Increasing resilience is a priority in modern pig breeding. Recent research shows that general resilience can be quantified via variability in longitudinal data. The collection of such longitudinal data on weight, feed intake and feeding behaviour in pigs has been facilitated by the development of technologies such as automated feeding stations.The goal of this study was to investigate resilience traits, which were estimated as deviations from longitudinal weight, feed intake and feeding behaviour data during the finishing phase. A dataset with 324,207 records between the age of 95 and 155 days on 5,939 Piétrain pigs with known pedigree and genomic information was used. We provided guidelines for a rigid quality control of longitudinal body weight data, as we found that outliers can significantly affect results. Gompertz growth curve analysis, linear modelling and trajectory analyses were used for quantifying resilience traits.h2 = 2.9%–20.2%), in feed intake (9.4%–23.3%) and in feeding behaviour (16.2%–28.3%). Additionally, these traits have good predictive abilities in cross-validation analyses. Deviations in individual body weight and feed intake trajectories are highly correlated (rg = 0.78) with low to moderate favourable genetic correlations with feed conversion ratio (rg = 0.39–0.49). Lastly, we showed that some resilience traits, such as the natural logarithm of variances of observed versus predicted body weights (lnvarweight), are more robust to lower observation frequencies and are repeatable over three different time periods of the finishing phase.To our knowledge, this is the first study comparing resilience traits from longitudinal body weight, feed intake and feeding behaviour data in pigs. We demonstrated that the resilience traits are lowly to moderately heritable for deviations in body weight (Our results will help future studies investigating resilience traits and resilience-related traits. Moreover, our study provides first results on standardization of quality control and efficient data sampling from automated feeding station data. Our findings will be valuable for breeding organizations as they offer evidence that pigs’ general resilience can be selected on with good accuracy. Moreover, this methodology might be extended to other species to quantify resilience based on longitudinal data.The online version contains supplementary material available at 10.1186/s40104-023-00901-9. Resilience in livestock usually refers to the ability of animals to be minimally affected by  stressors and/or to cope with these stressors and quickly return to their optimal production level –5. As suRecently however, several studies showed that increasing within-family and within-individual uniformity can improve animals’ general resilience. Blasco et al.  and FormThanks to technological developments, longitudinal data can be collected on a large scale in practice . For insIn this study we will investigate the genetic background of resilience proxies based on longitudinal body weight, feed intake and feeding behaviour data in a Piétrain pig population. This study is the first to examine the value of body weight deviations based on trajectory analysis as a novel proxy for resilience. Moreover, it is unique in its comparison and analysis of deviations in weight, feed intake and feeding behaviour over time, as previous studies have only focused on deviations in weight, feed intake or feeding behaviour. Lastly, we investigate the influence of observation frequency on the stability of resilience traits and the influence of observation period on the repeatability of resilience traits. Therefore, the repeatability of resilience traits over different stages of the finishing period (observation period) was studied as well as the impact of less data points per individual (observation frequency). Genetic parameters, such as heritability, genetic coefficient of variation and genetic correlations are estimated for the resilience traits. In an effort to better understand the value of genomics in selection for resilience, we assessed the predictive abilities using pedigree relationships or single-step genomic evaluation.2 per pig). Water was provided ad libitum in each pen from one nipple drinker and feed was provided with an automatic feeding system (AFS): Nedap pig performance testing feeding station . Individual recordings of weight (accuracy of 0.5 kg), feed intake (accuracy of 1 g), visit duration (accuracy of 1 s) and number of visits were obtained with the AFS per day. The daily records were calculated as summary statistics based on a pigs’ daily feeding station visits. Before data quality control (QC), the dataset comprised of 7,880 pigs born between May 2017 and September 2021. In total, these pigs had 522,122 AFS recordings for weight, feed intake, feeding duration and number of visits (on average 66 records per pig for each trait). Moreover, for all these pigs with AFS recordings, individual weights were also recorded by technicians at birth, 14 days of age, start of test (81 ± 5 d and 32.6 ± 7.6 kg) and end of test (161 ± 12 d and 114.3 ± 13.1 kg). At the end of test, muscle thickness and fat thickness were measured via ultrasound probing between 3rd and 4th last rib using Exago (IMV) device for these pigs.The study was carried out on Piétrain pigs from Hendrix Genetics (Hypor Maxter). The nucleus pig test barn (France) consisted of 14 compartments with 10 pens per compartment and on average 15 pigs per pen (1.0 mn = 874) or above 160 kg before an age of 160 days  were set to missing . Additionally, only pigs with at least twenty AFS weight recordings were retained to ensure a sufficient number of records per individual for the accurate estimation of resilience traits. After this first step of QC, 6,831 pigs and 505,990 AFS weight records were retained.This study investigates variability in longitudinal data from AFS, and links this variability with underlying biological/genetic factors. Therefore, it is vital that variability due to technical errors and/or noise are removed as much as possible. In a first step of quality control, outlier correction limits were designed based on population statistics to identify and exclude gross weight recording inaccuracies. Specifically, AFS weight recordings below 10 kg , similar to . RMSE waFor daily feed intake (FI), visit duration and number of visits, values exceeding the average plus four times the standard deviation were set to missing . Hereafter, individual and pen RMSE were obtained for these traits by regressing them on age. However, no outliers were detected using this method. After these QC steps, the dataset contained 6,457 pigs with 438,132 feed intake records, 437,753 visit duration records and 436,886 number of AFS visit records.n = 75), (ii) maximum age < 120 d (n = 226 pigs), (iii) > 30% missing records for weight or feed intake (n = 240 pigs) and (iv) < 20 d with AFS records (n = 188 pigs). The final dataset after QC comprised 5,939 pigs  with 324,478 AFS weight recordings, 323,775 feed intake recordings, 323,304 visit duration recordings and 322,910 number of visit recordings between 95 and 155 days of age (75% of pigs and ~ 62% of records before QC). The pigs originated from 1,273 dams and 130 sires . Pedigree consisted of 9,369 pigs with a pedigree depth ranging from 13 to 19 generations. Genomic information  was available for 6,726 pigs in total, of which 5,160 pigs (87% of dataset) had own phenotypic records. The evolution of weight in function of age for data after QC is shown in Fig. Next, only AFS records were kept between an age of 95–155 d to standardize age limits across animals. These thresholds were selected because most of our AFS recordings fall within this range Fig.  and becaake n = 20 pigs anAfter QC, traits were operationalized. Average daily gain (ADG) was estimated asAverage feed intake (AFI) per individual was estimated as total feed intake divided by number of days with a feed intake record. Feed conversion ratio (FCR) was estimated by dividing ADG by AFI.nls function and the Gompertz growth curve formula , as well as skewness  and the lag-one autocorrelation   Fig. , followimentclass2pt{minimlm function in R). As growing pigs (95–155 d) are more or less in a linear phase of their growth curve [weight). Here, we used MSE instead of RMSE to make lnMSEweight equivalent to lnvarweight.Next, similar to Putz et al. , a lineath curve , this ling pigs 9–155 d arweight_standardized). Pigs with a high lnvarweight_standardized hence showed great variations in weight over time, compared to the population mean. Second, additional deviation traits were derived from trajectory analysis using the trajr package in R [TrajDerivatives function) and the straightness (TrajStraightness function). The trajr package estimates mean speed as:A major challenge with modelling has to do with some circularity: expected weights are also estimated based on the observed weights, and these “expectations” might come from a biased curve , 22. To age in R   and/or visit duration (QRdur) was in the 5% lowest quantile using quantile regression (QR) on age over all pigs .Finally, after estimating these traits per pig from the daily AFS recordings, estimates deviating by more than four standard deviations from the mean were set to missing (184 for blupf90 suite of programs [h2) was estimated as the proportion of additive genetic variance divided by total variance. Likewise, the common environmental effect (c2) was estimated as the proportion of variance explained by random contemporary group effects (c), divided by total variance. For the resilience traits, the genetic coefficient of variation (GCV) was estimated as a measure of evolvability or possible selection response of a trait [The programs  was used a trait . For theribed by , 34, 35 y is the vector with phenotypes for the studied trait; b is the vector containing the fixed effects  and covariates (maximum age); a is the vector of additive genetic effects , which is assumed to follow a normal distribution for the pedigree matrix (A) using only pedigree relationships:H matrix, combining both pedigree (A) and genomic (G) relationship matrices following [c is the vector of contemporary group effects (113 levels), assumed to follow a normal distribution I the identity matrix; e is the vector of residual effects, assumed to follow a normal distribution X, Z and W are incidence matrices for respectively fixed effects, random animal effects and random contemporary group effects. The random contemporary group effect c is a combination of farm, compartment and date of entrance at farm. Contemporary groups with less than ten pigs were combined in a remainder group (165 pigs).All single trait animal models were of the form:ollowing –38 usingrg) between traits were estimated via bivariate animal models of the form:Likewise, genetic correlations , assumed to follow a normal distribution e1 and e2 are the vector of residual effects, assumed to be independently normal distributed X1, X2, Z1, Z2, W1 and W2 are incidence matrices for respectively fixed effects, random animal effects and random contemporary group effects.Cross validation was performed using three data masking strategies: within family masking, across family masking and temporal masking. For within and across family masking, we decided to use 5-fold cross-validation with random masking of 20% of the data (based on ), resultremlf90 software. Observed phenotypes were adjusted for fixed and non-genetic random effects based on these results