Selection of pigs in Russia is carried out within the framework of separate large holdings. Such a system does not allow for the use of sufficiently large amounts of data  to obtain the most reliable breeding value estimates. This problem is especially relevant for low-inherited reproduction traits , which are the main ones for maternal pig breeds. In this regard, our study considered the possibility of improving the accuracy of the breeding value assessment of Large White pigs  through the use of genomic data obtained on a high-density hybridization chip.Replacement pigs’ genomic prediction for reproduction , meat, fatness and growth traits  was tested using single-step genomic best linear unbiased prediction ssGBLUP methodology. These traits were selected as the most economically significant and different in terms of heritability. The heritability for meat, fatness and growth traits varied from 0.17 to 0.39 and for reproduction traits from 0.12 to 0.14. We confirm from our data that ssGBLUP is the most appropriate method of genomic evaluation. The validation of genomic predictions was performed by calculating the correlation between preliminary GEBV (based on pedigree and genomic data only) with high reliable conventional estimates (EBV)  of validating animals. Validation datasets include 151 and 110 individuals for reproduction, meat and fattening traits, respectively. The level of correlation (r) between EBV and GEBV scores varied from +0.44 to +0.55 for meat and fatness traits, and from +0.75 to +0.77 for reproduction traits. Average breeding value (EBV) of group selected on genomic evaluation basis exceeded the group selected on parental average estimates by 22, 24 and 66% for muscle depth, days to 100 kg and backfat thickness over 6–7 rib, respectively. Prediction based on SNP markers data and parental estimates showed a significant increase in the reliability of low heritable reproduction traits (about 40%), which is equivalent to including information about 10 additional descendants for sows and 20 additional descendants for boars in the evaluation dataset. The inclusion of genomic data in animal evaluation procedure provides undeniable advantages in comparison with traditional methods, which use information about animal relationships and productivity results only ,2. The fThe second element is to increase the accuracy of animals’ breeding value estimates of traits with low heritability, such as reproduction traits (sow productivity). In pig breeding, selection programs for improving animals of maternal breeds are mainly focused on genetic changes in the population based on the characteristics of reproduction (prolificacy). Estimates of animals (both boars and sows) for these traits, even with offspring data, have a relatively low level of reliability. This is a limiting factor that reduces the effectiveness of breeding program. Thus, the additional use of genomic data is intended to increase the accuracy of breeding value estimates by these traits, thereby making the selection criteria more objective ,4,5.The accuracy of GEBV depends on some main parameters, in particular the level of linkage disequilibrium (LD) between markers and quantitative trait loci (QTL); the accumulated data size of production records; the quality of pedigree and phenotype measurements; the number of genotyped animals; trait heritability; the actual distribution of QTL effects; and the optimality of the statistical model ,6,7,8,9.−1) [Currently, a number of approaches have been developed to calculate genomic breeding value estimates: GBLUP (with the inverse of the convenient relationship matrix replaced by the inverse of the genomic relationship matrix G−1) ,8, Bayes−1) ,12,13 an−1) . Each of−1) . The mai−1) ,16,17. S−1)  obtainedAt the same time, the ssGBLUP is not inferior to multi-stage methods in accuracy of estimates obtained. The method allows for more complex models to be applied and also automatically defines weights of all sources of information for the final breeding value ,19. The Selection in pig breeding in the Russian Federation is built within the framework of separate large agricultural holdings with a closed cycle of genetic resources reproduction. In this regard, the organization of purebred populations’ pig breeding is based on the main traits of phenotypes data collection, the individual breeding value assessment, selection of individuals in breeding groups and future generation parents’ selection. The overall effectiveness of the entire system depends on the accuracy of each of these elements. The inclusion of genomic evaluation in the system implies additional costs incurred by the business. In this regard, there are a number of questions about the economic feasibility of implementing such a method ,24. SincAn important practical stage in genomic selection implementation is the validation of genomic predictions based on correlation and regression analysis with highly reliable breeding value estimates  or adjusPossibilities of the genomic data application for the needs of livestock breeding are also associated with a number of areas. These are the development of low-density SNP chips (less than 3000 markers) , which cThe objectives of our study are to test the ssGBLUP approach for genomic prediction of replacement pigs’ main selection traits breeding values in two different productivity aspects. The first is to assess the genomic data contribution to reliability improvement of low-heritable reproduction traits’ EBV . The second is to determine the effectiveness of replacement animals’ genomic selection by meat and fattening traits .Data about Large White pigs’ phenotypic measurements of meat, growth and repro-duction traits were used. The information about individuals born between 2015 and 2020 was included in the data sets. The assessment was performed in terms of meat and fatness productivity according to the following characteristics: backfat thickness over 6–7 ribs , muscle depth  and days to 100 kg . The following reproductive qualities were taken into account: the number of all piglets born (TNB), and the number of piglets born alive (NBA) based on the results of the first farrowing.The data set on meat and growth traits had 41,941 records, of which 5445 were boars and 36,496 were sows. The reproduction data set comprised 9433 records of first sow farrowing .A high-density GGP Porcine HD hybridization chip  with 70,000 SNPs for all major pig breeds  was used for boars and sows genotyping. Genome-wide genotyping data was presented in the form of 1483 individual genotypes from the GenomeStudio 2 software .p-value set to 10−6; and animal records with Mendelian errors in allele inheritance. The presence of such errors is treated as errors in the records of animals’ pedigree.The following were excluded from genomic data: SNPs with an accuracy  less than 0.2; animal records with a genotype absence rate of more than 10%; SNP markers absent in more than 10% of genotyped animals; SNP markers with a minor allele frequency of less than 5%; SNP markers deviating from the Hardy-Weinberg equilibrium, with the threshold Statistical processing of phenotypic data was carried out using the STATISTICA 10 package. Pedigree records were analyzed using the CFC software package . PrimaryThe fixed effects significance on the variability of the dependent variable was evaluated on exact Fisher criterion basis. Models with other sets of random effects  were tested with the Akaike criterion, resulting in optimal models usage in the study.G = α G + β A22, where the coefficients α = 0.95 and β = 0.05 (by default in BLUPF90 package). To reduce bias in GEBV, we use the default in BLUPF90 procedure whereCalculation of genomic relationship matrix G was carried out in BLUPF90 package using a linear method proposed by VanRaden . To overA−1) is replaced by H−1, which uses both pedigree and genomic information [H−1 constructed as [A is the pedigree-based relationship matrix for both genotyped and non-genotyped animals, A22−1 is the inverse of a pedigree-based relationship matrix for genotyped animals only, G is the genomic relationship matrix adjusted to the same scale as A22 and τ = ω = 1 (by default) [In the ssGBLUP model, the relationship matrix (ormation . The fixucted as :H−1=A−1+default) .The reliabilities of EBV(GEBV) were calculated according to standard error, as follows:The reference group of individuals, information about which formed the genomic database, was represented by 1194 individuals, born in 2015–2019, with information on 46,277 SNP markers. The structure of the pedigree data of the studied population of pigs and the reference group of genotyped individuals is shown in EBV) that exceeded the average level for the entire studied population (Rel > 0.36). The group