Background: Prior observational studies have reported that higher levels of vitamin D are associated with decreased caries risk in children. However, these studies are prone to bias and confounding so do not provide causal inference. Genetic variants associated with a risk factor of interest can be used as proxies, in a Mendelian randomization (MR) analysis, to test for causal association with an outcome. The objective was to estimate the causal association between serum 25-hydroxyvitamin D (25(OH)D) (the commonly measured vitamin D metabolite in blood) and dental caries using a two-sample MR approach which estimates the causal effect of an exposure on an outcome.Methods: A total of79 genetic variants reliably associated with 25(OH)D were identified from genome-wide association studies and used as a proxy measure of 25(OH)D. The association of this proxy measure with three outcome measures was tested; specifically: caries in primary teeth , caries in permanent teeth in childhood and adolescence , and caries severity in adulthood proxied by decayed, missing and filled tooth surfaces (DMFS) counts .Results: The estimated causal effect of a one standard deviation increase in natural log-transformed 25(OH)D could be summarized as an odds ratio of 1.06  for caries in primary teeth and 1.00  for caries in permanent teeth in childhood and adolescence. In adults, the estimated casual effect of a one standard deviation increase in natural log-transformed 25(OH)D was 0.31 fewer affected tooth surfaces Conclusions: The MR-derived effect estimates for these three measures are small in magnitude with wide confidence intervals and do not provide evidence for a causal relationship between 25(OH)D and dental caries. Dental caries is a disease process which can lead to irreversible damage to tooth tissues. Initially hydroxyapatite crystals in the enamel, dentine and cementum tissues are demineralized when acidic by-products from bacterial fermentation of simple carbohydrates lead to low pH and mineral undersaturation in the tooth surrounding fluids. Eventually demineralization is followed by a proteolytic destruction of the organic substances of the tooth tissues and a cavity is formed D) (the commonly measured vitamin D metabolite in blood) and caries in childhood , so may These studies do not allow for direct causal inference but have led to the hypothesis that vitamin D supplementation in childhood may be a way to prevent dental caries at a population level. To test this hypothesis, controlled trials have been conducted. A meta-analysis of controlled clinical trials supported causal effects of vitamin D, but also highlighted substantial heterogeneity in estimates, risk of bias in individual studies and strong evidence for publication bias in the available literature . There iMendelian randomization (MR) is an alternative way to estimate causal effects, when randomized controlled trials are unfeasible or inconclusive. This method uses genetic variation, which is reliably associated with the exposure of interest, as a proxy measure of the exposure. These genetic variations are often single base pair changes in germline genotypes, termed single nucleotide polymorphisms or SNPs. A higher number of 25(OH)D increasing variants is associated with higher average serum 25(OH)D concentration. Due to the essentially random assortment of alleles during meiosis, the proxy measure of 25(OH)D concentration is unrelated to traditional confounding factors at population level. For example, individuals with two 25(OH)D increasing alleles at a particular SNP will smoke no more or less than those who have zero 25(OH)D increasing alleles at the same SNP. This is in contrast to serum 25(OH)D measures which, at a population level, show a strong inverse association with smoking  cannot alter germline genotypes, the direction of causation is from the proxy measure of exposure to the outcome, therefore reducing the risk of bias from reverse causation D on caries using MR, but was underpowered to provide precise estimates or fully interrogate the assumptions of the MR method D on three caries related traits  using data from published genome-wide association studies (GWASs).The data for SNP-exposure and SNP-outcomes were extracted from published GWASs. GWASs are used in genetic research to identify genetic variants that are associated with a specific trait.-8 in at least one published GWAS); b) were identified in a population of European ancestry participants; c) were conditionally independent of other SNPs in the same genomic locus in conditional analysis (SNPs to be used as proxies for 25(OH)D exposure were identified as those that a) were strongly associated with 25(OH)D exposure D–proxying SNPs on dental caries were obtained by extracting association statistics for these variants from published results.https://doi.org/10.5523/bris.pkqcnil6e9ju2nyreblt3mvwf  were obtained from a genome-wide meta analysis of adult cohort studies based in the USA, Germany, Sweden and Finland D are associated with caries outcomes then there must be an effect of 25(OH)D on caries. This intuition is formalized using a series of statistical tests under an analytical paradigm termed two-sample MR (xy) and standard error (SE) from the two-sample MR was converted into an interpretable OR with 95% confidence intervals (CIs) as follows:For caries in the primary dentition and permanent dentition in children and adolescents each causal effect estimate  in the Swedish GLIDE database who were originally recruited through the Northern Sweden Health and Disease Study D proxying SNP in turn. This analysis aims to identify SNPs with outlying causal effect estimates and ensures that the overall estimate of the causal effect of 25(OH)D on dental caries was not driven by a single or few SNPs with large effects. In cases where a small number of SNPs have large effects on caries (but not on 25(OH)D), this may imply that these SNPs act on caries through mechanisms unrelated to vitamin D (pleiotropic effects) and violate the core assumptions of the MR method.Weighted median sensitivity analysisFor each outcome, a weighted median (WM) sensitivity analysis of the SNPs was undertaken. The WM provides a consistent estimate of the causal effect, provided that 50% of the weight in the analysis stems from non-pleiotropic, and therefore valid, variants.MR-Egger regression sensitivity analysisFor each outcome, a MR-Egger regression of the SNP-outcome on the SNP-exposure with the y-intercept unconstrained was carried out. The y-intercept tests for the presence of directional pleiotropy, since when the SNP-exposure association is zero the SNP-outcome association should also be zero. The MR-Egger regressions rely on the instrument strength independent of direct effect (InSIDE) assumption that the strength of the SNP-exposure association should not correlate with the strength of any pleiotropic effects across the instrumental variants , where β and ƒ denote the effect estimate and the effect allele frequency of the allele on a standardized phenotype, respectively  estimates can suffer from weak instrument bias, which arises when confounders in the genotypic subgroups in samples are not perfectly balanced. If the IVs are weak, they explain less variation in the phenotype and therefore the difference in confounders between the subgroups might explain more of the variation in phenotype. If this bias is present, a confounded false-positive association between exposure and outcome may be found  to the seven missing SNPs were identified. Proxy SNPs were used for four SNPs  where the r2 was 0.7 or greater. The remaining three missing SNPs  were excluded from the exposure data as no suitable proxy was identified, leaving 79 SNPs for analysis for caries in primary teeth and caries in permanent teeth in paediatric populations.In total, 83 SNPs met the criteria for inclusion as proxies for 25(OH)D; these were selected from the most recent published GWAS of 25(OH)D  were removed for being palindromic with intermediate allele frequencies , leaving 78 SNPs for analysis. Information about the 79 SNPs is provided inFor caries in primary teeth, binary data for 17,035 children (aged 3–12 years) were available from nine studies of European ancestry children. Overall, 41% of participants were classified as having caries . For caries in the permanent dentition in children and adolescents, binary data on 13,386 participants (aged 6–18 years) were available from seven studies of European ancestry. In total, 44% were classified as having caries . For DMFS, quantitative data on 26,792 adults (aged 18–93 years) were available from nine studies (eight studies were of European ancestry and one study was of admixed Hispanic/Latino ancestry). For each of these studies, SNP-caries effect estimate summary statistics were extracted for the 81 SNPs associated with 25(OH)D .In paediatric populations, the estimated causal effect of 25(OH)D on caries in primary and permanent teeth was essentially null . For carIn adult populations, the estimated causal effect of 25(OH)D was in the direction of a small protective effect, with higher 25(OH)D associated with fewer caries-affected tooth surfaces . This effect was again small in magnitude and the CIs did not provide evidence supporting a causal relationship .Leave-one-out sensitivity analysisLeave-one-out sensitivity analysis suggested that no single SNP was strongly driving the IVW point estimates for each trait, since there were no cases where excluding one SNP resulted in dramatic changes in the overall result. In addition, all the CIs overlapped with the other causal estimates in each sensitivity analysis, further suggesting that all SNPs estimated a single common exposure .Weighted median sensitivity analysisWeighted Median sensitivity analysis for all three traits provided effect estimates similar in magnitude to the IVW primary analysis . This suMR-Egger sensitivity analysisThe MR-Egger sensitivity analysis for all three traits provided effect estimates similar in magnitude to the IVW primary analysis and WM analysis . The MR-Phenotypic sensitivity analysisSensitivity analyses were undertaken excluding questionnaire-derived data. The sensitivity analyses included 11,344 children with data on caries in primary teeth and 7,480 children and adolescents with data on caries in permanent teeth. The effect estimates had similar interpretation to the effect estimates in primary analysis but with reduced precision  P=0.45; caries in permanent teeth in children and adolescents: OR=1.11  P=0.59).Sensitivity analysis for ancestrySensitivity analysis of DMFS, which excluded a study of Hispanic/Latino ancestry included 14,975 adults. The effect estimate was small and had reduced precision compared to the principal analysis, but agreed with the estimate from the principal analysis within the 95% CIs. The sensitivity analysis estimate was , P=0.64).Sensitivity analysis using 7 independent SNPSSensitivity analyses using 7 independent SNPs, located in the the GC and CYP2R1 loci, that have biological relevance to 25(OH)D was carried out, to avoid horizontal pleiotropy and confirm the null associations. Results of this sensitivity analysis were consistent with the principal analysis ; caries in permanent teeth OR= 1.02 ; DMFS 0.52 surfaces .Strength of instrumental variablesThe 81 SNPs combined were considered to be a strong proxy for 25(OH)D in the MR study. The SNPs explain approximately 4.4% of the total variation in 25(OH)D levels in populations of European ancestry.This study tested for causal effects of serum 25(OH)D on dental caries using an MR approach. The main findings are near-null causal effect estimates for all three dental caries traits which do not provide evidence to support a causal association between 25(OH)D and dental caries. The effect sizes seen in this study agree with some reported associations in the observational literature, for example a weak association with a similar OR (0.97) found byOne limitation of the MR approach is statistical power, since this method requires large samples to produce precice causal estimates. The apparent disagreement in the results of this study and previous literature might be due to statistical power, the wide 95% CIs for all three caries outcome traits reflect the need for much larger sample sizes. We did not see large effect sizes which could be interpreted as suggesting that confounding in the observational literature and publication bias in results of controlled clinical trials has potentially over-estimated the effect sizes, however we also consider other explanations as discussed below.