Genomic selection (GS) plays an essential role in livestock genetic improvement programs. In dairy cattle, the method is already a recognized tool to estimate the breeding values of young animals and reduce generation intervals. Due to the different breeding structures of beef cattle, the implementation of GS is still a challenge and has been adopted to a much lesser extent than dairy cattle. This study aimed to evaluate genotyping strategies in terms of prediction accuracy as the first step in the implementation of GS in beef while some restrictions were assumed for the availability of phenotypic and genomic information. For this purpose, a multi-breed population of beef cattle was simulated by imitating the practical system of beef cattle genetic evaluation. Four genotyping scenarios were compared to traditional pedigree-based evaluation. Results showed an improvement in prediction accuracy, albeit a limited number of animals being genotyped . The comparison of genotyping scenarios revealed that selective genotyping should be on animals from both ancestral and younger generations. In addition, as genetic evaluation in practice covers traits that are expressed in either sex, it is recommended that genotyping covers animals from both sexes. In livestock breeding programs, genomic selection (GS) is a method that uses genomic information to estimate breeding values and rank selection candidates. GS has shaped modern breeding programs and contributed substantially to the increase of genetic progress for various economically important traits, especially in dairy cattle . The advtaurus and B. indicus) in the beef industry  or daughter yield deviation . This meWhile genotyping of the ancestral sires in a sense that they have contributed much more than their contemporaries to the current generations seems logical, however, sustainable genetic gain in a breeding scheme needs accurate selection in younger generations as well. In addition, similar to dairy cattle, genetic progress in a breeding scheme in beef is not only driven by bulls but also depends on the superiority of the dams of the candidates. Based on this, we hypothesized that prediction accuracy in selection candidates might differ when genotyping is only on ancestors, younger generation, and is restricted for males or females. Thus, the main aim of this study was to evaluate genotyping strategies in terms of prediction accuracy as the first step in the implementation of GS in beef. In particular, the goal was to compare and to contrast the importance of genotyping the ancestors and distributing the genotyped individuals for the two sexes on the predicting accuracy. The study was conducted under the assumption that a number of animals being genotyped is the main constraint.Using QMSim software, a historical population of beef cattle was simulated based on the forward-in-time process . In totaIn the second step, to enlarge the base population, 100 Founder males and 100 Founder females were selected randomly from the last generation of the historical population (Expanded generations) and were mated randomly for another eight generations. In the third step (Breed formation), five random samples, as the base for five breeds (A-E), were randomly chosen from the last generation of the previous step. In this step, mating and selection were also random within each breed, producing two offspring per dam for 30 generations. In the last step (step 4), population structure was simulated to mimic the production and genetic evaluation system in practice such that parameters were chosen to be realistic five breeds with different sizes were simulated for 15 generations. Selection in all breeds was based on EBVs using pedigree-based BLUP, and the culling of animals was based on age. It was assumed that pedigree was available for all breeds without error, and base animals in this step were considered as meta founders . Sire and dam replacement ratio was different across breeds .Five scenarios were compared in terms of prediction accuracy of selection candidates in the last generation (15th generation). Scenarios included a reference scenario (Ref. Sc) without genotypic data and scenarios with both genotypic and phenotypic information (Sc. 1 to Sc. 4). In all scenarios, it was assumed that phenotypic and pedigree data were available for the last 15 generations, and animals could be genotyped from the 7th generation onward. In all genomic scenarios, 5,000 animals could be genotyped. Genotyping scenarios differed in the method applied for the selection of 5,000 animals to be genotyped. In Sc. 1, the genotyping strategy was focused on young animals and only male progenies from the pool of selection candidates of generation 15 were selected randomly. In Sc. 2, genotyping was only on males but both young animals and ancestral sires could be genotyped. The criteria for selection of ancestral sires were that a sire should have at least 10 progenies in the population to be selected for genotyping. In Sc. 3, both male and female progenies from generation 15 could be genotyped. In Sc. 4, randomly selected ancestral sires, ancestral dams, and selection candidates  were selected to be genotyped. The number of male and female animals with genotypic record in each scenario across breeds is presented in In all scenarios, irrespective of the availability of genotypic data, two other factors were considered to evaluate their impact on the prediction accuracy of the selection candidates. The first factor was the number of phenotypic records which could be collected and used for the genetic evaluation. It was assumed that 20, 60, and 100 percent of the animals could have phenotypic observations . This phenotyping scenario was considered to cover a range of traits such as birth weight, where data are collected routinely in practice, and a scarcely recorded trait such as meat quality, where normally 20% of animals in the evaluation would have records available in practice. The second factor was the simulation of a sex-limited trait where either males   or females  could have phenotypic records. A schematic representation of the genotyping and phenotyping scenarios is in n = 800 QTL were considered. Both SNPs and QTL were selected from the segregating loci of the last generation of the historical population with a Minor Allele Frequency (MAF) of greater than 0.1 and were randomly spaced across the genome. Recurrent mutation rate of A genome consisting of 29 pairs of chromosomes with a total length of 2,319 cM was simulated. For each animal, single nucleotide polymorphisms (SNPs) markers with the density of 50 K and l to 0.4 .A single trait with a heritability of 0.3 and phenotypic variance of 1.0 was simulated. The True Breeding Values (TBV) for each animal were calculated as follows:Where For the reference scenario, a single-trait BLUP with UPG was used to estimate the breeding values. In this scenario, all five breeds were analyzed together in a multi-breed model. EBVs were estimated based on the model (2):Where Where For genomic scenarios (Sc. 1–Sc. 4), the ssGBLUP with meta-founder was used. In the meta-founder approach, a modified  matrix HΓ−1=AΓ−Where Where Matrix 𝚪 represents within and across population relationship matrix. The structure of variance-covariance of meta-founder was estimated as The genetic evaluation analysis was performed under the restricted maximum likelihood (REML) approach using an animal model in the BLUPF90 family software . The preIn all scenarios, the use of genomic information irrespective of phenotyping strategy, increased the prediction accuracy compared to the Ref. Sc . The ranAs expected within each scenario, higher percentage of phenotype availability, resulted in higher prediction accuracy. The trend was similar for all scenarios and irrespective of the availability of records on both or either of sex. Results also show that when the trait of interest could be measured on both sex , on average prediction accuracy was higher than when trait was sex-limited. For sex-limited traits, prediction accuracy was similar whether it was measured on males or females, however, mean accuracy were slightly higher when trait of interest was measured on male animals .We investigated the potential of applying GS in beef cattle when the aim was improving prediction accuracy in selection candidates. Four selective genotyping scenarios were compared to traditional pedigree-based evaluation. Results showed fair improvement in prediction accuracy, albeit a limited number of animals being genotyped.One challenge with applying GS in practice is that there are many selection candidates, and genotyping all of them is often impractical. Genotype scenarios should only genotype small proportions of selected candidates welcomed by breeders because results have shown that significant investments in the genotype of selected candidates are not necessary to take full advantage of the benefits of genomic selection . As the Natural mating of multiple sires is the most common mating system in beef cattle production, despite the management advantages of this mating system, it does not allow for identification the paternity of the progeny . So, becIn practical situation, all animals being evaluated rarely have phenotypic information, and the records are not widely available for traits such as disease and meat quality. To address this issue, we considered different phenotypic scenarios in our simulation. In all studied scenarios, with decreasing phenotypic records, prediction accuracy also decreases, which indicates a direct relationship between phenotypic records and prediction accuracy .Genomic prediction in beef cattle provides accuracy higher than the average of parents based on the pedigree of selected candidates. It can be equivalent to progeny tests based on a maximum of 10 offspring . In addiIn our study, genetic evaluation was based on multiple breeds information. When several breeds are combined in one assessment, there is generally no pedigree information among breeds. As a result, UPG  has beenpedigree . Legarrapedigree . AccordiThe pedigrees used in genetic evaluations may go back to a few base populations thought to be unrelated due to a lack of access to information . In addiWe investigated the potential of applying GS in beef cattle when the aim was improving prediction accuracy in selection candidates. Four selective genotyping scenarios were compared to traditional pedigree-based evaluation. Results showed fair improvement in prediction accuracy, albeit a limited number of animals being genotyped. Comparison of GS scenarios revealed that selective genotyping should be on animals from both ancestral and younger generations. In addition, as genetic evaluation in practice cover traits that are expressed on either sex, it is recommended that selective genotyping covers animals from both sexes as well.


