The sugarcane ratooning ability (RA) is the most important target trait for breeders seeking to enhance the profitability of sugarcane production by reducing the planting cost. Understanding the genetics governing the RA could help breeders by identifying molecular markers that could be used for genomics-assisted breeding (GAB). A replicated field trial was conducted for three crop cycles  using 432 sugarcane clones and used for conducting genome-wide association and genomic prediction of five sugar and yield component traits of the RA. The RA traits for economic index (EI), stalk population (SP), stalk weight (SW), tonns of cane per hectare (TCH), and tonns of sucrose per hectare (TSH) were estimated from the yield and sugar data. A total of six putative quantitative trait loci and eight nonredundant single-nucleotide polymorphism (SNP) markers were associated with all five tested RA traits and appear to be unique. Seven putative candidate genes were colocated with significant SNPs associated with the five RA traits. The genomic prediction accuracies for those tested traits were moderate and ranged from 0.21 to 0.36. However, the models fitting fixed effects for the most significant associated markers for each respective trait did not give any advantages over the standard models without fixed effects. As a result of this study, more robust markers could be used in the future for clone selection in sugarcane, potentially helping resolve the genetic control of the RA in sugarcane. Thus, GS has been evaluated in many crops  includinWe have previously reported two GS studies related to disease resistance, sugar, and yield-related traits using the same population and the same sets of markers as in the current study . The obj22.1Four hundred fourteen sugarcane clones from the second clonal stage of the USDA-ARS Sugarcane Breeding Program at Canal Point, Florida, program and 18 commercial cultivars from the USDA-ARS Sugarcane Breeding Program in Houma, Louisiana, were grown in a replicated field trial  and “CP 2.2-1), total fiber content (%), Pol (%), Brix (%), and moisture content (%). Sugar content (SC) was then calculated from the corrected Brix and Pol  in the form of tonns of cane per hectare (TCH) was calculated as the product of SW  and SP  and divided by 1,000. Theoretical recoverable sucrose (TRS) was calculated from the juice data and fiber concentration to estimate the sugar yield (-1) as suggested by -1) as tonns of sucrose per hectare (TSH) was estimated as:Cane yield . Mosaik  calling, and filtering protocols were described in detail in our previous reports . After e. Mosaik  was usede genome . Using Fe genome , the SNP2.4The best linear unbiased predictors (BLUPs) of the RA of the five tested traits for the sugarcane clones were analyzed using a single mixed linear model as described below:aikhjy was the vector of phenotypic value of genotype a from population i (a column describing which entry is the tested clone or one of two checks (CP00-1101 and CP96-1252) evaluated from the thh row of the thk column from the thj replicate in trial, γj was the effect of thj complete replicate, rjh was the effect of the thh row and the thj replicate, cjk was the effect of thk column within the thj replicate, iø is the fixed effect of the thi population  following entry mean basis as H2 = σ2g/(σ2g + σ2e/R), where σ2g and σ2e reflect variance associated with genotype and error, respectively, while R indicates the number of replications. The Pearson correlation coefficients among the five tested RA traits and resulting heat map were conducted in GraphPad Prism 9 software (www.graphpad.com).where ML-R 4.0  was used2.5An additive relationship matrix was constructed for the 432 clones in the genotypic dataset described above using the AGHmatrix package v2.0.4 in R . Genotyp2.6Multivariate mixed-linear models for the five traits of interest were fit using an Average Information Restricted Maximum Likelihood (REML) algorithm  implemeny was a vector of phenotypes from K cropping cycles that is partitioned into y’ = y’1, y’2, y’3]’ was a vector of fixed effects for each cropping cycle; kZ was an kxcn incidence matrix of unique clones c in cropping cycle k. and u’ =  was an matrix of random clone effects and each vector ku’ has length c; and e’ =  was a vector of residuals where each vector ke’ has length kn. Clone effects were assumed to follow a multivariate normal distribution with mean zero and an unstructured variance–covariance matrix equal to cI was a C x C identity matrix, U was a K x K matrix in which cropping cycle variances of clone effects kI was a K x K identity matrix and E was a K x K matrix with residual variance within a cropping cycle where GWAS was performed using the R packages GWASpoly v2.10  for each2.7https://sugarcane-genome.cirad.fr/ and a default e-value of 1e-10.Sorghum reference genome was used, since it is the closest diploid organism to sugarcane. Genes physically located nearby the significant associated markers with traits were investigated for their possible annotation and function in the literature. The tag sequences containing SNPs associated with traits of interest were also BLASTed against the sugarcane reference genome “R570” using the genomic resource provided at 2.8via 5-fold cross-validation repeated 25 times. The 5-fold validation was completed by randomly selecting four-fifths of the individuals for the training and the remaining fifth as the validation population. Iteration number was used to set the seed for randomization of genotypes into the test and training sets to ensure the same training set was used to develop all four models. Details of the four models tested, namely, the Ridge regression BLUP , Additi[RR-BLUP ], Reprod for five traits, EI, SP, SW, TCH, and TSH, was estimated. The distribution, mean values, ranges, and variance components of these values are included in  to high Table 1.Highly significant correlations were observed among the five tested RA traits in sugarcane Figure 23.2A principal component biplot was created using the SNP data to examine the population structure among the tested clones Figure 33.3The RA of the five traits  were subjected to GWAS analysis and results of marker–trait associations were included in Since many observed p-values deviated from the uniform distribution of expected and observed probability of obtaining an association with the respective traits, the quantile-quantile (Q-Q) plots demonstrated a potential for false-positive associations. Results revealed that most of the observed p-values follow a uniform distribution, but the few in linkage disequilibrium (LD) with a causal polymorphism have significant p-values in the tail Table 2.qRATCH-cS10) associated with the RA of TCH comprises three SNPs  covering 4.3 Mb on chromosome 10. The SNP (S10_53811870) with the strongest association with the RA of TCH  is located at position 53,811,870 bp on chromosome 10.A single putative QTL was considered when multiple loci associated with a trait were within a 5-Mb interval. The QTL nomenclature was according to 3.4Arabidopsis, rice, and “R570” (sugarcane monoploid genome) gene families and function. The LD contour plot near the most significant SNP (S03_8476715) associated with the RA of SW showed that there are several small LD blocks within a large one, indicated by red color in the plot and most of the SNPs have linked each other as indicated by the strong LD  with closely located genes (<20 kb from the SNP) were included in trong LD Figure 53.5The prediction accuracy for the five RA traits was evaluated using four GS methods with and without highly significant SNPs as fixed effects , and four metrics were used to evaluate the GS models . Results of genomic prediction and CI for the RA of EI, SP, SW, TCH, and TSH are presented in S03_8476715 located on sorghum chromosome 3 was used most frequent 60 times as fixed effect during predicting the RA of SW followed by S10_53811870 SNP located on chromosome 10 was used as fixed effect for predicting the GEBV of three  RA traits. Both the SNPs showed significant association during the GWAS analysis using GWASpoly in this study.The name and frequency of SNPs utilized as fixed effect in the four GS models for the five RA traits were included in 4The profitability of sugarcane production is highly dependent on the RA of sugarcane cultivation. A genomic analysis of the sugarcane RA for the five traits was conducted through a combined GWAS and GS approach. GWAS results discovered a total of 14 significant marker–trait associations. A total of seven putative candidate genes associated with the RA in sugarcane were identified. Thus, the findings of the genomic analysis conducted in this study provide new information and a potential avenue for improving the RA traits in sugarcane through molecular breeding.Worldwide, the sugarcane industry is facing increased planting costs and shortages of laborers. Thus, improvement of the RA is one of the most important target traits in breeding because replanting costs can be reduced by planting varieties that have a high RA, thereby increasing profitability. There are three main methods that have been reported to calculate the RA in sugarcane . AlthougThe molecular and genetic basis of the RA variation has received little attention in scientific literature. Thus, it has been unknown what molecular mechanism accounts for sugarcane’s ability to ratoon. To break through this impasse, we have conducted GWAS analysis for the five RA traits, detected putative candidate genes, and explored GS for the first time. In GWAS, mutations and potential functional markers statistically associated with complex quantitative traits are identified as underlying causal genes, mutations, and genetic variants . However2 around Rubisco and uses the NADP malic enzyme to increase the sugar yield (S03_8476715) with the RA of SW is physically colocated with the gene Sobic.003G096000 in the sorghum reference genome, which was annotated as an aldolase superfamily protein (S07_59440142) with the RA of EI, TCA, and TSH was linked with gene Sobic.007G160050 and Sh07_t012030 in the sorghum and sugarcane monoploid “R570” reference genome, respectively. These genes are functionally annotated as dicarboxylate carrier 2 and regulate the transportation of metabolites such as malic acid and abscisic acid (ABA) in the cell for controlling several energy-related pathways glycolysis, gluconeogenesis, and the TCA cycle in the plant (S10_53811870) associated with three RA traits  and picked up most frequently as a fixed-effect covariate during genomic prediction was linked with sorghum gene Sobic.010G195000. This gene is actively participating in plant development and growth. It has been shown that a double mutant of this type of gene reduced plant growth specially root length (The probe sequences of the significant seven associated markers with the RA of the five traits were utilized to pinpoint the putative candidate genes possibly regulating the RA in sugarcane. Sugarcane stubble morphology studies have found that deep (long) roots, a large number of buds, a large number of live buds, and a large number of permanent roots , along war yield . The mos protein . It also protein . An addihe plant . It has he plant . Anothert length . It may GS was experimentally evaluated in this study using four different GS methods in conjunction with the GWAS results incorporated as fixed effects to determine the prospect for future sugarcane hybrid breeding. The RA of five yield and sugar traits  were evaluated, and 5-fold cross-validated prediction accuracy differed by traits and models. The overall prediction accuracies were moderate (0.21–0.36). We are unable to compare our results, since there are no GS reports published on the RA traits. However, prediction accuracies on other yield, sugar, and disease traits in sugarcane were found to be low to moderate in several previous reports . The nonThis is the first study of its kind for the RA, and while prediction accuracies are low to moderate, these are likely good enough to begin testing GS practically for the RA. There is promise on the ability to increase prediction accuracies by addressing some of the known issues in marker development and genome complexities as mentioned above, as the heritability of the five RA traits is quite high. The findings of this study lay the groundwork for opening a new avenue for improving RA of sugar- and yield-related traits in sugarcane through breeding.https://doi.org/10.15482/USDA.ADC/1528986.The genotype data presented in the study are deposited in the Ag Data Commons repository : MI contributed to the conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, resources, supervision, visualization, writing—original draft, and writing—review and editing. KC contributed to the formal analysis, software, visualization, and writing—review and editing. PM contributed to the conceptualization, resources, and writing—review and editing. AH-K contributed to the writing—review and editing and resources. AL contributed to the writing—review and editing. All authors contributed to the article and approved the submitted version.