-8). In addition, the F-statistics are >10, therefore the IVs are considered to be strong, owing to the large sample size of available outcomes for the three traits under study D . To help address this, nearly all participants included in the MR analysis were of European ancestry and individuals are assumed to only differ with respect to the 25(OH)D loci under study. In addition, the SNP-25(OH)D SNP-caries association statistics were obtained from models which adjusted for population substructure, as described in the respective GWAS publications. There was little evidence for inflationary bias in the summary statistics of these models, suggesting that population stratification was well-controlled. Finally, sensitivity analyses were performed using more stringent exclusion criteria for ancestry in adults, and the results of these analyses agreed with the primary analysis. These approaches help protect against violations of the second assumption, but as a result of sample restriction, the results from this study are only applicable to individuals of European ancestry and are not necessarily generalizable to other ethnic populations. As a more general observation on external validity, the children included in this study were recruited from wealthy countries with longstanding public health messages regarding both vitamin D and dental caries. Thus, the prevalence of both vitamin D deficiencies and caries may be lower in the study population than in other groups, and care is needed in extrapolating the findings to populations in other countries or indeed historical studies in the same countries.The third assumption states that the genetic instrument must be associated with dental caries, only through its effects on 25(OH)D and not via any alternative pathways (referred to as pleiotropy). If any single SNP had strongly pleiotropic effects acting on the outcome through a mechanism other than through 25(OH)D, this would be shown in the leave-one-out sensitivity analysis as an outlier. No such outliers were identified. Groups of SNPs with pleiotropic effects would result in a discrepancy between the results of IVW and WM analysis, which we did not observe. Finally, groups of SNPs with unbalanced horizontal pleiotropic effects would result in a detectable intercept term in MR-Egger regression analysis.This experiment used a two-sample design which places natural limits on the scope of the experiment. For example, it was not possible to test for non-linear effects of 25(OH)D or describe the full characteristics of the SNP instruments. However, within these limits we have tested for we did not find evidence of violations of the assumptions of the method.In summary, MR has provided a genetic approach to assess causality between 25(OH)D and dental caries, an association which is currently not fully understood. This study did not find evidence supporting a clinically relevant causal association. Although the assumptions required by this method appeared to be valid, results were similar across different caries outcomes and findings were consistent in sensitivity analysis, we acknowledge that statistical power was a limitation. At this moment in time the results do not suggest that vitamin D supplementation is likely to be an effective population-level risk reduction strategy for alleviating the burden of disease from dental caries where there is no suspicion of vitamin D insufficiency. In the future, larger GWAS for caries may provide more precise quantification of the role of vitamin D or other modifiable risk factors in the aetiology of this complex and important disease.https://doi.org/10.5523/bris.pkqcnil6e9ju2nyreblt3mvwf  were extracted from this data release.Non-Commercial Government Licence for public sector information.Data are available under the The authors have addressed all my comments properly. I have no further comments.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Epidemiology; Mendelian randomization; vitamin D; dental healthI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. I am satisfied with the revisions and have no further comments.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:NAI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Thank you very much for the opportunity to review this manuscript. This is a very interesting and novel study exploring whether there is evidence of a causal relationship between vitamin D and caries in children and adults. The authors have used a Mendelian Randomization (MR) approach to estimate the causal effect between serum 25(OH)D and caries. Title: This reviewer would suggest a change in the title so that it would be more informative to readers and indicate the type of study it is. Perhaps it would be better as “Examining the causal relationship between 25-hydroxyvitamin D and caries in children and adults: a MR analysis approach.” Abstract: the use of the term “null hypothesis” may not be familiar for readers from some regions of the world. It would be more clear to just state what the hypothesis was and whether the findings support the causal relationship or not. Introduction: is appropriate. The only comment would be that the authors have reported on findings from cross-sectional studies. Is it worth mentioning findings from any prospective studies looking at 25(OH)D during periods of tooth formation and caries at a later time point? Methods: perhaps the addition of an extra sentence or two about genome-wide association studies (GWASs) would help future readers. Would it be possible to add a small figure that shows the various GWASs whether data were obtained from to conduct this MR study? Results: well described. Discussion: as recommended earlier, it might be best to rephrase and avoid the use of the term “null hypothesis”. Tables and figures: appropriate.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Vitamin D and dental caries, early childhood caries, dentistry.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Many thanks for taking the time to review this manuscript and for your valuable comments. We have carefully considered each comment and made changes in the paper where required. Please find point-by-point responses below. Reviewer comments are in bold type. Responses are in plain type. Sections quoted from the revised manuscript are in italics.Title: This reviewer would suggest a change in the title so that it would be more informative to readers and indicate the type of study it is. Perhaps it would be better as “Examining the causal relationship between 25-hydroxyvitamin D and caries in children and adults: a MR analysis approach.” 1.  Thank you for the comment, we have changed the title to:“Examining the causal association between 25-hydroxyvitamin D and caries in children and adults: a two-sample Mendelian Randomization approach.”2. Abstract: the use of the term “null hypothesis” may not be familiar for readers from some regions of the world. It would be more clear to just state what the hypothesis was and whether the findings support the causal relationship or not. Thank you for the suggestion, the term null hypothesis has been removed and reworded.“The MR-derived effect estimates for these three measures are small in magnitude with wide confidence intervals and do not provide evidence for a causal relationship between 25(OH)D and dental caries.”“The MR-Egger regression analysis for all three traits found y-intercepts compatible with 0  indicating no unbalanced horizontal pleiotropy.” In addition the term null hypothesis has been removed from the Results subsection MR-Egger sensitivity analyses: 3. Introduction: is appropriate. The only comment would be that the authors have reported on findings from cross-sectional studies. Is it worth mentioning findings from any prospective studies looking at 25(OH)D during periods of tooth formation and caries at a later time point? Thank you for the comment. We have added information from an additional study carried out during tooth formation to the introduction. “A 6-year follow up double-blind randomized clinical trial found that high-dose vitamin D supplementation during pregnancy was associated with reduced odds of enamel defects in the offspring .”  et al. is mentioned in the discussion already. A prospective study by Gyll4. Methods: perhaps the addition of an extra sentence or two about genome-wide association studies (GWASs) would help future readers. Would it be possible to add a small figure that shows the various GWASs whether data were obtained from to conduct this MR study? Thank you for the comment. A new figure  has been added to the methods section to further explain the two-sample MR method.“The data for SNP-exposure and SNP-outcomes were extracted from published GWASs. GWASs are used in genetic research to identify genetic variants that are associated with a specific trait. Figure 1 shows the steps involved in performing a two-sample MR.”“Figure 1: A diagrammatic representation of the two sample mendelian randomization analysis. Instruments are identified from the exposure 25(OH)D GWAS , corresponding SNP effects are extracted from the outcome GWASs. The exposure and outcome SNP effects are harmonized and then mendelian randomization analysis is performed. .” 5. Discussion: as recommended earlier, it might be best to rephrase and avoid the use of the term “null hypothesis”. Thank you for the comment the term null hypothesis has been removed from the discussion as well.“The main findings are near-null causal effect estimates for all three dental caries traits which do not provide evidence to support a causal association between 25(OH)D and dental caries.”  