M mediates the relationship between the genetic component of outcome Y and outcome Y itself. However, this approach suffers from attenuation bias, as PGSs capture only a  part of the genetic variance of a given trait. To overcome this limitation, we developed MA-GREML: a method for Mediation Analysis using Genome-based Restricted Maximum Likelihood (GREML) estimation.Mediation analysis is commonly used to identify mechanisms and intermediate factors between causes and outcomes. Studies drawing on polygenic scores (PGSs) can readily employ traditional regression-based procedures to assess whether trait M and Y. In addition to typical GREML parameters , MA-GREML estimates (i) the effect of M on Y, (ii) the direct effect , and (iii) the indirect effect . MA-GREML also provides standard errors of these estimates and assesses the significance of the indirect effect.Using MA-GREML to assess mediation between genetic factors and traits comes with two main advantages. First, we circumvent the limited predictive accuracy of PGSs that regression-based mediation approaches suffer from. Second, compared to methods employing summary statistics from genome-wide association studies, the individual-level data approach of GREML allows to directly control for confounders of the association between viz., that M precedes Y and that environmental confounders of the association between M and Y are controlled for. We conclude that MA-GREML is an appropriate tool to assess the mediating role of trait M in the relationship between the genetic component of Y and outcome Y. Using data from the US Health and Retirement Study, we provide evidence that genetic effects on Body Mass Index (BMI), cognitive functioning and self-reported health in later life run partially through educational attainment. For mental health, we do not find significant evidence for an indirect effect through educational attainment. Further analyses show that the additive genetic factors of these four outcomes do partially  and fully  run through an earlier realization of these traits.We use analytical derivations and simulations to show the validity of our approach under two main assumptions,  cause) of a trait (outcome) is mediated by another trait (intermediate factor). This method has two main advantages. First, it captures the total additive genetic variance as estimated using GREML rather than the smaller part of the genetic variance typically captured by polygenic scores. Second, the individual-level data approach GREML takes allows users to directly control for confounders in the model. We use analytical derivations, simulations, and empirical data to validate the underlying model that is used to quantify to which degree an intermediate trait mediates the relationship between the genetic component of the outcome and the outcome itself. We conclude that MA-GREML is an appropriate method to estimate genetic mediation and to test the significance of the indirect effect. We implemented the estimation procedure in the freely available Python-based software package MGREML, available at https://github.com/devlaming/mgreml/.Mediation analysis is instrumental to identify intermediate factors between causes and outcomes. We developed Mediation Analysis using Genome-based Restricted Maximum Likelihood (MA-GREML) estimation to quantify to which degree the genetic component ( Mediation analysis is widely used to identify the mechanisms and intermediate factors in between causes and outcomes . The oriMotivated by the desire to control for genetic confounding , the earThe Baron and Kenny approach, though, to assess mediation draws upon conventional regression techniques in a sample of unrelated individuals. By establishing that the effect of the independent variable on the outcome variable significantly changes upon inclusion of the mediating variable in the model, one can assess mediation , 8. By dM and outcome Y, but also the genetic variance of Y that is mediated by M  and the genetic variance of Y that is not mediated by M . Importantly, these estimates are not subject to the typical attenuation bias one would expect in a mediation analysis using a noisy approximation of the genetic component of M and/or Y, viz., a PGS.Originally, GREML estimation has been developed to quantify the contribution of random, additive effects of single-nucleotide polymorphisms (SNPs) to variation in a trait , 21  models, in which the main explanatory variable is only allowed to impact the outcome variable through the mediator, can be conceptualized as a restricted version of the MA-GREML model. This restriction boils down to the so-called rifiable , 25. MA-rifiable . EvidencWe use analytical derivations, simulations, and empirical data, to validate the structural model underlying MA-GREML and the procedure developed to test the significance of mediating effects. For the empirical analyses, we draw on longitudinal data from the US Health and Retirement Study . While wdirect effect and indirect effect) as functions of the genetic and environmental (co)variance of M and Y. Fourth, we propose a simple likelihood-ratio test (LRT) to assess whether the indirect effect differs from zero. Finally, we compare MA-GREML estimation to a step-wise approach involving several univariate GREML analyses that resembles the procedure suggested by Baron and Kenny [In this section, we first revisit univariate and bivariate GREML estimation. Second, we present our main SEM, its underlying assumptions, and the interpretation of the latent factors in the model. Third, we formulate the main quantities of interest  of the confounders and additive, random effects of the SNPs, where each SNP is assumed to have the same explained variance (R2) with respect to the trait of interest [R2 per SNP may vary across the genome are possible. Such a specification is referred to as a heritability model [The default form of GREML estimation considers a single trait interest , 21. Othty model . Typicalty model , 29.Y can effectively be decomposed into (i) a latent genetic factor GY, (ii) a latent environmental factor EY, and (iii) a contribution from the fixed-effect confounders. This decomposition can be perceived as a simple SEM, involving two latent factors, viz., GY and EY. The covariance of a given genetic factor between two individuals is equal to the corresponding element from the GRM, which reflects genetic similarity between individuals based on SNP data. Thus, the more genetically similar two individuals are, the more similar they tend to be in terms of GY . The covariance of a given environmental factor is assumed to be zero across individuals: environmental exposures (other than those captured by the fixed-effect confounders) are assumed to be uncorrelated across individuals. Without loss of generality, we also assume that environmental factors have unit variance and that all latent factors have an expectation equal to zero.Under this model, Y accounted for by GY  and EY . Quantities Y provides estimates of these VCs while controlling for user-specified fixed-effect confounders. In this framework, the SNP-based heritability (Y is defined asY) accounted for by the additive SNP effects under the specified heritability model.Omitting fixed effects in all equations for presentation purposes, this simple SEM can be written as:ts (VCs) . Univaribetween traits (denoted by M and Y). Genetic covariance quantifies the phenotypic covariance between two traits accounted for by random SNP effects under the specified heritability model. Environmental covariance quantifies the remaining phenotypic covariance. These covariances are also referred to as VCs. In this framework, the genetic correlation between M and Y is defined asBivariate and multivariate generalizations of GREML , 22, 31 M and the second genetic factor affecting both M and Y. Such a SEM would only be one possible representation of many statistically equivalent models, that can all yield a valid A relatively parsimonious SEM for two traits could be a model in which there are two genetic factors, with the first genetic factor only affecting For the purpose of MA-GREML, we make the following set of assumptions, which yields the SEM shown in A1The heritability model has been correctly specified.A2M precedes outcome Y and has homogeneous effect b on Y.Mediator A3M influences Y only through M.The environmental component of A4environmental confounders) are controlled for.Factors that violate A3  there exists no other latent environmental factor that has a direct effect on both M and Y and (ii) what then remains in terms of E and E* is uncorrelated. The reason for introducing Assumption A4, as we show in Section C in M and Y is purely driven by the causal effect of M on Y. Thus, Assumption A4 allows to consistently estimate b using the environmental covariance. We stress that the set of environmental confounders included in the model should not contain colliders, as their inclusion in the model leads to collider bias.Assumption A4 is equivalent to the following two requirements: after controlling for the observed environmental confounders , and even though it may be convenient to use publicly available GWAS summary statistics from well-powered GWASs for the desired type of mediation analyses, adequately controlling for such confounders of the association between M and Y remains difficult—in a GWAS one