ACTN3 rs1815739 and PPARA-α rs4253778 gene polymorphisms are variants frequently associated with athletic performance among different populations. However, there is limited research examining the pre-and post-test results of some variants of athletic performance in soccer players. Therefore, the presented research is to examine the relationships between the ACTN3 rs1815739 and PPARA-α rs4253778 gene polymorphisms and athletic performance improvement rates in adaptations to six weeks of training in elite soccer players using some athletic performance tests.Current research on athletic performance focuses on genetic variants that contribute significantly to individuals’ performance. Twenty-two soccer players between the ages of 18 and 35 voluntarily participated in the study. All participants were actively engaged in a rigorous six-day-a-week training program during the pre-season preparation period. Preceding and following the training program, a battery of diverse athletic performance tests was administered to the participants. Moreover, Genomic DNA was extracted from oral epithelial cells using the Invitrogen DNA isolation kit , following the manufacturer’s protocol. Genotyping was conducted using real-time PCR. To assess the pre- and post-test performance differences of soccer players, the Wilcoxon Signed Rank test was employed.ACTN3 genotype variable, it was observed that there were no statistically significant differences in the SJ (Squat Jump), 30m sprint, CMJ (Counter Movement Jump), and DJ (Drop Jump) performance tests (p > 0.05). However, a statistically significant difference was identified in the YOYO IRT 2 (Yo-Yo Intermittent Recovery Test Level 2) and 1RM (One Repetition Maximum) test outcomes . Regarding the PPARA-α genotype variable, the statistical analysis revealed no significant differences in the SJ, 30m sprint, CMJ, and DJ performance tests (p > 0.05). Nevertheless, a statistically significant difference was observed in the YOYO IRT 2 and 1RM test results Upon analyzing the results of the soccer players based on the PPARA-α rs4253778 and ACTN3 rs1815739 that underpin enhancements in endurance (YOYO INT 2) and maximal strength (1RM) aspects of athletic performance. However, to comprehensively elucidate the mechanisms responsible for the association between these polymorphisms and athletic performance, further investigations are warranted. It is thought that the use of field and genetic analyses together to support each other will be an important detail for athletes to reach high performance.The current study demonstrated significant enhancements in only YOYO INT 2 and 1RM test outcomes across nearly all gene variants following the six-day-a-week training program. Other performance tests, such as the 30m sprint, SJ, CMJ, and DJ tests did not exhibit statistically significant differences. These findings contribute novel insights into the molecular processes involving  Sports scientists and coaches have observed that there are significant differences in physical performance improvement among individuals and that exercise loads contribute significantly to performance improvement by triggering individual behavioral changes. Understanding how and why genetic diversity affects behavioral changes in response to stimuli is crucial. With the completion of the Human Genome Project in 2003, there was a significant increase in the number of genetic analyses due to the technological acceleration in genetic analysis methods. Genetic research is now focused on studies that involve multiple genes rather than just one gene region. These studies examine and interpret the entirety of the genetic information (genome) transmitted to the organism, as well as the multi-factorial processes that encode proteins .ACE) and α-actinin-3 (ACTN3) genes and athletic performance. Similarly, it is well-known that the Peroxisome Proliferator-Activated Receptor Alpha (PPARA-α) gene is highly influential in the development of physical performance.The degree of physical activity influenced by genetic variations can be determined by various factors. The presence or absence of a nucleotide in a specific region of a gene, for any reason, can make individual differences apparent. Among the most important factors contributing to the success of top athletes are environmental adaptation, optimal training loads, and random genetic sequence matches –4. In adACTN3 gene, which is the most extensively researched, is associated with the production of the α-actinin-3 protein that plays structural and regulatory roles in muscle contraction [ACTN3 gene in skeletal muscle activity is associated with superpower and exceptional endurance performance [ACTN3, the main gene of the Z line that controls muscle contraction intensity, is also responsible for the production of the actin-binding protein alpha actin-3 [ACTN3 protein can cross-link with the cells of fast-twitch muscle fibers through thin actin filaments. The ACTN3 gene polymorphism is determined by the presence of X or R at the R577 position. The R577X polymorphism (rs1815739 of the gene) codes for alpha-actinin-3, which substitutes an arginine residue at codon 577 instead of an early stop codon, resulting from the substitution of the "C" base at position 1,747 in exon 16 with a "T" base. Most importantly, this polymorphism triggers the production capacity of strong muscle contractions [In the context of the development of athletic performance, the traction –12. Alphtraction , 13. Theformance . ACTN3,  actin-3 , 16. Theractions , 17, 18.ractions , 20.PPARA-α). The PPARA-α rs4253778 polymorphism located on the 22nd chromosome (22q12-q13.1) is associated with aerobic endurance and strength. PPARA-α is a molecule that contributes to the occurrence of metabolic variations depending on various nutritional conditions, especially related to carbohydrate, lipid, and amino acid metabolism [PPARA gene, which supports fatty acid activation, has been associated with the utilization of fatty acids, especially in the heart and skeletal muscles. The PPARA-α transcription factor regulates not only lipid, glucose, and energy balance but also body weight and vascular inflammation. While PPARA expression is observed at low levels in various tissues such as the pancreas, it is found at high levels in tissues that catabolize fatty acids, including the liver, skeletal muscle, and heart.Another important gene that triggers the provision of energy sources needed for long-term efforts through carbohydrates and fats is the peroxisome proliferator-activated receptor alpha . In activities that require long-term effort where aerobic capacity is dominant, the GG genotype is associated with increased fatty acid oxidation in skeletal muscles and it has been observed that this genotype is more advantageous compared to others. It has been found that individuals with the GG genotype, who efficiently use the amount of oxygen sent to the tissue in order to maintain the continuity of muscle activity during long-term and low-intensity (more than 30 minutes) physical activities, also have higher proportions of slow-twitch type I muscle fibers [Type I (slow-twitch) muscle fibers have higher levels of PPARA-α expression  compared to type II (fast-twitch) muscle fibers. In addition to regulating the expression of a number of important muscle enzymes involved in fatty acid oxidation , PPARA-αcapacity , 23. Thee fibers . While pe fibers –26, carre fibers .ACTN3 rs1815739 and PPARA-α rs4253778 gene polymorphisms and physical performance characteristics in professional soccer players.In literature, there are currently approximately 251 gene polymorphisms have been associated with athlete status, of which 128 genetic markers were positively associated with athlete status in at least two studies. Among these, 41 have been reported to be associated with endurance performance, 45 with power, and 42 with strength performance . Given tThe current study included professional soccer team players : 180.62 ± 4.88; body weight (kg): 74.42 ± 5.42) from the North Macedonia Super League. At the University of Tetova in North Macedonia, written informed consent forms containing all information such as the study protocol and results, were signed by the athletes before the study, and then an oral DNA sample was collected from each soccer player using a cotton swab for genetic analysis. Tetowa University Ethics Committee approved the study protocol (18.05.2022/02-1474/1) and the study procedure was in accordance with the principles of the Helsinki (II) Declaration. A six-week exercise program was applied to all soccer players. The physical performance characteristics  of the players were measured before and after the exercise program.All participants were involved in a six-day-a-week training program  and specific exercises (up to six maximum attempts at the exercises being tested). Players were given three minutes of rest after warming up before performing the tests. Jump tests were conducted using Optojump equipment in the study. Participants repeated the jumping tests five times. the Squat Jump (SJ) test is a widely employed method to assess an athlete’s explosive lower-body power, focusing on their muscle strength and power capabilities. The Counter Movement Jump Test (CMJT) is utilized to gather valuable insights into an athlete’s explosive leg strength. On the other hand, the Drop Jump Test is a valuable tool for obtaining information about an athlete’s reactive explosive strength, which involves the ability to generate force rapidly during a quick change of direction . Data co2/ 8 ve g = 9,81 ms2). The takeoff and landing positions of a specific jump were considered valid for the study if they were visually comparable. The best-effort record was used for data analysis [In the present study, SJ and CMJ tests were used to measure participants’ vertical jumping capacity. Before the jump attempt in SJ, the legs were held in a static position at 90° knee flexion angle for 2 seconds without any warm-up exercises. To prevent variations in the jumping coordination pattern during CMJ, the athletes were instructed to extend their lower extremities fully upward immediately after the downward movement, and all jumps were performed with hands on hips. Each jump was attempted five times at 15-second intervals using an Infrared Contact Platform  to measure flight time (t), which was used to calculate the distance (h) that the body’s center of mass traveled during the jump . When the athlete lands on the platform, they quickly transition from the eccentric contraction phase to the amortization and concentric contraction phase. In this way, the device measures jumping performance .In the last decade, numerous studies have been conducted on the physical requirements and exercise profiles of several sports that involve intermittent activities such as basketball and soccer –33. ThesThe one-repetition maximum (1RM) test is often considered the “gold standard” for evaluating a person’s strength capacity outside of the laboratory. Simply put, it can be defined as the maximum weight that a person can lift for only one repetition using proper technique. The purpose of the test is to determine the maximum weight that can be lifted for a complete repetition of an exercise, based on the assessment of different muscle groups.The basic procedures applied for the 1RM (or any multiple RM) test are summarized below:The participant warms up by performing