Polygenic risk scores (PRS) have been successfully developed for the prediction of human diseases and complex traits in the past years. For drug response prediction in randomized clinical trials, a common practice is to apply PRS built from a disease genome-wide association study (GWAS) directly to a corresponding pharmacogenomics (PGx) setting. Here, we show that such an approach relies on stringent assumptions about the prognostic and predictive effects of the selected genetic variants. We propose a shift from disease PRS to PGx PRS approaches by simultaneously modeling both the prognostic and predictive effects and further make this shift possible by developing a series of PRS-PGx methods, including a novel Bayesian regression approach (PRS-PGx-Bayes). Simulation studies show that PRS-PGx methods generally outperform the disease PRS methods and PRS-PGx-Bayes is superior to all other PRS-PGx methods. We further apply the PRS-PGx methods to PGx GWAS data from a large cardiovascular randomized clinical trial (IMPROVE-IT) to predict treatment related LDL cholesterol reduction. The results demonstrate substantial improvement of PRS-PGx-Bayes in both prediction accuracy and the capability of capturing the treatment-specific predictive effects while compared with the disease PRS approaches. To try to predict an individual’s drug response using genetic data, most studies have used traditional polygenic risk score (PRS) methods. Here, the authors develop a pharmacogenomics-specific PRS method, which can improve drug response prediction and patient stratification in pharmacogenomics studies. Efficacy PGx studies have great potential to guide treatment options by integrating routine pharmacogenomic screening into clinical development and proposing novel strategies for identifying genetic markers that impact efficacy for new compounds 4. In the domain of precision medicine, many associations between genetic variation and inter-individual difference in drug response have been discovered to tailor treatments to the genetic makeup of the patient4. However, the conventional single variant PGx biomarkers or drug response predictors usually rely on large effect sizes. Genetic variants with small but genuine effects may not reach the significance threshold in a typical PGx analysis of hundreds to a few thousand subjects. Recent developments in disease genetics reveal that the polygenicity, i.e., many small genetic effects, is present in many complex traits5. This observation provides evidence to support modeling and predicting disease status by combining the effects of many weak signals.Pharmacogenomics (PGx), an important tool for precision medicine, studies how pharmacokinetics, pharmacodynamics, efficacy, and safety responses to drugs are associated with genetic information at the molecular level of treated subjects7, cancer9, etc. A large variety of methods have been developed for constructing PRS. To name a few, they include (1) the unadjusted method which builds PRS using the unadjusted effect estimates of SNPs across whole genome; (2) the Clumping and Thresholding (C+T) method10 which builds a few PRSs using independent SNPs that pass different significance thresholds and the optimal threshold is selected according to their performance in an independent study; (3) the Lassosum method11 which uses penalized regression to select informative SNPs by incorporating linkage disequilibrium (LD) information, and (4) the Bayesian regression methods, e.g., LDpred12, LDpred213, and PRS-CS14, which shrink the marginal effect sizes with respect to LD. Among them, the more sophisticated penalized regression and Bayesian regression methods have been shown to achieve better performance over the C+T method13.Polygenic risk score (PRS), defined as the weighted sum of the effect sizes of many polymorphisms, is a rapidly emerging tool in the disease genetics field. PRS reflects the overall genetic risk of a phenotype of interest and can be used as a stratification mechanism for downstream analyses and decision making. In disease genetics, PRSs have been successfully developed for multiple complex diseases including coronary artery disease15. Despite recent development in PRS methods and their exciting applications in disease genetics, similar analytic methods have largely not yet been successfully adapted to drug responses in PGx16. There are emerging examples published so far, which build PRS from disease GWAS using SNPs with treatment unrelated prognostic effects only and then test whether the PRS is predictive of drug responses in one or several PGx studies21. However, this current practice of building disease PRS and applying to PGx data  has not been fully justified in theory. In fact, we can show (Results section) that the PRS-Dis approach relies on a very stringent assumption that every variant selected for constructing PRS should have a constant ratio between its genotype main effect and genotype-by-treatment interaction effect, which may not be true in real PGx data. On the other hand, to the best of our knowledge, only a few published studies directly build PRS from drug-related data for safety or efficacy PGx prediction. For example, Lanfear et al.16 build an efficacy PGx PRS for β-blockers using observational data. Koido et al.22 build a PGx PRS for drug-induced liver injury using a method similar to C+T. Lewis et al.23 build an efficacy PGx PRS for clopidogrel response in terms of cardiovascular outcomes using single-arm clinical data. However, there is limited methodological development on the adaptation of PRS methods in disease genetics to PGx where data from both treatment and placebo (or control) arms are available.Like many complex traits, most drug responses in PGx are extremely polygenic14 to PGx GWAS, the PRS-PGx-Bayes method is able to infer the posterior prognostic and predictive effects simultaneously.To tackle the challenges of the complex drug response prediction and the lack of state-of-the-art PGx PRS methods, we propose to shift from the disease PRS approach to the PGx PRS approach by jointly modeling the genetic main effect and the genotype-by-treatment interaction effect . We systematically extend the current PRS-Dis methods to construct both prognostic and predictive PRSs for drug response prediction in PGx studies. These methods include PRS-PGx-Unadj (Unadjusted), PRS-PGx-CT (Clumping + Thresholding), PRS-PGx-L, -GL, -SGL , and PRS-PGx-Bayes (Bayesian regression) methods. Our proposed methods use only PGx genome-wide association summary statistics and an external LD reference panel except for the penalized regression-based methods, which require access to individual level genetic and phenotypic data. Moreover, by extending the idea of global-local scaling parameters from disease GWAS24 PGx GWAS summary statistics data25 to predict treatment-related LDL cholesterol reduction. The drug response prediction results demonstrate a substantial improvement of PRS-PGx-Bayes in both prediction accuracy and the capability of capturing the predictive effect over alternative methods.Our simulation studies demonstrate that PRS-PGx methods generally outperform the PRS-Dis methods across a wide range of genetic architectures and PRS-PGx-Bayes is superior to all other PRS-PGx methods. These methods are further applied to the IMPROVE-IT n patients and m SNPs for a drug response:Y denotes a quantitative trait (drug response), T the binary treatment assignment, X the n × p matrix of covariates, and G the n × m genotype matrix; β is a m × 1 vector of prognostic effects , α is a m × 1 vector of predictive effects , and ϵ is the random error. In practice, the phenotype Y can first be adjusted by the covariates X and the treatment T, before application to any PRS-PGx algorithms. For simplicity, we will use Y as the phenotype after such adjustment in the later discussion.We consider a high-dimensional regression model of b =  is assumed to be fixed in the PRS-PGx-Unadj, PRS-PGx-CT and PRS-PGx-L, -GL, -SGL methods and random in the PRS-PGx-Bayes method. Specifically, for each j = 1, ⋯  , m, we consider the following prior distribution of bj =  in the Bayesian approach:ϕ is a global scaling parameter that is shared across multiple SNPs and controls the degree of the model sparseness; ψj and ξj are local and marker-specific scaling parameters; ρj is the marker-specific correlation between the two effect sizes βj and αj; and g is a probability density function of a random matrix.The regression coefficient best = . The prognostic and predictive PRSs are defined as the weighted sum of the selected SNPs’ genotypes, where the weights are the estimated prognostic and predictive effect sizes, respectively,In PRS-PGx methods, SNPs are used for the construction of prognostic PRS and predictive PRS based on their estimated The predictive PRS is useful for patient stratification by aggregating the differential treatment effects. We can define the PGx PRS asGi, i = 1, . . . , m are standardized: EGi = 0, var(Gi) = 1; (ii) E(ϵ) = 0, var(ϵ) = σ2; (iii) b is defined in Eq.  is the observed response of a subject in the treatment arm with SNPs Gi, i = 1, . . . , m; SDis and SPGx∣(T = 1) are the perfect polygenic scores for this treated subject from disease GWAS and PGx, respectively. We will drop the condition notation ‘∣(T = 1)’ hereafter when there is no ambiguity. We prove  → h2. If, however, we have strong interaction effect and no main effect, R → 0, then cor2 → 0. This observation is consistent with the intuition that disease PRS approach, which ignores the treatment-by-genotype effects, is less ideal if such effects are strongly present.In the scenario that all interaction effects are independent of main effects, i.e., , m, Eq.  is reduc2 ≤ h2 and the equality holds if and only ifSDis works only under an extremely stringent assumption that every causal variant must have the same interaction effect proportionate to its main effect. We also consider the situations when the regression coefficients βi, αi, i = 1, . . . , m, are fixed constants  as external reference panel, we can calculate the cor2 = h2(1 − 0.54), which means the PRS developed from any disease GWAS can at most explain 46% genetic variability of the drug response. In addition, we also calculate the ratio of genetic main effect to interaction effect, c, for the SNPs (after clumping with 250 kb window size and LD r2 > 0.8) across whole genome  and the top SNPs defined by p-values of 2df (joint G and G × T) two-sided test26 less than three thresholds 1e−06 (m = 16), 1e−05 (m = 81), and 1e−04 (m = 472), respectively. Figure By using the IMPROVE-IT PGx GWAS summary statistics data and 1000 Genomes (1KG) Phase 3 data  may vary from different LD blocks. The full details of data generation process are provided in the “Methods” section.We simulated SNPs’ prognostic and predictive effects from a bivariate normal distribution, as described in Eq. . SpecifiR2 and the statistical significance of its predictive effect. Similarly, among three penalized regression approaches, PRS-PGx-GL was consistently favored in the current simulation setting. Therefore, in the remaining simulations and real data analyses, we focused on only PRS-Dis-LDpred2 among all PRS-Dis methods; and only PRS-PGx-GL among all the PGx penalized regression methods.Before we compared PRS-PGx and PRS-Dis methods, we first assessed the performance among the three disease PRS methods  and the three machine learning-based PRS-PGx methods , respectively. As shown in Supplementary Fig. SPGx quantified by R2 between the observed and predicted phenotypes in two arms; (ii) the predictive effect measured by the −log10 from the two-sided likelihood ratio test (LRT) of Spred × T interaction; and iii) R2 of the SPGx under treatment and control arms, respectively. The statistical details about the above analyses are provided in the “Methods” section.Five polygenic prediction methods, PRS-Dis-LDpred2, PRS-PGx-Unadj, PRS-PGx-CT, PRS-PGx-GL, and PRS-PGx-Bayes, were compared across different settings of sample sizes, number of causal variants, heritabilities