Pnpo, Ttll6, and GM11545 with bone mineral density, and Psmb9 with weight. However, replication at a nominal threshold of 0.05 between the two component studies was low, with less than one-third of loci identified in one study replicated in the second. In addition to overestimates in the effect size in the discovery sample (Winner’s Curse), we also found that heterogeneity between studies explained the poor replication, but the contribution of these two factors varied among traits. Leveraging these observations, we integrated information about replication rates, study-specific heterogeneity, and Winner’s Curse corrected estimates of power to assign variants to one of four confidence levels. Our approach addresses concerns about reproducibility and demonstrates how to obtain robust results from mapping complex traits in any genome-wide association study.Combining samples for genetic association is standard practice in human genetic analysis of complex traits, but is rarely undertaken in rodent genetics. Here, using 23 phenotypes and genotypes from two independent laboratories, we obtained a sample size of 3076 commercially available outbred mice and identified 70 loci, more than double the number of loci identified in the component studies. Fine-mapping in the combined sample reduced the number of likely causal variants, with a median reduction in set size of 51%, and indicated novel gene associations, including  Combining samples, through meta- or mega-analysis, has become routine in human genome-wide association studies (GWAS) of complex traits as a way to augment power by increasing sample size and to ensure robustness of results by replicating findings. Genetic mapping of complex traits in rodents has favored the use of linkage analysis in crosses between inbred strains, with many different inbred strain combinations being employed. In addition, few studies examine the same phenotypes. Therefore, few rodent studies have lent themselves to meta- or mega-analysis  or panelIn this paper, we combine results from two independent laboratories, one at the University of Oxford (OX) in the United Kingdom  or 4 (UC) per cage.All animals originated from the same colony of outbred mice, the Crl:CFW(SW)-US_P08 stock, maintained by Charles River Laboratories in Portage, Michigan at the time of the study. Details about the OX and UC mice are described elsewhere  as well as summary data representing changes in activity over time  that are likely correlated. Descriptions of all phenotypes can be found in The effect of covariates, including sex, weight, and batch, was tested in each data set separately (OX and UC) and introduced in a linear model to calculate residuals when the effect was significant (weight was retained for mapping body weight). OX and UC residuals were then quantile-normalized before being merged in a single data set used for the genetic analysis. In both centers the baseline activity of the mice was recorded during the first 30 min of testing. The activity of the OX mice was recorded at 16 weeks after placement into a new plastic home cage (46 cm×15 cm×21 cm) using a photoactivity system from San Diego Instruments  which has seven infrared photobeams crossing the width of the cage floor.The UC mice, ∼51 days of age at the time of test (SD = 4), were injected i.p. with physiological saline (0.01 ml/g body weight) immediately prior to the test and placed in the center of an OF chamber  consisting of a clear acrylic arena (40 cm×40 cm×30 cm) placed inside a frame containing evenly spaced infrared photobeams.Four phenotypes were analyzed for this study: locomotor activity initial (0–15 min), locomotor activity end (15–30 min), locomotor activity total (0–30 min), and activity decay. Activity decay was defined as the decrease in activity from beginning to end.In OX, conditioned fear (CF) was tested over 2 days in four San Diego Instruments chambers with mice at 17 weeks of age. On the first day of the test mice were subjected to a 13-min training session during which they received two electric foot shocks  preceded by a 30-s tone. In the morning of the second day of the test the mice were placed in the same enclosure for 5 min and fear associated with the context was measured by the amount of freezing. In the afternoon the animals were placed in a different enclosure for 5 min where they were subjected to two 30-s tones without any paired electric shock. Freezing behavior during all sessions of the test was scored using a VideoTrack automated system .UC mice were tested for CF at ∼63 days of age (SD = 2.9) over three consecutive days, each consisting of a 7-min trial: on the first test day, mice were conditioned to associate a test chamber and a tone  with a shock ; on the second test day, the mice were re-exposed to the same context, but no tones or shocks were given; on the third day, mice were exposed to the conditioned stimulus (the tones), but in a different environment. Mice were tested in four chambers obtained from Med Associates  and immobility, or “freezing” behavior, was recorded by analyzing digital video with Freeze Frame software .Phenotypes from the conditioned fear tests consisted of eight measurements of immobility collected in OX and UC mice. On day 1, we measured average proportion of freezing during the pretraining interval  before exposure to tones and shocks (baseline freezing D1), as well as the average proportion of freezing during exposure to the first conditioned stimulus corrected for baseline freezing . On day 2, we measured freezing during the context test corrected for baseline (corrected freezing to context). On day 3, we measured the average proportion of time freezing in the altered setting during the 30-s intervals in which the tones were presented corrected for baseline (corrected freezing to cue). These measurements were done on day 2 in Oxford and on day 3 in UC.UC mice were tested at a mean age of 74.5 days (SD = 2.2). During prepulse inhibition (PPI), the mice were exposed to loud pulses (120 dB) that caused them to exhibit the startle response. Occasionally, the exposure to the loud pulse was preceded by a barely perceptible “prepulse” (3–12 dB over background levels), which inhibited the startle response to varying extents. The UC PPI testing procedures follow protocols detailed in previous papers .OX mice were tested at ∼17 weeks of age following a different protocol , capturing mouse movement using a piezoelectric accelerometer, then converted into digital data and recorded on a computer. Before the start of each test day, the apparatuses were calibrated according to the manufacturer’s instructions.Five phenotypes were analyzed for this study: startle habituation difference, startle habituation ratio, startle, PPI with +6 db prepulse, and PPI with +12 db prepulse. In UC, startle habituation difference was defined as the average startle amplitude during the fourth pulse-alone trials subtracted from the average startle amplitude during the first pulse-alone trials. In OX this was calculated by subtracting the last block from the first block for the 120 dB stimulus. In UC, startle habituation ratio was defined as the average startle amplitude during the first pulse-alone trials divided by the average startle amplitude during the fourth pulse-alone trials. In OX, this was calculated by dividing the first block for the 120 dB stimulus by the last block. Startle was defined as the average startle amplitude to the 120 db pulses during the second and third blocks. PPI with +6 db prepulse was defined as the average prepulse inhibition during blocks 1 and 2 to the 6 dB prepulse. PPI with +12 db prepulse was defined as the average prepulse inhibition during blocks 1 and 2 to the 12 dB prepulse.Weight of hind limb muscles and length of tibia were collected by the same experimenter for both the OX and UC mice. Following sacrifice, at ∼20 weeks for the OX mice and ∼90 days of age for the UC mice , one leg was cutoff just below pelvis, tubed, and transferred into a −70°C freezer, then shipped on dry ice to Dr Arimantas Lionikas at the University of Aberdeen. On the day of dissection, the leg was defrosted and two dorsiflexors, tibialis anterior (TA) and extensor digitorum longus (EDL), and three plantar flexors, gastrocnemius (gastroc), plantaris, and soleus, were removed under a dissection microscope and weighed to a precision of 0.1 mg on a balance . Then, the soft tissues were stripped off from the tibia, and the length of the tibia was measured to a precision of 0.01 mm with a digital caliper .In the OX mice, bone mineral content of the tibia was measured with the Faxitron MX-20 scanner  using methods adapted from  in UC mice. Tail length (in cm) was measured at the time of sacrifice, as the distance from the base of the tail to the tip of the tail .Sample BAM preprocessing including mapping and quality control is as previously described . The 3.1P-values were retained. The code for the peak caller is available on the Github repository (Data availability).The genotype likelihoods at 97,452 independent SNPs, obtained using STITCH, were used to perform associated studies in the joint set of Oxford and Chicago samples. The genotype likelihoods were converted to dosages of the alternate allele. Using the dosages for the combined set of samples, association analyses were performed using linear mixed models in GEMMA (v0.96)  with a pP-values from the linear mixed model association analyses were computed in two different permutation approaches. First, we used a “naïve” permutation approach .The significance thresholds for the approach . In thisP-value was retained from each permutation replicate across the 2300 phenotype-replicate combinations. Again, the significance threshold was computed as the 5th percentile of the distribution of the most-significant P-values. The significance thresholds obtained using the decorrelated method were close to the naive permutation method .We constructed local LD weighted genetic relatedness matrices (GRMs) using imputed dosages at the common set of SNPs for Oxford and Chicago studies separately, and a combined GRM for all mice in both studies, using LDAK version 5.9 . We used the fraction of simulations f(Δ) within Δ to determine CIs for the original phenotype data. Thus, we identified the range of SNPs within 1.5 Mb of the top SNP and with a log P drop less than Δ to define the 100f(Δ)% CI for the QTL. We used the pruned SNPs within the 95% CIs as input to the SusieR fine-mapping method. We ran the