Variation in genes involved in the absorption, distribution, metabolism, and excretion of drugs (ADME) can influence individual response to a therapeutic treatment. The study of ADME genetic diversity in human populations has led to evolutionary hypotheses of adaptation to distinct chemical environments. Population differentiation in measured drug metabolism phenotypes is, however, scarcely documented, often indirectly estimated via genotype-predicted phenotypes. We administered seven probe compounds devised to target six cytochrome P450 enzymes and the P-glycoprotein (P-gp) activity to assess phenotypic variation in four populations along a latitudinal transect spanning over Africa, the Middle East, and Europe . We demonstrate significant population differentiation for all phenotypes except the one measuring CYP2D6 activity. Genome-wide association studies (GWAS) evidenced that the variability of phenotypes measuring CYP2B6, CYP2C9, CYP2C19, and CYP2D6 activity was associated with genetic variants linked to the corresponding encoding genes, and additional genes for the latter three. Instead, GWAS did not indicate any association between genetic diversity and the phenotypes measuring CYP1A2, CYP3A4, and P-gp activity. Genome scans of selection highlighted multiple candidate regions, a few of which included ADME genes, but none overlapped with the GWAS candidates. Our results suggest that different mechanisms have been shaping the evolution of these phenotypes, including phenotypic plasticity, and possibly some form of balancing selection. We discuss how these contrasting results highlight the diverse evolutionary trajectories of ADME genes and proteins, consistent with the wide spectrum of both endogenous and exogenous molecules that are their substrates. Physicians often observe that their patients can react differently to the same medical treatment: for some patients, the drug will prove inefficient, whereas for others, it might provoke side effects, sometimes rather serious. These differences in response to exogenous compounds are phenotypic differences that can be due to several factors, including genetic factors, potentially unevenly distributed among human populations. Using a standardized protocol, we demonstrate significant variation among Ethiopians, Omanis, Greeks, and Czechs in six out of seven measured phenotypes linked to 70–80% of drugs commonly used in the clinic. Significant genetic associations are evidenced by GWAS for four of these phenotypes, but genome scans of selection fail to detect positive signals in the genetic loci identified. We hypothesize that the results of our joint analysis of phenotypic and genomic diversity are best explained by diverse mechanisms, including phenotypic plasticity and possibly balancing selection, but do not appear to favor positive selection as an adaptation to specific chemical environments.http://pharmaadme.org) to publish a list of “evidence-based ADME genes”, that is polymorphic genes known to be involved in the differential absorption, distribution, metabolism, and excretion of drugs  that screen entire genomes in relation to drug-related phenotypes, such as metabolism rate or side effects . One sucreatment . Similarde scale . Howeverde scale , and thede scale . MoreoveMost of the already identified ADME genes display significant population variation, which is indicative of the existence of common variants with differentiated population frequency distributions . The strAlthough studies on world-wide populations are becoming more frequent, some regions are still underrepresented, notably the African continent and the Middle East, contrary to European and East Asian populations . MoreoveIn this study, by implementing a safe and efficient protocol to both collect individual DNA and measure blood concentrations of six probe drugs and their specific metabolites, as well as the P-glycoprotein (P-gp) substrate concentration in the same individuals, we jointly undertook phenotyping and genotyping of four human populations located along a latitudinal transect from Africa to the Middle East and Europe, that is Ethiopians, Omanis, Greeks, and Czechs . Each pometabolite/AUCsubstrate ratio, except for the transporter activity of P-gp, which was measured directly by the AUCsubstrate, that is the P-gp probe drug concentration (fexofenadine). Hence, higher ratios correspond to faster enzymatic activities, except for fexofenadine, for which a higher AUCsubstrate corresponds to a slower drug transporter activity.Using the concentrations of both substrates and their metabolites measured at three time points after probe drugs administration with the Geneva cocktail, we calculated areas under the curve (AUC) and defined an individual phenotype  as the AUCKruskal–Wallis and Wilcoxon two-by-two tests, to compare the AUC substrate/metabolite ratios for the Geneva cocktail compounds among populations, yielded differentiated results  online. We then defined three discrete classes of enzymatic or transport activity  for each of the seven compounds of the Geneva cocktail tested, using thresholds shown in  online bFrom the DNA extracted from saliva samples, and after quality controls  online, CYP2B6, CYP2C9, CYP2C19, and CYP2D6, led to significant signals.For each of the seven phenotypes measured in this study, a GWAS was performed with the GEMMA program , after tAandB) and the FUMA gene-based GWAS (CandD). FUMA identifies a single genomic risk locus (GRL) in this same area, spanning a 50 kb region encompassing CYP2B6 and pseudogene CYP2A7P1, with the top lead SNP rs201788928 (which shares the same position with long indel rs59423078) located in the first intron of CYP2B6 . On the other hand, FUMA gene-based GWAS points to an association with genes CYP2C9 and CYP2C19 on chromosome 10, as well as with SLC8A3 on chromosome 14 (CandD), but a single GRL is identified, which is characterized by the SNP lead rs34368405, an intergenic poly-A CNV located 2 kb upstream of CYP2C9 , whereas the FUMA gene-based GWAS indicates an association with genes around CYP2C19 (on chromosome 10) and with TPM4 on chromosome 19 (CandD). The three GRLs identified by FUMA match the significant areas detected in the classical GWAS , which are also identified by FUMA as GRLs , rs13055841 (an intronic SNP in gene SREBF2), and rs28568508 (an intergenic variant lying between CYP2D6 and the CYP2D7P pseudogene) as top lead . Finally, the mapping analyses prioritize 24 genes by a combination of the three mapping methods, all located in a wide genomic area spanning almost 1.5 Mb around CYP2D6 , the GWAS on phenotypes of caffeine and midazolam metabolism and fexofenadine transport, linked, respectively, to CYP1A2  and the P-gp (fexofenadine transport) phenotype is output by the FUMA gene-based GWAS of ABCB1 activity (CandD).Contrarily to the enzymatic activities encoded by genes of the CYP2 family , due to their apparent skewness. In total, the four tests identify 46 candidate areas and 215 candidate genes (A).First, we compared the two most differentiated populations for each of the evaluated phenotypes, using the XP-EHH method . Four XPtivities  online,   in the comparison of Ethiopians with Omanis, in that of Africans and Middle Easterners with Europeans, as well as in that of Omanis with Greeks .As an alternative to XP-EHH, which is based on an analysis of variation in linkage disequilibrium, we also compared the same four populations with the population branch statistic (PBS) method , which ielection  online. h Greeks  online, h Greeks  online. te zones B.rehh  are not mirrored in the iHS scans, and similar to the XP-EHH and PBS results, neither of the seven genes targeted by the Geneva cocktail is identified with the iHS approach (C). Interestingly, however (and contrarily to the results of the two former approaches), a single match with the “evidence-based ADME genes” list of PharmaADME was found with iHS for the NAT1 gene in the Omani population. This result is in line with previous studies reporting signals of directional selection in the sequence diversity of NAT1 identified with classical tests of selective neutrality, such as Tajima's D test  substrates, as well as in various essential metabolism pathways such as the synthesis of steroid cholesterol and lipid compounds , when analyzing only women.The GWAS of dextromethorphan demethylation identified two candidate zones. The first zone is located around ividuals . The secc target . Howeveranalyses  online, osome 22  online. ASB13, located on chromosome 10, as a candidate gene in addition to CYP2D6. ASB13 encodes a protein containing an ankyrin repeat motif and a SOCS box domain that targets proteins in order to proceed to their proteasomal-mediated degradation . SLC8A3 encodes the sodium/calcium exchanger 3 protein , a plasma-membrane protein that mediates the exchange of sodium/calcium ions across the cell membrane . However, we could not identify any apparent link between this gene and the activity of CYP2C9. It is nevertheless interesting to note that other sodium/calcium exchanger genes have been previously categorized as ADME genes (pharmaadme.org).On average, the Omanis are faster than the other three populations in the hydroxylation of flurbiprofen into hydroxyflurbiprofen, a metabolic function carried out by the enzyme encoded by reported , the linnclusive , and reqThe enzymatic activity of CYP2C19 is responsible for the oxidation of omeprazole and was found slightly, but significantly, faster in Czechs than in the other tested populations. Compared with phenotypic predictions based on markers leading to poor or rapid metabolism , our resCYP2C19, which confirms that the examined phenotype is at least partly determined by distinct genetic variants in that gene  primarily to environmental and physiological factors (such as age and sex), rather than to specific genetic factors  factors shaping inter-population variability, with genetic factors being only responsible for inter-individual variability, thereby in line with the lack of selection signals found in our XP-EHH, PBS, and iHS scans (CYP2C19 (and CYP2D6 too) is consistent with the main demographic model of human expansion over the globe, whereas this is not the case for CYP2C9 , do not correspond in our dataset with regions of unusually large allele frequency change that would be detected as PBS signals. We are aware that the detection threshold chosen in the PBS scans is very stringent  it has never been identified before.Despite that numerous candidate genes were output by our XP-EHH tests  online, d Omanis  online, d Omanis , was alrd Omanis . The twon Omanis  online, n Omanis . The natn Omanis , which an Omanis . Moreoven Omanis . As expen Omanis , genes fn Omanis  online. n Omanis  online, n Omanis . This sironments . Such poNAT1 gene region in Omanis, no other ADME gene was identified by our iHS scans.Note that methods such as XP-EHH and PBS are able to detect signals of strong directional selection on variants that are close to fixation, whereas iHS has the potential to detect softer selective sweeps, and also balancing selection . The actCYP2D6, are contained in many fermented foods and are used by the organism for dopamine biosynthesis, although they can also have a nefarious effect because high consumption of this type of molecules can cause hypertensive crises  in East African populations reflects local adaptation to a diet rich in toxic alkaloids , and their evidenced association with genetic variants, were shaped by local adaptations to diets with different compositions in fat. As proposed above, while CYP2C9 may be subject to directional selection (either adaptive or purifying), this does not seem to be the case for CYP2C19 . However, our results do not support the hypothesis that environmental variables correlating with latitudinal distance influenced the diversity of phenotypes, because in such case, the greatest differences would be expected between the two extremes