and effect sizes. The tuning parameters such as the p-value threshold in PRS-PGx-CT, the penalty parameter in PRS-PGx-GL, and some prior distribution parameters in PRS-PGx-Bayes were selected via 5-fold cross-validation (CV). The 1000 Genomes Project European population data was used as an external reference panel for LD. The performance was evaluated in an independent testing set (sample size = 1000) in terms of (i) the prediction accuracy of R2 of different methods in the treatment and control arms, respectively  across different methods in Fig. R2. P-values, obtained by the two-sided LRT of SPGx from Y ~ SPGx under two arms, respectively, were provided in Supplementary Fig. In addition to prediction accuracy, we summarized the predictive p-values  under four different genetic architectures, from no signal  = 0), sparse signals  = 0.001), to dense signals  = 0.01 or 0.1). In a scenario when no causal variants were simulated , PRS-PGx-CT misidentified a few SNPs. However, the type I error rate was still well controlled. More specifically, PRS-PGx-CT misidentified 7 SNPs, which is comparable to the expected number of false positives . Both PRS-PGx-GL and PRS-PGx-Bayes shrank prognostic and predictive effect sizes to zero. In this scenario when 5, 50, or 500 causal variants were simulated, PRS-PGx-Bayes more accurately estimated the genetic effects compared with the other methods.As shown in Supplementary Fig. R2 and −log from using the full genotype matrix to using uniform blocks with size 200. However, such differences across different types of blocks are limited, especially between LD blocks and full genotype matrix. For example, when P = 0.001, compared to using the full genotype matrix, the LD block approach only decreases R2 by 0.4%. The relative decreases of LD block approach compared to the full genotype approach was summarized in Supplementary Table To further assess the impact of different implementation strategies, we also performed additional simulations where PRS-PGx-Bayes function was applied on LD blocks jointly . The simulation settings remained the same as described in Fig. H2 = 0.1 and 0.5 and the results showed a very similar pattern , the sparse group Lasso-based method (PRS-PGx-SGL) performed the best among the three penalized regression methods, while PRS-PGx-Bayes still outperformed all the other methods , it was not surprising that PRS-Dis-LDpred2 was at least comparable to most PRS-PGx methods in terms of the prediction accuracy R2. But still, the performance of the PRS-Dis methods was much worse than PRS-PGx methods in terms of capturing the predictive effect.To assess the performance of the proposed methods under different heritabilities, we conducted sensitivity analyses by setting β/α) for each causal variant was either 0 or ∞. The details of data generation are provided in the “Methods” section.We simulated completely separate sets of prognostic and predictive SNPs so that no SNPs were both prognostic and predictive. Under this condition, the ratio of main to interaction effects  = 0.01. The tuning parameters were selected via cross-validation. The computation was completed on a single core of 2.4 GHz Intel Core i5. We summarized the result in Supplementary Fig. m2 to m3, where m denotes the number of variants. The result also shows that it took roughly 5.9 hours for the largest LD block and 1 h for the median-size LD block . As a sensitivity analysis, we also explored scenarios by randomly choosing 1000, 3000, 5000, 7000, 9000 SNPs from that block. The real genetic data was obtained from the IMPROVE-IT trial with a sample size of 5661. The effect sizes and phenotype data were simulated with heritability fixed at 0.3, We applied the four proposed PRS-PGx methods  and the other two PRS-Dis methods (PRS-Dis-CT and PRS-Dis-LDpred2) to the IMPROVE-IT PGx GWAS summary statistics data to predict the low-density lipoproteins cholesterol (LDL-C) log-fold change at 1-month from the two treatment arms. The two treatment arms are the treatment arm with the combined therapy (Ezetimibe + Simvastatin: 10 mg + 40 mg) and the active control arm with monotherapy (Simvastatin: 40 mg). We adjusted for the age, gender, prior lipid-lowering (PLL) therapy, early glycoprotein IIb/IIIa inhibition in non-ST-segment elevation acute coronary syndrome (EARLY ACS) trial, high-risk ACS diagnosis, baseline LDL-C level, and five top principal components when generating the IMPROVE-IT summary statistics data for the LDL-C drug response phenotype.p-value cutoff for PRS-PGx-CT, penalty λ for PRS-PGx-GL and  for PRS-PGx-Bayes). We compared performance across different methods with the results summarized from the testing set. The prediction accuracy was measured by R2 and summarized in Table p-values, and shown in this table as well.To apply PRS-PGx methods to the IMPROVE-IT data, we used nested cross-validation. More specifically, the IMPROVE-IT data was split into five folds in the outer layer of cross-validation with four for training and one for testing. The training set was used to obtain the PGx GWAS summary statistics. In the inner layer of cross-validation, the training set was further split into four folds, three for training and one for validation, to select the optimal tuning parameters . On the other hand, the PRS-Dis method PRS-Dis-LDpred2 was superior to PRS-PGx methods in terms of R2 under the control arm, which might be partially due to the fact that we used the disease GWAS statistics data with much larger sample size  for constructing the disease PRS in the PRS-Dis analyses. But our proposed method PRS-PGx-Bayes still provided a comparable R2 prediction performance . In terms of predictive p-value, PRS-PGx-Bayes yielded a much more statistically significant predictive (or interaction) p-value 5.4e−05 while compared to 0.033 from PRS-Dis-LDpred2. In addition, p-values obtained by the LRT of SPGx showed very similar pattern as the R2 in either treatment or control arm. Table SPGx from the model Y ~ SPGx under treatment arm were all negative across different PRS-Dis and PRS-PGx methods, indicating that a larger PRS would result in more LDL reduction after 1-month treatment of Ezetimibe + Simvastatin. In the meantime, PRS-PGx-Bayes method outperformed the others with the largest absolute value of effect size Spred from the model Y ~ T + Sprog + T × Spred were all negative across all methods, implying that a larger predictive score would result in a larger treatment effect . PRS-PGx-Bayes method is also superior to the others with the largest absolute value of effect size Consistent with previous simulation results, the two PRS-Dis methods performed poorly in terms of We further compared the patient stratification performance across different methods with the results summarized in Fig. p-value cutoff determined as 5e−08 and PRS-PGx-CT selected 91 SNPs into the PGx PRS with the optimal p-value cutoff selected as 1e−07. The number of SNPs identified by PRS-PGx-CT is much smaller because the sample size of the IMPROVE-IT PGx GWAS data is much smaller than that from the disease GWAS summary statistics data. For PRS-PGx-Bayes, it tends to shrink effect sizes of most SNPs close to zero , which is consistent with our simulation results. Distributions of the predictive effect sizes of the whole genome SNPs estimated by PRS-PGx-CT, -GL, and -Bayes are shown in Supplementary Fig. https://genetics.opentargets.org/).In terms of variant selection, PRS-Dis-CT identified 280 SNPs to construct disease PRS with the optimal p-value thresholding method (PRS-PGx-CT), three penalized regression methods , and a novel Bayesian regression method (PRS-PGx-Bayes). Except for the penalized regression-based algorithms, all the other methods can take PGx summary statistics as input without relying on individual-level genotype and phenotype data. Compared with the PRS-Dis methods, the PRS-PGx approaches can shrink variants’ main and interaction effect sizes simultaneously, and construct PGx scores including a prognostic PRS and a predictive PRS. Thus, in our PRS-PGx-Bayes method, we propose to accommodate both effects and their correlation by modeling a variance-covariance matrix. Although the inverse Wishart (IW) prior is widely used, in this paper, we choose to use the hierarchical half-t prior27 instead due to the limitation of IW . Moreover, by introducing global-local continuous shrinkage priors on SNP effect sizes, our proposed PRS-PGx-Bayes method is more robust to varying relationships between main and interaction effects compared to other PRS-PGx methods. Our extensive simulation studies and the PRS analyses based on the IMPROVE-IT PGx GWAS data show that the proposed PRS-PGx methods generally outperform the PRS-Dis methods in terms of the prediction accuracy R2 and the significance of their predictive effects. Furthermore, the PGx PRS Bayesian approach (PRS-PGx-Bayes) is superior to all the other methods, under different genetic architectures. Interestingly, we find that the C+T method (PRS-PGx-CT) can outperform the penalized regression-based methods  in some of the scenarios. This pattern was also observed in Mak et al.11. Our results suggest that Lasso-based methods are sensitive to the noise, and perform poorly when the signal-to-noise ratio is small , a clumping and 29 can result in decent power to detect variants associated with drug response phenotypes and further enable good drug response prediction performance of PGx PRS. On the other hand, the availability of summary statistics from PGx GWAS is expected to increase quickly . Therefore, statistical methods customized for PGx PRS analysis are urgently needed for drug response prediction and patient stratification in PGx GWAS studies, with the ultimate goal of achieving precision medicine. Unfortunately, there are currently very limited efforts of constructing PGx PRS and successfully applying to efficacy-based PGx studies23. The most popular practice of PRS analysis strategies in literature is to construct PRS from disease GWAS and apply to PGx data. We used rigorous statistical modeling to prove that PRSs built from disease GWAS lack the prediction potential in reaching the full heritability of drug responses. Qualitatively speaking, the disease PRS approach is only able to fully predict drug responses under an extremely strong assumption that all variants used for constructing PRS have the same relationship between their main effect and interaction effect . The violation of such assumption may result in very limited predictive power. For example, we showed in our theoretical analysis that any PRS developed from disease GWAS can explain at most 46% variability of the LDL-C drug response in the IMPROVE-IT PGx GWAS data. In addition, the disease PRS likely lacks the molecular specificity to be directly informative for clinical interventions30 since the PRS is built from variants with only prognostic effects . This may partially explain why there has been little investigation into PGx PRS and very few successful PGx PRS applications have been published for predicting drug responses in the PGx space so far. One successful example using an analysis strategy most closely resemble our PGx PRS analysis strategy is from Lanfear et al.’s paper16. A direct PGx polygenic response predictor (PRP) was constructed from a genome-wide analysis of β-Blocker (BB) x SNP interaction and successfully predicted all-cause mortality  in European ancestry patients with reduced ejection fraction heart failure16. The authors built the PRS with the selected variants  with only predictive effects  and then applied to the validation PGx GWAS data for PRS effect evaluation and patient stratification. Compared with the PGx PRS construction in this example, the PGx PRS analysis strategy embedded in our PRS-PGx methods constructs both the prognostic and predictive PRSs by jointly modeling both effects in the base GWAS data and then tests them on validation data. This provides a systematic way to understand the full picture of PRS’ association with drug response. Furthermore, in addition to the p-value thresholding