Activity traits were low to moderately heritable (22–35%) and showed low genetic correlations with production traits and physical abnormalities. We demonstrated a simple and cost-efficient method to extract body dimension parameters and activity traits. These traits were estimated to be heritable, and hence, can be selected on. These findings are valuable for (pig) breeding organizations, as they offer a method to automatically phenotype new production and behavioral traits on an individual level.Pig breeding is changing rapidly due to technological progress and socio-ecological factors. New precision livestock farming technologies such as computer vision systems are crucial for automated phenotyping on a large scale for novel traits, as pigs’ robustness and behavior are gaining importance in breeding goals. However, individual identification, data processing and the availability of adequate (open source) software currently pose the main hurdles. The overall goal of this study was to expand pig weighing with automated measurements of body dimensions and activity levels using an automated video-analytic system: DeepLabCut. Furthermore, these data were coupled with pedigree information to estimate genetic parameters for breeding programs. We analyzed 7428 recordings over the fattening period of 1556 finishing pigs (Piétrain sire x crossbred dam) with two-week intervals between recordings on the same pig. We were able to accurately estimate relevant body parts with an average tracking error of 3.3 cm. Body metrics extracted from video images were highly heritable (61–74%) and significantly genetically correlated with average daily gain (r Increasingly, pig farming is facing challenges such as environmental sustainability, animal welfare, farming efficiency and global competition. Innovations in genetics are considered crucial to solve these issues3. Traits related to robustness and resilience are now as relevant to animal breeding programs as those related to (re)production2. There is obviously a need for precision livestock farming technologies to optimize high-throughput pig phenotyping, which to this day remains difficult, labor intensive and costly4.Pork already accounts for almost 40% of meat production worldwide, but its global demand is expected to grow even further5 used top-view 3D-cameras to predict body weight, muscle depth and back fat in finishing pigs. Other studies used CVS to assess body positions and posture6, aggression7, sow behavior8 and feeding10. Only a few studies analyzed the genetics of these phenotypes4. In beef cattle, heritability and genetic correlations were estimated using image analysis for carcass traits11. Also, genetic parameters for coat color and conformational traits have been estimated in dairy cattle using images from web catalogs12.Computer vision systems (CVS) could enable non-invasive phenotyping of body composition, behavior and physical abnormalities in pigs, but there have been few practical applications of automated scoring of morphological and/or behavioral traits. Fernandes et al.2) estimates ranging from 20 to 60%15. Behavioral traits have been shown to be heritable as well, but h2 estimates differ considerably3. These estimates were most often based on manual scoring of behavioral traits that are susceptible to observer bias16. For example, activity scores during weighing were estimated to be low to moderately heritable (h2 = 10–23%), although there are environmental confounds that influenced these estimates19. Notably, Ott et al.16 showed that CVS activity scores were highly correlated with human observations (r = 0.92). Other studies related changes in activity scores in the pen to tail biting, infections22, or residual feed intake23. Incidentally, the relationship between activity during weighing and pen aggression was low to moderate (r = 0.15–0.60)24.Morphological traits of pigs such as body length, width and height are reported to be moderately to highly heritable, with heritability -based technology. DLC enables to create reliable and transferable tracking models29 to quantify various  traits with limited observer influence. We combine classical weighing of pigs with video analysis to estimate new phenotypes. We obtain measurements of body length, hip and shoulder width and surface area, as well as activity scores of pigs during weighing. Based on these body composition and activity traits, we estimated genetic parameters such as heritability and genetic correlations in a population of slaughter pigs with known pedigree. All annotated videos and tracking models are available on: 10.17605/OSF.IO/QKW5Y.The current study describes a method that uses open source DeepLabCut (DLC) softwareAll experimental procedures were approved by the Animal Ethics Committee of KU Leuven (P004/2020), in accordance with European Community Council Directive 86/609/EEC, the ARRIVE guidelines and the ILAR Guide to the Care and Use of Experimental Animals. Researchers obtained informed consent for publication from all identifiable persons to display and reuse videos.2 per pig). Food and water were provided ad-libitum in each pen throughout, from one trough and one nipple drinker.The study was carried out on 794 female and 746 castrated male Piétrain x PIC Camborough pigs , which had a mean age of 83.4 (± 2.2) days and a mean weight of 30.6 (± 5.1) kg at the start of the experiment. Observations were made during the fattening period which could span up to 120 days and ended when pigs reached a body weight of approximately 115 kg. Per sire, a median of 26 crossbred piglets  were allocated in equal numbers to two identical pens in mixed-sex groups. The pig building  consisted of seventeen identical compartments with eight semi-slatted pens (2.5 m × 4.0 m) per compartment and on average thirteen pigs per pen ; the presence and size of umbilical hernia ; ear biting wounds  and tail biting wounds  reader, a video camera and a computer Fig. . The gro227 was installed in an Anaconda environment with Python 3.7.7.30 on a custom-built computer running a Windows 10 64-bit operating system with Intel Core i5-vPro CPU processor (2.60 GHz) and 8 GB RAM memory. Training, evaluation and analysis of the neural network was performed using DeepLabCut in the Google Colaboratory (COLAB) (https://colab.research.google.com/).DeepLabCut 2.2b.827 as described in Nath et al.32. A minimalistic eight body part configuration  from the last frame32. These outlier frames were refined manually and hereafter added to the training dataset for re-training. In total, 150 outlier frames were extracted from six novel videos containing one single pig to improve tracking performance (± 25 frames per pig). The final training dataset consisted of 577 (95%) frames and a test dataset of 30 frames (5%). The network was then trained again using the same features as the first training. Additional File Seven videos of approximately one hour recorded on two different days were selected to include variable pig sizes (20–120 kg) and each video contained multiple pig weighings. From these seven videos, several frames were extracted for annotation using k-means clustering in DeepLabCut. We first annotated 457 frames (~ 1 frame per pig) which were split into a training dataset 95%; 434 frames) and a test dataset . The network was trained in Google Colaboratory using the ResNet-50 architecture with a batch size of 2. We trained our algorithm until the loss function reached an optimum, which indicated a minimal loss with a minimum number of iterations in this study. Next, we compared mean pixel errors of several models within this optimal region. Models with lowest mean pixel errors were visually checked for body part tracking performance on entire videos. Hereafter, the model that performed optimal was tested for flexibility using unseen single pig videos with pigs of variable size (20 vs 120 kg) weighed on different days. As model performance was suboptimal at first, poorly tracked outlier frames were extracted using the DeepLabCut ‘jump’ algorithm4 frames 33. Frames with a mean tracking probability < 0.1 over all eight body parts were removed . This large reduction in number of frames (± 50% removed) was mainly caused by video frames without any pigs, for example in between weighing of different pens or in between weighings of pigs.After posture extractions of body parts using DeepLabCut, body dimension parameters were estimated. The raw dataset contained body part positions and tracking probabilities of 5,102,260 frames. Individual pig IDs were first coupled with video recordings based on time of measurement from the weight dataset. The following steps and analyses were performed in RNext, for every weighing event, start and end points were determined to estimate body dimensions and activity traits. For a specific weighing event, a subset was first created containing all frames between the previous and next weighing event. The time of entrance and departure of the pig on the weighing scale was estimated using the x-position (in pixels) of the tail base, as the movement of pigs was predominantly along the x-axis , to assess the stability of estimates.Hip width, shoulder width and body length of a pig were estimated by using the median value of the distance between certain body parts over all frames for a specific weight recording Table , Fig. 2.35 for left and right shoulder, left and right hip and the tail base. For each body part, pixel coordinates were extracted, trajectories were rescaled from pixels to cm