of the transect, namely between Ethiopians and Czechs. The only phenotype for which such a tendency could be suggested is omeprazole transformation, in which CYP2C19  is involved. However, as no signal of local  selection in the ADME genes targeted by the Geneva cocktail was indicated by our XP-EHH and PBS tests on the phenotypically most differentiated populations, and not either by the population-specific iHS test , or the expected single-gene association , or more complex associations additional to those involving the primary gene (dextromethorphan and omeprazole metabolism). Our results, therefore, highlight different patterns of influence of genetic and/or environmental factors on each of the ADME systems examined.Although the precise mechanisms that influence the evolution of ADME genes, more specifically of the seven genes targeted by the Geneva cocktail, are not yet fully identified, our study shows that they are likely to be fairly complex. The population approach adopted in this study, which analyzed both phenotypic and genotypic data, provided some essential insights into these systems. First, we demonstrated the existence of differences in average enzymatic activities between populations for six of the seven phenotypes examined. Second, we evidenced either a lack of any apparent genetic-phenotypic association  was approved by the competent ethics commissions and is described in the Sampling sites in four countries, that is Ethiopia, Oman, Greece, and the Czech Republic , were chPhenotypes were computed from concentrations of substrate drugs and their corresponding metabolites in blood samples collected at +2, + 3, and +6 h after ingestion of the seven probe-drug compounds’ Geneva cocktail. These phenotypes are AUC ratios and represent the rate of metabolization of each molecule in each individual. In addition, on the basis of his/her AUC ratio, each individual was assigned to one of three discrete classes  of enzymatic or transporter activity. A full description of the method used for the computation of phenotypes is presented in For each of the seven phenotypes, populations were compared with Kruskal–Wallis and Wilcoxon two-by-two tests to highlight possible differences in AUC distributions, whereas proportion tests were applied to the discrete phenotypes datasets. Extensive analysis of potential confounders was performed by sensitivity analyses that compared subsets of populations, such as for example only women, only individuals not smoking, etc.  online.B, DNA extracted from collected saliva samples was processed with an Illumina CoreExome24 array including 555,356 built-in markers that we further enriched with 4,000 custom markers  online. fig. 1> 0.05, leading to a total of 5,773,125 markers  web platform  were used as input for genome-wide association analyses. Prior to GWAS, new markers were imputed from our genotyping data with the Michigan Imputation Server (MIS), using Minimac4 , and a m markers  online.  markers  to perfo markers  online tplatform  online.We compared the two most differentiated populations for each of the evaluated phenotypes to identify genes potentially under selective pressures related to the metabolism of exogenous compounds, using the XP-EHH method  and the evac167_Supplementary_DataClick here for additional data file.


Eucalyptus pauciflora grown in a common-garden field trial established at the hot and dry end of the species distribution on the island of Tasmania, Australia. We related the within-population variation in leaf economic  and hydraulic  traits, measured from two-year-old plants, to two-year growth performance (height and stem diameter) and to a fitness component . When performance-trait relationships were modelled for all traits simultaneously, statistical support for direct effects on growth performance was only observed for leaf thickness and leaf density. Performance-based estimators of directional selection indicated that individuals with reduced leaf thickness and increased leaf density were favoured. Survival-performance relationships were consistent with size-dependent mortality, with fitness-based selection gradients estimated for performance measures providing evidence for directional selection favouring individuals with faster growth. There was no statistical support for an effect associated with the fitness-based quadratic selection gradient estimated for growth performance. Conditional on a performance measure, fitness-based directional selection gradients estimated for the leaf traits did not provide statistical support for direct effects of the focal traits on tree survival. This suggested that, under the environmental conditions of the trial site and time period covered in the current study, early-stage selection on the studied leaf traits may be mediated by their effects on growth performance, which in turn has a positive direct influence on later-age survival. We discuss the potential mechanistic basis of the direct effects of the focal leaf traits on tree growth, and the relevance of a putative causal pathway of trait effects on fitness through mediation by growth performance in the studied hot and dry environment.Selection on plant functional traits may occur through their direct effects on fitness (or a fitness component), or may be mediated by attributes of plant performance which have a direct impact on fitness. Understanding this link is particularly challenging for long-lived organisms, such as forest trees, where lifetime fitness assessments are rarely achievable, and performance features and fitness components are usually quantified from early-life history stages. Accordingly, we studied a cohort of trees from multiple populations of  Plant functional traits are morpho-physio-phenological traits that impact attributes of plant performance and ultimately fitness . Howeverfitness-based selection gradients  and a multivariate phenotype  assessed on the same individuals, providing radients . Howeversee also . For a psee also . When fisee also . This thEucalyptus pauciflora, using within-population variation in leaf economic and hydraulic functional traits, two growth performance attributes (height and diameter) and a fitness component , measured in a common-garden (multi-population) field trial established at the hot and dry end of the species distribution on the island of Tasmania, Australia  leaf traits, and the assessment of growth performance attributes, were undertaken at age 2 years from planting (June 2016) when the trees were in the non-reproductive stage. 2) and leaf density (mg/mm3). Leaf thickness was measured using digital callipers, avoiding the influence of major leaf veins; leaf area was measured from scanned images obtained by using a digital scanner; and leaf density was calculated from leaf area, leaf thickness and dry weight. The leaf hydraulic traits measured were stomatal density (number/mm2), stomatal length (μm) and vein density (mm/mm2). Stomata observations were made with a microscope by using the nail polish impression method, as described in Details on the sampling and measurement of leaves are provided in Growth performance was assessed using maximum tree height and stem diameter, commonly used measures of the productivity of forest trees . Tree heThe fitness component studied at an early life-history stage was the time-averaged survival between 2 and 7 years from planting. At seven years from planting, trees of the sampled cohort were scored as a binary outcome whereby a tree was classified as dead (scored as 0) when no live plant tissue was evident above ground or the plant was in very poor vegetative health, otherwise it was classified as alive (scored as 1). Of the sampled trees, 70% survived to the final assessment. Yet, while pertaining to the 5-year interval, observations showed that virtually all the recorded mortality occurred between ages 4 and 7 years.Our experimental observations have a two-level hierarchical structure, where individual trees are clustered into seed-source populations. We favoured a fixed-effects (FE) model specification in the data analysis, as our interest was focused on the estimation and statistical inference for within-population effects of continuous predictors . This approach accommodates the dependence among observations that may occur when they are clustered into higher-level groups, while also preventing any bias on regression coefficients of lower-level covariates due to omitted variables at the higher level  see Su was usedPerformance-trait relationships were studied by estimating and testing the effects of focal functional traits on a growth performance measure, under the following general linear model:ijky is the observation on a measure of growth performance (height or diameter) for the thi individual tree within the thj population  at the thk block ; 0β is an intercept term; ju is the fixed effect of the thj population; kr is the fixed effect of the thk block; xhijk is the value of the thh covariate , pertaining to the observation on a functional trait for the thijk individual; hβ is the regression parameter for the thh covariate, representing the average within-population effect of a functional trait on the performance measure; and ijkε is a random residual.where xhijk data in Equation (1) were mean standardized, and then expressed on a (dimensionless) percentage scale for comparison of regression parameter estimates  t-test on the estimated hβ coefficient, and by computing the associated 95% confidence interval. Residual plots and other diagnostic statistics indicated that the model assumptions were met .For either performance response variable, least-squares estimation of model parameters and their standard errors were undertaken for specifications of Equation (1) that included: each trait individually; the three traits within the economic or hydraulic sets; and all the six functional traits across both sets. Statistical inference about the effect of We approached size-dependent mortality from the perspective of evaluating the strength and form of selection acting on growth performance, via its influence on time-averaged (i.e. over the five-year period studied) tree survival used as a fitness component. Using individual variation within populations, a generalized regression analysis was applied to model the dependence of tree survival on individual performance values, and subsequently derive (fitness-based) selection gradient parameters for performance. Under the assumption of only directional selection acting on performance, we first modelled a linear term for a performance variable in the generalized linear FE model defined as:ijky is the observation on an individual’s survival (i.e. a binary outcome of 0 or 1), g(.) is a link function relating the conditional expectation [E(yijk | xijk) = μijk] of the binary response to the linear predictor ηijk = β0  +  uj  +  rk  +  β1xijk ; xijk is a row vector for an individual from the design matrix X of all the explanatory variables included in the linear predictor; 0β, ju and kr are defined as before; ijkx is the value of the performance variable - height or diameter - for the thijk individual; and β1 is a regression parameter for the effect of performance on survival. The binary responses were assumed to be Bernoulli distributed with a parameter ijkμ, which is the conditional probability of tree survival [i.e. P(yijk = 1 | xijk) ] in our case; g(.) used the logit link function, such that g-1(.) is the inverse of g(.), exp denotes the exponential function, and β is a column vector of coefficients comprising all fitted parameters. The ijkx in Equation (2) refer to observations that were mean standardized through their division by the grand mean ijkη. In this context, note that the ijkx data were population-mean centered (by subtracting the thj population), in order to avoid multicollinearity and to obtain covariate values for the estimation of a within-population, nonlinear effect of growth