The authors have performed a two-sample Mendelian randomization (2SMR) study to investigate the potential causal association between serum 25-hydroxyvitamin D (25(OH)D) and dental caries in children and adults. They have used publicly available summary statistics from GWASs of serum 25(OH)D and dental caries for the analyses. Little evidence was observed to support a causal effect of serum 25(OH)D on dental caries.Major comments:The answer to the research question is warranted in the dental health. The authors have used the latest GWAS summary statistics and applied primary and several robust MR methods. They have also performed sensitivity analyses excluding questionnaire-based outcome data for children and a study of Hispanic ancestry for DMFS. The conclusion and the implication of the study were based on the solid results and the three MR assumptions were addressed well in the discussion.P<6.6 x 10-9) in one of the latest GWAS for 25(OH)D. Use of many genetic variants with unclear biological functions likely results in horizontal pleiotropy of an association. In the study by Manousakiet al, partially shared heritability between 25(OH)D and socio-economic traits were indeed observed. The existence of pleiotropy likely changes the estimates away from the null association. However, in theory it is possible that pleiotropy leads to a false negative finding. It would be advisable if the authors could perform extra sensitivity analyses using only independent SNPs related to four loci  that have biological relevance to 25(OH)D to avoid horizontal pleiotropy and confirm the null associations.The authors used statistically driven approach to select genetic variants, i.e. included all common variants (MAF≥5%) that are associated with serum 25(OH)D at a given level of statistical significance  indicate the precision of the observed association estimates and reflect the sample size, post hoc power analysis is generally not recommended.I would like to discuss the necessity for post hoc power analysis with the authors. The power analysis that the authors applied is designed mainly for one-sample MRMinor comments:Please write clearly that the study is a two-sample MR in the title, abstract and the introduction section.In the Methods:2 cut-off value should be given.One of the selection criteria for SNPs used as proxies for 25(OH)D exposure is “SNPs had independent effects”. The used linkage disequilibrium (LD) RGWAS of DMFS: DMFS as a count variable has a strongly positively skewed distribution with a large stack of zero counts for those without caries. How the zeros were handled when DMFS scores were generated?In the Results2 statistic (a measure of the variance in the exposure explained by the genetic variants) is more proper. In the Results, it reads “In total, these SNPs explain approximately 4.4% of the total variation in 25(OH)D levels in populations of European ancestry”. How was this 4.4% calculated? In the study by Manousakiet al., 138 SNPs explained 4.9% of the variation of 25(OH)D level.The F statistic is usually reported in one-sample MR studies to represent strength of the instrumental variables (IVs). In a two-sample setting, the RFor Table 2, could the authors rearrange the order of SNPs in the same way as in Table 1 and give chromosome positions for easier reading?et al., the effect estimates and P values were based on meta-analysis .Table 1. Number of participants should be given for the SNP-25(OH)D association. According to the study by ManousakiTable 3: use OR instead of “Transformed effect”, OR and beta correspond to genetically determined one standard deviation increase in natural log-transformed 25(OH)D.Figures 1 and 2: on risk for caries (binary); Figure 3. full name of DMFS.The tables and figures should be interpretable by themselves. Add more details in table footnotes and figure legends: e.g.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Epidemiology; Mendelian randomization; vitamin D; dental healthI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Thank you for taking the time to review this submission and for the valuable comments. We have considered each comment and made changes in the paper where required. Please find point-by-point responses below. Reviewer comments are in bold type. Responses are in plain type. Sections quoted from the revised manuscript are in italics.P<6.6 x 10-9) in one of the latest GWAS for 25(OH)D. Use of many genetic variants with unclear biological functions likely results in horizontal pleiotropy of an association. In the study by Manousaki et al, partially shared heritability between 25(OH)D and socio-economic traits were indeed observed. The existence of pleiotropy likely changes the estimates away from the null association. However, in theory it is possible that pleiotropy leads to a false negative finding. It would be advisable if the authors could perform extra sensitivity analyses using only independent SNPs related to four loci  that have biological relevance to 25(OH)D to avoid horizontal pleiotropy and confirm the null associations.1. The authors used statistically driven approach to select genetic variants, i.e. included all common variants (MAF≥5%) that are associated with serum 25(OH)D at a given level of statistical significance . These are located in the GC and CYP2R1 loci. These variants were selected as they were available in the outcome data and they had a minor allele frequency of 0.05 or above as stated in the SNP-exposure methods. The results of this analysis were concordant with the main analysis and had similar interpretation. We have added a paragraph to the results section:Sensitivity analysis using 7 independent SNPS “Sensitivity analyses using 7 independent SNPs, located in the the GC and CYP2R1 loci, that have biological relevance to 25(OH)D was carried out, to avoid horizontal pleiotropy and confirm the null associations. Results of this sensitivity analysis were consistent with the principal analysis ; caries in permanent teeth OR= 1.02 ; DMFS 0.52 surfaces .”2. I would like to discuss the necessity for post hoc power analysis with the authors. The power analysis that the authors applied is designed mainly for one-sample MR. As confidence intervals (CIs) indicate the precision of the observed association estimates and reflect the sample size, post hoc power analysis is generally not recommended. Thank you for this comment. We agree that the power analysis applied is redundant given the reported confidence intervals and have therefore removed Table 4 and this analysis from the methods, results and discussion. The discussion has been reworded to describe the wide confidence intervals as an indication of the greater sample size that is needed to provide more precise effect estimates.“The apparent disagreement in the results of this study and previous literature might be due to statistical power, the wide 95% CIs for all three caries outcome traits reflect the need for much larger sample sizes. We did not see large effect sizes which could be interpreted as suggesting that confounding in the observational literature and publication bias in results of controlled clinical trials has potentially over-estimated the effect sizes, however we also consider other explanations as discussed below.”3. Please write clearly that the study is a two-sample MR in the title, abstract and the introduction section. Thank you for this comment. This description has been added to the title, abstract and introduction section.2 cut-off value should be given.4. One of the selection criteria for SNPs used as proxies for 25(OH)D exposure is “SNPs had independent effects”. The used linkage disequilibrium (LD) R2>0.9 were excluded. For further details of GCTA-COJO analysis please refer to the section "Approximate Conditional Association Analysis" in the following paper titled “Genome-wide Association Study for Vitamin D Levels Reveals 69 Independent Loci”: https://pubmed.ncbi.nlm.nih.gov/32059762/. Thank you for this comment. The SNPs were chosen based on a conditional analysis implemented in GCTA, as reported in the original manuscript. Variants were selected if they reached genome-wide significance conditional on the lead variant in the same locus. Variants situated more than 20,000 base pairs away and variants with LD R“c) were conditionally independent of other SNPs in the same genomic locus in conditional analysis .”5. GWAS of DMFS: DMFS as a count variable has a strongly positively skewed distribution with a large stack of zero counts for those without caries. How the zeros were handled when DMFS scores were generated? Thank you for this comment. The GWAS for DMFS was carried out in adult populations. DMFS scores were regressed on age, age squared, and study specific-covariates and the residuals were plotted. We did observe skewed distributions for other traits (for example number of teeth), but not for DMFS, where the residuals were approximately normally distributed. This may be because the study population included middle aged and older adults with high disease burden. DMFS residuals were subsequently treated as a continuous trait in GWAS analysis.6. 2 statistic (a measure of the variance in the exposure explained by the genetic variants) is more proper. In the Results, it reads “In total, these SNPs explain approximately 4.4% of the total variation in 25(OH)D levels in populations of European ancestry”. How was this 4.4% calculated? In the study by Manousaki et al., 138 SNPs explained 4.9% of the variation of 25(OH)D level.The F statistic is usually reported in one-sample MR studies to represent strength of the instrumental variables (IVs). In a two-sample setting, the R Thank you for this comment. While we aimed to provide some information on the potential strength of the MR instrument, we accept that the F statistic is potentially confusing and have therefore removed this part. We have amended the Results section:Strength of Instrumental Variables“The 81 SNPs combined were considered to be a strong proxy for 25(OH)D in the MR study. The SNPs explain approximately 4.4% of the total variation in 25(OH)D levels in populations of European ancestry.”2 of the 25(OH)D reported in the results was estimated as the sum of variance explained by each SNP in the experiment. The variance of each SNP was given using the formula provided by the authors of the Vitamin D GWAS paper; variance explained ≈2β2 ƒ(1– ƒ), where β and ƒ denote the effect estimate and the effect allele frequency of the allele on a standardized phenotype, respectively. This information has been added to the methods section. The R2 ƒ(1– ƒ), where β and ƒ denote the effect estimate and the effect allele frequency of the allele on a standardized phenotype, respectively .”“The total variance explained in 25(OH)D by the SNPs was estimated as the sum of the variance explained by each SNP in the experiment. The variance of each SNP was given using the formula provided by the authors of the 25(OH)D GWAS paper: variance explained ≈ 2β7. For Table 2, could the authors rearrange the order of SNPs in the same way as in Table 1 and give chromosome positions for easier reading? Thank you for the suggestion, Table 2 has been rearranged to the same way as Table 1.8. et al., the effect estimates and P values were based on meta-analysis .The tables and figures should be interpretable by themselves. Add more details in table footnotes and figure legends: e.g.Table 1. Number of participants should be given for the SNP-25(OH)D association. According to the study by Manousaki  Thank you for the comment. More details have been added to the footnotes and figure legends.9. Table 3: use OR instead of “Transformed effect”, OR and beta correspond to genetically determined one standard deviation increase in natural log-transformed 25(OH)D. Thank you for the comment. The DMFS effect estimate is not on a odds ratio scale and therefore we prefer to use the term ‘transformed effect’ for this variable. The column has been renamed to “Odds ratio/transformed effect”.10. Figures 1 and 2: on risk for caries (binary); Figure 3. full name of DMFS. Thank you for this comment. We have added the information as suggested.