method, the more advanced (Bayesian regression based) method PRS-PGx-Bayes can also be applied to such application examples, which has been demonstrated to outperform the simple p-value thresholding method in our simulations and real data analyses. In summary, our drug response prediction analysis strategies based on proposed PRS-PGx methods, especially PRS-PGx-Bayes, are highly recommended over the various strategies and existing methods from current literature.Despite the successful application of polygenic risk score in disease genetic studies for disease prediction and stratification, the PRS analyses in PGx studies from randomized clinical trials are usually more complex. On one hand, although sample sizes from PGx studies are usually smaller than those in disease GWAS, the significantly larger effect sizes from PGx studiesThe genetic architectures of responses from a single drug or drug classes determine the proportion of variants across the human genome contributing to their heritability and the proportion of variants with small-, moderate- or large-effects in capturing the heritability. Our research tackles the challenges of PRS methodology in PGx so that PRS can be directly and accurately used for drug response prediction and patient stratification. However, it is usually challenging to find two independent PGx GWAS data or a summary statistics data from a relatively large PGx GWAS study with the trait same or similar as that in an independent testing PGx GWAS study. In addition to discovering the top variants with large effects associated with drug responses (like what most current PGx GWAS do), we call for also carefully examining genetic architectures of drug responses in PGx GWAS and sharing summary statistics data from those studies in the public domain. Furthermore, recording both the prognostic and predictive (the genotype by treatment interaction) effects  as GWAS summary statistics in future PGx studies from randomized clinical trials and sharing them will enable the wide application of the proposed PRS-PGx methods and accelerate the development and deployment of PRS based on PGx data for precision medicine.31 have shown that Genotype-by-Environment Interaction (GEI or G × E) may explain a significant proportion of phenotypes compared with the main genotype (G) test in disease genetics studies. Our proposed PRS-PGx methods can be directly applicable to the scenario of genome-wide GEI test or G + G × E joint test if environmental variables are considered in the PRS development and deployment in disease genetics. In addition, although we mainly demonstrate the PRS-PGx methods in the analysis of continuous drug response phenotypes, all of them can be directly applied to binary  and survival  phenotypes except for PRS-PGx-Bayes. It is straightforward to extend the PRS-PGx-Bayes to analyzing binary and survival endpoints by adopting the Bayesian logistic regression32 and Bayesian Cox proportional hazards model35 instead of the Bayesian linear regression. How to derive their posterior probabilities remains a question for future research. Moreover, our methods are developed based on single-ethnic population . A direct application of this score to other ethnic groups may result in considerable loss in prediction accuracy. With the rapid growth of non-European genomic resources in recent years, it is, therefore, of great both research and public interests to extend the proposed PRS-PGx approaches to the trans-ethnic scenario in future. In addition, for the purpose of effect size shrinkage, the currently existing Bayesian methods lack a systematic way to determine the optimal prior. For example, the LDpred method uses the spike-and-slab prior12; the SBayesR method uses the spike-and-slab prior by replacing normal with mixture normal36; the DPR method uses the Dirichlet process prior37; and Griffin and Brown use the Normal-Gamma shrinkage prior38. The PRS-CS method14 and our proposed PRS-PGx-Bayes method use one of the most popular continuous shrinkage priors . One potential drawback of the Horseshoe prior is that there has been no consensus on how to place a prior for ϕ based on the information about the sparsity39, neither by grid searching for the optimal ϕ (as in PRS-PGx-Bayes), nor by applying full Bayesian inference (as in PRS-CS). Thus, another possible future research direction is to systematically study the impact of different priors on PRS-PGx-Bayes performance and then determine the optimal one. Furthermore, if the Horseshoe prior is used, we may further improve the PRS-PGx-Bayes algorithm by automatically updating  based on the sparsity information instead of using grid searching for the tuning parameters. Last, we suggest applying our proposed methods by LD blocks. Expanding the size of blocks  may slightly improve prediction accuracy but also significantly increase computational costs. On the other hand, further reducing the size of blocks  can reduce run-time but also possibly increase the bias by missing long-range LD. We believe that the by LD block strategy is a right trade-off between decent modeling accuracy and feasible computational time. We also acknowledge that future work is needed to further improve the computational efficiency to incorporate more SNPs for simultaneous analysis.Recent studiesAs next-generation sequencing and other genetic platforms become much cheaper and more routinely embedded within PGx research studies, more rare variants may be evaluated and discovered to associate with drug responses. There will be an increasing need to consider pharmacogenomic variants, both common and rare with either large, moderate, or small effects to predict patients’ drug responses. The PRS-PGx methods we develop are promising in advancing precision medicine by improving drug response (efficacy and/or safety) prediction in PGx studies. Our efforts of developing the PRS-PGx methods, which identify optimal ways for PRS construction by jointly modeling the prognostic and predictive effects, is an important step for accelerating the translation of PRS to clinical practice.The four PRS-PGx methods  we propose are described in this section. We leave the brief description of the PRS-Dis-LDpred2 method to Supplementary Method Y and G have been standardized. Furthermore, we assume the residual variance σ2 follows a non-informative scale-invariant Jeffreys prior, that is, p(σ2) ∝ σ−2 as suggested by Ge et al.14 Regarding the priors of effect sizes, we extend the idea of global-local scale mixtures of normals14 to the two-dimensional scenario as indicated in Eq.  is an absolutely continuous and two-dimensional mixing density function.Recall that the Bayesian regression model has been specified in Eq. , and we σ2, ϕ, and Mj, j = 1, 2, ⋯ , m, and the summary statistics  b isWe first note that, given the prior information ψj ≡ 1, ξj ≡ 1, and ρj ≡ 0, Eq. . Second, assume a one-to-one treatment-placebo allocation ratio (μT = 0.5), unlinked genetic markers (σij ≡ 0 for i ≠ j) and ρj ≡ 0 . We can derive the formulas explicitly for fj ≡ f → 0, we can show that:tj = sj = 1 indicates no shrinkage while tj = sj → ∞ yields full shrinkage. The correlation c between Gj and Gj × T also contributes to the second part of the numerator because the bias induced by the positive correlation needs to be corrected.To provide further insights on Eq. , we cons≡ 0, Eq.  is equivMj denotes the variance-covariance matrix. It is a common practice to use an inverse Wishart (IW) distribution as the conjugate prior for the covariance matrix of a multivariate normal distribution. However, the IW prior has its own limitations. The IW prior imposes a dependency between the correlations and the variances: larger variances automatically imply the absolute value of the correlation near one while small variances imply the correlation near zero41. Therefore, in this study, we propose to use the hierarchical half-t prior27, which is more flexible than the IW prior by adding the degrees of freedom parameter to the scale matrix. Specifically, we assume−1 denotes the inverse Wishart distribution with scale matrix Bj and v degrees of freedom and G is a Gamma distribution. Equation , σ2, ,  sequentially based on their posterior distributions  estimated using data from the 1KG European sample, and further conduct multivariate update of the effect sizes within each LD block. The distributions of block sizes in 1KG and IMPROVE-IT data are summarized in Supplementary Fig. In practice, by using LD information from an external reference panel , the method can be applied to PGx GWAS summary statistics and does not require individual-level genotype and phenotype data. PRS-PGx-Bayes approach updates PRS-PGx-Bayes: Performed within each LD blockSprog) and predictive PRS (Spred). The unadjusted PGx PRS is the sum of all genetic markers across the whole genome, weighted by their marginal prognostic and predictive effect size estimates  p-values not passing the threshold PT, will be shrunk to zero. And then the remaining SNPs are kept with both types of effects. We consider PT ∈ {5e−08, 1e−07, 1e−06, 1e−05, 1e−04, 0.001, 0.01, 0.1, 1} in this paper. The PT value that produces the highest prediction accuracy in a validation data set is selected, and the predictive performance is assessed in an independent testing set.The PRS-PGx-CT method constructs both the prognostic and predictive PRS using the variant LD-clumping and https://cran.r-project.org/web/packages/glmnet/index.html):Xj = , and bj = . ∣∣ ⋅ ∣∣1 and ∣∣ ⋅ ∣∣2 stand for L1-norm and L2-norm, respectively. Assuming if a SNP is included into the model, both prognostic and predictive effects of that SNP may be non-zero, then Group Lasso43 (PRS-PGx-GL based on gglasso R package v1.5 https://cran.r-project.org/web/packages/gglasso/index.html) might be appealing by considering each genetic marker as a group:pj = 2 denotes the group size. Finally, if we assume sparsity at both the group and individual feature levels, we also consider the Sparse Group Lasso44 (PRS-PGx-SGL based on SGL R package v1.3 https://cran.r-project.org/web/packages/SGL/index.html) whose penalty is a linear combination of penalties from Lasso and Group Lasso:In the PRS-PGx-L, -GL and -SGL methods, penalized regression is used to solve Eq.  with indn = 5661 in the PGx subset population) and the 1KG European sample (n = 503) as an external LD reference panel. 