than in previousyears. Further, the increase in the number of publications can be seen in Fig. 1.It can be said that the number of publications in an increasing trend is an indicator that the field is still developing. The increasing trend in thenumber of citations is faster than the increasing trend in the number of publications.The 3.2The country collaboration network's map consists of 125 nodes and 185 links. Figure 2 shows the countries and cooperation networks of theseauthors. Each circle represents the country's outputs, and each link describes their collaboration, as shown in Fig. 2.The top 10 countries in terms of the number of publications and centrality are listed in Table 1. The United States was the most productive country inthe breeding value studies  of the countries and institutions.The network's density is calculated as 0.0239. Furthermore, the burstness is a valuable metric for determining which country is particularly active;as shown in Table 2, the People's Republic of China has had a strong burst over the last 3 years. According to Table 2, the People's Republic ofChina, South Korea, and Brazil have been the most active countries in recent years. As a result of cluster analysis for countries, the modularityTable 2 shows the top 10 countries and top 10 institutions with the citation bursts and their years of popularity. The red line indicates the activecitation burst duration between 2000 and 2021, whereas the blue line corresponds to the inactive duration.The institution's collaboration network's map consists of 680 nodes and 694 links. Figure 3 shows the institution and cooperation clusternetworks. Cluster refers to a group of related publications that share similar characteristics for institutions. Each circle represents theinstitution's outputs and each link describes its collaboration. We can understand from the density of connections that the most active institutionscooperate with each other. As a result of the cluster analysis for institutions, the modularity As shown in Table 1, the most active institution was found at the University of Wageningen, followed by National Institute for Agricultural Research(INRA) and the University of Guelph. Moreover, the University of Wageningen and INRA are key nodes with the highest betweenness centrality value(respectively 0.19 and 0.15) because the centrality is greater than 0.1, which means they play an important role in connecting other institutions. TheUniversity of Toulouse and Northwest A&F University are today's active institutions with a strong citation burst, as shown in Table 2.3.3Journal of Animal Breeding and Genetics with 1124 (15.89 %) academic studies, followed by Archives of Animal Breeding with 920(13.01 %), Journal of Dairy Science with 713 (10.08 %), Journal of Animal Science with 420 (5.94 %), and Genetics Selection Evolutionwith 391 (5.53 %). In other words, approximately 50 % of the academic studies in this field have been published in these five journals. Thejournals' co-citation map is a complex network consisting of 1231 nodes and 13070 links. According to the analyses of this network, the Journal of Animal Science had the maximum co-citation counts (3880); next was the Journal of Dairy Science (3667). These journals are “core journals” with highpublication and co-citation volume.A total of 7072 academic studies on breeding values were published in 146 different scientific journals. The journal with the highest number ofpublications was the 3.4There were 14 115 different authors in 7072 papers. The 10 most active and prolific authors and their quantitative metrics are listed in Table 3.Ignacy Misztal was the most productive researcher, with 79 papers in this field. As shown in Table 3, the following research by Legarra et al. (2009), of which Misztal is a co-author, “A relationship matrixincluding full pedigree and genomic information”, and Hayes et al. (2009), “Invited review: Genomic selection in dairy cattle: Progress and challenges”,plays an important role in breeding value studies. Author T. H. E. Meuwissen has the highest centrality value (0.08) compared to other authors. Therefore, it isa key point in connecting with other authors. The author network consists of 1022 nodes and 1965 links. As a result of the cluster analysis of thenetwork, the modularity Author analysis examined authors of 7072 academic studies on breeding value, and cited author analysis also examined authors cited in thosestudies. The cluster summary is given in Table 4. The software does not display the names of clusters with low silhouette values in the clustersummary table, clusters visualization, or timeline . The silhouette value of all clusters was greater than 0.7; therefore, theclusters were well clustered in a homogeneous structure. Clusters no. 0 (dry matter intake) and no. 1 (single-step genomic prediction) are stillactively studied sub-field topics.Table 4 shows the cluster size, silhouette values, and mean (years) of the clusters automatically selected.The citation burst, which is