of validating animals included 3 boars (mean Rel = 0.59) and 148 sows (mean Rel = 0.41). For those, phenotypic information was excluded from the phenotypic datasets (for sows), and information on the relationship between validating animals and their offspring (189 boar offspring and 548 sow offspring) was removed from the pedigree. On the formed set basis ), the validating animals’ breeding value was estimated using the ssGBLUP method with information on 1194 genotyped animals (reference group).The validation dataset for reproduction traits was constructed from whole dataset (WD) without validating animals’ records. Validating animals were ones with the estimates reliability (Relmeat) was formed, in which 110 animals (10 boars and 100 sows) were selected, the reliability of the EBV estimates (Rel) exceeded the threshold value of 0.8 . The phenotypic records of these individuals were removed from the dataset, and the relationships between the validated animals and their offspring were set to null in the pedigree.Validation of genomic assessments for meat and fattening qualities was carried out similar to described procedure. For this, partial dataset (PDPD), genomic prediction based on pedigree and genotype (GEBVPD), genetic estimates on pedigree, own phenotype and offspring record (EBVWD) and genomic EBV based on all available information (GEBVWD).Thus, for validating animals, some estimates were obtained from a whole (WD) and partial (PD) data sets: parental average  and compared their values, calculated using only pedigree data and combined information about the relationship of individuals (pedigree + genomic data) .The analysis shows a slight shift in the indicators of variances and heritability coefficients by traits (from 2 to 8%). Such results characterize, on the one hand, the relatively low contribution of genomic data to the clarification of the relationship between all individuals , taken into account when calculating estimates. On the other hand, this indicates an adequate level of consistency between the values in the probabilistic relationship matrix (matrix A) and the matrix that considers the already realized relationship (matrix H) between individuals. This provides the basis for using genomic data in assessing  and predicting the breeding values of individuals in the study sample.The analysis of inbreeding coefficients calculated on pedigree data and pedigree-genomic information  indicates that the probabilistic values are refined by including information about the genome. All values of the matrix A diagonal elements were equal to 1 , the average value of the diagonal elements = 1; for the matrix G (genomic relationship matrix), these values ranged from 0.83 to 1.31 with an average of 0.97.WD and GEBVWD values obtained from all available information sources.To test the effectiveness of genomic data in refining the young animals’ prediction of breeding value without phenotype and offspring data, the estimates obtained by the ssGBLUP method were compared with the final EBVOne of the genomic estimates’ reliability criterion is their correlation with high reliable estimates of breeding value . The “coPD and GEBVWD values.Thus, for the samples of validating animals, relatively high indicators of comparability of estimates were obtained using “whole data” on the one hand and pedigree and genome data on the other hand , which provides a reason to assume the consistency of the obtained GEBVWD, GEBVWD) are a kind of reference, because they have the highest reliability. Correlation between pairs of estimates EBVWD and PAPD had higher values (r = + 0.39…+ 0.82) than correlation between EBVWD and GEBVPD (r = + 0.51…+ 0.77) for all traits. That is, the pedigree-based prediction is more accurate with respect to the final genetic estimates compared to genomic predictions. However, pedigree-based scores are worse at predicting the final genomic values compared to genomic predictions. At the same time, the genomic estimates conformity (GEBVPD and GEBVWD) is higher (r = +0.60…+0.90) than the pedigree-genetic estimates conformity (PAPD and EBVWD) (r = +0.39…+0.82). Thus, higher correlation coefficients of genomic estimates  in comparison with genetic ones indicate the superiority of the former. This is also confirmed by the highest level of confidence in the relationship between these estimates, which will be shown below.The final estimates  and high levels (for reproduction traits), which allows one to count on an acceptable level of animal selection efficiency based on the GEBVWD and GEBVWD depending on the predictions’ growth . In some studies [Of particular interest is the measure of changes in estimates (regression) EBV studies , the regWD, GEBVWD) on the preliminary predictions  and final genomic (GEBVWD) estimates (repr = 1.03…1.06), confirming the significant contribution of kinship relationships to the final assessment of individuals based on the BLUP AM procedure. At the same time, these indicators for meat qualities varied within the limits of bmeat = 0.54…0.93. The determination coefficients (R2) that characterize the prediction reliability [2) were obtained by analyzing the GEBVPD estimates: brepr = 0.59…0.71 (for reproduction traits) and bmeat = 0.50…0.65 (for meat and growth traits); R2 varied between 0.17 and 0.57.The inflation was defined by regression with the final high reliable estimates  characterizes prediction reliability [2 were obtained by analyzing the regression of both final estimates on PAPD: R2repr = 0.55…0.67 (for reproduction traits), R2meat = 0.15…0.37 (for meat and growth traits) and conventional EBVWD on GEBVPD: R2repr = 0.48… 0.57 (for reproduction traits), R2meat = 0.17…0.30 (for meat and growth traits). Similar to the correlation coefficients, the determination coefficients’ highest values were for GEBVPD and GEBVWD for all traits (R2 = 0.36…0.81).The determination coefficient R characteWD, GEBVWD) by traits were: +0.94, +0.91 and +0.93 for BF1, MD and Age100, respectively, and +0.89 for both TNB and NBA. The change in the final estimates due to the inclusion of genomic data was more noticeable for low-inherited reproduction traits. In this regard, the reliability of the final genomic breeding value estimates is particular important.Thus, the results show that preliminary genomic estimates have the greatest predictive power (reliability) in relation to the final genomic breeding values. Inclusion of genomic data increases the animals’ breeding value accuracy and sustainability in comparison to the conventional estimates (BLUP AM). Correlations between final genetic and genomic estimates  increases the reliability of the estimation. The reliability of the estimates surpasses of the assessment by pedigree + own phenotype, allowing animals to get values comparable to the assessment results based on three sources of information ).For meat and fattening indicators, the reliability of estimates for pedigree and genome is at the level of estimates for pedigree and own phenotype.The main advantage of genomic prediction over pedigree estimation is the ability to differentiate the breeding value of complete siblings. Scores based on the parent averages will be the same for all descendants from the same parent pair. At the same time, genomic prediction allows one to select the most promising animals guided by the total value of the SNP effects-provided correction. Thus, when forming a group of replacement young animals, even before weaning piglets in the same litters, piglet leaders can be identified, characterized by the most desirable indicators of GEBV. In this case, the element of genomic assessment of meat and fattening qualities allows one to reduce the cost of raising all piglets from one litter to 100 kg (even before obtaining their phenotype information) via the most promising individuals’ early selection in the herd replacement groups.To be certain of animals’ selection based on genomic estimates’ (GEBV) implementation superiority in comparison with the selection by parental averages (PA), the average means of the groups’ formed on these criteria were compared. The intensity of selection was 50%, that is, for each of the criteria, the animals were selected according to their excess over the average value for the entire group. The results are presented in The average EBV analysis for groups of individuals indicates a greater efficiency of selection based on genomic estimates for meat and fattening qualities. The superiority of the group formed by GEBV is 66%, 22% and 24% in terms of “fat thickness over 6–7 thoracic vertebrae”, “muscle depth” and “days to 100 kg”, respectively. Simultaneously, the selection of the characteristics of reproduction in terms of efficiency did not have significant differences when using PA and GEBV