Meat quality and composition traits have become valuable in modern pork production; however, genetic improvement has been slow due to high phenotyping costs. Combining genomic information with multi-trait indirect selection based on cheaper indicator traits is an alternative for continued cost-effective genetic improvement.Data from an ongoing breeding program were used in this study. Phenotypic and genomic information was collected on three-way crossbred and purebred Duroc animals belonging to 28 half-sib families. We applied different methods to assess the value of using purebred and crossbred information (both genomic and phenotypic) to predict expensive-to-record traits measured on crossbred individuals. Estimation of multi-trait variance components set the basis for comparing the different scenarios, together with a fourfold cross-validation approach to validate the phenotyping schemes under four genotyping strategies.The benefit of including genomic information for multi-trait prediction depended on the breeding goal trait, the indicator traits included, and the source of genomic information. While some traits benefitted significantly from genotyping crossbreds , multi-trait prediction was advantageous for some traits even in the absence of genomic information .Our results show the value of using different sources of phenotypic and genomic information. For most of the traits studied, including crossbred genomic information was more beneficial than performing multi-trait prediction. Thus, we recommend including crossbred individuals in the reference population when these are phenotyped for the breeding objective. Meat quality and carcass composition in commercial animals are becoming traits of high interest and economic value in the pork industry. Increased consumer preference for pork with high nutritional and appealing organoleptic characteristics , 2 and tThe most straightforward approach to predict crossbred performance for traits of economic interest is to use purebred performance for cheap and routinely measured growth traits. For example, intramuscular fat can be measured post-mortem with wet-lab analyses or with an ultrasound probe on the live animal, with the latter being a predictor of the former. The correlated response that can be achieved for crossbred gain using purebred performance depends largely on the genetic correlation between crossbred and purebred performance. This might be affected by differences in the rearing environment, the genetic distance between the breeds used for crossbreeding, and differences in genetic architecture and trait definitions for purebreds versus crossbreds , 4. SeveAnother possible approach is to use crossbred performance for growth and routinely measured carcass traits for indirect selection of crossbred meat quality. Recent studies have found moderate to high genetic correlations of meat quality and carcass traits with growth and performance in crossbred animals , 12, whiIn the era of genomic selection, genetic gain for crossbred performance depends not only on the phenotyping strategy but also on the genotyping strategy used. While simulation studies have shown that genomic selection can increase accuracy of selection indices for crossbred performance, its effect will depend on marker density, size of the reference population, and the relationship between training and validation populations –19. In pThe availability of phenotypic and genomic information on crossbreds allows the evaluation of different genotyping and phenotyping strategies in terms of selection efficiency for commercial traits of interest. Therefore, the aim of this study was to evaluate the impact of single and multiple-trait models with different phenotyping and genotyping strategies to predict crossbred meat and carcass quality traits.The animals used in this study included commercial crossbred (CB) and purebred Duroc (PB) pigs from 28 paternal half-sib families raised at The Maschhoffs LLC . Crossbred pigs were from a three-way cross that involved 28 purebred Duroc boars and crossbred Yorkshire a et al. . Phenotya et al.  and BergTraits recorded on the CB progeny included the meat quality traits of intramuscular fat (cIMF), Warner–Bratzler slice shear force (cSSF), Minolta L* (cM.L), Minolta a* (cM.a), Minolta b* (cM.b), pH (cPH), subjective color (cSCOL), subjective firmness (cSFIR), and subjective marbling (cSMAR); the carcass composition traits of loin weight (cLOI), belly weight (cBEL), ham weight (cHAM), loin depth (cCLD), backfat depth (cCBF), and carcass average daily gain (cCDG); and the growth traits measured on live animals at market weight of live loin depth (cLLD), live backfat (cLBF), and live average daily gain (cLDG). Growth and some carcass traits  were recorded on all the individuals, but the meat quality and carcass dissection traits  were recorded on a selection of individuals that were chosen in equal numbers from each ‘group’, defined as the paternal half-sib individuals of the same sex and housed in the same pen. Four to five individuals were chosen for each group: the individual closest to the group’s mean for carcass growth, and the individuals closest to the + 1 standard deviation, + 2 standard deviations, − 1 standard deviation, and − 2 standard deviations from this mean. This selection effectively covered the whole within-group distribution and can be viewed as a viable routine phenotyping strategy. The individuals that were chosen for additional phenotyping were also chosen for genotyping.longissimus dorsi muscle using a handheld MPI pH meter fitted with a glass electrode . Thirty minutes post-slicing (during which oxygenation of the myoglobin occurs), the instrumental color traits were measured using a Minolta CR-400 Chroma meter , including cM.L (luminosity), cM.a (redness), and cM.b (yellowness). Subjective measures of pork quality were recorded at the same time, including cSCOL (5 categories), cSFIRM  and cSMAR (6 categories). Further details on the subjective color and marbling categories are in Wilson et al. [Both cCBF and cCLD were measured after slaughter using a Fat-O-Meater probe  near the 10th rib. cCDG was calculated by dividing the difference between the hot carcass weight and birth weight by the pig's age at slaughter. Carcasses were split, blast-chilled for 90 min, and then separated into primal cuts, such that cLOI, cBEL, and cHAM could be measured. Separation and preparation of the loins to measure meat quality traits were done as described by Wilson et al. . cPH wasn et al.  and on sn et al. . Measuren et al. .Traits recorded on purebred animals included intramuscular fat (pIMF), backfat (pLBF), loin depth (pLLD), loin eye area (pLLA), nursery average daily gain pLDG1), and finisher average daily gain (pLDG2). Purebred Duroc pigs were kept in a facility free of the porcine reproductive and respiratory syndrome and porcine epidemic diarrhea. After weaning, the pigs were moved to nursery conditions on another site, after which the pigs entered a mid-test phase and were allocated to single-gender pens. At this point, pLDG1 was calculated by dividing the difference between mid-test weight and weaning weight by the number of days spent in the nursery. When the pigs were off-tested at 180 days, pIMF, pLBF, pLLD, and pLLA were measured using an ultrasound probe . The trait pLDG1 was calculated by dividing the difference between the pig's off-test weight and mid-test weight by the number of days spent in finishing . The num, and finTraits that are routinely measured in the purebred nucleus line, such as growth traits, could serve as a cheap alternative for improving carcass phenotypes in crossbreds. Using purebred growth measures, we constructed several selection scenarios to quantify their relevance in the selection for crossbred carcass traits  from the BLUPF90 family of programs , 26. TheWe used fourfold cross-validation based on k-means clustering to compare the different scenarios for their ability to predict unobserved phenotypes in CB individuals. Dissimilarities between the 28 purebred sires were derived from the pedigree relationship matrix and were used to allocate individuals to clusters, maximizing intra-group and minimizing inter-group additive genetic relationships , 28.For the ST scenario, breeding values were estimated by masking phenotypes for one of the four groups and prediction accuracy was calculated as the correlation between the estimated breeding values and the (masked) phenotypes adjusted for the fixed effects . For the CB-Live, CB-FOM, and CB-Color scenarios, the phenotypic information for the predictor traits was included for all groups and the phenotypic information for the trait to be predicted  was masked for the validation group. For example, cLBF is recorded on groups 1–2–3–4, but cSSF  is recorded only on groups 1–2–3.Different genotyping strategies were also evaluated. A first scenario, defined as 'No Genotyping', was used as a baseline, and predictions were obtained using pedigree relationships. In the case of ST, CB-Live, CB-FOM, and CB-Color, the genotyping scenarios 'Stage-1' included genotypes for the 28 PB sires and the 914 dams of CB individuals; 'Stage-2' included genotypes for the 1252 CB individuals and their 28 sires; and 'Stage-3' included genotypes for the sires, dams, and CB individuals. When PB phenotypes were included, the genotypes for the 1200 PB individuals were added to each of the respective genotyping scenarios. A diagram of the information used for each phenotyping/genotyping scenario is in Fig. ) was calculated as described by Lourenco et al. [For the genomic prediction scenarios, the same models  and 2)  2) were cument}H , 30. Theo et al. , accounto et al. , 33. A mHeritability estimates for crossbred and purebred traits using single-trait models are in Table Estimates of genetic correlations among purebred traits Table  were posSignificant  estimates of genetic correlation of purebred live daily gain with crossbred traits ranged from low to moderate and were all positive, with the highest estimate equal to 0.54 between pLDG1 and cPH. Estimates of genetic correlations of purebred ultrasound growth traits with crossbred traits ranged from low to high and included positive and negative estimates. Both pLLA and pLLD had high and positive genetic correlation estimates with cCDG  and moderate to high and positive estimates with cCLD . Purebred backfat (pLBF) had the strongest genetic correlation estimates with crossbred meat quality traits, including cM.b (0.60), cPH (0.58), and cIMF (0.55). Purebred intramuscular fat also had the strongest genetic correlation estimates with crossbred meat quality traits, including high and positive estimates with cIMF (0.95), cSFIR (0.87), and cSMAR (0.79), and moderately high estimates with cBEL (0.66), cPH (0.58), and cSSF (0.49). A heatmap of the genetic correlation estimates between crossbred and purebred traits is in Fig. The crossbred live growth traits (in CB-Live) had low to moderate genetic correlation estimates in both directions with the meat quality breeding objective traits, ranging from − 0.51 between cLLD and cIMF to 0.56 between cLDG and cSFIR. Estimates of genetic correlations of cLOI with cLDG and of cLOI with cLLD were high and positive, at 0.90 and 0.83, respectively. Estimates of genetic correlations of the carcass traits in the CB-FOM scenario with the meat quality breeding objective traits were mostly non-significant, except the estimates for cCBF, which showed correlations of − 0.41 and 0.40 with cSSF and cIMF, respectively. Two traits, cCLD, and cCDG had high and positive estimated genetic correlations with the sole carcass composition trait in this analysis, cLOI . The color and pH traits in the CB-Color scenario had moderate to high genetic correlation estimates in both directions with the meat quality breeding objective traits, ranging from − 0.72 to 0.96. The cIMF trait had strong and positive genetic correlation estimates with two Minolta color traits, cM.L (0.96) and cM.b (0.95), and a strong and negative genetic correlation estimate with cPH (− 0.72). Other traits had strong genetic correlation estimates, i.e. cSSF with cM.a (0.72) and cSCOL with cM.L (− 0.70). A heatmap of estimates of genetic correlations among crossbred traits that served as breeding objectives for the selection index and traits that were included in the crossbred selection criterion scenarios is in Fig. Population stratification is reported in Fig. The accuracies of prediction (mean and standard deviation across the four folds) for the breeding objective traits cIMF, cSSF, cSMAR, cSFIR, cSCOL, and cLOI for the seven phenotyping scenarios  and the four genotyping scenarios (‘No genotyping' and Stage-1 through Stage-3) are presented in Fig. Phenotyping scenarios had a substantial impact on the accuracy of predictions for cLOI and cSCOL and had a moderate impact on cSFIR, cCBF, and cCLD. For cLOI, the CB-FOM and CB-Live phenotyping strategies resulted in the highest accuracies, with estimates ranging from 0.69 to 0.77 and from 0.42 to 0.44, respectively, across the four genotyping strategies. For cSCOL, the highest prediction accuracy was obtained for the CB-Color phenotyping scenario, with estimates ranging from 0.36 to 0.39, while accuracy was never higher than 0.15 for the other scenarios. For cSFIR, the CB-Live and CB-FOM scenarios had a moderate advantage over the different phenotyping scenarios, with accuracies around 0.20, compared to less than 0.15 for the other scenarios. For cCBF, prediction accuracies were higher for the PB-2 and PB-3 than for the PB-1 and ST scenarios; this was especially noticeable for genotyping strategies that did not include the crossbred individuals (No genotyping and Stage 1). A similar trend was observed for