As a natural extension in the application of these models, in this study, we incorporated in vitro information of three major OATP1B1 genetic variants into a previously reported PBPK model to predict the impact of OATP1B1 polymorphisms on human PK. Using pravastatin and rosuvastatin as examples, we showed that the predicted plasma concentration–time profiles in groups carrying different OATP1B1 genetic variants reasonably matched the clinical observations from multiple studies. This modeling and simulation approach may aid decision making in early pharmaceutical research and development as well as patient-specific dose adjustment in clinical practice.Physiologically based pharmacokinetic (PBPK) models are increasingly being used to provide human pharmacokinetic (PK) predictions for organic anion-transporting polypeptide (OATP) substrates based on  The organic anion-transporting polypeptide 1B1 (OATP1B1), encoded by the gene SLCO1B1, is an uptake transporter expressed on the basolateral membrane of human hepatocytes. OATP1B1 has a broad substrate specificity and is important in both systemic and liver exposure of many drugs.3 OATP1B1 genetic variants with decreased activity may increase systemic exposure, and hence the chance of dose/concentration related adverse drug reactions. For example, the incidence of severe myopathy with statin (OATP1B1 substrates) therapy is related to dose.4 On the other hand, genetic variants with increased activity may decrease the plasma concentration to subtherapeutic levels. Additionally, the impact of OATP1B1 polymorphisms on liver exposure, associated pharmacodynamic effect of liver-targeting compounds, and potential toxicity of nonliver-targeting compounds have not been well characterized and deserve attention.Membrane transporters play a significant role in the pharmacokinetic (PK) profiles of many drugs,SLCO1B1 single nucleotide polymorphisms (SNPs) have been identified, among which there are two that are relatively common and widely studied, i.e., c.521T>C in exon 5 and c.388A>G in exon 4 . The two SNPs lead to four haplotypes, known as *1a , *1b (c.388G and c.521T), *5 (c.388A and c.521C), and *15 (c.388G and c.521C).3 Adding to the complexity, individuals can be homozygous or heterozygous for these haplotypes. Compared with *1a, the haplotypes *5 and *15 usually have decreased uptake rate,3 while *1b may increase the uptake rate due to increased OATP1B1 expression.5 Although the prevalence and functional implications of other SLCO1B1 SNPs lead to different haplotypes ), these have not been widely studied as the four haplotypes listed above.3Multiple SLCO1B1 variants through physiologically based pharmacokinetic (PBPK) modeling. Such a model could aid decision making in early pharmaceutical research as well as patient-specific dose adjustment in clinical practice. Rosuvastatin and pravastatin are used in this study to demonstrate the prediction strategy, because there are multiple in vitro pharmacogenetic and in vivo genotyped pharmacokinetic datasets available for the two compounds.The aim of this study is to predict human PK of OATP1B1 substrates for carriers of CLact, tot), biliary clearance (CLbile), passive diffusion clearances in liver , absorption rate (ka), and the fraction of the dose absorbed from gastro-intestinal (GI) tract (Fa·Fg) are estimated by fitting nongenotyped mean intravenous infusion and oral dosing data.7 Model characterization of the data and the values of both fitted and predicted parameters generated in this study are indicated in Figure 1 and Table 1. The model-derived values of Fa·Fg for pravastatin and rosuvastatin  are lower than what would be obtained via noncompartmental analysis . This is likely due to an overestimation of Fa·Fg via noncompartmental analysis in the presence of enterohepatic recirculation. Consistent with this, a smaller difference is observed with pravastatin due to the relatively greater contribution of renal (rather than biliary) clearance.The nongenotyped clearance and absorption parameters including total hepatic active uptake clearance  for the Caucasian population are calculated from in vitro data and nongenotyped active uptake clearance through OATP1B1  (Table 1) as described in the methods section, where *5 and *15 are treated as the same group due to relatively similar activity of the two variants.8 With calculated values of CLact, *1a, CLact, *1b, and CLact, *15, the model reasonably predicts observed human plasma pravastatin profiles of Caucasian *1a, *1b, and *15 groups .The active uptake clearance of OATP1B1 *1a, *1b, and *15 . A similar prediction is not done for rosuvastatin due to the lack of the genotyped rosuvastatin pharmacokinetic data in Japanese population.A previous publication indicates that there is an intrinsic ethnic variability in the activity of OATP1B1,Figure 3). The differences between the observations and predictions are within 70% of the observed values for plasma AUC, 75% of the observed values for Cmax, and 10% of the observed values for tmax (Table 2). Considering the relatively large intra- and interstudy variability (Table 2), the observed and predicted pharmacokinetics are reasonably close.Pravastatin as well as rosuvastatin concentration–time profiles of c.521TT and c.521CC groups were also reasonably well predicted under the assumption that (in the absence of c388A>G information) these largely represent the *1a and *15 genotypes , blood to plasma ratio (RB/P), unbound fraction in liver tissue , renal clearance , CLact, OATP1B1, active uptake clearance through non-OATP1B1 transporters , CLbile, CLpass, liver, ka, and Fa·Fg. The liver concentration is also sensitive to these parameters except for RB/P and CLrenal. Plasma concentration is more sensitive towards CLact, OATP1B1 than the liver concentration (data not shown), consistent with a previous PBPK study for pravastatin.10 In addition, the plasma concentration is sensitive to different parameters during different phases , consistent with previous observations.11 AUCplasma is sensitive to fu, p, CLact, OATP1B1, CLact, other, CLbile, CLpass, liver, and Fa·Fg. In general, results are similar between pravastatin and rosuvastatin among *1a, *1b, and *15 groups, except that pravastatin AUCplasma is not sensitive to CLact, other, and rosuvastatin AUCplasma is not sensitive to CLact, *15.The sensitivities of plasma and liver concentration, and AUCCLact, tot in genotyped populations are calculated using in vitro data, a local sensitivity analysis was performed on predicted CLact, tot  towards the parameters estimated in the in vitro assays  (Table 3). In general, as the fraction of CLact, tot mediated by OATP1B1 increases, the predicted CLact, tot across populations is more sensitive to the ratio between CLact, *1a and CLact, *1b . When OATP1B1 mediates over 60% CLact, tot, the predicted CLact, tot for all three genotyped populations is relatively sensitive to the ratio between CLact, *1a and CLact, *1b , while only CLact, tot,*15 is sensitive to the ratio between CLact, *1a and CLact, *15 under most situations . Simulated plasma concentration–time profiles using the values of the ratio between CLact, *1a and CLact, *15 reported from two in vitro studies  are consistent with the sensitivity analysis results in Table 3, that only CLact, tot,*15 is sensitive to the ratio between CLact, *1a and CLact, *15.Because the predicted SLCO1B1 polymorphisms on transporter activity for selected OATP substrates and human PK have been established in both in vitro and in vivo studies;3 however, a mechanistic model describing these behaviors and connecting in vitro discoveries with in vivo observations has not been developed previously. In this study, we incorporated in vitro OATP1B1 information for genetic variants into a previously published PBPK model for OATP substrates12 to predict PK profiles of variant carriers.The effects of in vitro estimated fraction of OATB1B1 in total hepatic active uptake clearance and the ratio of uptake activities between variants. In combination with hepatic clearance and absorption estimated from average plasma concentrations of ungenotyped population, the proposed approach can reasonably predict plasma concentration–time profiles for genotyped groups. The prediction relies on the key assumptions that the contribution of OATP1B1 to total uptake and the effect of SLCO1B1 polymorphisms on uptake activities are relatively consistent between in vitro and in vivo conditions, and only CLact, OATP1B1 changes without the need for adjustment of other parameters and the model structure.The uniqueness of this approach relies on the use of 9 the model can also predict observations in Japanese pravastatin study14 . Application of the same approach to rosuvastatin pharmacokinetics determined in ungenotyped Japanese subjects suggests that the ethnic difference in OATP activity may be larger for this compound . As such, further study is required to understand the genotype-dependence of rosuvastatin pharmacokinetics in Japanese and to determine the ethnic dependence of OATP uptake for this compound.Accounting for differences in OATP1B1 activity between Caucasians and Japanese,15 We further assume that in Figure 3 the c.521TT group is the same as the *1a group in the studies for which SNP c.388A>G or c.463C>A is not sequenced;18 the assumed *1a  group may be confounded by *1b  and/or *14  genotypes. The *1b genotype is associated with the increased OATP1B1 expression level,5 while the *14 genotype may be associated with the reduction in the intrinsic OATP1B1 uptake rate.19 In addition, in Figure 2 where data are digitized from the studies sequencing c.388A>G,20 *1b group could also be confounded by *14 carrier if c.463C>A was not genotyped. We further assumed that polymorphisms of other transporters only play minor roles in drug disposition and do not affect plasma PK, and no gene interactions between OATP1B1 and other transporters. The use of these assumptions is supported by the good agreement between predictions and observations in the two case examples.Given the available data, several assumptions are made to simplify the problem. We assume that the fraction of three OATP1B1 variants in nongenotyped Caucasian IV studies follows that in European population. We assume that heterozygotes have the same activity as the homozygotes if the pharmacokinetic study grouped heterozygotes and homozygotes together. This may lead to the misprediction of the pharmacokinetics, because heterozygous clearance may be different from homozygous clearance.In vitro assay data indicate *1b increases OATP1B1 expression by twofold,5 which is assumed to affect all substrates. In the prediction for rosuvastatin, we assume that the uptake rate of *1b is higher than that of *1a and *15. However, the observed in vivo *1b uptake rate is slightly lower than *1a rate.22 The predicted AUC0–24 h of the *1b group (24.1 ng·h/ml) is less than the predicted AUC0–24 h of the *1a group (32.6 ng·h/ml), in contrast with the observation that average AUCt0– of *1b group is slightly higher than that of the *1a group.22 The reason for the inconsistency is unknown, but can be that protein expression differences do not proportionally translate to functional