estimates as a criterion. In our opinion, this is due to the low heritability and repeatability of both breeding value estimates for these traits (PA and GEBV).Genomic selection significantly changes the entire system of animal breeding. However, the accuracy of genomic selection itself is determined by many factors, such as the size of the reference population, the density of markers, the heritability of the trait, the effective population size, and the distribution of QTL effects ,37. AchiThus, since the development of the theoretical basis and the creation of the first reference groups of genotyped animals, an impressive amount of data on the genotyped individuals has been accumulated. Creating a reference population problem was described by H. Song et al., 2017 . They coSeveral studies are devoted to calculating the most accurate (objective) genomic relationship matrix ,16,38,39Heritability coefficient estimates by traits, calculated using pedigree and genomic data, in our study were in the range of 2–8%. Similar values of both the levels of heritability and their limits of change due to the inclusion of information about genomes were obtained in the studies of J. Hidalgo et al. (2020) . ChangesIn the Veerkamp et al. (2016) study on Holsten-Friesian cattle, a comparison of the genomic relationship matrices’ (GRM) (full sequence data and preselected SNP from genome-wide association) usage effectiveness analysis was made. The authors found that when selected variants were used, accuracy of genomic predictions decreased and the proportion of total variance explained was considerably smaller . At the For the population we studied, the refinement of the variance estimates was quite low for all the analyzed traits, which may be due to the limited genotyped reference group of individuals and a slight change in the indicators (relative to the entire data set) of the relationship based on genomic data. Therefore, the change was 8% for the reproduction traits and 3–6% for the meat and fatness traits.n” subsets of data formed from the complete set and correlates these predictions with the observed values (adjusted phenotypes). The second verification option involves obtaining genomic estimates for animals with high accuracy of conventional EBV [WD and GEBVPD scores . Regression coefficients (bias index) ranged from 0.50 to 0.65 for meat and fattening qualities and from 0.59 to 0.71 for reproduction traits. Similar values of the accuracy and bias of genomic estimates were obtained in the study of Song et al. (2017). Accuracy for the backfat thickness was +0.49, for days to 100 kg varied from +0.49 to +0.53, and for the reproduction traits from +0.53 to +0.59; unbiasedness for meat and fattening qualities varied from 0.65 to 0.88 and for the reproduction traits from 0.78 to 1.00.There are currently two main ways for accuracy of genomic prediction’s determining: cross-validation and verification based on high-precision estimates of breeding value. Cross-validation is performed on multiple evaluation of genomic predictions obtained with “onal EBV . In our 2 varied from 0.36 to 0.81) versus the pedigree prediction (from 0.15 to 0.67).It is worth noting that the correlation between final genetic and genomic breeding value estimates was significantly higher for traits with relatively high heritability over low-heritable traits (from +0.91 to +0.94 vs. +0.89). This indicates a greater contribution of genomic data to estimates for low-heritable traits with a limited number of phenotypic observations. In our opinion, the higher correlation between preliminary and final GEBV for all traits in study (from +0.60 to +0.90) compared to PA final EBV correlation (from +0.39 to +0.82) suggests the use of genomic data for predicting the breeding value of individuals. This assumption is also supported by the higher reliability of the genomic prediction  research .2 = 0.05) and the number of teats  due to the use of genomic data [According to VanRaden (2008), calculations performed on simulated data showed that young animals’ reliability could be more than 60% versus 32% of the parent average value [mic data . In our Preliminary genomic estimates (based on pedigree and genomic data) are more relevant predictions of final genomic breeding value estimates for pigs by reproduction, meat, fatness and growth traits compared to pedigree-based predictions, which is confirmed by higher accuracy (correlation) and reliability (determination). Genomic estimates are superior to pedigree-based estimates in terms of reliability and therefore are better criteria for the selection of replacement young animals. Genomic data usage in the animal evaluation on reproduction traits is comparable to the presence of 10 to 20 additional descendants in the evaluation dataset.


Human athletic performance is a complex phenotype influenced by environmental and genetic factors, with most exercise-related traits being polygenic in nature. The aim of this article is to outline some of the challenge faced by sports genetics as this relatively new field moves forward. This review summarizes recent advances in sports science and discusses the impact of the genome, epigenome and other omics (such as proteomics and metabolomics) on athletic performance. The article also highlights the current status of gene doping and examines the possibility of applying genetic knowledge to predict athletes’ injury risk and to prevent the rare but alarming occurrence of sudden deaths during sporting events. Future research in large cohorts of athletes has the potential to detect new genetic variants and to confirm the previously identified DNA variants believed to explain the natural predisposition of some individuals to certain athletic abilities and health benefits. It is hoped that this article will be useful to sports scientists who seek a greater understanding of how genetics influences exercise science and how genomic and other multi-omics approaches might support performance analysis, coaching, personalizing nutrition, rehabilitation and sports medicine, as well as the potential to develop new rationale for future scientific investigation. The ever-advancing number and standard of sporting competitions has led to an increased demand to improve the existing methods used by scientists to understand athletic capability and to drive the innovation of new recommendations for athletes to improve both health and athletic performance. Sport and exercise scientists study the adaptation of athletes to training and competition using a variety of physiological, biochemical and biomedical indicators. In recent decades, exercise physiologists have begun to elucidate the cellular and molecular responses to exercise that lead to adaptation at the whole-body level. In recent years, an increasing number of post-genomic analysis methods have been employed in the search for new markers in sports that can be used to quantify changes in the physiological and functional state of the human body ,2. GenetIn the present review, we provide perspective on the current state of sports genomics and a range of potential applications for genomic data in elite sport. Here, we review the current understanding of genetics in modern sport, including several recent large-scale studies that have successfully identified and replicated the associations of genetic variants with athletic performance phenotypes. We subsequently explore recent studies that have focused on the potential of using genomic information to complement injury prevention and pre-participation screening. We will also address what impact the advancing fields of bioinformatics and omics approaches  are likely to have on the way we understand athletes’ physiology and whether information gained through these approaches could help to improve athletic performance.A comprehensive search of the Medline and PubMed databases was conducted. The last query was on 1 December 2021. Findings were limited to English articles. Articles published before 1990 were excluded, unless no newer information had been published on a topic or their inclusion was necessary for understanding. Due to the wide spectrum of this topic of article, several different search terms were used in the following layout: (sports genetics) OR (sports genomics) OR (exercise genomics) AND (XXX). All of the articles identified were evaluated by their title and/or abstract and included if considered appropriate. Additionally, references of the articles cited were searched for to find further publications not detected by the literature search.Interest in the genetic contribution to human sports performance can be traced back to the early 1970s, though the first major book on the topic, Genetics of Fitness and Physical Performance by Bouchard and