and a smoothed trajectory was created using the TrajSmoothSG function. From these smoothed trajectories, the following activity-related features were derived: mean and standard deviation of speed and acceleration (‘TrajDerivatives’), a straightness index (‘TrajStraightness’) and sinuosity (‘TrajSinuosity2’).Trajectory analysis was performed using the R-package ‘trajr’36. The straightness index is calculated as the Euclidean distance between the start and the endpoint divided by the total length of the movement36. The straightness index is an indication of how close the animal’s path was to a straight line connecting the start and final point and varies from 0 to 1. Thus it quantifies path efficiency whereas the closer to 1, the higher the efficiency. In our experiment, this path efficiency will be highest when a pigs walks in a straight line during weighing (straightness index = 1). Any deviations from this straight line—due to an increased activity of the pig during weighing—will lower the straightness index towards zero. Sinuosity tries to estimate the tortuosity of a random research path by combining step length and the mean cosine of an animals’ turning angles37. The sinuosity of a trajectory varies between 0 (random movement) and 1 (directed movement).The straightness index and sinuosity are related to the concept of tortuosity and associated with an animals’ orientation and searching behaviorIn this study we hypothesize that mean speed, straightness index and sinuosity are related to pigs’ activity during weighing. In an extreme case, a pig will walk in a straight line towards the RFID reader, stand motionless until weight is recorded and continues its walk in a straight line after the gate is opened. This would result in a low mean speed (m/s), a sinuosity > 0 and a straightness index of 1. We hypothesize that more active pigs will present more lateral movements, increasing the mean speed and lowering the straightness index and sinuosity. So generally, more calm pigs during weighing will display a lower mean speed, although they might have run with a high speed towards the RFID reader.The estimations of body dimensions using video recordings analyzed with DeepLabCut were validated by an independent set of 60 pigs after the initial experiment. These pigs came from five pens of different ages (92–166 days) and were measured manually for tail-neck length and hip width using a simple measuring tape. Pig surface area was estimated for the manual recordings as the multiplication of tail-neck length and hip width. The manual estimates for tail-neck length, hip width and pig surface area were then compared to the estimates from the video analysis by calculating Pearson correlations and root mean squared error (RMSE).24. The average activity score per pig was then compared with automated activity scores by calculating Pearson correlations.Automated activity traits were validated by comparing these values with manual activity scores given by five trained observers. Video footage of 1748 pig weighings were manually scored for pig activity by at least two observers per pig on a scale from 1  to 5 (very active). This ordinal activity scale was constructed based on D’Eath et al. and Holl et al.2 for pig surface area (96 records). If the standard deviation of the estimated hip widths over frames within one weighing event of a pig was > 8.9 cm, the record was set to missing.After estimation of body dimension and activity traits, additional quality control was performed. First, estimates of hip and shoulder width, tail-neck length and pig surface area were set to missing for records with frame by frame standard deviation estimates higher than the mean + 3 × standard deviations for all records. The thresholds were 10.2 cm for hip distance (132 records), 11.8 cm for shoulder distance (135 records), 20.6 cm for tail-neck length (121 records) and 0.058 m2 for pig surface area (85 records) and 3.7 kg for weight (86 records).Second, for every individual with at least four records , outliers were determined using a second order polynomial regression on the variable of interest in function of age in days. Based on the distribution of the difference between observed and predicted phenotypes for all animals, a threshold for exclusion (record set to missing) was set as three times the standard deviation of the differences. The thresholds were 2.1 cm for hip distance (61 records), 2.2 cm for shoulder distance (58 records), 6.4 cm for tail-neck length (75 records), 0.021 mThe final dataset after data cleaning included 7428 records from 1556 finishing pigs descending from 73 Piétrain sires and 204 crossbred dams. Pedigree comprised 4089 animals, where the median pedigree depth of Piétrain sires was 15 generations  and 3  for crossbred dams.blupf90 suite of programs38. Genetic variances and heritabilities were estimated with average information REML, implemented in airemlf90 and invoked with the R-package breedR39 with the options “EM-REML 20”, "use_yams" and “se_covar_function”. Genetic parameters were first estimated on the full dataset and thereafter on subsets per pigs’ weight recording (1 to 8). The first weight recording, for example, corresponds with a dataset of 1176 pigs between 78 and 89 days of age  was estimated as the proportion of variance explained by random environmental effects (c), divided by total variance.We estimated genetic parameters (heritability and genetic correlations) using the g) between traits were estimated using bivariate animal models (airemlf90). Genetic correlations were first calculated between all possible trait combinations using the full dataset. Hereafter, the genetic correlations within traits for all pairwise weighing events were estimated (so two recordings of the same trait were treated as two different traits). By doing this, we can evaluate if a trait genetically changes over time.Genetic correlations (ry is the vector with phenotypes for the studied trait(s); b is the vector containing the fixed effects  and covariates (age); a is the vector of additive genetic effects (4089 levels); c is the vector of random environmental effects (65 levels); e is the vector of residual effects; X, Z and W are incidence matrices for respectively fixed effects, random animal effects and random permanent environmental effects. The random environmental effect c is a combination of date of entrance at the fattening farm and weighing date. Every two weeks, a new batch of pigs arrived at fattening farm. Parity of dams consisted of four classes ’.The estimated animal models were of the form:Performance of the network was evaluated by computing both train and test errors. These errors are measured by the average Euclidian difference between the pixel coordinates from the manual annotations and the DeepLabCut estimations on the training dataset and test dataset. The mean pixel error on the training dataset without probability cut-off (p-cut-off) was 99.2 pixels or 3.4 cm and 95.9 pixels or 3.3 cm for the test dataset Table 3ideo S2. 2). However, estimates using video analysis were on average 7.4 cm higher for tail-neck length and 2.3 cm for hip width. Moreover, automated activity scores were validated by comparing them with manually obtained activity scores given by trained observers. Pearson correlations between manual recordings and video analysis were moderate to high for mean speed (r = 0.49), straightness index (r = − 0.57) and sinuosity index (r = − 0.32). After combining these three automated activity traits in an ‘activity index’ , Pearson correlation with manual activity scores increased to r = 0.62. The inter-observer Pearson correlation for activity score was moderate to high as well, ranging from r = 0.55–0.84. Pairwise correlation plots for all validations are provided in Additional File To validate the performance of the automated pose estimation algorithm, estimated tail-neck length, hip width and pig surface area were compared with manual recordings. Pearson correlations between manual recordings and video analysis were high for tail-neck length , hip width  and pig surface area , standard deviation of tail base speed (6.3%) and mean and standard deviation of tail base acceleration (8.7% and 5.5% respectively). However, h2 estimates were low to moderate for mean speed (24.5%), straightness index (25.9%) and sinuosity (23.8%) of tail base and estimates were even higher for these traits computed from left and right shoulder and hip trajectories  were observed between body dimension parameters and ADG, and low to moderate negative correlations (rg = − 0.46 to − 0.34) were found between body dimension parameters and meat percentage. Mean speed was highly negatively correlated with straightness index (rg = − 0.93) and sinuosity (rg = − 0.84). Low genetic correlations (rg = − 0.34–0.19) were observed between body dimension parameters and behavioral parameters. Very low genetic correlations were estimated between behavioral parameters and tail biting, ear biting and ear swellings  software, we developed a model for pose estimation of individual fattening pigs. We were able to estimate relevant body parts accurately with an average tracking error of 3.3 cm. Using the tracking output we were able to estimate body dimensions and behavioral activity traits. DLC estimations were validated by using manually collected body dimensions as a golden standard. Pearson correlations between the automated estimations and manual observations were high, ranging between 0.80 and 0.94. Moreover, automated