SusieR method  were estimated by simulation. At each QTL, a residual phenotype was constructed by removing the effect of the top SNP at the QTL from the phenotype vector used in the QTL mapping above. This abolished the effect attributable to the QTL whilst maintaining genetic contributions from elsewhere in the genome. One thousand SNPs were then randomly selected, subject to the constraint that they were within 2.5 Mb of the top SNP and were polymorphic in the subset of individuals phenotyped for the trait. A causal variant was simulated at the SNP, with an effect size matching that of the top SNP, taking account of the allele frequency, and added to the residual phenotype. A local scan of the region using the same mixed model but the simulated phenotype was performed and the location and log z-scores of the significant variants in the discovery study and the corresponding z-scores in the replication study are used as input to learn the three unknown parameters (k is a genetic locus used as input to the method. The second (WC+C) corrects for both Winner’s Curse and study-specific effects for the discovery , 97,452 SNPs were retained for subsequent mapping .As expected, the genetic correlation between OX1 and OX2 was indeed close to 1 for all phenotypes . The staMethods; P threshold of 5.79  are on average less than those for physiological traits (blue dots). The absolute median effect size for the behavioral loci was 0.16 and 0.18 for physiological traits, indicating that the behavioral QTLs tended to have smaller effect sizes than the physiological QTLs. P and effect sizes.The genetic architecture of the phenotypes is polygenic; namely many loci each contribute a small effect to the heritability . Figure P-value of the top SNP was recorded. These simulations were used to compute the empirical distribution of the change in P-value between the most highly associated SNP and the causal SNP (Δ). The 95% CI was estimated as the maximum Δ among the lowest 95% of simulations. We estimated the 95% CI for every QTL using a previously published simulation procedure in which, for each locus, we randomly implanted causal SNPs that matched the true QTL’s observed effect size and simulated phenotypes for all samples (R2 < 0.99) of variants and the LD between these variants as input to susieR. SusieR uses these variants to compute a number of credible sets that are designed to contain the causal variant with high probability . We obtained credible sets of SNPs for 62/70 QTLs for which the fine-mapping algorithm converged. R2 > 0.99) of variants in the credible sets. We used the variants within these credible sets as a high confidence set of variants for downstream analysis.In order to identify putative causal variants within the 95% CIs previously computed, we applied a fine-mapping framework called susieR (We generated gene-based annotations for the variants in the credible sets using the ANNOVAR software. Ninety-nine percentage of variants were in noncoding regions of the genome. 32 variants in the credible sets were nonsynonymous mutations within 22 coding genes (compared to 4 nonsynonymous variants in UC and 16 nonsynonymous variants in OX). We interrogated the NHGRI-EBI catalog of human GWAS and the Mouse Genome Informatics database to determine if any of these 22 genes have been previously associated with relevant traits of interest . Of the P < 5.1e−05) for multiple testing of combinations of QTLs and other phenotypes. Phenotypes often derive from the same test (such as activity measured over different lengths of time or measures obtained from different muscles). To report our results we created two phenotype categories (Behavior and Physiology). Results are shown in We examined the effect of the 70 QTL significant in the mega-analysis on the other 23 traits, to determine the extent of colocalization between traits. We found that 45/70 QTLs had effects on more than one trait, after applying a Bonferroni correction  data set. After mapping all traits in either just the OX or just the UC sample and applying empirically derived thresholds , we identified 32 QTLs in the OX data set and 22 in the UC data set. Six of the 32 QTL discovered in OX replicated in the UC (19%); of the 22 QTL identified in UC, 7 replicated in OX (32%). There were also instances where the mega-analysis failed to confirm findings in a component study  to replication rates in our samples. When power is low, variants that pass the significance threshold are more likely to have inflated effect size estimates, meaning they are less likely to be replicated, a phenomenon that is often called “The Winner’s Curse.” Put another way, when power is low, the positive predictive value falls, so that for a given z-scores from the replication study. The method compares two random effects models: one that accounts for Winner’s Curse (WC) and another that accounts for both Winner’s Curse and study-specific heterogeneity due to confounding (WC + C). We compared the expected replication rates under each of these random effects models to the observed replication rates. If the observed replication rate is well explained by the WC model, there is relatively little heterogeneity. However, if the observed replication rate is better explained by the WC + C model, there may be heterogeneity between the studies.We applied a statistical framework that jointly models Winner’s Curse and study-specific heterogeneity due to confounding . Fourteen of the 70 variants in five phenotypes fell in this category. Finally, the fourth category contained the remaining variants. Twenty-seven of the 70 variants in 12 phenotypes fell within this category. A summary of the QTLs found in each of the categories is shown in By combining phenotypes and genotypes from two independent laboratories, we identified 70 loci for 23 complex traits in a population of 3076 commercially available outbred mice. The large sample delivered a median QTL interval size of 1.1 Mb. Fine-mapping reduced the number of likely causal variants, with a median reduction in set size of 51%. For all traits analyzed our results are consistent with a polygenic architecture, in which the vast majority of causal variants likely lie in noncoding parts of the genome, and with the existence of a considerable degree of pleiotropy, a pattern commonly recognized from GWAS of human complex traits  and TA (chr12:83514944) and is expressed highly in these tissues . Through fine-mapping and annotation of variants in the causal set, we implicated the Ptdins3P , providiTmem92, which has been previously found to be associated with heel bone mineral density in humans in a number of studies (Tbx18 gene which was implicated by a confidence level 2 QTL for plantaris weight (chr9:87855594), was previously associated with body size phenotypes in human GWAS studies and mouse KO studies  in the mega-analysis after correcting for Winner’s Curse. A QTL for EDL weight (chr13:113025220) in confidence category 3 implicated gene Larp4b, which has been previously associated with atypical femoral fracture and ankle injury in human GWAS . Additionally, the EDL QTL (chr2:86662262) had low estimated power after correcting for Winner’s Curse, and the body weight QTL (chr17:37094289) had a high estimated level of heterogeneity between studies. None of the seven genes have been previously associated with related phenotypes in the literature and are unlikely to be true associations. However, there is evidence to support the candidacy of genes at some of the QTLs in this category. For example, a QTL for soleus weight (chr13:9242435) implicates Pnpo, Gm11545, Psmb9, and Ttll6) represent novel targets for functional validation in follow-up studies. In particular, Pnpo lies at the bone mineral density QTL chr11:96912238, in confidence category 1. The bone mineral density phenotype had relatively low estimated levels of heterogeneity between the OX and UC cohorts, and the power of the mega-analysis after correcting for Winner’s Curse was close to 100%.While these examples highlight genes that have previously been associated with similar phenotypes in mice or humans, other genes  of QTL discovered in OX replicated in UC and just less than one-third (32%) of QTL identified in UC replicated in OX. We explored reasons for the low rates of replication and came to the following conclusions.Unknown confounds between laboratories limited replication, but this depended on the phenotype. By applying a method that detects the effect of heterogeneity, we observed that for one phenotype there was no impact of heterogeneity (weight of the EDL muscle), while on others the impact was substantial (up to 60% of the variance explained in the case of weight of the plantaris muscle). Some of the differences likely reflect systematic differences between the way the mice were treated at the two testing sites: mice born in Portage, Michigan, were shipped either to Chicago by truck (∼200 km) or to Oxford by truck and plane (∼6100 km). Other factors may include differences in the way the animals were handled or a variety of other laboratory specific differences.P-values and effect sizes reported in discovery cohorts; for example, a replication rate of 48% was found in a survey of 100 papers .Current human GWAS usually report results only from their meta-analyses, following the argument that joint analysis is more powerful than replication-based analyses . Code used to perform the analyses in this paper can be found at https://github.com/jzou1115/CFW_code.The authors affirm that all data necessary for confirming the conclusions of the article are present within the article, figures, and tables. The data and results presented in this paper require no additional permissions for use. Raw data from our study is deposited in Dryad: G3 online.This work was partially supported by National Institutes of Health grants . J.Z. is supported by a National Science Foundation Graduate Research Fellowship under Grant DGE-1650604. Publication charges for this article have been funded by 1R01MH115979.J.F., A.A.P., and R.M. conceived the study. J.Z., J.F., and S.G. performed the bioinformatics analysis. C.P. and J.N. prepared the phenotypes. R.W.D. generated the genotypes. J.Z., C.P., S.G, N.C, A.L. A.A.P., and J.F. wrote the manuscript. All authors read and approved the final manuscript.The authors declare that no conflict of interest.jkab394_Supplementary_Figures_TablesClick here for additional data file.jkab394_Supplementary_DataClick here for additional data file.