considered an important point in the research field, is given in Table 5 for the authors. A burst term is defined as anauthor who changes in literature with a sudden frequency during a given period.E. Kalm made the strongest citation burst between 2000 and 2007. The most active author, I. Misztal, is in the top 10 in the citation explosion withhis works between 2019 and 2021, and his works belong to cluster no. 1 (single-step genomic prediction). B. J. Hayes, M. P. L. Calus, R. F. Veerkamp, Bijma P,J. E. Pryce, and M. E. Goddard, active and influential authors in the field, belong to the no. 0 (dry matter intake) cluster.Table 5 shows the top 10 authors with citation bursts and their years of popularity. The red line indicates the active citation burst duration between2000 and 2021, whereas the blue line corresponds to the inactive duration.According to the cited author analysis result, T. H. E. Meuwissen (citation count: 1061) ranked first, followed by P. M. VanRaden (citation count: 992), andI. Misztal (citation count: 727). The field of study of these three authors belongs to cluster no. 1 genomic predictions determined from the citedauthor analysis (see Table 4). The highly co-cited authors are known to significantly influence the development of a particular field. Therefore, itcan be said that these authors made an important contribution to the development of the breeding value field.3.5The co-occurrence of keywords can effectively reflect research hotspots and boundary issues and provide research support . The keyword network's map consists of 756 nodes and 9549 links. It means that, on average, each determined keyword was connected with12.65 other keywords. The 20 most frequently used keywords are shown in Table 6.The most popular keyword was “selection” . Analysisof CiteSpace is conducted not only by considering the documents downloaded from databases but also by analyzing the references they have cited intheir text. Therefore, the document co-citation analysis generates a network that consists of both cited documents and citing (the ones downloadedfrom scientific platforms) . The co-citation network of a total of 116 624 cited references related to 7072 academic studiesconsisted of 1546 nodes and 7531 links, and each determined document was connected with 4.87 other references. The first 10 most cited academicstudies are given in Table 8.P. M. VanRaden's article “Efficient Methods to Compute Genomic Predictions” in 2008  was referenced in 283 of the selected breedingvalue studies, and the article was cited 2610 times in total. At the same time, this article with the strongest citation burst has the feature ofbeing an interesting article in a short time. Another P. M. VanRaden academic study in 2009, “Invited Review: Reliability of genomic predictions forNorth American Holstein bulls” , was referenced 238 times in breeding value studies. B. J. Hayes's 2009 review “Invited review:Genomic selection in dairy cattle: Progress and challenges”  was referenced 211 times and cited 1028 times. The reference withthe greatest centrality values is the “Positional candidate cloning of a QTL in dairy cattle: identification of a missensemutation in the bovine DGAT1 gene with major effect on milk yield and composition” article by Grisart et al. (2002) .Table 9 shows the cluster size, silhouette values, and mean (years) of the clusters automatically selected.The cluster no. 0 genomic prediction is the largest cluster, consisting of 264 nodes. It had a silhouette score of 0.797, which is relatively lowin homogeneity. The references composing cluster were, on average, published in 2010. The second-largest cluster no. 1 was a group of 186 nodes with a silhouette score of 0.858 and a publication year that, on average, was 2014. The third-largest cluster, no. 2 , was a group of 139 nodes with a high silhouette score of 0.945 and was, on average, published in 1998. As seen in Fig. 6, the timelineview would finally provide an overview of the evolution of clusters in the breeding value field over time and show whether these improvements havecontinued over the years. Single-step genomic evaluation and feed efficiency clusters are active sub-fields .In the co-citation reference network, 476 nodes indicated a citation burst in their history (see Table 10 for the strongest 20 citation burst). Inparticular, in the first 10 nodes with the highest magnitude of citation bursts, eight belonged to cluster no. 0 (genomic prediction). It was seenthat Gilmour et al. have three strong citations burst for different ASReml user guide versions among the top 20 citations burst. ASReml, a statisticalpackage that fits linear mixed models using residual maximum likelihood (REML), has been given references by many authors. It can be said that linearmixed model structures have been frequently preferred in breeding value studies since the 2000s. The second document with the strongest citationburst, “A new approach for efficient genotype imputation using information from relatives,” was authored by