typically only controls for (a rather limited set of) confounders of the association between the SNP and the outcome, and not for confounders of the associations between various traits when identified from the environmental (co)variance permeating the summary statistics.In light of Assumption A4, a notable advantage of GREML is that it uses individual-level data, allowing users to directly control for aforementioned confounders of the association between omic SEM , for insG and G* are latent genetic factors, E and E* are latent environmental factors, M is the observed mediator, and Y is the observed outcome. Although observed environmental confounders are not drawn in this figure (for the sake of clarity of the diagram), these can readily be controlled for by MA-GREML.In the SEM shown in G is the genetic component of M that is also permitted to affect Y directly, whereas G* is the genetic component of M that is only permitted to have an indirect effect on Y, viz., through M. To understand why both G and G* are relevant for this model, first observe that without G the model would simply enforce full mediation: in that case there would only be genetic factor G* of which its effect on Y runs fully through M. Second, without G*, the model would enforce a perfect genetic correlation between M and Y as both traits are then affected only by shared genetic factor G in that case, irrespective of whether there is no mediation, partial mediation, or full mediation. For a more elaborate explanation for why both G and G* are needed in the model, we refer to Section D in Conceptually, E*  is required to satisfy Assumption A5: M has E permits Y to have b = 0. Here, we emphasize that MA-GREML would still yield valid estimates if applied to a dataset for which in truth e = 0 . Thus, E merely enhances versatility of MA-GREML. A similar argument could be made in favor of including an idiosyncratic genetic factor G′ that only affects Y: here MA-GREML also still yields valid results b2 to the genetic variance of Y (indirect effect). Moreover, as G is the only genetic factor that has a direct effect on Y (with coefficient c), the genetic variance not mediated by M is given by c2 (direct effect).The underlying equations of the SEM in M on Y , the direct effect, and the indirect effect are the main quantities of interest  full mediation if the direct effect = 0 and the indirect effect > 0, (ii) partial mediation if the direct effect > 0 and the indirect effect >0, (iii) no mediation if the direct effect > 0 and the indirect effect = 0. There is also a degenerate case in which both the direct and indirect effect are equal to zero, implying that outcome Y has M is not relevant.In MA-GREML, the effect of M on the right-hand side of Gc is the direct contribution of G to Y and Gab + G*gb is the contribution of G and G* running through M. This formulation reveals that whenever abc ≠ 0, these two contributions are correlated (as both then involve G). In that case, the genetic variance of Y comprises a cross term equal to 2abc, in addition to the direct effect and indirect effect. In other words, the genetic variance of Y can and often will differ from the sum of the genetic variance mediated by M and the genetic variance not mediated by M. This deviation cannot clearly be categorized as either part of the direct or indirect effect. We also abstain from that, and treat it as a cross term.Substituting b, the direct effect, and the indirect effect) are always identified provided Assumption A5 holds true. For instance, in case of full mediation (c2 = 0 and (a2 + g2)b2 > 0), the SEM cannot differentiate between contributions from G* and G. More specifically, in case of full mediation, g2 and a2 are not separately identified. However, a2 + g2 still is identified.b, the direct effect, and indirect effect. Derivations of the SEs can be found in Section C.4 in In Section C in  the VCs , the delW as follows:W should asymptotically follow a χ2(1) distribution [viz., Assessing the significance of the indirect effect is currently advocated to be the most appropriate test for the presence of mediation . Given wribution . Howeverribution  and the ribution . This isa2 + g2)b2, it can only be non-zero if two conditions are met: (i) b ≠ 0 and (ii) a2 + g2 > 0. Conversely, this implies that the parameter space permitted by the null hypothesis of no indirect effect  within parameter space ii) within parameter space To obtain the test statistic, we therefore perform the MA-GREML analysis (i) a2 + g2 > 0 and (ii) b ≠ 0. While (i) concerns the power to detect that M has a non-zero heritability, (ii) is about the power to detect the environmental covariance between M and Y. The latter is typically high in empirical applications provided that there is sufficient overlap between the sets of individuals for whom M and for whom Y is observed). Therefore, the power to detect M serves as a reasonable upper bound for the power to detect the indirect effect. For this purpose, the GCTA power calculator [M for a given sample size. This power calculator can be accessed online at https://shiny.cnsgenomics.com/gctaPower/. For example, the power to detect M with true N = 10, 000. The power remains high at 97.7% in a sample of this size if M again  and/or the genetic variance that directly contributes to Y does not also affect M .MA-GREML takes a bivariate approach in which mediator compared . In SectM and Y would provide an estimate of b that is confounded by genetic factor G, and, in turn, uses this confounded estimate to back out which part of the genetic variance of Y is not mediated by M. In any other situation than the described extreme scenarios, this leads to inconsistent results.In short, mirroring a main critique on the step-wise regression-based mediation analysis procedure , a proceBaseline scenario with partial mediation , we also consider three scenarios with no mediation , viz., Scenario (i) where a2 + g2 = 0, b = 1, and c2 = 1; Scenario (ii) where a2 + g2 = 2, b = 0, and c2 = 1; Scenario (iii) where a2 + g2 = 0, b = 0, and c2 = 1. Finally, we consider one scenario with full mediation , viz., Scenario (iv) where a2 + g2 = 2, b = 1 and c2 = 0. Section I in Using simulations, we analyze whether the MA-GREML estimators are consistent and if the LRT for the presence of an indirect effect behaves appropriately . Besides a Baseline), estimates indeed reveal both the direct and indirect effect; for the scenarios with no mediation (i–iii), the estimated indirect effect is indeed very close to zero; for the scenario with full mediation (iv), the estimated direct effect is very close to zero, as expected. We conclude that MA-GREML consistently estimates the effect of M on Y, the direct effect, and the indirect effect, irrespective of whether we have no mediation, partial mediation, or full mediation.The simulation results are based on 100 runs. i) the optimum of the likelihood of the model under the null hypothesis  b2 = 0) is typically found in a2 + g2 = 0 and where b can differ from zero) and, therefore, the LRT tends to have two degree of freedom. p-values resulting from the LRTs compared to the expected distribution of p-values. We find that the observations lie somewhat below the 45 degree line. Thus, the LRT is conservative in case M has no genetic variance at all, implying a reduced false-positive rate in that case. By the same token, this finding implies that the LRT is underpowered in case M has a very low but non-zero Regarding the behavior of our LRT for the indirect effect, in Scenario (ii), the optimum of the likelihood of the model under the null hypothesis is mostly found in b = 0 and where a2 + g2 can exceed zero) and, therefore, the LRT tends to have one degree of freedom. p-values resulting from the LRTs compared to the expected distribution of p-values. With the observations being close to the diagonal, we find that the empirically observed p-values closely follow their theoretically expected distribution in case b = 0 and (a2 + g2)>0. Thus, also in this setting, we do not suffer from an inflated false-positive rate. Moreover, here we find no evidence of any loss in statistical power . Within the set of scenarios where the null hypothesis of no indirect effect holds true , we judge this scenario to be the most realistic one because in practice mediators will have a non-negligible Y.In Scenario (iii) the observed distribution of p-values resulting from the LRTs compared to the expected distribution of p-values are shown in a priori determine whether the optimum of the likelihood of the model under the null hypothesis will be in either p-values, like in Scenario (i), are somewhat conservative. We conclude that also in this scenario the procedure for testing the significance of the indirect effect sufficiently controls for Type I errors. Again, we note that in practice a setting in which the mediator is not heritable is not realistic.Finally, for Scenario . The HRS is a longitudinal panel study that surveys a representative sample of approximately 20,000 individuals aged 51 years and older (and their spouses) in the United States of America . Collectp < 10−6, and selection of unrelated individuals from European ancestry with <5% SNP missingness, we construct the GRM for a sample of 8,652 individuals using 1,188,298 SNPs.To construct the genomic-relatedness matrix (GRM) we need for