The majority of homozygous WFFS horses are aborted during gestation. Clinical signs of affected horses include fragile skin, skin and mucosa lacerations, hyperextension of the articulations, and hematomas. In spite of its harmful effect, a relatively high frequency of WFFS carriers has been found in Warmblood horses, suggesting a heterozygote advantage. Thus, in this study our aims were to: (1) estimate the frequency of WFFS carriers in the Swedish Warmblood breed (SWB), (2) estimate the effect of WFFS carrier genotype on performance traits in two SWB subpopulations bred for different disciplines, and (3) simulate the potential effects of balancing selection and different selection strategies on the frequency of carriers.Warmblood fragile foal syndrome (WFFS) is a monogenetic defect caused by a recessive lethal missense point mutation in the In total, 2288 SWB sport horses born between 1971 and 2020 were tested for the WFFS mutation and had estimated breeding values (EBV) for ten traditional evaluating and 50 linear descriptive traits.The frequency of WFFS carriers calculated from a pool of 511 randomly selected SWB horses born in 2017 was equal to 7.4% and ranged from 0.0 to 12.0% among the whole set of tested SWB horses, starting from 1971 till 2020. The effect of the WFFS carrier genotype was significant for several EBV mainly related to movements and dressage traits and especially for horses not bred for the show jumping discipline. Using simulation, we showed that balancing selection can maintain a recessive lethal allele in populations such as the SWB breed over generations and that the frequency is expected to slowly decrease in absence of balancing selection. Finally, we showed that selection against carrier sires can result in a more rapid decrease of the frequency of the mutant allele over time.Further research is needed to confirm the apparent association between equine performance and the WFFS carrier genotype. Identification of such associations or new causative mutations for horse performance traits can serve as new tools in horse breeding to select for healthy, sustainable, and better performing horses.The online version contains supplementary material available at 10.1186/s12711-021-00693-4. A deficit in LH1 causes mechanical instability of the affected tissues due to the reduction in collagen crosslinks [Warmblood fragile foal syndrome (WFFS) is an autosomal monogenetic disease that has raised much concern among warmblood horse breeders in recent years due to its negative effects on foal and foetus survival , with imosslinks .PLOD1 lethal allele in several European Warmblood horse breeds, including the Hanoverian, Selle Français, KWPN, Oldenburg, and Westphalian studbooks [Previous analyses based on pedigree data suggested segregation of the tudbooks . In spittudbooks –8. In cotudbooks . The lettudbooks , 9. Nevetudbooks .Warmblood sport horses are strongly selected for performance, mainly for the dressage and show jumping disciplines. Several of the traits under selection favor a high degree of joint mobility, smooth and supple movements, and elastic gaits in dressage horses, and extreme athleticism and power in show jumping horses. The clinical characteristics of hypermobility in horses are not comprehensively described, but there is a consensus that some horses are extremely flexible, supple, or even hypermobile. In humans, hypermobility is classified as a disease, although people with hypermobility spectrum disorders often outperform in sports that demand high levels of mobility . ConsideThe WFFS allele has been reported to be associated with estimated breeding values (EBV) for conformation, gaits, and dressage traits in warmblood horses . TherefoThere are several examples of recessive deleterious or lethal alleles in domestic animals that appear to have been driven to high frequency by balancing selection. If a detrimental allele also has a favorable effect on a trait under selection or is in tight linkage disequilibrium with such a favorable variant, its frequency may increase in spite of its deleterious effect on survival or fertility. This is the case with a series of alleles in dairy cattle –15, beefA genetic test for WFFS is available and is currently used in the SWB to avoid mating between carriers. This policy improves animal welfare by eliminating the risk of affected foals. However, this genetic test could also be used for marker-assisted selection against the WFFS allele. The outcomes and effectiveness of such selection depend on its implementation in the breeding scheme and on whether the frequency of WFFS carriers is driven by balancing selection. Based on the economic and welfare relevance of WFFS, together with the suspected balancing selection on this deleterious allele in Warmblood breeds, our aims were to: (1) estimate the frequency of WFFS carriers in the SWB breed, (2) estimate the effect of the genotype of the WFFS carriers on performance traits, with a specific focus on the two SWB subpopulations that were previously reported to reflect horses selected for two different sport disciplines , and (3)Genotype data from 2288 sport horses  born between 1971 and 2020 were analyzed in this study. To evaluate the frequency of WFFS carriers in the current generation of SWB horses, 511 randomly selected horses born in 2017, i.e., before the genetic test for WFFS became widely used, were genotyped specifically for this purpose. In addition, 380 previously analyzed SWB horses [https://rdrr.io/github/camult/DRP/) in R [Estimated breeding values (EBV) from the routine genetic evaluation in 2020 were available for all the 2288 genotyped horses included in the study. The available traits can be divided into two categories: (1) traditional evaluating traits and (2) linear descriptive traits. The evaluating traits are related to the breeding objective and their EBV are obtained with multi-trait animal models based on data from tests and competition results of young horses. The ten evaluating traits included: type, correctness of legs, walk, trot, canter, temperament for gaits, jumping technique, jumping temperament, show jumping and dressage . Fifty lP/) in R  based onP/) in R , removinbased on  using thPLOD1 gene by a TaqMan genotyping assay on a StepOnePlus™ instrument  by adding 1.5 μL gDNA to TaqMan™ Genotyping Master Mix and Custom TaqMan™ SNP Genotyping Assay , for a final volume of 15 µL. The following genotyping parameters were applied: a 10-min hold at 95 °C, followed by 40 cycles of 15 s at 95 °C, and 1 min at 60 °C. Each genotyping plate contained four standard samples: one non-template control, one sample homozygous for the reference allele (Allele 1/Allele 1) and two samples from known WFFS carriers (Allele 1/Allele 2). Two WFFS carrier controls were used to increase the Allele 1/Allele 2 cluster size, since no homozygous individuals for the lethal mutant allele were available.DNA from 380 SWB horses was available from our previous study . For allTo characterize the trend in the frequency of WFFS carriers over time, we calculated the frequency of carriers within nine birth year cohorts, starting in 1971 and 2020, and considering a birth interval of 5 years.We estimated the effect of the WFFS carrier genotype on performance using weighted analysis in ASReml 4.1 , where ted as in  where thollowing . The EBVollowing . Thus, imentclasspt{minimaIn order to model the management of a lethal allele in SWB, we used stochastic genetic simulation to investigate the effect of balancing selection and selection against carriers of a recessive lethal allele, using a population modelled on SWB horses. Simulations were performed with R  and the 5 at one million generations ago to 500 at 100 generations ago, and to 100 in the first generation of the simulation. The coalescent simulation resulted in haplotypes of biallelic variants, which were used to form the genomes of the first simulated generation.The genomes of the founder population were created with the MaCS coalescent simulator, run within the AlphaSimR program , using tWe simulated 30 chromosomes of 100 cM, each carrying three randomly chosen causative variants for the breeding goals, which were modelled as two correlated traits with additive genetic effects. The effects of the causative variants on the two breeding goals were drawn from a bivariate normal distribution, with a correlation of 0.3 between the effects on the two breeding goals, which resulted in an average genetic correlation of 0.3 in the first generation  were selected for Breeding Goal 1 and 180 (60%) were selected for Breeding Goal 2. These proportions are chosen to roughly reflect the proportions of non-show jumping and show jumping horses in the SWB studbook.https://swb.org/betacknings-och-folstatistik/). For example, the decile that sired the smallest number of offspring during this period sired 0.27% of the offspring, while the highest decile sired 58% of the offspring. Based on these proportions, the 6000 offspring were apportioned to deciles of the selected sires, with an equal number of offspring assigned to each sire within the decile. For example, sires in the lowest three deciles were each given one offspring, while sires in the highest decile were each assigned 116 offspring. The whole distribution is shown in Additional file The selected sires were randomly mated to the 3000 dams to produce 6000 offspring, with a 50:50 sex ratio. We proportioned the number of offspring to sires based on their phenotype for the breeding goal they were selected on, assigning a larger proportion of the offspring to high-ranking sires. The distribution was based on the proportion of offspring sired by each decile of sires based on the SWB foal statistics from 2008 to 2017 . In scenarios without balancing selection, we selected the lethal allele at random from 400 neutral variants per chromosome with no effects on either breeding goal. In scenarios with balancing selection, we selected the variant, within the frequency span, that had the largest positive (favorable) effect on Breeding Goal 1 as the lethal allele. The aim was to reflect the associations between WFFS carrier genotypes and the breeding goal traits, that has, a positive effect on non-show jumping traits, but little evidence of negative effects on show jumping traits.We also ran a simulation scenario for which the genetic effects of the lethal allele were close to the estimates obtained for the WFFS allele. In this case, after selecting the lethal allele as described above, we modified the genetic effect of the lethal allele to be seven units for Breeding goal 1 and zero units for Breeding goal 2. This is similar to our estimate of the association of the WFFS allele with dressage traits and the absence of substantial effects on show jumping traits  were excluded from selection and mating.Scenario 2: Genetic testing to avoid carrier-carrier mating. Under this scenario, carrier sires were included in the breeding program but could only be mated to non-carrier dams, and vice versa for carrier dams. This is similar to the current policy for known lethal alleles in the SWB studbook. We assumed that genetic testing was free from error.Scenario 3: Selection against carriers. Under this scenario, carrier sires were excluded from breeding. We varied the intensity of the selection, from strong to weak, by allowing a number  of the top males for each breeding goal to be used