activity scores had moderate to high correlations with manually scored activity traits (r = 0.32–0.62). This validation indicates that our methodology is adequate in quantifying general pig activity, certainly since inter-observer correlations for activity were similar (r = 0.55–0.84).40. Combining genetics with analysis of body conformation and animal behavior may help designing more sustainable and robust breeding in the future40. However, defining and scoring behavioral read-outs in pigs on a large scale for application in breeding programs remains problematic. Additionally, large datasets with a sufficient pedigree structure are required to estimate heritabilities and genetic correlations accurately41. Here, we were able to combine genetic information with direct behavioral read-outs. Heritabilities of body dimension parameters were high (h2 = 61–74%) and even somewhat higher than estimates found in literature (h2 = 30–60%)15. This could be attributed to high standardization and low environmental variability in our study: all measurements took place in the same experimental fattening farm within the same season (January–July 2021). This is also reflected in the low estimates for permanent environmental effects (c2 = 7.7–17.6%) for these traits. Heritability estimates were low to moderate for the activity traits mean speed, straightness index and sinuosity (h2 = 22–35%). These estimates are similar to those of manually scored activity and handling traits in pigs during weighing (h2 = 10–23%)24.Focusing only on increased production comes at the cost of an increased amount of production-related diseases and disorders such as leg problems in fattening pigsg = − 0.34–0.19), which indicates that pigs can be selected for both types of traits simultaneously. These findings are in line with Holl et al.17 reporting low genetic correlation between activity score and back fat thickness (rg = − 0.11 to − 0.16) as well as a moderate association between activity score and growth (rg = − 0.38). Similar to Ohnishi and Satoh42, genetic correlations were high between body dimension parameters and average daily gain (rg = 0.81–0.92). Despite these high genetic correlations, combining body dimension parameters with weight recordings remains relevant in (pig) breeding. Body length, for example, is correlated with the number of vertebrae, teats and litter size44. Activity traits mean speed, straightness index and sinuosity were highly correlated, presumably because these traits can be traced back to the activity level of pigs during weighing.No adverse correlations were found between activity traits and body dimension parameters  was first brought to a central hallway, after which pigs were weighed one after the other. Pigs usually reached the RFID reader within a few seconds, after which it took about 10–20 seconds to actually weigh them. Hence, the mean speed we estimate mainly indicates to which extent a pig moved back and forth while being weighed. Moreover, sinuosity and straightness indices are mostly used in ecological studies under the assumption that animals can move freely36. In our setup, pigs were restricted to a limited area of approximately 2.5 m × 0.6 m . This limitation could be tackled by using a real-time version of DeepLabCut in combination with a RFID reader48 or improvements in animal identification using computer vision systems, which currently is a major challenge. The developed model could also be expanded to detect very specific pig behaviors during weighing, such as jumping or turning around (U-turns), using SimBA  software (https://github.com/sgoldenlab/simba)49. During our experiment, several pigs tried to escape the weighing scale by trying to jump out, which might be an indication of flighty animals3. Furthermore, our model could be expanded to identify tail and ear biting marks and/or skin injuries, as demonstrated by Blömke et al.50. Adding a side view camera and/or 3D-camera would possibly allow us to refine our analyses, although it would increase complexity, and therefore limit its on farm use in large scale programs. This would enable us to estimate pig height, muscle depth and back fat5, or perform gait analysis related to lameness or other locomotor problems51.Although our neural network was developed for specific characteristics and settings , the network can be relatively easily generalized or expanded, as explained by Winters et al.In the present study, we estimated pigs’ body dimension and activity traits using automated pose estimation on recorded videos. Our methodology expands the standard routine of pig weighing with a computer vision system, which is able to accurately phenotype both pigs’ body dimensions and activity traits. Moreover, we validated our results and showed these traits are heritable and show no adverse genetic correlation with production traits. These methods are valuable for (pig) breeding organizations to phenotype new production and behavioral traits automatically.Supplementary Information 1.Supplementary Video 1.Supplementary Video 2.


Feed efficiency comprised of residual feed intake (RFI) estimated in both periods. Natural antibodies binding to keyhole limpet hemocyanin (KLH-IgM) and specific antibodies binding to Newcastle disease virus (NDV-IgG) measured at 16 and 28 weeks of age represented immune traits pre- and post-maturity, respectively. In the growing period, 1,820 records on ADG, KLH-IgM and NDV-IgG, and 1,559 records on ADFI1 and RFI were available for analyses. In the laying period, 1,340 records on AFE, EM, KLH-IgM and NDV-IgG, and 1,288 records on ADFI2 and RFI were used in the analyses. Bi-variate animal mixed model was fitted to estimate (co)variance components, heritability and correlations among the traits. The model constituted sex, population, generation, line and genotype as fixed effects, and animal and residual effects as random variables. During the growing period, moderate to high heritability (0.36–0.68) was estimated for the production traits and RFI while the antibody traits had low (0.10–0.22) heritability estimates. Post-maturity, the production traits and RFI were moderately (0.30–0.37) heritable while moderate to high (0.25–0.41) heritability was estimated for the antibody traits. Genetic correlations between feed efficiency and production traits in both periods showed that RFI had negative genetic correlations with ADG (−0.47) and EM (−0.56) but was positively correlated with ADFI1 (0.60), ADFI2 (0.74) and AFE (0.35). Among immune and production traits, KLH-IgM and NDV-IgG had negative genetic correlations with ADG , AFE  and EM  but were positively correlated with ADFI1  and ADFI2 . Genetic correlations between RFI with KLH-IgM  and NDV-IgG  were positive in both production periods. Feed intake, RFI and antibody traits measured in both production periods were positively correlated with estimates ranging from 0.48 to 0.82. Results from this study indicate selection possibilities to improve production, feed efficiency and immune-competence in indigenous chicken. The genetic correlations suggest that improved feed efficiency would be associated with high growth rates, early maturing chicken, high egg mass and reduced feed intake. In contrast, improved general (KLH-IgM) and specific (NDV-IgG) immunity would result in lower growth rates and egg mass but associated with early sexual maturation and high feed intake. Unfavorable genetic correlations between feed efficiency and immune traits imply that chicken of higher productivity and antibody levels will consume more feed to support both functions. These associations indicate that selective breeding for feed efficiency and immune-competence may have genetic consequences on production traits and should therefore be accounted for in indigenous chicken improvement programsThis study aimed at estimating genetic and phenotypic relationships among feed efficiency, immune and production traits measured pre- (9–20 weeks of age) and post- (12 weeks from on-set of lay) maturity. Production traits were average daily gain (ADG) and average daily feed-intake (ADFI Gallus gallus domesticus) play significant roles in nutrition, food and income security in many rural households in most countries in the tropical regions  structure  in Naivasha-Kenya. Two populations of chicken exist at the research station; an indigenous chicken (IC) population and a synthetic breed population known as KALRO chicken (KC). The IC population comprises of ecotypes from various agro-ecological zones grouped into three phylogenetic clusters based on major histocompatibility complex (MHC) linked microsatellite markers . Clustertructure . The synerations . Based o12)  of these chicken comprising of both ML and EL were considered. A total of 1820 chicken were included in the study.The two populations are under continuous selection to develop meat (ML) and egg (EL) lines . The ML 12) . The EL 12) . The chi12) . The selManagement of the experimental population followed standard operating procedures of the breeding program at NRI-KALRO . Birds wvia the wing vein at the ages of 16 and 28 weeks, respectively. These age points were considered based on the average age at point of maximum growth rate (16 weeks) and sexual maturity  of the third generation. Blood sampling procedure was carried out without anesthesia and no chicken was killed for sample collection. Post sampling, birds were given multi-vitamins and observed for any post-trauma effects. Plasma was extracted from the blood samples for further