differences. Assuming that *1b does not increase OATP1B1 expression level in rosuvastatin studies, we re-estimate clearance for the three variants without the expression difference incorporated, leading to a result consistent with clinical observations (data not shown). In addition, if c.463 C>A is not sequenced in these study,22 subjects carrying *14 may exit in *1b or c.388A>G group. Since *14 may lead to reduction in intrinsic uptake rate as discussed above, it would compensate for increase in mean uptake rate due to *1b carriers.in vivo study shows that the SLCO1B1 polymorphism  has no impact on fluvastatin PK.17 Based on this result, several studies claim that fluvastatin is not an OATP1B1 substrate,24 which leads to an obvious inconsistency with in vitro uptake assay results where fluvastatin is an OATP1B1 substrate.25 This phenomenon can be explained using our approach. Previously published in vitro pharmacogenetic studies have shown that this SNP does not result in impaired OATP1B1 uptake activity for fluvastatin.27The mechanistic modeling approach proposed here helps our understanding of the pharmacokinetic properties of OATP substrates in populations carrying OATP variants. For example, a previous CLact, other, fluvastatin is believed to be the same between *15 and *1a group here, hencein vitro, SNP c.521T>C will not change the in vivo PK profile of fluvastatin, because *15 and *1a have the same hepatic clearance. In fact, the impact of SLCO1B1 polymorphisms is known to be compound dependent.27 As such, in vitro functional evaluation of OATP polymorphisms can provide useful information on the prediction and interpretation of clinical pharmacokinetics.As such, even if fluvastatin is an OATP1B1 substrate as shown plasma is not sensitive to CLact, *15, mainly because given the current model parameter values, clearance of rosuvastatin by OATP1B1 *15 is not significant compared with clearance by other transporters. Similarly, pravastatin AUCplasma is not sensitive to CLact, other, largely because the clearance of pravastatin by other transporter is not significant compared with clearance by OATP1B1. The genotyped CLact, tot is calculated from in vitro data. To assess the impact of variability in the in vitro data on the calculated CLact, tot, we performed another sensitivity analysis (Table 3). Based on this analysis, if in vitro assay results indicate OATP1B1 mediates more than 60% of the CLact, tot, it is likely that the variability in the in vitro estimated intrinsic activity ratio between CLact, *1a and CLact, *1b will affect the calculation of the genotyped CLact, tot, and the PK prediction for each genotype. On the other hand, even if CLact, tot is solely mediated by OATP1B1, the variability in the in vitro estimated ratio between CLact, *1a and CLact, *15 may only affect CLact, tot, *15 estimation .In the sensitivity analysis, the rosuvastatin AUC29 potential liver toxicity of nonliver-targeting compounds ,30 or potential drug–drug interactions in the liver.31 Additionally, although the current model uses nongenotyped human plasma data as a starting point, when combined with previously published modeling efforts to predict mean human pharmacokinetic response for compounds in the preclinical development,12 the model has the potential to prospectively predict pharmacokinetics in OATP1B1 genetic variant populations without using any human data.In this study, we performed analysis with PBPK rather than traditional pharmacokinetic modeling. For OATP substrates, the PBPK model has the ability to predict the pharmacokinetics in the liver, where tissue concentration to plasma concentration ratio is not constant. This is important in estimating efficacy for liver-targeting compounds ,et al. recently published a study where a PBPK model was applied to assess the impact of OATP1B1 genetic variation on the pharmacodynamics of rosuvastatin.29 They estimated clearances by fitting genotyped human plasma data and evaluated the impact of OATP1B1 genetic variation on the pharmacodynamics. Our study uses in vitro estimated clearances together with nongenotyped clinical pharmacokinetics to prospectively predict how the OATP1B1 genetic variation affects the pharmacokinetics. Although using a different approach to evaluate the clearance values of the genotyped groups, and a slightly different liver model structure  versus five pairs of liver blood and liver tissue) as well as different physiological parameters, the sensitivity analysis in our study reaches a similar conclusion as the published study:29 the rosuvastatin concentration in plasma is more sensitive to the genetic variability of OATP1B1 while the liver concentration is less sensitive. As such, the genetic variation in OATP1B1 may not affect the pharmacodynamic effects of liver-targeting compounds as much as the systemic pharmacokinetics. However, the OATP1B1 genetic variation may affect pharmacodynamics or toxicity in other tissues due to its effects on systemic exposure.Rose in vitro functional pharmacogenetic data can be used to support reasonably accurate predictions for groups carrying specific variants through the proposed PBPK modeling approach. Such an approach may be useful in the evaluation of drug candidates in drug discovery, the design of clinical trials and ultimately for dose adjustments in clinical practice. Lastly, this framework also provides a starting place from which to systematically evaluate some simplifying assumptions which are currently necessary due to the lack of information as more data become available .In conclusion, these results indicate that Supplementary Figure S2) is based upon a previously published PBPK model.12 Equations were added to describe enterohepatic recirculation.The structural model where CLact, tot is modeled as the sum of CLact, OATP1B1 and CLact, other:CLact, tot, CLbile, CLpass, ka, Fa·Fg were estimated by fitting the model to plasma concentration–time course data following intravenous infusion and oral dosing in nongenotyped studies.7 The ratios of CLpass between liver, adipose and muscle and values of all other parameters are fixed at previously published values.12 The model is implemented in MATLAB  and differential equations are compiled as a MEX file . A stochastic global optimization method, differential evolution (DE) with nonlinear sampling, was used to estimate these parameters as described before.34 The 95% confidence intervals for the optimized parameters were approximated using a residual bootstrap method developed before.12The nongenotyped Fa·Fg is also estimated using a previously published noncompartmental method35 to compare with the value estimated from fitting PBPK model to plasma data. The liver blood flow and RB/P in the noncompartmental analysis are set to the values we used in the PBPK model.CLact, OATP1B1 in Eq. 6 with predicted CLact, *1a, CLact, *1b, or CLact, *15 to generate new CLact, tot. For example, for the group carrying *15 after pravastatin dosing, we haveTo predict PK profiles of carriers of specific genetic variants, we keep all the parameters in PBPK model unchanged, but replace the nongenotyped 8 The diplotypes are restricted to homozygous *1a/*1a, *1b/*1b, and *15/*15 . For the published concentration–time curves17 without information about SNP c.388A>G, we attribute the c.521TT group in these studies as *1a, and the c.521CC group as *5 and *15.To simplify the problem, *5 is treated as *15 considering the relatively similar activities of the two variants.23 The remaining 17% is believed to be due to OATP1B3. We assume that pravastatin is not the substrate of other uptake transporter ), because currently there is no evidence. However, if later research indicates other uptake transporter is involved in hepatic uptake of pravastatin, such information should be incorporated into calculation. With the fitted CLact, tot, we calculate CLact, OATP1B1 and CLact, other for nongenotyped population as below:Consistent with that reported previously, we assumed the fraction of pravastatin uptake clearance due to OATP1B1 to be 83%.36 while 77% of the remaining 65% is mediated by OATP1B1.37 As such, OATP1B1 is expected to account for 50% of total uptake clearance of rosuvastatin.For rosuvastatin, 35% of total active uptake is due to NTCP activity,CLact, OATP1B1 is calculated from CLact, tot estimated using mean data,7CLact, OATP1B1 of nongenotyped population is treated as the average values of active uptake clearance of three major OATP1B1 genetic variants weighted by the proportion of participants carrying them .Because CLact, tot,7 we assume that the proportion of OATP1B1 genetic variants follows the reported proportion in the European population ,38 considering the participants in the intravenous infusion studies were Caucasians.7Since the participants were not genotyped for OATP1B1 polymorphisms in the published studies with intravenous infusion data from which we estimate in vitro assay results. For pravastatin, the intrinsic uptake activities of *1b and *15 are reduced to 81% ,in vivo model were conducted as before11 where each compound specific parameter is raised by 1% with respect to its value in the PK simulations for genotyped Caucasian populations. The values of the plasma and liver concentrations throughout the time course, and AUCplasma are obtained. Sensitivity coefficients are normalized to both the parameter value and the model output value.Local sensitivity analyses for the CLact, tot for *1a, *1b, and *15 populations to the parameters estimated from the in vitro assays . In this analysis, population parameters  and parameters estimated by fitting in vivo data  are fixed. The intrinsic uptake activity ratio between CLact, *1a and CLact, *1b  is raised by 1%; and the value of the genotyped CLact, tot  is re-evaluated. The local sensitivity coefficient is calculated as the difference between the new value of the genotyped CLact, tot and its nominal value (Table 1), divided by the nominal value and 1%. As the new value of genotyped CLact, tot is also sensitive to the estimated fraction of CLact, tot mediated , the sensitivity analysis was performed over a range of fractional OATP1B1 contributions between 0.2 and 1.In addition, using Eqs. 7–14, we evaluated the local sensitivity of predicted in vitro data might impact these results, concentration–time profiles of pravastatin were generated over the range of CLact, *1a and CLact, *15 intrinsic uptake activity ratios reported in the literature  its value is available from two independent studies, while values of most other parameters are only available from single in vitro studies; (ii) its value of pravastatin shows the largest difference between two studies ;36 and (iii) OATP1B1 contributes over 80% of total hepatic active uptake of pravastatin, hence has a more pronounced impact on pharmacokinetics. The intrastudy variability is not included in the current study, considering it is generally small with the coefficient of variation below 60%.37To visualize how actual interstudy variability in the .20 ref.  and 0.50.20 ref. ). The raT.M., R.L., and H.B. wrote the manuscript. T.M., R.L., and H.B. designed the research. R.L. performed the research. R.L. analyzed the dataThe authors declared no conflict of interest.