Multiple testing procedures controlling the false discovery rate (FDR) are increasingly used in the context of genome wide association studies (GWAS), and weighted multiple testing procedures that incorporate covariate information are efficient to improve the power to detect associations. In this work, we evaluate some recent weighted multiple testing procedures in the specific context of GWAS through a simulation study. We also present a new efficient procedure called wBHa that prioritizes the detection of genetic variants with low minor allele frequencies while maximizing the overall detection power. The results indicate good performance of our procedure compared to other weighted multiple testing procedures. In particular, in all simulated settings, wBHa tends to outperform other procedures in detecting rare variants while maintaining good overall power. The use of the different procedures is illustrated with a real dataset. In genome-wide association studies (GWAS), hundreds of thousands of genetic markers, , are simultaneously tested for an association with a phenotype of interest. In this context, the most common approach consists in using single-marker methods . To avoiThe two major multiple testing error criteria used in GWAS are the family wide error-rate (FWER), which is defined as the probability of obtaining at least one false positive, and the false discovery rate (FDR), which is defined as the expected proportion of falsely rejected hypotheses over all rejected hypotheses . The latIn GWAS, the FWER has been traditionally used to control multiplicity with methods such as the classical Bonferroni procedure. To account for the correlation structure induced by linkage disequilibrium between SNPs, different approaches have been proposed to derive significant thresholds based on an estimation of the effective number of independent SNPs . HoweverThis single-marker strategy combined with multiple testing procedures has made it possible to identify hundreds of genetic variants associated with several diseases, usually close to previously unsuspected genes. However, only a small fraction of the phenotypic variations has been explained, reflecting a large part of missing heritability . Many reIndeed, rare variants are poorly covered by genotyping arrays and are usually filtered from GWAS, since the overall idea is that frequent genetic variants explain a large part of the heritability in common diseases and are easier to detect in populations . Thus, mVarious approaches have been proposed to address the problem of detecting rare variants. A popular strategy consists in using burden tests that collapse rare variants in a genomic region into a single burden statistic . HoweverOne of the drawbacks of standard multiple testing approaches is that all hypotheses (that correspond to SNPs in the context of GWAS) are considered as interchangeable. However, the statistical or biological properties of individual tests are usually different, so some tests have greater power than others. Moreover, classical multiple testing methods do not use prior knowledge which can improve the detection power of associated variants . Using wp-values or the test statistics by inverse weights)  . Thus, tweights) .We distinguish two main approaches defining weights. The first consists in defining external weights, based on prior scientific knowledge of the data . The secIn this work, we evaluate recent weighted FDR controlling procedures in the specific context of GWAS. We also introduce a new adaptive procedure called wBHa in order to prioritize the detection of genetic markers having a low MAF by letting the procedure adapt a weighting function in order to maximize the overall power.To evaluate the procedures, we conducted an extensive simulation study. Among the procedures using informative covariates to define weights, we considered wBH , FDRreg In the next section, we briefly describe the statistical framework, the main evaluated methods, and present our new wBHa procedure. We also detail the simulation study that was conducted to evaluate the procedures. The following section presents the results of the simulation study and we illustrate the use of our method with a real public dataset. Finally, in the last section, we conclude with a discussion on the different approaches.m denote the total number of null hypotheses tested. Among them, m0 null hypotheses (H0) are true while m1 null hypotheses are false, i.e.,  m1 alternative hypotheses (H1) are true. When a multiple testing procedure is applied, R null hypotheses are rejected and W = m − R null hypotheses are not rejected. The different outcomes are summarized in R can be observed while the numbers of true positives (TP), false positives (FP), true negatives (TN) and false negatives (FN) are unobservable random variables.Let FWER = ℙ(FP > 0) while the false discovery rate is defined as FDR controlling procedures, most of them being based on a preliminary calculation of p-values.Using these notations, the family-wise error rate is defined as pii =  be the p-values calculated for the m tested hypotheses and let P be the corresponding random variables. In the classical two components mixture model, the population of p-values can be viewed as a mixture of two sub-populations corresponding to null and alternative hypotheses. Thus, the marginal distribution of each p-value can be written: Let f0 denotes the null density, f1 the alternative density, and π0 = Pr(Hi = 0) and π1 = Pr(Hi = 1) with Hi being the random variable such that Hi = 0 if the null hypothesis is true, Hi = 1 if the alternative hypothesis is true. Note that if the test statistics are continuous random variables, then, under the null hypothesis, the p-values follow a uniform distribution over the interval .where X =  be an informative covariate defined as any continuous or categorical variable, independent from the p-values under the null hypothesis, and informative on the statistical properties of the hypothesis tests. In this work, we consider MAF as covariate to prioritize the detection of rare SNPs with strong effects over more frequent SNPs with weaker effects, since the individual power is lower for SNPs with low MAF than for common variants. Nevertheless, other covariates can be considered, such as the signal quality, sample size, or the distance between the genetic variant and the genomic location of the phenotype in expression-QTL analysis, etc. , the BH procedure controls the FDR at level π0α  (The linear step-up procedure (BH) proposed by R ≤ π0α) .π0 is estimated from the data  was introduced by π0 is estimated. However, this quantity is made dependent on the covariate, leading to: π0(xi) = Pr(Hi = 0|Xi = xi) and π0(xi) representing weights specific to each hypothesis. Thus, noting zi the test statistics, the two-components mixture model can be written: The false discovery rate regression (FDRreg) procedure introduced by f1(zi) is taken to be a location mixture of null density which is assumed to be Gaussian. The mixing proportion of f1(zi) is fitted via a predictive recursion algorithm , an approach similar to Qvalue is proposed but the ratio Pi>λ|Xi = xi) is estimated from a logistic regression model.The science-wise false discovery rate (swfdr) procedure introduced by lfdr), introduced by The covariate adaptive multiple testing (CAMT) procedure introduced by FDR = 𝔼(lfdr|P ∈ Γ) where Γ is a rejection region for the pvalues  and ki are estimated from an EM algorithm in order to find the optimal threshold t that allows the FDR to be controlled at the desired level.The principle of CAMT is to replace the ratio G groups according to the ordered values of the covariate. Then, positive weights are assigned to each group g in order to maximize the number of rejections.The Independent Hypothesis Weighting procedure (IHW) was introduced by m hypotheses into k folds independently of the pvalues and covariates. For each fold, an optimization problem is applied to the hypotheses of the k − 1 remaining folds in order to derive weights g = 1, …, G) that maximize the overall power. Then, hypotheses of the held out fold lying in the group g are assigned weight To avoid overfitting, the authors introduced a hypothesis splitting approach which consists in randomly splitting the λ such that λ > 0. This regularization parameter allows for weights of successive groups to be relatively similar.To make the optimization problem convex, the authors proposed using the Grenander estimator instead of the empirical cumulative distribution function. In addition, to solve the optimization problem, they added a regularization parameter Finally, once the weights are estimated, a standard wBH procedure is applied with the resulting weights vector.In a context of GWAS where rare variants have strong genetic effects, it has been shown that using a weighted-Holm procedure with weights depending on MAF can substantially improve the power to detect associations . Here, wxi = MAFi to prioritize the detection of rare variants, but as mentioned before, other covariates can be considered, the proposed function being particularly adapted to any continuous informative covariate. Thus, our procedure is similar to the classical weighted BH procedure with weights prioritizing the detection of variants with low MAF. However, the introduction of the free parameter a makes it possible to optimize weights flexibly in order to improve the overall power.In the following, we set a consists in choosing the value leading to the maximum number of rejection R for a grid of values. In the event of tied values , the optimal a is set to the largest value of the longest interval defined by consecutive a values leading to the largest number of rejections. The steps leading to the optimal a are presented in Algorithm 1. Once the optimal a is obtained, wBH procedure is applied with the corresponding weights.In practice, the naive algorithm to obtain the optimal K datasets are generated by sampling p-values with replacement within the m tested hypotheses. For each dataset, the naive algorithm is applied leading to a set of K values: a1, …, aK. Finally, the optimal a value is obtained by calculating the average value of all ak values.To avoid overfitting, we consider a bagging approach : K datasBy choosing d choice .a.To sum up, our wBHa procedure not only enables the incorporation of external information to improve the detection power of rare variants but also makes it possible to search for optimal weights by maximizing the overall power for a class of weights defined as a function of a free parameter In the next section, we describe the simulation study we performed to evaluate the different procedures in a context of GWAS. _______________________  Algorithm 1: a Optimization Algorithm                                     _________     Input:   A m-tuple of p-values P =   and covariates            X = , a nominal level α ∈   for the            FDR and a number of folds K = 100.    Output:   Optimal a     for ki = 1,...,K  do         Sampling with remplacement of m K  hypotheses;   for a = 0,0.1,0.2,...,10  do        Application of wBH procedure at level α with w =     m ____∑ m     j=1  1 _ xaj  × 1 _ xai ;   Computation and saving of the numbers of rejections R;   end    Saving the values a leading to the maximum of R in an    ordered L-tuple (L ≥ 1) A = ;   if  L > 1  then        Computation of the successive differences in A ;   Definition of interval bounds from differences larger to the step 0.1  ;   Clustering of the L values of A within the v intervals thus defined ;   if  v = 1  then    Saving the maximum value of the vector A ;   else    Computation of the length of each interval ;   if  one of the intervals is longer than the others  then    Saving the maximum value in the longest interval ;   else    Saving the maximum value in the interval closest to 1 ;   end    end    end     end     Optimal a obtained by calculating the average of the K      values ;    To compare and evaluate the performance of the different procedures in a GWAS context, we performed a simulation study.G the genotype matrix with n lines corresponding to individuals (set to 2000) and m columns corresponding to SNPs . To code the genotype matrix, we considered an additive genetic model with 0 for a homozygous genotype for the reference allele, 1 for a heterozygous genotype, and 2 for a homozygous genotype for the alternative allele. Thus, G is a matrix of size n × m where Gij ∈ {0, 1, 2}.Note B = 10 and within each block, all variables are equicorrelated at level ρ. The different values considered for ρ are: 0, 0.10, 0.20, 0.35, 0.5 and 0.75.To mimic the linkage disequilibrium (LD) structure, we considered a model adapted from the work of p2 + q2 + 2pq = 1 where, for each SNP, p is the frequency of one of the two possible alleles and q = 1 − p.To obtain the genotypes, these continuous variables were discretized following the Hardy Weinberg equation: p as the MAF, so that p ≤ q. The MAF of the m0 non-causal variants were generated from a uniform distribution between 0.01 and 0.5 . The m1 causal variants were divided into four distinct subsets in which the MAF