use in antibody measurement.Ethical approval to conduct the study was provided by the Institutional Animal Care and Use Committee (IACUC) of KALRO - Veterinary Science Research Institute (VSRI) (KALROVSRI/IACUC019/30082019). Measurement of antibody traits during the growing period and laying period involved blood sampling of the experimental chicken 2 values. This was done separately for each plate to partly correct for plate differences and allow values to be comparable across plates.Natural antibodies binding to keyhole limpet hemocyanin (KLH-IgM) and specific antibodies binding to Newcastle disease (NCD) virus (NDV-IgG) were used to measure general disease resistance and specific resistance against NCD, respectively. The KLH antigen was considered due to its suitability to measure natural antibodies while NDV antigen was considered due to the negative impact of NCD on IC population . Titers 0.75). During the laying period, ADFI, average daily egg mass (EM), MBW and ADG were considered. At the onset of lay, initial body weight was measured, daily feed intake, egg number and weight recording commenced and continued until week 12, at which final body weight was measured. Estimation of ADG, ADFI and MBW were made as described in the growing period. Egg number was summed and egg weights averaged during the 12 weeks of test period. Thereafter, EM was calculated as the product of total egg number and average egg weight divided by the test period (12 weeks)  and feed conversion ratio (FCR) were used to assess feed efficiency during the growing and early laying phases. To estimate the efficiency measures, average daily feed intake (ADFI), average daily gain (ADG) and metabolic body weight (MBW) were considered during the growers’ phase (from 8–20 weeks of age). At week eight of age, birds were weighed , transferred to individual feeding pens and allowed a 1-week acclimatization period. Thereafter, daily feed intake recording commenced from week 9 and ended at week 20, upon which birds were weighed  again. Average daily gain was obtained as the difference between final and initial body weight divided by the test period. Cumulative feed intake from week 9 to week 20 was divided by the duration of test to obtain ADFI. Metabolic body weight was calculated based on the average between initial and final body weights raised to the power of 0.75 (BW2 weeks) . Feed colme4 package of R Software  related to the population; αki is the random regression coefficient  specific to ith bird for the observed traits and assumed to have ∼ N  distribution.Random effect models were fitted to compute RFI using Eq. Software . The modSoftware .RFIi=Yi12 and AFE. In addition, during the growing period, ADG, MBW, and ADFI were considered. In the laying period, body weight at first egg (BWAFE), egg weight of first egg (EWAFE), ADG, MBW, ADFI, EM, cumulative egg number (EN12) and average daily egg weight (EW12) 12 weeks post on-set of lay were considered.Production traits included data on the traits under selection in the study population; BWyi is the vector of observations for the antibody, feed efficiency and production traits (considering two at a time); bi the vector of fixed effects; ai the vector of random animal additive genetic effects assumed to be a ∼ N  in which A is the numerator relationship matrix and σ2a is the additive genetic variance; ei the vector of random residual effect assumed to be e ∼ N  in which I is an identity matrix and σ2e is the residual variance; Xi, and Zi are incidence matrices relating records to fixed and random animal effects, respectively. The pedigree used to construct the numerator relationship matrix consisted of 2,013 individuals (inclusive of those with and without records) from three generations. Assumed covariance structures of the random model terms among the traits are presented below.ai, ei, A and I were described in Eq. 2aii is the additive genetic variance for trait i; σ2aij is the additive genetic covariance between trait i and trait j; σ2eii is the residual variance for trait i; σ2eij is the residual covariance between trait i and trait j; zero covariance between additive genetic effect and residual effect was assumed.A series of bi-variate animal models Eq.  were fitiaj+eiejwhere: yp < 0.05) affecting the traits, sex, population, generation, line and genotype were included in the bivariate analyses as fixed effects. Likelihood ratio test was used to determine whether genetic correlations among the traits were significantly different from zero, by comparing Eq. r to z-transformation was used to test whether phenotypic correlations were significantly different from zero using the following test statistic:From preliminary tests of fixed factors significantly . In contrast, mean estimates of ADFI, MBW and ADFI were higher in the laying period than in the growing period. At the on-set of lay, mean estimates of BWAFE and EWAFE were 1616.81 g and 42.30 g, respectively. Twelve weeks post on-set of lay showed that the study population laid on average 54 eggs with an average daily egg weight of 47.86 g resulting to an average daily egg mass of 28.87 g/d. In feed efficiency measures (RFI and FCR), chicken had higher mean estimates during the laying period  compared to the growing period . In immune traits, mean estimates of KLH-IgM in both production periods were higher than the NDV-IgG estimates. Between the two production periods, the laying period had higher mean estimates for the antibody traits than the growing period. The coefficient of variation of traits measured in both production periods indicated wide dispersion of estimates from their respective means.Descriptive statistics on production, feed efficiency and immune traits measured during the growing and laying periods are presented in 12, ADG, RFI and FCR than residual effects. In contrast, residual effects were higher on MBW, ADFI, KLH-IgM and NDV-IgG than additive genetic effects. Across the traits measured during the laying period, residual effect was more eminent on AFE, ADG, MBW, EN12, EM12, KLH-IgM, and NDV-IgG than additive genetic effect. On the other hand, phenotypic variation due to additive genetic effect was highest in BWAFE, EWAFE and EW12 than additive genetic effects. Moderate to high heritabilities (0.32–0.68) were estimated for production traits  measured during the growing period. Among the feed efficiency measures, heritability estimates for RFI and FCR were 0.43 and 0.45, respectively. Low to moderate heritability was estimated in KLH-IgM (h2 = 0.22) and NDV-IgG (h2 = 0.10). At the onset of lay, heritability estimates for AFE, BWAFE and EWAFE were 0.31, 0.37 and 0.59, respectively. For egg production traits measured 12 weeks post onset of lay, heritability estimates for EN12, EW12 and EM12 were 0.23, 0.64, and 0.33, respectively. Contrary to the growing period, ADG (h2 = 0.02), MBW (h2 = 0.26) and ADFI (h2 = 0.37) measured during the laying period had lower heritability estimates. Feed efficiency measures in the laying period had lower heritability  estimates than in the growing period. Heritability estimates for KLH-IgM and NDV-IgG in the laying period were 0.41 and 0.25, respectively. These estimates were higher compared to those obtained for traits in the growing period.Variation due to additive genetic effects and residual effects, and heritability estimates are presented in g) and phenotypic (rp) correlations among production, feed efficiency and immune traits measured during the growing period are presented in 12, ADG, MBW and ADFI) were highly positive . Between feed efficiency and production traits, RFI was negatively correlated with BW12 (rg = −0.66), ADG (rg = −0.47) and MBW (rg = −0.52) but positively correlated with ADFI (rg = 0.60). On the other hand, FCR had positive (rg = 0.44–0.49) correlations with BW12, MBW and ADFI but was highly negatively correlated with ADG (rg = −0.79). Among antibody traits and production traits, negative correlations were estimated between KLH-IgM with BW12  and between NDV-IgG with ADG . Between feed efficiency and immune measures, ADFI and RFI was positively correlated with KLH-IgM (rg = 0.41–0.62) and NDV-IgG (rg = 0.34–0.58). Positive genetic correlation was estimated between RFI and FCR (rg = 0.51). Although non-significant, KLH-IgM and NDV-IgG had a negative genetic correlation. Phenotypic correlations among the traits followed a similar pattern as genetic correlations, apart from correlations between FCR with BW12, MBW, and RFI, and between RFI and BW12. Significant positive phenotypic correlations  were estimated among the production traits. Residual feed intake had positive phenotypic correlation with BW12  while FCR was negatively  correlated to BW12, ADG and MBW but was positively correlated to ADFI . There was a positive  correlation between the two measures of feed efficiency. Positive phenotypic correlations were estimated between KLH-IgM with ADG, MBW, and ADFI.Genetic (rg) and phenotypic (rp) correlations among production, feed efficiency and immune traits measured during the laying period are presented in p < 0.01) genetic correlations were estimated among egg-related traits  measured at the onset of lay. Age at first egg had negative correlation EN12 (rg = −0.54) but was positively correlated with EW12 (rg = 0.74) and EM12 (rg = 0.41). Body weight at sexual maturity was positively (rg = 0.46–0.72) correlated with MBW, EW12, EM12, and ADFI but was negatively correlated with ADG (rg = −0.38) and EN12 (rg = −0.59). Egg weight of first egg had