performance on the logit scale .where Reflecting few populations where all trees were either alive or dead, quasi-complete separation was detected in the data analysis, indicating that classical maximum likelihood estimation of model parameters and associated statistical inference would not be reliable. Consequently, the bias-reducing penalized maximum likelihood method developed by ijkη , whereas an average quadratic selection gradient was derived only when ijkη had a quadratic term for performance. Besides being summary measures of the strength and form of selection, these derivative-based estimates of directional and quadratic selection gradients describe the average slope and curvature, respectively, of the individual selection surface on the expected fitness scale  or from the performance-mediated analysis introduced by ijky, 0β, ju and kr are defined as in Equation (2); the logit link function was used in g(.), and thus P(yijk = 1 | xijk) was defined as described above. The linear predictor ijkη included linear terms on p covariates for: the functional traits alone; and the functional traits plus a given performance measure (height or diameter). In this sense, ijkη comprised firstly the three traits within the economic or hydraulic sets, and then all six traits across both sets. Incorporating height or diameter as a covariate in ijkη enables estimating the (direct) effects of functional traits on tree survival that are not mediated by growth performance. Consistent with previous models, the xhijk in Equation (3) were grand-mean standardized data  for the trait and performance covariates. The model parameters, statistical inference about the regression coefficients on the logit scale, and predictive ability of the fitted models, were estimated by using the methods described for Equation (2). Variance inflation factors reflecting within-population correlations between the covariates involved in the analyses were < 2, indicating a weak degree of multicollinearity.where ijkη. Nevertheless, preliminary analyses showed that including nonlinear terms did not improve the model fit on the logit scale, indicating that assuming linearity of covariates and the log-odds of survival in Equation (3) would be plausible. The cluster bootstrap procedure referred to above was applied to obtain the standard errors and the 95% BCa confidence intervals of the estimated average directional selection gradients.Under Equation (1), the performance consequences of within-population variation in the traits were evaluated by considering only linear covariate terms. To keep consistency, we estimated fitness-based average directional selection gradients (see p < 0.05) within-population correlation estimates between the functional traits (p = 0.002) was detected between leaf thickness and leaf area. For the set of leaf hydraulic traits, the correlation between stomatal density and stomatal length was highly significant and negative . Across the two sets of traits, stomatal density was significantly associated with leaf thickness and leaf density, with the correlation estimates being positive  and negative , respectively; a significant negative correlation  was also found between vein density and leaf density.There were five statistically significant (l traits Table 1.p < 0.05) p > 0.05) When the effects on height or diameter were modelled by using each functional trait as a single predictor, statistically significant (2R) in performance measures for the models fitted with single or multiple continuous predictors are given in The percentage of the total variation explained R2 in perfoBased on the logistic FE model including a linear (but no quadratic) covariate term for growth performance, average directional selection gradients derived for height and diameter showed statistical support against the null hypothesis of no effect on expected relative fitness  when growth performance was not modelled, there was no statistical support for a direct effect of any functional trait on tree survival when either height or diameter was included in the linear predictor in either the analyses of the three-trait sets Table 4 E. pauciflora individuals in the common-garden experiment, leaf thickness and leaf density had statistically significant direct effects on growth performance, but all the six leaf traits studied were found to be independent of tree survival, conditional on a growth performance measure (height or diameter). In turn, there was statistical support for a significant positive direct effect of height or diameter growth on tree survival. These results suggest that, in our study system, the effects of the focal functional traits on the observed fitness component could occur through their influence on growth performance.For the examined cohort of Given the above-mentioned findings, evaluating directional selection acting on the traits via their effects on performance may be pursued by performance-mediated analysis as originally described by wβ) can be estimated from the product of the performance-based regression coefficients [hβ in Equation (1)] shown in Performance-mediated analysis is justified by evolutionary quantitative genetic theory, and can be applied to estimate directional and nonlinear  selection gradients for multiple traits . Converswith age .A limitation in the comparisons described above for the performance-based and performance-mediated analyses concerns the common assumption in both approaches that the relationship of fitness with performance should be reasonably approximated with a linear function. Reflecting the cumulative distribution function of the logistic distribution, tree survival on the expected probability scale is typically nonlinearly related to a performance variable, even when a quadratic term for performance was not included in the logistic FE model. In addition, for a least-squares linear regression of a fitness response on a performance variable, The average curvature of the individual selection surface associated with the quadratic selection gradient estimated for either performance measure was not statistically supported Table 3,The percentage of the total variation in early-age growth performance explained by our performance-based regression models was moderate (< 16%), indicating that other factors can be important. This is expected given the multitude of exogenous  . SLA is E. pauciflora  and longer cells within each layer . Such anuciflora . Howevertic rate . For exaroplasts . Thus, troplasts .2 diffusion . Increases in leaf density are mainly due to increased cell wall thickness and more tightly packed cells, which could counter the positive effects of thinner leaves on internal COiffusion . Howeveriffusion . Thus, tiffusion . In addiiffusion . Variabliffusion , which iiffusion . Eucalypxception , with maxception . Factorsxception , and potxception . Water sxception , with trex situ even-aged plantings of forest trees, including eucalypts  associations with climate were detected for leaf thickness, with populations originating from home-sites with higher average maximum temperatures tending to have thinner leaves. Thus, in terms of temperature, this trend is consistent with expectations from our performance-based analysis of directional selection at the environmental conditions of the trial site, suggesting that leaf thickness may directly contribute to local climate adaptation in the wild. In contrast to leaf thickness, the trend for performance-based directional selection to favour denser leaves was not supported by trait-climate associations [i.e. no statistically significant trait-climate associations were detected for leaf density in E. pauciflora by There are numerous plant economic and hydraulic traits which can affect physiological function and plant performance in the wild, and our study was focused on the association of the variation in only six commonly measured leaf traits with an early-age growth performance and a fitness component assessed in our common-garden experiment. Nevertheless, for the identified performance-based directional selection favouring thinner and denser leaves, insights into their persistence and evolutionary significance can be gauged from the associations between economic traits and home-site climate variables, as observed at the population level by E. pauciflora in the wild. Nevertheless, the impact of the leaf hydraulic traits on growth performance and even tree survival may increase as competition for water increases as the studied stand ages, and climate change predictions are realised in the studied stand and the wild populations in Tasmania , indicating that directional selection acting on the traits was mediated by their effects on growth performance. We provided evidence that, at an early stage of the life cycle, within-population phenotypic variation in economic traits rather than hydraulic traits directly affected growth performance, which in turn had subsequently a positive direct influence on later-age survival. Performance-based estimates of directional selection indicated that thinner and denser leaves were favoured in the hot, dry environment studied. Any association of hydraulic traits with growth performance was indirect, resulting from phenotypic correlations among the measured traits. The modelling of the fitness-performance relationship suggested that, when compared with diameter, using height as a fitness proxy may provide a relatively lower error in estimators of directional selection from performance-based analysis of selection in our studied system. While the observed selective advantage of denser leaves may be transient or site specific, the alignment of the directional selection favouring thinner leaves with predictions based on trait-climate associations for maximum temperature suggests that variation in leaf thickness may directly contribute to local climate adaptation in the species.In the studied The datasets presented in this article are not readily available because they are currently being used in another study to be subsequently submitted for publication. Requests to access the datasets should be directed to the corresponding author for consideration.JCS conceived and conceptualised the research work undertaken in this study. BMP, SMP and GW were responsible for planning and coordinating the common-garden field trial, sampling design and trait measurements. JCS performed the data analyses, and prepared the associated document provided in Supplementary Material. JCS and BMP wrote the manuscript. All authors contributed to the manuscript revision, read, and approved the submitted version.Fundação para a Ciência e a Tecnologia I.P. (FCT), Portugal, through the Norma Transitória DL 57/2016/CP1382/CT0008 and UID/AGR/00239/2019. The FCT also supported this work through the funding provided to Centro de Estudos Florestais, Portugal (Unit Project Reference: UIDB/00239/2020). The trait data collection was co-funded by the Australian Government Department of Climate Change, Energy, the Environment and Water, and the Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia, as part of its partnership on the Biodiversity Knowledge Projects series (https://research.csiro.au/biodiversity-knowledge/), as well as the Australian Research Council Linkage (Grant LP120200380) and Industrial Transformation Training Centre (Grant IC150100004) Programs. BMP acknowledges support of the Australian Research Council Discovery grant DP190102053.The contribution of JCS to this research work was supported by We thank Tanya Bailey, Peter Harrison and Paul Tilyard (University of Tasmania) for their involvement in trial establishment and sample collection. We are grateful for support from Greening Australia in establishing the common garden trial, and Roderick and Kate O’Connor for supporting the trial on their property. We thank the reviewers for their time and valued comments on the manuscript.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.