P-values method’ (abbreviated as ‘ADA’). This method combines per-site P-values of variants that are more likely to be causal. Variants with large P-values  are discarded from the combined statistic. In addition to performing extensive simulation studies, we applied these tests to the Genetic Analysis Workshop 17 data sets, where real sequence data were generated according to the 1000 Genomes Project. Compared with some existing methods, ADA is more robust to the inclusion of neutral variants. This is a merit especially when dichotomous traits are analyzed. However, there are some limitations for ADA. First, it is more computationally intensive. Second, pedigree structures and founders' sequence data are required for the permutation procedure. Third, unrelated controls cannot be included. We here show that, for family-based studies, the application of ADA is limited to dichotomous trait analyses with full pedigree information.Family-based study design will play a key role in identifying rare causal variants, because rare causal variants can be enriched in families with multiple affected subjects. Furthermore, different from population-based studies, family studies are robust to bias induced by population substructure. It is well known that rare causal variants are difficult to detect from single-locus tests. Therefore, burden tests and non-burden tests have been developed, by combining signals of multiple variants in a chromosomal region or a functional unit. This inevitably incorporates some neutral variants into the test statistics, which can dilute the power of statistical methods. To guard against the noise caused by neutral variants, we here propose an ‘adaptive combination of  Studies in genetic epidemiology are important to uncover the genetic architecture of complex human diseases. The development of next-generation sequencing technologies has allowed for the mapping of all genetic variants across the human genome. With this, we can search for rare causal variants  <1%), which are mostly not genotyped in genome-wide association studies (GWAS) but are related to the etiology of complex diseases. Till now, many statistical methods have been proposed for rare variant association testing. Most of them were designed for population-based studies where unrelated cases and controls were recruited and analyzed Despite a variety of statistical methods, there are two concerns in population-based rare variant association studies. First, population stratification may cause false-positive results. This issue was tackled since the era of genome-wide association studies (GWAS). In GWAS where most genotyped variants were common, principal component analysis (PCA) WS) CMAT) SKAT) et al.famSKAT” that can account for members' relationships within families. This method was essentially equivalent to the method proposed by Schifano et al.ASKAT”) proposed by Oualkacha et al.et al.'s method was not originally designed for rare variant association testing. Svishcheva et al.SKAT (abbreviated as “FFBSKAT”) that was shown to be the fastest method to perform the kernel-based association tests for continuous traits. Svishcheva et al. have shown a pure coincidence of the P-values calculated by the famSKAT, ASKAT, and the FFBSKAT software programs Family-based study design will play a key role in identifying rare causal variants, because rare causal variants can be enriched in families with multiple affected subjects P-values method (abbreviated as ‘ADA’) has been shown to outperform the burden tests  in rare variant association testing for unrelated subjects, because ADA is more robust to the inclusion of neutral variants ADA method to deal with pedigree data, and compare its power performance with that of the burden test FFBSKAT method famSKATASKATFFBSKAT). We also apply the method to the Genetic Analysis Workshop 17 (GAW 17) data Testing for effects of rare variants individually is known to be underpowered. To strengthen association signals, both the burden tests and the non-burden tests combine information of multiple rare variants in a gene/region. This inevitably incorporates many neutral variants into the test statistics. Adaptive combination of iY be the trait value of the ith subject th element of ith and the jth subjects. Because the kinship coefficient of subjects belonging to different pedigrees should be 0, Let L tests for the L markers, we have P-values J candidate truncation thresholds, L markers, the significance score under the jth truncation threshold is lth marker has a P-value smaller than jth truncation threshold) and 0 otherwise. Throughout this study, the candidate truncation thresholds are specified as ADA method for population-based studies P-value truncation thresholds, say, ADA (results not shown).The test statistic in P-value truncation thresholds are considered, to correct for multiple testing, statistical significance must be obtained with permutations. We first construct the distribution of the significance score jS under the null hypothesis, where the transmission of haplotypes from parents to offspring is completely random, conditional on parental genotypes 8 = 256 possible patterns of allelic transmission. With a total of N families, there are at least 256N different permutations of the genotype data.Because multiple lth locus  is lth locus of Nos. 1 and 2, respectively; The above permutation procedure was extended from that used for trio data B permutations, say, B = 1000. For the bth permutation, the significance score under the jth truncation threshold can be calculated with Eq. (2), denoted as P-value of j = 1, …, J), where P-value of j = 1, …, J and P-values across the J candidate truncation thresholds for the observed sample and the bth permuted sample, denoted as MinP and MinPb) was simulated. Dichotomous traits and continuous traits were considered respectively.We generated sequence data with the SeqSIMLA software When evaluating type-I error rates, no causal locus was specified. When evaluating power, five SNPs/SNVs  were assumed to be causal. We did not restrict all causal variants to be rare/uncommon, because in reality causal variants could also be common. Let ‘signal proportion’ be the fraction of causal variants out of all variants. The signal proportion in our simulations was set at one of the three levels: 0.033  for all causal loci was assumed to be 0.05, 0.10, 0.15, 0.20, 0.25, and 0.30, respectively. Therefore, the marginal PAR for each causal locus was 0.01, 0.02, 0.03, 0.04, 0.05, and 0.06, respectively. In the SeqSIMLA software ith subject. It was determined by the model lth QTL of the ith subject  were assumed to be quantitative trait loci (QTLs). Causal variants were not restricted to be all rare or uncommon, because in reality common variants can be causal as well. Let ADA with the broad classes of the burden test (referred to as “Burden”) Kernel”) FFBSKAT method Burden and Kernel were implemented with the R package “pedgene” FFBSKAT was performed with the package “FFBSKAT” (http://mga.bionet.nsc.ru/soft/FFBSKAT/) FFBSKAT was performed only when continuous traits were considered, because it could not analyze dichotomous traits. Following the default setting of FFBSKATKernelj, j)th element of the diagonal weighting matrix jth genetic variant. The P-values of ADA were obtained with 1,000 permutations.We compared By setting the PAR or the proportion of variance explained by causal SNPs at exactly 0%, we evaluated type-I error rates by performing 10,000 replications. In each replication, 50 three-generation families each with 12 members shown in  were genm  × three levels of family numbers  × six levels of PAR  or proportion of variance explained by causal SNPs  ×1000 replications for each scenario). Across all these 108,000 replications, there were totally 540,000  causal SNPs/SNVs . Among these 540,000 causal variants, approximately 45% were rare (MAF<1%), ∼62% were uncommon/rare (MAF<5%), and ∼ 38% were common (MAF≥5%). In our simulation setting, causal variants were not limited to be rare, because in real situations causal variants could also be common.When we evaluated power, 1000 replications were performed under each scenario. In total, there were 54,000 replications in power evaluation for dichotomous traits and continuous traits, respectively , whereas ADA had the best performance when m = 150 . When m = 100 , these two methods had comparable performance. Burden was the uniformly least powerful test among the methods we compared, regardless of the size of m . To conclude, when the signal proportion was larger, Kernel was more powerful; when the signal proportion was smaller, ADA took the advantage of truncating noise variants and therefore was more powerful.Kernel was again the most powerful method when m = 50 . When the signal proportion was getting lower and lower , Kernel had a more substantial power loss. Burden was again the least powerful method, regardless of the size of m . Compared with Kernel, FFBSKAT and ADA were less vulnerable to the inclusion of neutral variants. FFBSKAT became the most powerful method when m = 100  or when m = 150 .When continuous traits were studied , Kernel β column was the change in mean quantitative trait due to a copy of minor allele.We then applied the four tests to the Genetic Analysis Workshop 17 (GAW 17) simulated data FFBSKAT, ADA, Kernel, and Burden. The P-values of ADA were estimated based on 1,000 permutations. Based on the power to detect causal genes, these methods were roughly ranked as FFBSKAT> ADA> Kernel ≈ Burden. FFBSKAT was the most powerful method. It was based on the linear mixed effects model, in which the kinship relatedness was captured by the random effects terms  when the pedigree is larger  or when the signal proportion is smaller  ADA to family-based studies and compare it with Kernel and Burden, the two commonly used methods for dichotomous traits. Simulation studies show that ADA is more powerful than other two competitors when the percentage of causal variants is smaller  and the non-burden test (Kernel), ADA is more robust to the inclusion of neutral variants when dichotomous traits are analyzed. However, there are some limitations for ADA. First, because this method is more computationally intensive, it is not realistic to apply it to genome-wide sequencing data. Second, pedigree structures and founders' sequence data are required for the permutation procedure implemented in ADA. Third, unrelated controls cannot be included in the ADA analyses. This work shows that, for family-based studies, the application of ADA is limited to dichotomous trait analyses with full pedigree information.Rare causal variants may play an important role in the etiology of complex diseases S1 FigThe distribution of genotype relative risk (GRR) of causal SNPs/SNVs when the overall population attributable risk (PAR) for all causal loci was assumed to be 0.05, 0.10, 0.15, 0.20, 0.25, and 0.30, respectively. Therefore, the marginal PAR for each causal SNP/SNV was 0.01, 0.02, 0.03, 0.04, 0.05, and 0.06, respectively.(PDF)Click here for additional data file.S2 FigStructures of the eight pedigrees  in the Genetic Analysis Workshop 17 data set, plotted by the R package “kinship2”.(PDF)Click here for additional data file.