the GREML analyses, we use the 2012 release of genetic data. These genetic data were obtained from the DNA samples collected from HRS participants in the years between 2006 and 2008. Participants signed consent forms stating that the genetic data could be used for non-profit research use only. Genotyping was carried out using the Illumina Human Omni-2.5 Quad BeadChip. After imposing the quality control filters recommended by the genotyping center, removal of non-autosomal SNPs and SNPs with minor allele frequency (MAF) < 0.05, genotyping call rate < 95% and Hardy–Weinberg 2), Cognition (index score ranging from 0–35), Mental health (index score ranging from 0–8), and Self-reported health (index score ranging from 1–5)—as available in the RAND HRS Longitudinal File 2018 (V1). By analyzing a variable that is realized in early life as mediator and health measures in later life as outcomes, we make sure that the mediating variable precedes the outcomes. We selected phenotypic data from the wave of data collection with the (combined) lowest number of missing values for these four outcomes (Wave 8). In addition, we analyze to what extent genetic effects run through an earlier realization of these four variables. That is, we analyze whether the Wave 7 measures mediate the additive genetic effect on the Wave 8 outcomes.Phenotypic data from the biennial waves of data collection in the HRS are harmonized by the RAND cooperation. We exploit information about educational attainment (years) and four health outcomes in later life—Body Mass Index  and birth year. MGREML also adjusts for the first 20 principal components of the genomic-relatedness matrix to control for subtle forms of population stratification , 38. Tabb of M on Y show that every additional year of educational attainment leads to a decrease in BMI of 0.222 kg/m2 and an increase in the cognition index score of 0.323. For mental health and self-reported health, the estimates are −0.044 and −0.046, respectively. We find that the effect of the additive genetic factor of BMI on BMI runs through educational attainment for just 1.4%, but this indirect effect is statistically significant (p = 0.025). For mental health, the indirect effect appears to be statistically insignificant (p = 0.182). The proportion mediated for cognition is 27.6% (p = 0.011). This percentage is roughly similar for self-reported health, 30.6%, and for this outcome the indirect effect is also significant (p = 0.010).The main results of the empirical analyses are presented in p = 3.615 × 10−4). For self-reported health, we observe a similarly high proportion mediated, i.e., 96.8% (p = 7.785 × 10−5). These estimates suggest that the additive genetic factor of these two outcomes is relatively time-invariant between consecutive data collection waves in the analysis sample. For cognition and mental health the proportions mediated are lower, 71.3% (p = 2.901 × 10−3) and 54.9% (p = 1.563 × 10−3), respectively, suggesting relatively strong impact of the environment on the development of these traits over the life-course.For the analyses with the lagged variables as mediators, the results are shown in the bottom of https://github.com/devlaming/mgreml) accompanying this tool comes with a full tutorial on its usage.We designed a framework to analyze to what extent a particular factor mediates the relationship between the additive genetic factor of a trait and the trait itself. By doing so, we particularly improve on the current practice of using polygenic scores for such analyses, because GREML enables capturing the full SNP-based heritability of a trait rather than just the ‘explained SNP-based heritability’ . The staM and outcome Y is correctly specified. Moreover, SNP-based heritability estimates may capture both direct genetic effects and gene-environment correlations such as genetic nurture effects , depending on the outcome analyzed [M on outcome Y, the impact of such effects on the MA-GREML estimates depends on the specific empirical application.Usage of MGREML for mediation analysis requires careful assessment of whether the assumptions of the model hold for the specific empirical application, in particular that the causal direction between mediator analyzed , 39. In Nevertheless, the assessment of mediation is of prime importance in the social sciences , becauseMoreover, Mendelian randomization (MR) models are widely used for causal inferences by exploiting the notion that the inheritance of alleles is random conditional of the genotypes of the parents . In MR mEven in case a biology-informed decision is taken regarding the subset of genetic variants to use in MR applications, the GREML-based mediation model can be used for such an informative analysis by constructing a GRM using these genetic variants only rather than a genome-wide scan of SNPs. For example, the GRM can be constructed using SNPs from specific chromosomes, SNPs in genes expressed in particular cell types , or SNPs with an allele frequency in a particular range, etc. –42. MoreThe empirical results from the Health and Retirement Study presented in the main text, however, suggest that for two reasons MR analysis based on a genome-wide scan of SNPs (or a PGS as summary of that) cannot be used to analyze the causal impact of educational attainment on BMI, cognition, mental health, or self-reported health in later life. While for BMI, cognition and self-reported health we do find a significant indirect effect, the proportion mediated is (far) below 100% suggesting that there are other channels than educational attainment through which the additive genetic factor for these two outcomes affects the outcomes. For mental health the indirect effect is statistically insignificant, both invalidating the exclusion restriction and the so-called relevance assumption that the instrument has explanatory power for the mediator . In the Thus, beyond the assessment of the relative size of indirect effects, these results show that results obtained using MA-GREML are of importance for validating assumptions of other methodologies. Therefore, we believe that the mediation analysis approach as developed in this study and implemented in the freely available MGREML tool will prove to be an important framework not only for the social sciences but also for genetic epidemiology.S1 Text(PDF)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.


Bubalus bubalis). Genomic prediction is an effective approach in animal breeding to improve selection and reduce costs. As buffalo genome research is lagging behind that of the cow and production records are also limited, genomic prediction performance will be relatively poor. To improve the genomic prediction in buffalo, we introduced a new approach (pGBLUP) for genomic prediction of six buffalo milk traits by incorporating QTL information from the cattle milk traits in order to help improve the prediction performance for buffalo. Results: In simulations, the pGBLUP could outperform BayesR and the GBLUP if the prior biological information  was appropriate; otherwise, it performed slightly worse than BayesR and equal to or better than the GBLUP. In real data, the heritability of the buffalo genomic region corresponding to the cattle milk trait QTLs was enriched (fold of enrichment > 1) in four buffalo milk traits  when the EBV was used as the response variable. The DEBV as the response variable yielded more reliable genomic predictions than the traditional EBV, as has been shown by previous research. The performance of the three approaches  did not vary greatly in this study, probably due to the limited sample size, incomplete prior biological information, and less artificial selection in buffalo. Conclusions: To our knowledge, this study is the first to apply genomic prediction to buffalo by incorporating prior biological information. The genomic prediction of buffalo traits can be further improved with a larger sample size, higher-density SNP chips, and more precise prior biological information.Background: The 90K Axiom Buffalo SNP Array is expected to improve and speed up various genomic analyses for the buffalo ( Genomic prediction is becoming increasingly important for animal and plant breeding programs because of its effectiveness in improving selection and reducing costs ,2,3,4,5.Bubalus bubalis), which is a key species for smallholder producers in developing countries  [Our main application of interest is in genomic selection of buffalos (d China) ,28,29,30d China) ,28,29,30d China) ,35,36,37Motivated by both methodological interest and the above two application questions, we propose a statistical approach to incorporate prior biological information in the widely used genomic best linear unbiased predictor (pGBLUP) to improve genomic prediction. We first simulated several scenarios to test the stability and advantages of the approach. Then, we applied the approach for the genomic prediction of buffalo milk traits by incorporating the known cattle milk trait QTL information from the animal QTL database .https://www.animalgenome.org/ (accessed on 10 October 2017)) to serve as priors to determine which set of SNPs belongs to The basic idea behind our approach is to fit the effect sizes of all SNPs as random effects relying on an LMM framework. We divided the SNPs into two groups based on a priori biological information, and we assumed