regardless of their carrier status. I.e., with 10 top carrier males allowed, any carrier among the top 10 males for each breeding goal were used for breeding but carriers outside the top 10 were removed. This procedure corresponds to allowing up to 6–8% (top 10) or 56–83% (top 100) carriers among the sires used for each breeding goal. All dams were used regardless of their carrier status. The policy of avoiding mating between carriers was maintained, thus the top carrier sires that were used could only be mated to non-carrier dams.In total, 207 horses (9.0%) among the 2288 genotyped SWB horses were identified as heterozygous for the WFFS mutation (N/WFFS). The remaining 2081 (91.0%) genotyped horses were homozygous for the wild-type allele (N/N). No homozygotes for the WFFS lethal mutation were detected. The number of horses genotyped per birth year cohort varied considerably, the smallest number was observed for the first birth cohort (1971–1980), with five genotyped horses, and the largest for the latest birth cohort (2016–2020), with 878 genotyped horses. No carriers were found in the first birth cohort and the frequency of WFFS carriers was highest (12%) in the fourth and fifth birth cohort, which included horses born between 1991 and 2000 .After removing horses with only PA EBV, 621 NS horses were used to evaluate the effect of WFFS on traditional evaluating traits  from the comparison of WFFS/N and N/N horses, followed by + 6.95 (+ 0.35 genetic standard deviation) for the trait “temperament for gaits”. In contrast, the horses that were homozygous for the wild-type allele (N/N) scored on average 2.50 higher for show jumping (+ 0.13 genetic standard deviation) compared to horses carrying the WFFS allele  between WFFS/N and N/N horses, followed by + 3.98 (+ 0.20 genetic standard deviation) for dressage  how balancing selection can maintain a recessive lethal allele in a population with similar features to the SWB breed, (2) that the decline in frequency of the lethal allele in the absence of balancing selection is expected to be slow, and (3) that selection against carrier sires can result in a more rapid reduction of the frequency of the mutant allele.In the light of these results, we discuss the frequency of WFFS in SWB horses compared to frequency estimates in other horse breeds, propose some evidence for balancing selection on WFFS, and discuss the potential effect of testing and selection on carrier frequency, thereby evaluating the implications for breeding practice.Our estimate of the frequency of WFFS carriers among the randomly selected samples 7.4%) was lower than estimates obtained in German and Brazilian Warmbloods , 7, and .4% was lRandom sampling from the population is fundamental when estimating carrier frequencies. Therefore, we recommend that estimates of frequencies based on non-random samples, such as the 12% frequency in SWB horses between 1991 and 2000 in our study, are interpreted with caution. These data clearly show that the WFFS allele was present in the SWB population, already in the middle 1980s, but the estimate of the frequency may be biased.The apparent discrepancy between 0% WFFS carriers found in 16 SWB  and 7.4%PLOD1 allele. In humans, a LH1 activity of 52% was measured in carriers of a similar PLOD1 variant  as in the equine lethal WFFS allele, which is consistent with an intermediate phenotype, plausibly presenting a higher degree of joint laxity [The association between WFFS carrier genotype and dressage-related traits suggests the potential presence of balancing selection, where natural selection against the WFFS recessive lethal allele is counteracted by its apparent advantageous effects, especially for traits related to movement and conformation. Our study further supports this positive effect of the WFFS allele on the linear descriptive traits of walk, trot and canter cadence, stride length, suppleness, and elasticity. These results were more evident in the case of NS horses, which are mainly selected for the dressage discipline. This could be explained by a stronger selection for high mobility in dressage horses, with extreme locomotion patterns, possibly as a result of impaired collagen structure due to a lower LH1 enzyme activity in carriers of the mutant t laxity . Likewist laxity . InteresEBV are not only estimated based on an individual’s own performance but include information from relatives, which could add uncertainty to the attributions of genetic effects to any specific genotyped locus. To overcome this, we excluded all the horses that had EBV relying on parental average only and used as response variable the de-regressed EBV to weight for differences in reliabilities and to account for heterogeneous variance.Our simulations showed that allele frequencies are not fully informative for balancing selection. At all time-points, there was a considerable overlap between frequencies of carriers in scenarios with and without balancing selection, which means that the presence of balancing selection cannot be inferred from the frequency of the WFFS allele alone.Nevertheless, in the simulations, differences in frequencies of carriers between the top individuals for the two breeding goals (representing selection for non-show jumping and show jumping horses) tended to occur only with balancing selection. In the absence of balancing selection, the lethal allele can still drift to reach different frequencies in the two subpopulations. However, on the one hand, genetic drift will be symmetrical, in that there is no bias towards a higher frequency in any one subpopulation. On the other hand, selection will drive the lethal allele towards a higher frequency in the subpopulation where there is a beneficial effect of the lethal allele on the breeding goal; the size of the difference will depend on the size of the effect. In the real data, we found that the frequency of WFFS carriers was almost four times higher in the top 10% NS horses than in the top 10% SJ horses (17.2% compared to 4.4%), which strengthens the hypothesis of balancing selection for carriers, especially in dressage horses. Thus, taken together, the differences in frequency of carriers between subpopulations of Swedish Warmblood horses, and the observation that the WFFS allele has a similarly high frequency across Warmblood breeds –8, are sWhile the policy of using genetic testing for WFFS to avoid mating between carriers to eliminate the risk of affected foals being born is beneficial for animal welfare, simulations showed that such testing and mating strategies are not expected to decrease the frequency of carriers over time. If, instead, the aim is to decrease or eliminate the WFFS allele in the population, selection against carriers would be needed because the decline of such a recessive lethal allele is slow, even in the absence of balancing selection, with the allele still persisting in the simulated populations at a frequency of ~ 5% after 20 generations.Selection against carrier sires led to a decline of the lethal allele, decreasing the frequency of carriers to less than 1% within four generations. Selection against carrier sires does not need to be complete, since allowing a few carrier sires to be included in the breeding program, did not make much difference in the rate of decline of the frequency of carriers. However, in practice, the effect of allowing carrier sires will also depend on to what extent they are used in breeding. Simulations have shown that the use of popular sires increases the risk of lethal alleles becoming frequent in a population . Thus, aAll in all, our results suggest that one course of action for reducing the frequency of WFFS carriers, without losing the gain in breeding goal traits, is to implement marker-assisted selection against at least a fraction of the carrier sires. Intuitively, this is possible when the excluded carrier sires are replaced by non-carrier sires that are almost as good in terms of their EBV for the breeding goal traits. However, this would be different when multiple known genetic defects have to be taken into account. With multiple defects, requiring sires to be non-carriers for all defects could substantially reduce the number of eligible males, resulting in compromised genetic gain and increased inbreeding. Thus, as Georges et al.  have argGiven that the generation interval of SWB is 10 to 11 years , testingPLOD1 c.2032G>A, (p.Gly678Arg) WFFS causative missense variant with performance traits in SWB horses, and in particular, highlighted a positive effect on movement-related traits. We also detected an association with conformation traits, since the carriers of the WFFS lethal allele had longer body conformation. Our findings suggest the potential presence of balancing selection for the WFFS allele in SWB horses that are bred for dressage purposes. However, further research is needed to confirm the apparent association of genes related to collagen deficiencies and joint laxity syndromes with equine performance. Using simulations, we showed that by selecting only carrier stallions if they are in the top (10%) for the breeding goal, the frequency of the deleterious allele would decrease substantially in a few generations, while maintaining genetic gain. In the future, we will aim at gaining more knowledge about performance traits under selection in sport horses by identifying associated genetic markers or novel causative mutations which might serve as new tools in horse breeding to select for healthy, sustainable, and better performing horses.This work revealed several significant associations of the Additional file 1: Figure S1. Decline in genetic correlation between the two breeding goal traits over generations of selection. The figure shows the genetic correlation between breeding goals in a scenario without balancing selection and avoiding carrier—carrier matings. Points show the average over replicates, with the error bars being the 5 and 95 percentiles. Figure S2. Distribution of offspring per sire used in the simulation. The figure shows the number of offspring per sire proportioned to sires in that decile. Figure S3. Balancing selection on the lethal allele has little effect on genetic gain in the simulation. The figure shows the average genetic values for simulated populations at generations 1, 5, 10, 15 and 20 for the two breeding goals, with error bars showing the 5 and 95 percentiles.Additional file 2: Table S1. Descriptive statistics of the traditional evaluating traits in the NS horses. The following values are reported for each traditional evaluating trait in the NS horses: number of observations, mean, standard deviation, minimum, 25th percentile, 75th percentile and maximum.Additional file 3: Table S2. Effect of the WFFS genotype on the DEBV for the linear descriptive traits for both NS and SJ SWB horses. Table S2 shows the effect of the WFFS expressed in significance level and in LSM of the DEBV for the linear descriptive traits per each subgroup (NS and SJ SWB). EBV for the linear descriptive traits in the SWB population have a mean value of 100 and a genetic standard deviation of 4 index units.Additional file 4: Table S3. Descriptive statistics of the traditional evaluating traits in the SJ horses. The following values are reported for each traditional evaluating trait in the SJ horses: number of observations, mean, standard deviation, minimum, 25th percentile, 75th percentile and maximum.