positive correlation with EW12 . Positive (0.39–0.75) genetic correlations were estimated between MBW with ADG, EN12, EM12 and ADFI. Cumulative number of eggs 12 weeks’ post-onset of lay was negatively (rg = −0.52) correlated with EW12 but was positively correlated with EM12 (rg = 0.93) and ADFI (rg = 0.40). Average daily egg weight had positive correlations with EM12 (rg = 0.66) and ADFI (rg = 0.61). Between feed efficiency and production traits, RFI was negatively  correlated with MBW, EN12 and EM12 but positively  correlated with EW12 and ADFI. Feed conversion ratio had positive (rg = 0.51–0.82) correlations with MBW, EW12 and ADFI but had negative (rg = −0.74 to −0.82) correlations with EN12 and EM12. Genetic correlations between immune and production traits showed that KLH-IgM was negatively (rg = −0.35 to −0.43) correlated with AFE, BWAFE, EN12, EW12 and EM12 but was positively (rg = 0.47–0.51) correlated with MBW and ADFI. Similarly, NDV-IgG was negatively (rg = −0.35 to −0.49) correlated with AFE, BWAFE, EN12, EW12 and EM12 but was positively (rg = 0.44–0.52) correlated to MBW and ADFI. Positive (0.33–0.50) genetic correlations were estimated between RFI and the antibody traits. Between RFI and FCR, positive (rg = 0.41) genetic correlations were estimated. Similar to the growing period, although non-significant, KLH-IgM and NDV-IgG were negatively (rg = −0.49) correlated. Although lower estimates, phenotypic correlations among production, feed efficiency and antibody traits followed a similar a pattern as the genetic correlations.Genetic , and between ADG measured in both production periods . On the other hand, positive  genetic correlations between two production periods were estimated for ADFI, KLH-IgM and NDV-IgG. Positive phenotypic correlation was estimated between MBW  and RFI  measured in both production periods.Genetic and phenotypic correlations of traits between the growing period and laying period are presented in 12 and ADG) and feed efficiency (RFI and FCR) traits during the growing period indicate that these traits could be selected for in indigenous chicken to develop meat lines of high growth rates and that are feed efficient. The magnitude of heritability estimates for these traits suggest relatively higher prediction accuracies would be expected and therefore, mass selection could be utilized to improve growth and feed efficiency  and feed efficiency (RFI and FCR) suggest selection possibilities to breed for indigenous chicken egg lines that are early maturing, with high egg productivity and are feed efficient. Apart from average daily egg weight, higher residual variance than additive genetic variance for egg production and feed efficiency measures indicate that family selection would be a more reliable strategy to negate the residual environmental effects and improve accuracies of breeding values for these traits  at 16 weeks than at 28 weeks of age suggests that relatively low prediction accuracies would be expected during the growing period compared to the laying period. In a commercial layer line divergently selected for natural antibodies at age 16 weeks for seven generations,  chicken . In this chicken .12 to develop meat lines, the positive correlation between BW12 and ADG indicate that improved body weight at 12 weeks will be associated with higher growth rates. Besides, BW12 is previously reported to have favorable genetic associations with hatch weight and market weight (24–26 weeks of age) in indigenous chicken  and egg production traits than with ADFI, implying that variability in FCR was majorly influenced by production traits than by feed intake. On the other hand, the genetic and phenotypic correlations indicated that FCR efficient chicken consumed less feed but had higher growth rates during the juvenile period and higher egg production. Although FCR does not account for metabolic weight, the significant correlation with MBW suggest that efficient chicken had low maintenance requirement. These favorable associations imply that FCR is a trait that can be considered in breeding objectives intended to improve the efficiency of producing indigenous chicken during the growing and laying periods. Furthermore, FCR has extensively been used to measure feed efficiency due to its ease of computation and its direct association of costs and profits to quantities of feed . HoweverDue to the distributing properties of the regression procedure used to obtain RFI in this study, the efficiency measure was phenotypically independent from the production traits used in its estimation. The positive genetic correlation between RFI and ADFI in both production periods indicate that improved efficiency would be accompanied by lower feed intake levels. Selection for feed efficiency measures is mostly aimed at reducing feed intake levels with a consequential effect on reduced feed costs, nitrogenous wastes and environmental footprint . To this12, and between NDV-IgG and ADG implied immune deficiencies in chicken of high body weight at week 12 of age and growth rate during the juvenile period. At sexual maturity, the negative correlations between AFE and the antibody traits suggested that early maturing chicken had higher KLH-IgM and NDV-IgG levels. Similar observations by Among immune and production traits, the negative correlations between KLH-IgM and BWBetween the immune traits, the negative correlation indicated an antagonistic association between KLH-IgM and NDV-IgG. However, the lack of significance of the estimate suggests the association may not be significantly different from zero and the traits are probably genetically different. Antibodies binding to KLH and NDV represent different functional B cell activities and mechanisms underlying their formation vary with respect to the nature of the antigen . For ins12 and AFE implies that improved body weight at week 12 would lead to delayed sexual maturity. This may be undesirable for the egg lines given the negative associations between AFE and egg numbers  and specific (NDV-IgG) immunity would result in lower growth rates and egg mass but associated with early sexual maturation and high feed intake. Unfavorable genetic correlations between feed efficiency and immune traits imply that chicken of higher productivity and antibody levels will consume more feed to support both functions. These associations indicate that selective breeding for feed efficiency and immune-competence may have genetic consequences on production traits and should therefore be accounted for in indigenous chicken improvement programs.


DGAT1, MGST1, PTK2, and SCRIB), disease-related , heat stress–related , and reproduction-related (7SK and DOCK2) genes. This study has shown that there are differences in the genetic mechanisms of milk production traits at different stages of lactation. Therefore, it is necessary to conduct research on milk production traits at different stages of lactation as different traits. Our results can also provide a theoretical basis for subsequent molecular breeding, especially for the novel genetic loci.The aim of this study was to investigate the genetic parameters and genetic architectures of six milk production traits in the Shanghai Holstein population. The data used to estimate the genetic parameters consisted of 1,968,589 test-day records for 305,031 primiparous cows. Among the cows with phenotypes, 3,016 cows were genotyped with Illumina Bovine SNP50K BeadChip, GeneSeek Bovine 50K BeadChip, GeneSeek Bovine LD BeadChip v4, GeneSeek Bovine 150K BeadChip, or low-depth whole-genome sequencing. A genome-wide association study was performed to identify quantitative trait loci and genes associated with milk production traits in the Shanghai Holstein population using genotypes imputed to whole-genome sequences and both fixed and random model circulating probability unification and a mixed linear model with rMVP software. Estimated heritabilities (h2) varied from 0.04 to 0.14 for somatic cell score (SCS), 0.07 to 0.22 for fat percentage (FP), 0.09 to 0.27 for milk yield (MY), 0.06 to 0.23 for fat yield (FY), 0.09 to 0.26 for protein yield (PY), and 0.07 to 0.35 for protein percentage (PP), respectively. Within lactation, genetic correlations for SCS, FP, MY, FY, PY, and PP at different stages of lactation estimated in random regression model were ranged from -0.02 to 0.99, 0.18 to 0.99, 0.04 to 0.99, 0.04 to 0.99, 0.01 to 0.99, and 0.33 to 0.99, respectively. The genetic correlations were highest between adjacent DIM but decreased as DIM got further apart. Candidate genes included those related to production traits ( Chinese Holstein cattle are derived from grading crossbreeding and selection between the local yellow cattle and Holstein, a breed that was mostly imported from Canada, the United States, France, and northern Europe and renamed by the Chinese Ministry of Agriculture in 1992 . Since tSince 1994, the Dairy Herd Improvement has been carried out in Shanghai, where millions of test day records are collected . Milk prGWAS is an effective method for identifying the genetic variations involved in complex traits. With the rapid development of high-throughput sequencing technology, many researchers have reported that the power of GWAS based on imputed whole-genome sequencing (WGS) variants on different traits in livestock, such as cattle , pig Wu, and chiThus far, many researchers have studied the Holstein population in different countries and provinces, including the north of China . A previTo evaluate the genetic