were generated from the following distributions:From here, we arbitrarily set  •UGroup 1 (Rare SNPs):  •UGroup 2 (Medium-Rare SNPs):  •UGroup 3 (Medium SNPs):  •UGroup 4 (Common SNPs): m1 by 4 with m1 ∈ {5, 10, 15, 20, 25, 50, 100, 150}. The remainder of this division was added to group 4. The effect size simulation is described in the next section.The number of SNPs in each subgroup of causal variants was obtained by the Euclidean division quotient of In the end, we set for each SNP: •Gij = 2 if  •Gij = 1 if  •Gij = 0 if qN., is the quantile function of the standard normal distribution.where Y =  be the studied phenotype. In our simulation setting, we considered quantitative and binary variables corresponding to studies for quantitative trait and case-control design, respectively.Let i: The values of the quantitative trait were generated from a linear regression model. Thus, for each individual ϵi were generated from a normal distribution σ2 was set as a function of the coefficient of determination R2 as in where the residuals R2 values to 0.2.We set i: For the case-control design, phenotypes were generated from a logistic regression model. Thus, for each individual β0 is the intercept corresponding to the expected mean value of Y when all G = 0. We set β0 so as to obtain balanced proportions of cases and controls in the samples.where βj correspond to the effect size of SNP j on the phenotype. For non-causal markers, we set βj = 0. For causal variants, we considered three different scenarios , the coefficients m and m1 values  with R2 = 0.5. However, due to excessive computational time for generating the data, we only considered the independent case with quantitative traits.To evaluate the different procedures with more realistic numbers of hypotheses in GWAS, we also simulated datasets with larger To mimic a more realistic correlation structure, we also simulated data based on a real dataset from a study on HIV infection . In thisk = 1 and the Euclidean distance.For the reasons of computation time mentioned above, we restricted our simulation to the genotype matrix corresponding to chromosome 6, which has been widely reported in the literature. All SNPs with more than 10 missing values were removed and the remaining missing values were imputed using the k-nearest neighbors method with From the imputed genotype matrix, MAF were calculated and SNPs were divided into four groups according to their MAF values: the first corresponding to the 558 SNPs with MAF between 0.01 and 0.05, the second corresponding to the 4,909 SNPs with MAF between 0.05 and 0.15, the third corresponding to the 6,674 SNP with MAF between 0.15 and 0.30 and the fourth corresponding to the 7,840 SNPs with MAF larger than 0.30.β values (effect sizes), we first estimated the regression coefficient of all significant SNPs when applying the wBH method to the original dataset at level α = 0.05. Then, we considered the absolute values of the four quartiles of the empirical distribution of these estimated coefficients to define the effect size of causal variants in each of the four groups. As for the fully simulated datasets, we considered scenario 1 in which rare causal variants have a greater effect than common variants, scenario 2 in which common variants have a greater effect than less frequent variants, and scenario 3 in which all effects are equally distributed among the four groups. Finally, the phenotypes were generated from a linear regression model with R2 = 0.8.Causal variants were randomly drawn in each group in the same proportions as for the fully simulated datasets. To set their FDR level of 5%. For each configuration, we simulated 500 datasets.Once the phenotypes and genotypes were generated, we applied the multiple testing procedures described in the previous section at a nominal xi being the MAF). For the other weighting procedures, the MAF was used as the informative covariate.In this work, we sought to prioritize the detection of rare variants having strong genetic effects in GWAS. Thus, for wBH, we set the weights analogously to wBHa by considering MAF) to avoid computational problems in the EM-algorithm due to large values of the covariate). In addition, to evaluate whether the proposed method is robust when the covariate is completely uninformative, we applied all procedures with a covariate drawn from a uniform distribution between 0 and 1 .However, as previously mentioned, other informative covariates may be considered. For example, we illustrate the use of the proposed method to prioritize common variants by replacing MAF by 1/MAF for all methods  by the empirical mean (and its corresponding standard error) of the numbers of true discoveries over the 500 simulated datasets divided by the number of causal variants contained in each subgroup.To evaluate the performance of the methods specifically in the subgroup of rare variants, we also estimated the average power in each subgroup To assess the FDR control of each procedure, we estimated the FDR by the empirical mean (and its corresponding standard error) of the observed false discovery proportion over the 500 simulated datasets.m. It also decreases with m1, since the global effect is distributed among a larger number of SNPs, making the individual effect of each causal variant more difficult to identify. In addition, for correlated datasets, the power tends to increase with ρ in all configurations.m1 , wBHa and wBH tend to be the most powerful procedures, although for the smallest m1 values in the quantitative case (m1 ≤ 15), BH, qvalue and swfdr are slightly more powerful  but the least powerful for binary traits (for m1 ≥ 100), the most powerful being IHW. Conversely, FDRreg has good overall power for binary phenotypes but is the least powerful procedure for quantitative phenotypes. Note that while wBHa is not always the most powerful procedure, it has quite good overall power in all configurations in comparison to the other procedures.In scenario 1 with independent markers, for small and intermediate values of powerful . For larm1 (m1 ≤ 50), wBHa, wBH, BH, qvalue and swfdr tend to be the most powerful procedures with similar results  , IHW tends to be the most powerful procedure. As in scenario 1, CAMT performs well with quantitative phenotypes but is the least powerful procedure for binary phenotypes.In scenarios 2 and 3 with independent markers, for small and intermediate values of m1 ≤ 50)  and S2. m1 is intermediate (in scenario 1) or small (in scenarios 2 and 3). Note that in scenario 1 with binary traits, wBHa tends to be the most powerful procedure in all configurations , wBHa and wBH tend to be the most powerful procedures while CAMT and IHW are more powerful for large values of m1 (m1 ≥ 100).The results obtained with simulations based on a real dataset are similar to those obtained with fully simulated data  and S7. m and with the number of causal SNPs m1 for each configuration. In addition, for correlated markers, the power tends to increase with the ρ value in all configurations.m1 (m1 ≥ 50), CAMT tends to be the most powerful procedure for quantitative phenotypes, but it is the least powerful one for binary phenotypes. Conversely, FDRreg performs well for binary phenotypes but it is the least powerful for quantitative phenotypes.In scenario 1 with independent markers , the wBHInterestingly, in intermediate scenarios (scenarios 2 and 3), our procedure and wBH tend to be the most powerful in the subgroup of rare variants for all configurations  and S9. m1 ≥ 50 for quantitative traits in scenario 1.In the correlation case, for all scenarios we obtained results similar to those obtained in the independent case  and S12.When considering simulations based on a real dataset  and S14,ρ in all configurations. Similar results were obtained in scenarios 2 and 3  in CAMT makes it possible to avoid computational problems but leads to very similar results in the cases where CAMT worked with 1/MAF (data not shown).When MAF is replaced by 1/MAF to prioritize common variants, wBHa is one of the most powerful procedures for detecting common variants in all scenarios  and S27 When using an uninformative covariate, the different procedures remain valid since the FDR is controlled at the desired level  and S36.GSE90102).To illustrate the results obtained with the simulated data, we applied the different procedures on a publicly available dataset from the study conducted by . One of −5), and all SNPs with a MAF less than 0.01. The MAF distribution of all SNPs is presented in For our analysis, we only considered autosomal chromosomes. Therefore, standard quality controls were applied: we removed all SNPs with a call rate of less than 95%, all SNPs with a significant deviation from the Hardy-Weinberg equilibrium , wBHa identified 106 significant results while the procedures leading to the smallest number of rejections were BH, qvalue and swfdr, which identified the same 43 markers. In addition, wBHa produced the largest number of rejections for SNPs with a MAF lower than 0.02.rs3772479 and rs2270569, are located in the FHIT and KIF9 genes, respectively, which have been reported to play an important role in inflammatory bowel disease (IBD) (Crohn’s disease being a type of IBD)  .In this study, we evaluated recent weighted multiple testing procedures in the context of genome-wide association studies. We also introduced a new procedure called wBHa that aims to prioritize the detection of genetic markers with a low MAF while letting the procedure adapt a weighting function in order to maximize the overall power.For independent datasets, wBHa performed well in the simulation study compared to the other procedures with a quite good overall power in all simulated configurations. As noted by a in the wBHa procedure.When considering the power to detect associations within the subgroup of rare variants, the non-optimal wBH procedure is the most powerful in all scenarios. However, wBHa is not only powerful in the subgroup of rare variants but also has good overall power. These results demonstrate the value of the optimization parameter Concerning FDR control, most procedures seem to correctly control the error criterion for independent datasets, although in some cases, the estimated FDR for all procedures (including BH) is slightly larger than the threshold. This can be explained by the small number of rejections, which leads to a large variability of the false discovery proportion. However, FDRreg does not appear to control the FDR in the case-control design, which is consistent with similar results obtained by For correlated datasets , we obtained similar results in terms of power. Thus, wBHa showed good performance compared to the other procedures. As expected, a loss of FDR control is observed with all procedures when the correlations increase, and the difficulty of defining true and false positives remains a challenge. However, in practice, the influence of correlations on FDR control may be limited by using methods such as LD pruning .To sum up, although wBHa is not the most powerful in all configurations, it showed good performance compared to other procedures in all configurations, not only in terms of overall power but also regarding the power to detect associations in the subgroup of rare variants. In particular, in scenarios 2 and 3 for which rare variants have moderate or low effect sizes, wBHa proved to be powerful in the subgroup of rare SNPs, thereby testifying to its value. Thus, wBHa allows the detection of rare variants while having overall power similar to that of that of other procedures, whatever the size effect of rare variants.To illustrate the results obtained with simulated data, we applied the different procedures to a real public dataset on Crohn’s disease. The results were consistent with those obtained with simulated data. The weighted procedures had good performance in terms of power, particularly for procedures based on informative covariates. Moreover, wBHa identified six specific rare variants that could not be selected by the other procedures. Among them, two markers are located in genes FHIT and KIF9, which have been reported to be involved in IBD, suggesting that they could be true associations. These results underline the value of wBHa in real data applications.In conclusion, adaptive weighted multiple testing procedures based on informative covariates show great promise in the context of genome-wide association studies. Our new procedure wBHa, which showed good performance in all settings, appears to be a good choice for prioritizing rare variants without loss of overall power.https://github.com/obryludivine/wBHa. A second GitHub repository is also available at https://github.com/obryludivine/wBHa_simulation. It contains the programs used to create the simulated datasets and allows our results to be reproduced. These projects have been archived on Zenodo on https://zenodo.org/badge/latestdoi/409590338 and https://zenodo.org/badge/latestdoi/402729574 respectively.The wBHa procedure is implemented in the R package wBHa which is available at 10.7717/peerj.15369/supp-1Supplemental Information 1Click here for additional data file.