5000 SNPs were randomly chosen from LD blocks 31 and 3242 on chromosome 19, which were matched between the IMPROVE-IT and the 1KG datasets. To mimic disease GWAS data, the sample size was increased to n = 20,000 via random mating , 1 − P), j denotes the j-th SNP, and k the k-th LD block. Equation ; and when ψ/ξ = 16 or 1/16 . It is worth noting that Eq. (αj = 0); for the rest half of the causal variants, only predictive effects were kept .We performed simulation studies using real genetic data from the IMPROVE-IT trial , where σ2 was determined by the heritability, which was set to 0.1, 0.3, and 0.5.Five clinical factors  therapy, EARLY acute coronary syndrome (ACS) trial, and high-risk ACS diagnosis) were considered as covariates. To mimic the disease GWAS data, the phenotype was generated asY in the testing set was first adjusted by the clinical factors X and the treatment T, and we obtained the adjusted phenotype Yadj. Then the predictive effect was measured by the p-value from the two-sided likelihood ratio test of Spred × T interaction in the model Yadj ~ Sprog + Spred × T. The prediction accuracies were quantified by R2 between the adjusted phenotype and the predicted ones from Yadj ~ SPGx under two arms, the treatment arm, and the control arm, respectively. P-values were also obtained by the two-sided LRT of SPGx under treatment and control arms, respectively. The entire workflow of the simulation studies is shown in Supplementary Fig. In each replicate, we randomly chose either 1000 or 3000 patients from PGx GWAS data as the PGx training dataset (to build the PGx PRS); and the other 1000 patients as the independent testing dataset . Specifically, the observed phenotype 24 . The ethics committee at each participating center approved the protocol and amendments. All IMPROVE-IT trials were carried out in accordance with the Declaration of Helsinki, current guidelines on Good Clinical Practices and local ethical and legal requirements. All participants provided voluntary written informed consent before trial entry. The details of the endpoint, genotyping, genotype QC, and imputation for this GWAS analyses are introduced elsewhere25. After GWAS QC and SNP imputation, there were 9,407,967 variants and 6502 subjects are available for analyses. The subjects were further filtered down to 5661 subjects for the GWAS analyses by excluding subjects who had a cardiovascular event prior to month 1, since cardiovascular events prior to this time point may affect LDL-C in a manner unrelated to treatment. A total of 5661 European subjects were included for analysis of the LDL-C endpoint.We applied two PRS-Dis methods (PRS-Dis-CT and PRS-Dis-LDpred2) and four PRS-PGx methods  to the prediction of the drug response (low-density lipoprotein cholesterol log-fold change at 1-month) from the IMPROVE-IT PGx GWAS although LDL-C was measured longitudinally at multiple time points. IMPROVE-IT is a phase 3b, multicenter, double-blind, randomized study to establish the clinical benefit and safety of Vytorin (Ezetimibe/Simvastatin tablet) versus Simvastatin monotherapy in high-risk subjects45 (http://csg.sph.umich.edu/willer/public/lipids2017/), were used to construct disease PRS in the IMPROVE-IT data (as the independent testing set). For PRS-PGx methods , due to the lack of independent PGx data for training, we alternatively proposed to use a 5-fold cross-validation to evaluate their performance. More specifically, we split the IMPROVE-IT data into five folds; in each CV step, we chose four of them as the training set, and used the remaining one as the testing set. The PGx GWAS summary statistics, obtained from the training set, were used to construct PGx PRS in the testing set, and only the prognostic and predictive scores of patients in the testing set were recorded. The above procedures were repeated and the PGx PRS for all the patients in the IMPROVE-IT PGx GWAS data (when they served as the testing set) were obtained. Finally, the predictive performance was evaluated in the same criteria as described in the “Simulations” section, as well as the quantile plot for patient stratification.For the PRS-Dis approaches (PRS-Dis-CT and PRS-Dis-LDpred2), the LDL-C disease GWAS summary statistics, obtained from the Global Lipids Genetics Consortium ResultsY1 is the on-treatment LDL-C response, Y0 is the baseline LDL-C response, β is the prognostic effect, α is the predictive effect and the covariate matrix X included age, gender, prior lipid-lowering therapy, early Acute Coronary Syndrome (ACS) trial, high-risk ACS diagnosis, and the top five principal components. As recommended by Zhang et al.25, we adjusted for the baseline LDL-C level Y0  in the model to appropriately control the type I error rate (or genome inflation). The PGx GWAS summary statistics data, including the prognostics and predictive effects (p-values, the SNP positions, the standard deviation of response, and the mean of treatment, were further used for the PGx PRS based drug response analyses. Detailed information about the summary statistics is provided in Supplementary Method It is worth noting that in each cross-validation, the GWAS summary statistics data were generated from the training set by running GWAS analysis with the model: encies MA, the twoFurther information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Alistipes, Butyricimonas, and Coprobacter, while for lv2, the associated components were Agathobacter, Fusicatenibacter, and Streptococcus. There was a significant difference in the lv2 scores between the groups with atopic dermatitis  and those without , and the genera identified for lv2 are associated with the suppression of inflammatory responses in the body. A logistic regression model to estimate the probability of atopic dermatitis morbidity with lv2 as an explanatory variable had an area under the curve (AUC) score of 0.66 when assessed using receiver operating characteristic (ROC) analysis, and this was higher than that using other logistic regression models. The results indicate that the latent variables, especially lv2, could represent the effects of atopic dermatitis on the intestinal microbiome in females. The latent variables in the SEM could thus be utilized as a new type of biomarker. The advantages identified for the SEM are as follows: (1) it enables the extraction of more sophisticated information when compared with models focused on individual bacteria and (2) it can improve the accuracy of the latent variables used as biomarkers, as the SEM can be expanded.The relationship between the human gut microbiota and disease is of increasing scientific interest. Previous investigations have focused on the differences in intestinal bacterial abundance between control and affected groups to identify disease biomarkers. However, different types of intestinal bacteria may have interacting effects and thus be considered biomarker complexes for disease. To investigate this, we aimed to identify a new kind of biomarker for atopic dermatitis using structural equation modeling (SEM). The biomarkers identified were latent variables, which are complex and derived from the abundance data for bacterial marker candidates. Groups of females and males classified as healthy participants [normal control (NC) ], and patients afflicted with atopic dermatitis only [AS ], with atopic dermatitis and other diseases [AM ], and with other diseases but without atopic dermatitis [OD ] were used in this investigation. The candidate bacterial markers were identified by comparing the intestinal microbial community compositions between the NC and AS groups. In females, two latent variables (lv) were identified; for lv1, the associated components  were  Fusobacterium as an indicator for colorectal cancer (detection) based on the results of linear discriminant analysis and effect size, while Lactobacillus, Bifidobacterium, and Akkermansia, using a random forest classification model as biomarkers (targeted biomarkers) for cognitive impairment. Most previous studies have focused on individual intestinal bacteria. However, there are over 1,000 species of intestinal bacteria in the human gut  conducted by the Benno Special Laboratory  at the Institute of Physical and Chemical Research (RIKEN), Japan. This project was approved by the Research Ethics Committee of RIKEN , and consent to research participation was obtained from all stool sample providers and questionnaire respondents.Stool samples from 17,952  different Japanese people aged 20–79 years were obtained for the ONAKA Care Project by the Benno Special Laboratory  at the Institute of Physical and Chemical Research (RIKEN), Japan . ProjectHelicobacter pylori treatment, hospitalization or surgery experience, status for prescription or over-the-counter medications, and for females only, their menstrual status and whether they were pregnant or breastfeeding. Disease morbidity was divided into 11 categories  and identified for each participant, including whether or not they were currently being treated. Free-form responses were also be collected for diseases that did not fit in the above 11 categories. Prescription or over-the-counter medication use was divided into 15 categories . Free-form responses were also collected for medicines other than those listed in the 15 categories.A questionnaire survey was conducted with the 17,952 participants who provided stool samples to gather information including the participants’ sex, age, body mass index (BMI), defecation frequency, lifestyle , morbidity, sleep status, the Center for Epidemiologic Studies Depression scale (CES-D), presence or absence of g for 5 min at 25–28°C, after which the supernatant was separated. Then, 700 μL of cold isopropyl alcohol and 70 μL of 3 M sodium acetate solution were added to the separated supernatant and mixed by inversion. The mixed solution was centrifuged at 20,800 × g for 5 min at 25–28°C, after which the supernatant was removed. The precipitated DNA pellets were washed twice with a 70% ethanol solution and dried using an evaporator . The dried DNA pellets were dissolved in 200 μL Tris-EDTA buffer and stored at −80°C.For DNA extraction, glass beads (400 mg) were added to 600 μL of Tris-EDTA saturated phenol/chloroform solution (phenol:chloroform = 1:1) and combined with 100 μL of 10% SDS solution and the guanidine thiocyanate solution, in which there was approximately 4 mg of a stool sample suspended. This mixed solution was homogenized  at 7,000 rpm for 20 s, and then allowed to stand at 70°C for 10 min. This homogenization and standing procedure were conducted twice, and afterward, the sample was cooled in a water bath at 15°C. The sample was then centrifuged at 10,621 × Deoxyribonucleic acid extracted from the stool samples (12.5 ng) was used for DNA sequencing. The variable regions V1–V3 of the 16S rRNA were the sequencing targets, and the PCR amplicons were generated using the 35F primer , maxN = 0, maxEE = 5, truncQ = 4, rm.phix = TRUE, compress = TRUE, multithread = TRUE, verbose = TRUE, and minLen = 50. A sample was extracted in which the total number of reads for each unique ASV sequence created was ≥ 5,000, and coverage-based rarefying  file output of the MiSeq DNA sequencing using the bcl2fastq software . The fastq file was processed using the clsplitseq software (version 0.2.2019.05.10),arefying  was condarefying  based onarefying .The following analysis groups were defined for each sex, using the available data for the 14,693 participants from which the ASV data were obtained, using a self-reported questionnaire. The number of participants , distribHelicobacter infection at the time of stool collection; (NC7) no experience of medical examinations regarding insomnia at the time of stool collection; (NC8) defecation frequency of 1–2 times a day, or 4–6 times a week; (NC9) frequency of alcohol consumption of ≤ 5 days a week, the amount of which equates to ≤ 180 mL of Japanese sake, or no alcohol consumption; (NC10) no history of smoking; (NC11) regular menstrual cycle, or menopausal, but females aged < 40 and had already undergone menopause were not included in the NC group ; (NC12) not menstruating at the time of stool collection ; (NC13) not pregnant or breastfeeding .The normal control (NC) group consisted of participants who satisfied conditions (NC1) to (NC13), which were as follows: (NC1) not afflicted by any disease; (NC2) no prescription or over-the-counter drugs taken; (NC3) BMI value ≥ 18.5 and < 25; (NC4) CES-D value < 16; (NC5) no experience of hospitalization or surgery; (NC6) no experience of taking drugs for Patients with atopic dermatitis and no other diseases (henceforth referred to as AS) were those who satisfied conditions (AS1) to (AS4), which were as follows: (AS1) afflicted with atopic dermatitis; (AS2) not afflicted by any other disease ; (AS3) no prescription or over-the-counter drugs taken; (AS4) not pregnant or breastfeeding .Patients with atopic dermatitis and other diseases (henceforth referred to as AM) were those who satisfied conditions (AM1) to (AM4), which were as follows: (AM1) afflicted with atopic dermatitis; (AM2) afflicted by a disease other than atopic dermatitis ; (AM3) no prescription or over-the-counter drugs taken; (AM4) not pregnant or breastfeeding .Patients without atopic dermatitis but with other diseases (henceforth referred to as OD) were those who satisfied conditions (OD1) to (OD4), which were as follows: (OD1) not afflicted with atopic dermatitis; (OD2) afflicted by a disease other than atopic dermatitis ; (OD3) no prescription or over-the-counter drugs taken; (OD4) not pregnant or breastfeeding .The microbial community composition data derived from the ASVs were converted using a centered log-ratio (CLR) transformation. The CLR transformation of the intestinal microbial community