cCLD. Leaving aside the genotyping strategy with commercial crossbred individuals, prediction accuracies for cCBF were zero for the ST and PB-1 phenotyping scenarios, compared to ~ 0.20 for the PB-2 and PB-3 scenarios. We did not observe substantial effects of the phenotyping strategy for the remaining traits, with prediction accuracies reaching the same level for the crossbred and purebred trait phenotyping scenarios as for the single trait strategy (ST) across all four genotyping strategies.When the genotyping strategy was changed from Stage-1 to Stage-2 or Stage-3, we observed a relevant increase in prediction accuracy for most of the fat-related traits, such as cIMF, cCBF, and cBEL. This was also observed for cCDG in some phenotyping scenarios. The genotyping strategies in which crossbred individuals had genotypes resulted in estimates of prediction accuracy that were, in most cases, higher than for the other scenarios. For cIMF, prediction accuracies for the Stage-2 and Stage-3 genotyping strategies were greater than 0.30 for all phenotyping scenarios, while it never surpassed that value for the other genotyping scenarios. For cCBF, cBEL, and cCDG, prediction accuracies increased to values greater than 0.25, 0.13, and 0.12, respectively, for the Stage-2 and Stage-3 genotyping strategies.The relative importance of meat quality and carcass composition traits in pork production, coupled with the high cost of phenotyping, has accelerated the need for effective strategies for genetic improvement of crossbred performance for these traits. In this study, we investigated the usefulness of multi-trait (genomic) selection for meat and carcass quality in commercial crossbred individuals.First, we estimated the genetic parameters of routinely measured traits in crossbred and purebred pigs. Although available, we did not use genomic data to estimate variance components in this study because, for some traits, all the phenotyped individuals were genotyped , whereas for other traits only some of the phenotyped individuals were genotyped , which explains the difference as compared to our previous work on the same data . Since gHeritability estimates for meat quality traits measured on commercial crossbred animals were low and mainly within the range of previous estimates reported in similar crossbred populations , 34. KhaFor the traits recorded on purebred individuals, estimates of the heritability of ultrasound growth measures were low and similar to those previously reported in crossbred animals (0.26 to 0.45) . AlthougPrevious estimates of the purebred–crossbred correlation for the same trait in pigs range from 0.47 to 0.99 for growth –8, from Due to the varying cost of collecting carcass measures on commercial crossbred animals, phenotyping the crossbred offspring of purebred selection candidates for cheap and routinely measured crossbred traits is a possibility worth exploring. The three crossbred selection criterion scenarios evaluated here represent potential avenues of phenotype collection. However, their effectiveness in selection will largely depend on the genetic correlation between these traits and the traits of interest (breeding objective traits). Genetic correlation estimates between meat quality and growth traits were mostly not significant, which suggests a lack of a shared genetic architecture. An exception to this was the moderate correlations of cCBF with both cIMF and cSSF. Khanal et al.  found siNot surprisingly, the meat quality traits in the CB-Color selection criterion scenario had the strongest genetic correlations with the meat quality breeding objectives. Other studies have also found significant and moderate to strong genetic correlations between meat quality traits, especially intramuscular fat, subjective color, and Minolta color traits , 34.In this study, we used two methods to assess the value of using purebred and crossbred information (genomic and phenotypic) to select for expensive-to-record traits measured on crossbred individuals. The estimation of variance components set the basis for the comparison of the different scenarios. A cross-validation was also used to test the ability of each model to predict future crossbred performance. We chose to predict crossbred performance for several reasons, such as (1) CB performance is the breeding goal, (2) using real data, we did not know the true breeding value of the PB selection candidates, (3) ranking the CB individuals appropriately means ranking the paternal half-sib families among each other as well as the individuals within the family. As for (1), we opted for the prediction of the trait itself because it is the performance of the CB individual that determines the remuneration to the farmer, while we adjusted the phenotype for the systematic effects that are controllable in the breeding scheme. As for (2), the accuracy of PB (G)EBV for CB traits was lower than ‘1’, and this would not have provided the proper ground for comparison of the models. For example, predicting the PB EBV or GEBV would have been a substantially different comparison and could have underestimated or overestimated the value of using genomic information. Finally, for (3), as the model was ranking appropriately the ~ 300 CB individuals, it was also (i) ranking the seven paternal half-sib families, which were sired by the seven PB Duroc sires while (ii) ranking individuals within the family. The feature of (i) challenges the model in identifying the PB individuals based on their CB progeny performance, while the feature of (ii) allows the evaluation of the impact of Mendelian sampling within family. Overall, we found that the prediction of CB individuals’ performance provides the best ground for comparison while providing the most precise picture for comparing different genotyping and phenotyping strategies.The estimates of genetic variances and covariances may be not completely exhaustive, since a cross-validation also acknowledges that the genetic (co)variances cannot completely capture the genetic architecture of a trait, and thus cannot reveal how the information flows across the populations used in this study. Moreover, cross-validation was also used to assess the impact of genotyping strategy on the (multi-trait) prediction of the crossbred traits.The PB-1 scenario mimicked the selection of purebred growth traits (pLDG1 and pLDG2) to improve the crossbred traits. The cross-validation did not show an advantage of this approach over direct phenotyping of the breeding objective. The PB-2 scenario, which included purebred ultrasound traits (backfat and muscle), showed an improvement over direct selection for cBF, cLD, and cLOI. The cross-validation did not show a comparable outcome for cIMF, cSSF, meat color traits  and cHAM. The PB-3 scenario  mildly increased the accuracy for cIMF but not for cHAM, cBEL, cSFIR, and cM.L.We investigated three phenotyping scenarios that aimed at improving crossbred traits that are expensive to measure using traits that could be more easily recorded on the same crossbred individuals. We chose measures of meat quality , both objective and subjective, as well as a measure of carcass quality (loin muscle weight), all requiring carcass dissection and wet-lab analyses for their assessment.The CB-Live scenario included measures that can be collected on live crossbred animals using a weight scale and an ultrasound probe. The cross-validation showed a clear advantage for using this scenario on cLOI, cSFIR and cSSF under this scenario. The CB-FOM scenario included traits that can be collected post-mortem without dissecting the carcass. This scenario showed a further improvement for cLOI of 75% in prediction accuracy. The CB-Color phenotyping scenario included objective meat color measures as predictor traits, which do not need dissection of the whole carcass to be recorded but require that the loin muscle is exposed, which increases labor requirements. The cSCOL trait showed a substantial increase in prediction accuracy under cross-validation, reaching the only non-null prediction accuracy under this scenario. Conversely, cIMF showed no particular advantage under this scenario.We compared the increase in predictive ability that can be achieved by including genomic information, either recorded on the sires, dams, or purebred/crossbred progeny. The Stage 1 genotyping scenario included the genotypes of the 28 sires together with the genotypes of the crossbred dams. In Stage 2, the genotypes of the crossbred individuals were included, whereas in Stage 3, genotypes of the purebred sires and dams, and of the crossbred individuals were included. Stages 2 and 3 aimed at mimicking scenarios where the crossbred individuals are effectively included in the training population. The use of genotypes of crossbred individuals is non-conventional, as they are not used for breeding, however they could add relevant information to the training population, especially when the economically important traits are recorded on the crossbreds. When purebred phenotypic records were included , the genotypes of purebred individuals were also included for all genotyping scenarios.The results showed a clear picture of the relevance of genomic information from each group . The inclusion of genotypes of the sires and dams was irrelevant in terms of predictive ability, under all the phenotyping scenarios and for all traits. A substantial advantage was found when including genotypes of the crossbred individuals, although this varied across traits and phenotyping scenarios. The traits that benefitted the most from including genotypes on crossbreds were the fat-related traits: cIMF, cBEL, and cCBF. The advantage for these traits was strong and consistent across the phenotyping scenarios and showed that inclusion of genotypes of crossbreds leads to an improvement in multi-trait prediction accuracies under any phenotyping scenario. These were followed by growth and meat quality traits, such as cCLD, cSCOL, cCDG, and cM.b.In this study, we tested the value of purebred genotypes only when their phenotypes were included, so the contribution of these two components could not be disentangled. However, it is evident that inclusion of genotypes/phenotypes on purebreds seldom improved the models' predictive ability. Comparison of the predicting performance within the PB-1/2/3 phenotyping scenarios showed that using genotypes from sires and dams (Stage-1) did not improve the accuracy of predicting crossbred performance and that the predictive ability increased only when genotypes on crossbreds was included, even in the presence of genotypes on purebreds.The results suggest a decisive advantage of including crossbred genomic information into the training population when performing genomic selection on traits recorded on the crossbred individuals if the validation is also done on those individuals. The same conclusions were drawn by Esfandyari et al.  using siWe compared genotyping and phenotyping scenarios using real data recorded on 15 traits. We used cross-validation to compare the phenotyping strategies. The overall results suggest that the advantage of genotyping vs. phenotyping depends on the target trait(s). Some traits saw a sizeable benefit from genotyping the crossbred individuals that were phenotyped. These traits are those that are routinely considered in genetic evaluation, such as cCDG, cCBF, and cCLD, but also cBEL and, ultimately, cIMF. Genomic selection for these traits could be performed without including additional traits in the selection index, provided that the reference population is constantly updated. Some other traits, i.e., cLOI, cSFIR, and cSCOL did not benefit from the inclusion of genomic information but did benefit from the inclusion of correlated traits in the multi-trait model. In this case, the crossbred individuals to be predicted were phenotyped for the predictor traits but had no information on the breeding goal trait. In particular, for cSCOL a minimal advantage was obtained from the inclusion of genomic information.Our results make it difficult to infer the reasons for a stronger advantage of genotyping vs. multi-trait phenotyping or vice versa. Overall, the traits for which there was an advantage of genotyping or multi-trait phenotyping showed comparable values of heritability and genetic correlations with the other traits. The factors that influence the advantage of including genomic information could be the presence of a different number of quantitative trait loci that determine the trait, but also the number of effective chromosome segments and the linkage disequilibrium between the marker loci and the loci that determine the traits . In turnWe also acknowledge the need to study the use of methods that incorporate marker information based on the breed of origin of the allele, which has produced promising results in some studies –43. HoweThis study investigated the use of different phenotyping and genotyping strategies to maximize predictive ability for crossbred performance using real data. We found sizeable genetic correlations between purebred growth and crossbred carcass, color, and meat quality traits. The cross-validation results showed that the accuracy of phenotype prediction differed between genotyping and phenotyping strategies. In addition, we demonstrated that availability of crossbred genotypes improved prediction accuracy across most of the phenotyping scenarios. We recommend the inclusion of crossbred individuals in the reference population when the breeding goal traits are recorded on these individuals. Our findings provide evidence of a genetic relationship between traits measured in crossbred and purebreds that could be exploited under several phenotyping and genotyping scenarios to improve crossbred performance.