several submaximal repetitions.With three to five-minute rest intervals between each attempt, four attempts are made to determine the 1RM (or any multiple RM).An initial weight is selected for the participant between 50–70% of their perceived capacity.The weight resistance is gradually increased by 2.5 kg increments up to 20 kg, until the participant can no longer complete the chosen repetition. To ensure consistency between attempts, each repetition is performed at the same movement speed and range of motion. Therefore, the protocol mentioned above was used to determine the 1RM of soccer players .The 30-m sprint performance was measured using the Witty Speed, Microgate Equipment, and ITA device. The soccer l players, who were standing 0.5m away from the starting line, performed two sprint runs. The sprint tests were conducted on an indoor running track to prevent the effects of weather conditions. The distance covered during a specific time was used to calculate sprint speed. A 5-minute rest period was given between the two trials, and the fastest time was recorded for analysis.ACTN3 rs1815739 and PPARA-α rs4253778 TaqMan SNP Genotyping Assays  were used by following the manufacturer’s guide. Genotyping was completed by using 5 µL of master mix, 3.75 µL of H2O, 0.25 µL of the assay, and 1µL (10 ng) DNA, for a total of 10 µL [ACTN3 TaqMan SNP Genotyping Assay, was identified . The operations were carried out with respect to the manufacturer’s instructions. All the genotyping procedures were carried out by Real-Time PCR . For genotyping process, of 10 µL . The T aThe statistical analysis of the data was conducted using the SPSS 25.0 computer program. Descriptive statistical methods  were used to evaluate the data. Based on the results of the Kolmogorov-Smirnov and Shapiro-Wilk tests, it was found that the data did not follow a normal distribution. As a result of these procedures, data from 16 soccer players with incomplete or incorrect information were excluded from the analysis, leaving data from 22 soccer players for further analysis. The Wilcoxon Signed Rank Test was used to examine the differences between pre-test and post-test performance of the soccer players. Hypotheses were tested with a 95% confidence interval and a significance level of p < 0.05.ACTN3 genotype variable, although no statistically significant difference was found in the SJ, 30m, CMJ, and DJ performance tests (p > 0.05), it was determined that there was a statistically significant difference in the YOYO IRT 2 and 1RM test results. . However, there was a statistically significant difference in the YOYO IRT 2 and 1RM test results  are largely influenced by genetic structure [ACTN3 and PPARA-α genes [The findings obtained from previous research indicate that the tructure . Muscle tructure , and it tructure . Additio-α genes –49.Many studies explain the positive impact of genetic variants on physical performance in professional or amateur soccer players , includiACTN3 gene [ACTN3 gene to achieve successful performance. Additionally, it has been proposed that XX genotypes have low levels of fast-twitch muscle fibers and testosterone. The aforementioned observations and studies indicate that the deficiency of alpha-actinin-3 limits the explosive force function required for speed running and power performance of fast-twitch muscle fibers [ACTN3 gene as a crucial determinant influencing the formation of various types of muscle fibers, thereby impacting an individual’s athletic performance. These studies have shown that elite sprinters or power athletes and healthy individuals with the RR genotype trigger more hypertrophy in the vastus lateralis muscles and have a higher proportion of fast-twitch muscle fibers [ACTN3 rs1815739 polymorphism and physical performance in 138 Brazilian soccer players reported no significant genotype-phenotype association [In some studies evaluating the effects of various genetic polymorphisms on speed, power, and strength performance in professional soccer players, positive relationships have been found between the TN3 gene , 57 and TN3 gene –60. Recee fibers . Numeroue fibers , 63. In ociation .ACTN3 RR genotypes, which are associated with elite athlete status, show better improvements in speed, power, and high-intensity resistance training compared to XX genotypes [ACTN3 R577X RR genotype compared to their non-athletic participants [ACTN3 RR genotype is more prevalent than the XX and RX genotypes, and similar findings have been observed in Russian  and Brazilian  soccer players [ACTN3 RR and RX genotypes with athletes who require high-intensity, explosive power in individual sports such as team sports and 100m sprint [ACTN3 RR genotype and R allele in registered professional athletes who require physical strength and power capacity in their activities [In previous studies, it has been reported that elite sprinters and athletes with a focus on speed, power, and strength have significantly higher frequencies of the R allele compared to controls , 65, 66.enotypes , 67–69. enotypes , 20, 70.icipants . Ulucan  players , 58, 72.m sprint , 17, whim sprint . Additiotivities .ACTN3 genotype frequencies in soccer players appears to be more homogenous compared to other sports. As a result, soccer players may exhibit a more balanced distribution of ACTN3 genotypes, accommodating the various physiological requirements of the sport. Similarly, other studies conducted in Brazil and Spain also failed to distinguish between professional soccer players and the general population [ACTN3 polymorphisms among soccer players showed similarity in both genotypes. On the other hand, Yang et al. (2003) found that there was no correlation between higher XX genotype frequency and endurance performance in endurance athletes compared to controls. The same researchers showed that although Kenyan and Ethiopian endurance athletes had excellent success rates, the frequency of the XX genotype was observed to be quite low in these populations [ACTN3 gene was not associated with resting muscle strength and the response of muscle strength to resistance exercise [ACTN3 genotype frequencies between the fastest sprinters of all time and non-athlete controls  [ACTN3 gene polymorphisms [ACTN3 R577X polymorphism and explosive leg power in elite basketball players, no association was found between the ACTN3 R577X polymorphism and the likelihood of being an elite basketball player when considering the results of squat jump (SJ) and countermovement jump (CMJ) tests [ACTN3 polymorphisms  were determined to be RX > RR > XX, and XX genotypes were observed at a relatively low frequency within the general population. These data suggest a trend that the ACTN3 polymorphism may be an indicator of natural selection for sports that involve several biomotor characteristics such as speed, strength, power, endurance, and agility, such as soccer. In this study, it was observed that the R allele in males had a higher probability of being an elite soccer player in terms of performance compared to participants with the X allele. However, it was also stated that personal skills are of great importance in the soccer field. In the continuation, Coelho et al. (2016) remarked that factors affecting high performance in team sports such as soccer cannot be attributed solely to physical fitness but can also be influenced by the technical skills and tactical applications of each individual [ACTN3 R577X is not an ideal genetic marker for identifying a talented soccer player. However, the results indicate that the likelihood of being a professional soccer player is higher for individuals with the ACTN3 RX genotype compared to other genotype combinations [ACTN3 genotype distributions (p > 0.05). This may be due to differences in anthropometric and biomechanical factors, as well as the presence of many other genes and environmental factors that determine physical performance along with fitness levels [Considering that soccer is a multifaceted sport that encompasses a combination of anaerobic, aerobic, and intermittent efforts during each match, the distribution of pulation . Considepulation , it can ulations . In addiexercise . Similarontrols) . Accordiontrols) , 78. In orphisms . Furtherorphisms . Anotherorphisms . SimilarJ) tests . The prel groups . In addil groups , 80 have studies , 84 suppdividual . The fininations . In a sts levels .ACTN3 variants [p < 0.05) [ACTN3 genotype, and it was observed that RR-genotyped soccer players had faster and higher jumping potential in short-distance runs [ACTN3 genotypes, but significant results were obtained in YOYO INT 2 test results, which were determined as TT > CC > CT genotypes, respectively , defensive players with XX alleles had lower quadriceps and hamstring isokinetic strength than RX and RR genotypes at all tested speeds. In RR genotype defensive players, however, it was observed that they had higher quadriceps muscle strength than RX genotypes at all three speed levels. Furthermore, it was stated that strength performance was higher in attacking and defensive players with the R allele compared to midfielders, and midfielders had lower strength and power capacity compared to other playing position [In literature, previous studies showed that there were differences between variants , 86, 87. < 0.05) . However < 0.05) , 88, whince runs . In the position . Soccer position . During position , while tposition , 94. Howposition , 95, 96.ACTN3 gene variants in players (n = 200) of the Super League, it was found that individuals with RR genotype completed a 30- meter distance in a shorter time compared to RX and XX individuals. Similarly, it was reported that individuals with the RR genotype completed jump tests in a shorter time compared to individuals with the RX and XX genotypes, while individuals with the XX genotype showed higher maxVO2 values in aerobic tests compared to the RR group (p < 0.05). The findings of this study suggested that ACTN3/RR genotype soccer players had a higher potential for faster short distances and higher jumping ability, while ACTN3/XX individuals exhibited the highest aerobic capacity values [Contrary to our study findings Table , in a sty values .ACTN3 gene polymorphism was found to be 28.6% CC, 38.1% CT, and 33.3% TT [ACTN3 CC genotype was associated with strength-related phenotypes in soccer players, and strength-related phenotypes were reported to be important for success in soccer [In a study conducted by Eken et al. (2021) on 21 professional soccer players, the distribution of 33.3% TT . The stu33.3% TT . In anotn soccer . In contPPARA-α gene polymorphisms, the acute super-compensation effects of instant shuttle runs were evaluated using the Optojump device and 10-second continuous vertical jump tests in an inactive control group, elite endurance runners, and soccer players. It was found that the PPARA-α GC and GG alleles had a more advantageous effect on the level of sudden recovery compared to other alleles, and there was a significant combined effect of multiple genes on sudden super-compensation [PPARA-α gene polymorphisms among SJ, DJ, and CMJ test results  compared to other allele groups . Similar findings were also observed in 1RM test results (CC > CG > GG)  compared to long-distance runners (19%) but had a higher CC rate (10%) in sprints and jumps [Alvarez-Romero and colleagues (2020) indicated that the C allele of the bilities . Howeverbilities . In the bilities . In anotnd jumps .Soccer is a high-intensity activity that requires a combination of speed, strength, and power parameters such as short sprints, fast attacks, jumps, and zigzag runs to achieve game dominance along with prolonged physical loads executed at moderate to low intensity. Individual abilities of players, technical and tactical applications, motivation, and fitness level are among the most important factors that affect the outcome, along with differences in performance resulting from interpersonal attraction in both competitive matches and training. Considering the fact that genetic characteristics affect the degree of adaptation to training, planning personalized training programs for individuals is thought to contribute to the improvement of both individual and team performance as well as the efficiency of in-game performance. Candidate genes that reveal athletic ability, when used in elite athletes performing at a high level, can provide important clues for the scope and intensity of training practices, loading, rest and recovery processes, and the emergence of in-game high-level performance. The key is whether the candidate genes studied can determine natural advantages or abilities as part of training practices. Undoubtedly, athletes who interact with the correct methods and practices will have a higher likelihood of achieving higher levels of performance , 28, 29.The present study acknowledges certain limitations that need to be taken into account when interpreting its findings. Firstly, the sample size is relatively small, potentially impacting the precision of the results and the significance of observed differences. A larger sample size could lead to more robust conclusions. Secondly, the focus on only two candidate genes in training adaptation might influence the evaluations in a biased manner. Considering additional genetic factors and their interactions could provide a more comprehensive understanding of the genetic reflections on athletic performance. Furthermore, it is essential to consider the homogeneity and quantity of the study groups when analyzing the relationship between genotype and athletic performance. These variables play a crucial role in ensuring the reliability and consistency of genetic associations with athletic attributes. To enhance the reliability and generalizability of the findings, future research efforts could integrate biochemical data from larger and more diverse groups across various sports disciplines. This approach would provide a more comprehensive picture of the genetic factors that impact athletic performance and contribute to the advancement of sports science.PPARA-α rs4253778 and ACTN3 rs1815739) that were examined in our research. By considering the genetic variations identified in our study, coaches, trainers, and sports scientists can use these performance tests as valuable indicators to assess the potential strengths and weaknesses of athletes in specific areas, such as endurance (YOYO INT 2) and maximal strength (1RM). These alternative assessments offer insights into the athletes’ abilities that may align with their genetic predispositions, enabling tailored training and performance enhancement strategies based on individual genetic profiles. Integrating genetic information into the evaluation process can further optimize training programs and aid in talent identification, ultimately contributing to the development of personalized training regimes for athletes, thus maximizing their athletic potential. Moreover, these results highlight the importance of considering specific genetic influences on different aspects of athletic performance to gain a more comprehensive understanding of the molecular processes underlying sports-related traits. Given this context, it is evident that more extensive research is essential to enhance the predictability of results, considering the potential influence of two candidate gene polymorphisms eliciting diverse characteristics that may positively affect the on-field performance of soccer players.The present study unveiled significant improvements exclusively in the YOYO INT 2 and 1RM test results across the majority of gene variants following the six-day-a-week training program. Other performance tests, such as the SJ, 30m sprint, CMJ, and DJ Test, did not show statistically significant differences. The findings suggest that the genetic variations studied are more closely associated with improvements in endurance (YOYO INT 2) and maximal strength (1RM) aspects of athletic performance rather than explosive power and speed-based measures. Indeed, the results of our study suggest that these tests, particularly the YOYO INT 2 and 1RM assessments, can serve as alternative tools to evaluate the athletic performance of individuals with specific genetic polymorphisms (


Lamb fattening on pasture needs to address consumer demands for the extensive production of animal products with high animal welfare standards and also reduce management load. Therefore, breeding programs should focus on adequate growth and fattening performance in pasture-based production environments. In practice, progeny testing to evaluate fattening performance is commonly performed indoors. This does not represent the actual production environment, which is on pasture in many farms. In this simulation study, different scenarios with varying progeny testing schemes for fattening traits were simulated. The results revealed that a higher number of available phenotypes generally increased the accuracy of breeding value estimation. The highest genetic gain was generated in the scenario with both station and field progeny testing. As the simulation design is highly flexible, the methodology used is applicable in other sheep populations after input data modification. Translating these results into practice could improve the conservation of traditional pasture-based sheep farming in terms of environmental and social factors and increase the income of sheep breeders by improving lamb growth.Strong differences between the selection (indoor fattening) and production environment (pasture fattening) are expected to reduce genetic gain due to possible genotype-by-environment interactions (G × E). To investigate how to adapt a sheep breeding program to a pasture-based production environment, different scenarios were simulated for the German Merino sheep population using the R package Modular Breeding Program Simulator (MoBPS). All relevant selection steps and a multivariate pedigree-based BLUP breeding value estimation were included. The reference scenario included progeny testing at stations to evaluate the fattening performance and carcass traits. It was compared to alternative scenarios varying in the progeny testing scheme for fattening traits (station and/or field). The total merit index (TMI) set pasture-based lamb fattening as a breeding goal, i.e., field fattening traits were weighted. Regarding the TMI, the scenario with progeny testing both in the field and on station led to a significant increase in genetic gain compared with the reference scenario. Regarding fattening traits, genetic gain was significantly increased in the alternative scenarios in which field progeny testing was performed. In the presence of G × E, the study showed that the selection environment should match the production environment (pasture) to avoid losses in genetic gain. As most breeding goals also contain traits not recordable in field testing, the combination of both field and station testing is required to maximize genetic gain. Most sheep are farmed under extensive production conditions on grasslands, often coping with inadequate nutrition and harsh climate conditions ,2. EfficIn Germany, Merino sheep are the predominant breed. In Southern Germany (states of Bavaria and Baden-Wuerttemberg), where nomadic sheep farming is still practiced, this breed accounts for up to 80% of the total sheep population . Lambs aSo far, trait recording for Merino lamb fattening traits has mostly been conducted at stations for progeny testing, which has the advantage of being in a highly controllable environment and the capacity to also test hard-to-measure traits like, e.g., feed conversion rate. At station progeny testing for fattening traits (ST), the lambs are fattened indoors with energy- and protein-rich concentrated feed. Thus, ST does not reflect the intended production environment (on pasture). This is expected to cause G × E effects and hence raises the question about expanding trait recording towards field testing (FT) to alleviate these putative G × E effects.The present simulation study aims to adapt the German Merino sheep breeding program towards pasture-based production systems. A realistic simulation of the current German Merino sheep breeding program was established as a reference to compare with several alternative breeding plans, differing mainly in their progeny testing environment (field and/or station), and assumed a G × E between station and field testing records for fattening traits. The study mainly focuses on fattening performance traits, as the differences in the production environment (indoor fattening vs. fattening on pasture) are expected to be the largest and G × E is likely to occur in German Merino particularly in such traits.https://github.com/tpook92/MoBPS, accessed on 9 November 2023. To establish a realistic founder population, real genotype data from 785 German Merino sheep genotyped with an Illumina OvineSNP50 v2 Bead Chip  were used. Animals were randomly mated for five generations to generate initial relatedness and a pedigree structure. The simulated founder population consisted of 469 breeding rams and 7185 breeding ewes according to the number of herdbook-registered Merino sheep in Bavaria and Baden-Wuerttemberg in January 2022. A general overview of the simulated breeding program is shown in A simulation of the current breeding program for German Merino sheep was carried out by using the R package Modular Breeding Program Simulator (MoBPS) . The R sThe concept of licensing describes the permission for a ram to be used as a breeding ram. In real breeding programs, herdbook breeders’ preselected males, ideally based on EBVs, are presented to the breeding association’s judges at central licensing and sale events at the age of 15 to 18 months. One requirement for licensing is that the sire of the young ram to be licensed is progeny tested for fattening performance. The licensed ram itself will then be progeny tested after his first lambing. If the licensed ram is sold to a production herd, no further data recording is performed. On the female side, replacement ewes are selected within the herds. Breeders’ preselected females, ideally based on EBVs, are presented to the responsible breeding manager at herdbook registration at an age of 10 to 15 months. Females which meet the requirements are subsequently classified as breeding ewes. Note that in the simulation study, no preselection based on subjective impressions of breeders was simulated.creating.diploid in MoBPS [creating.diploid in MoBPS [F, e.g., ADGF. A trait measured in station testing, ST, is indicated by the subscript S, e.g., ADGS. Genetic correlations between the respective traits F, ADGS), F, FLNS), F, UMDS), F, UFDS)) were set to 0.8 to simulate G × E between the two progeny testing environments for these four traits , which were randomly sampled from the SNP data with effect sizes drawn