Dominance and other non-additive genetic effects arise from the interaction between alleles, and historically these phenomena play a major role in quantitative genetics. However, most genome-wide association studies (GWAS) assume alleles act additively.We systematically investigate both dominance—here representing any non-additive within-locus interaction—and additivity across 574 physiological and gene expression traits in three mammalian stocks: F2 intercross pigs, rat heterogeneous stock, and mice heterogeneous stock. Dominance accounts for about one quarter of heritable variance across all physiological traits in all species. Hematological and immunological traits exhibit the highest dominance variance, possibly reflecting balancing selection in response to pathogens. Although most quantitative trait loci (QTLs) are detectable as additive QTLs, we identify 154, 64, and 62 novel dominance QTLs in pigs, rats, and mice respectively that are undetectable as additive QTLs. Similarly, even though most cis-acting expression QTLs are additive, gene expression exhibits a large fraction of dominance variance, and trans-acting eQTLs are enriched for dominance. Genes causal for dominance physiological QTLs are less likely to be physically linked to their QTLs but instead act via trans-acting dominance eQTLs. In addition, thousands of eQTLs are associated with alternatively spliced isoforms with complex additive and dominant architectures in heterogeneous stock rats, suggesting a possible mechanism for dominance.Although heritability is predominantly additive, many mammalian genetic effects are dominant and likely arise through distinct mechanisms. It is therefore advantageous to consider both additive and dominance effects in GWAS to improve power and uncover causality.The online version contains supplementary material available at 10.1186/s13059-023-03060-2. Dominance arises from non-additive interactions between different alleles within a locus. The pathways that cause dominance still remain to be clarified, despite intense scrutiny –3; suggeIn the quantitative genetics model proposed by Fisher in 1916 –15, the A given biallelic locus is additive if the phenotypic effect of the heterozygote is the mean of that of the two homozygotes. In this study, we define dominance to mean any non-additive within-locus interaction, classified as partial (PD), complete (CD), or over-dominance (OD), according to whether the phenotypic effect of the heterozygote lies within the range spanned by the homozygotes but is unequal to their average, or is equal to one of the homozygote effects, or is outside their range –18  have been mapped in animals  and in rimulina –56. In crimulina , 33. Simrimulina , number rimulina , meat qurimulina , and grorimulina . In planrimulina  and stririmulina ) and grorimulina , floweribidopsis  and sunfbidopsis , and hybrimulina ). Consisrimulina , growth rimulina , milk prrimulina , and reprimulina ) and pigrimulina , daily grimulina , 63, numrimulina , and grorimulina ).In contrast with these findings, studies in humans have generally reported that both dominance variance components are small –68 and dThere are also practical reasons why dominance is often ignored. First, modelling dominance requires extra degrees of freedom in fixed effects models, potentially reducing the power to detect purely additive effects. Second, if one is to model dominance effects using the mixed model framework, it follows that both additive and dominance variance components should be included in genetic background effects, which is computationally challenging. For these reasons, most GWAS in humans only consider additive variance components. However, where dominance heritability is large, this shortcut is potentially unsound and might reduce the power to detect genetic associations.Another important reason to consider dominance is that understanding the relationship between dominance at the level of gene expression and at the level of physiological phenotype may be key to establishing causal mechanisms. In yeast , 71, plaIn this study, we systematically investigate dominance across physiological and gene expression traits in three mammalian species, namely pigs , rats 7, and micWe integrated and analyzed published multi-phenotype and multi-omics data from three mammalian stocks: (1) F2 intercross pigs (hereafter F2 pigs) , containEach phenotype was normalized, and the effects of covariates removed, as described previously –79. All We used these SNP sets to construct additive and dominance genetic relationship matrices (GRMs) and performed quantitative genetic analysis. We dissected the contributions of dominance to the 584 organismal traits measured across the three populations, by simultaneously estimating both additive ing GCTA . Each phmentclass2pt{minimmentclass2pt{minimIn general, additive genetic effects explain more phenotypic variance than dominance effects across all three populations, in line with previous studies –79. Howementclass2pt{minimmentclass2pt{minimmentclass2pt{minimP-values than a model solely including additive effects (named the Add or “A-Model”) [P-value significance thresholds, (i) approximate 5% genome-wide significance (0.05/NSNP) and (ii) suggestive significance  . At eachWe adapted a long-standing definition—the degree of dominance , 18—to centclass1pt{minimaentclass1pt{minimaUsing suggestive genome-wide significance thresholds, the AD Model detected 352 QTLs for 182 F2 pig traits, 179 QTLs for 119 HS rat traits, and 116 QTLs for 73 HS mice traits  values of 119 (33.8%) pig QTLs by more than 4 units compared to the A model, and similarly in for 52 (29.1%) rat QTLs and 40 (34.5%) mouse QTLs  values of significant SNPs (either uniquely or concurrently) detected by AD compared to both A . Among suggestive SNPs detected by the AD, A, or D Models  values more significant than the QTL found by the A-model in the neighboring gene Myo1f. Conditional QTL mapping analysis shows that Bat3 and Myo1f are associated with nearby but unlinked SNPs , HS rats , and HS mice , via variance decomposition with GCTA and eQTL mapping with ADDO. For rat RNA-seq data, we made separate analyses for gene and isoform expression, where a gene’s expression level is defined as the sum of all its constituent isoform levels.P-value < 10−4).The heritability of most expression traits was lower than for physiological traits, but surprisingly a larger fraction of the variance was accounted for by dominance, even though, as we describe below, most cis-eQTLs are additive. Figure To eliminate instabilities when both variance components are very small, we repeated the analysis restricted to genes where both 10(P) = 5.8, 6.3, 5.1 for pig, rat and mouse) to stringent (− log10(P) = 8.5) to characterize the variable impact of dominance on cis and trans-eQTLs. The proportions of dominant eQTLs at different thresholds are presented in Additional file We mapped thousands of eQTLs  = 5.5 to 8.5), there are statistically significant enrichments. For example, at threshold 5.5 all the P-values across all seven tissues are < 0.0001.We cross-tabulated additivity vs dominance against trans vs cis-eQTLs using Fisher exact tests. To simplify results, we grouped additive and partial-dominant eQTLs as “generalized additive eQTLs” (G-Add eQTL) and complete- and over-dominant eQTLs as “generalized dominance eQTLs” (G-Dom eQTL). Overall, and consistent with other studies, most cis-eQTLs are additive while trans-eQTLs are enriched for dominance effects, although it is not the case that most trans-eQTLs are dominant  = 4.7) where eQTLs are more numerous, but it persists at more stringent thresholds  > 10) but which tag most of the hotspots (2333 liver and 2088 muscle pig eQTLs from 537 and 479 hotspots). We detected a representative dominance gene-level hotspot at chr10: 85–86 Mb in HS rat heart  suggesting these genes act independently. Interestingly, scatter plots of the expression of the genes underlying these trans and cis-eQTLs . Additional file Trans-eQTL hotspots  are ubiquitous and strongly enriched for dominance effects. For example, among suggestive significant pig eQTLs, there are 11,596 and 12,157 overlapping eQTLs that localize to just 594 and 606 separate regions in liver and muscle respectively . Hotspots are summarized in Additional file In addition, we detected hundreds of colocalized dominance eQTLs that regulate the same gene in different tissues within each population  in contrast to others that regulate different genes in different tissues , tabulated in Additional file P = 7.9 × 10−11) and heart (P = 5.1 × 10−3). Additional file HS rat gene expression was measured by RNA-seq, which made it possible to distinguish expression levels of alternative isoforms, and to investigate the dominance enrichment of isoform-based eQTLs. Using the Rn4 reference annotations of 34,721 transcripts in 24,688 genes, 74.5% of genes express only one known isoform, while 22.2% (5489 genes) express two or three isoforms  rather than being driven by the superiority of a few dominant alleles over deleterious recessive alleles (the dominance hypothesis). Heterosis—which is closely related to dominance , 91—is oThird, integrated modelling of dominance across organismal and gene and isoform expression traits may suggest causal mechanisms. We conjecture that physiological dominance in some cases arises from the complex genetic control of alternative isoforms, as evidenced by many instances where the relationship between phenotype and genotype appears to be