composition data was conducted using the aldex.clr function in the ALDEx2 package  and AS to rule out the effects of diseases other than atopic dermatitis. The effect size between the NC and AS was calculated using the aldex.effect function in the ALDEx2 package. In the ALDEx2 package, Monte Carlo sampling was used in the calculation process , and conp-value of each parameter, whose null hypothesis was that the estimated parameters equaled zero, was < 0.05; for the final model, we adopted those with values close to one for their goodness-of-fit index (GFI) and adjusted GFI (AGFI), values close to 0 for the root mean square error of approximation (RMSEA), and the maximum absolute value for the path coefficient from the latent variable to the variable representing disease morbidity. The estimation of each standardized parameter for the SEM was conducted using the standardizedSolution function in the lavaan package. The calculation of each GFI for the SEM was conducted using the fitmeasures function of the lavaan package. The calculations for each latent variable value in the structural equation model were conducted for participants belonging to each of the NC, AS, AM, and OD groups using the lavPredict function in the lavaan package. Here, method = “Bartlett” was set for the argument of the function. The usage of the lavaan package was based on We constructed SEM using the CLR-transformed intestinal microbial community composition data from all participants  in NC and AS groups and the atopic dermatitis morbidity data. The SEM used the cfa function of the lavaan package  be a latent variable in first SEM, xij  be an indicator of Fi, aij be a factor loading of xij calculated in first SEM, c be a covariance between F1 and F2 calculated in first SEM, and vij be a residual variance of xij calculated in first SEM. If F1 = ∼ x11 + x12 + … + xn1, F2 = ∼ x21 + x22 + … + xm2, y ∼ F1 + F2, F1 ∼∼ F2 are structural equations of first SEM inputted to the cfa function,Let 2.F1 = ∼ a11*x11 + a12*x12 + … + an1*xn1, F2 = ∼ a21*x21 + a22*x22 + … + am2*xm2, F1 ∼∼ c*F2, xij ∼∼ vij*xij.Set the structural equation of the new SEM for the cfa function as follows: The SEM used the CLR-transformed intestinal microbial community composition data for all females in the NC and AS groups based on the new SEM and the cfa function. At this time, the std.lv = TRUE, std.ov = TRUE, and check.gradient = TRUE were set as the arguments, and the estimation method was diagonally weighted least squares. Based on the result, the estimated values for each latent variable of the participants belonging to the NC, AS, AM, and OD groups were obtained using the lavPredict function. Here, method = “Bartlett” was set as the argument for the function. The estimations for the latent variable values were compared between groups using the Wilcoxon–Mann–Whitney rank sum test with the ggsignif package.The merged populations of the NC, AS, AM, and OD groups were randomly divided in a stratified manner with 80% as the training population and 20% as the verification population. The sample_frac and group_by functions of the dplyr package  was R version 4.0.3 ; and the standardized path coefficient from latent variable 2 (lv2), which was assumed to have a negative effect on atopic dermatitis, to the atopic dermatitis morbidity variable was −0.41 ± 0.12 (p < 0.01). The standardized factor loading values from lv1 to each of indicators of lv1 were as follows: 0.54 ± 0.10 for Alistipes, 0.53 ± 0.10 for Butyricimonas, and 0.44 ± 0.09 for Coprobacter. The standardized factor loading values from lv2 to each indicator of lv2 was as follows: 0.58 ± 0.11 for Fusicatenibacter; 0.42 ± 0.09 for Agathobacter; and 0.20 ± 0.08 for Streptococcus. The standardized factor loading values from the latent variables to each indicator were all significant at p < 0.05. The residual variance of the atopic dermatitis morbidity variable was 0.75, and the two assumed latent variables in the structural equation model for females explained approximately 25% of the variance of the atopic dermatitis morbidity variables (p = 0.30), and the standardized path coefficient from latent variable lv2 to the atopic dermatitis morbidity variable was −0.90 ± 0.93 (p = 0.33). The standardized factor loading values from lv1 to each indicator of lv1 was 0.30 ± 0.13 (p = 0.02) for Collinsella and 0.80 ± 0.22 (p < 0.01) for Oscillibacter. The standardized factor loading values from lv2 for each indicator of lv2 were 0.77 ± 0.24 (p < 0.01) for Ruminiclostridium 9, −0.20 ± 0.13 (p = 0.13) for the Christensenellaceae R-7 group, and 0.18 ± 0.12 (p = 0.12) for Erysipelatoclostridium. The residual variance of the atopic dermatitis morbidity variable was 0.42 , which was assumed to have a positive effect on atopic dermatitis, to the atopic dermatitis morbidity variable was 0.32 ± 0.11 , while that for the AS was significantly higher than that for the NC (p < 0.01). The value for the AM tended to be higher than that for the NC, but there was no significant difference observed (p = 0.12). There was no significant difference in the lv2 values for females between the NC and OD groups (p = 0.55), and the values for the AS and AM were significantly lower than those of the NC (p < 0.01).The latent variable values were calculated for each female from each parameter of the SEM in A SEM was constructed to estimate the lv1 and lv2 values for females when the participant’s atopic dermatitis morbidity was unknown (p < 0.05), but no significant differences were observed between the AM and NC, and no increasing tendency was observed (p < 0.05), and the atopic dermatitis morbidity probability of AM did not show any significant differences from that of the NC, but there was an increasing tendency , and the path coefficient from lv2 to the atopic dermatitis incidence status showed a variance of −0.41 ± 0.12  A model is trained using each participant’s lv2 (explanatory variables) calculated from the intestinal bacteria analysis data and atopic dermatitis prevalence variables for each participant collected in advance, and a model is constructed to estimate the disease risk for atopic dermatitis from the lv2; (2) For new participants, lv2est are calculated and the values are fit to the model in (1) to estimate disease risk. For comparison, we also constructed a model with lv1 as the explanatory variable and both lv1 and lv2 as explanatory variables. Estimates of disease risk were calculated by fitting the lv1 and/or lv2 estimates (lv1est and/or lv2est) of the validation population to the three models constructed using data from this training population, and the accuracy of each model was compared using ROC analysis. The results showed that the model with lv2 as the explanatory variable had the highest accuracy , was suggested to be a latent variable related to the inhibitory effects of inflammatory responses, but it is possible that there are participants whose Agathobacter abundance is not greatly different from that of healthy participants, and that the decrease in lv2 is caused by a decrease in the relative abundance of Fusicatenibacter or Streptococcus (in Fusicatenibacter or Streptococcus when there is no change in the abundance of Agathobacter). For such participants, a model that uses only the amount of Agathobacter present as an explanatory variable cannot accurately estimate the participant’s risk for disease.To compare these results with the disease risk estimates for atopic dermatitis made using individual bacterial genera, we compared the accuracy of the disease risk estimation model for atopic dermatitis with the relative abundance of individual bacterial genera as explanatory variables and further assessed the results using AUC analysis. The accuracy of the model constructed in this study using lv2 as the explanatory variable was higher than that of the model using the CLR-transformed abundance of individual genera as the explanatory variable, except for the model using variable . The modIn this study, we showed it is possible to use latent variables in SEM as explanatory variables to estimate the disease risk with satisfactory accuracy . We alsohttps://www.ebi.ac.uk/ena/browser/home), accession number: PRJEB57381. The other raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The 16S rRNA amplicon sequencing data presented in the study are deposited in the European Nucleotide Archive (ENA) repository . The patients/participants provided their written informed consent to participate in this study.HT, HM, and YB contributed to the conception and design of the study. YB conducted the DNA extraction, 16S rRNA amplicon sequencing, and questionnaire survey. HT, TI, and JK performed data curation and performed statistical and data analysis. HT, KO, KH, and HM wrote specific sections in the first draft of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.


Anaeroplasma, Paraprevotella, Pasteurella, and Streptococcus were significantly associated with PL, and Sharpea and Helicobacter exhibited significant association with IMF (p < 0.05). Furthermore, Paraprevotella was also identified as a critical microbe affecting PL based on the divergent Wilcoxon rank-sum test. Overall, this study reveals the effect of host genetics and gut microbiome on pig fat deposition traits and provides a new perspective on the genetic improvement of pig fat deposition traits.Fat deposition affects meat quality, flavor, and production in pigs. Fat deposition is influenced by both genetics and environment. Symbiotic microbe with the host is an important environmental factor to influence fat deposition. In this study, the fat deposition traits were measured in 239 individuals obtained from Tongcheng pigs × Large White pigs resource population. The interactions between genetics and gut microbiome in fat deposition traits were investigated through whole-genome sequencing and cecum microbial 16S ribosomal RNA sequencing. The results showed that the percentage of leaf fat (PL) and intramuscular fat content (IMF) were significantly influenced by host genetics–gut microbiome interaction. The effects of interactions between host genetics and gut microbiome on PL and IMF were 0.13 and 0.29, respectively. The heritability of PL and IMF was estimated as 0.71 and 0.89, respectively. The microbiability of PL and IMF was 0.20 and 0.26, respectively. Microbiome-wide association analysis (MWAS) revealed  Fat deposition is closely related to carcass and meat quality traits in pigs. Fat deposition in different parts of the body has different effects. Subcutaneous backfat thickness is an important target trait in pig breeding. There is a negative correlation between backfat thickness and carcass lean percentage. In the viscera, fat could support and protect organs, and it affects dressing percentage and feed consumption in pigs. In addition, intramuscular fat content (IMF) is an important factor determining the tenderness, juiciness, flavor, meat quality characteristics, and consumer acceptance . IMF is Fat deposition traits are a type of quantitative traits with moderate-to-high heritability. This quantitative trait is influenced by heredity and environment. The gut microbiota that are symbiotic with the host are irreplaceable environmental factors. The gut microbiome is known as the second genome of humans , which iTreponema in cecum has been reported to be associated with feed conversion  and a commercial breed (Large White pigs). We measured fat deposition traits (backfat thickness at shoulder (SBFT), loin (LBFT), rump (RBFT), average backfat thickness (ABFT), percentage of leaf and caul fat , and intramuscular fat content (IMF)). We performed whole-genome sequencing and cecal microbial 16S ribosomal RNA sequencing of 239 pigs to determine whether there are any interactions between the host genetics and gut microbiome. Then, we estimated heritability and microbiability for the detected interactions between host genetic–gut microbiomes and further screened candidate microbes influencing traits. This study provides a new perspective and opportunity for the genetic improvement of pig fat