With the broad application of genomic information, SNP-based measures of estimating inbreeding have been widely used in animal breeding, especially based on runs of homozygosity. Inbreeding depression is better estimated by SNP-based inbreeding coefficients than pedigree-based inbreeding in general. However, there are few comprehensive comparisons of multiple methods in pigs so far, to some extent limiting their application. In this study, to explore an appropriate strategy for estimating inbreeding depression on both growth traits and reproductive traits in a Large White pig population, we compared multiple methods for the inbreeding coefficient estimation based on both pedigree and genomic information. This pig population for analyzing the influence of inbreeding was from a pig breeding farm in the Inner Mongolia of China. There were 26,204 pigs with records of age at 100 kg (AGE) and back-fat thickness at 100 kg (BF), and 6,656 sows with reproductive records of the total number of piglets at birth (TNB), and the number of alive piglets at birth (NBA), and litter weight at birth. Inbreeding depression affected growth and reproductive traits. The results indicated that pedigree-based and SNP-based inbreeding coefficients had significant effects on AGE, TNB, and NBA, except for BF. However, only SNP-based inbreeding coefficients revealed a strong association with inbreeding depression on litter weight at birth. Runs of homozygosity-based methods showed a slight advantage over other methods in the correlation analysis of inbreeding coefficients and estimation of inbreeding depression. Furthermore, our results demonstrated that the model-based approach (RZooRoH) could avoid miscalculations of inbreeding and inbreeding depression caused by inappropriate parameters, which had a good performance on both AGE and reproductive traits. These findings might improve the extensive application of runs of homozygosity analysis in pig breeding and breed conservation. Inbreeding refers to the mating of individuals related to each other by ancestry, which increases the frequency of homozygotes in small populations . InbreedPED). However, pedigree-based methods only give expected probabilities of IBD rather than their real value (PH), derived from the genomic relationship matrix . Compared with FPED, the above SNP-based measures could avoid the effect of genealogical error and detect ID more effectively in simulation studies (ROH) was reported that it was more accurate for assessing individual inbreeding levels than other inbreeding coefficient estimators in recent studies (The degree of inbreeding is measured by the coefficient of inbreeding (F), which is defined as the probability of the 2 alleles at any locus are identical by descent IBD; . It is tal value  and are  studies . BesidesID was usually associated with the reduced expression of dominance effects, which was also used in genomic models to estimate dominance and epistatic genetic variance , and gestation length , of which 5,519 sows have both growth and reproductive phenotypes. All animals were born between 2013 and 2020.The evaluated growth traits were age at 100 kg (AGE) and back-fat thickness at 100 kg (BF), and they were adjusted according to the National Swine Performance Recording Standards of China (Supplementary File 1). Reproductive traits included TNB, NBA, and litter weight at birth.Genotyping was conducted using an SNP chip named CAU50K, including 43,832 SNPs (The map version used for SNP positions is Sscrofa10.2). Quality control of the genotype data was implemented by PLINK  was estimated from 27,341 animals with at least 1 trait. Due to missing pedigree information, FPED was only estimated for the 1,599 animals with both genotype and pedigree information.The pedigree-based inbreeding coefficients with an average depth of 8.23 generations were estimated by the R package “Pedigree” . FPED(toPH), the genomic relationship matrix (FGRM), and the observed vs expected number of homozygous genotypes (FHOM), and runs of homozygosity (FROH). Their detailed computation strategies were as follows:Genomic inbreeding coefficients were estimated by 4 different estimators, including the percentage of homozygous SNPs  were calculated by GCTA soft  was also estimated after SNP pruning  based on linkage disequilibrium (LD), with 11,270 SNPs remaining.FROH was based on ROH, which was detected based on the different criteria using PLINK v1.9 and on a hidden Markov model framework using RZooRoH (ROH(N). A minimum density of 1 SNP per 80 kb was set to prevent low SNP density from affecting ROH length. The formula (FROH(model) was calculated as the proportion of the genome in HBD classes.The model-based ROH detection method used RZooRoH. The software implements an efficient and accurate approach based on the hidden Markov model to identify homozygous-by-descent (HBD) segments associated with ROHs. We set a 2-state model (1R model), which estimated the probability of 2 consecutive markers to be either HBD or non-HBD. The genotyping error rate was set to 0.025%, as suggested by The inbreeding coefficients estimated by these methods were compared using Pearson’s correlation with the data set of 1,599 pigs with both genotype and pedigree information.The linear mixed model estimated ID on growth and reproductive traits. ID was estimated separately for AGE and BF using the following bivariate animal model: is a vector of observations,  is the relationship matrix.  is the incidence matrix relating records to additive random animal effects;In practice, the reproductive traits usually use the repeatability model to estimate breeding value, so we took the different strategies from growth traits to estimate ID on TNB, NBA, and litter weight at birth, as follows:t-test was used to obtain P-values.The statistical analysis was performed with the DMU program package , and a tWe used 3 kinds of parameter settings in PLINK and a model-based method in RZooRoH to detect ROHs in a Large White pig population with 1,600 pigs genotyped. The numbers of ROHs were 47,133, 39,529, and 31,721, with an average length of 10,827.20, 12,049.43, and 9,228.63 kb for the 3 rule-based methods for ROH_1, ROH_2, and ROH_3, respectively. RZooRoH detected 208,211 ROH segments with an average of 4,194.77 kb, which was over 4 times the number detected by PLINK. Using PLINK, the length of most ROHs was between 5 and 10 Mb, whereas 78% of homozygous segments were short (less than 5 Mb) using RZooRoH . The distribution of ROH incidence per SNP for ROH_1 was similar to ROH_2 . HoweverPED was estimated from all animals with phenotype and pedigree information, and FPED was estimated from the animals with genotype and pedigree information. The average FPED was equal to the average FPED at 0.01 (see Supplementary Table 1), and the average genomic inbreeding coefficients using different methods varied widely. The average FPH was 0.64 ± 0.02 ranging from 0.56 to 0.73. While the range of FHOM(pruned) was smaller after pruning based on LD  was 0.24 ± 0.03 ranging from 0.16 to 0.46, which was higher than other estimates based on ROHs.Fed on LD , both thPED were FHOM(pruned), FROH1, and FROH2 (r = 0.25), followed by FROH(model) (r = 0.23). The lowest correlation with FPED was FGRM1 calculated from the loose criterion for MAF (0.01). Except for FGRM, there were strong correlations between other genomic F. Although the correlation coefficient between FHOM and FHOM(pruned) was relatively lower (just 0.79), the correlations using the same approach and different parameters were strong. Compared with FHOM(pruned), FHOM had a lower association with FPED and a higher association with genomic inbreeding estimates. In contrast, the correlation coefficient between FGRM from pruning data for MAF > 0.01 and MAF > 0.05 was 0.98. Compared to FGRM1 (MAF > 0.01), FGRM2 (MAF > 0.05) had relatively strong correlations with other