Sargolzaei et al. (2014).At the same time, this document has the most up-to-date citation burst with a duration of 5 years (from 2016 to 2021). In the sigma metric, thedocument with the highest value was the one published by VanRaden (2008), with a value of 48.1. A high sigma node not only has astrategically important structural feature but also has unique temporal implications .4This study aimed to analyze the current status and trends between 2000 and 2021 publications on breeding value using CiteSpace. This bibliometricanalysis has shown that relevant research about breeding value was grown from year to year. The increase in the number of citations that is faster than theincrease in the number of publications indicates that the citations given to the publications made in this field are shown not only in the area butalso in other fields. This can also be explained by breeding value studies being a multidisciplinary field that includes genetics, statistics,biotechnology, and other related fields. In other words, breeding value studies are also cited in secondary fields. The studies of VanRaden,Meuwissen, and Misztal, the most cited authors in studies on breeding value, are of great importance for those working in this field. These studiesplayed a key role in the development of the field. P. M. VanRaden's 2008 study “Efficient Methods to Compute Genomic Predictions” is a significant workin the breeding value field, cited 4155 times. He is a prominent author who has developed efficient methods for processing genomic data to improve theaccuracy of estimated breeding value and simultaneously predict thousands of marker effects. Another study by P. M. VanRaden in 2009, “Invited review:Reliability of genomic predictions for North American Holstein bulls,” has been cited 1394 times. B. J. Hayes's 2009 study “Invited review: Genomicselection in dairy cattle: Progress and challenges” is another important study that has been cited 1878 times. The highest betweenness centralitybelongs to Mike Goddard's academic study “Genomic selection: prediction of accuracy and maximization of long term response” in 2009. Additionally,this document has a long period of citation burst (2010–2017). He studied the accuracy estimation of genomic selection and maximizing long-termresponse and noted that unless new markers were continually added to the estimation of breeder value, genomic selection would result in a fasterreduction in selection response than phenotypic selection . The most prolific author I. Misztal's article “A relationship matrixincluding full pedigree and genomic information” has great importance in the field. Although the United States was the most productive country in thebreeding value studies . However, Gianola andFernando (1986) provided an elegant and critical review of Bayesian methods applied in animal breeding before the Markov chainMonte Carlo (MCMC) era, with their work making an important contribution to Bayesian methodology applied to theoretical animal breeding andquantitative genetics. Based on the number of references to ASReml by many authors, we can also say that regression models and linear mixed models aregenerally used as statistical methods in breeding value studies. Strong cooperation has been identified between many developed countries andwell-known institutions. Although it is difficult to publish relevant articles in high-impact journals, breeding value studies have been keptup to date and have high co-citation counts. When compared with the journals where the most influential articles were generally published, it wasfound that the most cited articles on breeding value were published in A more in-depth analysis of the 10 prolific authors and the 10 strongest citation bursts showed that the most common topics mainly focused ongenomic prediction, single-step genomic prediction, and dry matter intake. Based on the assumption that hotspot analysis is the basis for assessingthe evolution of keywords in breeding value, we can say that hot topics today are “feed efficiency” and “genomic prediction”. The “genomicprediction” is the essential sub-study field in the active clusters that appears in the analysis results. Since its initial development in the early2010s, the single-step genomic best linear unbiased predictor has become the most popular methodology for genetic evaluations, including for genotypedand non-genotyped individuals. It has accurately estimated breeding values in animals and plants. A new breeding value has been studied in the lastdecade, combining the amount of feed saved through improved metabolic efficiency with the expected maintenance requirements . Therefore, breeding value involves a genomic component for residual feed intake (RFI) with maintenance requirements calculated from a genomicor pedigree estimated breeding value (EBV) for body weight (BW) estimated using adaptation traits . Today's whole-genomesequencing studies have created a strong need for faster and more scalable applications of basic functions such as logistic