deposition traits.The resource population is an advanced generation intercross population. F1 was obtained by crossing 36 Tongcheng sows  with 11 Large White boars . The F1 sows and boars were subsequently intercrossed to produce F2. Then, intercross was performed in each generation until F9 population was produced. In this study, a total of 239 individuals from 9 to 10 intercross generations including 119 castrated boars and 120 castrated sows were used for further analysis. All the pigs were provided by Yunzhi Farm  with coincident feeding and management conditions. All individuals were healthy, and they were not administered any antibiotics before being slaughtered.The phenotypes of 239 pigs were collected, including live body weight (BW) before slaughter, carcass weight (CW), backfat thickness at the thickest shoulder (SBFT), loin (LBFT), rump (RBFT), weight of leaf fat, weight of caul fat, and intramuscular fat content (IMF). Backfat thickness was measured using Vernier caliper. The average backfat thickness (ABFT) was an average of three measurements of backfat thickness at the shoulder, loin, and rump. The weight of leaf and caul fat was measured using a scale with an accuracy of 0.01 kg. The percentage of leaf fat (PL) was calculated as the weight of leaf fat divided by CW. Similarly, percentage of caul fat (PC) was calculated as the weight of caul fat divided by CW. The percentage of leaf and caul fat (PLC) was sum of PL and PC. A near-infrared spectroscopy analyzer was used to measure the IMF of the longissimus dorsi samples. The longissimus dorsi samples were collected from the penultimate 3rd to 4th thoracic vertebrae, about 20–30 cm long. The gut contents of 239 individuals were sampled from cecum after pigs were slaughtered, snap-frozen in liquid nitrogen, and stored at −80°C until sequencing. Spleen tissues were collected into 50 ml centrifuge tubes containing 75% alcohol by volume and stored at −20°C.All the animal experiments in this study were approved by the Animal Care and Use Committee of Huazhong Agricultural University.p-value <10–6.Genomic DNA samples were extracted from spleen of 239 pigs to construct the whole-genome sequencing libraries. Each library was sequenced using 150 bp paired-end reads with a HiSeq X5 instrument (Illumina). To minimize mapping error, low-quality reads were removed using FastQC software. The clean reads from each pig were aligned to the porcine reference genome using the Burrows-Wheeler Alignment tool (BWA ver 0.7.15) with default parameters. We subsequently used the Picard toolkit to sort the alignment results and remove potential PCR duplicate reads. The resultant alignments were indexed using SAMtools (ver 1.6) and processed with the Genome Analysis Toolkit . To call variants, we set a minimum quality score as 20 based on the bases and mapped reads. The single nucleotide polymorphisms (SNPs) of each pig were combined to obtain a common dataset of single nucleotide polymorphism (SNP) data, and the dataset was processed by GATK. Finally, the software PLINK (ver 1.90) was used for quality control of the obtained dataset with the following filtering criteria: SNP call rate >80%, minor allele frequency >1%, and Hardy–Weinberg equilibrium First, the total DNA of cecal contents was extracted to construct sequencing library, and 30 ng genomic DNA samples and their corresponding fusion primers were used to prepare PCR reaction system. PCR reaction was performed to amplify V3-V4 region of 16S ribosomal RNA (rRNA) with the primers of 338R (ACTCCTACGGGAGGCAGCAG) and 806R (GGACTACHVGGGGTWTCTAAT). A 468 bp segment was amplified by PCR with the number of tags of 50,000. The PCR products were purified by using Agencourt AMPure XP magnetic beads and dissolved in the Elution Buffer. Agilent 2100 Bioanalyzer was used to detect the fragment range and concentration of the library. According to the size of the inserted fragment, HiSeq platform was selected for pair-end sequencing. FLASH  was used to splice the sequences. Based on the sequence overlapping relationship, the paired reads obtained from pair-end sequencing were assembled into tags. The assembled tags were clustered into operational taxonomic units (OTUs) using software USEARCH (ver 7.0.1090). Clustering analysis was performed by using UPARSE under 97% similarity to obtain representative sequence of each OTU. Afterward, OTU representative sequences were aligned against the Greengene database by ribosomal database project (RDP) classifier (ver 2.2) software to obtain the annotation at the levels of phylum, class, order, family, and genus.1 according to the following equation:A total of 14,139,625 filtered SNPs were used to estimate the genomic relationship matrix (GRM) using the HIBLUP softwarepi is the frequency of the coded allele.In which M indicates m  × n (the number of loci) matrix, and All the OTUs were used to construct the microbial relationship matrix (MRM) by R script  based onmij is the estimation of microbial similarity in cecum of individual i and individual j; Xio and Xjo indicate the relative abundance of OTU o in the cecum of individual i and individual j; Xo represents the average relative abundance of OTU o in cecum of the whole population; In which The following multiple random effects model was established to estimate variance components of the target traits using HIBLUP software (see text footnote 1).y is the n × 1 vector of the fat deposition traits; μ is the overall mean; g ∼ N  software  accordiy is a vector of the phenotype; c is a vector of fixed covariates, including sex effect and body weight (BW); K is the matrix corresponding to c; and g1 is a vector of the total effects of all SNPs following g1∼ N . The microbiability was estimated by GCTA software with the MRM substituted for GRM. In our study, c contained six covariates, namely, covariates 1–6. Covariate 1 included BW and the first five host genetic principal components (PCs); covariate 2 included BW and the first two PCs generated at SNPs which were significantly associated with PL and IMF; covariate 3 consisted of BW, the first five host genetic PCs, and the first two PCs generated at SNPs which were significantly associated with PL and IMF; covariate 4 included BW, sex, and the first five host genetic PCs; covariate 5 included BW, sex, and the first two PCs generated at SNPs which were significantly associated with PL and IMF; covariate 6 consisted of BW, sex, the first five host genetic PCs, and the first two PCs generated at SNPs which were significantly associated with PL and IMF. The first five PCs were generated by the genome relationship matrix to account for population structure.where The Bayesian information criterion (BIC) was calculated to select the model with optimal covariates. The smaller the BIC value was, the higher the model’s fitting level was. The BIC was calculated according to the following formula:k is the number of model parameters, n is the number of samples, and L is the likelihood value.where The divergent Wilcoxon rank-sum test and microbiome-wide association analysis (MWAS) were used to identify the candidate microbes for target traits.n = 48) and lowest 20% (n = 48) were formed into two extreme divergent groups. The Wilcoxon rank-sum test was used to test the significance in 56 genera with average relative abundance greater than 0.01% between the two groups. By the same method, microbes were grouped in terms of the relative abundance of the genus, and the significance in individual traits between the two divergent groups was tested by the Wilcoxon rank-sum test with the P < 0.05 as the chosen significant test criteria.According to the distribution of fat deposition traits of the 239 individuals, the highest 20%  and gut microbiome (m) exhibited 0.62 and 0.20 independent effect on SBFT, 0.68 and 0.18 independent effect on LBFT, and 0.86 and 0.11 independent effect on RBFT, respectively. In addition, ABFT displayed 0.75 g effect and 0.18 m effect. Generally, the g effect was higher than the m effect in the presence of other environmental factors. RBFT exhibited the highest g effect among backfat thickness at three positions. The PC exhibited 0.40 g effect and 0.24 m effect. However, PL displayed 0.77 g effect, 0.10 m effect, and 0.13 effect of interactions between host genetics and gut microbiome (a). The multiple variance components completely explained the phenotypic variation of PL and IMF. The IMF showed 0.71 independent g effect and 0.29 a effect, but no independent m effect. The a effect of PL and IMF was estimated as 0.13 and 0.29, indicating that host genetic–gut microbiome interactions affected the formation of PL and IMF.The effects of host genetics and gut microbiome on all traits were different . The resThe heritability of PL was estimated as 0.71 and that of IMF as 0.89. PL and IMF were mainly affected by host genetics in the resource population . To estiCampylobacter and Paraprevotella exhibited significant difference between divergent groups of PL, while PL also showed significant difference between divergent groups of Campylobacter and Paraprevotella , whereas Sharpea with IMF. Moreover, Paraprevotella, Pasteurella, and Streptococcus were also significantly associated with PL (P < 0.05), while Helicobacter exhibited significant association with IMF (P < 0.05).The MWAS was used to investigate whether there were significant associations between genus relative abundance and traits (PL and IMF). The MWAS results are shown in Paraprevotella was significantly associated with PL in both methods (P < 0.05). As a significant candidate genus, Paraprevotella is one of the most important butyric acid-producing bacteria, and it may affect visceral fat deposition by producing butyric acid (a short-chain fatty acid).We adopted two methods (divergent Wilcoxon rank-sum test and MWAS) to screen microbes affecting PL and IMF at the genus level and found that Fat deposition trait is closely related to carcass and meat quality traits in pigs, and it affects economic income and feed consumption. Pig fat deposition traits are influenced by host genetics and gut microbiome. Our study found that PL and IMF were influenced by the interactions between host genetics and gut microbiome. Several studies have identified that host genetics have interacted with gut microbiota and some host genetic variations affect the gut microbiota . In our In our study, heritability estimation model was consistent with microbiability estimation model. Both sex and BW were used as covariates to explore the influence of cecal microbiome on traits. To correct the effect of host genome on traits, the first five PCs generated by the GRM and the first two PCs generated at SNPs which were significantly associated with PL and IMF are considered as covariates. In the previous study, the first two PCs and the first five PCs of population structure were used as covariates to estimate microbiability . In our Campylobacter and Paraprevotella were significantly associated with PL, and the MWAS results showed that Anaeroplasma, Paraprevotella, Pasteurella, and Streptococcus were significantly associated with PL. Among these genera, Streptococcus is reported as one of the most abundant bacteria in the gut microbes of Jinhua Pigs . The 16S ribosomal RNA sequencing data are available in the Sequence Read Archive (SRA) repository (SRA accession number PRJNA868520). Further information on the original data can be directed to the corresponding author.The genotyping data in PLINK binary format is available from the Figshare database (This animal study was reviewed and approved by the Animal Care and Use Committee of Huazhong Agricultural University.YW collected the samples, performed the data analysis and interpretation, and wrote the manuscript. PZ performed the data analysis and wrote the manuscript. MF, ZL, and TW measured pig phenotype and collected samples. ZW and XL participated in constructing mathematical models. BL and XZ conceived and designed the study and manuscript revision. All authors contributed to manuscript revision, read, and approved the submitted version.