Introduction: Spontaneous rupture of tendons and ligaments is common in several species including humans. In horses, degenerative suspensory ligament desmitis (DSLD) is an important acquired idiopathic disease of a major energy-storing tendon-like structure. DSLD risk is increased in several breeds, including the Peruvian Horse. Affected horses have often been used for breeding before the disease is apparent. Breed predisposition suggests a substantial genetic contribution, but heritability and genetic architecture of DSLD have not been determined.Methods: To identify genomic regions associated with DSLD, we recruited a reference population of 183 Peruvian Horses, phenotyped as DSLD cases or controls, and undertook a genome-wide association study (GWAS), a regional window variance analysis using local genomic partitioning, a signatures of selection (SOS) analysis, and polygenic risk score (PRS) prediction of DSLD risk. We also estimated trait heritability from pedigrees.Results: Heritability was estimated in a population of 1,927 Peruvian horses at 0.22 ± 0.08. After establishing a permutation-based threshold for genome-wide significance, 151 DSLD risk single nucleotide polymorphisms (SNPs) were identified by GWAS. Multiple regions of enriched local heritability were identified across the genome, with strong enrichment signals on chromosomes 1, 2, 6, 10, 13, 16, 18, 22, and the X chromosome. With SOS analysis, there were 66 genes with a selection signature in DSLD cases that was not present in the control group that included the TGFB3 gene. Pathways enriched in DSLD cases included proteoglycan metabolism, extracellular matrix homeostasis, and signal transduction pathways that included the hedgehog signaling pathway. The best PRS predictive performance was obtained when we fitted 1% of top SNPs using a Bayesian Ridge Regression model which achieved the highest mean of R2 on both the probit and logit liability scales, indicating a strong predictive performance.Discussion: We conclude that within-breed GWAS of DSLD in the Peruvian Horse has further confirmed that moderate heritability and a polygenic architecture underlies the trait and identified multiple DSLD SNP associations in novel tendinopathy candidate genes influencing disease risk. Pathways enriched with DSLD risk variants include ones that influence glycosaminoglycan metabolism, extracellular matrix homeostasis, signal transduction pathways. Spontaneous rupture of tendons and ligaments in response to trauma or chronic degeneration is a common injury shared across species. In humans, rotator cuff and Achilles’ tendon injuries are common diseases that often lead to chronic tendon degeneration . In horsPresently, there is little understood about the mechanism leading to DSLD. Strong breed disposition suggests a substantial genetic contribution to risk of DSLD. However, the genetic architecture of DSLD is unclear. To date, no estimate of DSLD heritability has been reported in any horse breed. Currently, there is no genetic test available that could assess a horse’s risk of developing DSLD.In the Peruvian Horse, as in other horse breeds, domestication and breed development has generated selective pressures on the genome to enable horses to work in agriculture, and transport. More recently, traits such as morphology and performance have been considered during selection for breeding . These gDuring past decades, attempts have been made to discover the genetic background of DSLD in the Peruvian Horse. These studies involved investigation of the molecular pathology of DSLD, disturbances to signaling pathways, a genome-wide association study (GWAS) with low density markers, and case-control differential gene expression analysis . WhetherConsequently, we recruited a reference population of Peruvian Horses phenotyped as DSLD cases or controls, undertook a GWAS and SOS analysis, and used the reference population to undertake polygenic risk score (PRS) prediction of disease risk. Discovery of strong DSLD candidate loci and genes influencing disease risk would represent a significant advance. Furthermore, we estimated heritability using a population-based pedigree to assess narrow-sense heritability. Identification of genomic regions that contribute to the genetic risk of DSLD will permit development of a genetic screening test to assess risk of DSLD in Peruvian Horses. Additionally, gene mutations that influence risk of DSLD in the Peruvian Horse represent important candidate genes for risk of human and canine spontaneous TLI and rupture. We confirmed moderate heritability and a complex genetic architecture for DSLD in the Peruvian Horse and show that PRS prediction using Bayesian ridge regression (BRR) is highly accurate at predicting risk of DSLD in this breed.Client-owned Peruvian Horses were recruited at the UW Madison School of Veterinary Medicine and Texas A&M College of Veterinary Medicine and through online advertising. Hair bulb samples pulled from the tail or mane, nasal swabs, or EDTA blood samples were collected from 183 Peruvian Horses for case control binary GWAS. The data set consisted of 80 cases and 103 controls. All owners gave informed consent to participate in the study. All procedures were performed in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health and the American Veterinary Medical Association and with approval from the Animal Care Committee of the University of Wisconsin-Madison  and Texas A&M University (Protocol AUP IACUC 2018-0443 CA). Preparation of the manuscript conformed with the ARRIVE guidelines. If available, a pedigree was collected from each horse to confirm purebred status. DSLD cases were diagnosed with information from the veterinary records, such as physical exam findings, lameness exam findings and photographs. Physical examination consisted of palpation of soft tissue structures in all four distal limbs for pain, swelling, heat, asymmetry or scarring. Range-of-motion and resistance to manipulation was assessed. Fetlock flexion after gait evaluation at the walk and trot on hard and soft surfaces was evaluated. As DSLD develops, the SL progressively thickens. Over time, the SL mechanically weakens and ruptures, resulting in a classic sign of dropped fetlocks in multiple limbs. In the severe case, obvious thickening and dropping of the fetlocks is evident compared with the normal standing posture . In horsDNA was isolated from buffy coat, hair bulbs obtained from the mane or tail, or nasal swabs . Blood was collected in EDTA-coated tubes. For genotyping, samples underwent DNA isolation using the Gentra Puregene kit . DNA quantity and purity were assessed using a Qubit 4 Fluorometer  and a Nanodrop Lite Spectrophotometer .https://www.ncbi.nlm.nih.gov/assembly/GCF_002863925.1/), was used throughout the study.Isolated DNA samples were stored at −20°C until genotyping. SNP genotyping was performed using the Axiom Equine Genotyping Array  which includes a total of 670,796 SNPs. Genomic coordinates based on the latest version of genome assembly, EquCab3.0 SNP collection  as formulated by A univariate logistic linear mixed regression model in R as implemented by the ‘gaston’ package  was usedX is an n by m matrix of centered genotypes and Where, where P-value thresholds were considered. One threshold was determined through a Bonferroni correction . As an alternative approach, we established genome-wide significance thresholds using 95% confidence intervals (CI) derived from the empirical distribution of P-values obtained under the null hypothesis of no association  as a reference, which has a length of 188.3 Mb. We calculated the SNP density on this chromosome by dividing the total number of distributed SNPs by its length. The result was approximately 190 SNPs per Mb. Based on this, we selected a window size of 0.5 Mb, which corresponds to ∼90 SNPs. Then we ran a logistic linear mixed model with two variance components by considering the following mixed model:Y is the vector of DSLD case-control status as the binary phenotype, Where P-value of the DCMS statistic using fractional ranks using the stat_to_pvalue  function in the MINOTAUR R package . SHAPEIT2 , number of segregating sites by length (nSL), a statistical test based on a measure of haplotype homozygosity (H12), and integrated haplotype score (iHS). The different statistics were combined using the decorrelated composite of multiple signals method (DCMS) . This me package  for all SHAPEIT2  was usedIn the next step, the genes identified in the top 5% of genomic regions identified from the local genomic variance analyses using GWAS, regional heritability, and SOS analyses underwent functional analysis using genes with biological relevance to tendon homeostasis. This step aimed to reduce potential bias and increase the specificity of our analysis, by screening out unrelated genes based on prior knowledge, functional annotations, and available literature on DSLD and related biological processes. By doing this, we aimed to focus our analysis on genes that were more likely to be directly involved in the condition.https://biit.cs.ut.ee/gprofiler/), to identify Reactome pathways that are enriched in the experiment. The false discovery rate was set at 0.05 (http://www.baderlab.org/Software/EnrichmentMap)  .We assessed the genetic diversity within the Peruvian horse population using PCA. This analysis enabled us to investigate the variation present within the population and gain insight into its genetic structure. To accomplish this, the genotypic information was also used to compute a GRM between all individuals in case and control groups . By perfFour different machine learning models: weighted subspace random forest (RF), gradient boosting machine (GBM), least absolute shrinkage and selection operator (LASSO), and elastic net (EN) were used to predict DSLD polygenic risk scores. A weighted RF model was used because the weighted form of the RF model can achieve high accuracy in classifying high dimensional data , such asThe third model was the LASSO approach  which isX, and the β, which induces sparsity in the solution, and λ≥ 0 is a tuning parameter. The ℓ1 penalty enables the lasso to simultaneously regularize the least squares fit and shrinks some components of Where, edictors , for exaThe glmnet function from the “glmnet” R-package was used for fitting LASSO and EN models. The cv.glmnet  function in this package was used to obtain optimum values for Four Bayesian regression models included Bayesian ridge regression (BRR), Bayes B (BB), Bayes C (BC) and Bayesian Lasso (BL) models were fitted and compared in terms of their prediction accuracy. We assumed that there is a genomic variable predictor, i.e., µ is an intercept or population mean, Where, 2 prior density, µ was assigned a flat prior density, and the marker effects  .BB and BC both have an extra hyperparameter ‘π’ which is the probability of a marker’s effect to be equal to zero or null and usually is assigned a Beta prior π ∼ beta , 1.0% , 2.0%  and 3.0%  and evaluated performance of all models. Overall, the best performance was when 1.00% of top SNPs was used for prediction.2) on the probit and logit liability scale to assess predictive performance of our models. R2 on the liability scale can be obtained by transforming R2 on the observed scale from linear regression, using the Robertson transformation  = 2 values to the total variance in the liability scale, we gain insights into the predictive power of our models and their ability to explain the underlying genetic variation.These formulas allowed us to quantify the proportion of the total variance in the liability scale that can be attributed to the genetic factors captured by the linear predictors in the probit and logit models . So, by n = 3) and control (n = 7) phenotypes was utilized. Ensemble prediction was applied, incorporating all eight prediction models . Finally, tuning of the posterior probability threshold for PRS (Polygenic Risk Score) prediction as a DSLD case was carried out using the validation set.Furthermore, to validate the predictive performance of the models, a separate validation set comprising 10 Peruvian Horses with known DSLD case (Pituitary pars intermedia dysfunction (PPID) was identified in two DSLD cases and two phenotype-negative controls, and equine metabolic syndrome (EMS) was identified in four DSLD cases and three controls from medical records . No contHeritability analysis included 1,947 Peruvian Horses and was conducted using pedigree