PRS-included variants displayed a variety of associations with known traits . The proposed methodology led to creation of the first-ever PRS for BMI in Greek adults and aims at promoting a facilitating approach to reliable PRS development and integration in healthcare practice.Quantifying the role of genetics via construction of polygenic risk scores (PRSs) is deemed a resourceful tool to enable and promote effective obesity prevention strategies. The present paper proposes a novel methodology for PRS extraction and presents the first PRS for body mass index (BMI) in a Greek population. A novel pipeline for PRS derivation was used to analyze genetic data from a unified database of three cohorts of Greek adults. The pipeline spans various steps of the process, from iterative dataset splitting to training and test partitions, calculation of summary statistics and PRS extraction, up to PRS aggregation and stabilization, achieving higher evaluation metrics. Using data from 2185 participants, implementation of the pipeline enabled consecutive repetitions in splitting training and testing samples and resulted in a 343-single nucleotide polymorphism PRS yielding an R According to WHO estimates for 2016, a considerable 49% and 13% of the global adult population presented overweight or obesity, whereas worldwide obesity prevalence has tripled since 1975 . DNA samples from the THISEAS study were extracted from whole blood and genotyped using the Illumina Metabochip (with about 200.000 SNPs). Prior to joint statistical analysis and PRS derivation, the phenotypic and genotypic data of the three populations were merged. While the phenotypic integration was straightforward and comprised the simple join of the common phenotypes across the three datasets, the following steps were followed for the genotypic data which were converted to PLINK  1.9 BED+As expected, the aforementioned process created many missing genotypes, especially regarding non-common SNPs between the two datasets. To impute them, an iterative imputation approach was followed using facilities from package snpStats. The package includes genotype imputation functions based on linear regression of neighboring SNPs. This process was repeated until no further genotype imputation was possible. For the remaining missing genotypes of the merged dataset, a k-nearest-neighbors-based imputation technique was applied, implemented in the R package scrime, version 1.3.5.The merging and the imputation process resulted in a merged NAFLD–THISEAS dataset. The OSTEOS dataset was merged with the latter by repeating all the aforementioned steps, resulting in a merged NAFLD–THISEAS-OSTEOS dataset. The final merged dataset was exported to PLINK format using functions from the snpStats package. Next, to enhance the pool of SNPs for PRS derivation, the merged dataset was extended using IMPUTE2 software  using thp-value < 10−9 were also excluded from further analysis.The first filter applied to genotypic data was to exclude poorly imputed genotypes; therefore, SNPs with an IMPUTE2 INFO score less than 0.9 were excluded. Additional genotype and sample filtering was performed using functionalities from the snpStats package. Specifically, SNPs with an SNP call rate < 95% and minor allele frequency (MAF) < 5% and samples with a sample call rate < 90% were excluded from further analysis. The resulting filtered dataset was further subjected to a second round of genotype filtering based on the Hardy–Weinberg (HWE) equilibrium, where SNPs with HWE After dataset filtering, principal component analysis (PCA) was performed to capture any underlying population stratification not reflected by the confounders used in the subsequent association tests using R package SNPRelate, version 1.30.1. Subsequently, regression models were fitted for each SNP against BMI phenotype using sex, age, NAFLD case/control and cardiovascular disease status along with selected PCs as correction covariates with the purpose of deriving summary statistics for each SNP, namely effects and statistical significance for contribution of each single SNP to the phenotype. The number of PCs was automatically selected using the Tracy–Widom statistic for assessment of the most significant PCs based on their eigen values . Four diSeveral PRS candidates were derived using PRSice2  combined2) as returned by PRSice2. At this point, it should be noted that the PRSice2 PRS R2 is the difference between the R2 of the “full” model, i.e., a regression model including all the covariates/confounders and the PRS, and the “null” or “reduced” model, i.e., a regression model only with the other covariates without the PRS. The PRS R2 values were collected for each iteration, resulting in a baseline distribution that would be used later for assessing the statistical significance of the final PRS. In each iteration, the following actions were performed: first, the total dataset was split to a training set  and a testing set . Then, the source set was used to perform de novo association tests for each SNP with four different methods  against the BMI phenotype. Sex, age, NAFLD status and several automatically selected PCs (varying between 5–12 across multiple iterations), using the Tracy–Widom test, were used as confounders in the regression models underlying each of the four methods, resulting in sets of summary statistics derived with each method. Then, these summary statistics were used along with the target dataset as inputs to PRSice2 for extraction of the optimal number of SNPs that would comprise a candidate PRS for the specific iteration. The aforementioned steps, from data splitting up to PRS synthesis with PRSice2, were repeated 100 times. At each iteration, several performance metrics were collected, among which the statistical significance of the PRS and the percentage of additional variance explained by the PRS  of each SNP in the 100 iterations was counted considering an SNP to be appearing at least 5 times in order to further proceed to the downstream procedures. Then, for each frequency, a PRS comprising the SNPs appearing equally or above this frequency was assembled with effects averaged over iterations where each SNP appears and evaluated using previously described source/target dataset splits and linear regression, resulting in a series of evaluation metrics, among which also the PRS R2 as described above. This was repeated for all observed frequencies and a distribution of PRS R2 values was created. The PRS R2. Within the respective databases, participants presented median BMIs in the spectrum of overweight for all three studies . BMI was not statistically significantly different between the NAFLD and OSTEOS studies but did present a statistically significant difference between the NAFLD and THISEAS as well as the OSTEOS and THISEAS studies (p < 0.001 for both pairs). Differences in age were also statistically significant between all studies .The anthropometric characteristics of the unified sample are described in p-value < 2.2 × 10−16), with men presenting higher values. Among the overall sample, 614 participants presented BMI in the range of 18.5–24.99 kg/m2 , whereas 875 and 579 participants presented overweight and obesity, respectively  less than 0.9, structural and copy-number variations were excluded from further analysis. All downstream analyses were based only on known variants . This process led to 1,454,104 variants interrogated for PRS candidates. With respect to samples, 1970 (94.6%) had complete phenotypic records for covariates interrogated in regression models and included in further analyses.2 metric returned by PRSice2, which measures percentage of BMI variability explained by the PRS in the regression models. The PRS R2 values for each method were averaged over 100 PRS derivation iterations , followed by GLM  and statgenGWAS . PLINK yielded the lowest average PRS R2 values but with the smallest variability across 100 iterations .Summary statistics for the merged dataset were calculated with BMI phenotype as a response variable and using the extended  and further filtered genotypic dataset. In order to properly estimate the effects of individual SNPs that potentially contributed to the BMI phenotype in the unified dataset, we applied four different frameworks for summary statistics estimation, namely a simple generalized linear model (GLM) as implemented in the R statistical language, the regression algorithm implemented in the R package statgen GWAS as well as the SNPTEST software and the more generalized PLINK framework. In all cases, the sex, age, NAFLD status and cardiovascular disease status of individuals were incorporated in the regression models as confounders, along with several automatically selected principal components to capture potential underlying population stratifications not reflected by the other confounders. The four sets of summary statistics were used as input to PRSice2 along with the target samples in an iterative PRS derivation procedure, as described in Materials and Methods. To evaluate the performance of each summary statistics estimation method, we used the PRS Rerations  and the After completion of 100 PRS derivation iterations, we assessed the stability of the extracted PRSs . We obse2 values were assembled . Notably, our iterative and aggregative PRS derivation process resulted in a PRS with ~10 times improved explanatory power  when leaving out 50 of samples, with the maximum value at 0.3279 ± 0.0114 . A final regression model using the 343-SNP PRS for BMI with the total merged dataset yielded a PRS R2 = 0.3241 . Finally, to evaluate the ability of the 343-SNP PRS to characterize close phenotypes, we created a regression model with the same covariates but using population weight instead of BMI. The model yielded PRS R2 = 0.2313  and excluding (reduced) the PRS while maintaining the other covariates . Overallp-value = 4 × 10−193) and a positive correlation, where increased PRS values were associated with increased BMI levels. As shown in The aforementioned 343-SNP PRS deriving from using SNPTEST displayed a statistically significant association for BMI . The iterative pipeline presented here attempts to address various matters on PRS extraction, namely selection of an appropriate threshold for SNP inclusion and prediction accuracy [The present study sought to investigate application of an automated pipeline for PRS extraction using data from the three Greek studies of NAFLD, OSTEOS and THISEAS. In this population of Greek adults, the constructed PRS displayed a statistically significant association for BMI, with an Raccuracy  as well In attempting to strengthen PRS construction methodology , this piTo the best of our knowledge, the present study constitutes the first attempt to develop a PRS for BMI using data from a Greek population and a previous attempt for construction of a PRS has only been referred to once before in the current literature, exploring Parkinson’s disease in older Greek adults . Impleme2 of 0.3241 is substantially comparable, and, in some cases, higher, than the ones presented in other PRSs from BMI, which include thousands of SNPs [Other attempts to create PRSs for BMI in populations of European ancestry are extensively described in the current literature, with an overall number of 56 BMI-related entries in the PGS Catalog [In the current study, we observe links for various indices related to cardiovascular profile for twelve out of the sixteen variants with GWAS-Catalog-identified associations. The