from a Gaussian distribution  were considered in the TMI; the ST fattening traits  were not included in the TMI but were used to increase the EBV accuracy of the former through their correlation with the FT fattening traits. The TMI weights approximately reflected the currently used TMI weights of German Merino sheep in routine evaluation [F, FLNF, UMDF and UFDF are not specifically included there. Since these are of interest for pasture-based breeding programs, these were considered in the present simulation TMI. The TMI was defined by the following index weights and EBVs, with the trait abbreviations explained in Animals were selected according to their total merit index (TMI) based on estimated breeding values (EBVs) and their economic values. Breeding value estimation was conducted for each single trait by applying a BLUP animal model to individuals of the last three generations. For each of the individuals, pedigree information from seven generations was utilized. Breeding values were estimated by using multi-trait BLUP models in the package mixBLUP ,21. The aluation ,23. HoweBreeding value estimation was performed once during each breeding cycle, but selection based on the TMI was performed at two timepoints: First, during the selection of progeny for future breeding animals  were phenotyped for NA. Currently, fattening traits in the German Merino sheep breeding program are exclusively measured in station progeny testing. The reference scenario mimicked the currently practiced station progeny testing scheme. Following this, fattening traits were modelled as traits measured exclusively in ST with eight male offspring tested per sire. In the following, the reference scenario is therefore referred to as ST. In total, two alternative scenarios were simulated in which the traits ADG, FLN, UMD and UFD were recorded in FT. Therefore, one scenario was simulated in which only FT was performed (FT) and another in which FT and ST were performed (ST + FT)  per breeding cycle over the 50 independent simulations for each scenario are presented in F, FLNF, UMDF and UFDF). The EBV accuracy for the breeding ram cohort was generally higher than that for the licensing cohort for the TMI and the four individual traits. Scenario ST + FT led to a significantly higher EBV accuracy compared to the other scenarios for both cohorts. The EBV accuracy in scenario FT was significantly different from the ST scenario in the licensing cohort but not in the breeding ram cohort. For the four FT traits, EBV accuracy in the reference scenario ST was significantly lower than that in both alternative scenarios for all four traits and both cohorts. Accuracies were highest in scenario ST + FT for all four traits and both cohorts. Accuracies in ST + FT were approximately 50% higher for FLNF, UMDF and UFDF than that in ST for both cohorts. For ADGF, the increase was even more pronounced. The accuracy in scenario FT was significantly lower than that in ST + FT but significantly higher than that in ST for both cohorts and all four FT traits.The presented EBV accuracies refer to the average across the 10 breeding cycles and 50 runs in the licensing and breeding ram cohorts. The results are shown in The level of inbreeding displayed as kinship increased linearly over the 10 considered breeding cycles but did not exceed a kinship of 0.02 in all scenarios with no significant differences between the scenarios see . RegardiIn pasture-based sheep breeding programs, the breeding goals are lamb growth and fattening performance on pasture. However, traits are often recorded in feed-intensive environments (ST) and thus do not match the actual production environment. Consequently, G × E might occur, which might hamper possible breeding progress. In this simulation study, possible strategies to adapt the German Merino sheep breeding program to the pasture-based production environment were investigated. Alternative scenarios were evaluated that differed in their progeny testing environment .To mimic the existing German Merino sheep breeding program, the software MoBPS  was applF, FLNF, UMDF and UFDF were available in the ST scenario. Thus, breeding value estimation was dependent on the ST traits ADGS, FLNS, UMDS and UFDS, which were genetically correlated (0.8) with their FT equivalents. The ST equivalents were, however, not weighted in the TMI, which explains both the low accuracy of EBVs and the low genetic gain in these traits. This was the result intended with the chosen correlation of 0.8. It was expected that the highest genetic gain would occur in scenario ST + FT, as this scenario showed the highest number of phenotyped individuals  and thus the highest EBV accuracy. However, except for ADGF, scenario FT generated the highest genetic gain for the FT traits. This is because for traits that are not recorded, no sufficient genetic gain could be obtained, although they were still present in the TMI. A stronger weighting of the FT traits ADGF, FLNF, UMDF and UFDF in the TMI would further increase genetic gain, but evaluating changes in the TMI weightings was beyond the scope of the study [The alternative scenarios simulated in this study suggest putative optimization strategies to increase genetic gain in a pasture-based production environment. Scenario ST + FT led to significantly higher genetic gain for the TMI compared to both the reference scenario ST and the other alternative scenario FT, as phenotypes were assessed both in FT and ST, thus enabling direct selection on all traits in the TMI. This was also reflected in the EBV accuracy. The reduced EBV accuracy of the licensing cohort compared to the breeding ram cohort was due to a greater utilization of information on closely related animals for the latter. In addition, the breeding ram cohort contained individuals from multiple breeding cycles with greater overall differences and less focus on within-family differences in the estimation. Further, the increased numbers of phenotyped lambs in the alternative scenarios see  comparedhe study .Selection index weights need to be derived from economic weights and should include economically relevant traits or traits correlated to economically relevant traits ,27. As mStudies investigating G × E in fattening traits in German Merino sheep regarding production environments, i.e., indoor fattening and fattening on pasture, were not available. Bishop et al.  found siBody conformation had the highest TMI weight. This trait is also important in pasture farming. Sheep need to have adequate body conformation and robustness to survive in harsh environments . Body coMontossi et al.  and PeteThe present simulation study used the German Merino sheep breeding population as an example for the identification of alternative strategies for adapting a sheep breeding program to its intended production environment (on pasture). We recommend implementing FT for fattening traits to complement the current ST (scenario ST + FT). Although trait recording is more difficult in pasture environments, this study indicates that efforts will pay off regarding genetic gain in sheep breeding programs for pasture-based production environments.


Gasterosteus aculeatus) across species pair and solitary lakes in British Columbia. We identify two genomic clusters across freshwater populations, which differ in benthic–limnetic divergent phenotypic traits and separate benthic from limnetic individuals in species pair lakes. Phylogenetic reconstruction and niche evolution modeling both suggest a single evolutionary origin for each of these clusters. We detected strong phylogenetic signal in benthic–limnetic divergent traits, suggesting that they are ancestrally retained. Accounting for ancestral state retention, we identify local adaptation of body armor due to the presence of an intraguild predator, the sculpin (Cottus asper), and environmental effects of lake depth and pH on body size. Taken together, our results imply a predominant role for retention of ancestral characteristics in driving trait distribution, with further selection imposed on some traits by environmental factors.Rapid evolution of similar phenotypes in similar environments, giving rise to in situ parallel adaptation, is an important hallmark of ecological speciation. However, what appears to be in situ adaptation can also arise by dispersal of divergent lineages from elsewhere. We test whether two contrasting phenotypes repeatedly evolved in parallel, or have a single origin, in an archetypal example of ecological adaptive radiation: benthic–limnetic three-spined stickleback ( Parallel occurrence of adaptive phenotypes across similar but geographically separate environments has long fascinated evolutionary biologists. There are two main mechanisms which can explain such a pattern. First, novel adaptive phenotypes may evolve rapidly and repeatedly in response to new ecological opportunity, that is parallel ecological speciation . AlternaParallel ecological speciation may involve multiple de novo mutations, each of which may lead to a similar phenotype but by a slightly different mechanism. In this instance, it is easy to conclude multiple independent origins. However, evolution is not linear but often reticulated, and in many cases, parallel ecological adaptation may involve repeated reuse of standing genetic variation, that is the same, potentially ancient mutation can be introduced to multiple independent populations via admixture . In thisThe benthic–limnetic axis of three-spined stickleback in British Columbia (“BC”), Canada, is an archetypal example of ecological divergence and speciation . It sepaWe investigate whether benthic–limnetic divergence in BC stickleback likely has a single or multiple evolutionary origins. We first characterize genomic and phenotypic divergence across populations and show that all freshwater individuals fall within one of two genomic clusters, one of which exhibits a more benthic phenotype and the other exhibit a more limnetic phenotype. We construct a maximum likelihood phylogeny using a stringently filtered dataset, removing all known quantitative trait loci (QTLs) in stickleback, and test for phylogenetic signal in ecologically relevant phenotypic traits. We construct a microevolutionary adaptive landscape for the BC radiation using recently available niche modeling techniques  to identWe collected stickleback and environmental parameters from 21 lakes surrounding the Strait of Georgia, BC , includiA), one incorporating the marine populations and approximately half of the freshwater populations (cluster 1) and the other comprising the rest of the freshwater lakes (cluster 2). Although marine populations formed part of cluster 1, they are considered separately here and in all further analyses because their presence in shallow coastal areas is transient and they likely represent the ancestral phenotypic state of all freshwater populations (B).We used two methods to quantify clustering within the genomic data. First, a coancestry matrix in fineRADstructure  datasetA, one inulations . Second,ulations B.C). Of these 60 SNPs, 28 fell directly within genes  using the LDna R package . Many evolutionary phenomena  result in linkage disequilibrium (LD) among multiple loci. This software is designed to detect independent clusters of linked loci, each of which is a signature of a different evolutionary phenomenon . Our LD fig. 2in genes  online. in genes  online.A–E). Cluster 1 