mediated via the expression of a particular isoform rather than by overall gene expression  that regulate the target gene. This could lead to non-additive relationships between TF concentrations and gene expression . If the In summary, incorporating dominance into quantitative genetic analysis uncovers important, complex, and interesting biology. We only have space here to highlight a few examples of dominance phenomena, but many more can be found in the supplementary tables and figures. Dominance analysis requires only a slightly different workflow compared to additive analysis. Hence, even though the greater part of trait heritability is typically additive, there are few if any disadvantages to searching for dominance and we recommend its routine use.The F2 pigs were established by crossing Chinese Erhualian and White Duroc pigs in 1998, as described . GenotypThe HS rats were descended from eight inbred strains  , by rotaRattus norvegicus genome, Ensembl RGSC3.4) for consistency with the version used for the array genotypes. For the gene expression levels, we first aligned clean reads to the rat RGSC3.4 reference genome using STAR (v.2.5.3a), and removed duplicated reads by Picard (v.2.5.0). Next, we estimated the raw read counts of each gene using featureCounts (v.1.5.2), and normalized the counts using the Trimmed Mean of M-value (TMM) method, implemented in edgeR (v.3.20.9). For the transcript levels, we used Kallisto (v.0.43.1) to estimate the transcript per million (TPM) of all transcripts in rat RGSC3.4 genome, followed by the quantile normal transformation before GWAS analysis.In total, the genotypes of 1407 individuals  were meaThe HS mice originated from eight inbred progenitors   with a sWe estimated additive and dominance variance components and heritabilities using GCTA  in a staAll QTL and eQTL mapping was done using the ADDO  toolkit.“Add (A) Model”: The three genotypes are recoded as 0/1/2 within each locus, i.e., as additive genotype dosages, in order to model additive genetic effects. The design matrix is thus a column of ones (for the intercept) and a column of genotype dosages. Except that the variance matrix incorporates dominance effects, the A model is equivalent to the usual additive model used in mixed model GWAS.“Dom (D) Model”: Genotypes AA/AB/BB are coded as heterozygote dosages 0/1/0, to detect loci where the effect of heterozygote AB is from the mean effect of two homozygotes AA and BB. The design matrix is a column of ones and a column of heterozygote dosages. “Add-Dom (AD) Model”: Genotypes are coded as two columns 0/1/2 (additive dosage) and 0/1/0 (heterozygote dosage). The design matrix is a column of ones and both of these columns, and which can model any type of dominance effect by suitable choice of the regression coefficients p-value of the ANOVA comparison of the models.“AvsAD Model”: We used ANOVA to compare the A Model and AD Model, to detect loci with significant non-additive effect. Statistical significance was reported as the negative base 10 log p-value thresholds to report significant QTLs: (1) a suggestive threshold (1/NSNP), where NSNP is the number of SNPs. Under the null hypothesis where no SNPs are associated, one false positive is expected per genome scan; (2) whole genome-wide significance (0.05/NSNP), in order to control for false positives caused by multiple tests, and where a false positive should occur once in every 20 GWAS. The width of each QTL was determined using 2 point LOD drop from the peak SNP at the QTL.We used two 2(r).We classified each QTL into a dominance type , based on the log ratio of T-statistics from the Add-Dom Model, i.e., eQTLs were classified as cis-acting if the eQTL localized to the same chromosome with its target gene and the minimum of left and right boundary distances between “Peak SNP” and “gene physical range” < 2 Mb; otherwise, it was classified as trans-acting.Additional file 1: Fig. S1.1. Examples of dominance classifications visualized with different coordinate systems for QTL category comparison. Fig. S1.2. Additive  and Dominance  Variance Components of all traits across three populations. Fig. S2.1. QTLs detected by AD model. Fig. S2.2. Comparison of QTL detection among A, D and AD models. Fig. S3.1. Over-dominant QTLs in F2 pigs. Fig. S3.2. Over-dominant HS rat QTLs. Fig. S3.3. Over-dominant QTLs implicate a novel causal gene regulating the proportions of CD4+ and CD8+ T cells in HS mice, compared to additive QTL modelling. Fig. S3.4. Conditional GWAS of the proportion of CD4+ cells in CD3+ cells in HS mice. Fig. S4.1. Homologous QTLs for mean corpuscular volume (MCV). Fig. S4.2. Homologous QTLs for mean corpuscular hemoglobin (MCV). Fig. S4.3. Homologous QTLs. Fig. S5.1. Trans-acting enrichment among dominant eQTLs in liver and muscle in F2 pigs. Fig. S5.2. Trans-acting enrichment among dominant eQTLs of amygdala and heart tissues in HS rats. Fig. S5.3. Trans-acting enrichment among dominant eQTLs of hippocampus, liver and lung tissues in HS mice. Fig. S6.1. Dominant cis and trans transcript eQTLs at the hotspot chr10:85Mb-86Mb in HS rat heart. Fig. S6.2. Interplay of immunology-related QTLs in HS rats. Fig. S6.3. Dominant eQTLs detected by AD model from two trans-acting hotspots  in HS rat heart. Fig. S6.4. Dominant eQTLs from a trans-acting hotspot in HS mice hippocampus. Fig. S6-5. Dominant eQTLs from a trans-acting hotspot located at chromosome 7 in HS mice lung. Fig. S6.6. Dominant eQTLs from a trans-acting hotspot located at chromosome 9 in HS mice lung. Fig. S6.7. Tissue-conserved and tissue-specific dominant eQTLs. Fig. S7.1. Transcript-specific antagonistic dominant eQTLs of RPL14 and LFI44 in HS rat amygdala and heart. Fig. S7.2. Transcript-specific synergistic eQTLs in HS rats.Fig. S8.1. A pleiotropic over-dominant pig QTL for kidney weight and small intestinal length colocalized with MTCH1 eQTL in F2 pigs. Fig. S8.2. Dominant QTLs for three rat hematology traits colocalized with cis/trans eQTLs in rat amygdala and heart (chr19: 53 Mb-55 Mb). Fig. S8.3. An over-dominant QTL for mouse glucose slope colocalized with eQTLs in mouse amygdala, liver and lung.Additional file 2: Table S1-1. Summary of datasets in F2 pigs, HS rats and HS mice, together with phenotype and genotype quality control methods applied in QTL/eQTL mapping; Table S1-2. Trait classes and total numbers.Additional file 3: Table S2. Phenotypic variances with standard errors, due to additive and dominance effects  of 242 physical traits in F2 pigs (Table S2-1), 206 physical traits of HS rats (Table S2-2) and 124 physical traits of HS mice (Table S2-3), together with average values across each trait class.Additional file 4: Table S3. Counts and proportions of significant QTLs under dominance types within each population, and the corresponding missing rate compared to the additive GWAS, under suggestive thresholds and whole genome thresholds. Additional file 5: Table S4. Descriptive statistics, effect classification and consequence annotation of all significant QTL detected by AD Model in F2 pigs (Table S4-1), HS rats (Table S4-2) and HS mice (Table S4-3). (Rows with -log10(P)values of ADvsA Model > 5 are in red, rows where -log10(P)values of AD Model > 6 are in blue.).Additional file 6: Table S5. Summary of novel pleiotropic QTLs detected by AD model within each population. (Different shared QTL regions are separated by graey background and the rows with red numbers indicate novel QTLs where -log10(P)values of ADvsA Model > 5).Additional file 7: Table S6. Summary of homologous QTLs of common physical traits across three stocks. (Different trait groups are separated by frame and QTL rows of different stocks are colored differently).Additional file 8: Table S7. Gene expression heritabilities and standard errors explained by additive and dominance effects estimated by genome-wide SNPs within each tissue of F2 pigs (Table S7-1), HS rats (Table S7-2) and HS mice (Table S7-3), summary of average additive and dominance variance components of each tissue across three populations (Table S7-4).Additional file 9: Table S8. Counts and proportions of significant eQTLs classified by dominance effect type within each tissue and each population under different significant thresholds (Table S8-1); Proportions of novel eQTLs detected by AD model compared to A model in cis/trans groups (Table S8-2).Additional file 10: Table S9. Classification of eQTLs detected by AD Model . Rows with -log10(P)values of ADvsA Model > 6 are in red, remaining rows with -log10(P)values of AD Model> 10 are in blue.Additional file 11: Table S10. Counts of significant cis-eQTLs and trans-eQTLs under four different effect types within each tissue of three populations under different significant thresholds, together with the correlation test between different genetic modes and different regulatory types .Additional file 12: Table S11. Summary of pleiotropic eQTLs . Lines in bold indicate cis-eQTLs.Additional file 13: Table S12. Summary of eQTLs shared across tissues within each population . All rows are ordered by (1) whether from the same gene (2) the -log10(P)values of ADvsA Model. Table S12-7, counts of shared eQTLs between different tissues within each specie.Additional file 14: Table S13. Counts of significant eQTLs classified by transcript compositions (one transcript or multi transcripts) and by dominance type, in HS rats heart and amygdala, together with the correlation test between dominance type (A/PD/CD/OD) and transcript compositions.Additional file 15. Review history.