genomic inbreeding coefficients. For ROH-based methods, the correlation coefficients among FROH1, FROH1(N), and FROH2 were around 1. Also, FROH(model) using RZooRoH had the highest correlation with other genomic F, followed by FROH1/FROH2 and FROH3.Every pair of inbreeding coefficients was considered statistically significant at the 0.01 level. Correlations between the pedigree-based and genomic inbreeding coefficients were weak, ranging from 0.11 to 0.25 . The higPED was used as a covariate in the model, no statistically significant results were obtained. However, FPED had a statistically significant effect on AGE, with each 10% increase of FPED extending 2.6 ± 0.5 days. Furthermore, an increase of 10% in FPH, FHOM, and FGRM1 was associated significantly with the extension of AGE by 3.0 ± 1.6, 1.1 ± 0.6, and 0.8 ± 0.3 days, respectively. For FHOM, LD-based SNP pruning declined the P-value and increased the estimate of ID on AGE, and there was a similar trend using the strict criteria . An increase of 10% in FROH(model) caused the extension of AGE by 1.8 ± 0.8 days, while not all inbreeding coefficients using ROH-based measures had a significant ID effect, such as FROH1. From the error bar plot . The estimates of ID on AGE with FPH, FROH(model), and FHOM(pruned) were close to the result for FPED. Nevertheless, compared to the other 2 methods, the disadvantage with FPH was a relatively large standard error of estimates. All pedigree-based and genomic inbreeding coefficients were hardly associated significantly with back-fat thickness. As a result, the error bar plot of estimates for BF was not shown in All the results of descriptive statistical analysis of traits and estimates of the ID are shown in the Supplementary materials (Supplementary Tables 2 and 3). When Fbar plot , it was PED significantly affected TNB and NBA, which is different from FPED. For litter weight at birth, there was no ID effect for both FPED and FPED. An increase of 10% in FPED contributed to a decrease of 0.5 ± 0.2 piglets both for TNB and NBA. Most genomic inbreeding coefficients significantly affected 3 reproductive traits. Specifically, the corresponding estimates of ID on TNB, NBA, and litter weight at birth were 1.4 ± 0.4 piglets, 1.3 ± 0.4 piglets, and 1.7 ± 0.5 kg, with a 10% increase in FPH. Moreover, the maximum estimate with FPH was much lower than the minimum estimate with FPED  had significant effects on reproductive traits, and corresponding estimates were about 0.6 piglets (TNB), 0.6 piglets (NBA), and 0.7 kg (litter weight at birth), respectively.FD . The estr = 0.99). However, the long homozygous regions in the genome might be ignored with strict criteria of minimal density (ROH_3). Combined with the distribution of ROHs on chromosomes , it was evident that 2 ROH hotspots for ROH_2 were absent for ROH_3, located on SSC1 (the position from 48.5 to 54.8 Mb) and SSC4 (the position from 48.2 to 53.4 Mb), respectively. The same gene region on SSC4 was also detected in pig populations of Poland  had little effect on the detection of genome regions with ROH hotspots overlapping and estimates of inbreeding ) and FROH. When SNP pruning had a significant impact on estimating inbreeding, the disadvantage of FHOM(pruned) was a lack of guidelines on the applicability of SNP-pruning parameters. However, according to the practical guide to ROH analysis offered by ROH. Moreover, the correlation coefficients of most ROH-based methods with other genomic methods were high, especially FROH(model). These results suggested that FROH had a distinct advantage in estimating the level of inbreeding under appropriate parameter settings or using the model.The average pedigree-based inbreeding coefficient with an average depth of 8.2 generations was small, and there were weak correlations between FROH was obtained by 2 computing methods, based on the number of SNPs in ROH and the length of ROHs, both used in previous literature (ROH1 and FROH(N) (r = 1) indicated that the 2 methods tended to be the same. Although the minimal density was recommended to 70 kb/SNP (ROH_3) by ROH1 (r = 0.67), FROH3 correlated poorly with F using other measures. It suggested that genome coverage was essential to estimating the level of inbreeding. The inbreeding coefficient of the model-based approach (RZooRoH) correlated with the estimates of the rule-based approach (r = 0.65–0.88), but its correlations with FPH and FHOM were higher. This result was probably because the rule-based approach ignored some short ROHs less than 5 Mb in length, which could be misidentified as identity by state.The Fterature . The higPED led to adverse effects of 2.6 ± 0.5 days, 0.5 ± 0.2 piglets, and 0.5 ± 0.2 piglets. These estimates for TNB and NBA were higher than that in Iberian pigs  had no effect on BF and litter weight at birth. Although a small dataset was not conducive to estimating ID, genomic measures had a good performance without an amount of information.We could observe the statistically significant ID associated with pedigree-based inbreeding coefficients from 27,341 animals, but not when intersection with phenotypes and genotypes was calculated. For AGE, TNB, and NBA, a 10% increase in FPED, using FPH generated a relatively large standard error and might overestimate ID on both growth and reproduction traits. For AGE, the estimates of ID, with FGRM for strict MAF criterion (MAF < 0.05) and FHOM after SNP pruning based on LD, had more power. Because the loose criterion of MAF and no SNP pruning contributed to overestimating the inbreeding of certain animals, ID was finally underestimated. If F derived from the genomic relationship matrix is used to estimate ID on growth traits, the MAF should be set to 0.05 instead of 0.01. In addition, the estimate of ID on AGE with FROH(model) was higher than with FGRM2 and near to FHOM(pruned). However, FROH1 had no significant effect on AGE. While there is no obvious evidence that model-based FROH is the best method of ID on AGE among SNP-based methods, the difference between FROH(model) and FPED is comparatively small.Compared with FHOM(pruned) and FPH. The results of ID using FGRM and all ROH-based F estimating for TNB and NBA were similar to studies in Iberian pigs  may underestimate it. Only SNP-based F revealed a strong association with ID on litter weight at birth, ranging from 0.5 to 0.8 kg per 10% increase. In contrast with AGE, ID on reproductive traits with SNP-based F had a lower P-value than pedigree-based F. There were many reasons for these differences, such as errors in pedigree records, expected or actual IBD, and the number of samples. Anyhow, both our study and studies in dairy cattle  did not need to worry about the impact of parameters and performed well on both AGE and reproductive traits in the estimate of inbreeding and ID. These findings might improve the extensive application of ROH analysis in pig breeding and breed conservation.In summary, we found pedigree-based and SNP-based measures had significant effects on AGE, TNB, and NBA, except for BF. Especially in terms of litter weight at birth, only SNP-based F revealed a strong association with ID. The estimate of ID in Fhttps://doi.org/10.25387/g3.17207063).Supplementary File 1 contains a list of all supplemental files. Supplementary File 2 contains supplemental files, figures, and tables. Supplementary Files 3 and 4 contain phenotype information of growth traits and reproductive traits for Large White pigs, respectively. In addition, Supplementary File 5 contains the pedigree of Large White pigs in this study. Supplementary Files 6 and 7 contain genotype and maker location information in PLINK .ped/.map formation for Large White pigs. Supplementary material is available at figshare  under grants Nos. 31661143013, 31790414 and 31972563, and China Agriculture Research System (No. CARS-35-01A).No potential conflict of interest was reported by the authors.