The joint analysis of multiple traits has recently become popular since it can increase statistical power to detect genetic variants and there is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. Currently, most of existing methods use all of the traits for testing the association between multiple traits and a single variant. However, those methods for association studies may lose power in the presence of a large number of noise traits. In this paper, we propose an “optimal” maximum heritability test (MHT-O) to test the association between multiple traits and a single variant. MHT-O includes a procedure of deleting traits that have weak or no association with the variant. Using extensive simulation studies, we compare the performance of MHT-O with MHT, Trait-based Association Test uses Extended Simes procedure (TATES), SUM_SCORE and MANOVA. Our results show that, in all of the simulation scenarios, MHT-O is either the most powerful test or comparable to the most powerful test among the five tests we compared. Increasing evidence shows that pleiotropy, the effect of one variant on multiple traits, is a widespread phenomenon in complex diseases . FurtherSeveral statistical methods have been developed for joint analysis of multiple traits. These methods can be roughly divided into three groups: combining the univariate analysis results, regression methods, and dimension reduction methods. For combining univariate analysis results, one first conducts the univariate test by performing an association test for each trait individually and then combines the univariate test statistics or combines the p-values of the univariate tests , 10–12. In this article, we first propose a maximum heritability test (MHT). Based on MHT, we develop an “optimal” maximum heritability test (MHT-O) to test the association between multiple traits and a single variant. In each step of MHT-O, we delete one trait that has the weakest association with the variant. Then, we find the optimal number of traits and use MHT to test the association between the optimal number of traits and the variant. Using extensive simulation studies, we compare the performance of MHT-O with MHT, Trait-based Association Test uses Extended Simes procedure (TATES) , SUM_SCOn unrelated individuals. Each individual has K  traits and has been genotyped at one variant. Let Y = T denote the random vector of K traits and X denote the random variable of the genotype score at a variant. Let yi = T denote the values of K traits and xi denote the genotype score of the ith individual, where xi is the number of minor alleles that the ith individual has at the variant. We can consider that y1,…,yn is a random sample from Y and x1,…,xn is a random sample from X.We consider a sample with Now, let us consider linear modelsY as VP = VG + VR  = var(X)ββT is the genetic variance due to the genotype scores X, where β = T; VR = var is the residual covariance after removing the genetic effect. var(X) can be estimated by β and VR can be estimated from the linear modelsβk is estimated by the least square estimator. Let rik denote the estimates of residuals εik. Then, the th element of VR is estimated by We partition the total phenotypic covariance of  VG + VR ; VG = vaY, w = T. The heritability of wTY can be written asLet us consider a linear combination of wTY depends on w and we can find a linear combination of wTY that has the largest heritability among all linear combinations of Y. We define the maximum heritability as the test statistic to test the association between these K traits and the variant. We denote this test as maximum heritability test (MHT). The MHT statistic can be written asλmax(A) denotes the largest eigenvalue of matrix A.If we define TMHT may lose power in the presence of a large number of noise traits. Therefore, we propose an “optimal” maximum heritability test (MHT-O) to test the association between multiple traits and the variant. MHT-O includes a procedure of deleting traits that have weak or no association with the variant. It has the following steps:However, the test statistic Y = , initialize r = K and Yr)(.Step 1. Given traits TMHT based on Yr)( denote Yr)T; x is the genotype score at the variant of interest; λ =  is the vector of effect sizes of the genetic variant on the K traits; f = T ∼ MVN, Σ = (1 − ρ)I + ρA, A is a matrix with elements of 1, I is the identity matrix, and ρ is the correlation between factors; γ is a K by R matrix; c is a constant number; and ε = T is a vector of residuals, and ε1,…, εK are independent, and εk ∼ N for k = 1,…, K.To evaluate the type I error rates and powers of MHT and MHT-O, we generate genotypes according to minor allele frequency (MAF) and assume Hardy Weinberg equilibrium. Then, we generate or model , 19y=λxBased on Eq , we consR = 1, λ = T, and γ = T.Model 1: There is only one factor and genotypes impact on all traits with the same effect size. That is, γ = diag, where i = 1,…,5.Model 2: There are five factors and genotypes impact on one factor. That is, γ = diag, where i = 1, 2.Model 3: There are two factors and genotypes impact on one factor. That is, R = 5, λ = T, and γ = diag, where i = 1,…,5.Model 4: There are five factors and genotypes impact on one trait. That is, R = 1, λ = T, and γ = T.Model 5: There is only one factor and genotypes impact on one trait. That is, β = 0. To evaluate powers, we let β > 0. In the simulation studies for evaluation of type I error rates and powers, we set MAF = 0.3 and ρ = 0.2.To evaluate type I error rates of MHT and MHT-O, we let To evaluate the type I error rates of the two proposed tests (MHT and MHT-O), we consider 20 quantitative traits. We also consider different sample sizes, different significance levels, and different models. In each simulation scenario, the p-values of MHT and MHT-O are estimated by 1,000 permutations and the type I error rates of the two tests are evaluated using 10,000 replicated samples. For 10,000 replicated samples, the 95% confidence intervals (CIs) for estimated type I error rates of nominal levels 0.05 and 0.01 are  and , respectively  multiple related traits are usually measured in genetic association studies of complex diseases; (2) there is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases; and (3) the power of existing methods decreases in the presence of non-associated traits. The proposed MHT-O includes a procedure of deleting traits that have weak or no association with the variant. Therefore, it can be robust to the existence and the number of non-associated traits. By deleting one trait that has the weakest association with the variant in each step, MHT-O can maintain high power in the presence of a large number of non-associated traits. This feature is essentially important when there exist a large number of correlated traits but there are no guidelines to select relevant traits. Our results show that MHT-O has correct type I error rates and is either the most powerful test or comparable to the most powerful test among the five tests we compared. No other methods in the simulation studies show consistent good performance.Due to the allelic heterogeneity and the extreme rarity of individual variants in rare variant association studies, the variant-by-variant methods for common variant association studies may not be optimal . It has Pi = T denote the first L PCs of the genotypes at a set of genomic markers for the ith individual. Let xi = α0 + αTPi + εi, respectively. Using yik and xi, we can make MHT-O robust to population stratification. However, the performance of using the PC approach to control for population stratification in MHT-O needs further investigations.The fact that population stratification can seriously confound association results has been long recognized in association studies based on unrelated individuals , 47. Sevp denote the p-value of the test and denote a random variableα is the significance level. Then, Pr(ξ = 1) = α and Pr(ξ = 0) = 1 − α because p follows a uniform distribution between 0 and 1 under the null hypothesis. Suppose there are R replicates. Let ξi denote the value of ξ for the ith replicate, where i = 1,…,R Then, the estimated type I error rate is given by Let α.We define S1 Fig−8. The number of replicates is 500. The number of permutations is 108.Sample size is 1000. Total number of traits is 20. The significance level is 5×10(EPS)Click here for additional data file.