latter could be explained by inclusion of data for THISEAS participants with diagnosed cardiovascular disease (19.58% of the participants). Although the mediating effect of BMI is usually accounted for when investigating the effect of genetic or polygenic risk scores on indices of cardiovascular disease, the reciprocal relation between variation in cardiometabolic indices levels and BMI levels has not been extensively demonstrated through BMI-PRS-included, CVD-related variants. Out of the associated SNPs, the C allele of the rs2710804-included variant presents the majority of reported associations, namely with cell count types  and even measurements of C-reactive protein. In this context, the negative effect of the T allele observed in our study (β = −0.1356) could denote a positive relation of the C allele with metabolic pathways of inflammation and disturbed immunological responses in the subsequent increasing effect of BMI values. Parabacteroides genus has been linked to body weight. The majority of studies denote a higher Firmicutes:Bacteroidetes ratio and a generalized reduction in species variation in individuals with increased body weight or obesity [Interestingly and among this PRS’s novel associations, we find two variants previously linked to gut microbiome measurements in populations of European ancestry. More specifically, Rühlemann MC et al. previously associated the rs480039 SNP with a 0.082571946 unit increase in P_Bacteroidetes abundance among German individuals . Similar obesity , and dif obesity ,37,38,39When referring to SNPs related to lifestyle, our suggested PRS included one variant related to well-being (variant rs17662327) and one variant associated with exercise (rs10252228). More specifically, in our sample, presence of the T allele of the former SNP was linked to a 0.1471 change in BMI levels. Previously, Okbay et al. demonstrated a 0.0182 unit increase in sentiment of life satisfaction or emotional well-being of adults for the T allele . Our stu2 measure for the number of each-time-associated SNPs, the pipeline aims to facilitate integration of PRS use in daily healthcare practice, for example as part of widely distributed consumer reports. It should be stressed that, as this methodology is based on the highest R2 values of the aggregate PRS candidates, it ensures high explanatory power of the reduced signature. At the same time, it mitigates any computational and data management burden imposed by PRSs with large (up to millions) numbers of SNPs. One of the great strengths of the present study entails implementation of our novel methodology for extraction of PRS, which enables effective management and analysis of the vast amounts of genetic data required for such analyses. The automated pipeline enables practical application of our suggested holistic approach for extensive examination of thousands of SNPs, leading to identification of various novel associations. Through the methodological approach of applying a repetitive process of continuous adjustment of the RLimitations of the present study mainly concern power given the restrained participant sample size available for conducting analyses. Another limitation refers to use of a unified database of participants from three different studies. It is possible that variation in participant characteristics and bias accompanying use of a large analogic sample size of participants with cardiovascular disease played a considerable part in identifying associations between BMI and SNPs related to regulation of cardiovascular indices. However, we determined that much of the potential variability introduced by the fact of joining three databases was successfully captured by one of the PCs incorporated in the model. In addition, although the hypothesized pathways through which the identified SNPs potentially affect BMI levels provide insight for novel relations, there is little evidence to establish direct causal relationships. However, the present analysis sets a foundation for the suggested causal SNPs, and further research is also needed to explore the possibility of relations through their role as proxies for different associated variants. The present paper describes creation of the first PRS for BMI in Greek adults by introducing use of a novel, automated pipeline for PRS extraction. The findings of this study lead to identification of several novel SNPs associated with BMI, potentially through their implication in various metabolic pathways related to traits of cardiometabolic profile and gut microbiome. Our data provide novel insights into interactions of various biological pathways implicated in formation of BMI levels and subsequently affecting its individual variation across different populations. The suggested pipeline aims at promoting maximization of PRS integration in daily healthcare practice by enabling rapid and straightforward development of risk scores. In this regard, this first-ever PRS of a Greek population highlights the need for further development of PRSs for anthropometric traits in larger databases of Greek adults and sets a foundation for wider use of the described iterative PRS methodology.


Substantial morbidity is common with these diseases. Previous within- and between-breed GWAS for HD, ED, and ACL rupture using array SNPs have identified disease-associated loci. Individual disease phenotypes are lacking in repository data. There is a critical knowledge gap regarding the optimal approach to undertake categorical GWAS without individual phenotypes. We considered four GWAS approaches: a classical linear mixed model, a haplotype-based model, a binary case-control model, and a weighted least squares model using SNP average allelic frequency. We found that categorical GWAS was able to validate HD candidate loci. Additionally, we discovered novel candidate loci and genes for all three diseases, including FBX025, IL1A, IL1B, COL27A1, SPRED2 (HD), UGDH, FAF1 (ED), TGIF2 (ED & ACL rupture), and IL22, IL26, CSMD1, LDHA, and TNS1 (ACL rupture). Therefore, categorical GWAS of ancestral dog populations may contribute to the understanding of any disease for which breed epidemiological risk data are available, including diseases for which GWAS has not been performed and candidate loci remain elusive.Here, we report the use of genome-wide association study (GWAS) for the analysis of canine whole-genome sequencing (WGS) repository data using breed phenotypes. Single-nucleotide polymorphisms (SNPs) were called from WGS data from 648 dogs that included 119 breeds from the Dog10K Genomes Project. Next, we assigned breed phenotypes for hip dysplasia  HD,  In recent years, organized efforts have assembled large repositories of whole-genome sequencing (WGS) data, such as the international Dog10K Consortium . The DogIn dogs, breed phenotypes rather than individual case-control or quantitative phenotypes have been used to study height, chondrodysplasia, body size, muscling, skull shape, ear type, snout length, fur length and quality, life expectancy, cognition, behavior, cesarean section rate, litter size, stillbirth rate, gestation length, and cancer mortality . DomestiDisease risk variants have undergone inadvertent selection within breeds due to genetic correlation caused by random fixation, linkage disequilibrium (LD), and pleiotropy . High diAlthough single-nucleotide polymorphism (SNP) array data can detect some fixed large-effect genetic variants, such data are underpowered compared to WGS SNPs for the discovery of all potential associations that may exist across many breeds . GeneticPrevious research has used a classical linear mixed model (LMM) approach and removed individuals from overrepresented breeds to balance breed representation . HoweverTo study WGS SNPs using breed phenotypes and unbalanced breed representation, we used four association models for GWAS of HD, ED, and ACL rupture. We were able to detect new biologically relevant ED associations and validate previously reported associations for HD. We also report HD’s association with genes that have been under selection since domestication. These domestication genes influence traits such as body size, ear phenotype, and behavior. This suggests that HD is a complex trait that may have been directly or indirectly influenced by the selection of morphological traits during canine domestication.High-depth WGS data from 648 canids, including 562 dogs from 119 breeds, were obtained from the Dog10K Consortium . Dog10K In an across-breed GWAS with breed phenotypes, it is critical that breed assignments are correct. To detect samples with inaccurate breed assignment, we performed neighbor-joining phylogeny analysis. We compared the 562 dogs with a breed assignment to a previously published dataset of 1,110 dogs from 129 breeds with SNP array data  . VCFtoolBreed phenotypes for HD, ED, and ACL rupture were assigned based on epidemiological data describing disease prevalence . BecauseVCFtools was used to retain biallelic SNPs, remove indels, and remove variants with a quality score under 20 . The quaA literature search was performed to create a comprehensive list of all previously reported associated loci with HD, ED, and ACL rupture in dogs.P values, and Bonferroni correction (0.05/number of variants) was used to identify significant associations for OFA HD and ED  (p < 6.59E-9) (p < 7.12E-9)   and hosp7.12E-9) . All Quaool in R .P values, and Bonferroni correction was used to identify significant associations for OFA HD and ED (stringent binary case-control p < 6.68E-9) (lenient binary case-control p < 6.64E-9) (p < 7.26E-9) (lenient binary case-control p < 7.13E-9) (A binary association was performed by selecting a group of breeds that are strongly protected from the trait (low-risk controls) and a group of breeds that are strongly predisposed (high-risk cases) for the association. Two different scenarios were compared. Initially, we used a stringent scenario with a full separation of cases and controls to ensure the prevalence cutoffs were disparate. Since a limited number of dogs from low- and high-risk breeds for HD, ED, and ACL rupture were available, we also tested a less stringent scenario where the threshold values produced case and control groups with a larger sample group size. The case-control binary association was performed using GEMMA with a genomic relationship matrix and sex as a covariate . As desc6.64E-9)  and hosp7.13E-9) .p < 1.68E-9) (p < 1.82E-9)   and hosp1.82E-9) . For comool in R .We used a simple linear regression model with weights on the averaged genotypes as our final approach to perform categorical GWAS with breed phenotypes. Initially, we computed the average of the SNP dosage for each locus in each dog breed. So, we had an X matrix (n*p) for each disease phenotype where n = number of dog breeds and p = number of SNPs. We then computed the principal components from the genomic relationship matrix and included the first five important principal components in the model. For each breed, the weight was the proportion of the genotyped individuals for each breed in the whole sample population. Finally, we ran the following model and estimated SNP effects in the R environment:p < 6.59E-9) (p < 7.11E-9)  within each breed. This model has the advantage of using all the SNP data and appropriately considers variation in the number of dogs in each breed. A Bonferroni correction was used to identify significant associations for OFA HD and ED (6.59E-9)  and hosp7.11E-9) .The CanFam3.1 build on the UCSC and Ensembl genome browsers was used to identify genes contained within the top candidate loci. Due to the increased density of the WGS SNPs no flanking regions were added for gene annotation.n = 5 wolves, n = 33 village/indigenous dogs, n = 562 domestic dogs (119 breeds), n = 14 mixed dogs/breeds, and n = 34 dogs of unknown breed) was performed  within a breed in the dataset. This model was developed to address the challenges of unbalanced breed representation in the repository data rather than excluding individuals to balance the number of dogs per breed. Genome-wide significance was set at Bonferroni correction of UGDH. The chromosome 16 locus was also reported as significant with the LMM GWAS. 2 SNPs on chromosome 10 and 1 SNP on chromosome 17 met genome-wide significance for the hospital HD data  to 1.58 (ACL rupture) –4. 