using the predictf90 software:The process of cross-validation was as follows. First, a univariate animal model (as specified before) was used on the full dataset using the Predictive abilities were estimated as the Pearson correlation between breeding values of a validation dataset (with masked phenotypes) and the adjusted phenotypes  correlations indicate the repeatability of a trait and whether a given trait genetically shifts over time.Finally, the impact of observation frequency and observation period were evaluated. First, the influence of observation frequency on parameter stability was evaluated. Based on the full dataset, subsets were made with 1 out of 4 records per animal (~ 2 records per week), 1 out of 7 records per animal (1 record per week) and 1 out of 14 records per animal (1 record every two weeks) . The body weight deviation traits skewweight (2.9%) and lag1weight (6.2%) were also lowly heritable. Standardizing weights before estimating lnvar increased h2 (12.1% for lnvarweight_standardized versus 11.0% for lnvarweight). However, h2 estimates for body weight deviations were highest for trajectory parameters straightness (15.5%) and mean speed (20.2%), which were moderately heritable. Deviations related to feed intake and feeding behaviour had higher h2 (20.7%–28.3%) than body weight deviations (8.9%–20.2%). Despite low to moderate h2, the resilience trait indicators had high genetic coefficients of variation: 20.5%–30.2% for body weight deviations and 29.1%–33.4% for feed intake and feeding behaviour deviations. QRFI (h2 = 9.4%) and QRdur (h2 = 16.1%) had low to moderate h2 estimates.An overview of the main trait distributions and their estimated weight and most other body weight deviation traits were high , except for skewweight . Furthermore, lnvarweight was phenotypically and genetically also moderately to highly correlated with deviations in feeding duration  and feed intake . Deviations in feed intake (lnMSEFI) were moderately correlated with deviations in feeding duration  but lowly correlated with deviations in number of daily visits . ADG was moderately correlated with straightness  and mean speed . Additionally, ADG was negatively correlated with the number of days with a very low feed intake , indicating that pigs with high ADG have less off-feed days. A similar pattern was observed for AFI. For FCR, a low to moderate favourable correlation was found with lnvarweight  and lnMSEFI , indicating that pigs with more deviations in weight and feed intake have a higher FCR.Phenotypic and genetic correlations Table  between An overview of estimated predictive abilities per trait using both pedigree relationships and single-step genomic evaluation for three cross-validation strategies is given in Table weight were low for all strategies (0.00–0.23). For the body weight deviation traits, the trajectory parameters mean speed and straightness showed the highest predictive ability accuracies with single-step genomic evaluation (0.38–0.60). Feed intake deviations showed higher predictive ability accuracies than body weight deviations, and single-step genomic evaluation seemed to relatively increase predictive abilities for feed intake deviations more. Predictive abilities for lnMSEdur, for example, increased by 54%, 21% and 33% respectively when adding genomics to across, within and temporal masking strategy.Predictive ability accuracies of skewrp = 0.85 and rg = 0.95 with 1 in 14 density), as it is estimated as the average gain over a long period. FCR fluctuates more with lower observation frequency: when considering only 1 in 14 data points, the phenotypic and genetic correlations with the full dataset drop (rp = 0.45 and rg = 0.76) and h2 drops from 22.1% to 10.1%. For the resilience traits, lnvar and lnMSE estimates were least dependent on observation frequency with rp = 0.44–0.76 and rg = 0.79–0.96 in the most extreme scenario, although the h2 estimates decreased substantially from h2 = 10.6%–23.3% to h2 = 5.1%–10.1%. Skewweight and lag1weight were strongly impacted by differences in observation frequency, with rp = 0.05–0.08 and rg = 0.02–0.14 in the most extreme scenario. The trajectory parameters mean speed and straightness were moderately affected by data density (rp = 0.29–0.33 and rg = 0.50 with one in 14 data points), but showed a smaller decrease in h2 estimate from h2 = 15.0%–21.4% to h2 = 13.1%–17.7%.Phenotypic correlations per trait for different observation frequencies are given as pairwise correlation plots in Additional file rp = 0.59–0.63; rg = 0.71–0.82 and rp = 0.48–0.55; rg = 0.65–0.85), although genetic correlations are low to moderate within time periods . This is in contrast to AFI, where correlations were also moderate to high between time periods . The body weight deviation traits lnvarweight, lnMSEweight, lnvarweight_standardized, straightness and mean speed show high correlations between time periods and the full dataset  and moderate to high correlations within time periods . In contrast, lag1weight and skewweight show in general low correlations over time periods . Feed intake deviations lnMSEFI, lnMSEduration and lnMSEn_visit showed moderate to high (genetic) correlations .The influence of observation period was studied by dividing the full 60-day dataset (95–155 days of age) in three 20-day time periods during the finishing phase . Phenotypic correlations per trait over time periods are given as pairwise correlation plots in Additional file weight_standardized and lnMSEFI, for example, were more robust to low observation frequencies (as low as one data point in fourteen days) and showed moderate repeatability over three 20-day time periods of the finishing phase.Increasing resilience is becoming priority in modern pig breeding , 6. Therweight, skewweight, lag1weight were based on [weight_standardized was based on Berghof et al. [weight uses the pigs’ individual data as a reference (based on growth curve modelling), whereas lnvarweight_standardized takes the population statistics as a reference. The deviations in feed intake and behaviour  were based on Putz et al. [weight, straightness, mean speed). We believe this approach is justified, as an expected weight evolution in the finishing phase of pigs is more or less linear [In the first part of our study, we quantified and evaluated several resilience traits. The body weight deviation traits lnvarbased on  after Gof et al.  after stz et al. , althougz et al. . In addis linear . Our hyps linear , we belis linear . Figure h2 for body weight deviation traits lnvarweight, lnvarweight_standardized and lnMSEweight in pigs  compared to lnvarweight  and lnMSEweight . This might be because lnvarweight_standardized corrects for a scaling effect, since changes in mean levels tend to change variance levels as well [h2 estimates (15.5% and 20.2%), whereas h2 of lag1weight (2.9%) and skewweight (6.2%) was very low, similar to Poppe et al. [h2 of feed intake deviations  and feeding behaviour deviations  were also comparable to previous studies in pigs by Putz et al. [h2 = 8%–26% for feed intake), Homma et al. [h2 = 31% for feed intake; h2 = 36%–40% for feeding behaviour), and Kavlak and Uimari [h2 = 7%–11% for feed intake; h2 = 16%–20% for feeding behaviour). Estimated GCV for lnvarweight and lnMSEweight were 21%–22% and were lower than GCV estimates of 29%–33% for lnvarweight_standardized, lnMSEFI, lnMSEdur and lnMSEn_visit, but in the same range as (22%–39%) [To our knowledge, this is the first study to report gs Table . Our h2 chickens . h2 and  as well , 41. Reme et al. . The esta et al.  (h2 = 31d Uimari  (h2 = 7%22%–39%) . These h22%–39%) .h2 for lnvarweight, straightness and mean speed were very low . Applying a limited quality control on a population level, for example applying minimum and maximum thresholds for weight as a function of age, increased h2 estimates to h2 = 5.7%–7.1% (results not shown). However, these estimates are still considerably lower than what is achieved in a dataset with a rigid, individual quality control. Here, standard guidelines on quality control of AFS data might be valuable, although there might be no “one size fits all” approach. Our advice is to always visually check the weight trajectories of individual animals with outlying resilience traits, for example lnvarweight > 3 standard deviations from mean, even after quality control.There are no standard guidelines yet on how to perform quality control of weight data from AFS. The quality control procedure in the current paper was based on the structure and identified issues from our dataset, combined with the methodology from previous work . We woulweight and lnMSEFI. This correlation implies that individual deviations in feed intake are rapidly reflected in weight perturbations. However, the correlation does not equal one, indicating that these various indicators of resilience may signify different aspects of pigs’ resilience. Here, changes in feed intake might be considered as a short term response to a challenge, as a challenged animals’ appetite is usually directly affected [weight or lnMSEFI, and FCR (rg = 0.39–0.49). As feed efficiency is one of the most important traits in pig breeding, this favourable correlation would facilitate an implementation of resilience traits into breeding programs. Correlations between lnvarweight and most other body weight deviation traits were high  except for skewness . These correlations indicate different traits mostly capture the same genetic variation, but some differences exist between traits. Since the weight trajectory parameters straightness and mean speed showed higher h2 and do not rely on complex modeling, these traits might be more interesting to implement in breeding programs. Additionally, straightness has a favourable genetic correlation with FCR (rg = −0.41) and ADG (rg = 0.38). Notably, lnMSEn_visits was lowly to negatively correlated with lnMSEFI  and lnMSEdur . Similar genetic correlations were found by [The data in Table affected , 2. Varifound by . These frpc) of these resilience traits in pigs is not yet known. Research on this topic is essential for pig breeding programs, as an rpc < 0.80 indicates crossbred information should be taken into account [rpc was estimated ranging from 0.16–0.47 (lnvar of egg production) to 0.56–0.63 (lag1 autocorrelation) [It should be noted that our data were collected in purebred pigs in a high health breeding farm. This is in contrast to commercial crossbred finishing pigs, which are typically raised in a more challenging environment with, for example, a higher disease pressure and more social stressors such as a higher pig density. The commercial conditions might elicit more easily differences in resilience . Nonethe account . For exaelation) . FurtherPredictive ability analysis using three masking strategies indicated good prospects for selection on most resilience traits Table . The acrweight_standardized seemed to be very stable with rp > 0.76 and rg > 0.96 between full dataset and only one weight recording every two weeks . These results reiterate the need for data standardization, particularly for traits with a changing average and variance over time such as weight. Whereas trajectory parameters straightness and mean speed seem to have highest h2 and predictive ability for body weight deviations, these traits are also more sensitive to low data densities, with rp = 0.29–0.33 and rg = 0.50 for 1 in 14 data density compared to the full dataset. Further, lnMSEFI showed to be quite stable with lower data