Early interactions with significant individuals affect social experience throughout the course of a lifetime, as a repeated and prolonged perception of different levels of care, independence, or control influences the modulation of emotional regulatory processes. As many factors play a role in shaping the expectations and features of social interaction, in this study, we considered the influence of parental bonding and genetic allelic variation of oxytocin receptor gene polymorphism (rs53576) over levels of experienced anxiety and avoidance in 313 young adults belonging to two different cultural contexts, namely Italy and Singapore. Results highlighted a major effect of maternal characteristics, care, and overprotection, with differences between the two cultural groups. Additionally, the interaction between rs53576 and maternal overprotection suggested different environmental susceptibility in the Italian sample and the Singaporean one. Implications for clinical work and future steps are described in the Conclusion. Fundamental works in disciplines related to developmental psychology have demonstrated that early social interactions with caregivers influence different aspects of child development, such as social relationships ,2,3, acaAccording to the original theories on attachment by Bowlby and Ainsworth, attachment-specific features were considered to extend beyond cultural boundaries or parenting practices; in other words, Bowlby’s and Ainsworth’s original theory was considered to be culturally universal in the sense that parental behavior that is sensitive to the child’s needs will result in attachment security across a diverse array of cultural and ecological contexts. Despite this assumption, the ecological components of attachment have always been seen as an important factor that needs to be taken into account, together with other features that might play a role in child-rearing behavior, such as socioeconomic status or ethnicity. In fact, one of the major concerns in the attachment-related research fields is the limitation due to results coming primarily from countries belonging to Western societies. With regards to cultural studies, Ainsworth’s canonical work in Uganda and Baltimore underlined that processes related to maternal behavior reflect specific universal features that cross cultural, geographic, and linguistic boundaries . In theiIn light of these considerations, it appears clear that, to have a proper understanding of parental bonds, it is essential to take a wider look at the concept of the familial nucleus according to the country where these family units are immersed. In the current study, we focused on two countries, one more Western oriented  and another one representative of a non-Western model (Singapore). In Italy, which is configured within individualistic countries, family is seen as an expanded network of relationships and ties, where much attention is given to the child. The relationship that forms between parent and child tends to be supportive and encouraging towards independence, presenting high levels of emotional bonding . On the OXTR) ; Singaporean sample N = 216; M = 73; F = 143; mean age = 21.51, SD = 1.83 [range 18∼31]).Parental Bonding Instrument: The Parental Bonding Instrument (PBI) is a 50-item self-report questionnaire that investigates participants’ perception of both maternal (25 items) and paternal (25 items) care and protection in their first 16 years of life. It was developed by Parker, Tupling, and Brown in 1979 using factor analysis from parents’ self-reporting their childhood experiences, the results of which yielded two factors: warmth/care (hereafter referred to as care) and overprotection [maternal 5 items aotection .Experience in Close Relationships-Revised: In order to evaluate anxiety and avoidance levels in close relationships, the Experience in Close Relationships-Revised was employed . The. TheExpeOXTR gene rs53576 region target. The forward and reverse primers that were used were 5-GCC CAC CAT GCT CTC CAC ATC-3 and 5-GCT GGA CTC AGG AGG AAT AGG GAC-3. For this DNA region, the frequency for the allelic distributions  differ among populations belonging to different ethnic groups. Although a unique consensus has still not been reached and may depend on the alleles’ distributions within a specific sample, the A allele is usually combined with the same heterozygous group A/G in the Western samples, whereas the G allele is more commonly paired with the heterozygous group A/G in the Eastern samples [This study adopted the same DNA derivation and genotyping procedure used by Bonassi and colleagues , using A samples ,35,44,45 samples ,46.Here, Western participants  having at least one A allele (A/A homozygotes or A/G) were classified into a single A-carriers group. Complementary to this, Eastern participants  having at least one G allele (G/G homozygotes or A/G) were treated as a single G-carriers group.ensembl.org), whereas the distribution in the Italian sample was 15% for A/A homozygotes and 85% for G-carriers. Specifically, genotype frequencies were as follows: A/A = 14 (14.43%), A/G = 35 (36.08%), G/G = 48 (49.49%). In the present Italian sample, the less frequent homozygous group A/A was merged with the heterozygous group A/G  before the analysis. Participants’ age  and sex p = 0.899) did not significantly differ between the G/G and A groups (see ensembl.org), whereas in our sample, the distributions were 37% for A/A homozygotes and 63% for G-carriers. Specifically, genotype frequencies were: A/A = 80 (37.04%), A/G = 94 (43.52%), and G/G = 42 (19.44%). Participants’ age  and sex p = 0.663) did not significantly differ between the A/A and G groups  did not significantly differ between the three groups. Participants’ age did not significantly differ between A/A and A/G , A/A and G/G , and G/G and A/G  (see Alleles’ frequency distribution and Hardy–Weinberg equilibrium parameters are reported in 323) see .Data were analyzed with R (Version 4.0.0). Statistical tests were differently planned for hypothesis-driven and exploratory analysis. The hypothesis-driven tests were performed separately on the Italian and the Singaporean sample, whereas the exploratory tests were conducted on the total number of participants . Overall, the statistical analyses followed a common procedure of variables’ evaluation and tests’ application.SDs above/below the mean. Any artificial restriction  was avoided to preserve the meaning associated with the scoring of each extreme observation. A high reliability of the subscales . Preliminary tests were made in each population group to exclude any significant effect on the ECR-R-dependent variables that could be attributed to participants’ sex and age for the exploratory analysis see . Univariales see  also excExploratory correlation matrices were generated for the Italian, Singaporean, and total samples to probe possible associations among the continuous variables .OXTR was entered at Stage 1 as a between-subjects factor of the regression to control for differences due to the genetics . Progressively, all the PBI subscales as continuous predictors were entered into each following step; specifically, maternal care was entered at Step 2, maternal overprotection at Step 3, paternal care at Step 4, and paternal overprotection at Step 5. In the final model (Step 6), the interaction between OXTR polymorphism variants and perceived parental bonding was tested.A series of hierarchical multiple regression (HMR) analyses was performed on the Italian and Singaporean sample separately to test the hypothesis that gene*environment interaction would predict the dependent variable (DV) adult anxiety . A series of HMR analyses was also conducted on the Italian and Singaporean sample separately to test the hypothesis that the gene*environment interaction would predict the DV adult avoidance . In summary, a six-step HMR was conducted for each ECR-R subscale  as the DV in reference to each sample . Therefore, in the total sample, an initial step was added in order to include the cultural variable  in the predictors of the regression model, for a total number of seven steps . Successively, all the PBI dimensions were singularly entered into each subsequent step following the order of administration of each sub-dimension of the PBI questionnaire. The final model (Step 7) was here defined by the interaction between culture, OXTR polymorphisms variants, and perceived parental bonding.A further series of HMR analyses was conducted on the total sample to test the exploratory hypothesis that culture would modulate the interaction between teps see . AllelicOXTR as factors) entered into the linear models, dummy coding was applied to compare each level of a given categorical variable to a fixed reference level. The factor OXTR presented two levels for the analysis on the Italian  and Singaporean  samples, but three levels were considered for the analysis on the total sample . The factor culture included two levels for the analysis developed on the total sample .To compare the significance of each progressive model, the analysis of variance (ANOVA) was used to analyze the contribution of each predictor added at every step. As the contrast coding system for the categorical variables  as predicted by the hypotheses.R, This type of regression is particularly targeted in research design with multiple variables , nested designs, and unpredictable outcomes or exploratory analyses ,49 and wFor each sample, the level of significance for the effects emerging from the final step of each HMR analysis was adjusted as a function of the two HMR analyses conducted on anxiety and avoidance, respectively  for the t-tests was considered to circumscribe the probability of committing a type I error. The magnitude of the significant effects for the t-test was estimated by Cohen’s d.For each sample, the significant main and interaction effects obtained from the final step of each series of HMR analysis were graphically represented by scatterplots with linear models and bar plots. A further assessment was advanced to inspect the direction of such effects. Pearson’s ariables . VariablWith regard to the significant main effects, the mean differences of the given ECR-R variable between low and high PBI subscales  as the DV see . A sevenp < pHMRs with the final step showing significant effects  = 0.62, residual sum of squares—RSS= 38,597, pF = 16.35, RSS = 33,425, pF = 12.55, RSS = 29,455, pF = 4.85, RSS = 27,921, pF = 0.00, RSS = 27,921, pOXTR and paternal bonding dimensions on anxiety was tested  = 0.32, RSS = 27,517, pt = 4.46, df = 95, r = 0.42, pt test  . Specifi=−0.49) . Means aHP1, no main effect of genotype or interactions between covariates and genotype were identified.Contrary to F = 0.18, RSS = 63,021, pF = 0.99, RSS = 62,733, pF = 0.15, RSS = 62,689, pF = 0.87, RSS = 62,438, pF = 1.59, RSS = 61,976, pHP2) interaction between OXTR and paternal bonding dimensions on avoidance was tested  = 1.74, RSS = 59,956, pt = −2.37, pt = −1.02, df = 214, r = −0.07, pt test   . Specifi = 0.11) .t = −2.37, ppt = 0.51, df = 134, r = 0.04, pt = −2.38, df = 78, r = −0.26, pHP2  .Means and standard error values for each effect on avoidance are available from F = 6.90, RSS = 85,763, pF = 0.03, RSS = 85,745, pF = 4.42, RSS = 84,592, pF = 1.03, RSS = 84,325, pF = 1.79, RSS = 83,858, pF = 1.11, RSS = 83,569, pOXTR, and PBI dimensions on avoidance (H3) was inspected  = 1.70, RSS = 73,827, pAt this last step, one one-way effect, three two-way interaction effects, and two three-way interaction effects were found. Means and standard error values for each effect on avoidance are available from t = 2.95, pt = −1.26, df = 311, r = 0.07, pt test  B.pThe main effect of paternal care  D, but nopThe two-way interaction between culture and paternal care , A/A and G/G , and A/G and G/G  (Two further significant two-way interactions between  = 0.17) F.OXTR and paternal care and between OXTR and paternal overprotection on adult avoidance did not fall below the level of acceptability  I. Likewi= −0.25) J.OXTR, and paternal care approached, but did not reach significance . Moreover, we explored a more intertwined model, adding the influence of the cultural environment (tested in HP3).Early interactions have been widely proven to have a crucial role in shaping psychological mechanisms that drive social interactions later in life. However, it is fundamental to take into account several factors that occur in the development of these psychosocial processes, such as genetic and cultural contributions. Starting from the evidence in the literature suggesting the key role of oxytocin ,56 and pOXTR variation in explaining