The dairy industry is known for its extensive use of artificial insemination, which has resulted in a population where most animals can be traced back to only a few sires. Due to their relatedness to the population, old influential sires could still contribute to the accuracy of genomic predictions. The objective of the study was to identify the impact of historically influential sires on the recent population. This was tested by constructing a genomic relationship matrix using recursion with different sets of sires. Differences in prediction accuracies with different sets are indicative of how important each set is. Recursion coefficients linking young animals to those sets reveal the relative importance of specific sires to the prediction accuracy of recent animals. The data included ∼10 million scores for stature and fore udder attachment (FUA) measured from 1983. Genotypes of 569,404 animals were available. Sire sets included the 100 most popular sires born within different time periods. Computations were with single-step genomic BLUP. In general, the younger sires had higher prediction accuracies than the oldest sires, even though they generally have fewer progeny. The accuracy of evaluation for stature was increased from 0.54 with the most popular sires born before 1981 to 0.69 with sires born from 2001 to 2010, while the accuracy for FUA increased from 0.47 to 0.61. The accuracy achieved using the overall 100 most used sires was 0.66 for stature and 0.58 for FUA. All 100 sires from each period were combined in a subset to determine the importance of each sire relative to all 400 animals in the combined subset. The highest relative impact of a sire that was born within the different time sets was 1.97 for Valiant (before 1981), 1.94 for Blackstar (1981 to 1990), 4.38 for Shottle (1991 to 2000), and 3.09 for Planet (2001 to 2010). The 3 sires among the 400 with the greatest impact were Shottle, Goldwyn (3.73), and Planet. The relative impact of a sire was not strongly related to the number of progeny. For instance, the relative impact of Durham with 34K progeny was 2.29, whereas the impact of O Man with 15K progeny was 3.13. The impact of a sire is also influenced by whether it was used as a sire of sires. Results show that younger sires are more relevant to the accuracy of breeding value prediction in the recent population. The dairy industry is known for its wide use of a relatively small number of bulls. The breeding values of animals can be conditioned on the breeding values of a subset of animals based on the relationships between this subset and all remaining animals using a recursive formula . When thCDCB) and contained information up to 2014. It included 9,817,252 animals in the pedigree, of which 330,837 were sires and 5,471,039 were dams. Phenotypes started in 1983 and included 6,550,442 animals with 10,067,745 measures for stature and 10,067,730 for fore udder attachment (FUA). These traits were used because they have shown to be type traits with substantial genetic change over time  or indirectly with SNP-BLUP, depends on an extension of this equation to reflect the genomic relationships between animals. The extended equation to condition the genomic breeding values of animal i on the genomic breeding values of the subset animals j isi > n, where ijp is a relationship between animal i and subset animal j, ju is the breeding value of the animal j,i, and n is the size of the subset  was obtained using all genotyped animals and the formulaM is a matrix of SNP content centered by twice the current allele frequencies, and ip is the allele frequency for SNP i  using the method described by G.In this study, the genomic relationship matrix  . Five dissGBLUP) was used to accommodate genotyped and nongenotyped animals with the BLUPf90iod2 package within the blupf90 software suite (adj) using the PREDICTF90 software package (adj divided by the square root of the heritability (0.45 for stature and 0.18 for FUA).Stature and FUA were analyzed in a 2-trait model as described by re suite . The ped package . These aICS) of the current population. The highest accuracies were reached using the 100 sires from the period 2001 to 2010 (0.69 for stature and 0.61 for FUA). This is approximately 30% more than using the older sires. Thus, the ICS are better captured by young sires. It has been shown that the accuracy of genomic breeding values is influenced by the generational distance between the reference and validation population , had fewer than 60,000 direct progeny. However, popular dairy sires are known to have hundreds of thousands of progeny. The bulk of the sires' offspring are in herds that do not participate in US animal improvement programs, such as US commercial herds and foreign herds.The “Millionaires Club” refers to sires that have produced over 1 million semen doses. One member is Jenny-Lou Mrshl Toystory (Toystory), born in 2001. At the time of his death in 2015, he had produced more than 2 million semen doses that were used in more than 20 countries. Despite his commercial popularity, he only had 14,726 registered progeny in the United States as of 2014. Based on our results, Toystory's relative impact ranks at 90 among the 100 sires with the most registered progeny. Currently, Toystory has more than 162K total daughters with milk production records in over 27 countries, of which ∼45K are recorded in the United States . Due to This study was limited to conformation traits, which are generally less commonly measured than production. As of 2022, O Man had a total of 61K daughters measured for production in the United States but only 15K for type traits . TherefoIn conclusion, historically popular bulls still capture some of the ICS in the recent population, but have a relatively low impact on the accuracy of predictions compared with younger sires. Consequently, including their information in genetic evaluations may not be necessary. The total number of progeny a sire has is not the only determinant of importance in the population. Specific bulls can have a greater impact when they are used as a sire of sires. The true impact of sires on the US Holstein population cannot be determined due to the lack of participation of commercial herds.


This study aims to comprehensively investigate the phenotypic and genetic relationships between four common lipids , chronic kidney disease (CKD), and estimated glomerular filtration rate (eGFR).case = 41,395, Ncontrol = 439,303), and eGFR.We first investigated the observational association of lipids (exposures) with CKD (primary outcome) and eGFR (secondary outcome) using data from UK Biobank. We then explored the genetic relationship using summary statistics from the largest genome-wide association study of four lipids , CKD  between HDL-C, TG, and CKD risk. Partitioning the whole genome into 2353 LD-independent regions, twelve significant regions were observed for four lipids and CKD. The shared genetic basis was largely explained by 29 pleiotropic loci and 36 shared gene-tissue pairs. Mendelian randomization revealed an independent causal relationship of genetically predicted HDL-C , but not for LDL-C, TC, or TG, with the risk of CKD. Regarding eGFR, a similar pattern of correlation and pleiotropy was observed.There were significant phenotypic associations (HDL-C: hazard ratio (HR) = 0.76, 95%CI = 0.60–0.95; TG: HR = 1.08, 95%CI = 1.02–1.13) and global genetic correlations (HDL-C: Our work demonstrates a putative causal role of HDL-C in CKD and a significant biological pleiotropy underlying lipids and CKD in populations of European ancestry. Management of low HDL-C levels could potentially benefit in reducing the long-term risk of CKD.The online version contains supplementary material available at 10.1186/s12967-023-04509-5. Circulating lipids play an important yet complex role in the development of chronic kidney disease (CKD). While large-scale prospective cohort studies have demonstrated the risk effect of triglycerides (TG) (risk ratio (RR) = 1.28, 95%CI = 1.16–1.41) , 4. IndeCD36 and PCSK9) have further been identified as affecting LDL-C and total cholesterol (TC) levels as well as CKD or kidney function [In the subsequent efforts raised to address these discrepancies, genetic data has been used to overcome these drawbacks. Notably, a modest genetic correlation underlying circulating lipids and CKD has been quantified by a twin study  . Multiplfunction –10. Morefunction –13. Despentclass1pt{minimafunction , the othmentclasspt{minimaentclass1pt{minimafunction –15, whicmentclasspt{minimaTherefore, leveraging the hitherto largest observational and genetic data, we aimed to comprehensively evaluate the relationships between four lipid phenotypes  with CKD and with estimated glomerular filtration rate (eGFR). We first examined the phenotypic association using individual-level data from the UK Biobank (UKB). We next performed genetic analyses to quantify global and local genetic correlations, identify pleiotropic loci, and detect putative causal relationships underlying these complex traits. We set CKD as the primary outcome of interest and eGFR as the secondary outcome. The overall study design is shown in Fig. International Classification of Diseases, tenth Revision (ICD-10) code N18. We excluded participants with a history of kidney disease at baseline , those with missing information of HDL-C, LDL-C, TC, or TG at baseline, or those receiving lipid-lowering therapy at enrollment and during follow-up, leaving 357,662 participants for the current analysis.UK Biobank data UK Biobank is a large population-based prospective cohort study with over 500,000 individuals aged 40–69 at the time of recruitment in the UK from 2006 to 2010 . All parP < 5 × 10–8) as instrumental variables (IVs) for HDL-C, LDL-C, TC, and TG, respectively  SNPs by applying clumping at a linkage disequilibrium (LD) threshold of r2 < 0.01 , involving 6,604 cases and 299,094 controls.CKD GWAS The hitherto largest GWAS of CKD was conducted by the CKDGen Consortium, meta-analyzing data from 23 participating studies combining 41,395 cases and 439,303 healthy controls  [P < 0.05.Baseline characteristics of participants from the UK biobank were described as mean ± standard deviation (SD) for continuous variables and count (percentage) for discrete variables. We constructed a Cox proportional hazards regression model with baseline-measured HDL-C, LDL-C, TC, and TG as the exposure. We used three sets of adjustments. Estimates in model 1 (basic model) were adjusted for age, sex, region, body mass index (BMI), hypertension, diabetes mellitus, and the top 10 genetic principal components. Estimates in model 2 were further adjusted for income, Townsend deprivation index, smoking, drinking, physical activity, sleep duration, antihypertensive medications, and hypoglycemic medications usage at baseline and during follow-up. Estimates in model 3 (full model) were further adjusted for lipid fractions. In the sensitivity analysis, we excluded participants with less than a year of follow-up or a diagnosis of CKD within one year after enrollment. We further divided plasma lipids into quartiles and repeated