The availability of genetic data has increased dramatically in recent years. The greatest value of this data is its potential for personalized medicine. Many new associations are reported every day from Genome Wide Association Studies (GWAS). However, robust, reproducible associations are elusive for some complex diseases. Ontologies present a potential way to distinguish between spurious associations and those with a potential influence on the phenotype. Such an approach would be based on finding associations of the same genetic variant with closely related, but distinct, phenotypes. This approach can be accomplished with a phenotype ontology that also holds genetic association data.Here, we report a structured knowledge application to navigate and to facilitate the discovery of relationships between different phenotypes and their genetic associations.OGA allows users to (1) find the intersecting set of genes for phenotypes of interest, (2) find empirical p values for such observations and (3) OGA outperforms similar applications in number of total concepts and genes mapped. The basic idea is to take phenotypes that are distinct, but that are related by their ontological relationships, and compare them by finding the genes that are common to them. If common genes are found that have associations with higher frequency than it would be observed by chance, it may indicate that this genotype-phenotype association is stronger than others, or that the genes involved in the association are indeed related to a common root phenotype and not just to their ontological descendants. This type of analysis can facilitate the generation of hypotheses based on this in silico replication and can facilitate data management as well as improve data accessibility.The impact of genetic associations (GA) is, in some cases, dramatic -3. For eA limited number of applications are currently available that allow linking existing genetic associations to structured knowledge of phenotypes. Two of these applications are Neurocarta  and the OGA was created by combining three important data sources: the Human Phenotype Ontology (HPO) to form in silico experiment, such as: what makes these four genes common to all of these phenotypes? Are these simply all associated with a root phenotype such as neurological abnormality (HP:0000707)? Are these genes related to an underlying characteristic common to all four conditions?Currently, OGA has 2,294  concepts linked to a total of 135,091  associations. A total of 205,707 links now exist in OGA between HPO and the associations in GAD/GWASdb, there are many more links (phenotype to association mappings) than there are associations, because links are many-to-many relationships between associations and phenotypes. The links to several phenotypes in OGA were removed, because the phenotype labels were too short and were causing unspecific matches, i.e. “EO”, “ALS”, “age” and “CAD”. In order to showcase the unique type of analyses that are possible with OGA, we selected four arbitrary neurological disorders: bulimia (HP:0100739), schizophrenia (HP:0100753), depression (HP:0000716) and psychosis (HP:0000709). Four genes common to all four phenotypes were found using OGA: COMT, HTR2A, SLC6A3 and SLC6A4. OGA provides an empirical p value for this observation by obtaining random samples of the same size as the associations established for each phenotype. OGA repeats the random sampling until the same number of genes common to all phenotypes is found by chance . For this example, after 1,000 samplings no set was found with four or more genes, which translates into an empirical p value of p < 0.001. Although, they are beyond the scope of this paper, some interesting questions are generated from this -3[in silico replication in ontologically related phenotypes. Table OGA allows for easy navigation of the ontology. Additionally, it displays the associations linked to the phenotype selected. Additionally, OGA allows the user to select several phenotypes of interest and to find all the genes common to the selected phenotypes. Furthermore, OGA allows the user to obtain an empirical p value by recreating the random samplings as described above. Although previous work has pointed to the feasibility and benefit of the structured knowledge links between phenotypes and genotypes , to our -3, which iOGA is meant to be a very simple to use application, the user can locate any concept present in HPO by typing any substring of the concept in the search box Project home page:http://code.google.com/p/oga-genetic-association/downloads/listOperating system(s): Platform independentProgramming language: Java-Java Run Environment (JRE)Other requirements: noneLicense: GNU GPLAny restrictions to use by non-academics: noneContact: jenriqueherrera@gmail.comOGA: Ontological tool of phenotypes with genetic associations; GAD: Genetic association database; HPO: Human phenotype ontology; GWAS: Genome wide association study; GDB: GWAS diagram browser.The authors declare that they have no competing interests.. VM, Participated in the design of the application and provided critical editing of the paper. JLS, Identifying the problem, design of the application and writing the paper. All authors read and approved the final manuscript.JEH, Formulated the questions, designed the application, implemented the applications, wrote the paper. DLH, Participated in the design of the application and provided critical editing of the paper


Furthermore, the new method is applied to a loblolly pine (Pinus taeda) dataset.In recent years, with the development of high-throughput sequencing technology and the commercial availability of genotyping bead chips, more attention is being directed towards the utilization of abundant genetic markers in animal and plant breeding programs, human disease risk prediction and personal medicine. Several useful approaches to accomplish genomic prediction have been developed and used widely, but still have room for improvement to gain more accuracy. In this study, an improved Bayesian approach, termed BayesBπ, which differs from the original BayesB in priors assigning, is proposed. An effective method for calculating the locus-specific π by converting Compared with the original BayesB, BayesBπ can improve the accuracy of genomic prediction up to 7.62 % for milk fat percentage, a trait which shows a large effect of quantitative trait loci (QTL). For milk yield, which is controlled by small to moderate effect genes, the accuracy of genomic prediction can be improved up to 4.94 %. For somatic cell score, of which no large effect QTL has been reported, GBLUP performs better than Bayesian methods. BayesBπ outperforms BayesCπ in 10 out of 12 scenarios in the dairy cattle population, especially in small to moderate population sizes where accuracy of BayesCπ are dramatically low. Results of the loblolly pine dataset show that BayesBπ outperforms BayesB in 14 out of 17 traits and BayesCπ in 8 out of 17 traits, respectively.For traits controlled by large effect genes, BayesBπ can improve the accuracy of genomic prediction and unbiasedness of BayesB in moderate size populations. Knowledge of traits’ genetic architectures can be integrated into practices of genomic prediction by assigning locus-specific priors to markers, which will help Bayesian approaches perform better in variable selection and marker effects shrinkage.The online version of this article (doi:10.1186/s12863-015-0278-9) contains supplementary material, which is available to authorized users. In the field of medicine, risk prediction of major diseases such as cancer is essential for taking preventive measures early before worsening –4. SimilWith the development of high-throughput sequencing technology and the commercial availability of genotyping bead chips in recent years, large numbers of single nucleotide polymorphisms (SNPs) covering the whole genome can be obtained quickly and cheaply. The utilizations of these genomic data to accelerate genome wide association studies (GWAS), disease prediction and personal medicine of human beings, and breeding programs of animals and plants are attracting more and more attention . The parπ) are non-effective, are more powerful than other models in both of QTL detection and genetic value prediction. Since 2001, many Bayesian methods for GS have been developed [In Meuwissen et al. , least seveloped , 31, 34,eveloped . The coneveloped .π), which is treated as a constant close to 1  in most situations [π and/or scale parameter S are treated as variable and sampled from relevant prior distributions. Additionally, changes to the distribution of marker effects and variances have been performed [Although widely used in the animal and plant breeding programs, the original Bayesian models have been shown to have some drawbacks , 35. Thetuations , 35. Thetuations . To overtuations  were deverformed , 37. Bayerformed , 39 useserformed  and Bayeerformed , the prip-values derived from GWAS into marker-specific weights, the locus-specific priors have been utilized in the genomic prediction of human traits via BLUP [Although Bayesian approaches outperform GBLUP under most circumstances, the priors assigned to the established Bayesian approaches still may have room for further improvement. It has been shown that genetic architectures of traits can influence genomic prediction accuracy . Therefovia BLUP , throughPinus taeda) dataset [y ~ N, where y denotes the phenotypes. For these traits, the traditional estimated breeding values, with high reliability, were close to the true breeding values. The variation of the regressed phenotypes of the loblolly pine was dramatically large  based on single markers is performed for three traits in the dairy cattle population. The logarithms of its Fig. . For milAT1 gene , 47 locap-values from ANOVA can be converted with formula (4) to a probability form, which can be used as a genetic-architecture-based π in Bayesian methods. The distribution of locus-specific π for three traits in the dairy cattle population can reveal the genetic architecture of these traits at some extent  of non-effective markers, which is assigned to be a fixed value close to 1 in BayesB while to be locus-specific constants calculated based on p-values from associating phenotypes to single markers in BayesBπ.In recent years, it has been shown that traits’ genetic architectures impact genomic prediction accuracy at some extent . Therefoπ and the likelihood ratio, where π is identical among markers as discussed previously. The non-effective marker proportion π is estimated from data in BayesCπ and BayesDπ [π is another important parameter that affects the decision whether markers are fitted in the model. The locus-specific π in our study assigns a more reasonable prior to the MCMC algorithm, and performs better variable selection. When genomic prediction is performed on extremely dense markers panels or full sequences [In the iteration of MCMC in BayesB , variabl BayesDπ , but is equences , approacπ was derived by converting p-values from associating phenotypes to single markers into probability form via formula (4) and then involved into the MCMC procedure directly. Our results show that priors derived through this strategy are consistent with prior knowledge about the genetic architectures of different traits in dairy cattle population. The magnitudes of π among these traits are highly variable, which reveals that the absolute values of π are at some extent impacted by the denominator of formula (4)  Fig. . Traits p-values of ANOVA and then calculating π for each category of markers, which will involves efforts to find suitable thresholds as that for genome wide association studies [One way of dealing with such conflict may be to firstly dividing genetic markers into different classes based on  studies . Alterna studies  can be c studies . These dp-values of ANOVA to a locus-specific π is an efficient methodology for traits controlled by major genes in moderate size populations. Furthermore, BayesBπ may serves as a favorable method for variable selection when full sequences data are involved into genomic prediction.In this study, we proposed and validated a modified BayesB method, BayesBπ, which can integrate prior knowledge