had a typically limnetic phenotype (fig. 3A–E). Population-level phenotypic data are given in To determine whether the genomic clusters differed phenotypically, we analyzed differences in group means for important benthic–limnetic divergent phenotypic traits: weight, gill raker length and number, armor PC1 , and shape PC1 . There fig. 3henotype  with a sCoancestry plots of genomic data from Alaskan stickleback populations . We constructed a maximum likelihood phylogeny for all populations using RAxML. Consistent with the coancestry and PCoA analyses, the topology showed clusters 1 (more limnetic phenotype) and 2 (more benthic phenotype) at opposite ends of the tree, with marine fish most closely related to cluster 1 (A). The two species pair lakes both contained limnetic individuals whose closest relatives were in cluster 1 (PAXL and PRIL) and benthic individuals whose closest relatives were in cluster 2 . Alternative approaches to filter our master SNP set, for example removing all SNPs with Fst between clusters ≥0.35 (mean Fst: 0.08) or Fst between lakes with sculpin present (a major selective agent for stickleback) or absent ≥0.35 (mean Fst: 0.07), before phylogenetic reconstruction with RAxML made no difference to the distinction of clusters 1 and 2 in either topology . Lake and stream stickleback were monophyletic by watershed in all six cases . The topology with the second highest weightings (topology 7) was also concordant and just involved a switching of the order in which the cluster 1 populations split from the root. The simple geographical hypothesis, with the two pairs of populations nearest to one another being most closely related (topology 3), received very little support. The highest ranking topology had more than twice the proportional support that the second most likely topology had, suggesting that there is a strong genome-wide signal in favor of the maximum likelihood topology.We also performed a topology weighting analysis on a subset of populations selected specifically to test the likelihood that the phenotypes associated with clusters 1 and 2 could have evolved repeatedly in situ. Topology weighting is a means by which to quantify relationships between taxa that are not necessarily monophyletic. It determines how support for each possible topology varies across the genome and allows quantification of the overall proportion of the genome which supports each possible tree. This allowed us to identify multiple highly supported phylogenies, so that we could determine whether any of those with high support involved a model in which the two clusters arose more than once. It also allowed us to quantify what proportion of the genome supports our most likely topology and how big the difference is between this and the level of support for the next most likely tree. To do this, we selected pairs of populations that were from different cluster, but were geographically as close together as possible. We did this twice with the two pairs being geographically as far apart as possible . We did this so that if much of the genome supported a “geographic” model, in which populations near to each other were more closely related, we would be as likely as possible to detect it. One marine individual was used as the outgroup. The topology with the highest weighting across all 50 bp sliding windows (topology 8) was concordant with the maximum likelihood phylogeny, with the two cluster 2 populations (DOUG and KLEN) forming a monophyletic clade and each cluster 1 population splitting off earlier, deeper to the root C. The toIf benthic and limnetic phenotypes had resulted from repeated, rapid adaptive divergence, phylogenetic signal  would be obscured, and trait distributions would instead mimic the adaptive landscape—that is variation in the relevant environmental characteristics. Therefore, we tested a null model that traits would be distributed randomly with respect to phylogeny, and an association of trait distribution with population-level relatedness was taken as evidence that benthic and limnetic niches were conserved from the ancestral state .P values <0.05, P values >0.05, We estimated phylogenetic signal at the population level, using mean phenotypic trait data, and collapsing nodes in the phylogeny by population , using the R package PhyloSignal. We also tested five simulated traits that had no true association with phylogeny. We identified phylogenetic signal in all five real phenotypic traits: weight, gill raker number, gill raker length, armor PC1, and shape PC1 (B). The first included all marine and cluster 1 populations, and the second, all cluster 2 populations. The best fitting model included no instances of convergence between selection regimes, that is each independent regime appeared only once across the phylogeny.Niche evolution modeling combines phylogenetic information with the distribution of phenotypic traits across the tree to identify the most likely number and location of selection regimes imposed across the whole phylogeny. It also identifies the number of instances of convergence (where the same regime appears multiple times across the tree). If the benthic phenotype had evolved repeatedly and independently across the phylogeny, niche evolution modeling should identify multiple instances of convergence of a benthic selection regime. We performed niche evolution modesling using the R package SURFACE . We ran fig. 4P value <0.05; P values <0.01 and <0.05, respectively). Lake surface area and calcium concentration did not affect any aspect of phenotype, and none of the environmental variables we measured affected the number of gill rakers, the length of gill rakers, or shape PC1  approach so that phylogenetic signal could be accounted for and fitted to the population means of phenotypic traits in R. Marine fish were excluded from all phenotype–environment modeling, because of the difficulty of measuring the environment of migratory marine fish. We found that freshwater fish had more body armor in the presence of sculpin  are an intraguild predator of stickleback, which, when present, select for a more limnetic phenotype  in the Fraser River valley, dated approximately to the end of the Pleistocene and caused by the failure of a large ice dam , were selected for sampling see , 1993, a2) using GoogleEarth and collected data on the mean depth (m) of each lake from either HabitatWizard  or from data collected in Oncorhynchus clarkii) and rainbow trout (O. mykiss) are major intraguild predators of stickleback, but both occur in both the littoral and pelagic zones (C. asper) are a benthic intraguild predator, and their presence selects for a more limnetic stickleback ecotype (The size and depth of a lake largely determine whether both benthic and limnetic habitats are present (in larger deeper lakes) or just benthic . Therefore, we measured the surface area . Samples of between 10 and 63 individuals (see Fish sampled from lakes containing species pairs (PAXT and PRIE) were visually classified as benthic or limnetic at the time of capture as well as being later measured for all phenotypic traits.To determine body size, fish were blotted and weighed to the nearest milligram. To assess body shape differences, each stickleback's left side was photographed using a tripod-mounted digital SLR camera fitted with a macro lens and macro digital ring flash. Images were scaled, and 13 landmarks were placed on each image using tpsDig, version 2.16 . LandmarTo assess differences in body armor, fish were first bleached and then stained with alizarin red to highlight external skeletal structures following standard procedure . Fish weFinally, the left primary gill arch was extracted from each individual. For each gill arch, the total number of gill rakers were counted, and the mean gill raker length was calculated by taking the mean of the length of the longest three rakers on each arch, measured to the nearest micrometer.DNA was isolated from fin tissue using Quiagen Blood and Tissue DNA purification kits. RAD-seq data were generated following r2 linkage disequilibrium matrix was generated using dataset 1  in Plink version 1.9  and 1 (complete linkage), and therefore, a relatively conservative LD threshold was set at R2 > 0.2. Thinning dataset 1  to unlinked loci resulted in a dataset with 9,668 retained SNPs.Many genomic tools, however, rely on the assumption that variants are independent, and therefore, SNPs in linkage disequilibrium must be removed for such analyses. To that end, we estimated linkage disequilibrium across the genome as a whole by calculating pairwise We used fineRADstructure  to constP values for post hoc comparisons were adjusted for multiple testing using the false discovery rate method  and they represent the likely ancestral state of all freshwater populations . To detee method .R2 value >0.2 using Plink version 1.9. This left 6,215 SNPs, dataset 2.Prior to phylogenetic analysis, we filtered our master dataset (dataset 1) to remove all known QTLs in stickleback. QTLs are loci of large effect and thus most likely to influence tree topologies in phylogenetic reconstruction. They are also extremely well mapped in stickleback , allowinTo construct a phylogeny of all sequenced individuals, we used a bootstrapped maximum likelihood-based approach, implemented in the hybrid version of RAxML , version 8.2.12 . The VCFTo assess the robustness of the maximum likelihood phylogeny, we also performed topology weighting using TWISST . TopologP values for each trait were estimated using the package phylosignal  and instances of convergent evolution (c) by identifying the best fitting model of trait evolution for our phylogeny and associated phenotypic traits. SURFACE begins by fitting a single peak Ornstein–Uhlenbeck (OU) model  by maximum likelihood. It then sequentially adds adaptive peaks to the model in a step-wise process and accepts each more complex model until Akaike information criterion (AIC) values are no longer improved. SURFACE then attempts to collapse regimes with the same optima in a process of step-wise backwards selection whereby if multiple optima are the same, the AIC of the model is improved by reducing the number of model parameters. We ran SURFACE using the same collapsed phylogeny and associated trait data that were generated to estimate phylogenetic signal. The tree was converted to the ouchtree format, and the best fitting model of trait evolution was estimated under an AIC threshold of 0 (any improvement in AIC should be accepted) using the SURFACE R package.As we aim to detect whether benthic and limnetic characteristics have evolved a single time or repeatedly across the radiation, we also used the R package SURFACE  to estim2], presence/absence of prickly sculpin, pH, and calcium concentration [mg/L]). Terms were then removed sequentially, with the least significant terms removed first, until the reduced model was no longer a significant improvement on the fuller model under the P < 0.05 threshold. Model comparison was conducted using Wald tests. Phylogenetic effects for each phenotypic trait were accounted for in each model following the principles set out in To investigate associations between environmental characteristics and divergence in phenotypic traits, we used a PGLS approach, so that phylogenetic signal could be accounted for in the models, using the ape , nlme P, and geimsad191_Supplementary_DataClick here for additional data file.