New forms of the coefficient of determination can help to forecast the accuracy of genomic prediction and optimize experimental designs in multi-environment trials with genotype-by-environment interactions.In multi-environment trials, the relative performance of genotypes may vary depending on the environmental conditions, and this phenomenon is commonly referred to as genotype-by-environment interaction (GThe online version contains supplementary material available at 10.1007/s00122-021-03972-2. In plant breeding, the best performing genotypes for a given trait are identified through field trials. Depending on the environmental conditions , the relative performance of genotypes may vary, and this phenomenon is commonly referred to as genotype-by-environment interaction  thilkinson , (iii) tThe advent of high-throughput genotyping has revolutionized the methods used to select for quantitative traits by enabling the genomic prediction of the genetic value of unobserved genotypes  or using the same correlation squared . Several studies have focused on the allocation of genotypes to plots in one  confirm the value of multivariate genomic prediction models in the context of METs with GT trials, In this study, we considered a general multivariate model where response variables corresponded to the same trait in different trials. This multivariate model is presented here in a vectorized form:P and Let us assume standard GBLUP (SGBLUP) with within-trial GBLUP (WGBLUP) with multi-trial GBLUP (MGBLUP) with where t, t and t. Let us also define t and Depending on the assumptions on mentclass2pt{minimFor all models, the estimation of parameters was done using a Bayesian framework. SGBLUP was implemented using the R package "BGLR"  were compared for their x.Using the wheat dataset and simulated traits, the ability of Using the wheat dataset and simulated traits, all CD criteria were evaluated for their ability to forecast the Incomplete and unreplicated design (I) where a given genotype is only observed once in a single trial,k replicates (Ck) where a given genotype is observed k times in each trial.Complete design with Different MET experimental designs were considered that are illustrated in Fig. These MET experimental designs were compared to optimized experimental designs (Opt) obtained by maximizing either the mean of entclass1pt{minimaThe optimization procedure was implemented using the R-package "TrainSel"Using the wheat and maize datasets and real traits, genomic prediction models and experimental designs were compared using the same CV procedure as for simulated traits, except that the same phenotypes were used for all CV replicates. For the comparison of models and experimental designs using the maize datasets, TSs of 138 and 53 genotypes were defined in populations T1 and T2. All remaining individuals were used as TRS for the comparison of models or as candidates for the comparison of experimental designs . The phenotype-based reliability of genomic prediction (http://hdl.handle.net/11529/10548141 (file: "Data_Wheat_Iranian_Set_3.RData.gz"). The maize datasets (genotypic and phenotypic data) are available at: http://hdl.handle.net/11529/10548369. Supplemental figures and tables are available in: "Supplementary file 1". R code is provided to apply the MGBLUP model, compute all CD criteria, and apply the optimization: "R_code.R" and "R_functions.R".The wheat dataset (genotypic and phenotypic data) is available at: Based on the wheat dataset, the entclass1pt{minimaUsing the wheat dataset and real phenology traits, the phenotype-based entclass1pt{minimaentclass1pt{minimaThe properties of the new CD criteria  were in general lower with an increasing number of replicates. The The ability of the new CD criteria to forecast the Those standard experimental designs were compared to optimized experimental designs obtained based on entclass1pt{minimaFor real traits in all datasets, optimized experimental designs were obtained based on nce Fig. . Like foIn METs, GIn our study, accounting for Gentclass1pt{minimaWhen applying genomic prediction in METs with GAs MGBLUP is a multivariate linear mixed model, it is possible to derive CD criteria that can be used to forecast and assess the s et al.  for genos et al.  for multIn our study, we showed based on simulations that the expected es Laloë , 2017a.While the cost of genotyping is becoming cheaper and should continue to decrease over the next decades, the cost of phenotyping is likely to remain expensive. These costs translate into constraints on the number of plots to which genotypes can be allocated. It is therefore important to select the genotypes whose evaluation will allow accurate prediction of the entire breeding germplasm available.In genomic prediction, phenotypic observations for a genotype can help to predict the genetic value of other genotypes to which it is related. In this context, it is often more favorable not to replicate genotypes within and across trials, but to observe as many genotypes as possible in the overall design, as illustrated here see Fig. , MoehrinThanks to this consistency, optimization criteria based on In this study, we chose a conservative approach in that we computed the optimization criterion on all the candidates, excluding the TS used for validation. This corresponds to a scenario where we aim at checking the ability of our experimental design to predict another sample from the same population as one of the candidates. When the target population is different from the population of candidates, one can compute directly the criterion on the TS, making a targeted optimization as proposed by Akdemir and Isidro-Sánchez .In this study, we demonstrated the value of a genomic prediction model that accounts for GFrom a plant breeding perspective, we propose the following guidelines when setting up and analysing a MET experiment with genomic information available: (i) analyse data from trials jointly by accounting for GSupplementary file1 (PDF 1304 kb)Supplementary file2 (ZIP 4 kb)Below is the link to the electronic supplementary material.


Roots are essential for water and nutrient uptake but are rarely the direct target of breeding efforts. To characterize the genetic variability of wheat root architecture, the root and shoot traits of 200 durum and 715 bread wheat varieties were measured at a young stage on a high-throughput phenotyping platform. Heritability of platform traits ranged from 0.40 for root biomass in durum wheat to 0.82 for the number of tillers. Field phenotyping data for yield components and SNP genotyping were already available for all the genotypes. Taking differences in earliness into account, several significant correlations between root traits and field agronomic performances were found, suggesting that plants investing more resources in roots in some stressed environments favored water and nutrient uptake, with improved wheat yield. We identified 100 quantitative trait locus (QTLs) of root traits in the bread wheat panels and 34 in the durum wheat panel. Most colocalized with QTLs of traits measured in field conditions, including yield components and earliness for bread wheat, but only in a few environments. Stress and climatic indicators explained the differential effect of some platform QTLs on yield, which was positive, null, or negative depending on the environmental conditions. Modern breeding has led to deeper rooting but fewer seminal roots in bread wheat. The number of tillers has been increased in bread wheat, but decreased in durum wheat, and while the root-shoot ratio for bread wheat has remained stable, for durum wheat it has been increased. Breeding for root traits or designing ideotypes might help to maintain current yield while adapting to specific drought scenarios. As roots are responsible for water and nutrient uptake, they are a key aspect to consider when developing varieties with increased tolerance to abiotic stress.Two complementary strategies may help in developing drought-adapted plants. The first is to increase water use efficiency (WUE), for instance, by optimizing evapotranspiration or photosynthesis. The second is to optimize water uptake from the soil . Accordi–1 . Atta etIn the past, breeding mostly focused on above-ground traits so any effects of breeding on root traits are probably derived from an indirect selection. Some authors found evidence of indirect selection for root traits  whereas Studying root traits is highly complex because field root phenotyping is difficult, expensive, and labor-intensive. Dating back to Weaver in 1926 who excavated the whole root system architecture (RSA) and until recently, phenotyping mainly focused on easily measurable traits  that only roughly describe the RSA  with metIn an HTPP, the root traits of a large number of individual plants can be observed in controlled conditions at early developmental stages . The groUntil now, HTPP has shown contrasting results in detecting the relation between root traits measured in controlled conditions and field performances. Some wheat traits affecting grain yield in specific environments have been identified. These include higher root density at depth to improve deep water capture , deeper The lack of field validation may be due to genotype × environment (G × E) interactions. Indeed, RSA ideotypes to optimize agronomic performances are different from one environment to another. As a result, relationships between root traits measured in controlled environments and agronomic performances are inconsistent across locations and/or years . For insThe HTPP, nevertheless, provides large amounts of heritable root phenotypic data, some of which have led to the discovery of many QTLs for RSA in controlled conditions . QTL mapTriticum aestivum), two previously assembled panels were studied. The first panel, BW_div, is composed of 450 varieties sampled from 4,506 varieties in a worldwide collection (For bread wheat (llection  by minimllection . The comTriticum durum) panel includes 100 elite European lines (DW_EPO_elit), as well as 100 pre-breeding lines (DW_EPO_div). The latter evolutionary pre-breeding population (EPO) lines are derived from an open-pollinated population based on the intercrossing of about 650 accessions from wild subspecies and elite lines. To promote the allogamy rate, male sterile plants have been included and maintained in this population at a frequency of 20% over successive generations 2, 2 mM MgSO4, 2 mM CaCl2, 50 μM Fe EFTA, 10 μM H3BO3, 4.5 μM MnCl2, 0.2 μM Mo(Na2O4), 0.4 μM CuCl2, and 0.7 μM ZnCl2. A RhizoTube has a diameter of 17 cm and a depth of 49.5 cm (Plants were phenotyped in three experiments in the Plant Phenotyping Platform for Plant and Microorganism Interactions (4PMI) HTPP at INRAE-Agroecology . Seeds w 49.5 cm .Each of the three experiments  consisted of 1,125 RhizoTubes, each containing two plants of the same variety which were divided between two experimental units, one with 475 RhizoTubes, the other with 650 RhizoTubes. Plants were watered in the RhizoTubes with 250 ml of the same nutrient solution from day 1 to 4. The water content was maintained at the field capacity from day 5 to day 26 (harvest day) to ensure that no hydric stress occurred. The available water capacity was 1,542 mm. The temperature was on average 21.8°C during the day and 18.4°C during the night. Rhizotubes with varieties from the same wheat panel were grouped together. The RhizoTubes were grouped into 23 blocks in the two experimental units, with six check varieties (four bread wheats and two durum wheats) grown in each block to take any spatial effect into account. The 1,125 RhizoTubes were as follows:-Nine hundred and fifteen rhizotubes for 915 cultivars .-Seventy two rhizotubes for image calibration .