into genomic prediction by assigning locus-specific priors to genetic markers. We conclude, based on the results of genomic prediction for three traits in German Holstein dairy cattle and 17 traits in a loblolly pine dataset, that firstly, for traits controlled by large effect genes, BayesBπ can improve the genomic prediction accuracy and unbiasednesses of BayesB and BayesCπ. Secondly, knowledge of the genetic architecture can improve the performance of Bayesian models in genomic prediction by assigning locus-specific priors to markers. Thirdly, converting http://www.g3journal.org/content/5/4/615/suppl/DC1.Two datasets, a dairy cattle and a loblolly pine dataset, are used to validate the new genomic prediction model. The cattle population consists of 5024 individuals . Three thttp://www.genetics.org/content/190/4/1503/suppl/DC1.The publicly available loblolly pine dataset consists of 927 lines from the United States, of which 17 traits related to growth, wood quality, disease resistance, and development were recorded . For comy is a vector of phenotypic values; μ denotes the overall mean; u is a vector of additive genetic merits for all individuals, which is assumed to be multivariate normal u~N; σu2 denotes variance of additive genetic merits; G is a marker-derived numerator relationship matrix [e is the model residuals, where e ~ N; σe2 denotes the residual variance; and X and Z are incidence matrices linking the overall mean and additive genetic merits to the phenotypes, respectively. The original and modified BayesB are involved in the estimation of marker effects in the training population. The statistical model of both methods can be written asy is a vector of phenotypic values; b is a vector of fixed effects ; gi~N is the vector of residuals; σe2 is the residual variance; X is the design matrix for b; and zi is a vector of indicators for genotypes of marker i with values equal to 0, 1, and 2 to indicate the marker genotypes 11, 12, and 22, respectively. The marker effect variance π), where the degree of freedom v = 4.234 and scale parameter S = 0.0429 [σe2) is a scaled inverse χ-squared distribution with parameters v = −2 and S = 0.The statistical model for GBLUP in this study can be written asp matrix ; e is thyi centered at σ2. According to Meuwissen et al. [Gibbs sampling is used in the MCMC algorithm to obtain samples of each parameter from its full-conditional posterior distribution. Given a Gaussian response variable, the likelihood of which is n et al. , the priConditional posterior of each parameter can be deduced from the joint posterior density. However, we cannot use these conditional posterior distributions directly for estimating parameters because all of them are conditional on other unknowns. While we can introduce a MCMC procedure based on a Gibbs sampler to solve this problem. The general steps of Gibbs sampler  are given below.μ, gi and Initialization of parameters. Initialize v = 4.234, S = 0.0429, ni is the number of haplotype effects at the ith segment.Update the σe2. First adjust e with e = y − Xg − 1n′μ, then update σe2 by drawing a single sample from x− 2.Update the μ by sample from Update the overall mean Xij is the column of X of effect gij; gij = 0 equal to g except that the effect of gij is set to zero.Update effects of all chromosome segments by sampling all effects fromRepeat step 2 to step 5 for a large number of cycles.π) of markers are non-effective and the prior distribution of v = 4.234 and S = 0.0429. The Gibbs sampler of BayesA will not move through the entire space of method BayesB, because the sampling of gi′gi > 0. This problem is resolved by sampling gi simultaneously using a Metropolis-Hasting (MH) algorithm. Thus, the Monte Carlo Markov Chain (MCMC) algorithm of BayesB consists of running a Gibbs chain as in BayesA, except that samples of α is sampled from a uniform distribution. If α ≥ 1 − π, the variance of marker effects is not resampled and set as 0 or not updated according to the likelihood ratio. However, the variance is sampled from an inverse χ-squared distribution and accepted according to the likelihood ratio when α < 1 − π. If the variance is 0, the effect of current marker is set as 0, otherwise it is sampled from its posterior distribution. Therefore, the updating of marker effects is affected by the variance.BayesB uses a prior that a large proportion  in BayesBπ. Our new method is termed BayesBπ because it is an improved version of the original BayesB by assigning genetic architecture based priors. Calculation of GBLUP, BayesB, and BayesBπ are conducted with our in house programs, while BayesCπ is conducted with R package “GBLR” [In this study, the MCMC in Bayesian methods are iterated 10,000 times with 100 cycles in Metropolis-Hastings algorithm, and the first 2000 iterations are discarded as burn-in. Samples from the remaining iterations are averaged to obtain estimates of marker effects. In BayesB, e “GBLR” .p-values derived from the analysis of variance (ANOVA) to a probability form. ANOVA is performed by the R software package (http://www.r-project.org/) on single markers in the reference population to get the p-values. The model for ANOVA can be written asy is a vector of phenotypes; X is a design matrix linking records to the fixed effects included in b; Z is a design matrix indicating the genotypes of individual SNPs; g is the effect of single markers; and e is a vector of residuals. Then the p-values derived from ANOVA on single SNPs are transformed to the locus-specific π through the formulaπi is the locus-specific π of ith marker; ω = − log10(p); ωi = − log10(pi); pi is the p-value of ith marker; and p is the vector of p-values of all markers.From the aspect of whole genome, π is the proportion of non-effective markers. However, from the aspect of single markers, π is an important parameter which decides the extent to which a marker is fitted in the model, and thus affects the estimation of marker effects. Therefore, π should be different among markers, which is consistent with our prior knowledge that some genome segments have large effects and others show moderate to zero effects across the whole genome. Here we propose a method to obtain the locus-specific π based on traits’ genetic architecture. The locus-specific π is obtained by rescaling In BayesBπ, the locus-specific π of SNPs are obtained from the reference population through the method mentioned above. In the following MCMC algorithm, each marker uses its corresponding π to perform the estimation of variances and marker effects.β is the extent of improvement with our new method compared to the original BayesB; GEBVs and σy are the standard deviations of GEBVs and model response variables; accBπ and accB are accuracies of our new method and that of the original BayesB, respectively.The accuracy of genomic prediction is defined as the correlation between the GEBVs and the response variables . Regression of the GEBVs on the response variables are performed, and the regression coefficients are taken as the genomic prediction unbiasednesses. The accuracy and unbiasedness of BayesBπ are compared with that of GBLUP , the orihttp://www.g3journal.org/content/5/4/615/suppl/DC1 for dairy cattle dataset and http://www.genetics.org/content/190/4/1503/suppl/DC1 for loblolly pine dataset, respectively.The data used in this study are online available through


Pcombined = 2.44 × 10−15). Our study also replicated 16 genetic associations related to five hematological traits with genome-wide significance (P < 5 × 10−8) that were previously established in other ethnic groups. Of these, variants influencing platelet count are distributed across several genes and have pleiotropic effects in coronary artery disease and dyslipidemia. Our findings may aid in elucidating molecular mechanisms underlying not only hematopoiesis but also inflammatory and cardiovascular diseases.Hematological traits are important health indicators and are used as diagnostic clinical parameters for human disorders. Recently, genome-wide association studies (GWAS) identified many genetic loci associated with hematological traits in diverse ethnic groups. However, additional GWAS are necessary to elucidate the breadth of genetic variation and the underlying genetic architecture represented by hematological metrics. To identify additional genetic loci influencing hematological traits , we conducted GWAS and meta-analyses on data from 12,509 Korean individuals grouped into population-based cohorts. Of interest is EGF, a factor plays a role in the proliferation and differentiation of hematopoietic progenitor cells. We identified a novel EGF variant, which associated with platelet count in our study ( Hematological metrics are used as essential medical indicators . MaintenIn addition, previous studies illustrated that significant differences in hematological traits exist between ethnic groups. For example, African Americans tend to have lower white blood cell counts, whereas persons of Japanese descent generally have fewer red blood cell-related anomalies than typically seen in other populations , 11. TheIn this study, we sought to identify additional ethnic Korean-specific genetic variants associated with five hematological traits: hemoglobin (Hb), hematocrit (Hct), red blood cell count (RBC), white blood cell count (WBC), and platelet count (PLT). To achieve our aim, we thus carried out a GWAS and meta-analysis in Korean populations to look specifically for effects related to these metrics. Subsequently, we performed pleiotropic association analyses and functional annotation of the identified trait-associated loci. Our results may not only highlight the biologically important role of genetic variants in hematological traits found in Korean populations but also provide useful insight on understanding genetic diversity between ethnic groups.http://dx.doi.org/10.1155/2015/914965). And more detailed explanations of each cohort were previously described . Our, 6q23.3 P = 2.05 × 10−12) (Pcombined = 2.44 × 10−15) (http://hapmap.ncbi.nlm.nih.gov/) .We identified a novel intronic variant, rs2282786, located on EGF at 4q25 that associated with lower platelet counts  . This va× 10−15) . We alsoWe examined associations between seven PLT-associated variants with genome-wide significance and other traits related to CAD and lipid profile, including TC, TG, LDL, and HDL . Two varPcombined = 2.44 × 10−15) by combined meta-analysis in 43,594 individuals   . This SNhttp://www.sanger.ac.uk/resources/software/genevar) . It may play a role in growth, proliferation, and differentiation of megakaryocytes and platelet production. Previous studies reported that activated platelets induced by inflammation may secrete EGF and proinflammatory substances for subsequent thrombus formation in an inflammation-hemostasis cycle that is a tightly interrelated pathophysiologic process , 26. It . It may To date, many studies have reported genetic factors associated with hematological traits via GWAS across diverse ethnic groups –7, 22. AIn summary, we illustrated that a genome-wide approach identified genetic variants contributing to phenotypic variation of hematological traits in Korean populations. We identified one novel ethnic specific variant associated with PLT that localized to a key regulator of hematopoiesis and confirmed previously implicated loci that were associated five hematological traits. We also provided pleiotropic effects of PLT-associated variants that may support the biological role of genetic determinants for hematological traits. Our findings may help identify biological pathways that contribute not only to hematopoiesis but also to inflammatory and cardiovascular diseases in humans.Supplementary Figure 1: The quantile - quantile plots for five hematological traits in discovery stage.Supplementary Figure 2: Population diversity of rs2282786.Supplementary Table 1: Descriptive statistics of samples analyzed in this study.Supplementary Table 2: Descriptive statistics of samples analyzed in this study.Supplementary Table 3: Association results of 32 variants that were verified in the replication stage.Supplementary Table 4: Replication of previously reported loci associated with hematological traits in Korean populations.