parameter of milk production traits, we collected the test-day records from the farms of Shanghai Bright Dairy and Food Co., Ltd. from primiparous cows born between 1995 and 2020 with the regular and standard performance of DHI. In total, there are 1,968,589 records for the first lactation of 305,031 cows from 260 farms with the following criteria : 1) age The derivative-free approach to multivariate analysis (DMU) package was used to estimate breeding values using the random regression test-day model (RRM) . Due to ith herd-test day; jth calving month–age; kth calving year–season; l; l; mth covariate of Lengendre polynomial; Here, The variance-covariance matrix is as follows:Here, us model .Data from 3,489 genotyped animals were used in this study. In addition, 222 bulls from Run 2 of the 1000 Bull Genome Project were included . The 3,4http://broadinstitute.github.io/picard/), local realignment around indels and base quality scores recalculation were conducted using GATK v3.6  larger than 0.05 were retained. STITCH v1.5.3 (The extremely low-coverage whole genome sequencing used the Illumina Hiseq4000 platform to sequence the genomic DNA extracted from cow hair-follicle according to the manufacturer’s protocol. All of the raw sequence data were filtered using Fastp v0.20.0  with defATK v3.6  based onols v1.9  (set 1) H v1.5.3  was usedp < 10–6) as calculated by means of the Fisher’s Exact Test, and SNPs with MAF lower than 0.05. For the quality control of the samples, animals with a call rate lower than 0.95 were excluded from the analysis.For all the genotype data, only the autosomal chromosomes and SNPs with known positions in the UMD 3.1 bovine assembly map were considered. Genotype quality control for all the panels excluded SNPs with a call rate lower than 0.90, SNPs with deviations from the Hardy–Weinberg equilibrium . We used Perl script to match phenotype samples ID with genotype samples ID to obtain the genotype file which has phenotype. Finally, genotype data were filtered by PLINK v1.9 with the parameters “--geno 0.1 --hwe 0.000001 --maf 0.05 --mind 0.05”. Only autosomal SNPs were considered in this study, and IDs without phenotypes were excluded.To determine the level of population stratification, we plotted the population structure by PCA. Principal component analysis (PCA) was conducted using GCTA v64  on 3,016We performed powerful GWAS analyses of six milk production traits  in different lactation stages  in the Shanghai Holstein population using FarmCPU (Fixed and random model Circuitous Probability Unification) and MLM (mixed linear model) based on imputed WGS data with the rMVP software . FarmCPU−8 and 5 × 10−6 as genome-wide and suggestive significance threshold to correct false positive findings due to multiple testing (As suggested by  testing .https://david.ncifcrf.gov/). In all analyses, the p-value < 0.05 was considered significantly different.We extended the positions of significant SNPs 150 Kb upstream and downstream and then updated to the Ensembl (UMD3.1 genome version). Identification of the closest genes to significant SNPs was obtained using Ensembl annotation of the UMD3.1 genome version. GO enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the candidate genes were performed using the DAVID 6.8 Functional Annotation Tool  that contained or overlapped with the candidate genes. After matching, the number and function of variants were identified, and these variants were used for subsequent analyses.Genetic correlations between test-day MY, test-day FY, test-day PY, test-day FP, test-day PP, and test-day SCS at different stages of lactation estimated in RRM are ranged from 0.04 to 0.99, 0.04 to 0.99, 0.01 to 0.99, 0.18 to 0.99, 0.33 to 0.99, and -0.02 to 0.99, respectively . For theThe imputation accuracy was 0.95, which was evaluated by the internal information score generated by STITCH itself for the extremely low-coverage whole genome sequencing. The imputation accuracy for GGP Bovine LD v4 and GGP Bovine 50K imputed to GGP Bovine 150K was 0.98 and 0.99, respectively. Then the imputation accuracy for imputed GGP Bovine LD v4 and GGP Bovine 50K to WGS (222 bulls from Run 2 of the 1000 Bull Genome Project as reference panel) was 0.97 and 0.97, respectively. The accuracy for GGP Bovine 150K to WGS was 0.97. All the genotypes imputed to WGS were merged, and the final genotype data file contained 19,105,311 SNPs. After the filtration, 8,686,483 loci and 3,016 individuals were retained to be used in the GWAS. The PCA revealed that the Shanghai Holstein population are subdivided into five differentiated groups by the first two principal components, which explained 17.25 and 10.96% of the genetic variability in the analysis, respectively, and about 28.21% of the variation is explained by the first three PCs together .Due to the highest genetic correlation estimates being observed between adjacent test days, we only displayed the Manhattan plot of TD7 for all six traits . The Manp-value < 5 × 10−8 as the threshold, a total of 984, 1,150, 1,291, 1,229, 1,018, and 1,477 significant SNPs detected by FarmCPU for all DIM of MY, FP, FY, PP, PY, and SCS, respectively. 279, 429, 36, 175, 85, and 42 SNPs were identified as significant by MLM for all DIM of MY, FP, FY, PP, PY, and SCS, respectively . The most significant SNPs for FP at mid and late lactation both were BTA14:1801116 . For FY, the candidate genes contained the most significant SNPs for TD7, TD35, TD50, TD140, and TD280 were DSP, MAML3, PRKG1, WDR34, and SLC1A3, respectively. For PP, the candidate genes contained the most significant SNPs for TD7, TD35, TD50, TD140, and TD280 were DCLK2, AHCTF1, OCLN, MROH1, and HSF1, respectively. The most significant SNP associated with PP at late lactation was BTA14:1807140 . For PY, the candidate genes contained the most significant SNPs for TD7, TD35, TD50, TD140, and TD280 were CTNND2, CSMD3, WWOX, ARHGAP10, and LMAN2L, respectively. For SCS, the candidate genes contained the most significant SNPs for TD7, TD35, TD50, TD140, and TD280 were NFKBIE, ABCF1, MYZAP, TTLL7, and DNAH9, respectively.We paid more attention to the candidate genes which contained or were near to the most significant SNPs associated with different milk production in five lactation stages . For MY, the candidate genes contained the most significant SNPs for TD7, TD35, TD50, TD140, and TD280 were NDST4, ICAM2, KCNMA1, LRP5, KALRN, IQCA1, MANBA, SCRIB, COL22A1, MORN1, APBA2, ZMYND8, WWOX, BFAR, CECR2, GALNT16, SPOP, and CPEB3. For FP, a total of 20 candidate genes for at least two lactation stages contained significant SNPs, including DGAT1, ADAMTS3, ZKSCAN7, CTNNA3, CDH23, ELM O 1, SLC15A5, ESR1, NRP1, BOP1, RPH3A, ATRNL1, FAM21A, MGST1, USH2A, WDR87, SYNRG, RANBP17, ANKRD55, and PRIM2. For FY, 11 genes associated with at least two lactation stages. For PP, 18 genes involved in at least two lactation stages, including ZMYND8, AHCTF1, TSHR, RALYL, RYR2, ORC2, MAP1S, MT O 1, NRP1, TECPR2, LRP5, NADSYN1, SMC5, KCNQ5, MAP2K6, OCLN, PBX1, and PRKG1. For PY, 15 candidate genes involved in at least two lactation stages, including WWOX, TMEM132C, NDST4, GUCY1A2, CTNND2, MANBA, MCC, KCNIP1, ITGA2, CTNNA3, SCRIB, CCDC33, MACROD2, PITPNB, and FDXR. For SCS, 27 candidate genes involved in at least two lactation stages, including PCDH15, ELM O 1, LDB2, SH3GL2, COL22A1, NUDCD1, HMCN1, CCDC63, GALNS, ADTRP, C1QTNF7, LPAR1, MYZAP, PLCB1, SLC38A9, LANCL2, SLC35F3, DKK2, KCNIP4, TRIM11, RERG, ACOXL, DDX54, DNAH9, ERICH1, MTA1, and B3GALNT2.We further identified the genes which were candidate genes for more than two lactation stages or traits. For MY, there were 18 candidate genes for at least two lactation stages, including p-value adjusted using the Bonferroni approach  was considered to be the threshold value for significantly enriched GO terms and pathways. As shown in The The number and function of variants identified using QTL annotation are listed in In this research, we estimated various genetic parameters in a large population of Shanghai Holstein that had been regularly measured for six major dairy traits throughout lactation since 1995. This estimation was performed by using a random regression model for the first time in Shanghai. Currently, there are many studies for different Holstein populations . We founCurrently, there are many studies on the submodels in the random regression test day model. The results of these studies showed that the lactation curves of milk production traits obtained by different researchers were also quite different . Since 1In our study, except for FP, other traits showed that heritability reached its maximum in early lactation. The heritability of MY varied from 0.16 to 0.27, with the lowest value in peak lactation. In general, the trend for MY heritabilities was like the trend found by We analyzed the genetic correlation between different test days and found that it was highest (close to 1) on adjacent test days but gradually decreased with increasing DIM intervals, which was consistent with previous studies. SNP chips are customized chips based on existing SNP information, and new SNP cannot be found. The coverage of genotyping-by-sequencing accounts for only about 5% of the whole genome and many SNPs are missed. WGS can find SNPs on a genome-wide scale without causing the omission of SNPs . CompareNDST4 is associated with milk fever in the U.S. Holstein cattle  in the GWAS of TD7 FP is located in the intronic region of ATP2B2. PLBD1 is an important candidate gene for fatty