the above analysis. All statistical analyses were performed using SAS version 9.4 . Statistical tests were two-sided, and significant levels were set at P < 6.25 × 10−3  and suggestive −3 ≤ P < 0.05.We quantified the genome-wide genetic correlation using LD score regression (LDSC) . The genP < 2.12 × 10–5 = 0.05/2,353) was applied to determine statistical significance.We further estimated the pairwise local genetic correlation using SUPERGNOVA . This alHet, a statistic that is more powerful when heterogeneity exists. We used the PLINK clumping function to obtain independent shared variants (parameters: -clump-p1 5e-8 -clump-p2 1e-5 -clump-r2 0.1 -clump-kb 500) [PCPASSOC < 5 × 10–8 and Psingle-trait < 1 × 10–5 (for both traits). Novel shared SNP was defined if all following conditions were satisfied: (1) the SNP did not reach genome-wide significance (5 × 10–8 < Psingle-trait < 1 × 10–5) in original single-trait GWAS; (2) the SNP was not in LD (r2 < 0.1) with any of those previously reported genome-wide significant SNPs in single-trait GWAS. Ensemble Variant Effect Predictor [We next conducted a Cross-Phenotype Association (CPASSOC) analysis to identify potential pleiotropic variants that affect both traits . We comp-kb 500) . Signifiredictor  was applWe performed a fine-mapping analysis using FM-summary  to identWe performed a transcriptome-wide association study (TWAS) using FUSION based on 49 GTEx (version 8) tissue expression weights to investigate gene expression in specific tissues . We firs−3  was applied, and 6.25 × 10−3 ≤ P < 0.05 was defined as suggestive significance. An effect estimate was considered putative causal if it was significant in IVW and showed directional consistency in the MR-Egger regression and weighted median approach. We conducted an additional sensitivity analysis by excluding palindromic IVs with strand ambiguity and replicating the significant associations using nonoverlapped data. We further performed a multivariable MR (MVMR) approach to disentangle the independent effect of each lipid exposure while controlling for genetic predisposition to the remaining lipids traits, type 2 diabetes (T2D)[F-statistic [Two-sample MR analyses were performed using summary statistic data to investigate the causal relationship. The inverse-variance weighted (IVW) approach was applied as the primary method . As a cotes (T2D), hypertetes (T2D), and BMItes (T2D). Finallytatistic  for eachThe baseline characteristics of UKB participants included in the observational analysis were presented in Additional file P = 1.00 × 10–4) and TG (P = 2.66 × 10–5). No significant result was found for either LDL-C ( P = 0.95) or TC ( P = 0.93). For kidney function, a similar pattern of correlation was observed , we identified twelve genomic regions  demonstrating a significant local genetic correlation for lipids and CKD .Detailed annotations of each variant are shown in Additional file For all identified pleiotropic SNPs, we determined a 99% credible set of causal SNPs using FM-summary, providing targets for downstream experimental analysis. In particular, we identified only one candidate causal SNP in the 99% credible set for HDL-C and CKD rs1047891) with a posterior probability of 1.00. With regard to kidney function, more causal variants were identified for lipids and eGFR. Lists of candidate causal SNPs at each pleiotropic locus are shown in Additional file 047891 wiColocalization analysis was next conducted to determine whether genetic variants driving the association between different traits were the same. We identified 36.4% of shared loci colocalized at the same candidate SNPs (PPH4 > 0.5) for HDL-C-CKD, including two novel shared SNPs (rs780094 and rs12951387). Regarding kidney function, more causal variants were identified  with the risk of CKD . For kidney function, no causal association was found between genetically predisposed lipids level and eGFR.We continued to conduct a bidirectional MR to evaluate potential casual associations motivated by the significant shared genetic basis. We only identified a causal relationship of genetically predicted HDL-C  , yet it became nonsignificant in MVMR.In the reverse-direction MR, genetic predisposition to CKD or eGFR did not seem to causally affect lipids levels. Genetically predicted CKD was only significantly associated with a higher level of TG  [FNIP1, PFKFB2, LINC00393, GCKR, RER1, and IGF1R) [GGNBP2, ITSN2, and FBXL17) [CHCHD1, PRR12, PKP3, PPM1B, COL8A1, and ACTN4) [ADAM15, RAB5A, and GAB1) [GGNBP2, encoding a gametogenetin-binding protein associated with obesity [GCKR, is a well-characterized pleiotropic SNP. The T-allele of this SNP is associated with a reduced HDL-C level [Results from cross-trait meta-analyses demonstrated the biological pleiotropy between lipids and CKD or kidney function. Multiple novel shared loci were mapped to genes implicated in alcohol intake ( HOMER2) , 39, glud IGF1R) –43, obes FBXL17) , 43, biod ACTN4) –49, and nd GAB1) –52, refl obesity , 43, 53. obesity . SNP rs7-C level  but a lo-C level  and greaOur findings have important clinical and public health implications. Firstly, HDL-C is a causal protective factor for CKD. Our study indicated the long-term renal-protective effect of managing low HDL-C levels. As clinical evidence showed non-significant improvement of kidney function or reduction CKD incidence via current dyslipidemia treatments primarily targeting cholesteryl ester transfer protein inhibition , it is pF-statistics [F-statistics were over 289 for lipid instruments and over 56 for CKD instruments in our study, indicating that the potential bias stemming from sample overlap is likely to be minimal. Our sensitivity MR analysis utilizing non-overlapping GWAS data yielded similar results, thereby mitigating the potential for false positive findings. Furthermore, other genome-wide cross-trait analysis approaches demonstrate robustness in dealing with sample overlap [We acknowledge several potential limitations. Firstly, our findings were restricted to individuals of European ancestry. As the relationships between lipids and CKD risk show significant racial differences , future atistics , the F-s overlap –19. Thir overlap . Recent  overlap , 62. DesThe current study confirms a putative causal effect of HDL-C on CKD through an observational and genetic analysis of European ancestry. Dyslipidemia management targeting low HDL-C levels could help mitigate the long-term burden of CKD. Future studies should consider the potential for non-linear relationships as well as HDL functionality in CKD.Additional file 1: Table S1. Characteristics of genetic instruments of HDL-C. Table S2. Characteristics of genetic instruments of LDL-C. Table S3. Characteristics of genetic instruments of TC. Table S4. Characteristics of genetic instruments of TG. Table S5. Characteristics of genetic instruments of chronic kidney disease. Table S6. Characteristics of genetic instruments of estimated glomerular filtration rate. Table S7. Data sources, sample sizes, number of instruments, and F-statistics. Table S8. Baseline Characteristics of UK Biobank participants by chronic kidney disease status during follow-up. Table S9. Observational associations between lipids and the risk of chronic kidney disease. Table S10. Pleiotropic SNPs identified by cross-trait meta-analysis between lipids and chronic kidney disease. Table S11. Pleiotropic SNPs identified by a cross-trait meta-analysis between lipids and estimated glomerular filtration rate. Table S12. Detailed annotation of shared SNPs of HDL-C and chronic kidney disease identified from a cross-trait meta-analysis. Table S13. Detailed annotation of shared SNPs of LDL-C and chronic kidney disease identified from a cross-trait meta-analysis. Table S14. Detailed annotation of shared SNPs of TC and chronic kidney disease identified from a cross-trait meta-analysis. Table S15. Detailed annotation of shared SNPs of TG and chronic kidney disease identified from a cross-trait meta-analysis. Table S16. Detailed annotation of shared SNPs of HDL-C and estimated glomerular filtration rate identified from a cross-trait meta-analysis. Table S17. Detailed annotation of shared SNPs of LDL-C and estimated glomerular filtration rate identified from a cross-trait meta-analysis. Table S18. Detailed annotation of shared SNPs of TC and estimated glomerular filtration rate identified from a cross-trait meta-analysis. Table S19. Detailed annotation of shared SNPs of TG and estimated glomerular filtration rate identified from a cross-trait meta-analysis. Table S20. List of SNPs in the 99% credible set identified from fine-mapping analysis for each CPASSOC-identified locus shared between HDL-C and chronic kidney disease. Table S21. List of SNPs in the 99% credible set identified from fine-mapping analysis for each CPASSOC-identified locus shared between LDL-C and chronic kidney disease. Table S22. List of SNPs in the 99% credible set identified from fine-mapping analysis for each CPASSOC-identified locus shared between TC and chronic kidney disease. Table S23. List of SNPs in the 99% credible set identified from fine-mapping analysis for each CPASSOC-identified locus shared between TG and chronic kidney disease. Table S24. List of SNPs in the 99% credible set identified from fine-mapping analysis for each CPASSOC-identified locus shared between HDL-C and estimated glomerular filtration rate. Table S25. List of SNPs in the 99% credible set identified from fine-mapping analysis for each CPASSOC-identified locus shared between LDL-C and estimated glomerular filtration rate. Table S26. List of SNPs in the 99% credible set identified from fine-mapping analysis for each CPASSOC-identified locus shared between TC and estimated glomerular filtration rate. Table S27. List of SNPs in the 99% credible set identified from fine-mapping analysis for each CPASSOC-identified locus shared between TG and estimated glomerular filtration rate. Table S28. Results from colocalization analysis for each pleiotropic locus identified from CPASSOC between lipids and chronic kidney disease. Table S29. Results from colocalization analysis for each pleiotropic locus identified from CPASSOC between lipids and estimated glomerular filtration rate. Table S30. Significant shared transcriptome-wide association analysis results between lipids and chronic kidney disease after Conditional/Joint Analysis. Table S31. Significant shared transcriptome-wide association analysis results between lipids and estimated glomerular filtration rate after Conditional/Joint Analysis. Table S32. Estimated causal association between HDL-C and chronic kidney disease using non-overlapped data.Additional file 2: Figure S1. Mendelian randomization analysis between chronic kidney disease and lipids traits. The boxes denote the point estimate of the causal effects, and the error bars denote 95% confidence intervals (95%CI). MVMR models were adjusted for body mass index, hypertension, and type 2 diabetes. HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; TG, triglycerides; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; MVMR, multivariable mendelian randomization.