colleagues, was not published until 1997 . AnotherSports genomics is the scientific discipline focusing on the organization and function of the genome in elite athletes and aims to develop molecular methods that may be used for sports medical practices, personalized exercise training, nutrition prescription and the prevention of exercise-related injury and/or disease ,10. ReceHuman physical performance traits (PPTs) and adaptations to exercise training both result from the interaction of multiple intrinsic and extrinsic factors, including those categorized as environmental, genetic, anatomical, physiological and psychological . Common In recent years, the potential to use genetic information to maximize athletic performance and prevent injuries has been proposed ,16,17,18ACE) gene, which is a known regulator of blood pressure, have been shown by multiple studies to be a determinant of endurance potential in athletes. Other examples of candidate genes that have received notable attention, and which have subsequently been associated with phenotypes underpinning sprint, power or endurance performance, include the skeletal muscle isoform of α-actinin 3 (ACTN3), adenosine monophosphate deaminase (AMPD1), muscle creatine kinase (CKM), insulin-like growth factor (IGF1) and the family of peroxisome proliferator-activated receptor (PPAR) genes. The associations of common genetic variants in these and other genes with a range of athletic performance phenotypes are comprehensively reviewed elsewhere [Considerable research has already been conducted regarding the roles of genetic and epigenetic factors in training adaptation and the determination of elite sports performance. Research investigating the genetic basis of PPTs was initiated by observational studies conducted in twins, families and pedigrees, evolving from genetic epidemiology research into the study of DNA sequence biomarkers ,4. The fnase AMPD, muscle lsewhere ,30,31,32At end of 2021, the total number of DNA polymorphisms associated with athlete status was 220, of which 97 markers have been found significant in at least two studies  . FurtherHFE rs1799945, MCT1 rs1049434, MYBPC3 rs1052373, NFIA-AS2 rs1572312, PPARA rs4253778, PPARGC1A rs8192678 and VEGFR2 rs1870377 for endurance; ACTN3 rs1815739, AMPD1 rs17602729, CPNE5 rs3213537, CKM rs8111989 and NOS3 rs2070744 for power; LRPPRC rs10186876, MMS22L rs9320823, PHACTR1 rs6905419 and PPARG rs1801282 for strength; and COL1A1 rs1800012, COL5A1 rs12722, COL12A1 rs970547, MMP1 rs1799750, MMP3 rs679620 and TIMP2 rs4789932 for soft-tissue injuries [injuries ,37,38.MSTN) gene lead to extreme growth of skeletal muscle, indicating that myostatin is a key regulator of muscle mass [MSTN mutations that lead to an inactive gene product have been shown to produce double skeletal muscle mass in mice, sheep and cattle. Such knowledge may be highly valuable in helping to explain the underlying causes of certain diseases and may subsequently aid the development of new treatment methods. Indeed, increased myostatin expression might contribute to the loss of muscle mass through normal aging, suggesting that exercise modalities known to reduce myostatin expression and/or pharmacological inhibition of myostatin, might represent suitable treatments designed to prevent the age-related loss of muscle mass in non-athletic cohorts [To date, studying the genetic profiles of elite athletes has revealed DNA sequence variations that are associated with a competitor’s predisposition to excel in strength, power, sprint or endurance sports, their intrinsic vulnerability to sports-related injuries and their individualized nutritional requirements . The majcle mass . Specifi cohorts . The canThe majority of sports genomics studies to date have adopted the candidate gene approach. This involves investigators selecting genes and variants they believe are related to a PPT of interest. Researchers first conduct case–control association studies to determine whether a genotype or allele is more prevalent in a cohort of athletes than a control group containing healthy non-athletes from the general population ,9. TypicThe most up-to-date GWAS platforms can screen more than one million DNA loci simultaneously in order to detect regions associated with traits of interest. One of the advantages of the GWAS approach is that it is unbiased with respect to genomic structure and previous knowledge of the trait (hypothesis-free), in contrast to candidate gene studies where a priori knowledge is used to identify candidate loci contributing to the trait of interest. In addition, some GWAS platforms enable the investigation of epigenetic modifications. To date, four GWAS have been performed in athletes ,42,43,44Despite the obvious advantages of the GWAS approach, an overarching limitation of these studies is that in some countries, and in certain sporting disciplines, there is a limited number of elite athlete cohorts (cohort homogeneity). This reflects another common constraint within exercise genomics, which is that research is often conducted using small sample sizes. Accordingly, major collaborative efforts are required to increase participant numbers and sufficiently advance the impact of exercise genomics research . In recoIn the field of sport performance research, genomics focuses on identifying genetic variants associated with PPTs, response to exercise or future prognosis of injury. DNA, RNA, protein and metabolite often have complementary roles in PPTs to jointly perform a particular biological function. High-throughput technologies enable omics studies  and provide researchers with a greater understanding of the flow of information, from the original process of PPTs biology  to the functional consequences. Multi-omics approaches integrate data obtained from different omics levels to understand their interrelation and combined impact on the biological mechanisms of physical performance. PPARGC1A, PPARD), accompanied by a concomitant overexpression of those genes [Epigenetics is a rapidly advancing field within molecular exercise physiology, referring to changes in gene function in the absence of changes in DNA sequence ,22. Epigse genes ,22,23.Proteomic and metabolomic research methods offer a quantitative measurement of metabolic profiles associated with exercise, with some researchers attempting to identify the distinct metabolic profiles of athletes from a range of different sports. Metabolomics studies characterize exercise-related adaptations that affect an abundance of small-molecule metabolites serving a wide range of biological functions, such as biomarkers of cardiometabolic fitness in skeletal muscle . “MolecuThe application of proteomics and metabolomics to sports research has already revealed a number of functional proteins and metabolic pathways that are altered by exercise in general or which demonstrate variable responses between different sporting disciplines. The majority of changes have been detected in cytoskeletal structure or within energy-related pathways. In addition, omics approaches have recently allowed scientists to uncover the transducers and profiles of secretomes , that is, the biologically active substances secreted from cells and tissues  via autocrine, paracrine and endocrine systems, as an emerging subdiscipline of proteomics to study exercise-regulated signals ,10. HoweA key objective for integrating omics technologies into sports science is the prospective ability to prescribe personalized nutrition and training programs to athletes. Understanding the genetic, epigenetic and epigenomics underpinnings of an athlete’s nutritional requirements may provide a valuable tool to further optimize his or her sporting performance . In addiCOL1A1, COL3A1, COL5A1 and COL12A1) and matrix metalloproteinase-encoding genes (MMP12 and MMP3) that are associated with injuries such as Achilles tendon pathology and anterior cruciate ligament rupture [Injury prevention is a priority for athletes, coaches and practitioners of sports medicine for whom the diagnosis, treatment and prevention of injuries constitute a large part of a daily practice. Exercise-induced injuries to the musculoskeletal system depend on race, age, body composition, physical activity/inactivity levels and their multifactorial interaction with other extrinsic and intrinsic factors. An individual’s genetic profile is a recognized intrinsic factor predisposing individuals to a higher risk of suffering a musculoskeletal injury and/or necessitating longer recovery after intense exercise . Recentl rupture ,15,16,49 rupture ,18.TTN, ABCC9, DMD, MYPN and SCN5A, amongst others, have been implicated in the causes of familial cardiomyopathy. Athletes in soccer, basketball, cross-country and long-distance running are most vulnerable