densities with rp = 0.44 and rg = 0.84 between full data and 1 in 14 scenario. Moreover, phenotypic and genetic correlations for deviations in feed intake were high over different time periods, with rp = 0.48 and rg = 0.80 between lnMSEFI_early and lnMSEFI_late, and rp = 0.73–0.87 and rg = 0.90–0.97 between 20-day time periods and the total 60-d period. These results show that, similar to FI, feed intake deviations are moderately repeatable over time: pigs with a high variability in feed intake at the start of the finishing phase, will generally also have a high variability in feed intake at the end of the finishing phase. Observational period and frequency had a large impact on skewweight and lag1weight. Therefore, these indicators might not be useful for data with a low observation frequency and/or a short observation period.As indicated by Berghof et al. , the freh2, GCV and predictive ability. Additionally, these traits seem to be robust to changes in observation frequency and period. However, our study also suggests to include body weight deviations as resilience indicator in breeding programs, as the (genetic) correlations with feed intake and feeding behaviour resilience traits substantially differed from one. We hypothesize that body weight deviations reflect more moderate term responses to external challenges, whereas feed intake and feeding behaviour better reflect short term responses to external stressors. For body weight deviation traits, we recommend to perform a rigid quality control of body weights, as we found that outliers can significantly affect results. Although we provide some guidelines for QC of AFS body weight data, most studies currently still perform an ad-hoc QC. Future work on (more) uniform guidelines for QC could further improve standardization and replicability of results across studies. Regarding quality control of body weights based on AFS data, future studies should focus on more uniform guidelines. We also recommend standardizing weights over time. Finally, the trajectory analysis traits straightness and mean speed showed promise as body weight resilience traits as they had the highest h2 and predictive ability and a favourable (genetic) correlation with FCR. However, these traits seem more sensitive to observation frequency.In light of our findings, we provided suggestions on the choice of resilience traits to include in a breeding program. The inclusion of resilience traits based on feed intake and feeding behaviour deviations show to be most promising, with highest h2 = 3%–28%) with good predictive abilities. Moreover, we suggested new, promising resilience indicators based on trajectory analysis with higher h2 and predictive ability, although these traits were more sensitive to observation frequency. Next, we were the first to report the influence of observation frequency and observation period on these resilience traits and showed that feed intake and feeding duration deviations are very robust to low data density and moderately repeatable over time. Within body weight deviation traits, lnvarweight_standardized seemed most robust to low data density, stressing the need for weight standardization over age when quantifying body weight deviations. Our results can help the design of future studies to look at the relationship between these resilience traits and resilience-related traits such as mortality and disease incidence, and to estimate the purebred-crossbred correlation. We believe our findings will be very useful for pig breeding programs, and will aid in the improvement of pigs’ general resilience by selective breeding. We recommend the inclusion of resilience indicators from both feed intake and body weight deviations in breeding programs, as they could offer valuable insights into different aspects of pigs' resilience. Moreover, we are confident our methodology can be extended to other species as well.To our knowledge, this is the first study comparing resilience traits from longitudinal body weight, feed intake and feeding behaviour data in pigs. We showed these resilience traits are lowly to moderately heritable (Additional file 1: Fig. S1. Gompertz growth curve distribution and parameters.Additional file 2: Fig. S2. Quantile regressionof feed intake and visit duration.Additional file 3: Table S1. Genetic correlations between all trait combinations using bivariate models.Additional file 4: Table S2. Predictive abilities as Pearson correlation between masked breeding values and corrected phenotype.Additional file 5: Fig. S3. Pairwise correlation plots for all evaluated traits with full datasets and reduced datasets.Additional file 6: Table S3. Estimated genetic correlations between the full dataset and reduced datasets.Additional file 7: Fig. S4. Pairwise correlation plots for all evaluated traits with full datasets and reduced datasets.


Multi-locus GWAA revealed seven additional QTL, including one on BTA7 (60 Mb) which is adjacent to a known heifer conception rate (HCR) QTL (59 Mb). Positional candidate genes for the detected QTL included male and female fertility loci (i.e. spermatogenesis and oogenesis), meiotic and mitotic regulators, and genes associated with immune response, milk yield, enhanced pregnancy rates, and the reproductive longevity pathway. Based on the proportion of the phenotypic variance explained (PVE), all detected QTL  were estimated to have moderate (1.0% < PVE ≤ 2.0%) or small effects (PVE ≤ 1.0%) on the predicted probability of pregnancy. Genomic prediction using GBLUP with cross validation (k = 3) produced mean predictive abilities (0.1692–0.2301) and mean genomic prediction accuracies (0.4119–0.4557) that were similar to bovine health and production traits previously investigated.Subfertility represents one major challenge to enhancing dairy production and efficiency. Herein, we use a reproductive index (RI) expressing the predicted probability of pregnancy following artificial insemination (AI) with Illumina 778K genotypes to perform single and multi-locus genome-wide association analyses (GWAA) on 2,448 geographically diverse U.S. Holstein cows and produce genomic heritability estimates. Moreover, we use genomic best linear unbiased prediction (GBLUP) to investigate the potential utility of the RI by performing genomic predictions with cross validation. Notably, genomic heritability estimates for the U.S. Holstein RI were moderate ( One persistent challenge to maximizing U.S. dairy production while also enhancing sustainability via genomic selection is subfertility among Holstein cows and heifers, with low milk production due to impaired fertility being one of the most frequent reasons for culling and a major obstacle to increasing dairy profitability . Modern k-fold cross validation  expressing the predicted probability of a pregnancy previously developed for comparison and ranking of Holstein cow fertility across diverse U.S. geographic regions . Addition = 4 herds; midwest, n = 6 herds; southeast, n = 1 herd; southwest, n = 5 herds) were enrolled in a fertility study beginning at parturition and subsequently monitored for both health and reproductive events by members of the research team using standardized protocols   and low-fertility individuals (diagnosed not pregnant at 60 days after two postpartum AI attempts) and among the lowest 7.25% RI , as recently described (File S4). After selection, genomic DNA was isolated from bovine blood using the Promega Wizard Genomic DNA Purification Kit  according to the manufacturer's recommendations. Genomic DNAs were quantified and evaluated for purity (260/280 ratio) via NanoDrop . Collectively, 2,505 of the selected cows produced high-quality genomic DNA (96.3%) and were submitted to GeneSeek Neogen  for Illumina Bovine 778K genotyping.Holstein cows were selected for genotyping on the Illumina 778K assay  by defining two groups within each farm and calving season as follows: high-fertility individuals  and among the highest 15% RI , which yielded 2,448 Holstein cows and 628,526 SNPs for analysis. For GWAA's and genomic predictions with cross validation, the Holstein RI  was used as the dependent variable  (lF + p × log(n), where lF is the full-model log-likelihood, p is the number of model parameters  used in the particular multi-locus model, and one for each additional fixed effect covariate used in all of the models), where n is the sample size; Extended Bayesian Information Criterion (EBIC) as follows: q is the initial number of model parameters  and with p − q genotypic marker covariates (cofactors) under the assumption that these will only be selected from the best n markers; the related Modified Bayesian Information Criterion (MBIC) as follows: m representing the total number of markers being tested in the current multilocus step; Bonferroni criterion as: for forward selection models only, and an optimal model is defined as the model with the most genotypic marker covariates (cofactors) for which the best P-value obtained from the preceding EMMAX scan was below the Bonferroni threshold; Multiple Bonferroni Criterion as follows: an optimal model is the model with the most genotypic marker covariates (cofactors) whereby all of which have individual P-values below the Bonferroni threshold, with the threshold used being 1/(20m) and with m being the total number of markers tested in the current multi-locus step; and Multiple Posterior Probability of Association as follows: an optimal model is the model with the most genotypic marker covariates (cofactors) where all have posterior probabilities of association ≥ 0.50. Posterior probabilities of association are based on Bayesian priors pr of 1/m for every marker (and for every step), with m being the total number of markers being tested in the current multi-locus step and where the Bayesian factor for marker k is as follows: h0 and mrssk being the values of the Mahalanobis Residual Sum of Squares (RSS) for the base model and for testing with the marker k, respectively. Posterior odds and posterior probability were calculated as follows: P ≤ 5e − 05) , and thereafter, SNP filtering by call rate (>15% missing excluded), MAF (<0.01 excluded), and Hardy–Weinberg equilibrium (excludes SNPs with HWE variable . The genibed (G) , as compescribed . In addiescribed . Brieflyescribed . Likewisescribed . Variancproduced . The mod5e − 05) .k-fold cross validation (k = 3), we used GBLUP as previously described and implemented in SVS v8.8.3 (Golden Helix), where we again estimate the variance components using the REML-based EMMA technique with a GRM (G)  but also assume that u are the additive genetic merits [i.e. genomically estimated breeding values (GEBVs)] for these U.S. Holstein cows, which are produced from m genotypic markers as u = Mα, where M is a n × m matrix and α is a vector where αk is the allele substitution effect (ASE) for marker k. For this study, we used overall normalization for matrix M, as implemented in SVS v8.8.3 (Golden Helix), and explored solutions with and without fixed effect covariates , to produce GEBVs for all Holstein cows as well as estimates of ASE for all SNPs  as Xfv as well as the predicted values of https://doc.goldenhelix.com/SVS/latest/svsmanual/mixedModelMethods/overview.html#gblupproblemstmt. For the GBLUP predicted Holstein RI phenotypes, we report relevant summary statistics from the k-fold (k = 3) cross validations (n = 10 iterations) computed in SVS v8.8.3 (Golden Helix) as follows: Pearson's Product–Moment Correlation Coefficient as sy and k = 3; n = 10 iterations) , divided