acid composition in milk  of HSF1 that was related to thermo tolerance in Chinese Holstein cattle through association analysis ; thus, the mechanisms affecting the traits are similar. n cattle . In a pr15 of PY . Jiang en cattle . DGAT1 w with FY . ADAMTS3 of cows . TECPR2 breeding . Also, Tn cattle . PRKG1 p in milk , 1. Meane traits . TMEM132n traits . HMCN1 in humans . This re in milk . ACOXL ipathways . PLCB1 p content . B3GALNTn et al. . A previmastitis , and the in milk . The mosanalysis . NFKBIE esponses .MROH1, an important candidate gene for milk protein composition, is located in a 1.85–2.11 Mb region on BTA14 that has been shown to be associated with 305-days and peak milk production in cows. In addition, the model for selecting is also important. RRM is a feasible alternative to yield more accurate selection and culling decisions. RRM provides information about the temporal variation of biological processes underlying the studied traits to exploit for management and breeding purposes , disease-related genes , heat stress–related genes , and reproduction-related genes (7SK and DOCK2). The genes and QTLs related to heat stress are important to investigate the mechanism of response to heat stress, such as ITGA9, which can act as an important gene for heat-resistant breeding of Shanghai Holstein.In our study, an RRM with fifth-order of Legendre polynomials was an appropriate model for genetic evaluation of six milk production traits in Shanghai Holstein populations. The main results showed that genetic parameters and breeding values were successfully estimated. The results of genetic correlations demonstrated that combining the milk production traits tested on different lactation into a single trait can lead to inaccurate estimates of the genetic value of dairy cows. At the same time, the measurement and recording of milk for some adjacent lactation periods can be simplified without affecting the reliability of parameter estimation using RRM. Then, we detected significant SNPs and candidate genes associated with different traits in different lactation stages, mainly including milk-related genes (


Mungbean is a warm-season annual food legume and plays important role in supplying food and nutritional security in many tropical countries. However, the genetic basis of its agronomic traits remains poorly understood. Therefore, we resequenced 558 Chinese mungbean landraces and produced a comprehensive map of mungbean genomic variation. We phenotyped all landraces in six different environments. Genome-wide association studies (GWAS) produced 110 signals significantly associated with nine agronomic traits, for which several candidate genes were identified. Overall, this study provides new insight into the genetic architecture of mungbean agronomic traits. Moreover, the genome-wide variations identified here should be valuable resources for future breeding studies of this important food legume. Vigna radiata L.) is one of the most important warm-season legume crops. Rich in essential amino acids, vitamins, and micronutrients, mungbean offers many benefits to human health  underlying complex traits is the genome-wide association study (GWAS) . This stIn this study, we aimed to better understand the genetic basis of agronomic traits and genetic variability in mungbean landraces from geographically diverse regions in China. To this end, we resequenced 558 accessions with 9.83-fold coverage depth. We analyzed genomic variation, evaluated phenotypic variation across six environments, and performed GWAS for nine agronomic traits. Our results present a collection of genes or alleles that may be helpful for enhancing the genetic diversity of mungbean varieties, and provide valuable genomic information for future mungbean breeding programs.Five hundred and fifty-eight Chinese mungbean landraces were used for resequencing and phenotypic data collection. The accessions were selected on the basis of the germplasm database records of geographic origin and phenotypic variation to maximize genetic diversity. These mungbean lines are from 16 provinces in China, spanning most of the geographic range of mungbean. Detailed information of the 558 accessions is listed in For phenotyping, all accessions were grown in six natural environments at four different locations in 2019 to 2021: Ezhou , Hubei province, in 2019 (2019_EZ) and 2021 (2021_EZ); Lingshui , Hainan province, in 2019 (2019_LS) and 2020 (2020_LS); Wuhan , Hubei province, in 2021 (2021_WH); and Gucheng , Hubei province, in 2021 (2021_GC). In 2019_LS and 2020_LS, all accessions were sown in late October and harvested in early January of the following year, in the tropical environment of Lingshui on Hainan Island. All accessions planted in 2021_WH were sown in mid-April (spring growing season) and harvested in early July. In the other three environments, the 558 landraces were planted in summertime at different planting dates from early to late June, with harvests in October. Two replicates were performed in each environment. Each plot in the six environments contained one row 2 m in length, with 11 plants per row, 20 cm between plants within each row, and 30 cm between rows. Phenotypic trait data were collected according to a quantitative and descriptive method previously published for descriptors and data standards . All 11 For each accession, genomic DNA was extracted from young leaves of a single two-week-old plant using the cetyltrimethylammonium bromide (CTAB) method . At leasCleaned reads for each accession were mapped to the mungbean reference genome using BWA v0.7.17  with defTo ensure accuracy of the variants, we performed a two-step filter. Firstly, hard filtering was applied to the raw variant set using VariantFiltration in GATK, with parameters ‘QD < 2.0 || MQ < 40.0 || FS > 60.0 || SOR > 3.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0’ applied to SNPs, and ‘QD < 2.0 || FS > 200.0 || SOR > 10.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0’ applied to indels. Secondly, the variant set output from GATK was further filtered using PLINK v1.9  with then .The final variant data set of the entire population was used for GWAS. Kinship matrices of relatedness between the accessions were calculated using the “-gk” function of Genome-wide Efficient Mixed-Model Association (GEMMA). These kinship matrices were then used to correct the population structure. The association analysis was performed using GEMMA, which was designed to handle large dataset analysis. The qqman R package  was usedVC1973A reference genome (N/S) and transition to transversion SNPs (Ts/Tv) is 0.74 and 1.95, respectively  for the nine traits in the mungbean genome . Based on the peak SNP  of the association signal, two haplotypes were identified in all 558 accessions. Accessions carrying the CC allele exhibited increased seeds per pod and pod length compared to accessions carrying the TT allele  (Vradi05g00200). Leucine-rich-repeat receptor-like kinases are involved in polar auxin transport in plants  Table S9n plants , and auxn plants . We therVradi03g06500, a gene encoding a Calvin cycle protein, CP12-2, whose orthologue in Arabidopsis regulates flowering time  was located within the ninth intron of Vradi04g07810 .ZW, CL and XH conceived and designed the experiments. XH, XW, YX, LiL, LiaL, HC and LS performed the experiments. XH analysed data and wrote the manuscript. All authors contributed to the article and approved the submitted version.This work was supported in part by the National Key Research and Development Program of China (2019YFD1001303 and 2019YFD1001300), China Agriculture Research System (CARS-08), and the National Natural Science Foundation of China (32101808).We thank Professor Xin Chen , Professor Jing Tian , Professor Bin Zhou , Professor Xu Zhu , Professor Yanlan Wang , and Professor Huijun Zhu  for providing part of mungbean accessions used in the study.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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Compared to medium-density single nucleotide polymorphism (SNP) data, high-density SNP data contain abundant genetic variants and provide more information for the genetic evaluation of livestock, but it has been shown that they do not confer any advantage for genomic prediction and heritability estimation. One possible reason is the uneven distribution of the linkage disequilibrium (LD) along the genome, i.e., LD heterogeneity among regions. The aim of this study was to effectively use genome-wide SNP data for genomic prediction and heritability estimation by using models that control LD heterogeneity among regions.The LD-adjusted kinship (LDAK) and LD-stratified multicomponent (LDS) models were used to control LD heterogeneity among regions and were compared with the classical model that has no such control. Simulated and real traits of 2000 dairy cattle individuals with imputed high-density (770K) SNP data were used. Five types of phenotypes were simulated, which were controlled by very strongly, strongly, moderately, weakly and very weakly tagged causal variants, respectively. The performances of the models with high- and medium-density (50K) panels were compared to verify that the models that controlled LD heterogeneity among regions were more effective with high-density data.Compared to the medium-density panel, the use of the high-density panel did not improve and even decreased prediction accuracies and heritability estimates from the classical model for both simulated and real