GEBV-S) or not (VCGEBV). The third method uses restricted maximum likelihood (REML) estimates directly from univariate and bivariate analyses (VCREML) by ignoring that the variance components should refer to the line of interest, rather than to the line in which the trait is expressed. We validated these methods by comparing the resulting predicted bounds of The genetic correlation between purebred (PB) and crossbred (CB) performances  than with VCGEBV (0.44–0.57). With VCGEBV-S, the estimated With both VCWe conclude that using REML estimates of variance components within and between parental lines to predict the bounds of The online version contains supplementary material available at 10.1186/s12711-022-00773-z. The genetic correlation between purebred (PB) and crossbred (CB) performances , for the trait expressed in line 2 . Suppose the causal loci and their true average effects are known, the vector of additive genetic values of individuals in line 1 for the trait expressed in line 2  were mated to dams from a Large White line (LW) to produce F1 sows, which were crossed with sires from a synthetic boar line (S) to produce the three-way CB (i.e. Sx(LRxLW)). In total, we used 17,100 animals from line S, 6611 animals from line LR, 8587 animals from line LW, and 4173 three-way CB. The synthetic boar line S is a combination of the LW and Piétrain breeds and was created around 1975 [2 > 600), SNPs that were located on sex chromosomes, and unmapped SNPs. The positions of the SNPs were based on the Sscrofa11.1 assembly. Finally, SNPs with a minor allele frequency (MAF) lower than 0.01 in any of the lines were excluded, yielding 35,595 SNPs that were used in all the analyses. All genotyped animals had a frequency of missing genotypes lower than 0.05 and were therefore retained for further analyses.About 80% of the animals were genotyped with the Illumina 50K single nucleotide polymorphism (SNP) chip and the remaining 20% were genotyped with a custom Illumina 25K SNP chip. All genotypes were imputed to 50K within each purebred line using the FImpute v2.2 software . First, focal line) in terms of average effects for PB performance in the parental lines, because the difference between these average effects determines the value of In a previous study, Duenk et al.  derived entclass1pt{minimafocal line to refer to the line for which trait line to refer to the line where the trait is expressed (LW). The predicted upper bound of The present study builds on the work of Duenk et al.  and aimsmentclass2pt{minimEquations  to 6) s s6) showGEBV) relies on the assumption that the GEBV are estimates of true additive genetic values. Thus, the GEBV may be used as proxies for the additive genetic values in the focal line, and the required variance components can be approximated by computing the covariances between these GEBV and their variances. A disadvantage of VCGEBV is that the variance of GEBV is expected to be smaller than the variance of the additive genetic values (GEBV-S), where the GEBV were corrected for shrinkage by dividing them by the square root of their reliabilities.The first method (VCREML). It uses estimates of genetic variances resulting from a univariate analysis of the trait line, and estimates of genetic covariances resulting from pairwise bivariate analyses between parental lines. On the one hand, this method ignores that the required across-line variance components should correspond to individuals in the focal line, and instead uses the components estimated in the trait line. The across-line variance in the focal line can differ from the variance in the trait line due to differences in genotype frequencies between lines. On the other hand, REML estimates are generally accurate, and commonly reported. Furthermore, this method may result in accurate approximations when the genotype frequencies in the focal line are similar to those in the trait line.The third method for approximating variance components was based on REML estimates  w w6) wereGEBV, the required variance components are approximated by using the GEBV as proxies for true additive genetic values. We first analysed phenotypes in each PB line separately using a univariate model, while including genotypes of all PB lines to obtain GEBV for all PB animals. For example, for the phenotypes in line S, the model was:With VCmentclasspt{minimas et al. , 14. TheGEBV is that the resulting variance components relate correctly to the individuals in the focal line.The variances that appear in Eqs.  to 6) w w6) were in Eqs.  to (6) wGEBV-S), we used the same approach as VCGEBV, but we divided the GEBV by the square-root of their individual reliabilities to correct for shrinkage. The individual reliabilities were computed by the MTG2 program [Variances and covariances of GEBV can be poor approximations of genetic variance components, because GEBV are subject to shrinkage. For example, the variance of GEBV is a factor the GEBV . Therefo program , using t program . This pr program  and Blan program , except  program  and Blan program  scaling REML), we used genomic REML estimates of variance components within and between lines to approximate the required parameters. Approximated variances and standard deviations resulted from univariate genomic analyses in the trait lines. For example, With the third method , followed by VCGEBV-S (0.21–0.40) and VCREML (0.15–0.22) , followed by the LR (0.19) and LW (0.15) lines.On average across traits, VCEBV-S 0.2–0.40 andThe estimated heritabilities for PB performance across traits ranged from 0.23 (TGR for line S) to 0.54 (BFE for line LW) (Table The estimated 72 Table . On averThe estimated genetic correlation between lines (W) Table . On averThe objective of this study was to empirically validate expressions that predict the bounds of GEBV). Compared to the other methods we proposed, the advantage of VCGEBV is that the resulting variance components correspond to the individuals in the focal line, and not to the mated lines. Our results showed that VCGEBV resulted in a high fraction of cases where the predicted bounds captured the estimated The first method to predict the bounds of GEBV is that the variance of GEBV is expected to be smaller than the variance of additive genetic values due to shrinkage, resulting in biased estimates of variance components. Thus, we included a second method (VCGEBV-S), where GEBV were corrected for shrinkage by dividing them by the square root of their reliabilities. Although VCGEBV-S generally resulted in a smaller range than VCGEBV, it also resulted in a high fraction of cases where the estimated A disadvantage of VCREML). Our results show that, across traits and lines, VCREML resulted in more accurate prediction of the bounds of GEBV and VCGEBV-S because it resulted in the highest fraction of cases (14 out of 15) where the predicted bounds captured the estimated REML do not correspond to the individuals of the focal line, but to those of the trait line . We therefore investigated whether the within-line variance of the trait line was comparable to the across-line variance of the focal line. The within-line variance in the trait line is proportional to heterozygosity  . However similar , 29. As Across PB lines and traits, the heritability estimates ranged from 0.23 to 0.54, and were generally in line with estimates from recent studies , 30–32. The estimated genetic correlations between lines (Although the prediction of The present study shows that the bounds of neficial . In the neficial , the preIn conclusion, using REML estimates of variance components within and between parental lines to predict the bounds of Additional file 1: Table S1. Estimated additive genetic variances. Description: Sum of variance of marker genotypes (Table S2. Estimated residual variances. Description: Estimated residual variances (


Ten genetic variants were identified to be associated with growth and fatness traits in two Large White pig lines from different genetic backgrounds by performing both within-population GWAS and cross-population GWAS analyses. These ten significant loci represented eight candidate genes, i.e., NRG4, BATF3, IRS2, ANO1, ANO9, RNF152, KCNQ5, and EYA2. One of them, ANO1 gene was simultaneously identified for both two lines in BF100 trait. Compared to single-population GWAS, cross-population GWAS was less effective for identifying SNPs with population-specific effect, but more powerful for detecting SNPs with population-shared effects. We further detected genomic regions specifically selected in each of two populations, but did not observe a significant enrichment for the heritability of growth and backfat traits in such regions. In summary, the candidate genes will provide an insight into the understanding of the genetic architecture of growth-related traits and backfat thickness, and may have a potential use in the genomic breeding programs in pigs.Growth and fat deposition are complex traits, which can affect economical income in the pig industry. Due to the intensive artificial selection, a significant genetic improvement has been observed for growth and fat deposition in pigs. Here, we first investigated genomic-wide association studies (GWAS) and population genomics  to explore the genetic basis of such complex traits in two Large White pig lines ( In the past decades, Large White pigs had experienced intensive artificial selection for the fast growth rate and a high lean percentage . Their eGenome-wide association study (GWAS) is a powerful tool for revealing the genetic basis of quantitative traits across the whole genome, which usually use high-density SNP genotypes in livestock and poultry population. Numerous GWASs in different pig populations have successfully identified many candidate genes associated with important production traits, such as growth, carcass  and reprIn this study, the objectives were 1) to conduct GWAS for AGE100 and BF100 within two Large White pig lines with distinct genetic backgrounds; 2) to detect shared loci in cross-population GWAS; 3) to integrate GWAS with selection signatures to explore whether the associated loci are under selection. The findings here will help unravel the genetic background of these two complex traits in pigs.th to 12th ribs were measured. The initial and ending dates were recorded. To uniform the data, the measured age was adjusted to 100 kg live weight using the equation  . Pigs wir2) was calculated using PLINK (v1.90) (Ne) was computed by SNeP software (v1.1)  was conducted using the remaining SNPs to obtain eigenvalues and eigenvectors by PLINK (v1.90) . In addi (v1.90)  within ee (v1.1) .y is the vector of target phenotypes of individuals; μ is overall mean; b is the vector of fixed effects: sex (two levels) and year-season in which seasons were comprised of four levels ; g is the vector of the SNP effects, X is the matrix of