by the genomic heritability estimate /log10.For all genomic predictions with k-fold (k = 3) cross validations were performed using automated random sampling to define the validation set (i.e. to predict on) and the training set, for the specified value of k, with all validation set phenotypes set to missing in each fold.All GBLUP h2 = 0.1654 ± 0.0317– 0.1826 ± 0.0326), regardless of model optimality criteria (h2 = 0.2539 ± 0.0347) and GBLUP (h2 = 0.2550 ± 0.0348) when no fixed-effect covariates  were included in the models. However, the proportion of variance explained by the fixed-effect covariates (0.1193) suggests overestimation of these genomic heritability estimates, which is somewhat unsurprising considering that Holstein cows were selected for genotyping by defining two groups (i.e. high fertility and low fertility) within each farm and calving season (see methods). Nevertheless, moderate heritability estimates for the U.S. Holstein RI produced here (h2 = 0.1654 ± 0.0317– 0.1826 ± 0.0326) support the potential for positive economic and production gains resulting from the implementation of genomic selection; particularly since low heritability estimates have routinely been produced for most dairy fertility traits worldwide (We utilized two related approaches implemented by EMMAX  to generr models . All EMMcriteria . Higher File S1), with detailed summary data for QTL detected by EMMAX described in P ≤ 5e − 05) (SLC4A4 and GC) previously associated with Holstein milk and protein yields, somatic cell score or clinical mastitis, DPR, and CCR (DCK) (CENPC) in humans and Drosophila (i.e. spermatogenesis) (SDAD1) in planarian, humans, and fish  (5e − 05) . Notably and CCR  Sahana File S1, P ≤ 5e−05; File S2), thereby demonstrating a high degree of reproducibility among models with different optimality criteria, as would be expected when true associations and/or causal variants exist within the dataset , spermatogenesis and enhanced sperm quality , regulation of T-cell immune responses and early pregnancy pathways (VTCN1), increased pregnancy rates , bovine RD , milk yield (GRK5), and the efficacy of ritodrine treatment for arresting preterm labor and birth in humans (GRK5) (File S2)  detected for Nellore heifer fertility, as prioritized by the proportion of variance explained  or small effects (PVE ≤ 1.0%) on the probability of a pregnancy following AI . Genomic inflation factors for each optimal step  provided no evidence of spurious associations via population substructure.A multi-locus EMMAX approach to GWAA which employed both forward inclusion and backward elimination  replicatk-fold cross validation and random sampling (k-subsamples (k = 3), with one subsample selected (i.e. within a discrete fold) to serve as the validation set for genomic prediction. Thereafter, the GBLUP model was fit using the remaining (i.e. k-1) Holstein subsamples within that fold (i.e. the training data), until all subsamples were used for both training and prediction (k = 3), and relevant summary statistics were produced (see File S3). Holstein GBLUP models fit with no fixed-effect covariates (i.e. farm and season) performed best with respect to classical measures such as mean predictive ability and mean genomic prediction accuracy, as previously defined  to conduct two GWAAs and subsequently detect a major QTL on BTA6 for DPR and CCR, but also produce evidence for moderate genomic heritability estimates as well as 12 additional QTL affecting the probability of a pregnancy following AI. Positional candidate genes identified for the detected QTL include a variety of male and female fertility loci, known milk yield QTL, loci generally associated with the cell cycle, and one QTL interval previously associated with bovine RD. Genomic prediction with three-fold cross validation produced evidence in support of potentially utilizing the U.S. Holstein RI for improving fertility in U.S. dairy cows and heifers. Moreover, our ability to capture a major-effect DPR and CCR QTL combined with moderate heritability estimates strongly suggests that the U.S. Holstein RI might be useful in genomic selection initiatives. Collectively, the results of this study demonstrate that new measures of Holstein fertility can lead to changes in knowledge and perhaps changes in action with respect to genetic improvement of U.S. dairy cattle.jkad043_Supplementary_DataClick here for additional data file.


Understanding the mechanisms underlining forage production and its biomass nutritive quality at the omics level is crucial for boosting the output of high-quality dry matter per unit of land. Despite the advent of multiple omics integration for the study of biological systems in major crops, investigations on forage species are still scarce.L. perenne with another species within the genus (L. multiflorum) relative to across genera (F. pratensis). However, conserved hub genes and hub metabolomic features were detected between pedigree classes, some of which were highly heritable and displayed one or more significant edges with agronomic traits in a weighted omics-phenotype network. In spite of tagging relevant biological molecules as, for example, the light-induced rice 1 (LIR1), hub features were not necessarily better explanatory variables for omics-assisted prediction than features stochastically sampled and all available regressors.Our results identified substantial changes in gene co-expression and metabolite-metabolite network topologies as a result of genetic perturbation by hybridizing The utilization of computational techniques for the reconstruction of co-expression networks facilitates the identification of key omic features that serve as central nodes and demonstrate correlation with the manifestation of observed traits. Our results also indicate a robust association between early multi-omic traits measured in a greenhouse setting and phenotypic traits evaluated under field conditions.The online version contains supplementary material available at 10.1186/s12864-023-09292-7. L. perenne L. multiflorum) and Festulolium loliaceum (L. perenne F. pratensis), for example. As high-throughput sequencing platforms reduced genotyping costs, genomics began to play a significant role across grass breeding programs, reshaping breeding pipelines aiming at the optimization of resource allocation mainly via genome-wide selection . The input file for the analysis of NMR data consisted of two subsets of 556 NMR variables for each pedigree class. NMR features were mean-centered and variable intensities were addressed via Pareto scaling, which uses the square root of the standard deviation to reduce the relative importance of high-variance features across the spectrum without much disturbance to the data structure.The impute file for the analysis of gene expression data consisted of two subsets of 4,767 features times the number of samples of each pedigree class. The reduced set of genes was obtained after further filtering out transcripts with more than 50% of samples having zero reads and retaining positions with at least 10 or more samples having 10 or more reads. Additionally, a filter on the expressional variance of non-zero elements was performed, selecting features ranked in the top 50th percentile as the variation for genes in the bottom may be largely due to non-biological noise. Finally, we retained only features common to both data sets followed by the addition of a pseudo count to the expression matrix, which was subsequently log(2)-transformed   specifieon [BIC] , yieldinIn order to reduce the computational burden, a dense search was performed over ested by . For thekage JGL  was usedk data set following the definition:Network analyses aiming for complexity reduction were performed in order to prioritize candidate genes and metabolomic features for further integration with phenotypes of interest. Initially, each  precision matrix Four adjacency matrices e igraph . InitialHub features were identified intramodule via maximum Kleinberg’s hub centrality score, which is the principal eigenvector of k)·A(k)T . By usin2 family. The genomic heritability was calculated as Single omic features were analyzed by fitting a linear mixed model of the form: mentclass2pt{minimPhenotypic variance within location was partitioned into the terms defined by the linear mixed model displayed in Equation mentclass2pt{minimlm times, combining l hub nodes and m phenotypic traits, for each data set, yielding correlations used to describe the existence of a significant association between the concentration of selected biological molecules and economically important phenotypes.l and phenotypic trait m, respectively. For incidence matrices The raw phenotypic data were analyzed alongside hub omic features in a multitrait genome-wide fashion via linear mixed models to investigate pair-wise additive genetic correlations. The bivariate model Eq.  and 9 wamentclass2pt{minimFor hypothesis testing, we also ran a constrained version of the bivariate model, setting the additive genetic covariance between submodels  freedom , 72. Mul freedom  procedurlm additive genetic correlations estimated by fitting the full bivariate model for each data set were retained along with the p-values and FDR-based significant associations and used for constructing the omics-phenotype weighted network graph. A visualization of the network was produced using the software Cytoscape 3.9.1 [The pe 3.9.1 , weighinTranscript protein sequences were subjected to local InterPro analysis using InterProScan v5.28-67.0 . Predictnum.trees equal to 100, sample.fraction equal to 0.1, max.depth of 6, and extratrees for the splitrule argument. The imputation was performed by looping over one chromosome at a time within clusters of SNPs created by running a complete-linkage clustering algorithm with Starting from the centered ssRanger . The misn is the number of variables. Therefore, the random forest model was fitted on the combined data sets, setting the number of decision trees to 2,000. Training out-of-the-bag accuracy (OOB accuracy) was reported as a performance metric. Finally, variable importance was computed via permutation.We used the best linear unbiased estimator (BLUE) of entries as response variables in the prediction study. The adjusted phenotypes were obtained by rearranging the terms and refitting the submodel in Eq. Three prediction scenarios were studied. First, we selected a subset of SNPs tagging common hub genes across data sets, the common hub genes, and the common hub NMR variables as three sets of regressors. The second scenario consisted of stochastically sampling 20x sets of 30 genes (then SNPs within these genes) and 32 NMR variables aiming to compare the prediction power contained in hub nodes with randomly sampled features. In the last scenario, we used all common SNPs, genes, and NMR variables as regressors. Besides comparing prediction accuracy with the previous scenarios, here we can assess a common prediction task where the goal is to evaluate the closeness of predicted and observed values using all available predictor variables.Large-scale computations were performed in the GenomeDK high-performance computing facility located at Aarhus University, Denmark. Mixed model analyses were fitted using DMU package version 6 . ModularAdditional file 1. Genetic structure via principal component analysis.Additional file 2. Variance components estimated via Restricted maximum likelihood (REML).Additional file 3. Exploring the genomic and NMR landscape of hub nodes.