Stipa krylovii, a dominant species in the Inner Mongolia Steppe of China, and evaluated the effects of phenotypic plasticity and genetic differentiation, the effects of climate variables on population trait differentiation, and traits coordinated patterns under each soil moisture treatment. We selected seeds from seven populations of S. krylovii in the Inner Mongolia Steppe, China, and carried out a soil moisture (2) × population origin (7) common garden experiment at Tianjin City, China, and measured ten plant traits of S. krylovii. General linear analyses were used to analyze how soil moisture and population origin affected each trait variation, Mantel tests were used to analyze population trait differentiation—geographic distance (or climatic difference) relationships, regression analyses were used to evaluate trait‐climatic variable relationships, and plant trait networks (PTNs) were used to evaluate traits coordinated patterns. Both soil moisture and population origin showed significant effects on most of traits. Aboveground biomass, root‐shoot ratio, leaf width, specific leaf area, and leaf nitrogen (N) content were significantly correlated with climate variables under the control condition. Specific leaf area and leaf N content were significantly correlated with climate variables under the drought condition. By PTNs, the hub trait(s) was plant height under the control condition and were aboveground biomass, root length, and specific leaf area under the drought condition. This study indicates that both phenotypic plasticity and genetic differentiation can significantly affect the adaptability of S. krylovii. In addition, soil moisture treatments show significant effects on trait‐climate relationships and traits coordinated patterns. These findings provide new insights into the adaptive mechanisms of zonal species in the semiarid grassland region.Understanding patterns of intraspecific trait variation can help us understand plant adaptability to environmental changes. To explore the underlying adaptation mechanisms of zonal plant species, we selected seven populations of  Stipa krylovii, and that the environmental changes can also affect traits coordinated patterns. These findings provide new insights to the adaptive mechanisms of distribution change of dominant species of zonal vegetation.This article provides that both quick environmental changes (soil moisture) and long evolutionary history (population origin) can significantly affect adaptability of  Therefore, exploring how traits of S. krylovii are affected by aridity (soil moisture) and whether the population origins affect the effects of aridity on traits would advance our understanding of its adaptive strategies and distribution region changes. Besides, exploring how soil moisture affects the correlations among traits or those between climatic variables and traits of S. krylovii is helpful for us to understand and predict this species evolutionary potential in the context of increasing aridity.Population responses to environmental changes showing differences are common in zonal vegetation  × population origin (7) common garden experiment. Ten traits of S. krylovii and their plasticity indices across two soil moisture treatments were measured. We explored how these traits were affected by soil moisture, population origin, and their interaction, and further analyzed how soil moisture affected the correlations among traits and the relationships between climatic variables and traits. Specifically, we proposed the following hypotheses based on the existing knowledge. Firstly, both genetic differentiation caused by population origin and phenotypic plasticity caused by soil moisture treatment would affect the population trait differentiation of S. krylovii, but their relative contributions would show trait‐specific  and their local climatic variables would be common , and the interannual variability of precipitation, that is, the coefficient of variation (CV) of precipitation, was calculated by the ratio of standard deviation to mean of 20 years (from 1990 to 2009).Five climatic variables of each seed sampling site, including the annual mean temperature, annual precipitation, temperature seasonality, precipitation seasonality, and interannual variation of precipitation, were selected following previous findings . During the experiment, all individuals survived and the position of the pots was changed every week to avoid position effects. The experiment lasted 50 days and ended on July 10, 2009.A two‐factor (soil moisture × population origin) common garden experiment was carried out at Nankai University . The experiment was established in plastic pots (20 cm in diameter and 21 cm in height), with a mixture of vermiculite and nutrient soil (1:1) as culture substrate, soil moisture condition was quantified by volumetric water content (VWC). Considering that the water retention of nutrient soil was weaker than that of the local habitat soil in Inner Mongolia Steppe, we set 25% VWC for control treatment and 8% VWC for drought treatment, supplying an amount of water equal to transpiration losses every other day. The second factor was population origin, including seven different populations Table . Each tr2.3On the 30th day of the experiment, 10 traits related to resource acquisition and investment were measured following standard protocols, with at least 10 replications per treatment. Plant height per pot was the distance between the highest stem leaf and ground level. Fully expanded healthy leaves were used for the measurement of leaf traits. Leaf length, length width, and leaf area per leaf were measured using a Li‐3100 leaf area meter , and the leaf dry mass was weighed after 72 h in an oven at 60°C. Specific leaf area was determined as leaf area / dry mass. Dried leaves were ground for the measurement of leaf N content using an elemental analyzer . The clean fresh roots per pot were used for the measurement of root length after harvest using the WinRHIZO root analysis system (EPSON A3 Transparency Unit / EU‐88). The ratio of root length to belowground biomass was calculated as specific root length.At the end of the experiment, the shoots and roots were carefully harvested by pot and dried in an oven at 60°C for 72 h to obtain the aboveground biomass and the belowground biomass. The ratio of belowground biomass to aboveground biomass was calculated as the root‐shoot ratio (RSR).PI) was quantified by using the trait values calculated under both soil moisture treatments with the following formula  of multiple comparison.2.5lm function in R (R 4.1.1), with soil moisture and population origin as fixed factors, and initial tillers as a covariable. Variance explained by each factor was calculated by the mean square of each factor into its variance component  were performed using the 2.6distm function of the “geosphere” package (version 1.5‐18). The climate difference matrix was calculated by Euclidean distance between populations after z‐score standardizing of the five climatic variables  using the dist function of the “geosphere” package (version 1.5–18). Population trait differentiation was also calculated by Euclidean distance using the dist function of the “geosphere” package (version 1.5–18) for data collected under each soil moisture treatment.In order to investigate whether population trait differentiation is affected by the geographical distance or climatic difference of population origin, we calculated the geographical distance matrix and climatic difference matrix among seed sampling sites. The geographical distance (km) was calculated according to the longitude and latitude of sampling sites using the Pearson function of the “vegan” package (version 2.6‐4).Finally, the Mantel test was used to test the correlation of trait‐geographic distance matrixes, and trait‐climate difference matrixes using the 2.7lm and polynomial functions in R, with climate variables as independent variables and traits as dependent variables.The relationship between each trait measured under each soil moisture treatment and each climate variable in Table 2.8igraph” (version 1.4.1) in R. Before the analyses, correlations between traits are tested by Pearson correlation.Plant trait networks (PTNs) enable us to observe association among plant traits. We used it to analyze traits coordinated patterns by the package “ED), diameter (D), average path length (AL), and average clustering coefficient (AC) were selected as the key parameters , closeness (C), and betweenness (B) were selected as the key parameters  describes the density of edges connecting traits, and a PTN with a higher edge density (ED) may have more efficient resource utilization and integration.The edge density (D) and the average path length (AL) are the maximum value and the average value of the shortest path length between any two traits, respectively. A PTN with a higher D or a higher AL means higher independence among traits, which is less easy to form functional clustering.The diameter (AC) is the average of clustering coefficients of all traits, and a PTN with a higher AC means more possibility to form traits' clusters to achieve specific functions.The average clustering coefficient (k) is the number of connections in which a trait node is significantly related to other traits. A trait with the highest k in a PTN can interact with more other traits and further adjust the whole traits network, and is called a hub trait.The degree (C) is the reciprocal of the shortest mean path between a trait and another trait. A trait with a relatively higher C indicates that the trait is more closely related to other traits.The closeness (B) of a trait is the number of all the shortest paths through the trait. A trait with the highest B in a PTN can play the role of “mediators” in connecting coordinates several subnetworks or functional clusters, and is called mediator trait.The betweenness  out of the ten traits revealed significant regression relationships with at least one climatic variable, while no traits showed significant regression relationships with the annual precipitation Figure .Under the control condition, aboveground biomass showed a convex relationship with the interannual variation of precipitation Figure . Root‐shUnder the drought condition, specific leaf area had a significant negative relationship with annual mean temperature Figure , and lea3.3The correlation coefficients and significance between any pairwise traits were shown in Table ED and AC were lower under the control condition (0.305 and 0.593) than those under the drought condition (0.428 and 0.938); while the D and AL were higher under the control condition (5 and 2.639) than those under the drought condition (2 and 1.077) , and aboveground biomass showed the highest closeness (C𝑖 = 0.063) and betweenness (Bi = 16) among all traits. Under the drought condition, aboveground biomass, root length, and specific leaf area showed the highest values for degree, closeness, and betweenness among all traits  showed the highest contribution to variance in leaf width and specific leaf area Table  demonstr4.2Under the control condition, the finding that populations from higher temperature seasonality and precipitation seasonality had higher specific leaf area and higher leaf N content indicated that these populations are related to faster acquisition of light and nutrient resources. In recent years, many studies have shown the important role of seasonal climate in driving plant trait differentiation, especially specific leaf area of grasses ; data curation ; formal analysis ; methodology ; resources ; software (lead); validation (lead); visualization (lead); writing – original draft (lead); writing – review and editing . Baijie Fan: Data curation ; formal analysis ; methodology ; resources ; visualization ; writing – review and editing . Ziqing Gong: Data curation ; formal analysis ; software ; validation ; writing – review and editing . Luoyang He: Data curation ; formal analysis ; software ; visualization ; writing – review and editing . Lei Chen: Methodology ; resources ; supervision . Anzhi Ren: Conceptualization ; data curation ; methodology ; resources ; supervision ; writing – review and editing . Nianxi Zhao: Conceptualization ; data curation ; formal analysis ; funding acquisition (lead); methodology ; project administration ; resources (lead); validation (lead); writing – review and editing . Yubao Gao: Conceptualization ; data curation ; methodology ; resources ; validation .This work was supported by the National Natural Science Foundation of China .All authors had no conflict of interest to declare.Appendix S1Click here for additional data file.