ATP1A1, LRRC4C, SPCS2, XRRA1, CELF4, NTM, TMEM45B, ATE1, and FGFR2, were associated with udder traits, while the ENSCHIG00000017110, SLC9A8, GLRB, GRIA2, GASK1B, and ENSCHIG00000026285 genes were associated with litter size. These SNPs and candidate genes can provide useful biological information for improvement of the reproductive traits of goats.Reproductive traits are the basic economic traits of goats and important indicators in goat breeding. In this study, Dazu black goats , an important Chinese local goat breed with excellent reproductive performance, were used to screen for important variation loci and genes of reproductive traits. Through genome-wide association studies (GWAS), 18 SNPs were found to be associated with kidding traits , and 10 SNPs were associated with udder traits . After gene annotation of the associated SNPs and in combination with relevant references, the candidate genes, namely  Goats are one of the most important livestock in China, and the goat industry plays an important role in improving the national economy. According to the report of the Ministry of Agriculture, there are nearly 120,000 Dazu black goats (DBGs) in southwest China. It is an important local goat breed in Chongqing and has been widely farmed because of its excellent reproductive performance. The birth rates of twins, triplets, and quadruplets can reach 47.27, 40.02, and 10.29%, respectively, in this breed [FecB gene has been identified as a high-breeding gene for sheep, goats still do not have a recognized candidate gene with a strong correlation with kidding traits. So, to understand the genetic basis of litter size traits, many molecular biology and genetic studies have been carried out to detect chromosome regions, candidate genes, and genetic markers associated with reproductive performance in goats [Reproductive characteristics are important economic traits in the goat industry, and improving the kidding traits of goats is particularly important for increasing the economic benefits of the goat industry. Litter size is a very important economic trait, and is affected by many factors, including ovarian follicular development, ovulation, fertilization rate, embryonic development, and embryo implantation , and litin goats ,6,7. Genin goats . Previouin goats , horn stin goats , body coin goats , and milin goats .MARF1, SYCP2, TMEM200C, SF1, ADCY1, and BMP5 genes. Xu et al. [BMPR1B, FBN1, MMP2, GRIA2, SMAD1, and CTNNB1 genes were associated with litter size. To study the genomic regions related to the teat and udder structure of Canadian Angus dairy cows, Devani et al. [Many candidate genes and genetic markers are associated with reproductive traits in mammals. Islam et al.  found seu et al.  conductei et al.  conductei et al.  conducteTo better understand the genomic regions and biological pathways affecting the reproductive traits of DBGs, we performed a genome-wide association analysis of seven reproductive traits. The SNPs and candidate genes identified in this study can provide useful biological information for improvement of the reproductive traits of goats.The Chongqing Key Laboratory of Forage and Herbivore approved this experiment. All goat experiments followed the Institutional Animal Care and Use Committee of Southwest University regulations (IACUC-20230306-16), and no animals were anesthetized or euthanized during this study.(1)Udder depth (UD) is the vertical distance from the root of the left udder to the top of the teat.(2)Teat diameter (TD) is the teat diameter of the left udder.(3)Teat length (TL) is the teat length of the left udder.(4)Supernumerary teats (SNTs) were observed and recorded with the naked eye.In this study, a total of 150 female DBGs aged 3–5 years and weighing 35–40 kg were selected from Tengda Animal Husbandry Co., Ltd. . We also collected data on litter size (LS) and used Microsoft Excel to calculate the average litter size (ALS), average litter size in the first three parity (ALS3), and average litter size in the first six parity (ALS6), and the udder traits of 80 does in constant lactation period were measured:The genome data used in this study were generated as part of a previous whole-genome resequencing study, where the sequencing of each sample was performed on the DNBSEQ-T7 platform  . The avep < 1 × 10−6. Phenotype quality control criteria: delete individuals with phenotypic deletion [kγk is the marker effect to be tested; ξ~N  is the polygenic effect; e~N  is the residual effect; and K is the kinship matrix inferred from the SNPs.Standard quality control of genotypes and phenotypes is needed in GWAS to eliminate the influence of abnormal genotypes and phenotypes on the results of the analysis. The genotype QC process removes individuals or loci that meet the following criteria: (1) deletion rate > 10%; (2) minor allele frequency (MAF) < 1%; and (3) Hardy–Weinberg equilibrium (HWE) with deletion . Since Gdeletion  to inferdeletion  and is wp-value corrected using Bonferroni was 6.01 × 10−8. Significant SNPs were visualized as a Manhattan map and quantile–quantile (QQ) map in R (v4.0.4) [Then, we used GEEMA to obtain significant SNPs, and the (v4.0.4) .http://www.openbioinformatics.org/annovar/, accessed on 14 September 2023) [Candidate genes were identified according to their physical positions and functions based on the ARS1 reference genome assembly, and the significant SNPs were annotated to the corresponding genes using ANNOVAR  measured in this study are presented in p-value distribution of reproduction traits found in the GWAS. Most of the SNPs did not deviate from the expected p-value, and only a few SNPs in ALS6 and TL showed a certain degree of deviation. After screening for and obtaining the adjusted phenotype, GWAS was performed on the identified SNPs. TBC1D22A and TAFA5, near the locus. ALS6 identified 16 important SNPs, and among them, eight significant differences were identified on chromosome 17, most of which were annotated to the GLRB gene. Additionally, one locus was identified on each of the chromosomes 6, 11, 13, 19, 27, and 28. UD identified a locus on chromosome 3 and annotated two genes ATP1A and ENSCHIG00000022779 near the locus. TD identified one locus on chromosomes 15 and 18, and annotated the LRRC4C and ENSCHIG00000000547 genes near it, respectively. TL identified one locus on chromosome 24 and 29 respectively, and two loci on chromosome 15. The loci on chromosome 15 were annotated to the XRRA1, NTM, and TMEM45B genes, while the locus on chromosome 24 was annotated to the ENSCHIG00000022267 and CELF4 genes. The locus on chromosome 29 was annotated to the SPCS2 gene. SNT identified one locus each on chromosomes 18, 23, and 26. The locus of chromosome 18 was annotated to the CD83 and ENSCHIG00000008871 genes, while the locus of chromosome 23 was annotated to the ATE1 and FGFR2 genes. The locus of chromosome 26 was annotated to the BICRA gene.The QQ plot in A total of 217 different GO entries were enriched in the selected reproduction-related candidate genes. ALS6 alone was enriched to 77 strips, while SNT, TD, UD, and TL were enriched to 63, 52, 17, and 8 strips, respectively. A large number of items were enriched in molecular functions and biological processes . The topKEGG analysis revealed a total of 40 significantly enriched pathways . UD enriLRP1B and GLRB, related to litter size. In addition, they also analyzed the effect of copy number variation (CNV) on the litter size of DBGs and found that the new CNV of the CBLB gene was significantly related to the litter size of DBGs [As litter size is an important limiting factor affecting the reproductive efficiency of goats, the identification and use of goat molecular markers to increase litter size can effectively accelerate the development of the goat breeding industry. In the study of E et al. , genome- of DBGs . Wang et of DBGs  divided GLRB gene. This gene is consistent with the candidate gene obtained by E et al. [TBC1D22A and TAFA5 genes annotated by the SNP did not correlate with goat reproductive performance. However, as a member of the TAFA family, TAFA5 can encode small secretory proteins in the central nervous system and has been shown to increase expression in human malignant tumors [TAFA5 was mainly expressed in the central nervous system, is co-located with vasopressin and oxytocin in neurons, and may be involved in the regulation of homeostasis.According to the National Animal and Poultry Genetic Resources Variety list 2021 Edition), there are eight goat breeds with an average kidding rate of more than 200%, one of which is DBG. The reproductive period of female DBGs is mostly between 5 and 7 years, while some high-yielding does can even reach eight years. The results of this study showed that a total of 1, 0, and 17 SNPs were associated with ALS, ALS3, and ALS6, respectively. These SNPs are mainly concentrated on chromosome 17, of which six SNPs are located within the  Edition,t tumors  and Bundt tumors  examinedNDST3, GLRB, and TRAM1L1 have been associated with reproductive traits of livestock. One study investigated the role of heparan sulfate polysaccharide (HSPG) in regulating uterine receptivity by targeting NDST3 deletion in mice. Results showed that although reproductive tract development and ovarian function appear to be normal in knockout dams, they remained infertile due to implantation defects [NDST3 can promote the synthesis of heparan sulfate (HS) and assist in embryo implantation [SLC9A8 is one of the regulatory genes of the sodium/proton reverse transporter (NHE) protein [TRAM1L1 has been associated with fecundity in Holstein cows using GWAS [GLRB was shown to be located in the acrosome region of spermatozoa [GLRB is related to the litter size of DBG [GRIA2 is part of the secretion mechanism of gonadotropin-releasing hormone (GnRH) [GLRB and GRIA2 genes were related to the hind leg hair traits of southern Chinese goats. In another study, Mahmoudi et al. [In this study, the significant SNPs associated with ALS6 were annotated to 14 genes. Among them, five genes including  defects . Palleriantation . In addi protein . The NHE protein . TRAM1L1ing GWAS , while Grmatozoa . Previoue of DBG . Finallye (GnRH) , which ae (GnRH) ,33,34. Te (GnRH) , throughi et al.  performeIn female mammals, the mammary gland changes dynamically throughout the reproductive cycle, which can be divided into five stages: embryo, prepuberty, puberty, pregnancy, lactation, and degeneration. This developmental process is mainly regulated by hormones, growth factors, and cytokines. Jena et al.  recordedDue to the limited number of teats, most does struggle to feed multiple kids. Therefore, teat number is an important consideration in goat breeding, playing a key role in the healthy growth of kids. In addition, supernumerary teats greatly affect the efficiency of machine milking, but Hardwick et al.  