No causal association was observed between RHR and ASB . The multivariable MR analyses including RHR and HRV also suggested no causal associations  and no genetic correlations between the heart rate measures and ASB were observed using LDSC . Sensitivity analyses suggested that our results are not likely to be affected by heterogeneity, pleiotropic effects, or reverse causation. These findings suggest that individual differences in autonomic nervous system functioning indexed by RHR are not likely to directly contribute to the development of ASB. Therefore, previously observed associations between RHR and ASB may arise from confounding, reverse causation, and/or additional study characteristics. Further causally informative longitudinal research is required to confirm our findings, and caution should be applied when using measures of RHR in interventions targeting ASB.Observational studies frequently report phenotypic associations between low resting heart rate (RHR) and higher levels of antisocial behaviour (ASB), although it remains unclear whether this relationship reflects causality. To triangulate evidence, we conducted two-sample univariable Mendelian randomisation (MR), multivariable MR and linkage disequilibrium score regression (LDSC) analyses. Genetic data were accessed from published genome-wide association studies (GWAS) for RHR  and ASB  for the univariable analyses, along with a third GWAS for heart rate variability  for all other analyses. Genome-wide significant ( Furthermore, up to half of individuals who display ASB in childhood continue to exhibit these behaviours through adolescence and adulthood7. Considering these long-term and pervasive adverse outcomes, it is important to understand the aetiology of ASB to inform early identification and evidence-based intervention efforts.Antisocial behaviour (ASB), which includes aggression, rule-breaking and acts of violence, imposes a substantial economic and social burden on the individual, their community and wider society. Individuals who display high levels of ASB are at risk of lifelong adverse outcomes, such as poor mental health, substance misuse, criminal behaviour and unemployment11. Physiological markers, such as those indexing autonomic nervous system (ANS) activity, are particularly important in elucidating potential mechanisms underlying the development of ASB13. Of these, resting heart rate (RHR), defined as the number of heart beats per minute while at rest, is the most well-studied. Observational studies frequently report a strong inverse relationship between RHR and ASB, where individuals with lower RHR display higher levels of various types of ASB, including child conduct problems, juvenile delinquency and adult violence19. Several meta-analyses have been conducted on this topic23, all reporting a robust association between the two traits, with one review stating that “resting heart rate is a possible causal risk factor for antisocial behavior”20.Numerous reviews exist on putative risk factors for ASB, which include environmental and neurobiological factors24 and sensation-seeking hypotheses25. According to the fearlessness hypothesis, an individual with a low RHR has a higher threshold for experiencing fear than individuals with higher RHRs, partly due to attenuated ANS responses to aversive stimuli. The typical links between poor behavioural choices  and aversive stimuli  are either not established or are insufficiently established in individuals with lower RHR. As such, an individual with lower RHR would have inappropriately low expectations of negative outcomes and be prone to repeat poor decision making. In support of this hypothesis, many behavioural experiments report that participants who show deficient fear conditioning and reduced anticipatory fear reactivity have lower RHRs and higher levels of ASB27.Various potential mechanisms have been proposed to explain the relationship between RHR and ASB. The two main theories are the fearlessness30.The sensation-seeking hypothesis states that individuals with lower RHR have low basal ANS activity and are chronically hypo-aroused. Hypo-arousal is an unpleasant physiological state and therefore individuals with lower RHR seek to increase their arousal to a normal level by engaging in ASB. Sensation-seeking has been shown to be associated with both RHR and ASB, with some evidence suggesting that sensation-seeking is a mediator between these two factors23 shows that the majority of studies have used small and/or selective samples, which can produce unreliable and ungeneralisable estimates. In recent years, authors have attempted to include larger, unselected samples followed up over time. The findings from these studies are more inconsistent than those from earlier studies reporting a strong negative relationship between RHR and ASB, with some more recent studies confirming earlier findings33 and others suggesting no relationship between these two factors37. There is also a paucity of research adopting causal inference approaches to help overcome the inherent biases of these studies, including confounding and reverse causation. For example, only two studies have used genetically informed family-based methods and both found that the relationship between heart rate and ASB is entirely explained by genetic effects, i.e. genetic confounding38. Interestingly, these studies show evidence of genetic covariation between RHR and ASB, whereby children with a genetic liability for lower RHR also have a genetic liability for ASB.Although the link between RHR and ASB is well studied, questions remain over whether these two phenotypes are causally related. This is in part due to limitations in the existing literature which prevent the drawing of causal conclusions. A closer look at the studies included in the four meta-analyses40 and ASB43. The two aforementioned genetically informed studies used methods which rely on knowledge of genetic relatedness between family members. Other genetically informed methods can be used to triangulate these findings by relying on different types of data and assumptions45. A useful genetically informed causal inference method is Mendelian randomisation (MR). MR is an instrumental variable approach that uses genetic variants  associated with an exposure of interest  as instrumental variables to assess the effect of an exposure of interest  on an outcome (e.g. ASB). MR provides causal effect estimates under the classic instrumental variable assumptions: the genetic variants indexing the exposure must be (1) associated with the exposure (relevance); (2) independent of confounders of the exposure-outcome relationship (exchangeability); and (3) only associated with the outcome through the exposure (exclusion restriction)47. If these assumptions are met, a significant association between the genetic variants and the outcome suggests a causal relationship between the exposure and the outcome.Genetically informed methods can exploit the heritability of both RHR48 and ASB49 to provide a new type of evidence for triangulation with previous observational studies. The majority of evidence suggests an association between lower RHR and higher ASB, although research using more rigorous approaches has questioned the strength of this association. Given these findings, the current study aims to investigate whether RHR has a causal effect on ASB.The current study will be the first to interrogate the potential causal effect of RHR on ASB using two-sample MR analyses. We will exploit powerful genetic data from two large, independent genome-wide association studies (GWAS) on RHR55. Univariable two-sample MR integrates summary level genetic data from two GWAS, one GWAS estimating the association between the single nucleotide polymorphism (SNPs) and the exposure , and the other, independent GWAS estimating the associations between the genetic variants and the outcome . The IVW estimator is a weighted regression of SNP-outcome effects on SNP-exposure effects where the intercept is constrained to zero and the weighting is based on the inverse of the variance, thereby reflecting the precision of each instrument54. The IVW estimator can be construed as a weighted average of the effect estimates across all SNPs .To identify potential causal effects of resting heart rate (RHR) on antisocial behaviour (ASB), we conducted two-sample Mendelian randomisation (MR) analyses using the inverse-variance weighted (IVW) estimator47. These included other MR methods such as MR Egger56, weighted median analysis57, MR Robust Adjusted Profile Score (RAPS)58, MR Pleiotropy RESidual Sum and Outlier (PRESSO)59 and contamination mixture methods60. We further checked for heterogeneity and horizontal pleiotropy using the MR Egger intercept, and we used the Steiger approach to rule out reverse causation61. A dictionary designed to provide a comprehensive and accessible overview of MR theory, methodology and interpretation is available online62.We also ran a number sensitivity analyses to test for potential violations of the MR assumptions63. By adding a second indicator we better capture the individual differences in ANS functioning underlying low RHR, in particular high levels of cardiac vagal control. As such we were able to assess effects of RHR on ASB independent of cardiac vagal control, for instance due to cardiac sympathetic control, and therefore elucidate possible mechanisms of ANS activity. As a positive control we also conducted MR with HRV as an alternative outcome, assuming a negative causal effect between RHR and HRV. Finally we conducted linkage disequilibrium (LD) score regression analyses65 to estimate genetic correlations between the heart rate measures and ASB. We followed the Strengthening the Reporting of Observational Studies in Epidemiology—Mendelian Randomization (STROBE-MR) guidelines66 (see Supplementary Table The autonomic nervous system (ANS) controls both RHR and resting heart rate variability (HRV). Therefore, to assess alternative explanations, we conducted further analyses, including a multivariable MR analyses using data from a GWAS of HRV63.The current study used data from three summary statistics files. There was no sample overlap between the RHR and the other two GWAS and limited overlap  between the HRV and ASB GWAS. Further information on the cohorts and measures used in all the original GWAS is available in Supplementary Table 48, which included 458,835 individuals from the UK Biobank67. The original GWAS controlled for smoking but as smoking is an important covariate for RHR and ASB30 and was not controlled for in the ASB GWAS we asked the study authors to rerun the analysis without controlling for smoking. RHR is expressed in beats per minute. Further information is available in the Supplementary Materials.The summary statistics for RHR were obtained from the largest and most recent GWAS on RHR70. We obtained summary statistics for HRV from the Genetic Variance in Heart Rate Variability (VgHRV) Consortium GWAS63 of 53,174 participants .Resting HRV captures the vagal effects on the sinoatrial node co-determining RHR and has also been considered a potential marker of ASB49. The ASB measures from these samples covered a broad range of behaviours including conduct disorder, aggression, and delinquency using study-specific scales in different age groups  GWAS, which includes 85,359 individualsTwoSampleMR71 . The same clumping and harmonisation parameters were used in all univariable and multivariable MR analyses.Prior to the main analyses, we conducted quality control procedures on the GWAS summary statistics, including harmonisation and clumping using default parameters