the levels of anxiety and avoidance. Perceived maternal features were progressively entered into the model, both resulting as strong predictors for anxiety, with care displaying a negative association and overprotection a positive one. In the subsequent steps, recalled paternal characteristics were added to the model, and the care dimension for the father resulted as a predictor for anxiety as well. This might reflect the diversified, but equally important, contributions that both parents produce in their offspring, leading to different outcomes as a consequence [OXTR rs53576 and the parental bonding dimension on anxiety or avoidance, not confirming the first hypothesis. Overall, these results suggest that the quality of familiar bonds in this population, where higher levels of expressed emotions often characterize the relationship between parents and offspring [HP1.With regards to the Italian sample, the first step considered only the sequence . In the sequence , and fursequence ,62,63,64sequence ,66. Moreffspring ,67,68, cConcerning the Singaporean sample, the development of HMRs on this population overall revealed distinct patterns of parental boding and genotype on adult attachment as a potential effect of Eastern cultures. As for adult anxiety, recalled paternal care and overprotection contributed more significantly to the model when respectively entered. Although the interaction between genotype and parental features did not improve the model in terms of the total amount of variance, an interaction effect between genotype and maternal overprotection emerged at this final stage. Such an effect was marginally significant, thus did not persist when the correction was applied.OXTR variations and maternal care reached statistical significance as well. In particular, the two variations followed a mutually opposite pattern of association, displaying a slight positive correlation for maternal care over avoidance levels for G-carriers and a negative one for A/A homozygotes. In line with HP2, the present cross-interaction supported the assumption of a differential sensitivity to environmental influences among carriers of distinctive genetic phenotype in OXTR rs53576 [OXTR rs53576 on prosocial mechanisms, such as empathy or seeking emotional support, outcomes on attachment style were not consistent. Our results may reflect that the boundary condition plays a stronger role when it comes to adult attachment dimensions [OXTR rs53576 on adult attachment dimensions in Eastern populations, considering the ethnic group separately [Unlike the Italian sample, neither genotype, nor the recalled parental features resulted as a significant predictor of adult avoidance across the different steps of the model whose variance remained stable. In the final step, where all the targeted variables were included, maternal care was the strongest predictor of avoidance in adult close relationships with a negative trend. Moreover, the interaction between  rs53576 ,56,69. Wmensions ,36,70; tparately ,36,71, aWe observed that the effects in the two groups differed, especially in the relationship between recalled maternal bonding and the attachment dimension in adult relationships, reflecting the typical traits of familiar bonds: with regards to the Italian sample, where great attention is projected on the child and levels of emotions expressed are high , the conWhile the gene-by-environment framework focuses predominantly on external factors that characterize individual experience, the gene–culture interaction field of research focuses on the socio-cultural context. Within this theoretical frame, variants conferring genetic susceptibility can amplify typical cultural tendencies , such asIn the context of the HMRs advanced for adult avoidance, culture still was a key variable that was able to increase the variance significantly. Discordant with the significant effects of the numerous parental features on the variation in anxiety, only maternal care enlarged the amount of explained variation in avoidance. In accordance with the final model observed from the HMRs on anxiety, the highest quantity of total variance was reached when the interaction among the culture, genotype, and parental bonding dimensions was verified on avoidance.From the model of the three-way interaction, multiple effects were observed. Firstly, maternal overprotection positively predicted adult avoidance when the cultural and genotype influences were not further examined. Secondly, the cultural component played a dominant role over experienced avoidance in adult close relationships. We observed that the effects on the two groups were different, especially in the relationship between culture and maternal overprotection. Specifically, maternal overprotection was positively associated with adult avoidance for both Western and Eastern samples. However, the Western-oriented participants reported lower mean levels of avoidance from the partner than the Eastern-oriented individuals. Mousavi and colleagues, although framing their exploration in a wider perception of anxiety, including social phobia, which is related to avoidant patterns of behavior, provided evidence that the perception of parental control or overprotection was reported in a stronger extent in a Caucasian sample .OXTr rs53576 was also identified. In this regard, the significant interaction between genotype and maternal overprotection predicted adult avoidance when the participants were conceived together and independently from the cultural belonging to the group. However, the proper interpretation of this effect faced several difficulties in light of the different alleles’ frequency between Western and Eastern samples.Thirdly, the contributing action of the genetic predisposition driven by Analyses referring to the gene-by-culture frameworks displayed an interesting result: the different levels of associations between the genetic carriers A/A, A/G, and G/G and maternal overprotection on the adult avoidance for the Western-oriented and Eastern-oriented sample disclosed peculiar attachment patterns as a function of a differential genetic susceptibility to the external environment ,76. HereIn this study, we focused on two different social environments: the family, within witch the person experiences early relationships and builds the first behavioral patterns, and the cultural context, in which the grown person adopts a combined set of individual and shared criteria in relating to others. One of the strengths of this study was to provide a further step in comparing two distinct environments, each consisting of parenting strategies and family composition. Another important element considered was the distinction between maternal and paternal bonding. In fact, although currently, the competence regarding child care is becoming more and more equal between the two parents, fatherhood is an area that is still under-explored, especially in reference to the temporal dynamics of attachment in adulthood. Avoidance might have been more strongly correlated as it could play a mediating role between recalled parental features and anxiety . In our


Enterococcus faecium, Klebsiella pneumoniae and Mycobacterium tuberculosis. We used this pipeline to determine sample sizes required to detect causal variants of different minor allele frequencies (MAF), effect sizes and phenotype heritability, and studied the effect of homoplasy and population diversity on the power to detect causal variants. Our pipeline and user documentation are made available and can be applied to other bacterial populations. PowerBacGWAS can be used to determine sample sizes required to find statistically significant associations, or the associations detectable with a given sample size. We recommend to perform power calculations using existing genomes of the bacterial species and population of study.Genome-wide association studies (GWAS) are increasingly being applied to investigate the genetic basis of bacterial traits. However, approaches to perform power calculations for bacterial GWAS are limited. Here we implemented two alternative approaches to conduct power calculations using existing collections of bacterial genomes. First, a sub-sampling approach was undertaken to reduce the allele frequency and effect size of a known and detectable genotype-phenotype relationship by modifying phenotype labels. Second, a phenotype-simulation approach was conducted to simulate phenotypes from existing genetic variants. We implemented both approaches into a computational pipeline (PowerBacGWAS) that supports power calculations for burden testing, pan-genome and variant GWAS; and applied it to collections of  PowerBacGWAS is a computational pipeline that uses existing genomic data to perform power calculations for bacterial genome-wide association studies. See refs. 23. for reviews on the topic.Bacterial genome-wide association studies (GWAS) are a group of comparative genomics techniques aimed at identifying genetic variants in bacterial genomes that correlate with a phenotypic trait that is variable in a population. The introduction of bacterial GWAS became possible as a result of the increase in the number of whole-genome sequenced isolates. In the last few years, bacterial GWAS have been applied to study the genetic basis of a range of bacterial traits including antibiotic susceptibility25, adapted from tools originally developed for human GWAS. The use of units of association that capture genetic variation in both the core and accessory genomes was an important development for bacterial GWAS; including the use of k-mers26, presence or absence of genes in the accessory genome25, and unitigs27. Methodological advances include the use of linear mixed models (LMMs), which better control for fine population structure, and the deconvolution of lineage and locus effects1. Phylogenetic approaches that test for the co-evolution of genetic variants and traits have also been developed29.The wider adoption of bacterial GWAS is becoming possible thanks to the development of specialised tools tailored to the peculiarities of bacterial genomes30 cannot be applied to bacteria due to the unique characteristics of bacterial populations .A prerequisite for a successful GWAS is to perform power calculations to ensure that there are sufficient numbers of strains in the study to find statistically significant associations, or alternatively to determine the effect sizes detectable with a given collection of isolates. Variables affecting the power to detect causal variants include those related to the causal variants per se, such as their minor allele frequency (MAF), effect size or how they are distributed across the phylogeny (e.g. degree of homoplasy); and those related to the bacterial population under study, such as the degree of population diversity and structuring. Approaches developed for power calculations in human GWASEnterococcus faecium , Klebsiella pneumoniae  and Mycobacterium tuberculosis (causes tuberculosis). The methodology is general and compatible for a range of genomic variation including point mutations, indels and variation in gene content. We have made the code public (https://github.com/francesccoll/powerbacgwas) and provide user documentation on how to apply it to other bacterial populations.In this work, we show how existing collections of bacterial genomes can be harnessed to conduct power calculations for bacterial GWAS and measure the effect of these variables on the ability to discover causal variants. We implemented two new approaches to perform power calculations  and here apply it to three different bacterial species of clinical importance: The successful identification of phenotype-genotype associations in bacteria is influenced by multiple factors which vary strongly between bacterial species such as population structure, gene presence/absence, homoplasy and phenotype heritability. It is therefore not possible to create common statistical methods of estimating power in GWAS studies such as those used in well-studied human populations. To estimate the sample sizes required to detect phenotype–genotype associations in bacteria we implemented two new approaches based directly on real bacterial genomes and phylogenies Fig. . In the E. faecium), a gram-negative (K. pneumoniae) and a species of limited genetic diversity (M. tuberculosis). To test the effect of strain diversity in a population on the power of detecting causal variants, we assembled a population representative of the species’ overall strain diversity (hereafter referred to as species-wide population), and a second population of lower genetic diversity, made up of samples from a single clade (single-clade population). Table Table E. faecium collection (n = 1432), we could use kanamycin resistance  caused by the aminoglycoside resistance aph(3′)-IIIa gene31  = 1083); and streptomycin resistance for the single-clade clade A1 population  determined by the streptomycin-resistance ant(6′)-Ia gene31   caused mainly by blaKPC carbapenamese  for the species-wide population (n = 2628), but could not use it for the single-clade ST288 population (n = 1193) due to the unbalanced proportion of resistant (95.4%) and susceptible cases (1.3%). Isoniazid resistance was used for both M. tuberculosis populations , which is determined by well-known katG mutations33.Next, we searched for a known antimicrobial resistance (AMR) phenotype–genotype relationship in each population that could be used to perform power calculations . For the species-wide tion n = 432, we cE. faecium and K. pneumoniae populations to detect acquired AMR genes, yielded very comparable results, both between populations of the same species and across species. Specifically, a sample size of 500 to 700 genomes was enough to detect moderate (OR = 5) to very large (OR = 100) effect sizes of genes present in at least 10% of the population  compared to the species-wide population, to detect genes of the same MAF and effect sizes , individual SNPs (variant GWAS) and mutated genes (burden testing GWAS) causing binary phenotypes. Table We next conducted power calculations for the detection of individual SNPs  of the same MAF, regardless of their effect size, a pattern we observed in all bacterial populations studied. We next studied the effect of the bacterial population on the power to detect SNPs. In M. tuberculosis, lower sample sizes were required in the single-clade population compared to the species-wide, to detect SNPs of the same MAF, effect size and degree of homoplasy  but higher in the single-clade population to detect low-homoplasic SNPs (acquired 1–5 and 5–10 times).The degree of homoplasy of causal SNPs, that is, the number of times SNPs arose independently in the phylogeny, had a big impact on the ability of GWAS to detect them Fig. . GWAS wepost hoc, to report on the limit of detection in terms of the lowest MAF and effect sizes detectable by GWAS in the bacterial population of study. Either way, conducting power calculations will require making a set of assumptions as to the type of causal genotypes (i.e. caused by the acquisition of genes or mutations), their MAF, effect sizes and heritability.In this work, we showed how existing collections of bacterial genomes can be harnessed to conduct power calculations for bacterial GWAS. Investigators can apply our approach as part of their study design to determine how many strains they would need to sequence and/or phenotype to successfully identify statistically significant associations. Power calculations can also be applied We implemented two approaches to perform power calculations, here labelled as sub-sampling and phenotype simulation approaches. The former requires a known genotype-phenotype relationship in the population of study. We chose antibiotic susceptibility phenotypes, as determined by in vitro susceptibility testing, as they were readily available for the strain collections we used; and because the genetic basis of AMR phenotypes is generally well understood. The advantage of using AMR genes is that they are common and have high effect sizes; thus, the original population can be sub-sampled and AMR phenotypes modified to achieve the desired reduction in MAF and effect sizes. The limitations are that AMR phenotypes may not always be available or balanced in the population of study. This analysis is also constrained by the underlying genetic architecture, and the maximum MAF and effect size of causal genotypes. To overcome these limitations, we implemented the phenotype simulation approach which allowed us to test the effect of a wider range of parameters (i.e. effect size and heritability) by simulating phenotypes from existing genetic variants of different MAF and homoplasy.Although it was expected that higher values of these variables  would lead to an increase in power, we were able to determine the exact sample sizes for different combination of parameters and in different populations. Sample sizes required to detect causal acquired genes in a pan-genome GWAS were similar regardless of the bacterial species and population. This was not the case for the detection of causal SNPs, which depended heavily on the population. This may be due to pan-genome sizes being relatively comparable between species and populations, as opposed to a higher degree of genetic diversification in chromosomal genes (number of SNPs). Here we show that MAF, homoplasy and effect size of causal SNPs all have a measurable effect in the sample sizes required to detect them. The fact that the magnitude of such effect varies by population points to population-specific factors having an influence too. Factors that may affect the performance of GWAS, and thus the estimated power, include the accuracy of phylogenetic reconstructions, which may be challenging to obtain in bacteria with high recombination rates, the degree of population stratification and patterns of linkage disequilibrium. These points highlight the fact that the power estimated using samples from one population may not hold true for others, and support our recommendation to conduct power calculations using real genomes for the specific bacterial population of study, which will capture real patterns of population structure and linkage disequilibrium. This should be feasible for well-studied organisms given the large and increasing availability of whole-genome sequenced strains in public repositories.34 of choice, as it has been shown to minimise false positives calls35. However, a limitation of Snippy is that missing SNP calls are not retained in Snippy’s output consensus sequence, so SNP MAF were calculated without considering SNP missing calls. Still, we expect this to have little effect on power estimates, as SNPs chosen to simulate phenotypes are randomly selected multiple times, which means that the power calculated across simulations will account for the multiple and most common missing call rates present in the population. We used a single GWAS tool (PySeer)24 and method (linear mixed model) to conduct GWAS. Recent work has shown that the power to detect causal variants depends on the GWAS method employed36. It was out of the scope of our study to benchmark and give recommendations on individual GWAS methods. Our pipeline can be easily adapted to accommodate other GWAS tools, as multiple steps in the pipeline, such as variant sampling or phenotype simulations, are independent of the GWAS method used. A requirement to run the phenotype-simulation approach is that the ancestral state of genetic variants can be reconstructed so that these can be selected based on their degree of homoplasy. This prerequisite may not be possible for bacterial populations with high recombination rates. Another limitation is that sequenced genomes from the population of interest must be already available. However, for species that are not well-studied, power calculations can be performed by simulating bacterial populations from a reference genome, as previously described36. Further work is needed to perform power calculations for complex bacterial phenotypes involving multiple loci (e.g. epistatic effects). It was out of the scope of this work to investigate and report false positives rates. False positives in bacterial GWAS may arise from a variety of reasons, including the degree of genotype missingness, sequencing batch effects, regions in the genome that are hard to genotype, or the degree of stratification and linkage disequilibrium of causal variants in the population. Future work is needed on how best identify and account for factors that give rise to false positives in bacterial GWAS.Our study has several limitations. Here we used Snippy as the bacterial SNP-calling pipelineIn conclusion, power estimates will only apply to the bacterial population for which power calculations were performed from, and may not be generalisable to other populations. We thus recommend the use of existing genomes of the species and population of interest. In our approaches, only phenotypes were changed or simulated – bacterial genotypes and populations were not in any case simulated or modified. Given the inherent differences among bacterial populations, we believe our approach will yield realistic estimates of the sample sizes required to conduct successful GWAS.E. faecium), a gram-negative (K. pneumoniae) and a species of limited genetic diversity (M. tuberculosis), respectively. For each species, a population representative of the species-wide strain diversity was obtained from available collections of whole-genome sequenced strains. A subset of this species-wide population with lower strain diversity, made up of samples from a single clade, was additionally selected (single-clade population).Three bacterial species were chosen to represent a gram-positive organism , wastewater treatment plants (n = 383), bloodstream infections (n = 782) and 11 NCTC strains. The E. faecium single-clade population consisted of 761 clade A1 isolates drawn from a haematology study38, where the original population (of 1477 isolates) was de-duplicated to retain a single isolate per subtype (strain type) and patient, and reduced down to 227 and 534 isolates from faecal and ward environmental sources, respectively. To increase the population of clade A1 genomes, we additionally included genomes from a UK hospital study (n = 292)39 and a UK nation-wide (n = 478)40 studies, which increased the population size to 1531 clade A1 genomes.For K. pneumoniae collection was assembled from seven studies47 and consisted of 2628 isolates. The selected publications describe nation-wide or international isolate collections and include human, animal and environmental samples from Africa, Asia, the Caribbean and Europe including their antimicrobial susceptibility phenotypes. We did not include collections from a single local source, e.g. a single hospital, to reduce the possibility of including closely related isolates with low genetic diversity originating from a local outbreak. The K. pneumoniae single-clade population consisted of 239 CC258 isolates from this collection, plus 39648 and 55849 CC258 isolates from an extra two studies.The M. tuberculosis, we chose a collection of clinical isolates (n = 2655) generated as part of a global drug resistance project2 and representing the four main human-infecting M. tuberculosis complex (MTBC) lineages (lineages 1 to 4). The M. tuberculosis single-clade population consisted of 1139 isolates belonging to the most common sub-lineage (sub-lineage 4.3) in this global collection.For K. pneumoniae genomes, Kleborate v1.0.050 was used to determine the species of all isolates, of which those with a weak species match or a match to any species other than K. pneumoniae were excluded. Genomes with >1000 contigs were also excluded. For M. tuberculosis2 and E. faecium38 genomes, the quality control filters applied to discard bad quality genomes are described in the original publications.Raw sequencing data was analyzed with the goal of obtaining three standard files: a multi-sample VCF, a pan-genome table and a phylogenetic tree. For de novo assembly pipeline based on Velvet51 and annotated using Prokka52 v1.11. Pan-genomes were computed using Panaroo53 v1.2.3 with strict stringency mode. Reads were mapped to the reference genome of each bacterial organism using Snippy v4.6.0 (https://github.com/tseemann/snippy), specifically, the K. pneumoniae HS11286 (NC_016845.1), E. faecium Aus0004 strain (CP003351), and M. tuberculosis H37Rv (GenBank accession NC_000962.3) reference genomes were chosen. Snippy was used as it is recommended as a general purpose bacterial SNP-calling pipeline34 and has been shown to minimise false positives35. Whole-genome alignments were created by keeping a version of the reference genome with only substitution variants (i.e. SNPs but not indels) instantiated (i.e. Snippy’s .consensus.subs.fa output file). Single-nucleotide polymorphisms (SNPs) were extracted from whole-genome alignments using snp-sites54 v2.5.1 and saved as a multi-sample VCF files . Missing SNP calls were not retained as these are not specified in Snippy’s .consensus.subs.fa files used. IQ-TREE v1.6.10 was used to create a maximum likelihood tree from the core-genome alignment. Recombination events  were detected by Gubbins v1.4.1055, using an appropriate outgroup and the IQ-TREE phylogenetic tree