information. There were 499 (25%) horses considered inbred based on the F coefficient. The mean F coefficient was 1.72% and showed a range from 0.012% to 28.2%. The mean F in the inbred horses was 6.1%. The sample population included 688 founder horses and 1,259 non-founder horses. There were 376 horses with no progeny and 1,570 horses with at least one progeny. The posterior density of the estimated genetic variance P-value threshold at P < 1E-7 and a permutation-based threshold at P < 7.39E-5. In total, 3 and 151 SNPs passed these thresholds respectively. The three SNPs that exceeded the Bonferroni significance threshold were located on chromosomes 4, 10, and 11. Candidate loci with significant SNPs that passed the Bonferroni threshold contained the NOG, AHR, and UBE3D genes. We identified a total of 200 DSLD candidate genes based on the less stringent permutation threshold and after we screened the gene list. After conducting a thorough functional investigation, 17 of them were considered functionally or biologically related to DSLD , for Reactome pathway enrichment analysis. Finally, 33 pathways from the Reactome data base which were most related to tendon homeostasis, were identified . SNP assp = 0.0329). The coefficients of determination (R2) were estimated for the predictive performance of four machine learning classifiers and four Bayesian models on both the probit liability and logit liability scale. On the probit liability scale, the Bayesian Ridge Regression (BRR) model achieved the highest mean R2 of 0.702, followed closely by the Bayes B and Bayes C models with mean R2 values of 0.699. These models demonstrated relatively strong predictive power in explaining the variation in the data. On the other hand, the Gradient Boosting (GB) model had the lowest mean R2 value of 0.506, indicating relatively weaker predictive performance compared to the other models.Predictive performance of both classification machine learning and Bayesian regression models using a five fold cross validation is represented in 2 of 0.718, followed by Bayes B and Bayes C with mean R2 values of 0.717 and 0.716, respectively. The GB model again showed the lowest mean R2 of 0.467, suggesting comparatively lower predictive accuracy. Among the machine learning classifiers, Random Forest (RF) had a mean R2 of 0.632, while LASSO and Elastic Net (EN) achieved mean R2 values of 0.562 and 0.682, respectively on the probit liability scale.Similar patterns were observed on the logit liability scale, where the BRR model exhibited the highest mean RWhen PRS prediction of a separate validation set of Peruvian Horses was performed using ensemble risk prediction, four DSLD control horses were incorrectly predicted using a posterior probability threshold of 0.5 for classification as a case . Tuning DSLD is a debilitating condition characterized by systemic deposition of proteoglycan in connective tissues that may yield insight into human TLI associated with similar matrix disturbances. We undertook a within-breed GWAS of DSLD in the Peruvian Horse using a linear mixed model to discover candidate loci and genes that influence risk of the disease. Our analysis showed that the disease has moderate heritability of 0.22 in this breed. Specific environmental risk factors for DSLD are poorly understood. Our results also show that DSLD has a polygenic architecture with risk loci spread across the autosomal genome. Novel TLI genes and pathways were highlighted from this research. PPID and EMS were occasionally identified with similar frequency in the horses in both the case and phenotype-negative control group.AHR, NOG and UBE3D genes. The AHR gene regulates transcription via the aryl hydrocarbon receptor. A role in tendon biology has not been defined, but it is possible that this gene may have a role in extracellular matrix degradation during aging (NOG is a 222 amino acid secreted protein known for binding and inactivating BMP4 and other proteins in the transforming growth factor beta (TGF) superfamily. NOG is known to play an important role in tendon development and homeostasis  . Becauseinopathy . GLI2 an healing . In humapatients . A mutatr matrix . A mutathenotype . Tenascihenotype . Collagee (MEP1A , anotherGLI2 exhibited a large selection signature in DSLD cases relative to phenotype-negative control horses. A candidate genomic region from SNP GWAS that also contains a positive selection signature is more likely to contain the causal genetic variant, particularly for diseases with a simple mode of inheritance, but not for complex traits (TGFB3 is found with aging, particularly in tendons exposed to mechanical overload (GLI1/GLI2/GLI3, is known to modulate matrix responses to load and healing in tendon injury, particularly at bone attachment sites (Through our SOS analysis, we found 66 candidate genes that exhibited a selection signature in DSLD cases that was not present in the control group, further supporting the hypothesis that DSLD has a polygenic architecture. Candidate genes include 25 non-coding RNA sequences, suggesting that regulatory SNPs may play an important role in the genetic contribution to DSLD . We alsox traits . Developx traits  and downoverload . The Indnt sites .Pathway analysis of candidate genes identified by GWAS, local variance analysis and SOS identified enrichment of pathways associated with glycosaminoglycan metabolism and extracellular matrix homeostasis. Additionally, signal transduction, particularly the hedgehog signaling pathway also showed enrichment. Glycosaminoglycans have a key role in extracellular matrix composition of tendons and disturbed metabolism of the extracellular matrix of tendon; accumulation of aggrecan in SL tissue and disturbance to decorin glycosylation are key features of DSLD . Mechano2 values and the corresponding SD, representing the variability of R2 values across the cross-validation folds. The low SD values indicate more stable and consistent predictive performance. We identified the BRR model as the best performing single model with a clinically relevant predictive accuracy in the reference population with an R2 that exceeds 0.7 using the top GWAS SNPs and sex as the only covariate in the predictive model. When we used ensemble prediction in a validation set of 10 independent Peruvian Horses, all horses were predicted accurately except one after tuning of the posterior probability threshold.The Peruvian Horse is a breed with a small effective population , enablinThese results fit with an earlier observation that PCA analysis using top DSLD GWAS SNPs reflecting breed categorical risk causes the population within the Peruvian Horse breed to form two distinct clusters in contrast to a single breed cluster when all SNPs are considered in the analysis . BecauseThere were several limitations to this work. The sample size in our study population was relatively small at 183 horses. The age was not available for all the cases. In the future, increasing the sample size may help detect additional associations, improve the accuracy of PRS prediction of disease risk, and enable SNP estimation of DSLD heritability. Consideration of athletic activity in prediction models may also be useful in the future. Further validation of PRS prediction is needed by predicting a larger independent test set of horses and evaluating prediction accuracy. It would also be important to follow predicted horses over time to confirm whether young horses predicted as cases develop DSLD later in life. Combining both genotype and pedigree data to estimate heritability could also be considered. In our regional window variance analysis, the choice of the threshold for selecting the top windows with the highest heritability was a subjective decision. A higher or lower threshold than 5% may have yielded different results. More work is also needed to further investigate the key pathways involved in the pathogenesis. RNA-Seq analysis of tendon tissue from DSLD case and control horses will likely help confirm key candidate genes and pathways. Further investigation of candidate genetic variants using whole genome sequencing is also warranted. By including all genes related to DSLD from GWAS, SOS and WIN, and shortening the gene list by selecting biologically compelling genes, we aimed to capture a set of genes that are biologically relevant to the phenotype of interest and increase the power of our pathway analysis to detect meaningful associations. Additionally, we assumed this approach would help reduce the impact of false negatives in our analysis compared with inclusion of all associated genes from our discovery analyses.In conclusion, our within-breed GWAS of DSLD in the Peruvian Horse has further confirmed moderate heritability and a polygenic architecture underlies the trait and identified multiple DSLD SNP associations. Pathways enriched with DSLD risk variants include pathways that influence glycosaminoglycan metabolism, extracellular matrix homeostasis, signal transduction, interleukin signaling, and apoptosis. PRS prediction using an ensemble prediction pipeline shows clinical promise as a genetic risk test for DSLD.


Selection based on yield alone may not be effective for yield improvement in plant breeding programs. Thus, in order to progress the genetic gains during selection, yield should be considered along with potential yield contributing traits. The objective of this study was to improve the genotype of amaranth and increase the effectiveness of selection in the program by identifying the correlation and path coefficients between yield and its relevant attributes. On 120 genotypes of amaranth planted during two growing seasons in 2020 and 2021, the study was carried out using an alpha lattice design with two replications. The results revealed significant positive phenotypic and genotypic associations on leaf yield, with leaf area, leaf breadth, branch number, leaf number, plant height at flowering, and grain yield all having positive direct effects. Similar strong positive phenotypic and genotypic relationships were found for grain yield and grain sink filling rates. Using path coefficient analysis, the direct and indirect effects of yield-related traits on yield were also determined. In addition to having a strong direct impact on grain output, the grain sink filling rates showed both phenotypic and genotypic evidence of substantial positive relationships with grain yield. It was further suggested that leaf yield in amaranth genotypes may increase through the indirect selection of plant height at maturity, leaf length, and terminal inflorescence lateral length, which showed such significant indirect influences, mostly through leaf area, days to maturity, and days to emergence, which displayed such strong indirect effects, primarily through plant height at flowering. This study consequently shows the need for traits with significant positive indirect impacts via leaf area to be considered indirect selection criteria for improving leaf yield in amaranth genotypes. The grain sink filling rate also significantly improved grain yield indirectly at both the phenotypic and genotypic levels, mainly via days to flowering and leaf yield. This demonstrated that selection that mainly targeted days to flowering, leaf yield, and grain sink filling rate would ultimately boost the grain yield in amaranth genotypes. Practically speaking, it is a pseudo-cereal because it is dicotyledonous and not a true cereal2. There are 87 species in the genus3. However, because of the paucity of systematic studies on Amaranths, the number of species is unclear, and the genus is regarded as challenging by systematics4.A diversified family of food crops known as amaranths is impressively adaptable to new environments despite a variety of biotic and abiotic obstacles5. They have a balanced amino acid profile and high seed protein concentration6. Amaranth is high in iron, calcium, potassium, phosphorus, protein, and lysine. It also contains vitamins A and C7. The Food and Agriculture Organization of the United Nations views them as a critical crop for food and nutritional security because of their nutritional value (FAO)8. Amaranths are a great substitute for cereal for those with celiac disease due to their low gluten level9.Amaranths are now becoming more significant in both human and animal nourishment11. Understanding the relationships between quantitatively inherited crop traits and their direct and indirect effects on yield is crucial for the success of