from the R package R (version 4.2.372) using the TwoSampleMR (version 0.5.771) and MendelianRandomization (version 0.7.073) packages. The LD score regression analyses were conducted using a publicly available command line tool available on GitHub: LDSC (https://github.com/bulik/ldsc)65.We performed all MR analyses in SNPs = 278; BIVW =  − 0.0004; 95% CI − 0.004, 0.004; p = 0.841; see Table After harmonisation, 300 genetic variants were available in both exposure and outcome datasets, of which eight non-inferable palindromic SNPs with intermediate allele frequencies were removed. For the remaining 292 variants, we investigated the direction of their effects using Steiger filtering. Fourteen SNPs showed higher associations with the outcome than the exposure and were removed, leaving 278 variants. The MR analyses of RHR on ASB using the IVW method did not support a causal effect, with an estimate close to zero and the 95% confidence intervals including the null . No directional pleiotropic effects were observed from the MR Egger intercept  or MR PRESSO global test for pleiotropy , which supports the exclusion restriction assumption.Table R2EXP = 0.05, R2OUT = 0.007), and therefore we were able to assume that if a causal effect existed it was from the exposure to the outcome, instead of the outcome to the exposure.The MR Steiger test revealed that associations between the genetic instruments and the exposure were 7.49 times higher than with the outcome (pmin = 0.605) and MR Egger (pmin = 0.598). The genetic instruments had a high average F statistic of 84.20 (range = 27.30–1185.15) and the I2GX statistic of 0.99 further showed that the results were not affected by weak instrument bias57.The results did not change when conducting leave-one-out analyses using IVW  as exposures. Variants that were significant in either of the two exposure datasets and were available in SNPs = 206; BIVW =  − 0.014; 95% CI − 0.017, − 0.011; p < 0.001; see Supplementary Table As a positive control, we conducted a MR analysis using RHR as the exposure and an alternative outcome that we assumed would be causally related to RHR, resting HRV. The results from these results were significant . There were significant genetic correlations between RHR and HRV but we did not find any significant genetic correlations between either heart rate measure and ASB , we report no significant effects for resting heart rate (RHR) on antisocial behaviour (ASB). Sensitivity analyses suggested that these results are unlikely to be affected by heterogeneity, pleiotropy and/or weak instrument bias. Additional analyses using a measure that captures the vagal contribution to RHR, heart rate variability (HRV), also did not produce any significant effects and there were no significant genetic correlations between any measure of heart rate and ASB.38, using a twin and a co-relative control design, our results do not support the hypothesis that the often observed association between RHR and ASB is directly causal. The null findings in the current study and the discrepancy between these and the phenotypic associations reported in previous research lend themselves to several alternative explanations.In line with two prior studies that controlled for unmeasured confoundersis causal but the current study was not able to detect this either on account of limited power due to the sample size of the outcome GWAS  or because RHR has a causal effect on specific, potentially “more extreme”, forms of ASB than those included in this study. The most recent meta-analysis on RHR and ASB found significant evidence of heterogeneity of effects, with the effect of RHR on ASB being largest for the most violence offenders and those with psychopathy21. Although the phenotype used here included “more extreme” forms of ASB  and clinical samples, these measures were combined with other “less extreme” forms  to create a broad measure of ASB. Therefore, it is possible that we were not able to detect a potentially true causal effect on specific forms of ASB. It should be noted that the only three other genetically informed studies found no evidence of an effect of RHR on ASB in childhood14 or in adulthood38 nor evidence of a genetic correlation between RHR and childhood aggression74. Future research should aim to investigate heterogeneity in the relationship between RHR and ASB by considering specific phenotypes of ASB.First, it may be that the relationship between RHR and ASB is not causal but may arise in part due to issues in data quality and/or publication bias in previous research. In terms of data quality, many existing studies have included small and/or non-representative samples. In the most recent meta-analysis on RHR and ASB21, over half of the studies included data from fewer than 100 participants . The funnel plots also showed evidence of publication bias, whereby extreme negative findings were more likely to have been identified and included in the meta-analysis than studies that reported either a null or positive effect of RHR on ASB. The availability of larger and more representative datasets, such as those included in the current analyses, lends itself to future research overcoming these data quality issues.Another potential explanation is that the relationship between RHR and ASB 38. However, using LDSC, we were unable to support this hypothesis. Our results suggested no evidence of genetic correlation between any measure of heart rate and ASB, in line with a recent GWAS on childhood aggression which also found no genetic correlation with RHR74. It should be noted that LDSC relies on common SNPs which only capture a fraction of the heritability of RHR and ASB so we cannot exclude that future studies using larger samples and/or including rarer variants may detect significant genetic correlations between RHR and ASB.A third explanation is that the association reported in previous research is driven by genetic confounding. Indeed, previous genetically informed studies suggest that the association between RHR and ASB is entirely explained by genetic effects20 over three quarters of the effect sizes included adjusted for no confounders . It may be, for example, that sensation-seeking behaviour, which has been found to be associated with both RHR and ASB30, could be a “common cause” for both low RHR and high ASB. These alternative causes of the association between RHR and ASB need to be investigated fully as simply adjusting for a larger number of putative confounders poses the risk of conditioning on mediators and colliders.A fourth explanation for the association between RHR and ASB is that it is driven by other confounders which are not adequately accounted for in previous research. In another recent meta-analysis77. Five of these studies found an inverse association between RHR and ASB76 and three studies found no relationship77. These results highlight the inconsistencies in the literature which may arise in part due to differences in study design, RHR and ASB measurement, analyses and confounder adjustment. Of note, only two of these studies employed causal inference methods38, with both using family-based genetically informed methods. In order to draw causal conclusions, triangulation using methods that utilise different but complementary assumptions is needed45. The current study adds to the existing evidence by relying on instrumental variable assumptions. However, further analyses with large, longitudinal datasets using causal inference methods is required to disentangle this relationship further.Potential time-varying confounders have also not often been considered previously. Repeated measures enable the examination of the temporal ordering of variables. By using causal inference methods it is possible to control for time-fixed unmeasured confounding  and/or measured time-varying confounders (e.g. g-methods). However, there is currently a lack of longitudinal studies looking at RHR and ASB. In the same meta-analysis, nine in ten of the studies included were cross-sectional . Indeed, we are aware of only eight studies with moderate sample sizes (i.e. including more than 100 participants) that use longitudinal data21. The null findings from the current study and the lack of high-quality evidence from previous research suggest that, although RHR may still be a robust indicator of ASB, RHR should not be interpreted as a causal risk factor for ASB until more rigorous, longitudinal research is conducted.Despite limited confounder adjustment, the use of small sample sizes, cross-sectional data, and evidence of publication bias, it has been argued that RHR measures could be incorporated into risk assessments and interventions for ASB49. Therefore, the current study may not have been powered to detect small causal effects. However, the clinical utility of such small effects, for instance using RHR as a basis to diagnose or intervene on ASB, is uncertain. As is often the case, once more data are available the analyses should be updated using GWAS with larger sample sizes.The current study has some key strengths, such as utilising data from two, large GWAS and using MR analyses, which can help strengthen causal inference when instrumental variable assumptions are met. However, we must consider certain limitations. As mentioned above, the ASB GWAS had a relatively small sample size  in comparison to other GWAS and reported a SNP heritability of 8.4%Another potential limitation that has already been discussed is that the phenotypic measurements in both the exposure and the outcome GWAS were heterogeneous. The exposure GWAS used a measure of RHR which was averaged over multiple measures and the outcome GWAS combined questionnaires that captured a broad range of ASB types. Therefore, we may not have been able to detect an association between RHR and ASB due to the heterogeneity in the measurements used.79. However, it may be that the genetic effects of RHR on ASB are different over the life course (e.g. during adolescence) and we were not able to detect this in the current study.Furthermore, it should be noted that the exposure GWAS used an older sample than the outcome GWAS sample, meaning that the SNP-exposure associations were measured later than the SNP-outcome associations. MR estimates are often interpreted as lifetime exposures but the effect of this on MR results is an issue of ongoing debateF statistics to support the relevance assumption; ensured the absence of significant horizontal pleiotropy to support the exclusion restriction assumption; and used a positive control.Finally, although more of a concern for significant MR findings, some of the instrumental variable assumptions of MR are not verifiable. To be confident that the assumptions were supported, we used genetic variants that reached genome-wide significance; checked for high We found no significant genetic correlation nor a causal link between RHR and ASB in preliminary analyses using currently available summary statistics. Therefore, our results do not support that the often-reported association between RHR and ASB is causal. We suggest that the association reported by observational studies may be due to biased estimates resulting from small, selective samples, publication bias, and from inadequate control of genetic and environmental confounders. Future research should aim to use larger samples and appropriately control for potential confounders by using longitudinal data and more robust study designs  and statistical analyses . Only by adopting a range of causal inference methods will researchers be able to further understand whether there is a causal relationship between RHR and ASB.Supplementary Information.