as the starting tree. The final tree produced by Gubbins, obtained by running RAxML v8.2.856 without recombination regions, was rooted on the outgroup and kept for further analyses . Linkage disequilibrium was measured with the r-squared metrics calculated by Plink57 for each population.For all collections, draft assemblies were generated using an automated 24 with Roary’s output pan-genome file as genotypes and AMR susceptibilities as phenotype, and BLASTing the top hits against the CARD database58. For M. tuberculosis, we identified causal AMR mutations as those in the VCF file also found in the TBProfiler database33. The MAF and effect size of AMR causal variants were calculated  antibiotic susceptibility phenotypes, as determined by in vitro susceptibility testing, are readily available for many strain collections; and (2) the genetic causes of antibiotic susceptibility are generally well understood. We chose antibiotics that met the following criteria: (1) had a balanced proportion of resistant and susceptible isolates; (2) were caused by either the acquisition of genes or mutations in chromosomal genes; and (3) there was a good correlation between the presence of a well-known antibiotic resistance-conferring gene (or mutations) and antibiotic susceptibility in the studied collections, as determined by a large odds ratio (Table  antibiothttp://sanger-pathogens.github.io/Roary/), a simple tab delimited binary matrix with the presence and absence of each gene in each sample. Because of the use of PastML59 and PLINK57 tools in the pipeline, VCF and Roary-formatted files need be converted to a CSV file compatible with PastML (https://pastml.pasteur.fr/help) using vcf_to_pastml_matrix.py and roary_to_pastml_matrix.py scripts, respectively; and to PLINK binary PED files (https://www.cog-genomics.org/plink/2.0/input#bed) using the scripts vcf_to_plink_files.py and roary_to_plink_files.py  to account for population structure. The kinship matrix for LMM is calculated from the phylogenic tree using PySeer’s script phylogeny_distance.py and thus is kept the same for all GWAS analyses  applied to the same population. Finally, the script process_gwas_runs.py is used to extract the LMM adjusted p-values of causal variants from Pyseer output files; and the R script plot_gwas_runs_subsampling.R to plot the results.The sub-sampling approach uses a known genotype-phenotype relationship to conduct power calculations and is implemented for binary phenotypes. It supports power calculations for acquired genes (pan-genome GWAS), VCF variants (variant GWAS) and burden testing, where VCF variants are aggregated within chromosomal regions (e.g. genes), and regions tested for association. The scripts prepare_gwas_runs_subsampling.py and prepare_gwas_runs_subsampling_roary.py implement this approach for VCF variants and the pan-genome, respectively. In addition to the variant file (VCF or pan-genome), this script requires a file with causal variants (a GCTA-formatted file with a list of variants IDs) and a Pyseer-formatted phenotype file, where the variants in the causal variants file are known to determine the trait in the phenotype file. A parameters file is also required to specify the range of MAF, sample sizes and effect sizes to test. First, the observed MAF and effect size of causal variants are calculated. Second, and for each parameter combination, the original phenotype file is modified to ‘artificially’ decrease the MAF  and the sample size  by assigning ‘NA’ phenotype labels to as many isolates as needed to achieve the chosen reduction in sample size and MAF. The effect size is reduced by swapping phenotype labels so that the number of false positives and false negatives increases at the expense of a reduction in the number of true positives and true negatives; only between isolates of the subsample to ensure MAF and sample size remain unchanged. This sub-sampling step is repeated as many times for each parameter combination as specified in the parameters file. Finally, prepare_gwas_runs_subsampling.py outputs a bash script with GWAS runs (as many as sub-samples) and a CSV file with all parameter combinations used. Pyseer v1.3.560 to simulate phenotypes. When simulating binary phenotypes with GCTA, we noted that the variable that had the biggest impact on the distribution of mutated and wildtype individuals among cases and controls, and thus association p-values, was the heritability, not so much the effect size (odds ratio) specified in the causal variants file. We thus decided not to use GCTA when specifying odds ratios to simulate binary phenotypes, but only when specifying heritability values or simulating quantitative phenotypes. The script simulate_binary_phenotype_vcf.py (and simulate_binary_phenotype_roary.py) was written to implement a custom method to simulate binary phenotypes for a given odds ratio.We implemented a second approach to conduct power calculations for when a genotype–phenotype relationship is not known in the population, for both binary and quantitative phenotypes. In brief, this approach consists in sampling existing genes from the pan-genome, variants from a VCF file or mutated regions (i.e. for burden testing) meeting a predefined MAF and degree of homoplasy, to then simulate phenotypes from these variants with a pre-defined effect size and sample size. The wrapper scripts prepare_gwas_runs.py and prepare_gwas_runs_roary.py implement this approach for VCF variants and the pan-genome, respectively , MAF and odds ratio to be simulated as parameters. The function scipy.optimize.least_squares is used in this script to solve the following set of equations:24 is used to run the GWAS using the linear mixed model (LMM) and the script process_gwas_runs.py to extract the LMM adjusted p-values of causal variants from Pyseer output files. The R script plot_gwas_runs.R is used to plot the strength of association of causal variants in the y-axis , as plotted in Manhattan plots) as a function of sample sizes  for different combinations of effect sizes and MAF. The results can also be plotted for different combination of heritability values and MAF. Note that the pipeline (in the parameters files) allows to vary the effect sizes or heritability values of causal variants, but no both at the same time. The effect of varying degrees of homoplasy on the association strength can also be plotted across sample sizes for a fixed MAF. For each set of parameters, the total number of GWAS replicates is determined by the number of times the variant sampling step is repeated multiplied by the number of times the phenotype simulation step is repeated. Power is calculated for each combination of parameters as the proportion of GWAS replicates in which the causal variant is above the Bonferroni-corrected genome-wide significance threshold.The number of times this phenotype simulation step is repeated can be chosen in the parameters file. This was set to 10 per set of simulation parameters in the analyses presented in this manuscript. The output of the phenotype simulation step is a Pyseer-compliant phenotype file for a given parameter combination. Finally, the wrapper script prepare_gwas_runs.py outputs a bash script with as many GWAS runs, and a CSV file with as many lines, as parameter combinations. As done for the sub-sampling approach, Pyseer v1.3.5We used a single causal variant  to simulate phenotypes, to assess the limit of detection of individual causal variants. The pipeline does not currently support the simulation of phenotypes from multiple causal variants.https://hub.docker.com/r/francesccoll/powerbacgwas) to facilitate usage. We have additionally implemented a Nextflow pipeline that uses this Docker image, to automate the multiple computational steps and parallelise the GWAS runs. Using the Nextflow implementation, the pipeline is reduced to only three steps: (1) preparation of input files, (2) ancestral state reconstruction and (3) GWAS runs. In the latter, the user can choose the type of variants, phenotype, GWAS method and approach to power calculations. The GitHub page (https://github.com/francesccoll/powerbacgwas)61 includes instructions on how to install PowerBacGWAS via Docker and Nextflow, or locally. The Usage Wikipage (https://github.com/francesccoll/powerbacgwas/wiki#usage) includes sections on how to run the pipeline using the Docker/Nextflow installation (‘Nextflow commands’) or how to run individual scripts using the locally installation . The computational time of running PowerBacGWAS depends on the number of parameter combinations, and number of variant sampling and phenotype simulation repetitions indicated in the parameters file. All these parameters combined determine the number of individual GWAS runs. We recommend to run the Nextflow pipeline with the LSF executor, if an LSF cluster is available, for faster running times, wherein each process is submitted as a separate job. Overall, for the six bacterial datasets used in this work, PowerBacGWAS used a median of 1,670 CPU hours (interquartile range: 548 to 3542) and a median duration of 3.1 h (interquartile range: 2–7.6 h) when using the Nextflow LSF executor.Given the multiple software tools, modules and package dependencies of PowerBacGWAS, we built a Docker image of the pipeline (Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplemenatary Data 1Supplementary Data 2Supplementary Data 3Reporting summary


Profitability of the beef industry depends on the performance of both finishing cattle and the breeding herd. Breeding programs need to be designed in a way that balances requirements for both systems, requiring knowledge of relationships between trait groups. This study examined the inheritance of growth, ultrasound and carcass traits and their relationships with maternal performance in New Zealand beef herds. Results have shown that genetic variation exists for traits measured in finishing animals such that direct selection for enhanced growth, ultrasound and carcass traits is feasible. Genetic correlations indicate that decreasing fat content in live animals at ultrasound scanning or at slaughter can lead to detrimental changes in the genetic merit of maternal performance. Thus, a reduction in reproductive performance in 2-year-old cows may occur while mature size genotypes are likely to increase, and this can impact on cow maintenance requirements. However, only low genetic correlations exist with body condition score in mature cows such that there is room to alter the fat distribution in finishing animals without impacting on body energy reserves in mature cows. Results indicate that identifying animals with favourable genotypes for both maternal and finishing performance is feasible when measuring animals for both trait groups.g = 0.53–0.95) and may be used as a selection tool for improved genetic merit of the beef carcass. Fat content determined via ultrasound scanning in the live animal or at slaughter in finishing cattle is positively genetically correlated with rebreeding performance (rg = 0.22–0.39) in female herd replacements and negatively correlated with mature cow live weight (rg = −0.40 to −0.19). Low-magnitude associations were observed between the genetic merit for carcass fat traits with body condition in mature cows.Research has shown that enhancing finishing performance in beef cows is feasible; however, any adverse impact of selection strategies for finishing performance on the performance of the maternal herd should be taken into account. The aim of this research was to examine the inheritance of growth, ultrasound and carcass traits in finishing beef cattle and to evaluate their correlations with maternal performance traits. Data were collected from a nationwide progeny test on commercial New Zealand hill country farms comprising a total of 4473 beef cows and their progeny. Most finishing traits were moderately to highly heritable (0.28–0.58) with the exception of meat or fat colour and ossification (0.00–0.12). Ultrasound scan traits had high genetic correlations with corresponding traits measured at slaughter (r The key driver for performance and profit in a beef cow herd is calf output relative to cow feed requirement ,2. The kMany researchers have published genetic and phenotypic parameters for either maternal or finishing traits ,12 but lWeik et al.  reportedAnimal ethics approval was obtained for all related procedures and measures involved in this study .The current study used data collected as part of an ongoing nationwide beef progeny test (BPT) on five large-scale commercial hill country farms in New Zealand. The BPT was established by Beef + Lamb NZ Genetics in 2014 to compare sire performance