to suffering SCD [In addition to the problem caused by sports injuries, there are a range of genetic conditions that may unknowingly increase the risk to an individual’s health when participating in exercise. The most severe examples include those that elevate the risk of sudden cardiac death (SCD). In this context, genetic data could potentially be applied during pre-participation screening for the identification of at-risk individuals and the prevention of SCD in sport. SCD refers to the unexpected natural death of an individual that is caused by cardiac arrest within a brief time interval  under conditions that would not normally be considered fatal, and it is the leading medical cause of death in young athletes during sport or exercise activity ,52,53. Hring SCD . Whilst ring SCD ,52, whicHigh-quality experimental data and accurate analysis continue to advance the understanding of the genetic and molecular basis of a range of human PPTs including sprint, strength and endurance performance. Here, we briefly describe a number of databases, as well as computational and statistical methods, that can be used when researching genetic markers related to PPTs.https://www.ebi.ac.uk/gwas/, accessed on 20 November 2021) to document genome-wide associations for PPTs and other traits of interest [Currently, several approaches are routinely employed to identify genetic variations affecting sport-related traits, including SNP array-based GWAS and next-generation sequencing (NGS) . GWAS uninterest . In addiinterest . Among t2 max, strength and/or specific anthropometric indicators of power output, associations with genetic markers are inferred using regression analysis or one-way analysis of variance (ANOVA) [There are two broad categories of computational and statistical methods in existence that are used to infer associations between genetic markers and PPTs: (i) methods for individual marker–trait association inference and (ii) methods utilizing polygenic scores to infer predispositions to enhanced physical fitness or sport-specific phenotypes. The first class of methods rely on simple statistical techniques that compare genotype and/or allele frequency distribution between cases and controls. For categorical traits, such as endurance versus sprint athlete status or power athlete status versus non-athlete control status, chi-square, Fisher’s exact or logistic regression tests are typically employed ,31,32,57 (ANOVA) ,59,60,61jAA = 2, SjAa = 1 and Sjaa = 0. Once scored, the sum of genotypes across an infinite number of variants can be calculated for each individual participant, meaning that possessing a greater number of trait-predisposing alleles from multiple variants will result in a higher TGS, indicating a greater predisposition to the trait of interest. For the total number of markers (N) included, the total genotype score is computed as: TGS = 100 × (Σj=1…NSj/2 × N). Subsequently, the discriminatory power of a TGS can be investigated in cohorts of athletes and/or controls in relation to a categorical or continuous trait. Where a statistically significant association with a trait can be established using individual markers in the first instance, a TGS can then be computed from that subset of markers, demonstrating statistically significant associations for each individual in order to quantify and score their polygenic predisposition to the trait of interest. Since the TGS concept was introduced, the approach has been adopted, and at times adapted, by several studies [SNAP25 rs362584 and ACTN3 rs1815739) has been described in gymnasts [In 2008, Williams and Folland  presente studies ,66. For  studies . Whilst  studies , suggestgymnasts .An additional problem arising from the relatively small samples of elite athlete populations is the limited ability to obtain strong evidence of genotype–phenotype associations. In such cases, published data of specific gene markers from multiple populations can be pooled in a meta-analysis to provide a stronger evidence based for genotype–phenotype associations ,69. In abFGF or NGF), this procedure could also be abused by athletic populations. However, understanding how gene therapy may be utilized for elicit purposes also allows experts to design new strategies to prevent doping in sport. For example, researchers have discovered that copy number variation in the UGT2B17 gene influences the efficacy of anti-doping methods and that tests designed to detect testosterone abuse may be less sensitive in athletes of some geographical ancestries where certain genotypes are more frequent [Advancing knowledge surrounding the genetic variants that predispose individuals to success in specific sporting domains also creates the risk that such information could be used for illegal doping. New technologies for the transfer of genetic material (DNA/RNA), known as a gene therapy, are actively being developed to treat severe disorders such as anemia, peripheral vascular diseases and muscular dystrophy ,71. Despfrequent ,71. In sEPO), vascular endothelial growth factor (VEGFA and VEGFD), insulin-like growth factor 1 (IGF-1), growth hormone (GH), phosphoenolpyruvate carboxykinase 1 (PCK1), myostatin (MSTN) and MSTN antagonists such as follistatin (FST) [MSTN, IGF, GH and rhGH (recombinant human GH) are the major determinants of successful performance in sprint/power sports, with EPO (and rEPO), VEGFA, HIF-1, PPARD and PCK1 associated with elite performance in endurance sports [Gene doping refers to the non-therapeutic use of gene therapy by healthy athletes to improve physical performance in sporting competition. Gene doping could have dangerous and even fatal outcomes, as the knowledge of gene therapy is still in its infancy . Throughin (FST) . These ge sports . In 2003e sports ,74. Accoe sports ,76. While sports ,71. The The current strategy to screen for gene doping involves the direct detection of cDNA sequences, and anti-doping laboratories must permanently incorporate new detection techniques in order to maintain a harmonized approach to detecting doping substances and methods on the worldwide scale .At present, the clustered regularly interspaced short palindromic repeats (CRISPR/Cas) system is one of the most frequently used tools for genome editing. It enables the specific modification of any desired DNA sequence and surpasses all existing alternatives for gene editing . HoweverIn 2021, WADA released guidelines concerning gene doping detection methods   and seveExercise and sport-specific training induce the stimulation of many biological networks contributing to the complex yet coordinated response of the human body to perturbations in homeostasis and mechanical demand. Developments of omics technologies have created new possibilities in the study of the molecular mechanisms underlying athletic performance. Technological advances in genome-wide association studies and the profiling of gene expression have each contributed to our understanding of the genes, pathways and biological networks involved in the response to exercise, as well as the presence of variability in training responses among individuals. Sports genetics and genomics may one day help individuals to achieve superior outcomes in their general health and wellness. In athletic settings, genetic information has the potential to identify individuals with a natural predisposition to achieving success in specific athletic activities and may also be useful for athletes seeking to understand and make improvements in specific performance attributes where their genetic predisposition may be less favorable. In addition, there is scope for genetic information to assist in strategies aimed at reducing the risks of injury and to augment the process of rehabilitation once an athlete suffers an injury. Further collaborative research is needed to confirm the potential influence of genetic variants in athletes from different sporting backgrounds to identify more informative markers of athlete health and performance. The use of genetic testing in sports presents a number of possibilities for athletes, coaches and medical personnel to understand athletes’ susceptibility for specific pathological conditions including sports injuries, sudden death and cardiomyopathies, among others. With improved study designs and the increasing availability of advanced analytical tools and statistical approaches, the interaction of genetic variability with sport and exercise can be better understood and potentially utilized to improve human health, wellbeing and performance. Over the next decade, the application of genomics in sport is likely to progressively improve through the increasing availability of complete omics databases and high-throughput screening approaches for investigating human performance.