that these two groups of SNPs have different effect sizes:uggested , u is anarge2P)) ,17,18. Rarge2P)) ,42,43,44MVN denotes the multivariate normal distribution, and When ect size ,43. Diffect size ,15,16,20ix (GRM) . σsmall2In the training dataset, about 80% of all individuals in the simulations, the SNP effects p-value < 10−4, genotype call rate < 95%, or minor allele frequency (MAF) < 0.01. All SNP positions were re-coded by the provider for confidentiality, and the genotypes of the population were used to simulate the phenotype in this study.German Holstein genomic prediction population ,45: The p-value < 10−5, genotype call rate < 97%, or MAF < 0.05. Six buffalo milk traits (peak milk yield (PM), total milk yield (MY), fat yield (FY), fat percentage (FP), protein yield (PY), and protein percentage (PP)) were recorded and adjusted to 270 days in milk, as suggested by [Water buffalo data : The genested by . The estested by . The derested by . The detested by . Both thhttps://www.animalgenome.org/cgi-bin/QTLdb/index accessed on 10 October 2017) during October 2017; those traits included milk yield, milk fat, milk protein, and some other milk component traits. Based on the Bos taurus UMD3.1 genomic assembly, the genes within 50 kb of the QTL regions were selected as genes associated with the trait. We only focused on the QTL regions with a length smaller than 40kb, which had the largest proportion of QTLs. After initial filtering, 2435 genes were selected to be associated with the milk traits, including some genes associated with several milk traits. Then, we only kept 396 genes s (10/25/50/100/500) segments were randomly selected as causal segments in our simulation settings, and the 10 SNPs in the center of each segment were then selected as causal SNPs; thus, the total number of causal SNPs (k) was 100/250/500/1000/5000, while the total number of SNPs in the causal segments was Set the causal segments: The genotype matrix was standardized, and the 42,551 SNPs were divided into 1000 approximately equally sized segments, with 42 or 43 SNPs in each segment; (2)Simulate the SNP effects and phenotype: Firstly, all SNPs were simulated with the small effects following a normal distribution (3)Five-fold cross-validation: The 5024 individuals were divided into five groups, with 1004 or 1005 individuals in each group. Each time, one group of individuals was set as the test dataset, while the rest of the groups of individuals were set as the training dataset . We applied the pGBLUP approach in two ways to predict the performance in the test dataset: only the SNPs in the causal segments were set in P method  and the P method  to compaWe used the real genotypes of the German Holstein genomic prediction population to simulate the phenotypes with the following steps:As the draft genomic sequence of the buffalo is currently not assigned to chromosomes, the chromosome and position for all SNPs in the 90K Axiom Buffalo SNP Array were based on the bovine UMD 3.1 genome sequence . This alhttp://cnsgenomics.com/software/gcta/#Download (accessed on 10 October 2017)). For BayesR, we used BayesR with the default parameters (https://github.com/syntheke/BayesR (accessed on 10 October 2017)). For the data cleaning and processing, PLINK (https://www.cog-genomics.org/plink2 (accessed in October 2017)) [https://www.r-project.org/ (accessed on 10 October 2017)) [For the GBLUP and pGBLUP, we used the GCTA  to perfor 2017))  and R (hr 2017))  were appIn our simulation studies, the causal regions were simulated and known. Firstly, we assessed the performance of different methods when only the SNPs in the causal segments were set in egments) A,B. If tegments) A,B. It secreased C, the precreased B. The felated fe C.Then, we assessed the performance of the pGBLUP when SNPs in both the causal segments and non-causal segments were set in segments . When ald better A and thed larger B. The pGIn our real data application studies, according to the prior biological information of 396 cattle-milk-trait-associated genes, 1279 SNPs within 10 kb of those genes were set in Three methods were applied for genomic prediction of buffalo milk traits using the GEBV and EBV as the phenotypes . The herWe proposed a new genomic prediction approach called the pGBLUP, which incorporates prior biological information in the LMM. Several methods incorporating prior biological information in the LMM were also developed recently using different strategies from ours: The BLUP|GA ,22,52,53dictions .In our simulations, the real genotypes of the cattle were divided into 1000 segments without considering the linkage disequilibrium (LD), which is not ideal. However, this should not affect our simulation purpose of illustrating the relationship between the predictive performance and e traits . While istimated C. The mihad fe>1 B. In sumhe GBLUP A.p-value < 10−5) to remove the variants due to the genotyping errors, which was also likely to remove the causal variants under selection, thus affecting the performance of the genomic prediction. The heritability estimations using the DEBV and EBV as the phenotypes were larger than those using the original records directly [We applied the pGBLUP to our published buffalo data  with anoary much ; BayesR directly ,60,61, bdirectly ,62. Whennd 0.439 , so therach 0.58 A; we belWe proposed a genomic prediction approach, the pGBLUP, which has the potential to improve the genomic prediction performance by incorporating the proper prior biological information. The pGBLUP uses heritability enrichment to quickly check the importance of the prior biological information. We also applied the pGBLUP to incorporate the milk-related QTL information from cows for genomic prediction of buffalo milk traits. We found that some cattle-milk-related QTLs also played an important role in buffalo milk production traits. We believe that genomic prediction of buffalo traits can be further improved with a larger sample size, higher-density SNP chips, and more precise prior biological information.


Unique features include network diagrams that show joint effects between variants for each trait and regional association plots that integrate fine-mapping results, all with user-controlled zoom features for an interactive exploration of potential causal variants across traits.flashfm-ivis is an open-source software under the MIT license. It is available as an interactive web-based tool (http://shiny.mrc-bsu.cam.ac.uk/apps/flashfm-ivis/) and as an R package. Code and documentation are available at https://github.com/fz-cambridge/flashfm-ivis and https://zenodo.org/record/6376244#.YjnarC-l2X0. Additional features can be downloaded as standalone R libraries to encourage reuse.Bioinformatics online. Genome-wide association studies (GWAS) have successfully identified many genetic variants that are associated with diseases and traits . As biolThere are few multi-trait fine-mapping methods that allow multiple causal variants at a single genomic region due to the computational complexity of many possible model combinations between traits. One approach is to restrict traits to have the same causal variants and allow for different effect sizes, as in fastPAINTOR , simplify complex results and give insight for further analyses (e.g. discovering shared variants of credible sets for different trait combinations).Flexible ways of viewing the joint effects of variants on one or more traits.Flashfm-ivis is currently built in R and minimizes the complex (inter-)dependency with other R packages (http://shiny.mrc-bsu.cam.ac.uk/apps/flashfm-ivis/) does not require users to have any programming skills, encouraging a wide spectrum of researchers to interact with and visualize their own results. It includes six Dashboard tabs that give access to various comparisons and summaries, links to download all codes (https://github.com/fz-cambridge/flashfm-ivis) and a link to finemap-ivis (http://shiny.mrc-bsu.cam.ac.uk/apps/finemap-ivis/).packages , making APOE region  thresholds can be controlled (Control widgets allow users to refine their data selection and then focus on key results; both credible sets and ntrolled .Individual regional association plots are presented for each trait and MPPs from fine-mapping are shown by the dot size . In SuppIn We provide area-proportional Venn diagrams that give an intuitive view of the degree of overlap between credible sets of the traits . InteracIn flashfm-ivis, that is accessible without any programming knowledge; it is available as an R package for those who prefer to run it from their own machine. It promotes exploration of fine-mapping results of several traits and helps with interpretation, as well as identification of variants for further follow-up. Users can interact with plots and decide on the final version of publication-ready plots for download. Flashfm-ivis output, such as lists of variants in credible sets for selected traits, is easily downloaded for further follow-up. If users are interested in only a single trait, there is an option to produce plots based on FINEMAP output only, finemap-ivis. We hope that flashfm-ivis will become standard practice for exploring fine-mapping results and will contribute to revealing the underlying mechanisms of diseases.We provide a user-friendly fully interactive web tool, btac453_Supplementary_DataClick here for additional data file.