breeding program selections12. Moreover, studies on the genotypic and phenotypic correlations among crop plant traits are useful for planning, assessing, and creating selection criteria for the targeted characters for selection in the breeding program13. Generally speaking, the strength of character association has a significant impact on the effectiveness of any crop development effort.The complex variable of yield is influenced by many different variables, notably polygenes, the environment, and genetic variability14. Keeping these facts under consideration, a planned effort was undertaken to evaluate different genotypes of amaranth under the agro-climatic conditions of Ethiopia. The selection of better genotypes for breeding programs depends on this information. There has not been a remarkable study conducted in Ethiopia to date that evaluates the relationships between yield components and plant characteristics and grain and leaf yield, as well as the direct and indirect effects of these relationships on grain and leaf yield in different amaranth genotypes. To obtain the yield data required for amaranth breeding and development, the objective of this study was to (1) quantify the magnitude of the correlation coefficient and path coefficients between traits of amaranth genotypes grown in Ethiopia. (2) Analyse the direct and indirect effects of quantitative features on grain and leaf yields. (3) Put out the selection criteria for boosting amaranth genotypes' grain and leaf yields.Path coefficient analysis is an efficient statistical technique specially designed to quantify the interrelationship of different components and their direct and indirect effects on yield. Partitioning of total correlation into direct and indirect effects by path coefficient analysis helps in making the selection more effectiveThe experiment was conducted in 2020 and 2021 at the agricultural experimental site, Hawassa University. It is geographically located around 275 kms from Ethiopia's capital, Addis Ababa, in the Sidama Region. The experimental area is situated at an altitude of 1709 m above sea level, at latitude 7°2′ 54.7503′′ N and longitude 38°30′ 17.1608′′ E  and international guidelines. The Ethiopian Biodiversity Institute field staff assisted in collecting the samples that were used as vouchers. The specimens were accepted by the National Herbarium (ETH) at Addis Abeba University after they had been placed there, dried, tagged, pressed, and given a number. The specimens were identified by systematist Melaku Wondafrash Ayitaged at the Addis Abeba University National Herbarium.A total of 120 amaranth genotypes were used in this study, with 34 genotypes coming from the Ethiopian Biodiversity Institute (EBI), 2 from the Melkasa Research Center, 15 from the Werere Research Center (Afar region), 8 from Sidama, and the remaining 61 genotypes being collected in the Southern Nations, Nationalities and Peoples Region, Oromia, and Tigray regions of Ethiopia in 2019 and characterized for various agro-morphological traits. One hundred eighteen members contain passport information, but the other two do not and are regarded as released varieties . On April 15th, 2020, and 2021, the seed was sown at one location in each season, which is under ideal growing circumstances and during the agricultural season. One growing season on the experimental site in one year is considered the environment. Seeds of various genotypes , thinning was done twice at a distance of 75 cm between rows and 30 cm between plants. According to Grubben and Van Sloten16 and Shukla, et al.17, the experiment followed standard cultural practices. Hand-hoeing was used to control weeds at 2-week intervals following germination and whenever necessary. A total of 12 plants were maintained in each plot.A tractor cleared the experimental field, correctly plowed it, and harrowed it. A manual hoe was used to prepare the ridges. The experimental design was an alpha lattice. A layout with 2 replications, 16 blocks, and 15 plots per block for amaranths was conducted. Each unit plot was separated by a 0.60-m distance between plots, a one-meter distance between blocks, and a three-meter distance between replications, with a plot size of 1.80 m in length and 1.50 m in width for a 2.7 m18. Characters that were not on the list but were deemed crucial for the characterization were included. To accomplish the aforementioned objectives, a two-season experiment was carried out at the Agricultural Research Site of Hawassa University, Ethiopia.Data on agro-morphological traits were collected either on a plot-by-plot basis or from ten randomly selected plants per plot. A list of the studied 24 agro-morphological traits with their descriptions and sampling methods is indicated in Table 19. The key plant breeding phenomena of collinearity diagnostic boosts the effectiveness of indirect selection for genetic improvement when the target characteristics have low heritability20. When there is a moderate to severe multicollinearity, the computed coefficients may have utterly irrational (higher than unity) values, and the variances linked to estimators of path coefficients may inflate, generating incorrect estimates21, and the variances associated with estimators of path coefficients may become inflated and leads to unreliable estimates22. By examining the correlation matrix or by using the variance inflation factor (VIF), tolerance limit (TOL), and eigenvalues, multicollinearity can be identified. Two explanatory variables may be multicollinear if there are strong coefficients (near + 1) between them in the correlation matrix23. This possibility can be verified using the VIF, TOL, and eigenvalues. Using the VIF, TOL, and COLLIN option methods, the PROC REG was used to perform the VIF, TOL, and eigenvalues (SAS).One of the requirements for accurate and trustworthy path coefficient estimation is the analysis of multicollinearity between any explanatory variables24. The VIF quantifies how much the variance of an estimated regression coefficient is increased due to the effect of multicollinearity25. The TOL, which is the inverse of the VIF, indicates that there is severe collinearity in the database when it is larger than 0.125. According to Montgomery et al.25, the correlation matrices' maximum eigenvalue (max) can be divided by the subsequent smallest eigenvalue (min) to determine the condition number (CN) for each trait. Montgomery et al.25 determined that the degree of multicollinearity in the matrices was categorized as weak when the estimates of CN were less than 100, moderate when they were between 100 and 1000, and severe when they were greater than 1000. Either eigenvalues or eigenvectors can be used simultaneously to find the multicollinearity issue. Therefore, removing the traits responsible for boosting the variance of the regression coefficient is essential unless the trait is of importance for improvement. Traits with a low eigenvalue and a high matching eigenvector indicate a striking collinearity problem22. Therefore, removing the traits that cause multicollinearity from the study is a quicker and more efficient way to solve this issue and enables obtaining the most precise path coefficients23. As a result, the correlation matrices, VIF, TOL, CN, and Eigen system analysis of the correlation matrix were used to perform the multicollinearity test between the researched features. In total 24 traits were measured and based on the multicollinearity diagnosis tests. These multicollinearity diagnosis tests allowed us to exclude variables like TSW, GFP, BLBL, TLBL, SD, NN, PL, and LT from the analysis of correlation since they were so strongly linked to multicollinearity in the matrix. However, multicollinearity is associated with DM according to grain yield path analysis and GSFR related to leaf yield path analysis; these features were removed as well from the analysis. The remaining 16 variables (or around 66%) and 14 variables (58.33%) were considered for correlation and path, respectively, for the analysis due to their low multicollinearity in the current study.A number greater than 10 indicates that the related regression coefficients are underestimated due to multicollinearity26. The coefficient of correlation was tested using ‘r’ tabulated value at n − 2 degrees of freedom, at 5, 1, and 0.1% probability level, where n is the number of genotypes.xy and rgxy are the phenotypic and genotypic correlation coefficient between traits x and y, Pcov and Gcov are phenotypic and genotypic covariance between traits x and y; σ2px and σ2py are phenotypic variances for traits x and y; σ2gx and σ2gy are genotypic variances for traits x and y, respectively.The collected data from the two years were combined and subjected to correlation and path coefficients analysis. Correlation analysis was performed for all possible combinations of traits after diagnosing the multicollinearity test. Phenotypic correlation, the observable correlation between two explanatory traits, which includes both genotypic and environmental effects, and genotypic correlation, the inherent association between two explanatory traits, were computed from the components of variance and co-variances as described by27 and developed by Wright28, the correlation coefficient was further divided into direct and indirect effects28. It is possible to tell whether a cause-and-effect relationship between two explanatory traits is real and independent of other traits by using path analysis. LY and GY were chosen for the path-coefficient analysis as the result (dependent) variables and the other traits as the causal (predictor) traits. The LY was also considered as an independent trait in path coefficient analysis for GY and vice versa. The direct and indirect effects of the independent traits on LY and GY were estimated by the simultaneous solution of the following equations which may be represented by the following general formula as applied by Dewey and Lu27.Using path coefficient analysis, which was proposed by Dewey and Lu27. The direct effect product in the path analysis and the dependent trait coefficient in the correlation analysis via all predictor traits are added to create the coefficient of determination (R2). The contribution of the remaining unknown factor was measured as the residual factor (RF), which was calculated with the following formula;Based on average data from the two years, path coefficient analysis was conducted using phenotypic and genotypic correlation matrices built up as X = Y − 1 * Z for LY and GY. The phenotypic and genotypic correlation coefficients of dependent traits (LY and GY) versus independent agro-morphological traits are represented in this matrix by the vector ′X. Vector Z is the direct effect of the route coefficients, and vector Y′ is the inversed value of the phenotypic and genotypic correlations for all conceivable combinations among the examined traits. The matrix inverse (MINVERSE) function of Microsoft Excel 2010 was used) compute the inverse of matrix Y′ after inverting the reciprocals and autocorrelations of the correlation coefficient matrix. Using the matrix multiplication (MMULT) function of Microsoft Excel 2010, the direct influence of path coefficients was estimated as the product of vector ′X and each row of vector Y′ inverse. As recommended, the value of the direct effect path coefficient was multiplied by the correlation coefficient in the matrix to assess the indirect effect of the path coefficient29. That is, if the RF value is low , the variability in the predictor characteristics fully accounts for the variance in the dependent trait, but a greater RF value suggests that other factors that have not been taken into account must be included in the study.The value of RF reveals how effectively the causative factors explain the dependent factor's variabilityp = 0.90), LA (rp = 0.77), DF (rp = 0.61), PHF (rp = 0.48), and PHM (rp = 0.45). The correlation coefficients of TILL (rp = 0.86), TISL (rp = 0.80), and GSFR (rp = 0.30) were also observed to be highly significant and positive with AIL. On the contrary, it revealed a very highly significant negative phenotypic correlation with PHM (rp = −0.56), BN (rp = −0.50), LN (rp = −0.35), PHF (rp = −0.54), DM (rp = −0.42), and DF (rp = −0.64). BN was significantly and positively correlated with PHM (rp = 0.74), LN (rp = 0.39), PHF (rp = 0.66), DM (rp = 0.70), DF (rp = 0.58), and DE (rp = 0.58), but very negatively correlated with TILL (rp = −0.48) and GSFR (rp = −0.36), as well as highly and negatively correlated with TISL (rp = −0.18). The results also demonstrated a significant negative relationship between LL (rp = −0.20 ), and DE (rp = −0.21), with the AIL. The LL had a very high positive correlation with LW (rp = 0.90), PHM (rp = 0.50), PHF (rp = 0.56), LA (rp = 0.76), DF (rp = 0.62), and GSFR (rp = 0.29), but a high negative correlation with DE (rp = −0.19). The AIL (rp = 0.86), TISL (rp = 0.78), and GSFR (rp = 0.44) were shown to have a very highly significant positive connection with the TILL. With PHM (rp = 0.78), BN (rp = 0.66), LL (rp = 0.56), DM (rp = 0.60), LA (rp = 0.32), DF (rp = 0.82), and DE (rp = 0.32), it was shown that the PHF had a very highly significant positive association with each of these variables. Strong positive associations with DM were seen for PHM (rp = 0.52), BN (rp = 0.70), LN (rp = 0.32), PHF (rp = 0.60), DF (rp = 0.60), and DE (rp = 0.60). The LA was identified to have a very highly significant negative association with DE (rp = −0.34), but a very highly significant positive connection with LW (rp = 0.77), LL (rp = 0.76), and DF (rp = 0.47). The DE was observed to have a very highly significant negative association with LA (rp = −0.34) but a very highly significant positive relationship with PHM (rp = 0.42), BN (rp = 0.579), PHF (rp = 0.32), and DM (rp = 0.36). DF was significantly positively correlated with BN (rp = 0.58), LL (rp = 0.62), PHF (rp = 0.82), LA (rp = 0.47), and DM (rp = 0.82). AIL (rp = 0.30), LL (rp = 0.29), and TILL (rp = 0.44) were found to have a very highly significant positive correlation with the GSFR, whereas BN (rp = 0.36), LN (rp = −0.31), and DM , BN (rg = 0.17), LL (rg = 0.86), TILL (rg = 0.68), LN (rg = 0.73), and DF (rg = 0.23) all exhibited a very highly significant positive correlation with the LW. The opposite was true, however, as it showed a very strong negative genotypic relationship (rg = −0.35) with DE. While the AIL displayed a highly significant and negative link with DM (rg = −0.14), it had a very highly significant and positive correlation with TISL (rg = 0.38) and TILL (rg = 0.27) instead. The PHM showed very significant positive relationships with the following variables: BN (rg = 0.26), LL (rg = 0.58), TILL (rg = 0.75), LN (rg = 0.70), DF (rg = 0.22), and GSFR (rg = 0.19); however, the PHM also showed very high negative associations with DE (rg = -0.47). With PHM (rg = 0.26), LL (rg = 0.19), and DM (rg = 0.22), as well as with LN (rg = 0.17), and TISL (rg = 0.13), the BN showed a very strong positive association. The TISL had a very highly significant negative correlation with GSFR (rg = −0.25) but a very highly significant positive correlation with AIL (rg = 0.38) and TILL (rg = 0.22). Concerning LW (rg = 0.73), PHM (rg = 0.70), LL (rg = 0.71), DM (rg = 0.21), and DF (rg = 0.28), the LN exhibited very highly significant positive relationships. Conversely, it showed a very strong negative genotypic relationship with DE (rg = -0.55) and LA (rg = -0.24). The DM noted that BN (rg = 0.22), LL (rg = 0.20), LN (rg = 0.21), and DF (rg = 0.28) all had very highly significant positive correlations. The DF was considerably positively connected with LW (rg = 0.23), PHM (rg = 0.22), LL (rg = 0.29), TILL (rg = 0.21), LN (rg = 0.28), PHF (rg = 0.25), and DM (rg = 0.28), but strongly negatively correlated with DE (rg = −0.22). With LW (rg = −0.35), PHM (rg = −0.47), LL (rg = −0.31), TILL (rg = −0.55), LN (rg = −0.55), DF (rg = −0.22), and GSFR (rg = −0.20), the DE displayed extremely strong negative associations. While TISL (rg = −0.25) showed a very strong and negative association, the GSFR showed extremely substantial and positive correlations with PHM (rg = 0.19).The analysis revealed that among the variables that contribute to yield, 60 associations out of a total of 91 relationships were significant at the genotypic level Table . The finp = 0.43), PHM (rp = 0.29), LL (rp = 0.40), PHF (rp = 0.37), LA (rp = 0.45), DF (rp = 0.41), and BN (rp = 0.23), all of which were highly significant. AIL (rp = −0.19), TISL (rp = −0.21), and TILL (rp = −0.19) all demonstrated a strong negative phenotypic connection with it as well. Additionally, LY had extremely strong positive genotypic correlations with PHM (rg = 0.25), LL (rg = 0.21), LN (rg = 0.18), TILL (rg = 0.17), LA (rg = 0.36), GY (rg = 0.20), and GSFR (rg = 0.18). Additionally, it showed a strong positive genotypic correlation with the BN, PHF, and DF , AIL (rp = 0.38), TISL (rp = 0.38), and TILL (rp = 0.51). Additionally, it showed a substantial positive phenotypic connection with both LW and LL . Additionally, it had a very strong negative phenotypic correlation (rp = −0.24), (rp = −0.30), and (rp = −0.28) with BN, LN, and DM respectively. GSFR (rg = 0.95), TISL (rg = 0.28), PHM (rg = 0.21), and LY (rg = 0.21) likewise displayed a very highly significant positive genotypic correlation with the GY. Furthermore, it showed a highly significant positive genotypic association with LN (rg = 0.16), a substantial positive genotypic association with DF (rg = 0.12), and a significant positive genotypic association with TILL (rg = 0.12).In the current study, GY and its yield-contributing characteristics had 17 significant correlations, of which 9 genotypic associations and 8 phenotypic relations were significant Table . The finTable Table Table Table 32, this aids in determining the attributes that will be used in the breeding scheme. Moreover, genetic correlations between two characters arise because of linkage, pleiotropic or developmentally induced functional relationships33. As a result, it is more important and may be used to build a successful selection system. However, the remaining pairs had higher phenotypic correlation coefficients corresponding to genotypic correlation coefficients suggesting that environmental factors influenced the inherent associations among the different traits under study.Understanding the relationships between characters is crucial for any crop improvement effort since it indirectly influences selection success. To find the component characters that can be used in selection to boost yield, a correlation analysis is conducted to evaluate the underlying correlations between distinct characters. The correlation coefficient measures the degree to which two variables are positively or negatively associated. In the current trial, from the two sets of correlation coefficients, 61(50.83%) of the 120 possible pairs of trait comparisons across traits showed that the genotypic correlation coefficient was higher than the corresponding phenotypic correlation coefficient, which showed that the association was largely due to genetic factors among various traits, and that enhanced genetic inherent association35. The LL had a very high positive correlation with LW, PHM, PHF, LA, DF, and GSFR. Similar to LW and LA, Jangde et al.34 found a substantial positive association between leaf length and both. In the current finding of PHM, BN, LL, DM, LA, DF, and DE, it was shown that the PHF had a very highly significant positive association with each of these variables. Singh36, discovered comparable results in the previous study on amaranth genotypes for plant height exhibited positive significant phenotypic correlations with days to 50% flowering and number of branches. The strong and positive link between leaf width and length also raises the prospect of enhancing both characteristics at the same time. A similar finding was reported by Olusanya37. BN was significantly and positively correlated with LN, PHM, and PHF, which indicates that selecting genotypes with taller plant heights would result in a greater number of branches, which is necessary for the production of leaf yields. Akter et al.38, came to the same results. DF was significantly and positively correlated with BN, PHF, and PHM. Similar findings were reported by Singh36. Days to maturity showed a positive and highly significant correlation with plant height. Similar results were reported by Shrivastav et al.39. The time of flowering are important adaptive trait in grain amaranth40. It was observed that leaf number and leaf area correlated negatively, suggesting that the more leaves amaranth produced, the smaller those leaves were. A negative correlation between the number of leaves and leaf area might act as a challenge in the improvement of leaf yield in amaranths. Furthermore, this pattern suggests that the length of the leaf and the area of the leaf decrease as the number of leave increase. Breeding strategies should focus on increasing the area of each leaf or on producing more leaves in each main branch. Similar results were reported by Gerrano et al.41.The phenotypic correlation analysis indicated that the LW was associated significantly and positively with LL which also correlated significantly and positively with PHM, PHF, LA, and DF. Other research findings also showed that the LL had a significant positive association with LW and PHM, and significant positive associations with LA were reported at the phenotypic level42. At the genotypic level, genes governing two separate traits are correlated positively at the coupling phase of linkage and negatively at the repulsive phase of linkage43. The relationship between leaf length and leaf width is very strong. Similarly, most of the leaf traits showed high positive correlations with one another, indicating that these traits are crucial for improving and selecting amaranth yields. The results of the trait correlations in this study imply that selection for these features in amaranth genotypes will be efficient and successful. Similar findings on garden peas were reported by Sharma and Sharma44. With the following variables BN, LL, TILL, LN, DF, and GSFR, the PHM demonstrated highly significant positive relationships. Similar results were reported by Shrivastav et al.39, and inflorescence length and days to 50% flowering were positively and extremely significantly correlated with plant height, according to Yadav et al.45. A positive and significant correlation with lateral inflorescence length, leaf length, leaf width, number of branches per plant, and days to maturity was obtained46. Similar results were found by Varalakshmi and Pratap Reddy47.Genotypic correlation between traits is important for identifying the most and least, important characteristics to be considered for the success of selection in breeding48, in their analysis. Therefore, consideration for these traits should be given in amaranth breeding programs. LY had significant genotypic and phenotypic relationships with LW, PHM, LL, PHF, LA, DF, and BN. The taller genotypes in the current study produced more leaves, which resulted in higher leaf yields; hence PHM as well as PHF did forecast the genotypes' high leaf-yielding potential due to their significant association. However, the fact that PHM and PHF had a negative association with GY suggested that taller genotypes had poor grain yields. Therefore, selecting a genotype for higher leaf yield by focusing on the taller genotypes and shorter genotypes would produce good grain yields. As a result, the breeding strategy gave higher priority to characters that increased LW, PHM, LL, PHF, LA, DF, and BN, increasing the chances of obtaining a genotype with a high leaf yield. Similar results were reported in amaranth genotypes by Varalakshmi and Pratap Reddy47, who reported that plant height, leaf length, and leaf width were positively and significantly correlated with the leaf yield. Moreover, choosing genotypes with taller plant heights will result in a better number of branches, which is required for the production of leafy vegetables, according to the statistically significant positive association between plant height and the number of branches. A similar finding was reported by Olaniyi49 Vegetable amaranths are said to benefit from late flowering and maturation since farmers would have more time to harvest the leaves50. A single gene controls early flowering in amaranth, and the dominant allele determines early flowering51. For grain production, early flowering and maturity would be preferable. The positive and significant correlations between yield and yield components in amaranth were also reported by53.For the leaf yield, the phenotypic correlation studies carried out for this investigation revealed a strong and positive association between economically important parameters such as LA, LW, and LL. The results show that any enhancement