The detection of family relationships in genetic databases is of interest in various scientific disciplines such as genetic epidemiology, population and conservation genetics, forensic science, and genealogical research. Nowadays, screening genetic databases for related individuals forms an important aspect of standard quality control procedures. Relatedness research is usually based on an allele sharing analysis of identity by state (IBS) or identity by descent (IBD) alleles. Existing IBS/IBD methods mainly aim to identify first-degree relationships (parent–offspring or full siblings) and second degree  pairs. Little attention has been paid to the detection of in-between first and second-degree relationships such as three-quarter siblings (3/4S) who share fewer alleles than first-degree relationships but more alleles than second-degree relationships. With the progressively increasing sample sizes used in genetic research, it becomes more likely that such relationships are present in the database under study. In this paper, we extend existing likelihood ratio (LR) methodology to accurately infer the existence of 3/4S, distinguishing them from full siblings and second-degree relatives. We use bootstrap confidence intervals to express uncertainty in the LRs. Our proposal accounts for linkage disequilibrium (LD) by using marker pruning, and we validate our methodology with a pedigree-based simulation study accounting for both LD and recombination. An empirical genome-wide array data set from the GCAT Genomes for Life cohort project is used to illustrate the method. The detection of related individuals in genetic databases is of great interest in various areas of genetic research. Most obviously, it is of interest in forensic studies aiming at identifying relationships between individuals such as paternity tests  of the number of IBS alleles over variants can be used to identify related pairs  is also often used to detect related pairs  can be estimated and used for relationship inference, since their theoretically expected values are known for the standard relationships  =  and full siblings have  = . For a given pair of individuals, these probabilities can be estimated by maximum likelihood  or full siblings (FS)) and second-degree relationships  by retaining only pairs with ϕ < 1/16. In addition, third-degree relationships (first cousins (FC)) can be eliminated by retaining only pairs with ϕ < 1/32 ,  or  or identical by descent (IBD). A scatterplot of the mean  =  and their kinship coefficient is ϕ = 3/16. A detailed derivation of these probabilities is shown in Appendix A. A 3/4S relationship is not very common, but is more likely to be present in GWAS studies with ever-increasing sample sizes. The 3/4S relationship has received very little attention in the literature, and the aim of this paper is to develop tools that distinguish 3/4S from full siblings and second-degree relatives.The sample size used in genetic studies, GWAS in particular, is progressively increasing owing to large human sequencing projects that involve genetic data from hundreds of thousands of individuals such as UK Biobank , P(G1/G2∣m = 1) and P(G1/G2∣m = 2) are the probabilities of observing each pair of genotypes given the number of IBD alleles  relationships and calculate three LR having FS, 3/4S, or 2nd in the numerator and having the UN relationship in the denominator. The common denominator makes the LR values comparable in order to distinguish 3/4S from FS and 2nd degree. The inference of relatedness for each pair of individuals is based on the largest LR value in the FS ~ UN, 3/4S ~ UN, and 2nd ~ UN ratios. The LRs are shown in Table We test our method for detecting 3/4S with data from the GCAT Genomes for Life cohort project .In this section, we evaluate the likelihood ratio approach to distinguish 3/4S from FS and 2nd relationships by using simulated data. Pedigrees were simulated from the genetic data of the individuals of the GCAT project, using the ped-sim method of Caballero et al. . We applFigure mentclass2pt{minimmentclass2pt{minimBoxplots of the kinship estimator recently proposed by Goudet & Weir  suggested this database to contain eight 3/4S pairs using a log-ratio biplot approach combined with discriminant analysis (LR-kinbiplot). Figures d Goudet . The colFigure In this paper, we show that the likelihood ratio approach is useful for distinguishing three-quarter siblings from FS and 2nd-degree relationships. Figure The estimated relationships for the GCAT cohort were to some extent confirmed by an analysis of the surnames of the participants, respecting their privacy. In Spain, people have a double surname, usually the first from the father and the second from the mother. This implies that FS and 3/4S pairs share two surnames, whereas 2nd-degree relationships share only one. All identified 3/4S pairs were confirmed to share two surnames, supporting that these pairs are not 2nd degree.The proposed LR approach multiplies the likelihoods over loci, under the assumption of independence. The existence of LD between variants violates this assumption. In order to approximately meet the requirement of independence, LD pruning of neighboring variants in a window is therefore recommended (Kling and Tillmar, The LR approach developed in this paper assumes known allele frequencies and non-inbred individuals. The first assumption seems reasonable given the large sample size used in this study. Inbreeding could be accounted for by the use of nine condensed Jacquard coefficients (Hanghoj et al., Supplementary material


Arabidopsis thaliana. We show that traits like flowering time are indeed influenced by distinct genetic effects in local populations. Furthermore, using gene expression as a molecular phenotype, we show that some genes are globally affected by shared variants, whereas others are affected by variants specific to subpopulations. Remarkably, the former are essentially all cis-regulated, whereas the latter are predominately affected by trans-acting variants. Our result illustrate that conclusions about genetic architecture can be extremely sensitive to sampling and population structure.Understanding the genetic architecture of complex traits is a major objective in biology. The standard approach for doing so is genome-wide association studies (GWAS), which aim to identify genetic polymorphisms responsible for variation in traits of interest. In human genetics, consistency across studies is commonly used as an indicator of reliability. However, if traits are involved in adaptation to the local environment, we do not necessarily expect reproducibility. On the contrary, results may depend on where you sample, and sampling across a wide range of environments may decrease the power of GWAS because of increased genetic heterogeneity. In this study, we examine how sampling affects GWAS in the model plant species  Genome-wide association studies (GWAS) have become the standard tool for analyzing the relationship between genotype and phenotype in populations. Pioneered in human genetics , GWAS arHowever, when working on traits that are likely to be involved in local adaptation, there is every reason to expect differences in the underlying genetic architecture. We expect allele frequency shifts for loci that are under selection. The magnitude of the changes will depend on the spatial or temporal scale, as well as on the strength of selection .Arabidopsis thaliana, which occurs throughout the northern hemisphere, and has been shown to be locally adapted  may be an example of this. The minor allele at this locus appears to be associated with late flowering across Europe, but is too rare to be detected except in Northern Sweden  causal polymorphisms in the different subpopulations. As noted above, given the high SNP density used, we do not believe this is a general explanation. More likely is extensive allelic heterogeneity, a phenomenon consistent with local adaptation, and well-demonstrated in thaliana .To further investigate the putative heterogeneity, we estimated the polygenic overlap between subpopulations using a method that estimates the correlation of marker effects across different samples based on GWAS summary statistics without trying to detect significant associations and thus potentially biasing the results . AlthougA. thaliana, few relevant data sets exist. The most relevant phenotypes we were able to find were stomata size and cauline leaf number, measured in 131 accessions from Sweden and 109 from the Iberian Peninsula . We calculated the power to identify the causal polymorphism as well as the number of false positive associations using a Bonferroni threshold and a thousand simulations for each scenario  online. Finally, we carried out GWAS on gene expression data for a large sample of world-wide accessions . It seemPerhaps not surprisingly, 780 (39%) of these filtered genes revealed a genome-wide