across a range of commercial environments over multiple years. An in-depth description of the animal management procedures and data collection is provided by Weik et al.  and WeikPerformance records were obtained for a range of traits for females within the breeding herd and for heifers and steers as part of the finishing operation. Female herd replacements were assessed for their maternal performance in terms of reproduction, weight, size and body condition, whereas animals destined for slaughter were measured for growth, ultrasound and carcass traits to examine finishing performance.Where appropriate, phenotypic records were pre-adjusted to either a constant age, weight, body condition score or hip height at the time of measurement, where pre-adjustments were based on continuous variables using the 4-step procedure of Reverter et al. . AdjustmMaternal traits were a subset of those previously described by Weik et al. , chosen BCS) or a constant hip height of 130 cm (MWTHH) to evaluate live weight of cows irrespective of stored energy reserves or size. Adjustments were made using either BCS or hip height as a linear and quadratic covariate, for MWTBCS and MWTHH, respectively.Mature cow live weight (MWT), mature cow body condition score (BCS) and mature cow hip height (MHH) were recorded on cows that were aged 3 years or older. Animals were measured for MWT and BCS at three timepoints throughout the year  and only one annual measure of MHH was made prior to calving. All three mature cow traits were pre-adjusted to 5 years of age. Age adjustments were made using a fixed-effects model with age (in years) with cows older than 12 years of age grouped in a single group and CG as factors in the model. Scoring BCS was through visual assessment of body energy reserves with 1 being emaciated and 10 being obese . The traWeaning weight of calves (WWT) was pre-adjusted to a constant 200 days of age using linear models with age (in days) as a covariate.Live weights for growing animals were recorded unfasted for WWT between 108 and 228 days of age, yearling weight (YWT) between 272 and 388 days of age and 18 month weight (W18) between 475 and 616 days of age. Weight traits were chosen based on their relevance for the New Zealand beef sector and are commonly recorded traits for growing animals within genetic evaluation programs worldwide . Live weP8), and fat depth at the 12/13th rib site (FDRIB). Scans were conducted by Breedplan-accredited technicians on replacement heifers and on steers and heifers destined for processing, resulting in records for 1904 steers and 2049 heifers. Recording coincided with W18, between 475 and 616 days of age. All ultrasound traits were pre-adjusted to a constant live weight of 432 kg, the mean live weight at 18 months of age, using linear and quadratic live weight as covariates. This adjustment was used to compare muscularity and fatness rather than absolute size.Ultrasound scanning was conducted on live animals for traits commonly recorded in pedigree-recorded cattle , namely Following ultrasound scanning, animals were assigned to slaughter management groups based on their live weight. Within these groups, all animals were slaughtered on the same day without further selection prior to slaughter. Measurements on the carcass included carcass weight (CWT), carcass eye-muscle area (CEMA), marbling score (MB), fat colour (FC), meat colour (MC), ossification score (OSS) and rib fat depth (RF) for a total of 1636 steers and 354 heifers and were recorded between 541 and 1029 days of age. The trait CWT was recorded at the time of slaughter, whereas grading measures were assessed by trained graders using the Meat Standards Australia chiller assessment system  on the dDefinitions of CG for maternal, growth, ultrasound and carcass traits are outlined in p < 0.05) was considered evidence for the presence of heterogeneous variances, and this was the case for MWT, MWTBCS, MWTHH, FDP8, FDRIB, MC and OSS. Consequently, these traits were scaled to homogenize the variances [Traits were tested for departures from homogeneity, and this was evaluated by examining the relationship between the CG mean and CG standard deviation (SD) ,29. A liariances  and thisariances .Each trait included in this study with number of records, range of data measure, mean and SD is presented in Data cleaning and formatting were conducted using R version 3.6.1 . RestricVariance components were obtained for all growth and ultrasound traits using univariate animal models. For carcass traits, variance components were estimated using bivariate models including W18 to account for any selection that occurred prior to data recording due to drafting of animals in lighter and heavier slaughter mobs. Genetic and phenotypic correlations were obtained among all maternal and finishing traits for which heritability was greater than 0.05 by estimating (co)variance components from bivariate animal models, or trivariate models (when a carcass trait and W18 were included).Models included various fixed effects which were not accounted for through pre-adjustments of the phenotypes. Those along with random effects fitted for each trait are shown in The general models used can be described as follows:OrOrThe expected values of Convergence was presumed according to the default value in ASREML when the log likelihood changed less than 0.002-fold the number of iterations and the change of individual variance parameter estimates was below 1%. The pedigree was traced back for up to two generations so that the numerator relationship matrix Results from univariate animal models for the estimation of variance components and heritabilities for growth and ultrasound traits as well as from bivariate analysis for carcass traits are presented in Estimates for heritabilities were moderate for the growth traits PWG and PYG and ranged from 0.33 to 0.35. Heritabilities were higher for both weight-related traits YWT and W18 with 0.53 and 0.50, respectively. Variance components increased for both additive-genetic and residual variances when estimated for the same trait in older compared to younger animals. The trait YHH was the most heritable growth trait at 0.55.P8 and FDRIB (0.52–0.58), but variances were lower than for EMA.Weight-constant ultrasound traits were moderately to highly heritable, and the lowest heritability estimate was for EMA (0.36). Estimates of heritability were generally greater among those traits related to fat deposition, namely IMF, FDHeritability estimates varied considerably across traits measured on the carcass at slaughter and were generally low to moderate. The most heritable carcass trait was CWT with 0.47 estimated at a constant age. Moderate heritabilities were also observed for the weight-constant traits CEMA (0.43) and MB (0.39). All other carcass traits were only lowly heritable and ranged from 0.07 to 0.28 for FC, OSS and RF. The trait MC was not heritable and variance components were low for both traits related to colour of the carcass (MC and FC).Genetic and phenotypic correlations among traits measured in finishing cattle are shown in P8 and FDRIB. Both, FDP8 and FDRIB were not correlated with EMA (0.06), but a moderate genetic correlation has been observed between EMA and IMF (0.36). Genetic correlations among carcass traits ranged from a highly negative correlation between CWT and FC (−0.63) to a high positive correlation between CEMA and FC (0.73).Weight traits of growing animals generally had high genetic correlations (0.70–0.88) among themselves, but correlations with the growth-related traits PWG (0.08–0.18) and PYG (0.10–0.68) were overall lower. Those traits related to fat deposition measured at ultrasound scanning were highly correlated and ranged from 0.68 to 0.85 among IMF, FDRIB and RF (0.95). Overall, traits related to the fat content of live animals at ultrasound scanning  compared to carcass traits (MB and RF) were moderately to highly genetically correlated and ranged from 0.38 to 0.95. Low genetic correlations were generally obtained for OSS or FC with all traits measured in live animals at ultrasound scanning (−0.19–0.23), but a moderate genetic correlation was observed between FC and EMA (−0.34).The trait CWT was only lowly genetically correlated with weight-constant ultrasound traits and ranged from −0.06 to 0.14. High genetic correlations were obtained among corresponding traits measured at either ultrasound scanning or at slaughter for EMA and CEMA (0.53), IMF and MB (0.70) and FDGenerally, phenotypic correlations were lower compared to genetic correlations among all traits measured in finishing animals. The highest phenotypic correlations were estimated among weight traits such as YWT, W18 and CWT and ranged from 0.56 to 0.77. Low to high phenotypic correlations were observed among traits related to fat deposition in either live animals at ultrasound scanning or at slaughter and ranged from 0.22 to 0.72.Genetic correlations among maternal performance and finishing traits are presented in P8 and FDRIB and those correlations were positive for RB (0.31–0.39) and negative for DtC2 (−0.42 to −0.35). Correlations among fat-related traits measured on the carcass at slaughter  were slightly lower for RB (0.22–0.36) but in a similar range for DtC2 (−0.59 to −0.29). The trait RB was lowly positively (0.11) correlated with EMA at ultrasound scanning after the effect of weight has been accounted for but was moderately negatively correlated with the corresponding trait measured at slaughter (−0.46).Genetic correlations of the reproductive traits RB and DtC2 with finishing traits were similar but with opposing signs except for PYG which was positively correlated with both reproduction traits. Traits related to growth in yearling animals  were positively genetically correlated with RB in the current study but correlations were low (0.11–0.18). Estimates were slightly greater for growth traits measured in older animals (PYG and W18) and ranged from 0.22 to 0.32. Moderate genetic correlations were estimated among reproductive traits and ultrasound scan traits related to fat deposition such as IMF, FDGenerally, MWT was highly genetically correlated with most growth traits, ranging from 0.62 to 0.92, but only a moderate correlation was observed with PWG (0.45). Similarly, the genetic correlation with CWT was high (0.82). Genetic correlations of MWT with all ultrasound and carcass traits other than CWT and FC were low to moderate and negative and ranged from −0.48 to −0.19. Overall, estimates of genetic correlations with finishing traits were similar compared to those with MWT after the effect of BCS has been removed. Adjusting MWT to a constant height generally reduced all genetic correlations but estimates were slightly higher for the carcass traits OSS and RF. Correlations among MHH and finishing traits were slightly lower but similar to those with MWT.The trait BCS was lowly genetically correlated with all finishing traits except for a moderate correlation with PYG (0.36) and OSS (−0.30). Body condition score was positively correlated with all ultrasound traits (0.18–0.29), but mainly negative genetic correlations were estimated with carcass traits other than CWT and those ranged from −0.30 to −0.07. No genetic correlation was observed between BCS and RF.D) and PWG, but estimates were high with all other growth traits in the current study (0.56–0.86). Genetic correlations of WWTD with ultrasound or carcass traits other than CWT (0.85) ranged from a low negative correlation with CEMA (−0.29) to a low positive correlation with IMF (0.22). The maternal component of WWT (WWTM) was negatively correlated with both PWG and PYG (−0.56 and −0.18) but correlations with other growth traits were high and positive (0.66–0.78). Genetic correlations among WWTM and ultrasound traits were low to moderate and positive, and the greatest estimate was observed with FDP8 (0.33). The genetic correlation for WWTM was moderate only for CWT (0.42), whereas all other traits recorded at slaughter only showed low correlations with WWTM (−0.06–0.21).No genetic correlation was observed between the direct genetic effect of WWT . Only low phenotypic correlations were observed among BCS and all finishing traits (0.08–0.18). The trait WWT was generally highly correlated with growth traits (0.72–0.88), but estimates were low for PWG (−0.27) and PYG (0.05). Almost no correlations were observed among WWT with ultrasound and carcass traits (−0.08–0.09), but a high estimate was obtained with CWT (0.51).Phenotypic correlations among maternal performance traits and finishing traits are presented in Heritabilities for the growth traits YWT and W18 were high compared with