of these characteristics will increase the potential LY of amaranth. Similar trends were identified by Kumar54, reported comparable results. As a result, amaranth should be chosen for future breeding programs to ensure the maximum exploitation of grain amaranth, as the photosynthetic capacity of a plant with longer leaves is predicted to be higher than that of a plant with shorter leaves55, and traits like a high leaf area, leaf length, and plant height enable optimal crop output when water is accessible as an essential agricultural input in sufficient quantities56. Grain yield exhibited a highly significant and positive phenotypic association with inflorescence length . Nyasulu et al.57, Kumar58, and Yadav et al.45, obtained consistent findings in amaranth species. As a result, characteristics may be prioritized during selection to produce genotypes with higher grain yield.The results showed that at the phenotypic level, GY with GSFR, AIL, TISL, and TILL have extremely high positive phenotypic correlations. Furthermore, it demonstrated a strong positive phenotypic relationship with both LW and LL. The results suggest that these traits might be simultaneously enhanced to raise grain yield either individually or in combination. Showemimo et al.45. Grain yield and leaf area, length, and width are strongly correlated with one another. Similar to grain yield, all leaf traits showed high positive correlations with one another, indicating that these features are important for selecting amaranth leaf and grain productivity. The findings of the trait correlations in this study imply that selection for GSFR, PHM, and LY will be efficient and beneficial in improving grain production in grain amaranth genotypes, as also found in garden peas by Sharma and Sharma44. In addition, the positive correlation between grain yields and days of flowering revealed that early flowering would be a viable choice for a greater grain yield in this scenario. These results appear to be in line with a general pattern that early blooming is linked to higher grain yields across species59. These findings appear inconsistent with a general trend for late flowering to be associated with high grain yield across species37.The genotypic relationship between GSFR, PHM, and LY with the GY was also very substantial and positive. It also revealed a highly significant positive genotypic association with LN, a substantial positive genotypic association with DF, and a significant positive genotypic link with TILL. These findings demonstrated that selection for any one of these traits that contribute to grain yield will result in increases in the other traits, thereby finally boosting the grain yield. To select genotypes with higher grain production, primary selection for characteristics like GSFR, PHM, and LY may be prioritized. There were similar trends for the association of GY with other traits  that may be prioritized60, for PHF in wheat and amaranths genotypes61, for the number of leaves in amaranths genotypes, Islam62 for leaf area, and Jangde et al.34 for leaf width . LL, TILL, and DE, on the other hand, were found to have the greatest negative direct effects on LY, both at the phenotypic and genotypic levels, showing that direct selection of these traits did not improve LY in amaranth genotypes. Following the present findings, the LL had a negative direct influence on LY in amaranth genotypes62, However, since PHM, LL, and TILL had such strong indirect effects, mostly through LA, while DM and DE exhibited such strong indirect effects, primarily through PHF, it was anticipated that LY in amaranth genotypes may also rise through an indirect selection of these traits. While TISL had positive direct effects on LY at the genotypic level, AIL had positive direct effects on LY solely at the phenotypic level. Additionally estimated was the residual effect, which establishes the unaccounted variability of the dependent variables (LY and GY). The characteristics that were included in the phenotypic and genotype path coefficient analyses explained 87.12% and 87.79% of the total variance in LY, respectively, according to the residual effects of 0.1288 and 0.1221. These results suggest that the independent variables taken into account in this study effectively represented the diversity of LY in amaranth genotypes. Other factors (at phenotypic level 12.88% and genotypic level 12.21%) that caused variations in LY, but were not taken into account in the current investigation, did exist.The relationship between any two features is merely depicted by their correlation coefficients, which do not reveal any potential underlying causes. The degree of the relationship between the yield and yield components can be determined by path coefficient analysis, which also helps to clarify the cause-and-effect interactions between different characters. Path coefficient analysis was performed to segregate the correlation coefficient into the direct and indirect effects of various features on yield. The yield-contributing features for LY and GY's varied phenotypic and genotypic correlation coefficients were further divided into direct and indirect effects. According to the findings, both phenotypic and genotypic levels of LY were positively affected directly by the LW, BN, LN, PHM, LA, and GY. This suggests that these traits can be used to develop a selection index that is both maximally reliable and effective at improving the leaf yield of amaranth genotypes. These findings confirmed the results of Aycicek and Yildirim48 for the plant height in amaranth genotypes. On the other hand, LW, TISL, LL, and DF had negative direct effects at both phenotypic and genotypic levels indicating that direct selection based on these traits could not improve the GY in amaranth genotypes. The results were also in agreement with the findings of Shrivastav et al.39, who reported that DF had a negative direct effect on GY in amaranth genotypes. The direct selection of LW, TISL, LL, and DF at both phenotypic and genotypic levels would decrease the GY; however, their indirect selection through mainly PHM may improve the GY in amaranth genotypes. PHM had beneficial indirect effects on LW, LL, TISL, and DF in addition to its beneficial direct effects on GY. For the indirect selection of high seed-yielding genotypes in amaranths, the DF may be a trustworthy indicator. Alake and Otusanya63, obtained consistent findings in amaranths genotypes. The direct effect of an AIL, BN, TILL, LA, and DE on GY was negative only at the genotypic level while LN and LY had a positive direct effect on GY only at the genotypic level. Other research findings also showed that LN had a positive direct effect at the genotypic level on GY in amaranth genotypes64. While LY had a negative direct effect on GY only at the phenotypic level, AIL, BN, TILL, and LA had positive direct effects on GY only at the phenotypic level. On grain yield in amaranth genotypes, the same observation was made for TILL, BN, LA, and AIL65. Furthermore, LW, AIL, BN, TISL, TILL, LN, DF, LY, and GSFR might be indirectly selected, mostly through PHM, to improve the GY at the genotypic level. According to the residual values of 0.1005 and 0.1140, the traits included in the phenotypic and genotypic path coefficient analyses, respectively, explained 89.95% and 88.6% of the overall variation in GY. This suggests that additional traits may also be influencing GY.A path analysis' findings revealed the importance of GSFR, PHM, and PHF traits for direct selection since they had considerable positive direct effects on GY both at the phenotypic and genotypic levels. This suggests that GY improvement in amaranth genotypes might be obtained through selection based on these characteristics. The result was congruity with66. The findings indicated that selection based on the phenotypic expression of LW, BN, LN, PHF, LA, and GY could be successful for LY improvement in amaranth genotypes these traits had positive direct effects on LY along with a significant positive association with LY at both phenotypic and genotypic levels. On the other hand, LL demonstrated negative direct effects on LY both at the phenotypic and genotypic levels, even though LL, DF, and GSFR had significant positive phenotypic and genotypic associations with LY. The occurrence of significant positive phenotypic and genotypic associations along with inverse direct effects between LY and these traits could be due to the high positive counterbalancing potential of the phenotypic and genotypic indirect effects exerted by these traits via mainly LN. The genotypic direct effect of DM on LY was negative despite the non-significant positive genotypic association between the two traits, indicating that the positive genotypic association between these traits was caused by the high and positive neutralizing genotypic indirect effect of this via chiefly by LA. Despite AIL's positive phenotypic direct effect on LY, its negative phenotypic correlation with LY might be because of AIL's significant negative phenotypic indirect effect, which largely acts through LA.When combined with path coefficients, correlation coefficients offer more accurate data that may be successfully predicted in crop development initiatives67 obtained consistent findings in rice, while Knott and Gebeyehou68, reported equivalent results in bread wheat. Likewise, GSFR, PHF, and LY exhibited significant positive genotypic associations with GY as well as positive genotypic direct effects, revealing that genotypic selection based on the phenotypic expression of these traits would be effective for enhancing GY in amaranth genotypes. Although PHM and PHF had positive phenotypic and genotypic direct effects on GY, their correlations with GY were negative and insignificant on both a phenotypic and genotypic level. This might be because PHM and PHF's indirect actions, which primarily affect BN, LL, and DF, have significant and detrimental neutralizing potential. The phenotypic and genotypic direct effects of LW, LL, and TISL were negative but their associations with GY were positive at genotypic levels. This was due to the high and positive neutralizing potential of the genotypic indirect effects exerted by both traits via mainly PHM, LN, LA, and LY. The phenotypic (DE) and genotypic (DF and DE) direct effects on GY and their respective associations with GY were negative and insignificant indicating that the GY improvement based on the selection of these traits could not be effective in amaranth genotypes.The GSFR had significant positive phenotypic and genotypic relationships with GY as well as positive direct impacts on GY, suggesting that selection for genotypes of amaranth based on the phenotypic expression of these traits may be successful in improving GY. Wei et al.Information from correlation may be augmented with the use of route analysis to select features that significantly increase the grain and leaf yield of amaranth genotypes and hence enhance selection efficiency in breeding. The studies of phenotypic and genotypic correlation and path coefficients revealed that LA was the variable that was most crucial for the indirect selection of LY since it exhibited a strong positive direct influence on LY as well as a substantial positive correlation with LY. Furthermore, it was suggested that LY in amaranth genotypes may also rise by an indirect selection of PHM, LL, and TILL, which exhibited such potent indirect influences, mostly through LA, and DM and DE, which showed such strong indirect effects, primarily through PHF. Therefore, this study pointed out that traits that showed significant positive indirect impacts via LA should also be taken into account as indirect selection criteria for LY improvement in amaranth genotypes. Similarly, the studies of correlation and path coefficients demonstrated strong direct impacts of GSFR on GY, and noteworthy positive correlations of both traits with GY were seen at the phenotypic and genotypic levels. Additionally, PHF strongly influenced GY at the genotypic level and had a substantial positive correlation with GY. At both the phenotypic and genotypic levels, the GSFR also had a significant indirect favorable impact on GY, mostly through DF and LY. This showed that selection mostly focused on DF, LY, and GSFR would ultimately enhance the GY in amaranth genotypes. However, because DE has detrimental direct impacts on LY and GY at the genetic level, breeding progress in these genotypes might be negatively impacted.