significant association  were significantly associated with the same SNP in both subpopulations (shared architecture), 25 (3%) were significantly associated with different SNPs in the same 50-kb genomic region in the both subpopulation , 92 (12%) were significantly associated with different SNPs at distinct genetic regions in the two subpopulations (genetic heterogeneity), and 182 (23%) appeared to show an specific association in one subpopulation only . The remaining are more ambiguous  online.cis- versus trans-regulation. Of the 110 genes with shared association between subpopulations, 99% were cis-regulated, whereas the opposite was true for genes with different regulation in the subpopulations. Here, 75% of the 182 genes that appeared to show a specific association in one subpopulation only were trans-regulated , which is linked to bacterial and downy mildew resistance  are predominantly in trans.It has been clear for over a decade that GWAS in plants often produce results that are strikingly different from those typically seen in humans. Major associations explaining substantial fractions of the phenotypic variance are common, likely because this variance is adaptive, and the allelic variants are maintained by selection . A preditrans-regulation is perhaps not surprising, as it seems likely that such adaptation generally involves expression changes at large numbers of loci. Many studies assume that polygenic adaptive traits are influenced by multiple loci with small effects, with contributions from only a few loci with larger effects , flowering time at 16 °C , stomata size , and cauline leaf number . AraPheno stores 1,163 world-wide A. thaliana accessions. We split the 888 European accessions into eight subpopulations of approximately equal sizes (103–119 accessions) (https://arapheno.1001genomes.org/study/52/). We selected the 665 accessions with full genome sequencing data, and created two subpopulations roughly matching the cauline leaf and stomata size data. The “Swedish” subpopulation contains 70 accessions from Sweden, 2 accessions from Denmark, and 2 accessions from Norway, whereas the second subpopulation from the Iberian Peninsula contains 83 accessions from Spain and 8 accessions from Portugal. The RNA-seq data have been generated in two distinct batches , but accessions from both subpopulations were predominantly present in the second batch, minimizing the risk of batch effects in the analyses.The phenotypic data used in this study were obtained from the AraPheno . The genAraPheno . Phenotyessions)  online. essions)  for 24,1https://github.com/arthurkorte/GWAS) implementing a fast approximation of the described in α = 0.05) by the number of SNPs with minor allele count greater five in each GWAS run. Permutation-based thresholds were derived from running 100 linear mixed models per phenotype with a random reordering of the phenotypic values . When the simulated causative marker explained 20% of the phenotypic variation, GWAS performed using all accessions resulted in the detection of this causative marker in 96.4% of the cases, albeit at a high false discovery rate (FDR) of 18.9%. Here, we consider an association as false, if it is more then 100 kb apart from the simulated causal marker. This high FDR dropped dramatically when a more stringent threshold of In each scenario, the causal marker was chosen randomly from all markers with a minor allele count greater five and set to explain 20%, 15%, 10%, and 5% of the phenotypic variance, respectively. To mimic population structure, 1,000 random markers were additionally assigned random small effects that are zero-centered; 1,000 simulated phenotypes were generated for each setting, resulting in a total of 12,000 simulated phenotypes. All simulated data were generated using a custom R script (P value threshold (First, we estimated the polygenic overlap among all subpopulations by comparing lists of significant SNPs. Since the comparison of significant SNPs between subsets showed no shared signals, we set a less stringent MiXeR (z-scores for each subpopulation was created and used to estimate the proportion of shared causal SNPs between subsets based on their beta and z-score distributions.Next, we estimated the polygenic overlap using the statistical tool MiXeR , which opwr.p.test function implemented in the R package pwr (cis- versus trans-regulation observed for genes with a specific local association. Genes, where the same genomic region was associated, were defined as genes having a global genetic regulation, if the same significant marker in both subpopulations was associated (110, P value was lower in the analysis of the respective subpopulation compared with the results of the combined population, as we argue that a true local association should be more significantly associated in the respective local subpopulation. Additionally, we also excluded genes, where different regions have been associated in the analysis of the combined population compared with the analysis of respective local subpopulation, to generate a high confidence list of genes with a distinct regulation in only one subpopulation. This procedure led to a set of 118 genes displaying a specific local association only in the Iberian subpopulation (cis, aka the same genomic region where the gene is located, or in trans. Here, we defined a cis-association, by a maximum distance of the associated markers to the respective gene of 100 kb. As a control, we also performed the same analysis described above with two random, nonlocal population of 91 and 74 accessions, respectively. These random populations have been sampled from the merged population of 165 accessions. The respective workflow and numbers are presented in The available RNA expression data contain transcription values for 24,175 genes. Before performing GWAS on the RNA expression data, we removed TEs and genes that are encoded by the organelle genomes, leaving 23,021 nuclear genes for further analyses. Next, we selected genes where the pseudoheritability estimate was above 0.5 and a statistical power analysis estimated that the power in GWAS was greater than 0.9 (using the method of kage pwr . This fited 110,  online, ted 110,  online, pulation  online apulation  online. The different lists of genes showing either globally the same regulation or a specific local architecture in one of the subpopulations where used for a GO-enrichment analysis. The analysis was performed using Gorilla  comparinMolecular Biology and Evolution online.msab208_Supplementary_DataClick here for additional data file.


There is a growing interest worldwide in genetically selecting high-value cut carcass weights, which allows for increased profitability in the beef cattle industry. Primal cut yields have been proposed as a potential indicator of cutability and overall carcass merit, and it is worthwhile to assess the prediction accuracies of genomic selection for these traits. This study was performed to compare the prediction accuracy obtained from a conventional pedigree-based BLUP (PBLUP) and a single-step genomic BLUP (ssGBLUP) method for 10 primal cut traits—bottom round, brisket, chuck, flank, rib, shank, sirloin, striploin, tenderloin, and top round—in Hanwoo cattle with the estimators of the linear regression method. The dataset comprised 3467 phenotypic observations for the studied traits and 3745 genotyped individuals with 43,987 single-nucleotide polymorphisms. In the partial dataset, the accuracies ranged from 0.22 to 0.30 and from 0.37 to 0.54 as evaluated using the PBLUP and ssGBLUP models, respectively. The accuracies of PBLUP and ssGBLUP with the whole dataset varied from 0.45 to 0.75 (average 0.62) and from 0.52 to 0.83 (average 0.71), respectively. The results demonstrate that ssGBLUP performed better than PBLUP averaged over the 10 traits, in terms of prediction accuracy, regardless of considering a partial or whole dataset. Moreover, ssGBLUP generally showed less biased prediction and a value of dispersion closer to 1 than PBLUP across the studied traits. Thus, the ssGBLUP seems to be more suitable for improving the accuracy of predictions for primal cut yields, which can be considered a starting point in future genomic evaluation for these traits in Hanwoo breeding practice. In beef production systems, carcass value is the main revenue source, and it is determined by both meat quantity and quality. Acceptable carcass and meat quality characteristics are of great importance in the beef industry, as consumers are willing to pay more for higher-quality products . Hanwoo,The availability of genome-wide marker panels and an increased number of genotyped individuals have enabled improvements in the accuracy of the estimated breeding value (EBV). Recently, the single-step genomic best linear unbiased prediction (ssGBLUP) method has been widely applied in routine genomic evaluation ,8,9,10. The dataset used in this study was derived