Glycine max) is a photoperiod-sensitive and self-pollinated species. Days to flowering (DTF) and maturity (DTM), duration of flowering-to-maturity (DFTM) and plant height (PH) are crucial for soybean adaptability and yield. To dissect the genetic architecture of these agronomically important traits, a population consisting of 309 early maturity soybean germplasm accessions was genotyped with the Illumina Infinium SoySNP50K BeadChip and phenotyped in multiple environments. A genome-wide association study (GWAS) was conducted using a mixed linear model that involves both relative kinship and population structure.Soybean (Dt1 gene was identified in the locus strongly associated with both DTM and PH. Ten candidate genes homologous to Arabidopsis flowering genes were identified near the peak single nucleotide polymorphisms (SNPs) associated with DTF. Four of them encode MADS-domain containing proteins. Additionally, a pectin lyase-like gene was also identified in a major-effect locus for PH where LD decayed rapidly.The linkage disequilibrium (LD) decayed slowly in soybean, and a substantial difference in LD pattern was observed between euchromatic and heterochromatic regions. A total of 27, 6, 18 and 27 loci for DTF, DTM, DFTM and PH were detected via GWAS, respectively. The This study identified multiple new loci and refined chromosomal regions of known loci associated with DTF, DTM, DFTM and/or PH in soybean. It demonstrates that GWAS is powerful in dissecting complex traits and identifying candidate genes although LD decayed slowly in soybean. The loci and trait-associated SNPs identified in this study can be used for soybean genetic improvement, especially the major-effect loci associated with PH could be used to improve soybean yield potential. The candidate genes may serve as promising targets for studies of molecular mechanisms underlying the related traits in soybean.The online version of this article (doi:10.1186/s12864-015-1441-4) contains supplementary material, which is available to authorized users. Arabidopsis, a model organism in plant biology. The floral integrator genes FLOWERING LOCUS T (FT) and SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 (SOC1) play a central role in flowering regulation. They promote the expression of a group of floral meristem identity genes such as APETALA 1 (AP1), LEAFY (LFY) and CAULIFLOWER (CAL) to initiate the floral transition of the plant , where σ2g is the genotypic variance, σ2gl is the genotype by environment interaction variance, k is the number of environments, r is the number of replications. Estimation of variance components was performed by the varcomp procedure in SAS version 9.3  with all effects considered to be random. To estimate the proportion of phenotypic variation explained by the mixed linear model (MLM) containing all identified loci, the likelihood-ratio-based R2 was calculated for each trait [DTF and DTM were recorded in the field as the number of days from planting to the date when 50% of the plants in a plot had showed the first flower and when 95% of the pods had ripened as indicated by mature pod color, respectively. DFTM was calculated as the difference between DTM and DTF (or days from flowering to maturity). PH was the average of four measurements per plot, and each measurement was recorded as the length of main stem from the ground to the top extremity of the plant at maturity. The model for the phenotypic trait was ch trait .The Illumina Infinium SoySNP50K BeadChip was used to genotype the population as described in a previous study , and 42,r2) of alleles using R package synbreed [r2 was calculated separately for the two chromosomal regions. The physical length of euchromatin and heterochromatin on each chromosome were defined as in SoyBase (www.soybase.org). Only r2 for SNPs with pairwise distance less than 10 Mb in either euchromatic or heterochromatic region of each chromosome were used to draw the average LD decay figure by R script using the equation described in a previous study [r2 dropped to half its maximum value [Pairwise LD between markers was calculated as squared correlation coefficient  and MLM implemented in the GAPIT R package [To minimize the effects of environmental variation, best linear unbiased predictors (BLUPs) of individual lines were calculated for each trait using the R package lme4 , and wer package ,35. The For the naive test, the equation wasFor the MLM analysis, the equation wasμ is the total mean, X is the incidence matrix relating the individuals to the fixed marker effects α, P is the incidence matrix relating the individuals to the fixed principal component (PC) effects β, and Z is the incidence matrix relating the individuals to the random group effects u obtained from the compression algorithm. The random group effects u follows a multivariate normal distribution with mean 0 and variance-covariance matrix 2KVg, where K is the kinship matrix, and Vg is the polygenic variance. The random error term e follows a multivariate normal distribution with mean 0 and variance-covariance matrix IVe, where I is the identity matrix and Ve is the error variance component. The optimal number of PCs to be involved in the MLM was determined by Bayesian information criterion of the model  was suggested by GAPIT. For PH, the results between the regular MLM and compressed MLM (cMLM) were very similar, thus a regular MLM (K model) was adopted too. While for DFTM, a K model was not applicable  could not detect significant association, thus cMLM (K + P model) with a compression level of 1.9 was used as suggested by GAPIT  < 0.05 or the empirical significance level at P < 0.001, whichever was more stringent. To assess the empirical significance of SNPs, we performed 1,000 permutations of genome-wide association analyses. Since we could not find appropriate permutation for K + P model, permutation was conducted for K model only, which was used for association analysis of DTF, DTM and PH as described above. For each iteration, the phenotype values and kinship matrix (K) in the MLM remained unchanged, while genotypes of each SNP were permuted. Briefly, we shuffled the rows randomly but kept the order of row names unchanged in a genotypic data file where each column represented one SNP and each row represented one germplasm accession. GAPIT was run with the same parameter setting as the original test for each trait. This method preserves the association between the phenotypes and K but eliminates the association between the SNP and P . As a result, we applied it to the K model only but not to the K + P model.For association analysis of DTF and DTM, regular MLM (www.soybase.org) were used as the source of candidate genes. The prediction of candidate genes was referred to the following preferences: i) genes of known function in soybean related to the trait under study, ii) genes with function-known orthologs in Arabidopsis related to the trait under study, and iii) genes pinpointed by the peak SNPs.Genes annotated in Glyma1.1, Glyma1.0 and NCBI RefSeq gene models in SoyBase (G × E interaction for PH (Table P < 10−4) for DFTM between 2011 AU and 2012WT to 0.91 (P < 10−4) for DTF between 2011 AU and 2012BK. Accordingly, a high heritability (>80%) was estimated for each trait, indicating that genetic effects play a predominant role in the performance of the traits  while negatively correlated to DFTM .Analysis of variance indicated that the effects of genotypes, environments, and their interactions were significant for all the four traits except the PH Table . AveragePH Table . There wts Table . Interesr2 = 0.23   to 22.3 kb/SNP on Gm13  within each LD block was kept, except in the case of the extensive LD blocks where multiple peak SNPs were found. Finally, 27, 6, 18 and 27 loci associated with DTF, DTM, DFTM and PH were identified across the 20 chromosomes  at this locus were 31.2 cm taller and matured 4.4 days later than those with the alternative allele , we further predicted candidate causal genes for loci significantly associated with each trait. A total of 18 candidate genes were predicted for 15 of the 27 loci associated with DTF  [r2 = 0.90). Glyma09g07980, identified at 7 kb away from the peak SNP of DTF16  [Based on the results of GWAS and genes annotated in SoyBase    .Figure 4Dt1 gene at 18.6 kb upstream of the peak SNP  on Gm19, which was associated with both DTM and PH  of locus PH21 on Gm18 , which is involved in auxin-induced shoot and root development [Glyma19g37180, encoding a putative pectinesterase, was identified in a small chromosomal region associated with locus PH24 on Gm19 where LD decayed rapidly  and population structure (P) is more effective than the MLM containing either K or P alone [P in the K model could vary with traits. The possible explanations might include: i) the phenotypic variation attributed to population structure varies with traits; and ii) the degree of overlap between K and P in controlling genetic relationships is different for individual traits [Kinship and population structure are known as the major confounding factors leading to spurious results in association analysis, and corrected MLM containing both kinship matrix  was observed in the present study. The relatively higher correlation between DTM and DFTM  indicated that DFTM had greater impact on DTM than DTF. In addition, the negative correlation between DTF and DFTM  indicated that for a certain early maturity group in soybean, shorter vegetative growth might imply longer reproductive growth to some extent, which may help accumulate more dry matter in seeds. The GWAS results showed that the flowering locus DTF16 was also associated with DFTM but not with DTM, while maturity loci DTM2 and DTM4 were associated with DFTM but not DTF. Therefore, we suppose that some flowering loci may also affect growth stage after flowering as reported in a previous study [Previous reports suggested that DTM and DTF would be highly correlated . Howeverus study , and somE1 through E8, and J) also affected flowering in soybean [E3 and E4 affected post-flowering photoperiod responses as well [E2 and one in E3, did not detect any of the loci at q < 0.05 either. Another explanation was that genetic variants might exist at