regression, genomicdistance evaluation, and linkage disequilibrium estimation. C. C. Chang et al.'s 2015 article “Second-generation PLINK: rising to the challenge oflarger and richer datasets” addresses this need . It is also an indication of this that this document has a strong and up to datecitation burst.As a matter of course, when it comes to animal breeding, the names of the pioneers of animal breeding such as Henderson who developed the best linearunbiased predictions and mixed models and Lund should not be forgotten. Because the time period of this study does not include the years before 2000,that is, the active years of such pioneers' names, it is not directly included in this study. Our primary aim in this study is to focus on how animalbreeding will take a direction in the future, recognizing that it has come from a certain point rather than a complete historicaldevelopment. Consequently, this study aimed to assess the importance of breeding value in animal science using various bibliometricanalyses. “Genomic prediction” provided the largest set of important clusters over various research topics associated with breeding valueestimation. It is only since the work of Meuwissen et al. (2001) that the study of genomic selection or prediction hasgained momentum. The main theoretical assumptions and associated statistical models to obtain the benefits of genomic prediction were firstdemonstrated by Meuwissen et al. (2001) by using simulated datasets. Genomic relationship matrices to obtain genomic breeding values were first defined by. However, the impact of different genomic relationship matrices  on theaccuracy of genomic prediction of breeding values has remained unclear . From this standpoint, the popularity of the “genomicprediction” cluster could be understandable, and further research in this field should be carried out to obtain breeding values with higher accuracyunder various experimental settings.In the mid-20th century, technological innovations in computation and methodological advances in genetic theory and statistics paved the way forpowerful multi-trait analysis. As more sophisticated analytical techniques for traits were developed and incorporated into selection programs,production began to increase rapidly, and the wheels of genetic progress began to turn . The evolution of breeding value studiesinto genetic breeding value over the past 20 years has been with the adoption of the idea of using DNA markers to improve the genetic gain rate. Inthis study's time period considered, it is seen that the developments in the first 10 years are static; however, the developments in the last 10years are more dynamic. The way has been opened for better genetic predictions with the identification of single genes or the quantitative trait loci(QTL). Many of the economically important traits in livestock are complex, continuously distributed phenotypes influenced by multiple polygeneslocated at QTL distributed throughout the genome . Remarkable advances in production efficiency have been made following theapplication of sophisticated selection strategies based on quantitative genetic theory.All aspects of animal health and welfare have an additional non-monetary value that cannot be measured economically. This extra value has twointerrelated aspects: the suffering of affected animals and the social impact. A merely economic approach will never capture the full value of suchfeatures . While the studies conducted in the past years have focused on economic value and accuracy, the studies conducted in recentyears have started to be studies that take into account technological developments and changing world conditions such as global warming. We believethat, in the future, international commitments and coordinated action to limit global warming directly related to greenhouse gas emissions, nutrientefficiency, and climate adaptation will play a more critical role in breeding goals than a purely economic approach. Ultimately, it is possible toevaluate and optimize breeding programs with different objective functions . Defining cultivation goals is one of the criticalentrepreneurial challenges of a cultivation organization. It always has to consider the expected future production conditions, demand structures, andsocio-economic context. Breeding goals will likely have to respond to society's expectations of animal welfare and growing skepticism about theover-industrialization of animal production . The livestock sector's new goals should consider reducing greenhouse gasemissions, environmental protection, and climate change. Climate change, environmental impact mitigation, and animal adaptation are current emergingissues in the livestock industry, and these will bring new breeding objectives and new research areas .5Although technological developments since the beginning of the last century have facilitated the calculation of breeding value, developments ingenetic theory have increased the complexity of calculations. With the development of technology, even more data