Two-sample MR analysis was conducted using summary-level data from UK Biobank  and FinnGen consortium . Polygenic risk score analysis using individual-level data in UK biobank was performed to complement the MR findings by examining the non-linearity of the association. Diabetic complications were taken as positive controls to validate the therapeutic effect of targeting GDF-15. Linear and nonlinear associations between genetically predicted GDF-15 levels and gallstones were estimated with stratification by the diabetic status. In the two-sample MR analysis, the odds ratio (OR) of gallstones was 1.09 , 1.03–1.15; p = 0.001) for one standard deviation increase in genetically predicted GDF-15 levels in the meta-analysis of two datasets. Polygenic risk score analysis found this association to be U-shaped (p = 0.037). The observed association was predominantly seen in nondiabetic population . An inverse association between genetically predicted GDF-15 levels and diabetic complications  was observed, validating the potential therapeutic effects of targeting GDF-15 levels. This MR study indicates that the increased risk of gallstone disease should be taken into account when considering GDF-15 as a therapeutic target for diabetic complications.Growth differentiation factor 15 (GDF-15) levels have been revealed as a robust biomarker for metformin use. We conducted Mendelian randomization (MR) analysis to explore the association between GDF-15 and gallstone disease to inform potential therapeutic effects targeting GDF-15. Four genetic variants associated with GDF-15 levels at  Growth differentiation factor 15 (GDF-15) is one of the members of transforming growth factor beta (TGF-β), specifically produced by macrophages and epithelial cells, as well as by adipocytes, with the basic effects on inhibition of macrophage activation, anti-inflammation, and maintenance of homeostasis .Experimental data on mice found that GDF-15 controlled appetite , reducedEvidence from some population-based studies has also been provided that increased GDF-15 might be a cause mediator or predicting biomarker in health-adverse outcomes, such as insulin resistance  and cardGallstone disease is defined by the occurrence of symptoms or complications caused by gallstones in the gallbladder and/or the bile ducts , which hEmploying genetic variants as instrumental variables for exposure  and Mendelian randomization (MR) analysis can strengthen causal inference by minimizing unobserved confounding and diminishing reverse causality . Therefop < 5 × 10–8) were identified from a meta-analysis of 13 genome-wide association studies including up to 21,758 individuals of the European ancestry  were selected as instrumental variables for GDF-15  and the FinnGen study . We used the Neale Lab second wave results of the genome-wide analysis in UK Biobank after the removal of individuals of the non-European ancestry, closely related individuals, individuals with sex chromosome aneuploidies, and individuals who had withdrawn consent. The association test was adjusted for age, sex, and up to 20 genetic principal components. The R5 release of FinnGen consortium data was used, where individuals with ambiguous gender, high genotype missingness, excess heterozygosity, and non-Finnish ancestry had been excluded. The association test in FinnGen was adjusted for age, sex, 10 genetic principal components, and genotyping batch.th March 2021 under the approved project application (ID: 66345). Polygenic risk scores for GDF-15 levels were constructed by summing the number of risk alleles  carried by each participant and weighted by the effect size estimates  of the four genetic instrument variables . Incident and prevalent gallstone cases were defined by corresponding codes in the International Classification of Diseases (ICD) versions 9 and 10 with information from national medical records . Diabetic complications, including type 2 diabetes with ketoacidosis, renal manifestations, ophthalmic manifestations, and neurological manifestations  were taken as positive controls to validate the effect of GDF-15 as a therapeutic target for diabetic complications. Individual-level UK Biobank data was accessed up to 6ariables  selectedp-value for the intercept in MR-Egger was used to detect horizontal pleiotropy  the univariate regression model without adjustment for any covariates; 2) the multivariable regression model with adjustment for basic covariates, including age, sex, body mass index (BMI), and the first 20 genetic principal components; and 3) the multivariable regression model with adjustment for a comprehensive list of covariates, including age, sex, BMI and the first 20 genetic principal components, deprivation index, smoking, drinking, physical activity, blood low-density lipoprotein cholesterol (LDL-C) levels, blood glucose levels, blood HbA1c levels, and metformin use. Considering confounding effects from type 2 diabetes  and mostp = 0.028) in UK Biobank, 1.08  in FinnGen, and 1.09  in the meta-analysis of the two datasets. Four GDF-15–associated SNPs showed consistent associations with gallstones in FinnGen (p for intercept >0.05), and no outlier in MR-PRESSO analysis  for one standard deviation increase in genetically predicted GDF-15 levels in a univariate model, and the effect estimate remained significant  after adjustment for additional full list of covariates. In the stratification analysis by diabetic status, the association between genetically predicted GDF-15 and gallstones was consistently observed in nondiabetic participants , indicating the observed association was not driven by diabetes (p = 0.605), and the power calculation indicated insufficient statistical power (13%) to detect this effect among diabetic individuals. Furthermore, we found a nonlinear U-shaped association by a regression model with cubic splines  (p = 0.228) when it is below the turning point, while genetically predicted high levels of GDF-15 (above the turning point) showed a significant positive association with an increased risk of gallstones .Characteristics of 339,197 participants in the UK Biobank are shown in diabetes . In diab= 0.037) . When rep = 0.023) for one standard deviation increase in genetically predicted GDF-15 levels.Higher genetically predicted GDF-15 levels were associated with a decreased risk of diabetic complications, validating the therapeutic effect on diabetic complications targeting GDF-15 levels; the corresponding OR was 0.77 , suggesting GDF-15 to be a potential biomarker in biliary diseases . LikewisThere were also some studies indicating opposite findings to ours. An experiment in mice found that metformin that elevates circulating GDF-15 levels  reduced In stratification analysis, the significant association between higher genetically predicted GDF-15 and gallstones was only observed in nondiabetic individuals. The insignificant association in diabetic patients could be due to the lack of study power (13%) and the possibility that genetic polymorphisms may explain a smaller variance of GDF-levels, given that metformin treatment is much more frequent in diabetic patients. In addition, we observed a U-shaped association between GDF-15 and gallstone disease. The nonlinearity of the observed association has important clinical implications on the dose of metformin use which should maintain GDF-15 at a reasonable level to balance the therapeutic effects on diabetic complications and the risk of gallstone disease.via inflammation  with causal inference potentials are needed to verify this association, and the corresponding underlying mechanisms also deserve further investigation.Our study has several strengths, including MR design that strengthened causal inference, consistent associations in two independent populations, the confined study population of the European ancestry that minimized the population structure bias, stratification analysis by the diabetic status that diminished unobserved confounding from diabetes, and examination of nonlinearity. In addition, our study validated the use of GDF-15 as a potential therapeutic target to reduce the risk of diabetic complications which could be regarded as positive controls. Nevertheless, limitations need attention when interpreting our results. We observed pleiotropic associations for rs2517481 but not for other SNPs at the genome significance level. However, this pleiotropy was supposed to generate a limited impact on our findings since this SNP explains a small variance in GDF-15 levels, and the observed association between GDF-15 and gallstones was mainly driven by rs1227734 located in the n GDF-15 , and theIn conclusion, this MR study suggests a detrimental effect of high GDF-15 levels on gallstone formation for the first time. This finding may indicate gallstone disease as a potential adverse outcome, when taking GDF-15 as a therapeutic target for diabetic complications as indicated by the metformin use.


P ≤ 1 × 10–5) marker-trait associations (MTAs) were identified for seven phenotypic traits. The phenotypic variance for MTAs ranged between 5.2 and 9%. A total of 64 associations were found in 19 unique putative gene expression regions. Among them, 12 associations were found in gene models with stress-related biological functions.Being a widely cultivated crop globally under diverse climatic conditions and soil types, maize is often exposed to an array of biotic and abiotic stresses. Soil salinity is one of the challenges for maize cultivation in many parts of lowland tropics that significantly affects crop growth and reduces economic yields. Breeding strategies integrated with molecular approach might accelerate the process of identifying and developing salinity-tolerant maize cultivars. In this study, an association mapping panel consisting of 305 diverse maize inbred lines was phenotyped in a managed salinity stress phenotyping facility at International Center for Biosaline Agriculture (ICBA), Dubai, United Arab Emirates (UAE). Wide genotypic variability was observed in the panel under salinity stress for key phenotypic traits viz., grain yield, days to anthesis, anthesis-silking interval, plant height, cob length, cob girth, and kernel number. The panel was genotyped following the genome-based sequencing approach to generate 955,690 SNPs. Total SNPs were filtered to 213,043 at a call rate of 0.85 and minor allele frequency of 0.05 for association analysis. A total of 259 highly significant ( The inbred lines involved in this panel were derived from various pools and populations, including several biparental pedigree populations. Further details about the pools and populations of CIMMYT’s tropical maize program can be found elsewhere .3 equivalents), porous (45% porosity), and moderately alkaline (pH 8.22). Saturation percentage of the soil is 26% with very high drainage capacity, while electrical conductivity of its saturated extract (ECe) is 1.2 dS m –1. According to American Soil Taxonomy , days of 50% silking (SD), anthesis silking interval (ASI), and plant height (PH) were measured using the standard phenotyping protocol for maize (––1). In 2018, in addition to grain yield, cob length (CL), cob girth (CG), and kernel number (KN) per cob were also measured by capturing digital photographs of harvested cobs of each plot. The ears were properly arranged and placed on a contrast background with equal spacing between the ears to avoid any overlapping. The ears were imaged using a digital camera at a uniform height. An image analysis was performed using the ImageJ software,1 open-source software using a script  developed at CIMMYT  , Dubai, United Arab Emirates (UAE), which is located about 23 km from the Arabian Gulf (Persian Gulf). The soils at ICBA experimental fields are sandy in texture, including fine sand , calcareous  software  in whichijkl is the trait of interest, μ is mean effect, Envi is the effect of ith environment, Repi is the effect of ith replicate, Blockj (EnviRepi) is the effect of jth incomplete block within ith replicate and environment, Genl is the effect of lth genotype, Envi and Envi × Genl are the effects of ith environment and environment by genotype interaction, and ∈ is the residue. Broad-sense heritability (h2) was estimated as follows:where Ywhere Zea samples). The criteria for filtering SNPs for GWAS and LD (linkage disequilibrium) analysis were based on DNA extraction was conducted following the modified CTAB method  on 3–4 week young leaves harvested from each inbred line involved in the panel. Single nucleotide polymorphisms (SNPs) were generated through GBS v2.7 using Illumina Hi-seq 2000/2500 at the Institute for Genomic Diversity, Cornell University, Ithaca, NY, United States. Physical coordinates of GBS SNPs were derived from AGPv2. A total of 955,690 SNPs were generated after imputing missing data points by accepting a 5% mismatch with the closest neighbor in small SNP windows across the entire maize database . The fitness of the model was determined by observing Q-Q (quantile-quantile) plots  were searched for the putative gene function in maize GDB in B73 RefGen_V3.3 Gene models having a stress related gene/biological function were considered as candidate genes.The SNPs  obtained for the association panel after imputation were further filtered to 213,043 SNPs using the multiple selection criteria of CR > 0.85 and MAF ≥ 0.05 for the GWAS analysis. The association between the filtered SNPs and the trait of interest was detected by employing a mixed linear model (MLM) in the SNP and Variation Suits v8.6.0 software  plots , 2. A maP > 0.001) significant genotypic variance for all the traits studied under salinity stress in both individual and across years. Environmental and genotype × environmental interaction variance was also significant for all the traits. The magnitude of genotypic variance and error variance was narrow for individual years, whereas the magnitude of error variance and G × E variance of across year analysis was multi-folded when compared to genotypic variance. Broad-sense heritability of the traits in 2017 and 2018 ranged from 0.4 (CL) to 0.9 (AD), whereas across-year heritability ranged between 0.2 (GYG) and 0.8 (AD) (The GWAS panel exhibited a highly (0.8 (AD) . In addi0.8 (AD) .