incidence associating each observations to the pertinent level of fixed effects, W is the incidence matrix relating observations to SNPs effects with elements coded as 0, one and two for genotype A1A1, A1A2, and A2A2, respectively, u is the random additive genetic effect of the individual and is assumed to be distributed as Z is incidence matrix for u, e is the random residual and is assumed to be distributed as I is the identity matrix and mlma option of the software GCTA (Version 1.93.3beta)  .p-values, direction of SNP effects and the number of individuals. In each case, threshold p-values were set to -log10 (1/SNPs) and -log10 (0.05/SNPs) for suggestive and Bonferroni-adjusted genome-wide significance, respectively. Quantile-quantile (QQ) plot of–log  was examined to determine how well GCTA accounted for population structure and family relatedness.Furthermore, we integrated two Large White pig lines to identify the candidate genes using combined-population GWAS and cross-population GWAS. In the combined-population GWAS, line effect was taken into account the fixed effect, and the analysis procedure was followed the single-population GWAS mentioned above. The cross-population GWAS also utilized the summary data of single-population GWAS to implement meta-analysis by METAL (version 2011–03–25) . In metaST, hapFLK and runs of homozygosity (ROH). The VCFtools software  was implemented. The candidate genes resided within the genomic regions of up- and downstream 500 kb around the significant SNPs were taken into account in our study. Functional annotation of the genes located in the regions of interest was performed with R package WebGestaltR  at various physical distances between two loci on all the autosomes. The average r2 at pair-wise SNP distance of less than 5 Mb on autosomes ranged from 0.432 to 0.582 in Canadian Large White population. The LD decay pattern in French population was similar to the Canadian line with average r2 ranging from 0.431 to 0.588. The average r2 decreased much more slowly with the increase of pair-wise SNP physical distance and remained constant beyond 1 Mb in two lines  was identified by cross-population GWAS to be significantly associated with AGE100 at the suggestive significant level, which was also detected in the combined-population  were significantly associated with BF100 at genome-wide significant level. These two SNPs were also detected by the cross-population GWAS analysis, which resided in the genic regions of and ANO1 ; Table 3and ANO1 .Three SNPs  were identified to be significantly associated with BF100 using the combined-population GWAS at the suggestive significant level ; Table 3Ne in the recent past, while the LD at short distances reflected the Ne in the distant past  that resided in the genic region of the  balance . In addi balance . In thisecretion , which cf β cell . CRISPR- animals . EYA2 hantiation . In addid to IMF , and EYAmp sheep .ANO1 gene was simultaneously identified for both two lines in BF100 trait. By integrating selection signatures with growth rate and backfat thickness relevant trait association studies, however, we did not observer that heritability of growth and fatness were significantly enriched in genomic regions under selection. Future study is needed to refine the genomic regions and identify candidate genes and candidate mutations affecting growth and fatness in pigs.In this study, we performed single-, combined- and cross-population GWAS analysis for growth and fatness traits in purebred Large White pigs that were from two separated foundation lines (Canadian and French lines). We demonstrated that the cross-population GWAS could be used to increase the power of GWAS. One candidate gene,


Pissodes strobi (Peck) is a major pest of spruces and substantially reduces lumber quality. We revisited a large Interior spruce provenance/progeny trial  of varying susceptibility, established in British Columbia. We employed multivariate mixed linear models to estimate covariances between, and genetic control of, juvenile height growth and resistance traits. We performed linear regressions and ordinal logistic regressions to test for impact of parental origin on growth and susceptibility to the pest, respectively. A significant environmental component affected the correlations between resistance and height, with outcomes dependent on families. Parents sourced from above 950 m a.s.l. elevation negatively influenced host resistance to attacks, probably due to higher P. engelmannii proportion. For the genetic contribution of parents sourced from above 1,200 m a.s.l., however, we found less attack severity, probably due to a marked mismatch in phenologies. This clearly highlights that interspecific hybrid status might be a good predictor for weevil attacks and delineates the boundaries of successful spruce population movement. Families resulting from crossing susceptible parents generally showed fast-growing trees were the most affected by weevil attacks. Such results indicate that interspecific ‘hybrids’ with a higher P. glauca ancestry might be genetically better equipped with an optimized resource allocation between defence and growth and might provide the solution for concurrent improvement in resistance against weevil attacks, whilst maintaining tree productivity.Tree planting programs now need to consider climate change increasingly, therefore, the resistance to pests plays an essential role in enabling tree adaptation to new ranges through tree population movement. The weevil  To keep pace with the current speed of climate change in terms of sustainable forest productivity, effective management of forest genetic resources must be developed. The assisted gene flow approach implementing transfer functions  shifts gTo achieve tolerance to various stressors, Janes and Hamilton  emphasizPicea glauca (Moench) Voss) and Engelmann spruce (P. engelmannii Parry ex Engelm.) as shown by the high levels of recombination events uncovered in hybrid populations [P. glauca) and above 1,800 m a.s.l. (P. engelmanii) according to recent hybrid index assessment using genetic markers [Spruces are the most important reforestation species in Canada, with hundreds of millions of seedlings planted each year. Interior spruce, a natural hybrid, has a long history of introgression between white spruce ; Coleoptera: Curculionidae) is one of the most important biotic threats for Interior spruce [Picea sitchensis (Bong.) Carr) species [P. abies (L.) H. Karst), which dominates the boreal and subalpine coniferous forests in Europe and has been an important plantation species in eastern North America, is also susceptible to the weevil [Interior spruce is also one of the economically most important species in western Canada and, due to its hybrid nature, can provide genetic resources needed to cope with climate change . The whir spruce  and the  species  reforeste weevil . The weee weevil . The whie weevil . Thus, te weevil . Adult wP. glauca ancestral proportions could mean increased resistance to the weevil in such interspecific hybrids. In general, host tree defences are most effective against local pests, while they weaken under relaxed or absent pest pressure. As the climate warms, these local pest populations may expand their ranges to higher latitudes or elevations, where they may encounter naïve hosts [While conifers have more or less effective defence mechanisms in place , 19, theve hosts , that isPicea glauca (Moench) Voss × P. engelmannii Parry ex Engelm., exist. It was started in 1973 with a seed collection from 173 ‘plus tree’ Interior spruce individuals growing in natural stands across north central British Columbia (Prince George Selection Unit), where subsequently four progeny test sites involving these open-pollinated (OP) families were established, with subsequent evaluation of the genetic control of juvenile height growth [The selection of initial plus trees entering the breeding program, and the subsequent selection of parental individuals for advanced generation breeding, has favoured trees with superior growth characteristics. Therefore, screening for resistance of seed sources usually occurs posterior to the detection of a pest or pathogen problem within the breeding program. Therefore, a way to perform mass screening for suitable genotypes is to establish new progeny trials from known seed sources and use artificial infestations to assess genetic control of resistance . These tt growth , 29. Furt growth , 30. Thet growth ; certaint growth , 31, 32.t growth , 31. Thet growth , 30.In the present study we revisit the entire Interior spruce weevil resistance screening trial encompassing 2,964 individual genotypes (42 families) of varying susceptibility, established in the interior of British Columbia . The samHere, we want to clarify the relationships between resistance to the weevil and, for the first time, pre-attack growth within and across all cross types. We also make direct use of information of parental origin of the selected crosses and investigate whether hybrid status in the studied Interior spruce infestation experiment could be a good predictor for weevil attacks. Our results may have important implications for the future management of breeding programs in Interior spruce, especially when the general environmental gradients under climate change will be considered in addition to the local environmental conditions considered here.Picea glauca (Moench) Voss × Picea engelmannii Parry ex Engelm.) parents, previously selected from wild stands and then screened for resistance to the white pine weevil Pissodes strobi (Peck)   attacks i (Peck) . In totai (Peck) ; the geni (Peck) . This api (Peck) . The claY is a matrix of phenotypes , β is a vector of fixed effects including intercept, disease status in previous year (A99) and replication effect, u is the vector of random effects including effects of cross, genotype, and plot. Each term in the random effects was modelled by Kronecker product of unstructured variance-covariance matrix and identity matrix. In the case of genotype, the identity matrix was replaced by average numerator relationship matrix [X and Z are incidence matrices assigning effects in vectors β, u to phenotypes of matrix Y. This model was performed for each cross-type separately. The reason for separate analyses by cross-type was based on the fact that separate breeding populations will be needed to also preserve cold-tolerance for instance. Additionally, the joint analysis combining data from all cross-types was performed with the cross-type effect in fixed terms. The model convergence was investigated by the Gelman-Rubin method [ith trait and ith trait. Similarly, the posterior estimates of genetic and environmental correlations between traits were estimated as follows:The multivariate mixed linear model using MCMC algorithm