CCND2 and SHANK2 genes as strong candidates affecting BF traits. The results of this study may provide additional insight into the genetic contribution of these genes to carcass meat production capabilities in pig breeding and enhancing lean meat percentages in pigs.Backfat thickness (BF) and loin muscle area (LMA) are important characteristics in pig breeding. The identification of the quantitative trait loci (QTLs) and genes closely associated with these carcass traits may be useful for pig molecular breeding. In this study, genome-wide association studies (GWAS) using imputation-based whole genome sequencing data of four selected phenotypic traits  were conducted in a total of 1131 pigs from three pure breeds . As a result, we highlighted SHANK2 gene was defined. In addition, two QTLs with 135.40 Kb (from 66.10 to 66.23 Mb) and 3.12 Kb (from 66.886 to 66.889 Mb) intervals containing CCND2 and TSPAN11 genes, respectively, were found on SSC5. For the BF-EBV trait, two QTLs (128.77 Kb from 66.10 to 66.23 Mb on SSC5 and 42.10 Kb from 2.89 to 2.93 Mb on SSC2) were identified. Notably, CCND2 and SHANK2 were the only candidate genes in their respective QTL interval. Furthermore, we detected a 3.33 Kb (66.106 to 66.110 Mb on SSC2) haplotype block which was detected as affecting the BF_EBV trait, which only contained the CCND2 gene. Thus, we suggested CCND2 and SHANK2 as strong candidate genes for regulating the BF trait for pigs. The empirical confidence intervals of the QTLs were 1.14 Mb (165.65 to 166.79 Mb on SSC6) for adjusted 100 kg LMA and 1.49 Mb (165.26–166.74 Mb on SSC6) for LMA-EBV. These two confidence intervals contained 13 and 28 annotated genes, respectively. Our results provide a deeper understanding of the genetic basis of pig carcass traits. The identified molecular markers will be useful for selecting breeding lines for breeding pigs with superior carcass traits.Improvement of carcass features is an essential goal in pig genetic breeding programs. Backfat (BF) and loin muscle area (LMA) are important carcass production metrics and useful indicators of pig production performance and lean meat rate. However, the genetic architecture of BF and LMA traits remains elusive. To identify quantitative trait loci (QTLs) and genes associated with these traits, we performed a genome-wide association study (GWAS) using imputation-based whole genome sequencing data for four phenotypes  in 1131 pigs from 3 breeds . After genotype imputation and quality control, 14,163,315 single nucleotide polymorphisms (SNPs) were retained for further analysis. For the adjusted 100 kg BF trait, using the 2-LOD drop method, a QTL with a 13.4 Kb interval (2.91 to 2.93 Mb on SSC2) and containing a  Sus scrofa) is widely recognized as an important agricultural livestock species and a crucial biomedical model. Pork has always been a major source of animal protein for people. Therefore, it is essential for pig breeders to concentrate their genetic selection efforts on improving growth rates and lean meat percentages. Backfat (BF) is a crucial carcass parameter in swine production because it influences the pigs’ growth rate, feed efficiency, and reproductive success [The domestic pig  and coiled-coil-helix-coiled-coil-helix domain 3 (CHCHD3) were recently identified as candidate genes for LMA, whereas insulin-like growth factor 2 (IGF2) and bone morphogenetic protein (BMP2) were shown to be associated with BF [PKM2) was shown to be associated with BF [With advances in sequencing technology, multiple genes, quantitative trait loci (QTL), and single nucleotide polymorphisms (SNPs) associated with pig carcass traits have been found. In the pig QTL database, 415 and 594 QTLs associated with LMA and BF were identified, respectively . Many pr with BF . In a La with BF . Further with BF . In thisIn this study, the 1131 experiment pigs consisted of three pure breeds, including 651 French Yorkshire, 294 Landrace and 186 Duroc. All pigs sustained uniform feeding conditions and were raised on three farms at Guangdong Guangken Group Co., Ltd. . Phenotypic records of BF and LMA were measured when the pigs were approximately 150 days old. BF and LMA were measured by B-mode ultrasound scanning at a point 5 cm from the dorsal midline at the 12/13th rib. The adjusted 100 kg BF was calculated by the following formula:LMA was adjusted to 100 kg using the formula below:The phenotypic correlation between the BF and LMA traits was estimated by Pearson’s correlation coefficient.The estimated breeding values (EBV) of the three pig populations for BF and LMA traits were estimated by best linear unbiased prediction (BLUP)  using pe2 0.89. Moreover, the reference population was mainly composed of Landrace (n = 651), Yorkshire (n = 543), and Duroc (n = 485) breeds. Thus, we submitted chip data from a total of 1131 pigs of three breeds in this study to SWIM for imputation together. The QC of imputation-based whole genome sequence data was carried out with PLINK [p-value greater than 10−6 were ruled out. Only SNPs located on the autosome chromosomes remained in this study. After QC, 1131 pigs and 14,163,315 SNPs remained for subsequent analyses. GCTA [Ear samples were collected from each pig and stored in 75% alcohol at −20 °C;. DNA was extracted from each sample and the quality of the DNA was determined using the A260/280 and A260/230 ratios. All animals were genotyped with the China Chip-1 Porcine SNP50 BeadChip , which contains 51,315 SNP markers across the entire genome. Genotyping was conducted as described by Ding et al. . The SNPn is the number of animals. We used a genome-wide significant threshold of p = 5 × 10−8 to declare the significance as described in previous study [p-value) was roughly equivalent to one unit of logarithm of the odds (LOD) value and all SNPs in each SSC with a LOD-score higher than the peak LOD-score  minus 2 were retained [Imputation-based GWAS for the four traits were conducted using GEMMA software v0.98.1 . The folus study . As descretained . Sscrofa 11.1 genome version . Candidate genes were selected in the candidate QTL region based on the 2-LOD drop method and haplotype block analysis. Furthermore, we manually queried the literature for the information about the associations between all candidate genes and the analyzed traits in this study.The PLINK v1.90 and LDBlockShow v1.40  were useSHANK2). We identified two QTLs on SSC5 with intervals of 135.40 Kb and 3.11 Kb. Cyclin D2 (CCND2) and Tetraspanin 11 (TSPAN11) were identified as candidate genes associated with these QTLs. For the BF-EBV trait, we detected one QTL on SSC5 with an interval of 128.77 Kb, and mined CCND2 as the candidate gene associated with it. Gene SHANK2 was also identified in a QTL with an interval of 42.10 Kb on SSC2 for BF-EBV trait. Manhattan plots of GWAS for adjusted 100 kg BF and BF-EBV traits and the corresponding Q–Q plots are shown in CCND2 gene.Furthermore, LD analysis around the most significant SNP for adjusted 100 kg BF trait on SSC5 identified one haplotype block of 3.10 Kb between 5_66210825 and 5_66213940 SNPs a. In addRNF220 was the only gene located in this block.For adjusted 100 kg LMA and LMA-EBV traits, we identified one QTL on SSC6 that surpassed the significant threshold. The empirical confidence intervals of the QTL were 1.14 Mb (165.65–166.79 Mb) for adjusted 100 kg LMA and 1.49 Mb (165.26–166.74 Mb) for LMA-EBV c,d. ThesBF and LMA traits are both complex quantitative traits regulated by a main effect gene and a set of micro effect genes in pigs ,22. WithCCND2 and SHANK2 genes were the only genes in the respective QTL support interval (a drop in LOD score of 2). Most importantly, CCND2 was the only gene in the LD block of the SNPs associated with the BF EBV trait. Therefore, CCND2 and SHANK2 may be considered strong candidate genes as quantitative trait gene (QTG) for pigs’ BF trait. CCND2 is involved in the growth of pancreatic islets, which regulate animal growth through its hormones [CCND2 gene may be related to the growth performance of pigs and is as a possible candidate gene for backfat conformation in Landrace [CCND2 was significantly associated with animal body weight and total body fat amount [SHANK2, a member of the Shank protein family, was found to be associated with childhood obesity, and to have an influence on estradiol blood concentrations [SHANK2 gene was identified as likely to affect the meat to fat ratio in pigs [Genotype imputation can be used to infer genotypes at untyped loci, thus playing an important role in current genome-wide association studies . In thishormones . In a GWLandrace . In addit amount . Our restrations ,28. In a in pigs . HoweverRNF220) (+/−) mice exhibited different typical amyotrophic lateral sclerosis (ALS) pathological features [RNF220 was the only gene in the LD block of the associated with pigs’ LMA trait. Therefore, we hypothesize that the RNF220 gene has an effect on the LMA in pigs and thus warrants further study. Peroxiredoxin 1 (PRDX1), a member of the peroxiredoxin family of antioxidant enzymes, regulates with hematocrit levels and hemoglobin concentration. As a candidate gene associated with the immune health in pigs, PRDX1 may indirectly affect the growth and LMA traits of pigs [In our study, a total of 23 candidate genes associated with LMA traits were detected in the region spanning from 165.65 to 166.74 Mb on SSC6. In particular, a study by Ma et al. showed that ring finger protein 220 (features . In our  of pigs . HoweverCCND2 and SHANK2 as strong candidate genes affecting BF trait. These results will advance our understanding of the genetic basis of complex traits in pigs. In addition, the identified SNPs will be useful for the genetic improvement of BF and LMA traits in pigs by exploiting associated SNPs in genomic selection.In this study, we conducted imputation-based GWAS for BF and LMA traits to detect significant QTLs and genes in large pig populations consisting of three breeds. Significant QTLs and genes were identified as being associated with the traits analyzed in pigs. We highlighted