1 SNP HD data . The reg HD data  is a kno , a measurement of hip joint laxity, in a population of British Labrador Retrievers (FBX025) gene that controls ubiquitin-mediated proteolysis is within the chromosome 25 HD region of interest . This candidate locus is strongly associated with hip osteoarthritis (OA), suggesting REG3A may influence canine hip OA through MAPK pathway signaling  and IL1B (interleukin 1β). IL1A and IL1B polymorphisms have been associated with human hip OA patients , an apoptosis enhancer that modulates osteoblast differentiation , which catalyzes the final step of anaerobic glycolysis. In chondrocytes under inflammatory stress, LDHA binds NAHD and increases oxidation and free radical generation, thereby augmenting the inflammatory response and OA severity (TNS1 (tensin 1), which is essential for extracellular fibronectin, collagen matrix formation, and myofibroblast differentiation  and dichotomous phenotypes (binary case-control). Breed phenotypes are ordinal categorical phenotypes. Using categorical phenotypes with models optimized to handle quantitative or dichotomous traits may result in lost information and decreased power .Biologically relevant associations were identified by all GWAS methods with no evidence that one method is advantageous. No methodology performed consistently better across all datasets. LMM, haplotype-based, case-control binary, and weighted least squares models all reported biologically relevant associations. No two methods had a constant overlap of significant findings over all diseases analyzed. Currently, there is little literature documenting candidate loci for ED and ACL rupture, limiting validation findings and hindering complete performance assessment of the models. Many significant associations do not contain annotated genes. This could be due to imperfect annotation of the reference genome. However, such regions could contain quantitative trait loci that explain variation in gene expression and associated disease risk.Several study limitations were identified. Genomic inflation with high lambda values was a consistent feature of haplotype GWAS. This could be due to the use of a fixed SNP window exacerbating allele frequency differences with the large, fixed haplotypes that exist across breeds of dogs . Althougn = 4 (Overall, the weighted least squares method was able to identify one SNP that was previously reported or detected by another model for all diseases. The weighted least squares method most often identified a moderate number of SNPs, but the Bonferroni correction appeared too conservative as even loci that had been previously reported did not meet genome-wide significance, such as the chromosome 16 locus identified from OFA HD SNPs. The advantage of the weighted least squares method is that it was developed to handle unbalanced datasets to allow the analysis of all sample data. A disadvantage to the weighted least squares model is if most of the samples of a particular breed did not have the large effect variant at a particular SNP, then that variant may go undetected during analysis because average allelic frequencies are calculated per SNP per breed. There is growing scientific interest in ordinal categorial GWAS approaches optimized for repository SNP data , and altn = 4 . More woDefining genome-wide significance dynamically for each model would also be helpful. Bonferroni correction may be too conservative for the weighted least squares method, as supported by the chromosome 16 locus identified from OFA HD SNPs that did not meet genome-wide significance but overlapped with a previous HD candidate locus. Conversely, a more stringent cutoff may be needed for haplotype GWAS to reduce the risk of false positives. FDR correction was not sufficient by itself to dampen the extreme genomic inflation of the haplotype-based analysis. The FDR correction of the LMM did limit the number of significant loci to fewer genomic regions and even had no reported significant loci for the OFA HD dataset.There were also limitations to the binary case-control GWAS regarding stringency parameters for breed classification as excessively stringent classification reduced sample size. Reduced sample size may increase the likelihood of a false-positive association that reflects a contrast of other breed phenotypes different from the complex disease of interest. This problem should self-correct as the number of genomes in the Dog10K project expands. Overall, the dichotomous case-control GWAS had the smallest number of significant SNPs detected for each disease revealing that information is lost when an ordinal categorical phenotype is arbitrarily made into a dichotomous binary case-control phenotype. Case-control GWAS may allow important large-effect disease-associated genetic variants of breeds considered moderate risk, not classified as high or low risk, to go undetected.Although SNP and dog filtering was robustly considered, including confirming the phylogeny of individual samples using a cladogram, while aiming to preserve as many dogs as possible in each breed group, further data filtering could have been considered, such as excluding individuals with very low sequencing depth. However, despite differences between HD breed prevalence  and diffFAF1, that warrants validation. Further investigation is needed to explore alternative methods for haplotype GWAS. Here, use of a moving window to dynamically define haplotype may be beneficial. Further optimization of the methods for the estimation of genome-wide significance appropriate for each model is also needed to appropriately control the false discovery rate. Categorical GWAS of ancestral populations may contribute to the understanding of any disease for which epidemiological risk data are available for the breed or ancestral population, including diseases for which GWAS has not been performed and candidate loci remain elusive.In conclusion, we investigated four approaches for across-breed GWAS with breed phenotypes for three major canine orthopedic diseases. The use of multiple GWAS methods helped us to detect significant associations. For HD, strong associations with genes that regulate morphology were found, suggesting that the intense selection that gave rise to diverse breeds of dog has also influenced breed risk for HD. We also discovered a strong association between ED and a biologically relevant gene,


N = 27,651 individuals) and conducted a genome wide association study (GWAS) meta-analysis of parent- or self-reported hoarding symptoms (HS). We followed up the results with gene-based and gene-set analyses, as well as leave-one-out HS polygenic risk score (PRS) analyses. To examine a possible genetic association between hoarding symptoms and other phenotypes we conducted cross-trait PRS analyses. Though we did not report any genome-wide significant SNPs, we report heritability estimates for the twin-cohorts between 26–48%, and a SNP-heritability of 11% for an unrelated sub-cohort. Cross-trait PRS analyses showed that the genetic risk for schizophrenia and autism spectrum disorder were significantly associated with hoarding symptoms. We also found suggestive evidence for an association with educational attainment. There were no significant associations with other phenotypes previously linked to HD, such as obsessive-compulsive disorder, depression, anxiety, or attention-deficit hyperactivity disorder. To conclude, we found that HS are heritable, confirming and extending previous twin studies but we had limited power to detect any genome-wide significant loci. Much larger samples will be needed to further extend these findings and reach a “gene discovery zone”. To move the field forward, future research should not only include genetic analyses of quantitative hoarding traits in larger samples, but also in samples of individuals meeting strict diagnostic criteria for HD, and more ethnically diverse samples.Hoarding Disorder (HD) is a mental disorder characterized by persistent difficulties discarding or parting with possessions, often resulting in cluttered living spaces, distress, and impairment. Its etiology is largely unknown, but twin studies suggest that it is moderately heritable. In this study, we pooled phenotypic and genomic data from seven international cohorts ( Hoarding Disorder (HD) is one of the most recent mental disorders to be included in the DSM-5  and ICD-The prevalence of HD in the population is estimated to be approximately 1% to 2.5% for both men and women –6, but aHS typically appear in early-to-mid-adolescence and, in contrast to many other psychiatric disorders, symptom severity increases with age –13. PsycN = 3974 twins, [N = 25,523, [The etiology of HD is largely unknown, though likely to be multifactorial in nature and related to a complex interplay of genetic, neurobiological, and psychosocial factors. Family studies have consistently shown that HS run in families , 19–22.  25,523,  reportedSLC1A1, a glutamate transporter gene [SETD3, a gene highly expressed in the brain and involved in apoptotic processes and transcriptomic changes, to be associated with HS [Linkage and GWAS studies of HS have been rare thus far and have largely been conducted in small samples of OCD or Tourette syndrome patients. Candidate gene studies in individuals with OCD have suggested (largely non-replicated) associations between HS and a number of candidate variants –31. Threter gene . One GWA with HS . Another with HS . The sam with HS ), a brieLarger samples are needed to increase power to detect significant genetic effects. Therefore, the current study aimed to conduct a GWAS meta-analysis of several large international cohorts from Sweden, the Netherlands, England, and Canada that included parent- or self-report hoarding scale data. We pooled data from seven population-based cohorts that together include 27,651 individuals (including 7012 twin-pairs), representing a more than eightfold increase in sample size compared to the previous study by Perroud et al. . We follChild and Adolescent Twin Study in Sweden  GWAS meta-analysis included individuals from seven different European-ancestry cohorts. Four cohorts are part of the Swedish Twin Registry , namely CATSS24 ), and thy (YATSS ). CATSS y (YATSS ), Spit fy YATSS , 42), an, anChildy  to five items  of the Hoarding Rating Scale Self-Report , while N = 8598), another part was genotyped on the GSA genotyping array (N = 606). For the GWAS analyses, the STR-CATSS cohorts  were pooled over each genotyping platform , forming two separate CATSS datasets (STR-CATSS-GSA and STR-CTASS-PC). Each of the six datasets  underwent stringent quality-control (QC), including the