will be available for use inselection . The problem will be determining which traits to choose fromwithin this dataset, which contains many traits, rather than measuring phenotypes. It is clear that future studies will seek an answer to the questionof which traits should be included in the breeding value. It is already know that the focus of selection had shifted from being purely production orientedtoward a more balanced breeding goal. This change has partly been due to increasing health and fertility problems and partly to social pressure andwelfare concerns. In addition to selection indices such as longevity, fertility, and health for sustainable breeding goals, studies that integratefeatures such as reducing greenhouse gas emissions and protecting the environment into selection indices will increase rapidly in the future.As a result, it is possible to say that future studies will focus on innovative issues that may have limited environmental impacts while reducingcosts and accelerating livestock productivity.Nevertheless, combining bibliometrics and visual knowledge maps provides researchers with a reliable way to review the literature, helping themcomprehensively and systematically understand the development and evolution of hotspots in a given field. This study aims to comprehensively presentthe studies on breeding values and examine the studies' development stages and potential trends. As a result, this study can be a pioneer in thisfield by revealing important research countries and institutions, influential journals, leading authors, cooperation between countries andinstitutions, general trends, and hot topics and can provide an excellent guide for further studies.


To assess their potential for beef production, this study describes the morphometrics, physical traits, and body weight of 1034 Indonesian beef cattle from eight breeds, namely Bali, Rambon, Madura, Ongole Grade, Kebumen Ongole Grade, Sasra, Jabres, and Pasundan. An analysis of variance in addition to cluster, Euclidean distance, dendrogram, discriminantfunction, stepwise linear regression, and morphological index analyses wereperformed to describe the differences in traits among breeds. The morphometric proximity analysis revealed two distinct clusters with a common ancestor, where the first cluster included Jabres, Pasundan, Rambon, Bali, and Madura cattle and the second included Ongole Grade, Kebumen Ongole Grade, and Sasra cattle, with an average suitability value of 93.20 %. This showed that the classification and validation methods can be used to distinguish breeds. The most important factor in estimating body weight was the heart girth circumference. Ongole Grade cattle had the highest cumulative index, followed by Sasra, Kebumen Ongole Grade, Rambon, and Bali cattle. A cumulative index value  Based on statistics from the year 2021m the national cattle population is A thorough understanding of farm animal characteristics is required for theeffective management of their genetic resources . Morphometrics and physical appearance are important traits in breeding programs . These traits are used for genetic conservation, in addition to determining whether animals are suitable for selection, and to improve their production and realize their potential. Felius et al. (2014) stated that distinct, breed-specific morphological traits are always noticeable, while molecular genetic variance does not provide comprehensive information about adaptation. Animal traits allow us to determine the best uses for each breed and are thus of interest to beef producers .While morphometric studies have examined several Indonesian cattle breeds,there is inadequate information on the relationship between body size andthe physical appearance of indigenous and local cattle in Indonesia. Bothmorphometric and physical measurements are very useful for assessing whetheranimals are suitable as beef producers and for optimizing their potential. Based on these considerations, this study assessed the morphometric and phenotypic performance of indigenous and local cattle in Indonesia to provide information on the potential of beef-producing cattle and inform the selection criteria of breeding programs.22.1This study examined 1034 adult cows  was measured using a digital scale  with a maximum capacity of 1000 kg and an error of 0.5 kg. Body measurements were made in centimeters (cm) using a calibrated ruler, measuring tape, and calipers. To avoid variation, the measurements were made by the same technician. Based on guidelines for the phenotypic characterization of cattle , 17 morphometric characters were measured, including head length (HdL), head width (HdW), horn length (HrL), horn circumference (HrC), ear length (ErL), ear width (ErW), chest width (CsW), withers height (WtH), heart girth circumference (HtGC), body length (BdL), heart girth (HtG), hip height (HpH), rump length (RmL), hip width (HpW), rump width (RmW), tail length (TiL), and tail length up to switch (TiLS). The cows were in the parallelogram standing