traits. Compared to the classical model, LDS effectively improved the accuracy of genomic predictions and unbiasedness of heritability estimates, regardless of the genetic architecture of the trait. LDAK applies only to traits that are mainly controlled by weakly tagged causal variants, but is still less effective than LDS for this type of trait. Compared with the classical model, LDS improved prediction accuracy by about 13% for simulated phenotypes and by 0.3 to ~ 10.7% for real traits with the high-density panel, and by ~ 1% for simulated phenotypes and by − 0.1 to ~ 6.9% for real traits with the medium-density panel.Grouping SNPs based on regional LD to construct the LD-stratified multicomponent model can effectively eliminate the adverse effects of LD heterogeneity among regions, and greatly improve the efficiency of high-density SNP data for genomic prediction and heritability estimation.The online version contains supplementary material available at 10.1186/s12711-022-00737-3. Since genomic prediction was proposed , the metIn general, LD information between markers is used to pre-select markers or construct LD-based haplotypes, but these processes have little impact on the accuracy of genomic prediction –8. HowevWhole-genome sequence (WGS) data and high-density SNP data have been used in animal genetic evaluation , 17. ComIt is generally assumed that the greater is the heritability (genetic variance) captured by the model, the higher is the prediction accuracy of the model. However, studies have shown that the variation of the estimates of heritability is not consistent with the variation of genomic prediction accuracy, i.e. the genomic prediction accuracy does not necessarily increase or it even decreases as the estimates of heritability increase . This maIn this study, we compared a series of models that control LD heterogeneity among regions with the classical model  to: (1) –6) were removed; after filtering, 336,977 SNPs (referred to as the 300K SNP panel hereafter) remained for further analyses.This study used a German dairy cattle population of 2000 bulls from Vereinigte Informationssysteme Tierhaltung Wirtschaftlicher Verein, which has previously been described in . All indhttps://github.com/SCAU-AnimalGenetics/LD-heterogeneity/tree/main/simphe.We established a series of simulation studies to explore the effects of LD heterogeneity among regions on genomic prediction and heritability estimation. The real dairy cattle genotypic data were used as a base for the simulations. Heritability and number of causal variants were fixed at 0.8 and 100, respectively. Using cument}j , where \g et al. . This mePrevious studies have shown that the genomic prediction accuracy of medium-density panels (20 to 50K) with SNPs evenly distributed throughout the genome is higher than that of high-density SNP panels . In the Pedigree-based estimated breeding values (EBV) for milk yield (MY), milk protein yield (PY), milk fat yield (FY), milk protein percentage (PP), milk fat percentage (FP) and somatic cell score (SCS) were available for all the bulls. Additional file ument}gi . DeregreThe GCTA model  was used-th SNP in the -th individual, which is represented by 0, 1 and 2. The additive Due to LD, SNPs will be repeatedly tagged. Using the LDAK software , we calcmentclass2pt{minimIn addition to constructing the LDAK model, we also used the LD-stratified multicomponent model to offset the influence of LD heterogeneity among regions. In this study, the regional SNP LD score was used to divide SNPs into five equal groups, corresponding to SNPs with very high, high, moderate, low and very low LD levels, respectively. The SNPs in each group were used to construct the respective GRM and then a multi-component model was established :4\documementclass2pt{minim by Eqs. , respecthttps://github.com/SCAU-AnimalGenetics/LD-heterogeneity/tree-save/main/regional-LD. The GCTA, LDAK, GCTA-LDS and LDAK-LDS models used in this study only consider additive genetic effects, so the GCTA-LDS and LDAK-LDS models did not consider the covariance of genetic effects corresponding to different GRM.In this study, we used the mean LD score to represent the regional LD. The mean LD score was fitted with ~ 100-kb segments using a sliding window approach [rget SNP . Our mett-tests were conducted between all models, with P values adjusted by Bonferroni correction.In this study, variance components and genetic values were estimated using the LDAK software . For eacThe mean LD score used to represent the regional LD ranged from 3.8 to 347.9 for the high-density panel Fig. a, which The GCTA, LDAK and GREML-LDS models were originally proposed to improve the unbiasedness of heritability estimates. In this study, the performance of each model in terms of genome prediction, heritability estimation and model fit were analyzed simultaneously to fully evaluate the effectiveness of each model in dealing with LD heterogeneity.Figure Figure Figure Additional file Figure Currently, studies on LD heterogeneity among regions have focused on the estimation of the heritability of human complex traits and diseases , 10. HowIn this study, all GRM were constructed based on the method proposed by Yang et al. . This meentclass1pt{minimatability . In thistability  should bentclass1pt{minimaentclass1pt{minimaDue to LD heterogeneity among regions, the genetic contribution of causal variants was overestimated in high LD regions and underestimated in low LD regions , 29, 30.This result confirms that GREML-LDS is effective for heritability estimation and proves that GREML-LDS is also beneficial to genomic prediction for livestock populations. Thus, GCTA-LDS and LDAK-LDS can be used as reliable models to control LD heterogeneity among regions, and the LD stratified genomic best linear unbiased prediction (GBLUP) or the single-step GBLUP models can be constructed based on these methods to eliminate the adverse effects of LD heterogeneity among regions and improve the accuracy of genomic prediction in livestock.It is generally assumed that the greater is the genetic variance (or heritability) explained by the model, the higher is the genomic prediction accuracy . HoweverDue to LD, the level of replicate tagging of SNPs is unevenly distributed along the genome Fig. b and se: Fig. S1With the higher-density panel, the differences in the level of replicate tagging between SNPs in high and low LD regions increase Table . Thus, iThe results of the estimation of variance components based on GREML-LDS showed that the causal variants of quantitative traits are not evenly distributed among LD levels Fig. . As discIn contrast to the simulated traits, LDAK-LDS gave the best genomic prediction results for almost all of the real dairy traits. In this case, SNPs should not only be grouped according to LD heterogeneity among regions but the SNPs in low LD regions should have a greater weight. Generally, SNPs in low LD regions also have a low MAF, and this is also true in the bovine genome data  of GCTA, LDAK, GCTA-LDS and LDAK-LDS based on the high- and medium-density panels for dairy cattle traits. Table S4. Estimates of SNP-heritability for dairy cattle traits by GCTA, LDAK, GCTA-LDS, and LDAK-LDS based on the high-and medium-density panelsAdditional file 2: Figure S1. Replicate tagging of SNPs on chromosome 29 without and after LD weighting. (a) Represents replicate tagging of SNPs without LD weighting in the high-density panel (300K). (b) Represents replicate tagging of SNPs after LD weighting in the high-density panel. (c) Represents replicate tagging of SNPs without LD weighting in the medium-density panel (50K). (d) Represents replicate tagging of SNPs after LD weighting in the medium-density panel. (e) Represents the distribution of replicate tagging of SNPs without LD weighting in the high-density panel. (f) Represents the distribution of replicate tagging of SNPs after LD weighting in the high-density panel. (g) Represents the distribution of replicate tagging of SNPs without LD weighting in the medium-density panel. (h) Represents the distribution of replicate tagging of SNPs after LD weighting in the medium-density panel. Figure S2. Performance of GCTA, LDAK, GCTA-LDS and LDAK-LDS in terms of genomic prediction (a), heritability estimation (b), and model fit (c) for simulated phenotypes that are controlled by causal variants with different tagging levels. The heritability of all simulated phenotypes was 0.5. Paired t-test was applied to compare the difference between models, with P values adjusted by Bonferroni correction. ***indicates significant differences at P < 0.001, ** significant differences at 0.001 < P < 0.01, * significant differences at 0.01 < P < 0.05, and ns indicates no statistically significant difference. Figure S3. Estimated heritability enrichment of simulated phenotypes in five LD groups. Phenotypes in (a) to (e) were controlled by very weakly, weakly, averagely, strongly, and very strongly tagged causal variants, respectively. Estimates of heritability enrichment were calculated from the GREML-LDS model. SNPs in the high-density panel were used to construct the GRM used in GREML-LDS. The red lines represent the true median of heritability enrichment for simulated traits. Figure S4. Relationship between LD score and minor allele frequency. The 336,977 SNPs in the high-density panel were used to calculate the LD score. The red line is the regression line across all 336,977 SNPs, its positive gradient, is especially apparent for SNPs with a MAF < 0.2.