removal of non-European ancestry outliers based on PCA and imputation using the HRC [N = 27,651) individuals with complete genotypic and phenotypic information.All participants were genotyped on SNP-arrays based on DNA from saliva or blood. One part of the STR-CATSS samples was genotyped on the PsychChip genotyping array  > 1%, and imputation-quality score > 0.8. All datasets were aligned to the HRC-reference. In case alleles were reported on different strands, they were flipped to the orientation in the HRC reference. Strand ambiguous A/T and C/G SNPs were removed if their MAF was ≥0.4. Remaining ambiguous SNPs were strand aligned by comparing MAF to the HRC reference . We thenHeritability estimates of each individual cohort were extracted from the GCTA association output. GCTA uses a restricted maximum likelihood (REML) approach  to estimp-values were computed by mapping SNPs to their corresponding gene(s) based on their position in the genome. Positional mapping was based on ANNOVAR annotations and the maximum distance between SNPs and genes was set to the default setting of 10 kb. Based on the results of gene analysis, competitive gene-set analysis was performed with default parameters. The 15,496 gene-sets were obtained from MsigDB v7.0, including ‘Curated gene sets’ consisting of nine data resources including KEGG, Reactome, and BioCarta, and GO terms consisting of three categories .We carried out a Multi-marker Analysis of GenoMic Annotation (MAGMA)  v1.08 asp-value thresholds based on the reported thresholds in the primary publications . For STR, PRS analyses were conducted separately for the three datasets  and were subsequently merged.To explore the genetic relationship between HS and other phenotypes, we calculated a range of PRSs based on large-scale GWAS summary statistics. We selected mainly studies of psychiatric disorders, i.e., OCD , DEP 5858, schizTo evaluate the relationship between each PRS score and HS in every cohort, we employed generalized estimating equations (GEE) in R . The GEE analysis accounts for the relatedness in the datasets. As SfS did not contain any related individuals, we carried out linear regression, as implemented within the PRSice2 pipeline. Again, the same covariates that were previously used in the respective GWASs were included.Q test [I² [I² statistic describes the percentage of variation across studies that is due to heterogeneity rather than sample variation and does, unlike Q, not inherently depend upon the number of measures included in the meta-analysis. Regardless of observed heterogeneity, we calculated a fixed effects model to evaluate the association of each PRS with HS. If there was considerable observed heterogeneity across study sites (I² > 0.5 and/or PQ < 0.05), we further calculated a random effects model.PRS estimates per discovery phenotype were summarized across all target datasets by means of an inverse variance meta-analysis using the metagen package in R. We examined heterogeneity in PRS estimates across the cohorts with Cochran’s Q test  and Higgtest [I² , 66. Q ip = 0.0001, p = 0.00001, and p = 0.000001) were performed between each pair-wise combination of cohorts as well as between LOO meta-analyses and the left-out cohort to identify any cohort in which the GWAS results markedly deviated from the rest of the cohorts. Sign-tests allow for the quantification of independent genomic regions that have the same direction of effect in two separate summary statistics. The output, in form of a ratio, gives an estimate of the percentage of genomic regions with the same direction of effect in the two compared datasets. A sign-test is a binomial test with the null-hypothesis = 0.5, with a ratio above 0.5 indicating convergence, and a ratio below 0.5 indicating divergence. While certain fluctuations in the sign-tests across different p-value thresholds are expected, depending on the true association of each SNP with the phenotype, we mainly aimed to assess whether a specific cohort or age-group markedly deviated from the rest.To identify if the summary statistics from any of the included cohorts substantially deviated from the others, we performed leave-one-out (LOO) GWAS meta-analyses and carried out a set of sensitivity analyses. With the replication module of the Ricopili pipeline, sign tests on the top SNPs .The distributions of each cohorts’ sum-scores have been maximally normalized  were rs117321479 (P = 1.36 × 10–7) on chromosome 12, rs78426839 (P = 3.12 × 10–7) and rs7567224 (P = 7.70 × 10–7) on chromosome 2, and rs72927972 (P = 9.09 × 10–7) on chromosome 18  and entails the gene SOX5. The region tagged by rs78426839 spans 22.1 kb (LD r² > 0.6) and entails the genes TUBA4B, DNAJB2, PTPRN, MIR153-1, RESP18, and DNPEP. The region tagged by rs7567224 spans 24.40 kb (LD r² > 0.6) and entails the gene CNTNAP5, while the region tagged by rs78426839 spans 94.3 kb (LD r² > 0.6). In addition, 19 independent SNPs with p < 1 × 10–5 were identified . The SNP-based heritability estimate of the GWAS meta-analysis using LDSC resulted in a total observed-scale heritability of 0.019 .For the twin cohorts, the additive genetic variance of HS, estimated based on the sparse genetic relatedness matrices, ranged between 0.26 (NTR) and 0.48 (TwinsUK), with estimates for the STR cohorts in between . Note that the SNP-heritability estimates based on the twin cohorts are largely driven by the twin resemblance . The heritability for SfS, only including unrelated individuals, was 0.11  associations between HS and PRSs based on all DEP, SCZ and EA, though not consistently across all target cohorts . When not taking the heterogeneity of the individual PRS estimates into account, the meta-analyzed PRSs of ASD and EA also showed significant associations with HS . As the associations with the PRS of EA showed substantial  heterogeneity, we further conducted random effects models to examine if the association with HS remains significant when accounting for this heterogeneity, resulting in a nominally significant association (EA: Prandom = 0.00765). The associations with the PRS of DEP, OCD, and ADHD did not show any significant associations with HS in the meta-analysis  and linear regression analysis (SfS) revealed Bonferroni-corrected significant  between each pair-wise combination of STR, NTR, SfS, and TwinsUK  and are therefore difficult to interpret. While the ratios of the sign tests for the other two p-value thresholds (p = 1 × 10–05 and p = 1 × 10–04) varied between 0.2 and 0.8, there is no apparent pattern indicating a systematic deviation of one cohort from the rest.First, we conducted sign tests at three different The LOO PRS analyses did not show a significant association between any of the PRSs and the HS score of the left-out cohort. While this could suggest heterogeneity across the cohorts, it is likely indicating a low power for this analysis considering that each discovery cohort had a rather low sample size and we only saw significant associations with PRS based on GWASs with considerably higher sample sizes . See Supplementary Tables P = 0.0303) in one of our cohorts (SfS) with unrelated samples. It suggests that, with sufficient power, specific genetic variants that are associated with HS will be eventually identified. SNP-based heritability of the meta-analysis as calculated with LDSC was low  and non-significant. We therefore did not conduct genetic correlation analyses as it is suggested to have a heritability Z-score of above 1.5  in order for the analysis to be meaningful [With 27,651 included individuals we conducted the largest GWAS study of HS in the population to date. Although we could not report any genome-wide significant SNPs, we found a significant contribution of common genetic factors to HS as indicated by a substantial genetic SNP heritability of 11%  entails the gene SOX5, which is a member of the SOX family of transcription factors involved in the regulation of embryonic development and in the determination of cell fate [SOX5 has been identified as a gene with a high pleiotropic effect on a broad spectrum of psychiatric disorders and has been associated especially with ASD, BIP, MD, and SCZ, and to a lesser degree also with OCD, TS, ADHD, and AN [SOX5 has been genome-wide significantly associated with 64 phenotypes, spanning a wide range of domains including psychiatric , skroticism ), reprodysis rs1121479 entroticism ), and enroticism , educatiroticism ). Given By current standards –61, the Another reason why we did not find a significant signal may lie in differences between the included study cohorts, beyond any heterogeneity that we were able to detect. Possible sources of heterogeneity across the datasets include age, instruments used to assess HS, or ascertainment of data. The SfS cohort stands out compared to the other cohorts with regards to age and questionnaire used. We addressed this issue by applying a framework of sensitivity and heterogeneity analyses. Neither in the genome-wide heterogeneity test, nor in the sign-tests or LOO PRS analyses did we observe a pattern that indicated a systematic deviation of one cohort from the rest. We therefore concluded that a meta-analysis of the cohorts is warranted and that the lack of signal is indeed most likely attributable to a lack of power. As most of the individuals included in the cohorts in this study were relatively young compared to the age when the full disorder debuts, there is the possibility that HS reported by younger individuals are the outcome of a somewhat different phenotype than HS reported at an older age.Neffective = 214,576; ASD: Neffective = 44,367; EA: Neffective = 245,621) can pick up genetic signals that may be associated with hoarding symptoms. We consider this a further indicator that additional power is needed to obtain reliable signal in a quantitative trait GWAS for HS. The lack of significant associations with the most described comorbidities in HD  was unexpected and possibly attributable to the currently modest size of the discovery samples.Cross-trait PRS analyses showed significant results. The genetic risk for schizophrenia was significantly associated with HS, while we found suggestive evidence for an association with autism spectrum disorder and educational attainment. This suggests that a well-powered GWAS such as those used in the cross-trait PRS analyses . As in any study based on self-administered instruments, it was not possible to rule out these other causes of HS. It will further be of interest to determine the extent to which HS in the population and clinical HD share the same genetic susceptibility. However, no case-control GWAS of HD exists, nor is HD assessed in large datasets like UK BioBank , 77, whiTo conclude, we found that HS are heritable, confirming and extending previous twin studies. Nevertheless, we had limited power to detect any genome-wide significant loci. Much larger samples will be needed to further extend our findings and reach a “gene discovery zone”. Further, additional samples should be more ethnically diverse to ensure that results are relevant to individuals of non-European ancestry . Future Supplementary InformationSupplementary Tables