-One hundred and thirty eight rhizotubes for the six check genotypes . Each check genotype was present in one rhizotube (two plants) in each block.Temperatures were measured in 66 locations spread over the experimental units and incident solar radiation was measured in 12 locations to correct for any spatial heterogeneity in the experimental units. Kriging was performed with the krige function of the gstat R package  to extraTwo types of traits were measured in control conditions. First, RSA-related traits were extracted from image analysis early during plant development. Five days after implantation, we measured the seminal root angle [angle between the leftmost and the rightmost seminal roots at 3 cm from the seed ] and theThe BW_elite panel was grown in fields from 2012 to 2016 in 42 environments, partly representing the diversity of growing conditions in France . These 4The DW_EPO panel was grown in 10 environments in 2018 or 2019 located in three main durum production areas in France  and in Italy  under both non-limiting (NL) and limiting (L) conditions , grain protein content , grain protein deviation (GPD), thousand kernel weight , grain number per m2 (GN), and date of earing  were measured.In each environment, grain yield at 0% moisture content , one genotype for depth , one genotype for width  and one genotype for CGY .Marginal means  per genotype were extracted from the SpATS model to perform further analysis. At this stage, a single genotype from the BW_div panel with trait values clearly outside the distribution was removed.The generalized heritability of the corresponding adjusted means was computed for each trait as in Adjusted means and heritabilities of the agronomic traits  had already been computed in previous studies for BW_elit . For theThe linear correlations between traits measured in the HTPP and agronomic traits measured in fields were computed. As DOE was significantly correlated to yield and yield components in some environments –3, residThe square root of the determination coefficients, that is, the value of R for each model, was computed. To account for multiple testing, we computed critical R with a Bonferroni correction based on the number of independent HTPP traits × the number of independent environments. The numbers of independent HTPP traits and independent environments were estimated as the number of principal component analysis (PCA) axes accounting for at least 90% of the variability of respectively the HTPP traits dataset and the GY dataset. We identified six independent variables for the HTPP traits in all panels and respectively 30, 21, and 21 independent variables in BW_elite, BW_div, and DW_EPO multi-environment trials, which resulted in 180, 126, and 126 independent tests for the different panels. PCA was computed using the PCA function of the FactomineR package .Multiple linear models were also used to relate agronomic variables to several root traits measured in the HTPP. Again, the R-value  of the models was analyzed for each environment independently.The mixed model used for association mapping  was as fY = Xβ + Zu + ε with Var(u) = Kσ2G and Var(ε) = Iσ2e, where u is the random polygenic effect, β represents the vector of fixed effects , K represents a matrix of genetic relatedness between individuals (see below), I is the identity matrix, X and Z are incidence matrices for fixed and random effects, respectively, and σ2G and σ2e are the respective polygenic and error variances. K was computed following the i the allele frequency at marker i.with V being the centered marker matrix and pEach SNP was tested successively with the function GWAS from the R package statgenGWAS which is based on the method presented in We computed a false discovery rate (FDR) threshold of 30% as described by To define QTL boundaries, we used a method inspired by 2 estimator from The R2”. Critical R2 (R2c) was defined as the 99.9th percentile of the distribution of unlinked R2 computed between 10,000 pairs of markers randomly sampled from different chromosomes. This threshold accounts for the risk of 0.1% of markers being in LD by chance.Clustering was performed with the UPGMA method using a cutoff of 1–“critical R2c for relatedness using the package LdcorSV . Correlations were tested only in BW_elite and in DW_EPO as environmental variables were only available for these two panels. Multi-trait models were tested using multiple regression with the lm and ANOVA function of base R.Quantitative trait loci (QTLs) were considered to co-localize when they had at least one marker in common in their confidence interval. Note that because of the high LD extend of the durum wheat panel, co-localization might be due to both genetic linkage or pleiotropic effects of the loci. For each HTPP QTL co-localizing with a field agronomic trait QTL, for each agronomic productivity trait , we regressed environmental variable values onto the QTL effect computed in each environment. Thus, for each HTPP QTL, we computed six regressions. Significant regressions were detected with a Bonferroni threshold accounting for the number of significant SNP tested × the number of independent variables  which reflects the good quality of the phenotyping . As a result, the root biomass also significantly correlated with most other above-ground traits, for example, with the number of leaves , and the number of tillers . Interestingly, the root-shoot ratio was negatively correlated with the number of leaves . This indicates that at a given date at the beginning of the season, late cultivars that have fewer leaves (due to the longer phyllochron) tended to allocate more resources to their roots than early cultivars that have more leaves at an equivalent stage.Significant correlations between root traits were similar in the three panels , 6. All r = −0.02, −0.23, −0.24) than in BW_div. The correlations between biomass measurements (root and above-ground) and geometric measurements  were relatively stronger in BW_div than in BW_elite. Similar phenomena were observed for durum wheat between the elite and EPO sub-panels.We also observed differences between the elite and diverse bread wheat panels , 6. RootIn BW_elite, significant correlations were found between grain yield and root traits but these correlations were not significant after correcting for phenology (DOE measured under field conditions) indicating that differences in earliness among the lines partly explain root trait variability . Among yr = 0.21, p-value < 0.05). Convex hull which represents the soil exploration capacity of the roots in the HTPP, was positively and significantly correlated with GYC in three environments . The strongest correlation involving convex hull was for the environment 17INRcle_WUE_IRR. That was also the environment where root width was the most significantly correlated with GYC , but root depth was not .For BW_div, most identified correlations disappeared after correction for earliness. However, significant positive correlations remained between root depth and GYC in three environments  but root biomass and above-ground biomass were not , the root-shoot ratio was significantly correlated to grain protein deviation (r = 0.19). In addition, in the environment QUALPREST_2019, GPD was also significantly correlated with root-shoot ratio  and with above-ground biomass . Among these six root QTLs, one was related to both root and above-ground biomass, and another was related only to agronomic variable non-corrected by DOE. Thus, we did not consider them in the following lines. Significant relationships between agronomic QTL allelic effects and environmental variables are presented in For the HTPP QTLs that colocalized with agronomic QTLs, we tried to identify an environmental variable that correlated significantly with the environment-specific agronomic QTL effect. This was possible for 6 QTLs  and had a negative impact in non-stressed environments  altering root and above-ground biomass in the HTPP also had a differential effect on GYC depending on the amount of incident radiation during winter. This QTA had a negative impact on GYC in low radiation conditions and a positive effect in high radiation conditions (r2 = 0.46).Third, one QTA altering root biomass had a contrasting effect on GYC depending on the number of days with temperatures below 0°C between the beginning of stem elongation and flowering. The effect was negative or null in warm environments and positive in cold environments  during the period from elongation to meiosis  of the multi-environment trial.critical = 0.91). In addition, the highest correlations were obtained with agronomic variables that were not corrected for earliness, indicating that earliness plays an important role in this correlation.Thirty-four QTLs related to root traits measured under 4PMI HTPP conditions were found in the durum wheat panel : one forp-value = 0.07 and 0.08, respectively). This was confirmed by the significant increase observed for Depth_80  over time. Such trends were not observed in durum wheat. In bread wheat, the mean number of seminal roots has decreased significantly  from 4.5 in varieties registered before 1940 to 4.2 for the newest cultivars. In durum wheat, the same phenomenon occurred but not to a significant extent . From 1940 to 2015, in bread wheat, newly registered cultivars had more tillers going from 2.4 tillers before 1960 to 2.75 for the most recent cultivars . The opposite was observed in durum wheat . Finally, in durum wheat, a significant increase in the root-shoot ratio was observed (p = 0.012) over the registration period studied.For most traits, no correlation between variety registration date and trait values was found , 21. HowIn this study, we characterized root traits on two bread wheat panels and one durum wheat panel grown in controlled conditions in three successive experiments, to investigate whether root traits measured in platforms can be related to field performances and to what extent any relationship at the trait or at the QTL level is environment-specific.2 = 0.73 for root angle and H2 = 0.67 for the number of roots in 2 = 0.75) and seminal root angle (H2 = 0.85) has been reported in an experiment with six independent replicates  and between aerial biomass and the root-shoot ratio. This might reflect developmental constraints and the trade-off in carbon allocation to the different organs of the plants. This interpretation is borne out by the narrower genetic variability of the observed root-shoot ratio compared to other traits.Consistent correlations were observed between aerial biomass and root biomass  might be due to cultivars with deep roots gaining extra access to nutrients and water, resulting in higher yield, especially in water-scarce environments and nutrient-poor environments . In the Second, in BD_EPO, some environmental characterizations were available, especially regarding water stress. We found two elements confirming our hypothesis. First, we observed better performances in durum varieties with high root-shoot ratios in QUALPREST_2019 and INRAE_2019_opt environments. The common features of these two environments were deep soil and the strongest water stress of the multi-environment trial. Specifically, the water stress index dropped early at tillering down to 0.72 compared to 0.9 for the other environments. Thus, for equal above-ground biomass, plants with higher root biomass were able to capture more water at depth and thus avoid yield loss due to stress.Third, using multi-trait approaches to better describe RSA, we found that the relationship between root traits and GYC was more important in water-deficient environments where roots are likely to play a major role in water uptake, especially in the most stressed environments .Lastly, in the BW_elit panel, environmental characterization was precise  but no cWe found numerous RSA QTLs that co-localize with agronomic performance QTLs. Assuming that the SNPs responsible for the variation in the RSA QTLs and the variation in agronomic performances are the same, some RSA QTLs have contrasting effects depending on the level of stress according to environmental covariates. If relationship patterns can therefore be explained at both the phenotypic and allelic level, it follows that alleles conferring specific RSA might be suboptimal in some environments and optimal in others. If so, it would be advisable for future breeding programs on root traits to carefully consider the characteristics of the target environments.Two types of cases were identified regarding the relation between RSA QTL effects on agronomic variables and environmental variables.First, we found some quantitative trait alleles (QTA) whose effects vary linearly with environmental variables. For instance, the QTA increasing root depth increases yield in stressed environments. Deeper roots might enable plants to access water from deeper layers and have been associated with high yield in some environments . The QTASecond, some QTA had a significant effect on an agronomic variable only when the environmental variable reached a certain threshold. For instance, the QTA allele increasing root angle  enables adaptation to distinct environments in bread wheat. In durum wheat, the relationships between root traits (particularly root-shoot ratio) and agronomic variables (particularly GYC) were stronger in water stress.At the allelic level, we found numerous root QTLs in both species. Some QTLs had an effect on agronomic performances varying linearly with environmental variables whereas others had an effect on agronomic performances only when the environmental variable reached a specific threshold.This highlights that multi-environment trials are required to evaluate the agronomic value of root phenotypes measured in HTPP and that breeders should prioritize root ideotyping for target environments.The original contributions presented in the study are included in the article/MC, RR, and PR analyzed the data. PR, CS, CJ, ML, WN, BD, VA, JL, and RR participated in the 4PMI experiments. CS, CJ, and RR designed the 4PMI experiments. ML analyzed the 4PMI images. TBWC provided the genotyping and field phenotyping data. PR, CS, CJ, ML, SL, KB, JL, and RR initiated the project. RR supervised the project and designed the experiments. MC wrote the manuscript under the supervision of RR, JL, VA, and PR. All authors reviewed the manuscript.SL, PD, and A-VD were employed by Biogemma Limagrain. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Adarb2, Ankrd26, Cacna1c, Cacng4, Clock, Ctu2, Cyp26b1, Dnah9, Gda, Grxcr1, Eva1a, Fam114a1, Kcnj9, Mlf2, Rab27b, Sec11a, and Ube2h) for these traits. Many of these genes have been implicated by human GWAS of various psychiatric or drug abuse related traits. In addition, there are other candidate genes that likely represent novel findings that can be the catalyst for future molecular and genetic insights into human psychiatric diseases. Together, these findings provide strong support for the use of the HS population to study psychiatric disorders.Many personality traits are influenced by genetic factors. Rodents models provide an efficient system for analyzing genetic contribution to these traits. Using 1,246 adolescent heterogeneous stock (HS) male and female rats, we conducted a genome-wide association study (GWAS) of behaviors measured in an open field, including locomotion, novel object interaction, and social interaction. We identified 30 genome-wide significant quantitative trait loci (QTL). Using multiple criteria, including the presence of high impact genomic variants and co-localization of cis-eQTL, we identified 17 candidate genes ( Many personality traits are predictors of vulnerability to addiction . For exaThe open-field test (OFT) is a widely used behavioral test for measuring anxiety-like and exploratory behavior in rodents –14. A roThe heterogeneous stock (HS) rats were originally derived from interbreeding eight inbred strains . An analad libitum. All rats were group-housed with 2–4 same-sex peers throughout the experiments to avoid social isolation. All procedures were conducted in accordance with the NIH Guidelines concerning the Care and Use of Laboratory Animals, as approved by the Institutional Animal Care and Use Committee of the University of Tennessee Health Science Center.The N/NIH heterogeneous stock (HS) rat (RRID:RGD_2314009), was created at the NIH in 1984 by interbreeding the following eight inbred founder strains: ACI/N, BN/SsN, BUF/N, F344/N, M520/N, MR/N, WKY/N and WN/N . The HS All HS rats  were adolescents when tests began. Their age was 31.8 ± 2.6 (mean ± STD) on the day of the OFT. Adolescent rats were used because the onset of many psychiatric diseases occur during this age . Each HSTwo OFT arenas were constructed using black acrylic glass, measuring 100 cm (L) × 100 cm (W) × 50 cm (H), which were placed side by side. The floors were covered by wood boards painted with either black or white acrylic paint  to contrast the coat of the animals . The test chambers were illuminated by a long-range, 850-nm infrared light  located 160 cm above the center of the two test chambers. No source of visible light was present during behavioral testing, with the exception of a flat panel monitor (Dell 1908FP). A digital camera (Panasonic WV-BP334) fitted with an 830 nm infrared filter  and located next to the infrared light source was used to record the behavior of the rats. All rats were released at the same corner of the test chamber, and data were collected for 1 h.https://github.com/chen42/RatSocialInteractionTest. The novel object was placed in the center of the arena before testing. The test duration was 20 min and was recorded using the same camera as that used in the OFT.This test was conducted the day after the OFT in the same arena. A cylindrical rat cage constructed using 24 aluminum rods (30 cm in length) spaced 1.7 cm apart was used as the novel object. The bottom and top of the cage (15 cm in diameter) were manufactured using a 3D printer from polylactic acid. The design can be downloaded from cm(L) × 60cm(W) × 50cm(H) by using a black board placed vertically in the arena. Two cylindrical cages described above were placed ~30 cm away from the walls on opposite sides . A randomly selected stimulus Sprague-Dawley rat of the same sex and similar weight as the HS test rat was placed into one of the cylindrical cages (kept the same throughout the experiment) 5 min before the HS subject rat was placed into the arena. The stimulus and subject rats were never housed together and thus were unfamiliar to each other. No social isolation was conducted on either rat. Each stimulus rat was used no more than once per day. The test duration was 20 min and was recorded using the same camera as that used in the OFT.This test was conducted the day after the NOIT. This test compares the preference of a subject rat for a stimulus rat restricted in a cylindrical cage  against an empty cylindrical cage. The test arena was reduced to 100Ethovision XT video tracking system  was used to analyze the videos recorded in all behavioral tests. After identifying the arena and calibrating the size of the arena, specific zones in the arena were outlined. For OFT and NOIT, one center zone, which was a circular region with a diameter of 20 cm, was used. For the SIT, one object zone and one social zone, both were circular regions with diameters of 20 cm, corresponding to the two cylindrical cages, respectively, were specified. The extracted data included the total distance traveled in the arena, the duration and the frequency the test rat was present in specific zones, the distance of the subject to the zones, and the latency of the test rat entering the zones. The center of the subject rat was used for all calculations. Phenotypic correlations were determined using the Pearson test.All phenotype data were stored in the C-GORD (RRID:SCR_021866) relational database. For genetic analysis, each trait was quantile-normalized separately for males and females; this approach is similar to using sex as a covariate. Other relevant covariates  were identified for each trait, and covariate effects were regressed out if they were significant and if they explained more than 2% of the variance. Residuals were then quantile-normalized again, after which the data for each sex were pooled prior to further analysis. This approach removed mean differences due to sex; further, it did not attempt to model gene-by-sex interactions.Genotypes were determined using genotyping-by-sequencing (GBS), as described previously . This prlog10(p) > 5.6, which was supported by a second SNP within 0.5 Mb that had a p-value that was within 2 − log10(p) units of the most significant SNP.GWAS analysis employed a linear mixed model, as implemented in the software GCTA , using ap-value that exceeded our permutation-derived threshold, it was considered to be a second, independent locus. This process was repeated , until no more QTLs were detected on a given chromosome. Linkage disequilibrium (LD) intervals for the identified QTL were determined by identifying those markers that had a high correlation coefficient with the peak marker (r2 = 0.6).There could be more than one QTL on the same chromosome for one trait. We resolve the dependency and determine their locations as follows: we used the top SNP from the most significant QTL as a covariate and performed a second GWAS of the chromsome in question. If the resulting GWAS had an additional SNP with a Genetic fine mapping were conducted using Credible Set analysis  and SuSir2 > 0.6). The gene expression data were collected from 88 naive adult HS rats. Five brain regions  were collected for RNA-seq from each rat . However, due to the large sample size, most of these correlations are highly significant (median −log10(p) is 7.8). In general, correlations of traits obtained from the same behavioral test are among the strongest. For example, frequency of visiting the center and duration of staying in the center are positively correlated in OFT (r = 0.76), and duration in the social zone and distance to the social zone in the SIT are negatively correlated (r = –0.76). Most of these correlations are expected from the definitions of these variables.We calculated Pearson correlation between the 23 traits . We founAmong the correlations of variables derived from two different behavioral tests, correlations for measures of distance traveled are among the highest  for traits are provided in h2 = 0.08). Heritability estimates for variables from the NOIT are slightly lower than that of the OFT; most of them are in the range of 0.21–0.29, with the exception of that for the latency of entering the center zone (h2 = 0.10). Heritability estimates for various measures of the SIT are in the range of 0.10–0.28. Interestingly, heritability estimates for measures on the social zone are consistently greater than those for the object zone.SNP heritability estimates . We identified two loci for Frequency of entering center zone and Total travel distance, 3 loci for Total distance to center zone. We found 4 NOIT traits have significant loci. Among them, Total distance to center zone has 3 loci and Mean distance to center zone has 2 loci. We did not find any significant loci for Frequency of entering center zone (h2 = 0.209 ± 0.041) and Latency of entering center zone (h2 = 0.100 ± 0.034). For SIT, we identified significant loci for all traits except Latency of entering object zone which has heritability of h2 = 0.082 ± 0.032. We found 2 loci for the traits Latency of entering social zone, Mean distance to social zone Total distance to social zone and Total travel distance. All genome-wide significant loci are shown in In To determine if traits that mapped to the same location are pleiotropic, we considered the minor allele frequency (MAF), and the strain distribution pattern (SDP) of the most significant SNP among the 8 founder strains that were used to create the HS. Using these criteria, we did not observe any pleiotropic loci between the traits analyzed in different tests. However, we did identify pleiotropic loci between the traits of the same behavior test. Most of these traits are highly correlated, as shown in Adarb2 within chr17:58Mb for latency of entering social zone in SIT. However, it is also possible that the causal allele is a regulatory variant that is located in this interval but regulates a gene outside of the identified interval.The number of genes within the identified QTL ranges from 1 to 127  located in the credible set identified by either one of the fine mapping methods. (2) the presence of moderate or high impact variants located within the gene, as predicted by SnpEff . We alsoACAN, ADAMTSL3, ALPK3, CPEB1, FES, FURIN, LINC00933, MIR9-3HG, MRPL46, NMB, POLG-DT, SEC11A, ZNF592, and ZSCAN2, Sec11a is the best candidate gene  but with high statistical significance. It is likely that these correlated traits are controlled by the same behavioral processes and thus are influenced by the same genetic factors. In fact, our genetic analysis did find several pleiotropic sites   . Cyp26b1man GWAS , 43. Anxman GWAS , 83. Cyprneurons . Most inbehavior .Eva1a is a candidate gene for the duration stayed in the center zone that contained the novel object. Eva1a is supported by strong cis-eQTL and a missense variant but has no literature support. Thus, further evaluating the role of Evala could potentially lead to new mechanisms for novelty seeking-like behavior. Sec11a, a candidate gene for total distance in the center zone, is associated with depression and schizophrenia  showed many deficits in social behavior. These included reduced levels of ultrasonic vocalizations during rough-and-tumble play, as well as social approach behavior elicited by playback of ultrasonic vocalizations  = 5.969, size of interval: 4.1 Mb, p-value did not reach genome-wide significance (-logP = 4.63). This region contains 69 known genes. Its syntenic region on human Chr15 (82.5-90.8 Mb) is a hotspot for human pyschiatric diseases, containing 30 SNPs and 14 genes  associated with generalized anxiety disorder, schizophrenia, bipolar disorder, obsessive compulsive disorder, attentions deficit hyperactivity disorder, autism spectrum disorder, and unipolar depression, smoking behavior, etc. These results are reported in 21 publications  of the four candidate genes for OFT have been associated with schizophrenia, rather than anxiety. However, many genetic variants are pleiotropic for multiple psychiatric diseases .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.