published values ,33. The Ultrasound traits recorded on the live animal in the current study were generally heritable and variable, thus direct selection for higher genetic merit in live animal scan traits is feasible. Most previous research has evaluated live animal ultrasound scan traits primarily on an age- rather than weight-constant basis and this needs to be considered when comparing results presented in this study with literature values. Generally, heritability estimates for ultrasound traits presented here were slightly greater compared to those values presented in the literature and the adjustment factors may explain part of the discrepancy. After adjustment of the animals to a constant weight at 18 months of age, differences in EMA are likely to be primarily due to differences in muscling rather than size. Traits related to size of the animals are likely to be more heritable than those traits related to muscling ability, and this may explain the lower estimates obtained in the current study when comparing the results to an age-constant EMA without accounting for differences in weight . CoppingBreedplan [The current study evaluated all carcass traits other than CWT on a weight-constant basis, whereas CWT was adjusted to a constant age. A similar approach has been presented by Reverter et al.  for genereedplan . They rereedplan  reportedreedplan  of 0.41 reedplan  on a weireedplan  indicatereedplan , howeverreedplan .P8 and FDRIB were highly correlated amongst themselves. Thus, genetic progress in one trait can lead to a correlated response in other scan traits in the same direction, and this is in agreement with the literature [RIB (0.06) or between EMA and FDP8 (0.06) at ultrasound scanning, which was in contrast with the moderate genetic correlation between backfat and longissimus muscle area (0.39) presented by Arnold et al. [Generally, genetic correlations among all growth traits were high, indicating that animals with favourable genotypes for one trait also have favourable genotypes for the other traits and this has also been observed by previous researchers ,34,40. Sterature . Howeverd et al. . Geneticd et al.  found eqRIB and RF of 0.86 reported by Kemp et al. [RIB is likely to be an accurate predictor of RF recorded on the carcass at slaughter and can be used as a selection tool for genetic improvement [Several researchers have examined genetic relationships between ultrasound traits and the corresponding traits recorded on the carcass at slaughter. Carcass traits measured via ultrasound scanning in live animals are used as indicator traits for carcass merit, allowing an early, more simplified prediction compared to measuring the actual economically relevant traits at slaughter . Kemp etp et al.  was consp et al.  of 0.94,p et al.  presenterovement ,41. SimiResults from this study indicate that RB in female herd replacements has only limited genetic correlations with growth and weight traits in finishing cattle. The same has been observed for DtC2 which describes a similar trait to RB using a continuous as opposed to binary measure of reproductive success. Increased reproductive performance in female herd replacements in terms of first-calving heifers getting back in calf early in their second mating season had a positive genetic correlation with fat traits in finishing animals at either live animal ultrasound scanning or at slaughter. Thus, genetic progress in the fat content of finishing cattle is likely to move the average of the replacement herd towards animals with higher pregnancy outcomes. Wolcott et al.  examinedMature cow weight was highly genetically correlated with growth and other weight traits such as YWT, W18 and CWT and this has been previously reported in the literature . Thus, bBody condition score was only lowly genetically correlated to all finishing traits in the current study. Contrary to common belief among breeders, the results have shown that the fat content in finishing animals at either ultrasound scanning or on the carcass is not strongly related to the energy reserves in mature cows as indicated by BCS. This indicates that if breeders want to increase the BCS of their cows by using fat traits in finishing cattle as a selection tool, a large increase in the genetic merit of ultrasound and carcass fat traits would be required for only a small change in BCS. Generally, a greater response in BCS is likely when including a direct measure of BCS in the selection program and breeders may improve BCS in their cows without greatly impacting the fattening potential of the finishing herd. Practical application may, however, be limited by the time delay in measuring mature cows and/or the number of cows available for selection.D and growth traits were generally high, and this agrees with literature values [D and this indicates only a limited impact on fat content and muscularity for finishing cattle at a constant weight when aiming for genetic progress in WWTD. Improving maternal genetic ability in terms of calf weaning weight tends to lead to a correlated response in weight and height traits of finishing animals in the same direction but those animals are less likely to exhibit greater genetic merit for growth rates until W18. Overall, WWTM was only lowly genetically correlated with carcass traits other than CWT and correlations were only slightly higher with those traits measured at ultrasound scanning. Thus, antagonistic effects on any traits related to performance in finishing cattle are unlikely when improving the genetic merit for WWTM. The genetic correlations among WWTM and the carcass traits CWT, RF and MB agree with the estimates presented by Crews et al. [Genetic correlations among WWTe values ,40. All s et al.  in Canads et al.  were, hoTo optimize the efficiency of the entire beef herd, knowledge of the relationships among traits that affect the maternal and the finishing operation is required. Generally, results indicate that genetic correlations exist among key maternal performance traits and finishing traits and those need to be taken into account to avoid potential antagonisms when developing selection programs and for genetic evaluations. Balanced selection is required to enhance production in both systems without compromising performance in correlated traits. Genetic correlations indicate that there is potential to reduce the fat content in either live animals at ultrasound scanning or at slaughter with only minor changes in body energy reserves in mature cows, but this may have a detrimental effect on the genetic merit for reproductive performance in 2-year-old cows. Thus, it is important to consider traits in a multi-trait context to achieve genetic gain in the entire system. Results indicate that it is possible to identify animals with favourable genotypes for finishing and maternal performance when measures are available for both trait groups.


Sesamum indicum L.) is one of the oldest oil-crops in the world. Despite its economic and agricultural importance, it is an ‘orphan crop-plant’ that has undergone limited modern selection, and, as a consequence preserved wide genetic diversity. Here we established a new sesame panel (SCHUJI) that contains 184 genotypes representing wide phenotypic variation and is geographically distributed. We harnessed the natural variation of this panel to perform genome-wide association studies for morpho-agronomic traits under the Mediterranean climate conditions.Unrevealing the genetic makeup of crop morpho-agronomic traits is essential for improving yield quality and sustainability. Sesame (Field-based phenotyping of the SCHUJI panel across two seasons exposed wide phenotypic variation for all traits. Using 20,294 single-nucleotide polymorphism markers, we detected 50 genomic signals associated with these traits. Major genomic region on LG2 was associated with flowering date and yield-related traits, exemplified the key role of the flowering date on productivity.Our results shed light on the genetic architecture of flowering date and its interaction with yield components in sesame and may serve as a basis for future sesame breeding programs in the Mediterranean basin.The online version contains supplementary material available at 10.1186/s12870-021-03328-4. Sesamum indicum L.), one of the oldest oil-crops in the world, was domesticated about 5,500 years ago from Sesamum indicum subsp. malabaricum , with some overlaps ,], with sPisum sativum L. [Twenty-eight SNPs were significantly associated with yield components. Of those, 20 were associated with SYPP, 7 for SNPP, and 1 for TSW. Seed size (TSW) is known to have moderate-high heritability in sesame , 57, as tivum L. ), was astivum L. , 60, whitivum L.  or genomtivum L. , may conCicer arietinum L. [SYPP and SNPP are highly polygenic traits associated with various anatomical and morphological traits . For SNPP and SYPP, we found one shared genomic region on LG2 overlapping with FD . It is Arabidopsis to interact with two flowering regulators transcription factors, CONSTANS and FLOWERING LOCUS C (FLC) [PYR1-like, a mutant allele of this gene is found to be associated with growth and productivity in rice [CHROMATIN REMODELING 19. This gene was found regulating floral organs development in Arabidopsis [Arabidopsis and associated with auxin conjugate and homeostasis [In sesame, allelic variation within flowering-related genes contributes to variation in flowering date , 66. In  C (FLC) . LOC1051 in rice . Two of bidopsis . Additioeostasis , and seveostasis . Yet, adThe functional annotation of these genes and the co-localization of them in the same genomic region demonstrated how the variation in FD and SYPP could be genetically controlled together. Further investigation is needed to study how this genomic region (and the genes inside it) interact with other identified genomic regions for a deeper understanding of the genetic basis and mechanisms underlying FD and SYPP variations in sesame.While sesame is still mostly grown under a traditional cropping system, future sesame breeding targets should focus on improving yield and adaptively to more diverse environments. Thus, elucidating the genetic architecture controlling phenological, morphological, and yield components traits will aid in understanding selection criteria and better genetic-based breeding. Here we established a new sesame panel (SCHUJI) and explored its genetic variation for morpho-agronomic traits under the Mediterranean climate conditions. We showed the benefits of using the globally distributed panel for discovering new alleles associated with these traits. A major genomic region on LG2 was found in association with flowering date and yield components, indicating the crucial role of phenology on sesame production. A better understanding of the genetic variability underlying flowering date in sesame will serve as a basis for improving sesame adaptively to new cropping systems. Moreover, it will enhance breeding efforts and enable turning this important crop from domestically grown to global production in intensive agriculture.Additional file 1: Supplementary Tables. Table S1. List of the sesame lines from the SCHUJI panel and their geographical origin. Table S2. Cross-validation error for K=1-10. Table S3. Mean, standard deviations (SD) and coefficient of variation (CV) of the measured traits. Table S4. Phenotypic Correlations and P-values among the measured traits. Table S5. Cluster means from the K-means clustering analysis. Table S6. Fixation index (FST) values between the subpopulation in the panel. Table S7. Genomic heritability estimates of the measured traits. Table S8. Genomic correlations among the measured traits. Table S9. Significant genetic markers (SNPs) that are associated with the traits in the experiment. Table S10. Candidate genes for flowering date and seed-yield traits.Additional file 2: Fig S1. Density distribution of reproductive zone measured in 2018 (blue) and 2020 (red).Additional file 3: Fig S2. Genome-wide linkage disequilibrium pattern in the SCHUJI panel. The red dashed line represents the non-linear trend and the green dashed line indicates the point where linkage disequilibrium drops to half of its initial value (0.22) at 58,774 base pairs.Additional file 4: Fig S3. Manhattan plots for (A) reproductive zone, (B) plant height and (C) number of branches per plant. Dashed line represents the genome-wide significant threshold.Additional file 5: Fig S4. Principal component analysis on genetic markers of the SCHUJI panel. Colors represent different clusters from the k-means cluster analysis. Red color represents cluster 3, green color represents cluster 1 and blue color represents cluster 2.