Charcoal rot (CR) caused by the fungal pathogen Macrophomina phaseolina is a devastating disease affecting soybean (Glycine max (L.) Merrill.) worldwide. Identifying the genetic factors associated with resistance to charcoal rot is crucial for developing disease-resistant soybean cultivars. In this research, we conducted a genome-wide association study (GWAS) using different models and genotypic data to unravel the genetic determinants underlying soybean resistance to сharcoal rot. The study relied on a panel of 252 soybean accessions, comprising commercial cultivars and breeding lines, to capture genetic variations associated with resistance. The phenotypic evaluation was performed under natural conditions during the 2021–2022 period. Disease severity and survival rates were recorded to quantify the resistance levels in the accessions. Genotypic data consisted of two sets: the results of genotyping using the Illumina iSelect 6K SNP (single-nucleotide polymorphism) array and the results of whole-genome resequencing. The GWAS was conducted using four different models  based on the GAPIT platform. As a result, SNP markers of 11 quantitative trait loci associated with CR resistance were identified. Candidate genes within the identified genomic regions were explored for their functional annotations and potential roles in plant defense responses. The findings from this study may further contribute to the development of molecular breeding strategies for enhancing CR resistance in soybean cultivars. Marker-assisted selection can be efficiently employed to accelerate the breeding process, enabling the development of cultivars with improved resistance to сharcoal rot. Ultimately, deploying resistant cultivars may significantly reduce yield losses and enhance the sustainability of soybean production, benefiting farmers and ensuring a stable supply of this valuable crop. Soybean (Glycine max (L.) Merrill.) is one of the most importantlegumes in the world due to the high nutritional valueand protein content of seeds . Accordingto the Agencies for Strategic Planning and Reforms of theRepublic of Kazakhstan Bureau of National Statistics, soybeanwas grown on 127.7 thousand hectares in Kazakhstan in2022 (https://new.stat.gov.kz/ru/industries/business-statistics/stat-forrest-village-hunt-fish/publications/5099/). To furtherdevelop the soybean industry in Kazakhstan, the Governmentof Kazakhstan has announced a new program known as“Northern Soybean” to expand the soybean area to 1.5 millionhectares (https://www.gov.kz/article/64601?lang=kk).The important factor severely limiting soybean productivityis its susceptibility to harmful fungal diseases . Strategies for the managementof soybean fungal diseases include cultural methods, seedappliedfungicides, and biological controls, but these have notbeen effective or widely adopted, and have provided limitedcontrol . Therefore, geneticresistance may be the most feasible and sustainable method bywhich to manage fungal diseases . Breedingfor resistance is difficult because most diseases are quantitativelyinherited and controlled by multiple genes. However,modern genomic methodologies may help elucidate resistancemechanisms and identify resistant genotypes to improvebreeding programs . For instance, breedingprojects can be coupled with genome-wide association studies(GWASs), as this approach can efficiently identify candidategenes for disease screening.Genome-wide association studies are becoming a routineapproach in the search for marker-trait associations  and can be efficiently applied to assess geneticvariations of important agronomic traits, including diseaseresistance . These studies use high-densitysingle-nucleotide polymorphism (SNP) arrays and variablepopulations to enhance the mapping resolution, which drasticallyimproves the identification of putative causal genes. Although there arepossibilities in respect of GWASs for the prediction of falsepositive associations, applying variable statistical algorithmscan be instrumental in controlling these. For instance, a mixedlinear model (MLM) that uses population structure and kinshipmatrices will significantly regulate inflation . However, this method may also remove true genesas background noise in certain studies of complex traits associatedwith the population structure. To overcome this obstacle,the Bayesian information and linkage-disequilibriumiteratively nested keyway (BLINK) employs a multiple-locitest method along with a fixed-effect model (FEM), Bayesianinformation criteria, and linkage disequilibrium information. Our previous GWAS of 182 soybean accessionsfor resistance to fungal diseases  allowed the identification of 15 marker-trait associations(MTAs) for resistance to fusarium root rot , frogeye leaf spot , and brown spot .In Kazakhstan, more than ten soybean fungal diseaseshave been identified , and one category of thesecomprises root rot diseases . The expansion of these studies isan obvious necessity in order to examine the genetic backgroundassociated with the resistance to harmful pathogens insoybean. One of the local harmful root rot diseases is charcoalrot (CR) caused by Macrophomina phaseolina, a soil- andseed-borne polyphagous fungus .Currently, only partial resistance has been recorded in soybean,while complete resistance to M. phaseolina has not beenreported in any plant species . In addition, it has been reported that no similarmarkers or genes have been found when comparing field andgreenhouse studies, suggesting that CR resistance in soybeanhas a complex molecular mechanism. Therefore, the searchfor more valuable resistance sources from which to identifyresistance genes should be continued. The main purpose ofthis study was to identify MTAs for charcoal rot resistance ina collection of 252 accessions from major soybean growingregions from around the world using the GWAS approach.The soybean collection analyzed in this study consisted of252 accessions, including 31 released cultivars and prospectivebreeding lines from Kazakhstan . Accessions from 20 countries were represented in thecollection and separated into five origin groups: Western andEastern Europe, North America, East Asia, and Kazakhstan.The collection was assessed in the experimental plots of theKazakh Research Institute of Agriculture and Plant Growing(south-eastern Kazakhstan) in the 2021–2022 period. Plantswere grown in one-meter-long rows with a 30 cm distance betweenadjacent rows and 5 cm between plants within rows. Theassessment of field resistance to CR was based on a generallyaccepted five-point scale: 1 – microsclerotia are not visible inthe tissue (I-immune); 2 – very few microsclerotia are visiblein the core, vascular tissue, or under the epidermis, and thevascular tissue has not changed color (R-resistant); 3 – vasculartissue is partially discolored, and microsclerotia partiallycover the tissue (MR-moderately resistant); 4 – vascular tissueis discolored with numerous microsclerotia embedded in thetissue, and microsclerotia are also visible under the outer epidermis in stem and root sections (MS-moderately susceptible);and 5 – vascular tissue darkened due to the large amount ofmicrosclerotia both inside and outside the tissues of the stemand root (S- susceptible) . The fieldexperiments for CR resistance were conducted in triplicate andin randomized order. The results were analyzed using StatisticalPackage for the Social Sciences (SPSS 22.0.0.0)  computer programsThe genotyping data consisted of two sets of SNP data.The first set (Set 1) was developed using the soybean 6KSNP IlluminaiSelect array  at TraitgeneticsGmbH . DNA samples were extractedand purified from single seeds of individual cultivars usingcommercial kits . The DNA concentrationfor each sample was adjusted to 50 ng/μl. SNP genotype analysiswas carried out using Illumina Genome Studio software(GS V2011.1). The second set (Set 2) was developed at theDepartment of the School of Life Sciences, Guangzhou University,China, using whole-genome resequencing (WGRS)technology based on the Illumina HiSeq X Ten system . For each of the accessions in the panel of 252,at least five μg of DNA was used to construct a sequencinglibrary with an Illumina TruSeq DNA Sample Prep Kit, accordingto the manufacturer’s instructions.The SNP datasets were filtered using a 10 % cutoff formissing data, and markers with minor allele frequency ≥ 0.05were considered for the genome-wide association studies.Numbers of hypothetical groups ranging from k = 1 to 10 wereassessed using 50,000 burn-in iterations followed by 100,000recorded Markov-chain iterations. The sampling variance ofthe population structure inference was estimated for each kusing STRUCTURE software  withfive independent runs. The delta K value (ΔK) was estimatedusing Structure Harvester . The Q-matrixwas developed based on the