showed tATP1A1, LRRC4C, XRRA1, TMRM45B, BICRA, and CD83, etc.) were annotated. Liu et al. [ATP1A1 of Holstein cows (n = 320) and found that ATP1A1 was associated with somatic cell score and 305d milk yield, which played a role in mastitis. The candidate genes that may affect SNT are BICRA, CD83, ATE1, and FGFR2. In previous studies, Scott et al. [BICRA led to a unique neurodevelopmental disorder. CD83 is a member of the Ig superfamily. Expressed in the form of membrane binding or soluble, it can be detected in a variety of activated immune cells. It is reported that CD83 participates in the regulation and balance of the immune system and can be used as an immunomodulator with therapeutic potential [CD83 promoted the growth and proliferation of ovarian cancer cells. In previous studies, FGFR2 has mainly been related to breast cancer. Bao et al. [GH overexpression may promote breast proliferation through FGF7 and FGFR2, and FGFR2 was expressed in endometrial epithelium and trophoblastic epithelium [FGFR2 in the mammary gland of a mouse model and found that FGF/FGFR2 drives the development of breast cancer, and inhibiting FGFR2 and blocking immune checkpoints can reduce the number of cancer cells [FGFR2 plays a crucial role in normal uterine development and normal uterine function in mice after birth. Arginyltransferase 1 (ATE1) is an evolutionarily conserved eukaryotic protein located in the cytoplasm and nucleus. It can regulate metabolism and apoptosis, resulting in decreased intercellular contact and chromosome aberration [DBGs not only maintain high reproductive performance, but also ensure a high survival rate of young kids, which is strongly related to the udder function of does, in theory. In this study, the udder traits of 80 lactating does were selected for GWAS. In this GWAS, 1, 2, 4, and 3 important SNPs were detected in UD, TD, TL, and SNT, respectively, and a total of 15 candidate genes (u et al.  analyzedt et al.  found thotential . In addio et al.  showed tithelium . Lei et er cells . Additioer cells  found therration .The studies mentioned above have made different efforts in exploring the factors affecting reproductive traits in goats, but there are differences in the results of the studies, and these differing findings suggest that reproductive traits in goats are influenced by complex genetic mechanisms. Subsequent studies will explore the function of this candidate gene and the mechanism of its effect on reproductive traits.ATP1A1, BICRA, CD83, ATE1, NDST3, GLRB, GRIA2, and GASK1B may be involved in the regulation of reproductive traits in DBGs. The significant SNPs and genes identified in this study will aid in the molecular-based breeding of goats to improve reproductive performance.In this study, the genome and reproductive phenotypic data of 150 DBGs were collected and combined with GWAS. Results revealed that the genes


White lupin is a promising high-protein crop, the cultivation of which is limited by a lack of adaptation to soils that are even just mildly calcareous. This study aimed to assess the phenotypic variation, the trait architecture based on a GWAS, and the predictive ability of genome-enabled models for grain yield and contributing traits of a genetically-broad population of 140 lines grown in an autumn-sown environment of Greece (Larissa) and a spring-sown environment of the Netherlands (Ens) that featured moderately calcareous and alkaline soils. We found large genotype × environment interaction and modest or nil genetic correlation for line responses across locations for grain yield, a lime susceptibility score, and other traits, with the exception of individual seed weight and plant height. The GWAS identified significant SNP markers associated with various traits that were markedly inconsistent across locations, while providing direct or indirect evidence for widespread polygenic trait control. Genomic selection proved to be a feasible strategy, owing to a moderate predictive ability for yield and lime susceptibility in Larissa (the site featuring greater lime soil stress). Other supporting results for breeding programs where the identification of a candidate gene for lime tolerance and the high reliability of genome-enabled predictions for individual seed weight. Lupinus albus L.) is a Mediterranean crop with a long history of domestication that has high potential interest in this context, because of its seed protein content close to 40% and other characteristics [Greater grain legume cultivation has a high priority for European agriculture, to enhance the sustainability of its agricultural systems ,2, reduceristics ,6. Its eeristics , the abieristics , and 8–1eristics . While beristics .2+ may directly affect Lupinus species [3− [2+ concentration and alkaline soils also reduce the growth and the nodulating ability of the nitrogen-fixing Bradyrhizobium microsymbiont of lupins [Bradyrhizobium strains adapted to such soils [A key factor that limits the cultivation of white lupin worldwide is its narrow adaptation with respect to soil type. Grain and biomass reductions can be expected in soils whose active lime, i.e., the soluble fraction of calcium carbonate based on Drouineau’s  method,  species , the mai species , and thrcies [3− . Indeed,cies [3− . High sof lupins ,20,21, bch soils .Various studies have highlighted the adaptation to moderately calcareous or alkaline soils of Egyptian landraces that evolved in such soils ,24,25, bPlant breeding for traits that are genetically complex and/or difficult to assess could become more cost-efficient by genomic selection, which combines phenotyping and genotyping data of a genotype sample (training population) representing a target genetic base (reference population) into a statistical model for the prediction of breeding values in future plant selection ,31. ThisThis study focused on a collection of 140 GBS-genotyped sweet-seed breeding lines representative of a genetically-broad reference population originated by crosses of four elite landraces with four elite sweet-seed cultivars or breeding lines performed according to a factorial mating design. The lines were evaluated for grain yield and other traits associated with performance in an autumn-sown environment of Greece and a spring-sown environment of the Netherlands that featured moderately calcareous and alkaline soils. The main objectives of our study were to assess the phenotypic variation, the trait architecture based on a genome-wide association study (GWAS), and the predictive ability of genome-enabled models for grain yield and its contributing traits, verifying as well the consistency of genotype adaptation patterns and genome-based results across the two growing regions.p < 0.05), except for the proportion of plants with seeds, which was not significant in the Dutch site   and Ens (the Netherlands), two sites that featured moderately calcareous, moderately alkaline soils . Actuallg unity) . The Greg unity) . p < 0.01; gr = 0.32, p < 0.10), while showing no genetic correlation for the last lime tolerance score or the average value of the two scores  of the DNA samples generated, on average, 1.93 million reads per sample. After alignment, SNP calling, and quality filtering, we obtained a set of 19,024 markers, which were further filtered for missing rate. We retained for molecular analyses 9815 polymorphic SNP markers mapped on the l et al. , after ir2 = 0.28) at 3442 bp, with single chromosome values ranging from 277 bp for chromosome 12 to 6649 bp for chromosome 24  reached half of its 90th percentile  and one in Ens  . We alsoWe assessed the intra-environment predictive ability of GS models as Pearson’s correlations between true and predicted phenotypes via ordinary cross-validation for the six traits that were the object of the GWAS. The list of best-performing GS models in We assessed the cross-environment predictive ability (where one location was used to train the model for trait prediction in the other location) for the meaningful scenario represented by traits that displayed moderately high genetic correlation for line responses across locations, i.e., individual seed weight and plant height. The soil active lime content of the test sites was nearly (Ens) or over (Larissa) two-fold that indicated in  as the tgr value of 0.32). Various reasons may account for this result. First, the site-specific adaptive responses of the genotypes were not only influenced by lime susceptibility, but also by adaptation to the contrasting climates and sowing times of the two sites. Secondly, the much greater impact on plant responses of lime tolerance in the Greek site than in the Dutch site  could itself be a reason for GEI, making lime tolerance variation important mainly in the former site. In this context, data from the Greek site are more relevant than those from the Dutch site for assessing the lime tolerance of the lines, also with regard to lime tolerance score values, which suffered low heritability besides referring to milder stress in the Dutch site. Lime tolerance scores themselves displayed definite inconsistency between locations, suggesting that they might have estimated the general plant vigor  rather than the intrinsic lime tolerance, especially in the Dutch site (featuring lower soil lime stress). However, white lupin lime tolerance proved to be a fairly elusive and only partly repeatable trait in earlier studies. For example, 17 cultivars exhibited only moderate correlation (r = 0.50) for lime tolerance, expressed as shoot biomass ratio between high-soil lime and low-soil lime environments, across experiments in Italy and Morocco [An important finding of our study was the low consistency of genotype yield responses across the two sites featuring a moderately calcareous soil  than the other seed components [The number of pods per plant was the grain yield component that displayed the highest correlation with grain yield in both locations. This result agrees with results for landrace germplasm evaluated in Spanish environments ,39. Indimponents , which wr2 and corresponding distance values essentially arising from the adoption of different window sizes for LD estimation (100 kb instead of 50 kb). However, the current LD decay was faster than that reported by Hufnagel et al. for a collection of cultivars and landraces [3+ according to the acquisition strategy I typical of non graminaceous species [The observed pattern of LD decay as a function of the physical distance was very similar to that reported for the same reference population of lines in an earlier study , with diandraces , in the andraces ,43 and tandraces ,45. In pandraces  and actiandraces , by redu species . Our resThe GWAS revealed very few or no significant SNPs for all traits in both sites, except for individual seed weight in Larissa, probably because of insufficient power to detect small-effect