from 3467 Hanwoo steers born between 2008 and 2017 at the Hanwoo Experiment Station, National Institute of Animal Science (NIAS), Rural Development Administration, South Korea. All steers were slaughtered at approximately 24 months of age. The pedigree data containing 18,809 animals was used. The phenotypic data of the primal cut yields were recorded in kilograms and composed of both unique and composite meat cuts from the forequarters and hindquarters. The 10 traits analyzed were bottom round, brisket, chuck, flank, rib, shank, sirloin, striploin, tenderloin, and top round; the locations of each cut on the carcass are illustrated in A total of 3745 animals (2957 steers and 788 bulls) were genotyped  with the Illumina BovineSNP50K BeadChip , including 52,791 SNPs on the 29 chromosomes. SNPs were excluded if they had a minor allele frequency less than 0.01 (8783 SNPs) and extreme deviation from the Hardy–Weinberg equilibrium (defined as the maximum difference between the observed and expected heterozygosity) greater than 0.15 21 SNPs). For the genotyped individuals, SNPs with missing genotypes were imputed using FImpute V3 software [ SNPs. Foy is the vector of observations for the trait of interest; b is the vector representing the fixed effects that included the slaughter date (180 levels) and slaughter age (days from birth to slaughter) as covariates for carcass traits; X is an incidence matrix related to the fixed effects; Z is an incidence matrix related to the random genetic additive effects; u is the vector of random genetic additive effects; and e is the vector of random residual effects. Random effects were assumed to be distributed as u ~ N  and e ~ N , where A is the numerator relationship matrix, I is the identity matrix, σa2 is the additive genetic variance, and σe2 is the residual variance.The estimation of variance components and heritabilities were performed through classical Bayesian inference under a single-trait pedigree-based animal model in gibbsf90test software  as folloFor this study, Markov Chain Monte Carlo (MCMC) chains comprising 550,000 cycles with the first 50,000 iterations discarded as burn-in  were implemented to estimate variance components and heritability as the posterior means of the corresponding sampled values. In addition to these criteria, the chain convergence was assessed by visual inspection. g) for each trait was considered as the genetic standard deviation divided by the mean value for the trait of interest [Moreover, the coefficient of genetic variation  method with pedigree-based relationship matrix and a single-step GBLUP (ssGBLUP) method with a combined relationship matrix constructed from genotyped and non-genotyped individuals and pedigree information. The analyses were performed using gibbsf90test software from BLUPF90 family . The estd by H−1  as follop matrix , which wp matrix .The two methods were compared in terms of population accuracy, dispersion, and bias. Validation was performed to evaluate these methods using the LR method as described by Legarra and Reverter . In brie dataset , and σu,n et al. . In addi dataset .Thus, The expected bias was defined as the difference between the means of EBV/GEBV in the partial and whole datasets, g values .The descriptive statistics for the 10 primal cut yields are given in The population accuracies for the primal cut yields obtained via the PBLUP and ssGBLUP methods using the partial and whole datasets are presented in was used . When the whole dataset for the prediction of breeding values was added in the analyses, accuracies ranged from 0.45 to 0.75 (average 0.62) for PBLUP and from 0.52 to 0.83 (average 0.71) for the ssGBLUP model, indicating an improvement in accuracy with ssGBLUP compared with PBLUP for the studied traits . In otheThe results obtained with a partial dataset were compared with those from the whole dataset in terms of accuracy in both the pedigree and genomic methods. The results show that the population accuracies using the whole dataset were higher than those derived from a partial dataset. Regarding the bias, predictions from the ssGBLUP method displayed less bias than predictions from the PBLUP method for the traits under study, except for rib. The average values varied from −0.12 to −0.01 for PBLUP and from −0.05 to 0 for the ssGBLUP method across all traits . Among tThe current study is the first attempt to compare pedigree-based BLUP (PBLUP) and single-step genomic BLUP (ssGBLUP) methods for prediction accuracy regarding primal cut yields in Hanwoo cattle. To maximize the profitability of the beef cattle industry, selection based on genomic information has become an effective tool in breeding programs. According to our results, primal cut yields are moderately to highly heritable traits, comparable to those estimated in Hanwoo cattle , SimmentIn this study, estimates of the accuracy, bias, and dispersion of primal cut yields were obtained via evaluation models that used only pedigree or a combination of pedigree and genomic relationship matrices using the LR method. Our results demonstrated that the ssGBLUP model outperformed PBLUP for all 10 traits in either a partial or whole dataset. A shift in the average accuracy from 0.26 to 0.46 was obtained by changing from a PBLUP to ssGBLUP method across the studied traits using the partial dataset. Similarly, the use of the whole dataset in analyses led to an average increase in accuracy from 0.62 to 0.71 by moving the model from PBLUP to ssGBLUP for all the primal cut yields. In other words, when genomic information was included in the analyses, the average improvements in the accuracy were + 20 and + 9 percentage points using partial and whole datasets, respectively, compared with pedigree-based BLUP due to the capture of variation in Mendelian sampling ,33. Furt2 = 0.39 and h2 = 0.43, respectively) among the feed efficiency traits in Nellore cattle [Among the studied traits, ssGBLUP provided a substantially higher prediction accuracy than the PBLUP method for bottom round, shank, top round, and brisket compared with the other traits. It is reasonable to assume that this higher accuracy could be due to the heritability of the traits of interest, which were 0.50, 0.50, 0.52, and 0.51 herein for bottom round, shank, top round, and brisket, respectively . The impe cattle . Concerning the ratio of the superiority of ssGBLUP over the pedigree-based method, the performance of the estimation of the ratio was improved by the added genomic information in ssGBLUP compared with the PBLUP method across all traits. The estimated value of the ratio indicates the increase in accuracy from pedigree to genomic predictions in both partial and whole datasets. The results indicate that the relative gain in the accuracy of GEBV with ssGBLUP was 65% using the partial dataset, whereas the ssGBLUP method was superior to PBLUP when the whole dataset was used, with an average increase or relative gain of 7% across all traits. In fact, these findings indicate the positive impact of genomic information on the genetic evaluations when no phenotypic data are available for the candidate animals or even when phenotypes are available on individuals; the addition of genomic information in the analysis can considerably improve the accuracy of prediction relative to pedigree-based models.According to our results, the increase in accuracy from partial to whole dataset  or underestimation (deflation), respectively. The results showed that the average absolute deviation of dispersion from 1 was less for ssGBLUP than for the PBLUP method. Therefore, the inclusion of all information available in the evaluation simultaneously reduced the overestimation and underestimation of the breeding values , and theConsequently, the results of the current study provide a comprehensive analysis of accuracy, bias, and dispersion for primal cut yields using the LR method, which can be applied to any model and any data structure ,55. Our Our aim was to investigate two methods, pedigree-based BLUP and ssGBLUP, for 10 primal cut yields using the LR method in terms of population accuracy, bias, and dispersion in Hanwoo cattle. The results indicated that the ssGBLUP model yielded considerably higher accuracy than the PBLUP model for all the studied traits. Concerning the bias and dispersion, predictions from the ssGBLUP method had less bias and a value of dispersion closer to 1 compared to PBLUP across the primal cut yields. It is worthy to note that the inclusion of all pedigree, phenotypic, and genomic information simultaneously in the ssGBLUP led to a 65% (7%) higher accuracy on average than PBLUP in primal cut yields using the partial (whole) dataset. Therefore, it seems that using ssGBLUP is a promising approach in future genomic evaluations targeting the improvement of weight in the more valuable primal cuts, consequently increasing the profitability of the Hanwoo beef production system.