final k-values. Population geneticanalysis was conducted using two sets of SNP data toconstruct a neighbor-joining tree with TASSEL software andfurther visualization using the iTOL online platform (https://itol.embl.de/).The GWAS for soybean resistance to CR in Southeast Kazakhstanwas conducted using MLM , amultiple-locus mixed linear model (MLMM) , fixed and random model circulating probability unification(FarmCPU) , and BLINK models  using GAPIT V3 software .The rMVP package  was used for the visualizationwith P ≤ 0.0002 thresholds. A QQ plot was appliedto evaluate the distribution of observed p-values comparedto the expected distribution under the null hypothesis of noassociation between genetic markers and the trait of interest.Disease resistanceField trial results obtained at the experimental stations of theSoutheast region suggested a clear difference in the developmentof charcoal rot. During the two-year period, the spreadand effect of CR pathogens on plants were stronger in 2021 1. The results indicate thatthe group of susceptible accessions comprised 23 samples(9.1 %) in 2021 and 12 samples (4.8 %) in 2022 .https://vavilovj-icg.ru/download/pict-2023-27/appx21.pdfSupplementary Materials are available in the online version of the paper:Five genotypes were susceptible to resistance to CR duringthe two years of the study. Among them were three accessionsfrom East Asia, i. e., cultivars Jin nong 62, Dong doe 027,and Mei feng 18 from China, and line 1034 from Korea. Inaddition, two accessions from East Europe (Osobliva fromUkraine and CH 147020-1 from Belarus) were susceptibleto charcoal rotGenetic variation in the soybean collectionbased on two sets of SNP markersThe final data after filtering consisted of 4495 (Set 1) and44,385 (Set 2) polymorphic SNPs. Set 1 consisted of 77.98 %transitions and 22.02 % transverse variants, and Set 2 comprised71.88 % transitions and 28.12 % transverse variants.The average length of chromosomes was 47.4 Mb for bothsets, and the average number of SNPs per chromosome was222.1 for Set 1 and 2219.3 for Set 2. The chromosome lengthranged from 37.3 Mb in Gm16 to 62.1 Mb in Gm18 for Set 1,and from 34.7 Mb in Gm11 to 58 Mb in Gm18 for Set 2. Thenumber of markers per chromosome varied from 185 in Gm11to 295 in Gm13 for Set 1, and from 1438 in Gm11 to 3438in Gm18 for Set 2. The average density of the SNP map wasone marker every 213 Kb for Set 1 and every 22 Kb for Set 2.The Structure Harvester results divided the studied collectioninto three clusters based on data from Set 1 and Set 2.No clear separation of accessions depending on the originwas recorded, regardless of the set chosen. The largest groupof accessions was distributed in the third cluster, which consistedof 165 genotypes in Set 1 and 168 samples in Set 2.Based on two sets of genotyping data, a phylogenetic treewas constructed using the neighbor-joining method .Population analysis based on resequencing data showed a cleardivision into four populations .Association mappingThe GWAS of soybean resistance to CR was conducted usingtwo sets of genotypic data (Set 1 and Set 2) and four models. The results of theGWAS using four models are shown in Supplementary Material2. The comparative results of the associations suggest that the most informative results were obtained using the MLMM,FarmCPU, and BLINK models (Table 1).The application of BLINK using Set 1 allowed the identificationof five quantitative trait loci (QTLs) associated withCR resistance, while the usage of Set 2 expanded the detectionto 11 QTLs that were significant at the threshold of P ≤ 0.002. The QQ plot confirmsthe reliability of the associations .The physical position of each critical SNP marker in theMTAs was overlaid with the positions of known QTLs (https://soybase.org/search/qtllist_by_symbol.php) (Table 2). Particularly,the assessment of the 11 QTLs listed in Table 2 indicatedthat 8 of them were reported in published reports in respectof plant resistance studies.The largest numbers of QTLs associated with CR resistanceusing Set 2 were identified in chromosomes 7, 9, and 15. Analysis of the genome physicallocations of associated SNP markers revealed that all identifiedSNPs were part of the coding DNA sequence (CDS) (Table 3).Each SNP in an intergenic position was considered for possiblefunctional annotation based on the proximity of closelylocated genes.The analysis of soybean CR development in Southeast Kazakhstanrevealed a strong environmental influence on thedistribution of pathogens and plant tolerance to the disease.Previous CR resistance studies in soybean were unsuccessfulin identifying major genes that completely resist thispathogen , indicating that the resistanceis quantitative and may rely on the efficiency of minor QTLs.Therefore, searching for resistant genotypes and applyingpowerful genomic tools are the obvious priorities for successfulCR resistance breeding in soybean. The current work wasconducted to identify QTLs associated with CR resistance insoybean using a genome-wide association studies.The study was based on using two genotyping sets (Set 1and Set 2) that drastically differed in the number of SNPs in thesame soybean collection. Set 1 included 4651 SNPs and wasextensively used in our previous GWAS projects , while Set 2 included 44,385 SNPs, which is roughlyten times higher than the number in Set 1. As expected, thepower of the GWAS using Set 2 (11 QTLs) was higher thanthat of Set 1 (five QTLs) (see Table 1) and relied on WRGStechnology, which allows more in-depth searching and discoveryof new genes associated with agronomic traits, as wellas the study of evolutionary mutations in the genome. Mostof the identified statistically significant QTLs were detectedusing the BLINK and FarmCPU models, which were appliedamong four different statistical approaches, including MLMand MLMM. In this study, BLINK and FarmCPU were themost successful approaches ,and additional estimations, including QQ plots, confirmedthat these models generally result in fewer false positives andidentify more true positives .Assessment of the identified QTL locations showed that,in most cases, they are distant from each other, or detected ondifferent chromosomes. Analysis of the SNPs in the identifiedMTAs revealed nine proteins associated with the immuneresponse to pathogens. The qMac.ph 3-1 interval containstwo genes (Glyma_03G0135900 and Glyma_03G0136400),both coding NB-ARC domain-containing protein, which isassociated with resistance to fungal pathogens . The qMac.ph 15-1 interval contains geneGlyma_15G118400 coding HMA domain-containing protein,which is associated with regulation of the defense responseto fungi.In addition, the gene Glyma_15G124500, which codesMLO-like protein associated with powdery-mildew resistance, is also located in this interval. TheqMac. ph 16-1 interval contains two genes, Glyma_16G031700and Glyma_16G033900, associated with the defense response.The late embryogenesis abundant protein LEA-2 subgroup codingtwo genes, Glyma_19G198800 and Glyma_19G198900,was located in an interval of qMac.ph 19-1 and is associatedwith disease resistance. These examples of SNPs in MTAs,including presumably newly identified genetic factors thathave not been matched to previously reported factors in theliterature, require additional validation studies. Thus, the identifiedQTLs may facilitate the discovery of new genes fordisease resistance and a better understanding of genotype ×environmentinteraction patterns. The identified SNP markers(see Tables 2 and 3) for each of the detected QTLs of CR resistancecan be efficiently used in marker-associated selectionprojects in soybean.Our GWAS of soybean resistance to CR has provided importantinsights into the genetic determinants underlyingresistance to this devastating disease. By employing twosets of genotypic data with different SNP density levels andutilizing four GWAS models, we identified eleven QTLs thatwere statistically associated with CR resistance. The GWASrevealed the complexity of the genetic architecture underlyingresistance to CR, indicating the involvement of multiple genesand molecular pathways. The identified genomic regions canserve as valuable targets for further functional validation andexploration of their specific roles in plant defense responsesagainst Macrophomina phaseolina. Overall, the study representsa significant step in understanding the genetic basis ofsoybean resistance to charcoal rot. The knowledge gained fromthis research may further contribute to developing resilientsoybean cultivars, ensuring a stable and sustainable supplyof this essential crop while minimizing the economic andenvironmental impacts of charcoal rot.The authors declare no conflict of interest.Akem C.N. Management of Soybean Diseases. IITA ResearchGuide 40. 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