LCT (rs4988235) and height or LDL-cholesterol were confirmed to be false positives whereas the association between LCT and body mass index was genuine. We provide formal evidence of subcontinental admixture in individuals with European ancestry, which, if not properly accounted for, can produce spurious results in genetic epidemiology studies.European-ancestry populations are recognized as stratified but not as admixed, implying that residual confounding by locus-specific ancestry can affect studies of association, polygenic adaptation, and polygenic risk scores. We integrate individual-level genome-wide data from ~19,000 European-ancestry individuals across 79 European populations and five European American cohorts. We generate a new reference panel that captures ancestral diversity missed by both the 1000 Genomes and Human Genome Diversity Projects. Both Europeans and European Americans are admixed at the subcontinental level, with admixture dates differing among subgroups of European Americans. After adjustment for both genome-wide and locus-specific ancestry, associations between a highly differentiated variant in  European ancestry individuals are not typically treated as admixed in genetic studies. Here, the authors detect higher than expected admixture in European populations, which could potentially affect the results of genetic studies if it is not accounted for. However, continental ancestry is not necessarily a single homogenous component of genetic diversity, but rather can be a composite of diverse subcontinental ancestries5. In some instances, differentiation between intra-continental populations is on par with or higher than differentiation between inter-continental populations6. Also, there are examples from pharmacogenetics of variants that are differentiated at the intra-continental level, such as in the case of abacavir hypersensitivity syndrome, for which the causal allele (HLA-B*5701) has a prevalence of 13.6% among Maasai in Kenya but a prevalence of ~0% among Yoruba in Nigeria7.Human genetic studies have primarily considered admixed populations to have resulted from interbreeding between two or more continentally separated populations10 and strong evidence of admixture within Europe by ancient DNA analysis12, European-ancestry populations are generally treated in association models as stratified but not as admixed at the subcontinental level. As a result, genetic epidemiology studies of Europeans or European Americans usually control for potential confounding effects of population stratification using genome-wide ancestry estimated by principal components analysis13, but do not control for locus-specific ancestry, which is inherent to admixed populations14. Potential consequences are that detection of causal genetic variation is hampered and estimation of effect sizes can be biased, leading to further negative consequences such as misestimation of polygenic adaptation15 and poor predictive performance of polygenic risk scores16.Despite genetic studies highlighting a clear pattern of North-to-South genetic variation in Europe18, which is the individual’s ancestry averaged across the entire genome, and locus-specific ancestry 21, which allows for inference of an individual’s ancestry at each locus. The power, resolution, and specificity of disease or trait mapping studies can be improved by leveraging both genome-wide and locus-specific ancestries23. To assess both genome-wide and locus-specific ancestries in admixed individuals, present-day populations are used as proxies for ancestral populations that serve as references for ancestry estimation. Considering that ~96% of participants in genome-wide association studies (GWAS) have European ancestry24, a comprehensive analysis is needed to evaluate the adequacy of European reference panels for ancestry analysis using European-ancestry individuals.Recently developed approaches have enabled the use of genome-wide data (either array-based genotype or whole genome sequence data) to assess admixture at two levels: genome-wide ancestry LCT) has been reported to be under positive selection and associated with height, body mass index (BMI), and low-density lipoprotein (LDL)28. The SNP rs4988235 is one of the most highly differentiated variants in Europe29, with derived allele (A) frequencies ranging from 93.1% in Swedes to 2.9% in Sardinians30. Importantly, rs4988235 and height are well known to covary following a north-to-south axis31, and the association between rs4988235 and height has been suggested to be spurious based on attenuation following adjustment for genome-wide ancestry27. Nonetheless, there are no association studies in European-ancestry populations that control for confounding at both the genome-wide and locus-specific ancestry levels to test the validity of the association between rs4988235 and reported associated traits.The prevalence of lactase persistence varies widely across Europe and the most strongly associated variant rs4988235 in the lactase gene  derived from projection of European Americans onto our new reference panel, rather than for PCs derived from study-specific unsupervised analysis. Altogether, this study indicates that full adjustment for subcontinental European admixture (at both genome-wide and locus-specific levels) should become best practice in genetic association studies using European-ancestry individuals, including the UK Biobank35, the Human Genome Diversity Project (HGDP)36, the Human Origins dataset37, a study of the Caucasus Mountains38, and a study of the Jewish Diaspora39  vs Southeast Europeans  represented the extremes along the first principal component , Coronary Artery Risk Development in Young Adults , Framingham Heart Study , Genetic Epidemiology Network of Arteriopathy , and Multi-Ethnic Study of Atherosclerosis . To assess continental-level structure, we evaluated our European-ancestry dataset with a worldwide reference panel  with the North European-associated ancestry cluster (Spearman’s rho = 0.814), and longitude was correlated with Southwest- (Spearman’s rho = −0.859) and Southeast-associated (Spearman’s rho = 0.579) European ancestry clusters  between the inferred European ancestry clusters: FST = 0.033 between North and Southwest, FST = 0.032 between North and Southeast, and FST = 0.028 between Southwest and Southeast. For these comparisons, European-associated ancestry clusters are genetically homogeneous populations identified by ADMIXTURE, not real-world populations. To put these amounts of genetic differentiation into perspective, FST estimates between European ancestry clusters are comparable to FST between British (GBR) and either Mexican  or Punjabi in Pakistan  samples  than FST between Mandenka and Luhya (FST = 0.011), i.e., between West and East Africans, and not as high as FST between inferred European ancestry clusters. The FST estimates in our analysis were in agreement with FST values reported by the 1000 Genomes Project35.Unsupervised analysis with ADMIXTUREree Fig. , suggestree Fig. . Formal ers Fig. . We obse17 analysis of European Americans  showed patterns of European ancestry clusters that differed by cohort  and FIT (a measurement of inbreeding) at SNPs throughout the genome in European American cohorts, consistent with subcontinental ancestry-related assortative mating in European Americans  than admixture dates for subgroup SW  and subgroup N . As a confirmatory analysis, we used LaNeta44 for admixture dating in European Americans, and we observed similar admixture times as inferred by MALDER  and height, which has been claimed to be a false positive result due to stratification27. In addition, we evaluated the associations of rs4988235 with BMI and LDL, which were recently identified in large GWAS meta-analyses using primarily European-ancestry individuals (up to 500K samples)26. These studies either adjusted association models for genome-wide ancestry using the first 10 principal components26 or there was no evidence of adjustment for European population stratification25. Using our integrated set of European American cohorts  of height using models unadjusted and adjusted for genome-wide and locus-specific subcontinental ancestry Fig. . As withrol Fig. , but didrho from 0.46 to 0.53, p < 0.001), with GENOA not showing a significant correlation (Supplementary Data ΔAIC up to 12.45)46 for some cohorts when models were adjusted for projection-derived principal components compared to study-specific principal components . Here, we tested the approach of deriving principal components from projection of target individuals onto an external reference panel (projection or supervised PCA). To evaluate the similarity between these two approaches using our European American data, we performed Mantel’s correlation test between individuals’ genetic distances computed from the top twenty principal components obtained from the unsupervised and projection approaches. We observed moderate correlation in four studies . This resource allows for estimation of subcontinental ancestry proportions by projection analysis based on publicly available, aggregated, and non-identifiable data. The end-user does not need to access, clean, integrate, or analyze individual-level reference data. Additionally, we made available a detailed tutorial for performing ADMIXTURE and PCA projection analyses and using locus-specific ancestry posterior probabilities as covariates in GWAS analysis using PLINK 2.350.First, we created a new reference panel of European genetic diversity by combining five genome-wide data sets51. Using multiple approaches to infer admixture, we showed that European-ancestry individuals are three-way admixed with wide variation in ancestry proportions. The demonstration that European Americans are ancestrally heterogeneous may have implications for calibrating locus-specific ancestry analysis19 with respect to the number of generations since admixture began. Most recent admixture dates estimated for European Americans corresponded to the large-scale Migration Period in Europe (300–800 AD)52, and were consistent with gene flow after the end of Roman Empire described in ancient DNA studies of the Viking Age11 and Anglo-Saxon migrations12. A limitation of our study is that current methods for dating admixture have a limit of resolution of approximately 100 generations and tend to be biased toward more recent admixture events. Another limitation of our study is a lack of ancestrally homogeneous reference populations or individuals corresponding to the Southeast European ancestry component.Second, our admixture analyses yielded formal evidence that European-ancestry individuals are admixed at the subcontinental level. Moreover, our results support the occurrence of subcontinental ancestry-related assortative mating as a social factor that shaped the genetic structure of European Americans in the US53. Southeastern European ancestry mainly represents descent from early Neolithic farmers from Anatolia who carried predominantly Y chromosome haplogroup G2a. Southwestern European ancestry mainly represents descent from Early to Middle Bronze Age southern steppe peoples (north and east of the Black Sea) who carried predominantly Y chromosome haplogroup R1b. Northern European ancestry mainly represents descent from Late Bronze Age northern steppe peoples (north and east of the Caspian Sea) who carried predominantly Y chromosome haplogroup R1a. Other ancestries not of European origin, such as Arabian, North African, and North Asian ancestries, have contributed to lesser extents to present-day Europe. Additionally, we have reconstructed the phylogeny of present-day ancestries47. One key inference from that reconstruction is that the ancestry reflected in early Neolithic farmers from Anatolia is likely the most recent common ancestor of present-day Southwestern European and Southeastern European as well as Arabian and North African ancestries.Previous work has described projection analyses of ancient DNA samples in terms of present-day ancestriesLCT) are associated with height, BMI, and LDL54. However, the association between rs4988235 and height has been suggested to be spurious due to uncorrected genome-wide ancestry27. Adjustment for genome-wide ancestry may not be sufficient to avoid false positive results and can mask true associations if ancestry is associated with the outcome55. Consistent with known potential confounding effects of ancestry56, we demonstrated that the lack of adjustment for both genome-wide and locus-specific ancestry can produce false positives in association studies