Preliminary evidence, to date, shows that breeding for low methane may result in animals with higher lean yields that are economically favorable even before carbon costs and environmental benefits are taken into account. These benefits were seen in animals measured for methane on fixed intakes and require validation on intakes that are allowed to vary.Global agreements in place to reduce methane emissions in livestock are a potential threat to food security. Successful but independent breeding strategies for improved production and lower methane are in place. The unanswered questions are whether these strategies can be combined and how they impact one another, physically and economically. The New Zealand economy is largely dependent on pastoral agriculture from grazing ruminants. The sheep industry produces ∼20 million lamb carcasses for export each year primarily from grass. Methane emitted from the fermentation of forage by grazing ruminants accounts for one-third of all New Zealand’s greenhouse gas emissions. Here, we use sheep selection lines bred for divergent methane production and large numbers of their relatives to determine the genetic and phenotypic correlations between enteric methane emissions, carcass yield, and meat quality. The primary objectives were to determine whether previously shown physiological differences between methane selection lines (differing by ∼12% in methane) result in a negative impact on meat production and quality by measuring close relatives. The results show no negative effects of breeding for lowered methane on meat and carcass quality. Gross methane emissions were highly correlated with liveweight and measures of carcass weight and negatively correlated with dressing-out percentage and fat yield (GR). Trends were similar but not significant for methane yield (g CH Greater than one-quarter of the Earth’s total land mass is used for grazing ruminants . The evoBefore undertaking the use of breeding for adaptation strategies in ruminants such as low methane emissions, it will be crucial for us to understand the impact of this on the animal’s digestive physiology and any potential negative outcomes on animal production .Evidence for differences in particle retention time, digesta passage rate, and rumen size in sheep bred for low methane  suggestsWe hypothesize that the physiological differences associated with low-methane-yield sheep would have the potential to affect the growth and performance of the sheep and the subsequent quality and conformation of the carcass. The objectives of the current study were to determine the relationship between methane emissions and carcass, and meat quality characteristics.All animal experiments were conducted to meet the guidelines of the 1999 New Zealand Animal Welfare Act and AgResearch Code of Ethical Conduct and were approved by the AgResearch Grasslands  and AgResearch Invermay  animal ethics committees.4/kg DMI) as detailed by Trait data from selected New Zealand sheep flocks, born between 2002 and 2013, were obtained from animals recorded on the Sheep Improvement Limited (SIL) database . MethaneA summary of the traits and their abbreviations are provided in 4, g/d) and methane yield (g CH4/kg DMI). Enteric methane emissions were measured on male and female lambs between 5 and 10 months of age (30–40 kg liveweight) in a facility with 24 respiration chambers as described by 4 yield selection line progeny in three birth years have confirmed that the methane phenotype on lucerne pellets is repeatable when the same animals received ryegrass-based pasture  at 8 months of age  was extracted from the SIL database, and to ensure that animals were comparable, data were scaled to a mean liveweight of 42 kg. The preslaughter liveweight  was recorded prior to slaughter at approximately 8 months of age. The dimensions of the M. longissimus  were made over the 12th rib by a commercial ultrasound operator, specifically eye muscle depth , width , and fat depth over the muscle .® (® (VS) traits included carcass weight , GR as an estimate of carcass fat depth , and the percentage of lean meat in the carcass , leg , loin , and shoulder . The dressing percentage was calculated as the ratio of carcass weight divided by PRESLT (DO%).The carcass yield and quality traits, measured post-slaughter, varied depending on the source of the animals but included hot carcass weight (kg), butt circumference , GR fat depth over the 12th rib at a distance of 110 mm from the midline , carcass length , and leg length . For a proportion of the animals that were processed through Alliance Group Ltd. processing plants, the traits estimated using the two-dimensional imaging system VIAscan®  were avaThe carcass weight (Carc Wt) is a consolidated trait, and for the majority of animals, it represents hot carcass weight; if the hot carcass weight was not measured on an animal, then the cold carcass weight or VSCWT was used, which is directly derived from the hot carcass weight.For a proportion of the animals, as indicated in The carcass data included records from the research and CPT flocks, and data from surplus ram lambs from the high and low methane selection lines (60 born in 2012 and 60 born in 2013). The meat quality data were only available on the ones from the research and CPT flocks.With the exception of the methane trait data, which were stored in an independent database, all other traits and animal data were extracted from the SIL database, together with pedigree information.4 emissions were expressed as gross emissions  and methane yield (g CH4/kg DMI). Intake was based on the measured DMI on the day of the methane measurement. A randomized block design was used, and significant systematic effects and covariates for gross CH4 and CH4/DMI were determined using a general linear model procedure  analyses undertaken using ASReml 3.0 .For ultrasound and early liveweight measures from live animals , all 1,825 animals that had been measured for methane also had records for these traits, therefore, a direct estimation of the genetic and phenotypic correlations was possible. For post-slaughter data, however, the numbers varied per trait. The analysis, therefore, primarily used pedigree relationships to estimate methane. This enabled the use of detailed carcass data collected from all flocks to provide estimates of genetic and phenotypic correlations with methane traits. There were some traits where there were no animals with both methane and the slaughter trait recorded. These were specialist measures of fat color, meat color, pH, and eye muscle area traits recorded on CPT animals. For these traits, the genetic and phenotypic correlations were completely extrapolated based on correlations scaled by relatedness using a numerator relationship matrix within a linear mixed model, as described in detail by The heritabilities for total methane production and methane yield were 0.25 and 0.11, respectively, while the heritabilities for carcass traits were moderate to high (0.14—0.65) . The staThe gross methane production was positively genetically correlated with liveweight, carcass weight traits, and eye muscle depth and width, while negatively correlated with CGRM-a predictor of carcass fat . These sThere were no significant genetic correlations between methane yield and carcass and quality traits. There was a significant positive phenotypic correlation with CGRM and negative correlations with dressing-out percentage and leg length.® lean yield traits tended to have a negative  genetic correlation with methane yield.Although not significant, the VIAscanAs the initial progenitors of the divergent methane lines were measured in the NZ central progeny test (CPT) flocks, and methane is heritable, we can predict methane yields in CPT animals. This meant that the associated detailed carcass data collected on CPT animals could inform likely trends and effects of selecting for methane in the NZ commercial sheep population.Heritability estimates for all carcass traits were moderate to high and close to previous estimates in New Zealand sheep . AbsolutAs expected, gross methane emissions were positively genetically correlated with liveweight but less so with carcass weight. There was a low negative genetic correlation between gross methane and dressing-out percentage and also a significant low negative genetic correlation with carcass fat. For methane yield, no genetic correlations with any of the meat and carcass traits were significant. But there were some significant phenotypic correlations that were similar to those previously published, such as higher fat content associated with higher methane yield and lower dressing-out percentage . These rvia genetics in the New Zealand industry.These results show that the use of breeding as a mitigation strategy for lower methane yields and the resulting physiological changes do not negatively affect meat quality or carcass traits. Low-emitting animals may even have greater economic value through slightly higher dressing-out rates, decreased fat, and increased meat yields. These results are of importance for all ruminant livestock production systems. They are of particular relevance for meeting the globally agreed targets through the development of robust selection indices that aim to reduce methane emissions while also increasing productivity. Meat production and meat quality are increasingly important goals in sheep production and therefore accurate estimates of genetic and phenotypic parameters of these traits with methane production are essential for any industry implementation. The current work will underpin methane reduction


In genetic studies of psychiatric disorders in the pre-genome-wide association study (GWAS) era, one of the most commonly studied loci is the serotonin transporter (SLC6A4) promoter polymorphism, a 43-base-pair insertion/deletion polymorphism in the promoter region (5-HTTLPR). The genetic association signals between 5-HTTLPR and psychiatric phenotypes, however, have been inconsistent across many studies. Since the polymorphism cannot be tested via available SNP arrays, we had previously proposed an efficient machine learning algorithm to predict the genotypes of 5-HTTLPR based on the genotypes of eight nearby SNPs, which requires access to individual-level genotype and phenotype data. To utilize the advantage of publicly available GWAS summary statistics obtained from studies with very large sample sizes, we develop a GWAS summary-statistics-based approach for testing the variable number of tandem repeat (VNTR) associations with various phenotypes. We first cross-verify the accuracy of the summary-statistics-based approach for 61 phenotypes in the UK Biobank. Since we observed a strong similarity between the predicted individual-level 5-HTTLPR genotype-based approach and the summary-statistics-based approach, we applied our method to the available neurobehavioral GWAS summary statistics data obtained from large-scale GWAS. We found no genome-wide significant evidence for association between 5-HTTLPR and any of the neurobehavioral traits. We did observe, however, genome-wide significant evidence for association between this locus and human adult height, BMI, and total cholesterol. Our summary-statistics-based approach provides a systematic way to examine the role of VNTRs and related types of genetic polymorphisms in disease risk and trait susceptibility of phenotypes for which large-scale GWAS summary statistics data are available. Large-scale human genetic studies have resulted in major breakthroughs for our understanding of the genetic architecture of complex human traits. SNP arrays for genome-wide association studies (GWAS) in up to millions of subjects are extensively used for the detection of common risk alleles for polygenic disorders such as schizophrenia  and bipoIn genetic