genes that are expected to be important for the control of crop yield, yield components, and plant stature. For individual seed weight, a polygenic trait architecture was supported not only by the detection of various putative QTL in one location, but by the fact that cross-environment GS prediction models exhibited just a modest loss in predictive ability (27%) compared with intra-environment prediction models despite the inconsistency for significant SNPs across locations. Such an outcome could only arise from the presence of many small-effect, statistically non-significant QTL that are consistent across locations.Genomic selection is highly relevant for polygenic traits if predictions are proved to be sufficiently accurate. The current intra-environment predictive ability values for grain yield and the lime tolerance score in the site featuring greater soil lime stress (Larissa), which were close to 0.34, were lower than those reported for white lupin grain yield in moisture-favorable or drought stress conditions of landrace genotypes (in the range of 0.47–0.58) and breeding lines (in the range of 0.67–0.78) ,40. HoweThe absence of sizable and consistent differences in predictive ability among GS statistical models is fairly common in general and was already reported in earlier white lupin studies that compared the same models  or a subIn conclusion, this study highlighted the importance of GEI effects when improving white lupin adaptation to calcareous soils, supporting efforts on a regional scale that may take advantage of genome-enabled predictions of grain yield and visually-assessed lime tolerance. Future proof-of-concept work will verify the actual genetic gains obtained from GS for these traits applied to independent germplasm. Other major results issued from our study are: (a) the identification of a candidate gene for lime tolerance, revealed by the GWAS; (b) the high reliability across contrasting locations of the genome-enabled prediction of seed weight; and (c) the usefulness of more pods per plant as a phenotypic selection criterion in early selection stages that lack grain yield data.2 to F5 inbred lines for each of the 16 crosses were produced from 2015 to 2017 by single-seed descent on-season and off-season generations. All crossing and seed multiplication work was carried out in isolation through insect-proof nets. Within-cross selection for low alkaloid content was performed (a) on F3 and F4 individual seeds by the fluorescence method [4 seed by a non-destructive test that was adapted to single seeds using the spectrophotometer method described in [5 inbred lines (60 per cross), of which 560 (35 per cross) were genotyped, and 192 (12 per cross) were multiplied in isolation in 2018 to obtain the F6 seed used for this study and to phenologically characterize these lines. Four to 10 lines per cross contributed to the current set of 140 test lines ribed in ,52. The st lines . The lin2O5, with manual weeding and no lupin seed inoculation. In Ens, characterized by a cool Oceanic climate, the material was sown at the beginning of April and was harvested by the end of October 2021. The crop was organically-managed, inoculating the seed by the commercial inoculant HiStick  before sowing, and performing occasional hand-weeding during the crop cycle. Each experiment was laid out according to a randomized complete block design with three replications per line, sowing 36 seeds per plot. Plots included 4 rows in Larissa and 3 rows in Ens, with rows at 25 cm spacing and seeds at 9 cm spacing on the row (density: 44.4 seeds/m2) in both sites. The 140 inbred lines were evaluated under field conditions in Larissa  and Ens , two sites that featured moderately calcareous, moderately alkaline soils along with a contrasting climate . In LariGrain yield at 0% seed moisture was assessed on a plot basis, after measuring the seed moisture on a sample of 100 seeds per plot. In Ens, the harvested pods were previously dried through a heated forced air drier for about 48 h. A lime tolerance score was visually attributed twice, i.e., at the end of the pod setting stage of the main stem, and at the end of the pod setting stage of the primary branches, according to the following scale definition: 1 (no stress): dark-green leaves, robust growth, 3–6 lateral branches, >20 pods/plant (only for the second observation); 3 (mild stress): chlorotic lower leaves, 2–4 lateral branches, 10–20 pods/plant (for the second observation); 5 (intermediate stress): yellow leaves along the whole plant, apparently limited shoot development, 1–2 lateral branches, 6–10 pods/plant (for the second observation); 7 (severe stress): yellow leaves along the whole plant, modest shoot development, abscission of leaves early in the ripening stage, 0–2 lateral branches, 2–5 pods/plant (for the second observation); 9 (extreme stress): yellow leaves along the whole plant, very poor shoot development, abscission of leaves and inflorescences, inability to complete the reproductive stage. Data analysis was performed for the latest score observation and for the average value of both observations. We also recorded the following traits: proportion of plants with seed; plant height at the end of flowering (measured on five test plants); average numbers of pods per plant and of seeds per pod ; and individual dry seed weight (assessed on 100 random seeds per plot). m is the trait mean value, and gs is the square root of the genotypic component of variance (s2g) estimated along with the experimental error (s2e) component of variance by the REML method. Trait broad-sense heritability was computed from these components of variance by the equation: n is the number of experiment replicates. We used 2H values to compute best linear unbiased prediction (BLUP) values as described in [® software 9.3 [An analysis of variance (ANOVA) including the factors lupin genotype and block was performed for each trait in each environment to assess the significance of the variation among lines and its extent as a genetic coefficient of variation computed as: ribed in , which wribed in  for one ware 9.3 . 5 plants of each inbred line using the DNeasy Plant Mini Kit . Nucleic acid was quantified by a Quant-iT™ PicoGreen™ dsDNA Assay Kit , checking its quality by 1% agarose gel electrophoresis. A trial digestion was carried out on 10% of the DNA samples using the Optizyme EcoRI restriction enzyme , to compare bands of cut and uncut DNA. The reaction was performed at 37 °C for an hour and the enzyme was deactivated at 65 °C for 20 min. DNA samples were sent to The Elshire Group Ltd. lab  for outsourced library preparation and sequencing. GBS data were generated according to Elshire et al.’s [ApeKI enzyme ; then, the library was amplified with Kapa Taq polymerase Alpha  by 14 PCR cycles. Sequencing was performed on a single Illumina HiSeq X lane, at 2 × 150 bp paired end. Adopting ApeKI as the restriction enzyme according to Elshire et al. [Genomic DNA was extracted from young leaves of Fet al.’s  method we et al.  was suppLupinus albus genome version 1.0 [https://www.whitelupin.fr/ (accessed on 3 November 2022). The final genotype matrix, in the form of a vcf file, was further filtered for quality using the vcftools software [−minQ 30 −max-non-ref-af 1 –non-ref-af 0.001. The resulting data set was filtered for monomorphic markers, minor allele frequency (MAF) > 5%, missing SNP marker rate < 10%, 20% or 30%, and a missing rate per individual < 50%. Following Nazzicari et al. [GBS raw reads were demultiplexed using Axe demultiplexer . Trimminsion 1.0 , which wsoftware  with pari et al. , we estii et al.  using thi et al.  with theBoth genotypes and SNPs were filtered for excess heterozygosis by using the mean plus three and two standard deviations as maximum thresholds, respectively.find.clusters, dapc and optim.a.score.The presence and pattern of population structure were investigated by a discriminant principal components analysis (DPCA) . We iterLinkage disequilibrium was estimated for SNPs within a 100 kb window in R by the LD.decay function from the package sommer . We usedA GWAS was performed on BLUP data of grain yield, the average value across observations of the lime susceptibility score, plant height, number of pods per plant, number of seeds per pod, and individual seed weight measured in each location, using 9815 mapped SNPs according to the Blink model  in R pacFor each of six focus traits, we tested several whole-genome regression models whose configuration resulted from the combination of four possible statistical models (described below), the presence or absence of population structure inputted as in the GWAS, and three thresholds of maximum missing rate per SNP marker . We first envisaged an intra-environment prediction scenario, assessing the predictive ability of GS models by a standard within-location 10-fold cross-validation. Each analysis was performed 10 times, reporting the average result, to ensure numerical stability. We also envisaged a cross-environment prediction scenario for two traits that displayed moderately high genetic correlation for line responses across locations, using by turns one environment for training and the other for validation (by splitting the training data in a 90/10 fashion as done for intra-environment predictions). The whole procedure was repeated 10 times for numerical stability. We considered four possible whole-genome regression models: Ridge Regression BLUP (rrBLUP), Bayesian Lasso (BL), Bayesian Reproducing Kernel Hilbert space (RKHS), and Weighted G-BLUP (WGBLUP). All models were implemented using R package GROAN .2q) is the vector of marker effects, and ε is the vector of residuals. The model is solved in a maximum likelihood context estimating the ridge parameter λ = σ2e/σ2q representing the ratio between residual and markers variance. When the population structure is included in the model the above formula is updated as follows:2b) is the vector of (fixed) effects corresponding to the population structure.rrBLUP  assumes BL  solves t2g, where G is the genomic relationship matrix and σ2g is the genetic variance for this model. In the context of RKHS the G matrix is considered as the reproducing kernel function mapping from each pair of markers to covariance. The system is then solved with a standard Gibbs sampling, as done in BL, with the same configuration to accommodate for population structure if required.RKHS model is used to solve the so-called genomic BLUP (G-BLUP) in the Bayesian context. First, a genomic kinship additive matrix G is computed according to . The matp values resulting from an association study. The association scores were programmatically computed inside each cross-validation cycle on the training set using statgenGWAS R package [i is the observed MAF of all genotyped individuals.WGBLUP  is very  package . Once th