position when measured. Figure 2 shows the biometric measurement points. In total, 12 physical characteristics/traits of the cattle were assessed, namely forehead coat color (FCo), dewlap size (DSz), hump size (HmSz), horn color (HrCo), horn orientation (HrOr), switch color (SCo), muzzle shape (MSp), muzzle color (MCo), coat color (CCo), hoof color (HoCo), hoof shape (HoSp), and vulva color (VCo). Table 1 summarizes the value ranges for each trait.2.3Descriptive statistics (mean and standard deviation) and an analysis ofvariance were used to examine the differences in the morphometric data among cattle breeds. For each morphometric trait, the least square means and standard error were calculated using the general linear model procedure. Through hierarchical cluster analysis, animals with relatively homogeneousmorphometric and physical data were grouped together, and the Euclideandistance between the groups was determined. The groupings are presentedhierarchically using a dendrogram. To determine the appropriateness of thegroupings, discriminant function analysis was performed .Stepwise linear regression analysis was used to determine the best BWestimation model . The coefficient of determination  wereestimated, namely the height slope index (HSI), width slope index (WSI), length index (LI), depth index (DI), foreleg length index (FLI), balance  might be attributable to environmental and breed factors. Previously, a strong relationship between morphometrics and productionpotential was reported . The values of HdL, HdW, ErL,ErW, HrL, and HrC reflect the appearance of the face and head of an animal. These values are very important for distinguishing among breeds; in fact, they are the basis on which different livestock breeds are generated. Furthermore, meat-related morphometric measures such as RmL, CsW, HtGC, andHpW can be used as indices of production performance and to characterizelivestock populations. These indices allow the assessment, management, andconservation of livestock based on morphological relationships . A larger body frame can support a larger body, where body sizeis associated with the structure of body components. BW is a proxy for bodysize .Physical characteristics must be considered in livestock breeding programs.In our study, the orientation of the horns varied among the animals and hadno bearing on breeding activities (Table 3). Physical characteristics arecritical for cattle identification and morphometric measurements and serveas basic data for breeding programs . The uniquecharacteristics of individual breeds can provide a foundation for breedingprograms, although it is often difficult to distinguish cattle breeds basedon physical characteristics because of the high within-breed variation. Interestingly, in Sasra cattle, a local breed inIndonesia that has the potential to be maintained, the percentages of hornedand hornless cattle were 31.25 % and 68.75 %, respectively. Sasra cattle with horns are thought to be the result of past random mating. These black cattle were developed after the introduction of artificial inseminationtechnology (1976–1982) at the Lembang Artificial Insemination Center, usingfrozen semen from Aberdeen Angus cattle . Aberdeen Angus cattle have black coats and are naturally polled. The recipient cows were mostly Ongole Grade cattle. Thehorned trait in cattle appears in homozygous recessives, while the polledcharacter is dominant .The genetic closeness between Rambon and Pasundan seen in this study, andthe wide gap between Bali and Kebumen Ongole Grade cattle, might be due totheir morphological appearance . Figure 1 clearlyshows that Rambon and Pasundan cattle appear similar but have distinctbreeding ranges, while Bali and Kebumen Ongole Grade cattle differ in bothappearance and distribution area. The morphometric distances (Table 4) andclustering  results of this study are consistent with the results ofphylogenetic analyses of various types of molecular data . Phylogeneticdiversity is the best objective criterion for conserving and protectingtaxonomically different breeds through genetic resource conservationprograms .The discriminant function analysis revealed overlap between the Rambon andPasundan cattle populations ; Table 5 shows this in more detail.Based on cross-validation, only individuals from the Bali, Madura, and Sasracattle breeds were grouped 100 % correctly; the average suitabilityvalue for the remaining breeds was 93.20 %. Even when there is someoverlap between centroids, differentiation between breeds is possible .Based on the equation shown in Table 6, the HtGC is an important quantitative measure for estimating animal BW. However, other variables not included in the equation must also be considered. The estimation of the relationship between 5The Indonesian cattle in this study formed two distinct clusters. The firstcluster consisted of Rambon, Jabres, and Pasundan cattle and had an averagesuitability value of 93.20 %; the second cluster consisted of Bali,Madura, and Sasra (average suitability value