Identifying selection on polygenic complex traits in crops and livestock is important for understanding evolution and helps prioritize important characteristics for breeding. Quantitative trait loci (QTL) that contribute to polygenic trait variation often exhibit small or infinitesimal effects. This hinders the ability to detect QTL-controlling polygenic traits because enormously high statistical power is needed for their detection. Recently, we circumvented this challenge by introducing a method to identify selection on complex traits by evaluating the relationship between genome-wide changes in allele frequency and estimates of effect size. The approach involves calculating a composite statistic across all markers that capture this relationship, followed by implementing a linkage disequilibrium-aware permutation test to evaluate if the observed pattern differs from that expected due to drift during evolution and population stratification. In this manuscript, we describe “Ghat,” an R package developed to implement this method to test for selection on polygenic traits. We demonstrate the package by applying it to test for polygenic selection on 15 published European wheat traits including yield, biomass, quality, morphological characteristics, and disease resistance traits. Moreover, we applied Ghat to different simulated populations with different breeding histories and genetic architectures. The results highlight the power of Ghat to identify selection on complex traits. The Ghat package is accessible on CRAN, the Comprehensive R Archival Network, and on GitHub. Many important traits in plants, animals, and humans are polygenic and depend on the cumulative effects of many loci, each contributing a small proportion of the total genetic variation . StudyinRecently, large sample sizes, especially as utilized in human genetic studies, have allowed a more powerful understanding of the genetic architecture of polygenic traits using genome-wide association studies (GWAS) . After cQPC, which detects adaptive divergence while controlling for population substructure. Conditional QPC detects adaptive divergence based on the excess of variation observed among populations versus variation observed within populations  ” and canParallel-Package {parallel}: Support for parallel computation, to speed the computation time in large data analysis (rrBLUP-Package {rrBLUP}: Ridge regression for estimating marker effects (RR-BLUP) .These may be installed and loaded before running Ghat by typing: >install.packages) and then typing > library and > library(rrBLUP) , step-1.For its simplest implementation, Ghat requires only 2 vectors of input. The first of these is a vector of allele substitution effects, and the second is a vector containing the allele frequency change between the 2 generations   are required , step-4 To demonstrate the power and flexibility of the Ghat R package, we used it to test for selection during the past 50 years of wheat breeding in Western Europe. To achieve this, we leveraged a dataset representing 50 years of commercial winter wheat varieties that were previously published by Voss-Fels and colleagues  biomass parameter traits, including above-ground biomass, kernels per sqm, and thousand-kernel weight; (3) quality parameter traits, including crude protein, protein yield, and nitrogen use efficiency; (4) morphological traits, including plant height, green canopy duration, and kernel per spike; and (5) disease resistance traits, including resistance to powdery mildew and resistance to stripe rust. All cultivars were genotyped with a 15K SNP Illumina Infinium iSelect genotyping array . All traits were tested in 3 different environments by Pearson correlations between phenotype and the year of release  , we can spearman correlations between the Ghat results from 3 and 50 years of phenotypes were estimated. A significant  correlation was found between Ghat values from 3 and 50 years of phenotypes, indicating that even in the absence of historical phenotypic information, Ghat can be used to understand historical patterns of selection.To dissect the history of selection in wheat more precisely, we divided the 50 years of breeding into 10 5-year time periods and applied the Ghat test successively for each period. We excluded the green canopy duration trait from this analysis since it was only measured under 1 environmental condition. For the remaining 14 traits, we calculated the average phenotypic value across the 3 different environmental conditions. For example, during the period from 1966 to 1975, significantly selected traits according to the Ghat test included grain yield, harvest index , above-g2h), sample size (n), trait polygenicity [number of QTL (nQTL)], and marker density (MD) on the performance of Ghat.To complement the above wheat analysis, we additionally generated a simulated cattle dataset with divergent selection to further test and demonstrate the efficacy of Ghat. This allowed us to evaluate how the Ghat package performs in terms of different genetic architectures and experimental parameters, which are never perfectly known using real data. In this analysis, we assessed the impact of trait heritability  in each dataset. Then we measured the P values coming from using these different datasets with different n. Group 2 was simulated to investigate the effect of h2 on Ghat performance. For this, we simulated 10 datasets with a constant n, nQTL, and MD, but with a different h2 (from 0.1 to 0.95) in each dataset. Group 3 investigated the effect of nQTL on Ghat performance. For group 3, we simulated 10 datasets with a constant n, h2, and MD, but with a different level of nQTL  in each dataset. Group 4 was simulated to investigate the effect of MD on Ghat performance. In this group, we simulated 10 datasets with a constant n, h2, and nQTL but with a different level of MD (from 30 to ∼ 1 million markers) in each dataset.Four groups of datasets were simulated for studying the performance of Ghat . Each grhttps://github.com/Medhat86/Selection-and-adaptation-on-simulated-bovine-genome).Genomic prediction methods like BayesR, BayesB, and BayesC assume that the allele substitution effects follow a distribution in which many effects are zero and some have moderate effects, subsequently these methods may for some genetic architectures lead to higher accuracy than the GBLUP/rrBLUP methods . The effn, h2, nQTL, and MD) to evaluate the detection accuracy by evaluating true discovery rates and false positives. Confidence intervals were obtained from P-values of the same traits from the Ghat permutation test using the formula from Ghat is calculated as the summation of the estimated effect of every SNP scored multiplied by its effect size, according to model 1. We implemented identical analyses on selected and unselected populations across 1,000 simulation replications for 4 different tests  under selection. Conversely, in the selected population 99.3% of replicated traits found highly significant under selection . The P-value also shows a decreasing trend with increasing n  in the selected population. Moreover, the confidence interval decreases with increasing n.Based on 1,000 replications, h2 on the power of Ghat. Higher h2 led to a more powerful test. When we tested the unselected population with a constant n = 400, nQTL = 150 and 10,020 markers, 100% of replicates were not significantly  under selection. In the selected populations 100% of replicates were significant for selection . The P-value shows a consistency decrease with increasing trait h2 (from 0.1 to 0.95 of h2).Based on 1,000 replications, nQTL on the power of Ghat. When we tested the unselected population with a constant n = 400, h2 = 0.5 and 10,020 markers, 99.7% of replicated traits were not significantly  under selection. In the selected population, 100% of replicated traits found highly significant selection . The power of the test increased with increasing the nQTL in the selected population. This can be restated to emphasize that, all else being equal, Ghat is more able to detect selection on traits that are more complex (i.e. controlled by a large number of QTL).Based on 1,000 replicates, P-values of the Ghat test for selection. When we tested the unselected population with a constant n = 400, h2 = 0.5 and 150 QTL, 90% of replicated traits were not significantly  under selection. While, in the selected population 100% of replicated traits were highly significant for selection . The P-value also shows an inconsistent increase of significance along with an increasing MD in the selected population (from 30 to ∼ 1 million MD). This may be caused by multicollinearity in the data when there are a high number of markers  and relatively small n .Based on 1,000 replications, https://cran.r-project.org/package=Ghat) for the 3 major operating systems: MS Windows, Linux/Unix, and Mac OS. We have also released the source code on GitHub (https://github.com/Medhat86/Ghat) so that users can compile for specific platforms or modify the code as needed.We have released Ghat package on CRAN  relative to current high-density marker panels and/or whole-genome sequencing approaches, so this analysis did not represent all the challenges associated with implementing Ghat in these settings. In particular, high-density panels include markers that are in strong LD, which must be properly accounted for in any Ghat implementation.To address this, we used a simulated SNP panel to further explore the utility of Ghat when high LD between markers is present. We found that Bayesian methods, such as BayesC , 2011. BUnlike methods for detecting selected loci, Ghat can quantify the direction of selection even in the absence of phenotypes from a preselection population. For breeders and breeding organizations, this represents a practical solution for tracing selection and adaptation history based on phenotypes measured from modern germplasm coupled with DNA samples saved from the past. From an economical perspective, this may allow a retrospective evaluation of traits that were inadvertently selected in the past, and therefore may productively serve as the targets of intentional selection in the future. Advances in phenotyping technologies make measuring novel traits increasingly possible (The Ghat test and implementation via the package described herein does not require immense sample size, individual genotypes, and/or genome-wide significant SNPs, as demonstrated by our implementation with a relatively modest sample size of 191 wheat individuals genotyped for 8,710 SNPs. Notably, this approach is more powerful when traits are controlled by a large number of QTLs (jkac319_Supplementary_DataClick here for additional data file.