implemented in the JWAS package  was usedp matrix . Similarn method  through αj is the intercept or threshold value for jth level of an ordered category and βi is the regression coefficient for ith explanatory variable. Elevation was directly used to order the origin of the parents, while the cross-type was recoded into 0 for SxS, 1 for RxS and 2 for RxR. The proportion of total variance explained by the model was estimated in R package ‘pscl’ as pseudo R2 by comparing the log-likelihood for the fitted model against the log-likelihood of a null model with no predictors implemented. Similarly, the linear regression was performed for continuous variables  using the ‘lm’ function implemented in the R package ‘base’ [R2.Since Interior spruce is the natural hybrid of white and Engelmann spruce with an elevational hybridization gradient, we tested the statistical significance of the origin of mother/father trees (in terms of elevation) and cross-type on the investigated traits through ordinal logistic regression for class variables  using ‘polr’ function implemented in R package ‘MASS’ as follows:e ‘base’ . The proThe spatial distribution of phenotypes at the study site is displayed in Additionally, cross-type explained 15% and 12% of the total variance in A00 and E00, respectively . As expeResults from linear regression confirmed the effect of cross-type for height at both years as statistically significant. Yet, the proportion of total variance explained by cross-type reached only from 4% (HT5) to 5% (HT4), which was therefore much lower compared to the total variance explained for the herbivory traits. The increase in mean height was around 5 cm for HT4 and 6 cm for HT5, respectively, with an increasing level of cross-type resistance from SxS to RxR .Since Interior spruce genetics reflects the natural hybridization between Engelmann and white spruces along an elevation gradient, we investigated whether the distribution of attack severity in the population can be attributed to parental origins. We found that mother trees originating from elevations between 600 m and 950 m had the highest proportion of progeny with no attacks, while mothers originating from elevations between 950 m and 1,200 m had the highest proportion of progeny with severe attacks . When weFor the distribution of egg puncture occurrences in the population according to parental origin . Again, We assessed heritability, genetic, residual, and phenotypic correlations across and within three distinct cross types . Results are summarized in Positive genetic correlations were observed between herbivory traits A00-E00 ranging from 0.19 (RxS class) to 0.84 (RxR class) and between growth traits HT4-HT5 ranging from 0.73 (RxS class) to 0.95 (SxS class). The genetic correlations between herbivory resistance traits and growth traits ranged from strongly negative in RxR class (reaching from -0.73 to -0.74) to moderately positive correlations in SxS class (reaching from 0.16 to 0.34). Additionally, the analysis across all cross-types and the RxS class showed a wide range of estimates from low negative to low positive (reaching from -0.08 to 0.11). A similar pattern was observed for the residual correlations with the largest estimates between disease resistance traits ranging from 0.85 to 0.94 and between growth traits ranging from 0.78 to 0.96. The residual correlations between disease resistance and growth traits ranged from strongly positive in the RxR class (reaching from 0.75 to 0.76) to low in the SxS class (ranging from 0.12 to 0.25) and moderate for the entire population analysis (reaching from 0.44 to 0.53) and in the RxS class (reaching from 0.25 to 0.39).The phenotypic correlations were all positive , again, vice versa. The relatively high narrow-sense heritability might result from high genetic diversity captured within the sampled parental trees originating from elevations 610 to 1220 m a.s.l. Since Interior spruce is a natural hybrid between Picea glauca and P. engelmannii which occupy a transitional elevation zone [P. engelmannii occupies subalpine environments requiring adaptation for short growing windows and extended winters with deep snow cover, P. glauca occupies a boreal environment that competes for light naturally, which selects for relatively fast-growing genotypes. Individuals within the hybrid zone were found to show advantageous features passed on from parental species, such as autumn cold tolerance (P. engelmannii) and fast growth (P. glauca) [P. glauca ancestral proportion were tallest  weevil obtained within RxR and SxS cross-type analysis and the relatively high narrow-sense heritability for tree height provides a potential for an effective response to selection for both traits. We stress here that the parental trees deployed in this study were selected solely on the basis of their resistance/susceptibility to white pine weevil attacks and regardless of their origin , 30, whiion zone , both sp glauca) . This tr tallest . Our hypd budset . Additiod budset  found a d budset  stated tf origin  for the  earlier , future Taking into account the origin of the maternal or paternal parent, the study of attack severity revealed a clear shift from resistant to susceptible genotypes when the parental origin surpassed 950 m a.s.l. . With reResistance status was tested in seven crosses, five RxR and two RxS families, which confirmed the resistance segregation pattern expected for a resistant genotype . Since PP. strobi performance, possibly due to the complete lack of coevolution of this highly sensitive exotic host with the local weevil [in situ.A Québec study performed on 45 Norway spruce genotypes originating from various European provenances, however, found no relationship between budburst phenology, traumatic response intensity and l weevil . Howeverl weevil . Thus, sl weevil , warmingl weevil . The impl weevil  will be Leptocybe gall wasp in Eucalyptus grandis [The genetic analysis performed across all cross types discovered weak genetic correlation between tree height and the severity of white pine weevil attack represents a great opportunity for simultaneous genetic improvement for both traits by finding individuals with above-average performance across traits. Such a weak relationship between disease resistance and productivity is rather uncommon. Recent studies showed that tree height is positively correlated with disease resistance such as white pine weevil in Norway spruce  or Lepto grandis . In a di grandis , and lat grandis , investiWhen each cross-type was analyzed separately, opposite patterns in genetic correlations between growth and resistance traits were found. While the RxR class showed strong negative correlations, the genetic correlations for the SxS class were moderately positive. Therefore, fast-growing trees in RxR class were least attacked while fast growing trees in SxS class were mostly attacked. However, the results of the SxS class should be treated with caution due to the small sample size. Additionally, the difference between these two cross types was in the severity of the attack. While the RxR class showed attack severity at level one which indicates that the tree was attacked but that the terminal growth was unaffected, the SxS class resulted in a more severe response (level two) with killed terminal shoots. Similarly to the all-cross-type analysis, the RxS class showed only weak genetic correlations between growth and resistance traits. Surprisingly, a different pattern was observed for the residual compared to the genetic correlations, and these residual correlations were strong and positive for RxR and moderate and positive for RxS and SxS. We argue that a common environment was created by the experimental design where each plot represented multiple individuals that belonged to the same family. This might have generated such rather strong residual correlations , explained by a strong covariance between the permanent environmental effects in the considered traits . For thiAs one of the defense mechanisms against bark invading insects, conifer trees are releasing oleoresin around the wound to flood deposited eggs, preventing them from further development into larvae and from causing irreversible terminal shoot damage. A significantly higher density of cortical resin canals was found in resistant compared to susceptible genotypes in Sitka spruce  and in wP. strobi attacks, whilst maintaining tree productivity. We hypothesize that the RxS hybrid crosses might have inherited high growth rates and dense resin canals from P. glauca but also some other features involved in resistance (or avoidance) mechanisms from P. engelmannii preventing severe attack of fast-growing genotypes. Such a feature might be a faster rate of bud flush. This is the first study that clearly highlights that hybrid status might be a good predictor for weevil attacks and that also delineates the boundaries of successful spruce population movement. These results that RxS hybrids might be genetically better equipped with an optimized resource allocation between defence and growth has important implications considering the future management of Interior spruce breeding programs and subsequent spruce plantation programs, which need to ensure that adaptation can be maintained across the environment of deployment. Further work is needed to employ genetic markers in efficient and early tree selection.Natural disturbances affecting forests are cumulative, highly interdependent, and involve biotic stressors, pests, pathogens individually or in association, and environmental factors, drought, fire alike. All of these depend on climate, conditions, which affect the phenology or life cycle of trees, pests, pathogens. Multi-trait selection is needed to create conifer forest resilience. Tree planting programs need to increasingly consider the consequences of a warming climate on organisms’ phenologies. The resistance to pests and pathogens plays an essential role in enabling tree adaptation to new ranges through tree population movement. This corroborates that resistance breeding programs are continuously important for managing insect pests and pathogen-related diseases. For example, stable positive genetic correlations between height growth and resistance to a local pest would be necessary for an effective multi-trait tree improvement program. Our study on the widely outplanted Interior spruce concluded that selection of fast-growing hybrid genotypes might be the solution for concurrent improvement in resistance against S1 Fig(JPEG)Click here for additional data file.S2 Fig(JPEG)Click here for additional data file.S3 Fig(JPEG)Click here for additional data file.S4 Fig(JPEG)Click here for additional data file.S5 Fig(JPEG)Click here for additional data file.S6 Fig(JPEG)Click here for additional data file.S1 Dataset(CSV)Click here for additional data file.