studies of psychiatric disorders, one of the most commonly studied locus in the pre-GWAS era , is the Since the 5-HTTLPR VNTR cannot be tested via available SNP arrays, we previously proposed an efficient machine learning algorithm to predict the genotypes of 5-HTTLPR based on genotypes of eight nearby SNPs . The preIn short, in a training dataset that has PCR-based genotypes for 5-HTTLPR and the eight SNPs considered for our machine learning algorithm , we fit In an ideal scenario, if the genotype data of 5-HTTLPR is available along with the phenotype of interest in a sufficiently large study sample, we can directly assess the association between 5-HTTLPR and the phenotype. The PCR-based assay to genotype the serotonin transporter promoter polymorphism has suffered from technical difficulties as demonstrated by the lack of Hardy–Weinberg equilibrium in genotype data for this locus . With thThe serotonin transporter (SLC6A4) promoter polymorphism is a tandem repeat polymorphism, also called the 5-HTTLPR, with two alleles: one long allele (L) and one short allele (S), hence the three possible genotypes SS, SL, and LL. Using additive coding, i.e., counting the number of L alleles in a genotype, the coded genotype can take three possible values: 0, 1, 2. Suppose, we have a reference dataset containing the genotype data of 5-HTTLPR and the surrounding eight SNPs tagging 5-HTTLPR . We note that the predicted component of 5-HTTLPR genotypes, γ1,…,γ8 does not depend on the distribution of the noise ϵ. Since the sample size of the Dutch dataset (n = 276) is much larger than the number of tag SNPs, a linear regression model is adequate for model fitting instead of a penalized regression.Here X denote the genotype data matrix for the eight tag SNPs ’ as: G is the genotype vector for 5-HTTLPR. Once we estimate γ, it is considered fixed in the down-stream analysis. If the 5-HTTLPR genotypes are missing in a different dataset, but the genotypes of the eight tag SNPs are available, we can use x is the genotype vector for the tag SNPs.Let Ps Table . Using tn unrelated individuals in a new dataset, Y denotes the phenotype of interest, G denotes the genotype of 5-HTTLPR that is unobserved. We assume that the individual-level data for Y and genotypes of the tag SNPs are available, but the 5-HTTLPR genotypes are missing. Consider a linear regression of Y on unobserved G as follows:Suppose, for X denote the genotype data matrix for the eight tag SNPs. Since G is unobserved, we can use our previously proposed machine learning method [G based on X, and perform a test for association between predicted G and Y.Let g method  to prediY and genotype data of the tag SNPs are also not available, we propose the following summary-statistics-based approach. We use the prediction model that was obtained from linear regression fitted in the Dutch reference dataset , where U follows a χ2 distribution with one degree of freedom.For a sufficiently large sample size, each element of α = 0. Therefore, using summary-level data on genetic association between a phenotype Y and the tag SNPs of 5-HTTLPR, the estimated prediction model for 5-HTTLPR genotypes (based on the Dutch reference dataset), and an estimate of the LD matrix of the tag SNPs, we can test for association between 5-HTTLPR and Y. We do not require any individual-level phenotype data and genotype data of 5-HTTLPR and related tag SNPs. We can use an estimate of V, the LD matrix of the tag SNPs, obtained based on the European individuals in 1000 genome data. To further increase the number of individuals in the LD reference panel, we combined the 276 Dutch individuals in our reference dataset with the European individuals in 1000 genome. We also provide the LD between 5-HTTLPR and its tag SNPs in Table Thus, given mentclass2pt{minimIn order to cross-verify the accuracy of the summary-statistics-based approach (VNTR.s), we conducted a phenome-wide association study for 5-HTTLPR and 61 phenotypes in the UKBB based on both of the individual-level prediction of 5-HTTLPR genotypes  and the p value of association while adjusting for relevant covariates, such as, age, sex, and top 20 principal components (PCs) of genetic ancestry. We refer to this approach as VNTR.g.First, we implement our previous machine learning approach  for 287p value of association between 5-HTTLPR and the phenotype.Next, we apply VNTR.s for the same phenotype in UKBB. We perform a linear regression of the phenotype on the genotype of each tag SNP of 5-HTTLPR, while adjusting for the same set of covariates, age, sex, and top 20 genetic ancestry of PCs. We obtain the summary statistics of association between the phenotype and each tag SNP from these regressions. We now assume that only the summary statistics for each tag SNP are available. Using the summary statistics and estimate of the LD matrix for the tag SNPs (as described above), we apply VNTR.s to compute the p values of association along with z-scores. We note that the genotype data for one of the tag SNPs, rs1061342 on chromosome 17  and theu et al. ). The top values obtained by the two approaches are very similar  and –log10(P.s), respectively. Pearson’s correlation coefficient between y and x based on 61 UKBB phenotypes was estimated as 0.98. Spearman’s rank correlation coefficient was estimated to be 0.9. If we fit a linear regression of y on x, we obtain the following regression equation: y = –0.16 + 1.15x. Here, the slope regression coefficient was estimated as 1.15 with a standard error of 0.03, and the intercept coefficient was estimated to be –0.16 with a standard error of 0.07. Similarly, we regressed x on y and obtained the following regression equation: x = 0.19 + 0.83y. We also performed the non-parametric Kolmogorov–Smirnov (KS) test to evaluate if x and y have the same distribution. The KS test p value was 0.99 that strongly implies that x and y have the same underlying distribution. A plot of the observed values of x and y  from psychiatric, cognitive, neurological phenotype domains, each based on reasonably powered GWAS with sample size of N > 50,000 subjects. Most of these studies are based on UKBB data, and all the studies are based on European ancestry. Since individual-level genotype and phenotype data used in these GWAS are not publicly available, it is very challenging to implement VNTR.g in this context.Using publicly available summary statistics data from GWAS with large sample sizes, we implemented VNTR.s to study association of 5-HTTLPR with a range of phenotypes mainly focusing on psychiatric and neurobehavioral phenotypes. We applied VNTR.s to nine psychiatric phenotypes in the psychiatric genetics consortium (PGC). We then examined 131 phenotypes using summary statistics available at the GWAS Atlas [p value 5 × 10–8, we did not find any significant association between 5-HTTLPR and the psychiatric phenotypes in PGC  promoter VNTR polymorphism (5-HTTLPR) on susceptibility of neurobehavioral traits and psychiatric disorders in the largest available samples for genetic studies. Despite the long-debated history of this locus as having direct as well as moderating effects on traumatic events and stress on depression –10  perform equally well, which gives confidence about the reliability of the GWAS summary-statistics-based approach. This method is analogous to that of the transcriptome-wide association studies (TWAS), which we previously developed  and succSupplementary material: A summary-statistics-based approach to examine the role of serotonin transporter promoter tandem repeat polymorphism in psychiatric phenotype


Moitree by gamma rays therefore produced high variation for the majority of quantitative measures examined. Henceforth, principal component analysis (PCA) and path coefficient analysis were conducted to identify and exclude redundant mutant genotypes with similar traits as the success of breeding is dependent on understanding the relationship between morpho-agronomic traits and seed yield. As shown by the findings of this research, the total quantity of pods per mutant plant should be given considerable priority. The identified mutant genotypes, such as lines 24, 43, 28, 33, and 10, may be used as parents in future breeding or released directly following trials.Lentil is a notable legume crop valued for its high protein, vitamin, mineral, and amino acid (lysine and tryptophan) content. This crop has a narrow genetic base due to the formation of gene pool barriers during interspecific hybridization within and across species. Mutagenesis may be seen as a novel and alternative breeding technique for the production of new diversity. For the identification of new alleles, the creation of mutants followed by selection in subsequent generations would be necessary. Induction of mutation in lentil cv.  Lens culinaris Medik) are an important pulse crop. It belongs to the Fabaceae family and contains 14 chromosomes (2n = 2x). . These results pointed to the genetic uniqueness of the newly created mutant types, which must be acclimatized by determining their ideal growth circumstances and particular agronomic practices. Yield is a polygenic trait with a complicated mode of inheritance, and direct selection for yield is seldom successful. Therefore, correlations between quantitative characteristics are influenced by the small cumulative effects of numerous genes governing trait expression. In fact, in mutant breeding initiatives, correlation analysis between character combinations is crucial for assessing the impact of yield characteristics on overall yield . So, it Moitree by 250 Gy of gamma rays resulted in considerable variance for the vast majority of quantitative parameters examined. Due to its significant connection with and direct impacts on seed yield per mutant plant, both PCA and path analysis suggested that selection should place a significant emphasis on the total number of pods per mutant plant. The findings of cluster analysis revealed that there are genetic differences among mutant families [Therefore, it was discovered that mutation induction by gamma rays was useful for establishing new sources of variability in lentils and avoiding breeding limitations. The induction of mutation in cv. families . The fouSelecting superior crop genotypes requires genetic diversity. Artificial selection has reduced allelic diversity since time immemorial. So, extending genetic diversity in a crop species may improve breeding effectiveness. Mutagenesis may be introduced directly to elite cultivars without being disturbed by the circumstances of linkage drag. Therefore, a popular lentil cultivar was irradiated for this study. This approach uses irradiation to create diversity while retaining fertility. Consequently, this research helped to the discovery of significant variables that directly or indirectly affect lentil yields. The highlighted traits may serve as selection criteria for hybridization and continuous selection-based lentil yield enhancement programs. In addition, the researchers identified a number of lines with diverse sets of traits that may be used to create superior kinds. Induced mutations at loci controlling commercially relevant features in chosen high-yielding mutants have efficiently contributed to diversifying the existing lentil genetic base and will be of invaluable use for future lentil breeding programs. In the coming days, Targeting Induced Local Lesions in Genomes (TILLING) might be used to confirm the mutants generated by this study at the genomic level.