Bayesian genomic prediction methods were developed to simultaneously fit all genotyped markers to a set of available phenotypes for prediction of breeding values for quantitative traits, allowing for differences in the genetic architecture (distribution of marker effects) of traits. These methods also provide a flexible and reliable framework for genome-wide association (GWA) studies. The objective here was to review developments in Bayesian hierarchical and variable selection models for GWA analyses.By fitting all genotyped markers simultaneously, Bayesian GWA methods implicitly account for population structure and the multiple-testing problem of classical single-marker GWA. Implemented using Markov chain Monte Carlo methods, Bayesian GWA methods allow for control of error rates using probabilities obtained from posterior distributions. Power of GWA studies using Bayesian methods can be enhanced by using informative priors based on previous association studies, gene expression analyses, or functional annotation information. Applied to multiple traits, Bayesian GWA analyses can give insight into pleiotropic effects by multi-trait, structural equation, or graphical models. Bayesian methods can also be used to combine genomic, transcriptomic, proteomic, and other -omics data to infer causal genotype to phenotype relationships and to suggest external interventions that can improve performance.Bayesian hierarchical and variable selection methods provide a unified and powerful framework for genomic prediction, GWA, integration of prior information, and integration of information from other -omics platforms to identify causal mutations for complex quantitative traits. The goal of genome-wide association (GWA) studies of quantitative traits is to identify genomic regions that explain a substantial proportion of the genetic variation for the trait, with the ultimate goal to identify causal mutations underlying the genetic basis of the trait. The standard GWA approach is to genotype a population that has been phenotyped for the trait(s) of interest and genotyped for many genetic markers across the genome and to analyze these data by estimating and testing the effects of marker genotypes on phenotypes using a regression-type of analysis for each SNP, one at a time , 2. The Some of these issues can be overcome by using the Bayesian multiple regression methodology that was originally developed for genomic prediction . BayesiaBayesian multiple regression methods for analysis of quantitative traits using genetic markers were originally developed by Meuwissen et al.  for genoAfter their introduction for genomic prediction, the Bayesian Alphabet methods, in particular BVS methods such as Bayes-B, were very quickly adapted for use in GWA to differentiate SNPs or windows of SNPs that are associated with phenotype from those that capture just pedigree relationships or noise. Fernando and Garrick  providedMethods for genomic prediction and GWA using Bayesian Alphabet methods were implemented in the software Gensel by Fernando and Garrick , as wellA concern of implementations of the Bayesian Alphabet methods for both genomic prediction and GWA is the computational demand of the MCMC methods that are employed. To overcome this, a fast non-MCMC approach for genomic prediction using Bayes-B based on the expectation maximization (EM) algorithm was developed by Meuwissen et al. . This meGianola  pointed An important advantage of Bayesian multiple regression methods for GWA is that they implicitly account for population structure by fitting all markers simultaneously. Similarly, in single-SNP GWA methods, fitting a polygenic effect based on genomic relationships has been shown to account for population structure and to avoid false positives . KärkkäiAvailability of sequence data is the ultimate p >  > n situation, where all possible genomic locations are interrogated on a usually relatively small number of sequenced individuals, although this can be increased by imputing SNP-genotyped individuals up to sequence if an appropriate sequenced reference population is available. Depending on the LD structure in the population, sequence data, however, does not necessarily improve mapping accuracy, especially when GWA is based on single-SNP methods or GBLUP . There iH matrix) by Legarra et al. [While increasing numbers of animals are being genotyped in breeding programs for different livestock species, still many animals that contribute phenotypic data are not genotyped. For genomic prediction, information from ungenotyped animals was initially incorporated using two-step methods that combined EBV derived using genomic data with EBV derived using pedigree-based BLUP by selection index methods . Others a et al.  and Chria et al. .Wang et al.  used theFernando et al. , 46 exteIn single-SNP mixed linear model GWA approaches, with each SNP fitted as a fixed effect one at a time, along with a polygenic effect based on a pedigree-based or genomic relationship matrix, significance testing is typically conducted using a standard test for a fixed effect in mixed linear models based on its estimate divided by its standard error. However, Gianola et al.  pointed For Bayesian multiple regression GWA methods, several approaches have been developed to identify which SNPs or windows of SNPs can be considered as explaining a substantial or significant proportion of the genetic variance. Because all SNPs are fitted simultaneously in these methods, these approaches must consider groups or windows of SNPs rather than individual SNPs, because the effect of a causative mutation may be distributed across multiple SNPs. Initial studies used the proportion of variance in GEBV among individuals in the population that is explained by a window or group of SNPs, i.e. the variance across individuals of the window GEBV  divided by the variance of genome-wide GEBV of those same individuals . Onteru An alternative criterion to identify association signals in Bayesian multiple regression GWA is an estimate of the proportion of genetic variance that is explained by a group or window of SNPs. Instead of a ratio of variances of GEBV, as in Boddicker et al. , this crThe posterior distribution of genetic variance explained by a window from the approach described in  also enaFor BVS methods, the MCMC samples after burn-in can also be used to compute the proportion of samples for which a particular SNP was included in the model with a non-zero effect, which is referred to as the posterior probability of association (PPA) for that SNP. Similarly, the proportion of samples for which at least one SNP in a window was included in the model can be computed, which was referred to as the window posterior probability of association (WPPA) by Fernando et al. . The WPPIn addition to analysis of individual traits, there is also an interest in understanding genetic correlations between traits, with particular interest in genomic regions that break unfavorable genetic correlations. Many studies have attempted to identify pleiotropic QTL regions by comparing results from single-trait GWA, i.e. by identifying overlap between windows that explain a large proportion of genetic variance across traits . This isIn a more formal approach, Jia and Jannink  extendedIn the multi-trait Bayesian GWA approaches of –69, the In summary, Bayesian multiple regression methods provide a flexible approach for the detection of pleiotropic QTL. However, the impact of prior assumptions on SNP effects across traits requires further investigation. Also, an important limitation of all methods that attempt to use genotype–phenotype associations to detect pleiotropic QTL is that they cannot differentiate the presence of a pleiotropic QTL from the presence of two closely linked single-trait QTL, depending on the extent of LD in the region. In addition, similar to the issues with inferences about genetic variance based on multiple-marker regression models raised by de los Campos et al. , estimatIdentification of pleiotropic QTL is important for understanding the biology behind traits and multi-trait GWA approaches are expected to increase power to detect QTL and increase the accuracy of genomic prediction. However, it is not clear whether pleiotropic QTL should receive specific attention in multi-trait breeding programs, beyond the emphasis they receive in standard total merit selection criteria based on GEBV.Structural equation models (SEM) aim at going beyond correlations to making inferences on causal relationships between variables, as first proposed by Wright  based onLi et al.  applied Momen et al.  adapted Given the typically limited statistical power and mapping precision of GWA studies, interpretation of GWA results requires substantial post-GWA analyses. These can be based on evidence from other -omics data, results from previous QTL or GWA studies, and functional biological information about genes located in the identified genomic regions, as reviewed by Uffelmann et al. . RamananRather than for post-GWA analyses, functional biological information can also be integrated in the GWA analysis. One strategy is the feature GBLUP approach employed by, e.g., Edwards et al. . In thisAn alternative approach is to incorporate prior biological information in the Bayes-R approach of Kemper et al. , as propcis gene expression regulation to be mostly oligogenic.Combining multiple omics data to achieve improved phenotype prediction and fine-mapping of causal loci can be performed in a stepwise post-GWA interpretation of GWA results, using other -omics data, meta-data from other GWA, and functional information, as reviewed by Uffelmann et al. . AlternaAn additional level of information that can inform causal relationships between genotype and phenotype is the proteome , which iFlutre et al.  introducAnother solution to missing data is to impute missing records using integrative techniques that use correlations and shared information across data sets , 104. ThHajiramezanali et al.  proposedWhile BVS methods continue to be improved, they remain computationally demanding, which in the era of increasing availability of sequence data on tens or hundreds of thousands individuals with millions of SNPs becomes a challenge. Improved Gibbs-samplers and non-MCMC algorithms are expected to alleviate some of the computational burden. van der Berg et al.  proposedIn addition to genomic prediction, Bayesian multiple marker regression methods provide a flexible and reliable framework for GWA studies and for integrating multiple sources of functional and genomic information (multi-omics data) to gain insight into the genetic architecture of complex traits. Further development of methods that are less dependent on choice of priors or that include more appropriate priors is, however, warranted, as well as of methods for integration of multi-omics, functional, and biological information.