–1 and > 5 days, respectively, with a very wide range for both traits . The mean of PH slightly varied between years and across years, but it had a comparatively narrow range. The low values of GY and higher values of ASI indicate the impact of salinity stress on the reproductive stage of the crop. A similar effect of salinity stress was found on other morphological traits as well.Mean GY, AD, and ASI in the panel was consistent between individual years and across years. The mean value of GY and ASI was < 4 thar2 = 0.1 and 1.38 kb at r2 = 0.2 (P < 1 × 10–5)  were shortlisted to 128,913 SNPs (CR = 0.9 and MAF = 0.1) for linkage disequilibrium (LD) decay estimation, and 213,043 SNPs (CR = 0.85 and MAF = 0.05) were used for the association analysis. The LD decay of the panel was 3.82 kb at r2 = 0.2 . In the  × 10–5) , 4 MTAs  × 10–5) . In indi × 10–5) . A total × 10–5) . The SNP × 10–5) .In this study, a total of 236 MTAs were found in 113 unique putative gene expression regions. In particular, 11 MTAs: 7 for GY and 4 for AD were found in gene models with stress-related biological functions . NotablyMaize, a widely adopted crop under a range of climatic conditions, is often planted on salt-affected fields, as many farmlands are salinized . Analyzir2 = 0.1 and 0.2, respectively, which is characteristic of tropical and subtropical maize lines. The high LD , qPHI9, and qPHI10   and lower than the observed broad-sense heritability explained based on phenotypic data. This gap, also known as the missing heritability , might be index) . Similare index) . This muAssociations (MTAs) in the expression region of genomes are of practical interest. In this study, 19 unique putative expression regions with various biological functions were associated with 64 SNPs. Plants develop a complex defensive or escape mechanism to overcome multiple stresses during the crop period through interconnected and diverse downstream signaling cascades by regulating genes . A few SThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/PZ and MS: conceptualization, investigation, and supervision. KS and MV: formal analysis. PZ, MS, and KS: methodology. PZ, MS, KS, and MV: visualization and writing – review and editing. KS, PZ, and MV: writing – original draft. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Genetic improvements in feed efficiency (FE) and related traits could considerably reduce pig production costs and energy consumption. Thus, we performed a genetic parameter estimation and genome-wide association study of four FE and FE-related traits, namely, average daily feed intake, average daily gain, the feed conversion ratio, and residual feed intake, of two pig breeds, Yorkshire and Duroc. The results demonstrate the genetic relationships of FE and FE-related traits with two growth traits, age and backfat thickness at 100 kg. We also identified many single-nucleotide polymorphisms (SNPs) and novel candidate genes related to these traits. In addition, we found many pathways significantly associated with FE and FE-related traits, and they are generally involved in digestive and metabolic processes. The results of this study are expected to provide a valuable reference for the genomic selection of FE and FE-related traits in pigs.MC4R and INSR genes. A gene enrichment analysis showed that FE and FE-related traits were highly enriched in the biosynthesis, digestion, and metabolism of biomolecules. This study deepens our understanding of the genetic mechanisms of FE in pigs and provides valuable information for using marker-assisted selection in pigs to improve FE.Feed efficiency (FE) traits are key factors that can influence the economic benefits of pig production. However, little is known about the genetic architecture of FE and FE-related traits. This study aimed to identify SNPs and candidate genes associated with FE and FE-related traits, namely, average daily feed intake (ADFI), average daily gain (ADG), the feed conversion ratio (FCR), and residual feed intake (RFI). The phenotypes of 5823 boars with genotyped data (50 K BeadChip) from 1365 boars from a nucleus farm were used to perform a genome-wide association study (GWAS) of two breeds, Duroc and Yorkshire. Moreover, we performed a genetic parameter estimation for four FE and FE-related traits. The heritabilities of the FE and FE-related traits ranged from 0.13 to 0.36, and there were significant genetic correlations (−0.69 to 0.52) of the FE and FE-related traits with two growth traits (age at 100 kg and backfat thickness at 100 kg). A total of 61 significant SNPs located on eight different chromosomes associated with the four FE and FE-related traits were identified. We further identified four regions associated with FE and FE-related traits that have not been previously reported, and they may be potential novel QTLs for FE. Considering their biological functions, we finally identified 35 candidate genes relevant for FE and FE-related traits, such as the widely reported  MC4R), a signaling molecule involved in the regulation of energy homeostasis [INSR) as a candidate gene for RFI. Several genes involved in the transport process, such as AQP4, SLC22A23, and SLC6A14, have been identified as potential candidate genes for FCR and ADFI in previous studies [https://www.animalgenome.org/cgi-bin/QTLdb/SS/index (accessed on 13 May 2022)). Studying genetic structures and variants affecting FE and FE-related traits does not provide enough detail since phenotype data collection is difficult. Fu et al. [Feed costs account for approximately 64–72% of the total cost of pig production . Improvieostasis , was ideeostasis ,12, and eostasis . Duy et eostasis  found th studies . An enri studies . These su et al.  found thu et al.  found thu et al. . IdentifTo further identify quantitative trait loci (QTL) and potential genes associated with four FE and FE-related traits in pigs, we conducted a GWAS on two purebred pig breeds, Yorkshire (YY) and Duroc (DD). In addition, we estimated the genetic parameters of FE and FE-related traits and their genetic correlation with two growth traits, backfat thickness at 100 kg (BF) and age at 100 kg (AGE).The data used in this study were all from a nucleus breeding farm in the province of Inner Mongolia of China. Details of the phenotype data collection and processing procedures are explained in y is the vector of observations for one of the six performance traits in univariate analyses, while in the bivariate model, y is the vector of observations composed of one FE-related trait and one growth trait. I denotes the identity matrix of adequate dimension, A denotes the numerator relationship matrix, and The best linear unbiased prediction (BLUP) method was applied to the six traits in this study to determine the (co)variance components. For an evaluation of the genetic relationships, the phenotypes have eight combinations between two growth traits with four FE and FE-related traits. The following univariate and bivariate animal models were used to estimate the variance components: of 0.75  was inclUsing the estimated variance or covariance component from the bivariate analysis, estimated genetic correlations were calculated as H) was as follows:G was calculated as Gr is a genomic matrix before weighting, calculated as VanRaden [A with H. Estimated variance components/GEBVs were determined based on BLUP and ssGBLUP using the DMUAI module in the DMU software package e. To eliminate the contribution of information from relatives, de-regressed estimated breeding values (DEBVs) were used as the response variables in the GWAS. According to the method proposed by Garrick et al. [y is the vector of the dependent variable (DEBV); m is the vector of the SNP marker effects; G is the realized relationship matrix constructed with markers [e is the vector of residual errors with a normal distribution I is an identity matrix, and X and W are the incidence matrices of y and are related to m and a, respectively.In the GWAS, a single-step genomic BLUP (ssGBLUP) ,20 was f+1−w A22 , where wk et al. , the DEBk et al. . The GWAk et al. :y=Xm+Wa+ markers , and σa2p = 0.05/N and p = 1/N, respectively, where N is the number of analyzed SNPs [For each SNP, the Wald statistic ,27 was ized SNPs .PigQTLdb  was usedt-test showed that the phenotypes among the breeds were significantly different for all six traits. In general, AGE has a moderate heritability, whereas BF has a moderate-to-high heritability [tability ,36. In ttability , which dtability ; the estExcept for BF and RFI in DD (0.07), non-ignorable genetic correlations (>0.10) can be found between two growth traits with FE and FE-related traits . HoweverP-values of the GWAS results were visualized using Manhattan plots and Q–Q plots , a candidate gene of ADFI, is a protein coding gene that is involved in ion-channel transport and the transport of glucose, bile salts, organic acids, metal ions, and amine compounds, and it has been identified as a candidate gene for backfat thickness in pigs [MC4R) and lectin, mannose binding 1 (LMAN1) were also identified as candidate genes for ADFI, and they have been reported to be associated with AGE [INSR) has repeatedly been reported to be an important gene affecting RFI in relevant studies [ZRANB3 and MCM6, which are essential for the initiation of genome replication.After merging overlapping “QTL windows”, we finally identified 8, 20, 2, and 7 QTLs associated with ADFI, ADG, FCR, and RFI, respectively . The fou in pigs . The melwith AGE ,11,12,13with AGE , respect studies ,41,42 bep < 0.05. The top 30 enrichment terms are shown in p < 0.01), which is involved in the covalent connection between sialic acid and substrate molecules and plays an important role in the metabolism of macromolecular organic substances. The ephrin receptor signaling pathway  enriched on ADG is very important for protein synthesis, and it regulates many major cellular processes that produce or use large amounts of energy and nutrients. Therefore, it is an essential pathway for FE and FE-related traits [In total, 617 enriched GO terms and 102 KEGG pathways were identified for candidate genes of FE and FE-related traits, of which, 96 GO terms and 4 KEGG pathways were considered statistically significant at FDR-corrected d traits . OverallIn conclusion, the results show that the heritabilities of FE and FE-related traits ranged from 0.13 to 0.36, and there were distinct genetic correlations (−0.69–0.52) of growth traits with FE and FE-related traits. We identified four critical genomic regions for ADFI (SSC18: 32.12–33.12 Mbp), ADG (SSC11: 67.29–68.29 Mbp), FCR (SSC15: 15.78–16.78 Mbp), and FCR (SSC8: 88.95–89.95 Mbp), and they might be potential new QTL regions for FE. We also further identified 35 potential candidate genes for four FE and FE-related traits, and they play an essential role in many biological processes, such as ion transfer, mitochondrial activities, and the macromolecule metabolism. Some interesting KEGG pathways and GO terms, e.g., sialylation, were found to have potential functions in FE in pigs. Overall, this study’s estimation of genetic parameters and GWAS results helps us better understand the genetic structure of FE and FE-related traits and provides important information for the future genomic predictions of these traits.