using European-ancestry individuals. By adjusting our models for locus-specific ancestry in addition to genome-wide ancestry, associations of rs4988235 with height and LDL were eliminated. In contrast, the association between rs4988235 and BMI remained after correcting for both genome-wide and locus-specific ancestry, suggesting an effect on weight but not on height. These results suggest that residual confounding by subcontinental European ancestry can produce spurious associations in genetic association studies, with consequences for estimation of polygenic adaptation15 and polygenic risk scores16 and for fine-mapping of genetic associations. Importantly, our results warrant further analyses on the impact of unmodeled European admixture on GWAS, polygenic adaptation, and polygenic risk scores in European-ancestry individuals, including those in large biobanks such as the UK Biobank32 in Europe and the All of Us Research Program33 and the VA Million Veteran Program34 in the United States.Third, studies of European-ancestry individuals have reported that genetic variants, principally rs4988235, in the lactase gene 36, the Human Origins dataset37, a study of the Caucasus Mountains38, and a study of the Jewish Diaspora39 , CARDIA (phs000285.v3.p2), FHS (phs000007.v32.p13), GENOA (phs000379.v1.p1), and MESA (phs000209.v13.p3).We compiled genome-wide data from five different studies: the 1000 Genomes Project--maf 0.01), per genotype missingness (--geno 0.05), per individual missingness (--mind 0.05), and deviation from Hardy Weinberg equilibrium (--hwe 1 × 10−6). We also pruned strand-ambiguous SNPs and SNPs in high linkage disequilibrium (--indep-pairwise 50 10 0.8).To reduce batch effects due to the integration of array-based genotype data and whole genome sequence data, we performed quality control analysis within and between datasets using PLINK 1.9, filtering by minor allele frequency  50. Based on these IBD probabilities, we calculated the pairwise kinship coefficient (Φij) as a function of IBD-sharing, Φij = 1/2δ2ij + 1/4δ1ij. We modeled the genetic relationships among individuals as networks58, in which pairs of individuals were linked if they had a Φij threshold ≥0.0884 . Then, we excluded related individuals using the maximum clique graph approach to minimize sample loss58. We performed unsupervised principal components analysis13 and unsupervised ADMIXTURE analysis17 on the European reference data. We performed unsupervised and supervised PCA and ADMIXTURE analyses using the reference data combined with the European American data. For supervised analysis in ADMIXTURE, we used as the ancestral references the European individuals with ≥90% of one of three ancestries based on unsupervised ADMIXTURE analysis. To evaluate the coverage of European diversity, we used the first two principal components to calculate convex hull areas40. We calculated ƒ3 statistics as implemented in ADMIXTOOLS41 to formally test admixture. We tested all possible combinations of two European sources and a target European American cohort, following the form f3. All ƒ3 statistics with z ≤ −3 were considered significant evidence of admixture. We used the top 20 principal components from population-specific and projection PCA approaches to calculate Euclidean distances. Then, we compared the correlation between the genetic distance matrices using Mantel’s test implemented in the R package vegan60.We used PLINK 1.9 to estimate the probability that individuals 42 to calculate the most likely number of clusters. To estimate the origin dates of admixture events, we calculated two-locus weighted LD decay statistics using MALDER43 within each cluster of European Americans. Given that background LD can have a confounding effect on the weighted LD curves, we used as reference populations North European (Lithuanian and Estonian) and South European  populations that did not show high LD correlation with the tested target populations. For confirmatory analysis for admixture dating in European Americans, we calculated three-locus weighted LD decay using LaNeta44. Because LaNeta requires larger reference sample sizes to fit LD decay curves than MALDER, we included additional North European (Finnish in Finland [FIN]) and South European  reference populations.We first combined all European American cohorts and performed supervised PCA by projecting the European Americans onto the European reference panel. We then used gap and elbow statisticshttps://github.com/zhanxw/checkVCF). We phased and imputed the genotype data using EAGLE2.461 and Minimac62, respectively, using the TOPMed panel available through the TOPMed imputation server63. After imputation, we retained high quality SNPs with minor allele frequency ≥0.01 and with either high imputation quality (info ≥0.95) or empirically determined genotype data.To generate valid VCF files before phasing, imputation, and association tests, we checked and corrected for monomorphic sites, consistency of reference alleles with the reference genome, variants with invalid genotypes, and non-SNP sites using the checkVCF.py Python script 19. Locus-specific ancestry estimates were performed using high quality imputed data. For ancestral references, we selected individuals with ≥90% North or South European ancestry as estimated in the unsupervised ADMIXTURE analysis. We performed inference in the PopPhased mode to correct possible phase errors. We set the number of generations since the admixture event (argument -G) at 50, the number of expectation maximization (EM) iterations (argument -e) at 2, and the window size (argument -w) at 0.2 cM. All other arguments were set at default values.Given that rs4988235 is highly differentiated between North and South European populations64. Our analyses were focused on unrelated European Americans, with relatedness determined by the maximum clique graph approach58. Models were adjusted for the genetic relationship matrix as a random effect and the four first principal components (PCs that were significantly associated with the outcome and explained between-population structure) and/or locus-specific ancestry as fixed effects. Genome-wide ancestry was accounted for using principal components derived from one of two approaches: study-specific unsupervised analysis or supervised (projection) analysis of individuals onto an external reference panel. To account for the uncertainty of locus-specific ancestry estimates, models were adjusted for locus-specific ancestry dosages calculated from the posterior probabilities of locus-specific ancestry. Similarly, we used genotype dosages to account for imputation uncertainty. We performed genome-wide association analysis (GWAS) of height using models unadjusted and adjusted for locus-specific ancestry using PLINK 2.3, which allows for the inclusion of SNP-specific covariates. For the GWAS, we adjusted models for the top 12 PCs significantly associated with height.To perform association analyses between rs4988235 and height, LDL, and BMI, accounting for different levels of control of population stratification, we used linear mixed models implemented in GENESISAll dbGaP studies (dbGaP Study Accession described in the “Methods” section) obtained ethical approval from the relevant institutions and written informed consent from each participant prior to participation. We obtained approval for controlled access (protocol number: 12-HG-N185) of each of the dbGaP studies.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-14Reporting Summary


P < 5 × 10−8) with amygdala volume and its nine nuclei. The univariate GWAS identified significant hits for eight of the ten volumes, tagging 14 independent genomic loci. Overall, 13 of the 14 loci identified in the univariate GWAS were replicated in the multivariate GWAS. The generalization in ABCD cohort supported the GWAS results with the 12q23.2 (RNA gene RP11-210L7.1) being discovered. All of these imaging phenotypes are heritable, with heritability ranging from 15% to 27%. Gene-based analyses revealed pathways relating to cell differentiation/development and ion transporter/homeostasis, with the astrocytes found to be significantly enriched. Pleiotropy analyses revealed shared variants with neurological and psychiatric disorders under the conjFDR threshold of 0.05. These findings advance our understanding of the complex genetic architectures of amygdala and their relevance in neurological and psychiatric disorders.The amygdala is a crucial interconnecting structure in the brain that performs several regulatory functions, yet its genetic architectures and involvement in brain disorders remain largely unknown. We carried out the first multivariate genome-wide association study (GWAS) of amygdala subfield volumes in 27,866 UK Biobank individuals. The whole amygdala was segmented into nine nuclei groups using Bayesian amygdala segmentation. The post-GWAS analysis allowed us to identify causal genetic variants in phenotypes at the 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 multivariate GWAS identified 98 independent significant variants within 32 genomic loci associated ( The amygdala is an interconnecting structure located in the temporal lobe that plays a fundamental part in the regulation of memory formation, fear reaction, anxiety behaviors, and encouraging mechanisms . AnatomiDespite previous imaging-genetics studies have attested to the extremely polygenic genetic architecture of the amygdala, with a heritability estimate ranging from 0.34 to 0.83 , 10, indIn this study, we aimed to delve into the common variant influences of amygdala volume, their associations with altered gene expression, genetic overlap with other brain phenotypes, and, importantly, significant associations with neurological and psychiatric disorders. The whole amygdala was segmented into nine nuclei groups using Bayesian amygdala segmentation  (Methodshttp://www.ukbiobank.ac.uk/) ADAMTS8 msk of AD .Intriguingly, the regions of conserved genomic partitions were particularly enriched by the most statistically significant partitioned heritability, which was noted by the ancient sequence age human promotor, a conserved region that is evident to exist before the evolutionary split of mammals . Using tSLC39A8, a gene well-known for its high pleiotropy, being associated with a range of traits besides SCZ, such as PD [SP4, which is highly expressed in the brain and was reported to be related with an increased burden of rare deleterious mutations in SCZ [MSRA, a member of the methionine-sulfoxide reductase system [The amygdala complex is composed of structurally and functionally distinct subregions that have critical yet differential roles in a range of disease states. We first identified the genetic overlap between amygdala volumes and ten neurological and psychiatric disorders via LDSC regression . Though ch as PD . This los in SCZ . rs78315e system , was repe system . FurtherThe present GWAS confronts some limitations. First, given that the UKB data were sampled from a cohort of British ancestry with a specific age range, the generalization of these findings to non-European ethnicities or specific clinical cohorts should be treated with caution. We hope diverse samples become increasingly available to further confirm our findings and provide novel discoveries. Second, the estimates of SNP-based heritability are narrow-sense, which only consider the additive genetic effects of common variants however ignore the effects of non-additive and rare loci as well as gene-gene/gene-environment interactions. Third, due to the lack of data on the amygdala segmentation in the ABCD cohort, we were unable to perform GWAS for each of the nuclei and the corresponding multivariate GWAS, and also the polygenic scores of each nucleus. Finally, since the bioinformatics tools relied on gene expression patterns in non-human samples or relatively small samples of postmortem human brains, the annotation results can only be considered suggestive rather than conclusive.In summary, the current study provides new insights into the genetic architecture of amygdala regions by identifying the significant genetic loci through multivariate GWAS, functional annotating for biological processes, analyzing genetic overlap with other traits, and showing evidence for an involvement of amygdala regions in common brain disorders. Taken together, these results advanced our understanding of the genetic architecture of the amygdala and shed light on further research into the neurobiological basis of its anatomy and associations with brain disorders.Supplementary Table