To investigate the effects of metabolic traits, lifestyle factors, and drug interventions on liver fat using the mendelian randomisation paradigm.Mendelian randomisation study.Publicly available summary level data from genome-wide association studies.Genome-wide association studies of 32 974 to 1 407 282 individuals who were predominantly of European descent.Genetic variants predicting nine metabolic traits, six lifestyle factors, four lipid lowering drug targets, three antihypertensive drug targets, and genetic association estimates formagnetic resonance imaging measured liver fat.Mendelian randomisation analysis was used to investigate the effects of these exposures on liver fat, incorporating sensitivity analyses that relaxed the requisite modelling assumptions.Genetically predicted liability to obesity, type 2 diabetes, elevated blood pressure, elevated triglyceride levels, cigarette smoking, and sedentary time watching television were associated with higher levels of liver fat. Genetically predicted lipid lowering drug effects were not associated with liver fat; however, β blocker and calcium channel blocker antihypertensive drug effects were associated with lower levels of liver fat.These analyses provide evidence of a causal effect of various metabolic traits, lifestyle factors, and drug targets on liver fat. The findings complement existing epidemiological associations, further provide mechanistic insight, and potentially supports a role for drug interventions in reducing the burden of hepatic steatosis and related disease. Further clinical study is now warranted to investigate the relevance of these genetic analyses for patient care. Fat accumulation in the liver contributes to the development of chronic liver disease and is implicated in adverse cardiometabolic outcomesSeveral metabolic, lifestyle, and pharmacological factors have been implicated in the accumulation of liver fat, however, evidence for their causal effects is limitedGenetic evidence supports causal effects of increased adiposity, type 2 diabetes (including raised fasting insulin levels), systolic blood pressure, smoking, alcohol consumption, and sedentary time watching television on increasing liver fatGenetic evidence supports protective effects on liver fat of higher low density lipoprotein cholesterol and high density lipoprotein cholesterol levels, but detrimental effects of higher triglyceride levelsNo strong genetic evidence to support effects of lipid lowering drug targets on liver fat, but some evidence supports blood pressure lowering through β blocker and calcium channel blocker antihypertensive drugs might reduce liver fatThe findings complement existing epidemiological associations, further provide mechanistic insight, and support a potential role for drug interventions in reducing the burden of hepatic steatosis and related diseaseFurther clinical study is now warranted to investigate the relevance of these genetic analyses for patient careThe accumulation of fat in the liver contributes to the development of chronic liver disease and is implicated in various adverse cardiometabolic outcomes.Use of mendelian randomisation leverages randomly allocated genetic variants as instrumental variables for investigating the effect of modifying an exposure on the risk of an outcome.This study is a two sample, mendelian randomisation study based on publicly available summary level data for nine metabolic traits, six lifestyle factors, four lipid lowering drug targets, three antihypertensive drug targets, and liver fat measured by magnetic resonance imaging. –8) with nine metabolic traits  and low density lipoprotein cholesterol (LDLC), and triglycerides) and six lifestyle factors  from corresponding genome-wide association studies (2r>0.01) were removed and the single nucleotide polymorphisms with the smallest P value for the genetic association were retained. Three additional smoking related traits  and located in gene regions corresponding to four drug targets (HMGCR (3-hydroxy-3-methyl-glutaryl-coenzyme A reductase), LDLR (low density lipoprotein receptor), NPC1L1 (Niemann-Pick C1-like 1), and PCSK9 (proprotein convertase subtilisin/kexin type 9)) were obtained from the Global Lipids Genetics Consortium.–8) were obtained from the International Consortium of Blood Pressure.2r<0.01 were selected as instrumental variables.Genetic variants proxying the effects of lipid lowering and antihypertensive drug treatments were obtained by use of approaches similar to previous mendelian randomisation studies.10.1136/bmjmed-2022-000277.supp1Supplementary dataSummary level data for hepatic fat measured by abdominal magnetic resonance imaging were obtained from a genome-wide association analysis of 32 974 generally healthy adults of European descent in the UK Biobank study.F statistic for metabolic and lifestyle factors in univariable mendelian randomisation analysis.F statistic was estimated to inform the instrument strength in multivariable mendelian randomisation analysis.We harmonised all variants serving as instrumental variables between the exposure and outcome data by effect allele. Given that a few single nucleotide polymorphisms were unavailable in the outcome data, we did not find proxies to replace missing single nucleotide polymorphisms. We calculate –5). We also conducted a Steiger directionality test to examine the possible reverse causality.The inverse variance weighted mendelian randomisation method was used as the main analysis. The model under the multiplicative random effects was used for the traits with at least three single nucleotide polymorphisms and that model under the fixed effects was used for the traits with one or two single nucleotide polymorphisms. The weighted median,We performed multivariable mendelian randomisation analysis for the metabolic traits, with adjustment for genetically predicted body mass index. This analysis had two aims: to minimise potential pleiotropic effects from body mass index on the associations between metabolic factors and liver fat, and to examine for potential mediating roles of metabolic factors in the association between body mass index and liver fat. Network mendelian randomisation was used to estimate the proportion of the total effect of body mass index on liver fat that is mediated through other metabolic factors.No patients or members of the public were involved in the design or reporting of this study. On publication, the study findings will be disseminated to the public through the authors’ institutional research media offices.F statistics were more than 10, which indicated limited bias caused by sample overlap , systolic blood pressure, smoking, alcohol consumption, and sedentary time watching television. Additionally, genetic evidence supports the protective effects on liver fat of higher LDLC and HDLC concentrations, but detrimental effects of higher triglyceride levels. No strong evidence supported the effects of lipid lowering drug targets on liver fat but some evidence showed that blood pressure lowering through β blocker and calcium channel blocker antihypertensive drugs might reduce liver fat. Our findings are consistent with existing understanding on the determinants of fat accumulation in the liver; namely, increased uptake of free fatty acids into the liver, impaired metabolism within the liver, and increased de novo lipogenesis.These findings make important advances in our understanding of hepatic steatosis and related diseases. Although evidence for a role of lipid lowering drugs in non-alcoholic fatty liver disease has been mixed, we did not identify any strong support in clinical studies,Our current work is largely consistent with previous epidemiological and genetic analyses. Existing epidemiological investigation has identified strong associations of fatty liver disease with obesity, type 2 diabetes, hyperlipidemia, hypertension, and metabolic syndrome.F statistics for these associations were more than 10, which indicated that the bias caused by this mild sample overlap should have been limited. However, conditional F statistic for smoking initiation was less than 10, which might introduce weak instrument bias in multivariable analysis of smoking initiation and alcohol consumption in relation to liver fat. Sixthly, use of genetic instruments from the genome-wide association studies with additional adjustment except for age, sex, and population structure factors  might introduce collider bias in mendelian randomisation analysis.The main advantage of the mendelian randomisation approach taken in this work is that the analyses is time and cost efficient by use of pre-existing, large scale genetic association data. The random allocation of genetic variants at conception means that the approach is less susceptible to the bias from environmental confounding and reverse causation that can hinder causal inference in a traditional epidemiological study design. However, this approach also has weaknesses. Firstly, the analyses were largely restricted to individuals of European genetic ancestry, thus limiting generalisability to other ancestry groups. Secondly, the mendelian randomisation paradigm explores the effects of small, lifelong changes in the genetically predicted levels of a risk factor on an outcome, in this case liver fat. This differs from estimating the effect of a clinical intervention, which might have a larger magnitude but over a shorter period of life. Similarly, our approach could not inform on the dose-response association between these risk factors and levels of liver fat. Thirdly, the mendelian randomisation approach is susceptible to bias from variants that maybe having pleiotropic effects on the outcome through pathways unrelated to the exposure being considered. Although we incorporated a range of sensitivity analyses to prevent confounding affecting our conclusions, this possibility cannot be entirely excluded. Fourthly, we considered the outcome of liver fat in this work, which in itself, might not directly cause disease. Additionally, we quantified liver fat was by use of a machine learning algorithm,In conclusion, we provided a wide angled investigation into the effects of metabolic traits, lifestyle factors, and pharmacological interventions on liver fat. The findings largely support the existing evidence on the role of cardiometabolic traits on hepatic steatosis, and further identify potential mediating mechanisms and pharmacological strategies for reducing the burden of related disease. Further work is now warranted to explore these findings in a clinical setting.