these major loci, but could not be captured due to the lack of SNP coverage. Notably, some genetic variants were actually undetectable by SNP genotyping. For example, the e1-nl allele, one of the natural variants of E1, was a deletion of the entire gene [Previous research showed that major maturity loci , SAM switches from vegetative growth to reproductive growth soon after photoperiod-induced floral transition, and stem growth stops [Dt1/Dt1) and vegetative growth of SAM continues until a cessation caused by the demand of developing seeds [Dt1 was strongly associated with both DTM and PH, indicated that Dt1 is very likely the causal gene for DTM5 and PH25. Because plant height is one of the major factors determining yield potential in soybean, PH25 with large effect on plant height may also affect soybean yield substantially. However, application of the preferred allele of PH25 locus needs to be careful, as it may also affect maturity dates.The SNP at the 45.0 Mb position on Gm19, a region similar to previously reported QTLs Pod mat 13–6 and Plant height 4–2 and 13–8 . Pectin is a structurally complex polysaccharide contained in primary cell walls of plant and has functions in plant growth, morphology and plant defense [Petunia inflate pectinesterase [Arabidopsis, a reduction in cross-linking of cell wall pectic polysaccharide resulted in dwarf phenotype [Arabidopsis by affecting the degree of pectin methyl-esterification [Glyma19g37180 encoding a putative pectinesterase was the most likely causal gene for PH24, a major-effect locus associated with plant height in soybean. Notably, unlike PH25, PH24 had no association with maturity, and selection of the desired allele of this locus might improve the yield potential of soybean without affecting the maturity dates.The locus PH24, explained 10% of phenotypic variation, was mapped to a small region on Gm19 where LD decayed rapidly. Three QTLs associated with PH were previously reported in the similar region  and 16 genes orthologous to Arabidopsis genes functioning in similar traits. It evidently demonstrates the high efficiency of GWAS in dissecting complex traits in soybean. A medium number of SNPs generated from the SoySNP50K analysis is capable of capturing genome-wide allelic variation, and candidate genes are regionally accessible for crops like soybean with a low LD decay rate. The genetic variants and trait-associated SNPs identified in this study will be useful for soybean cultivar improvement, especially for major-effect loci associated with PH that may have great potential for soybean yield improvement. Additionally, biological validation of the candidate genes will be also of great interest.In this study, 27, 6, 18 and 27 loci associated with DTF, DTM, DFTM and PH were identified via GWAS, respectively. Thirty-five candidate genes were proposed, including a function-known gene (


Miscanthus has been widely studied around the world, especially Miscanthus × giganteus owing to its high biomass yield in Europe and North America. However, the narrow genetic basis and sterile characteristics of M. × giganteus have become a limitation for utilization and adaptation to extreme climate conditions. In this study, we focused on one of the progenitors of M. × giganteus, Miscanthus sinensis, which was originally distributed in East Asia with abundant genetic resources and comparable biomass yield potential to M. × giganteus in some areas. A collection of 138 individuals was selected for conducting a 3-year trial of biomass production and analyzed by using 104 pairs of SRAP, ISAP, and SSR primers for genetic diversity as well as marker-trait association. Significant differences in biomass yield and related traits were observed among individuals. Tiller number, fresh biomass yield per plant and dry biomass yield per plant had a high level of phenotypic variation among individuals and the coefficient of variation were all above 40% in 2011, 2012, and 2013. The majority of the traits had a significant correlation with the biomass yield except for the length and width of flag leaves. Plant height was a highly stable trait correlated with biomass yield. A total of 1059 discernible loci were detected by markers across individuals. The population structure (Q) and cluster analyses identified three subpopulations in the collection and family relative kinship (K) represented high gene flow among M. sinensis populations from Southwest China. Model testing identified that Q+K was the best model for describing the associations between the markers and traits, compared to the simple linear, Q or K model. Using the Q+K model, 12 significant associations (P < 0.001) were identified including four markers with plant height and one with biomass yield. Such associations would serve an efficient tool for an early selection of M. sinensis and facilitate a genetic improvement of biomass yield for this species.As a great potential bio-fuel feedstock, the genus  Miscanthus, comprising of C4 perennial warm-season rhizomatous grasses  mapping, has been proved to be useful and powerful for genetic dissection of complex traits  which contains many important C4 crops including maize (Zea mays L.), sorghum (Sorghum bicolor L. Moench), and sugarcane (Saccharum officinarum L.) with rich genomic databases, and a large number of SSRs have been proven to have high transferability to M. sinensis  sequence to detect the genetic diversity and differentiate the collected M. sinensis population from southwest China . Briefly, each of the genotypes was cloned to three individuals using rhizome division and planted following a complete randomized block design, with one replicate per genotype in each of three blocks. Prior to transplanting, plant leaves were cut back to 8–10 cm with 6–10 tillers. All the individuals were transplanted to the Sichuan Agriculture University farm  in May of 2010, with an average annual precipitation of 1774 mm. The soil pH at the experimental site ranged from 5.3 to 5.5, and soil type was purplish loam with 1.46% organic qualitative content. Plants were well watered immediately after transplanting and no fertilizer or water was applied to the plants afterwards.A total of 138 Fresh young leaves from each individual were collected for genomic DNA extraction using the Plant Genomic DNA kit  according to the manufacturer's protocol. The quality and concentration of the DNA were determined by comparing the sample with known standards of lambda DNA on 0.8% (w/v) agarose gels and NanoDrop ND-1000 spectrophotometer . The isolated genomic DNA was diluted to 20 ng/μL for PCR amplification.M. sinensis that varied in morphology and geographic locations were selected for screening other markers based on Nie et al. , 0.8 μL primers (10 pmol/μL), 0.4 μL Golden DNA Polymerase  and 5 μL of ddH2O. Amplification was performed on a Peltier Thermal Cycler  under the following conditions: for SRAP and ISAP amplification, 5 min at 94°C for 1 cycle, followed by 5 cycles at 94°C for 1 min, 35°C for 1 min, and 72°C for 1 min, and then 35 cycles at 94°C for 1 min, 50°C for 1 min, and 72°C for 1 min, extended at 72°C for 10 min, then stored at 4°C; for SSR, 5 min at 94°C for 1 cycle, followed by 10 cycles at 94°C for 1 min, 55°C for 30 s, and 72°C for 40 s, decreased 0.5°C for annealing with each cycle, and then 35 cycles at 94°C for 1 min, 50°C for 30 s, and 72°C for 40 s, extended at 72°C for 10 min, then stored at 4°C. Electrophoresis was performed in a denaturing 6% polyacrylamide gel  to separate allele sizes. The gel was stained by AgNO3 solutions.For PCR amplification, the total volume of each PCR reaction system was 20 μl, containing 3 μL template DNA (20 ng/μL), 10 μL of Mix  was measured at the ground level to the top of the plant. The total number of tillers in each plant (TN) was counted after harvest. The fresh biomass yield of per plant  was evaluated with autumn harvest in October. In 2012 and 2013, in addition to H, TN, and FW, several other morphological traits associated with biomass were measured. The main tiller diameter (TD) was measured approximately 10–15 cm from the base of the plant on three randomly chosen tillers. Number of main stem internodes (NI) was counted and the length of the main internode (LI) was measured. The length of flag leaf (LF) and length of longest leaf (LL) were measured from the ligule to the tip along the central vein of the leaf. The width of flag leaf (WF) and width of longest leaf (WL) were measured for the width of the blade at half-leaf length for the leaf which was recorded for measuring the length. Plants were harvested about 20 cm above the soil surface, and the whole above-ground biomass was weighed as FW in October. The harvested tissue were then dried in an oven at 105°C for 1 h, followed by 70°C for 3 days, for determining dry biomass yield per plant . All the plants in the field were cut about 20 cm to avoid rhizome damage and facilitate quick re-growth in the following season.* 100%—for detecting the discrete level of the data.Analysis of variance (ANOVA) and correlation analysis of morphological traits were performed using SPSS 17.0 software . Effects of both environment (different measurement year) and individuals on various traits were determined using the Least Significant Difference test model. Pearson correlation coefficients were calculated for correlation analysis. The coefficient of variation (CV) was calculated using the following model—CV = SD/Mean PIC), calculated by the following model PICi = 2fi(1 − fi) , and 1 −fi is the frequency of the null allele.The alleles of molecular markers were scored manually for the population as band presence (1) or absence (0), and each of them was treated as an independent character regardless of its intensity. A present/absent data matrix was constructed to analyze the genetic diversity and population structure. The discriminatory power of different primers was evaluated by means of polymorphic information content  of 138 T) was divided into gene diversity within populations (HS) and the gene diversity among populations (DST). These parameters were calculated according to the equation HT = HS+DST. The genetic differentiation coefficient (GST) was calculated as a ratio of DST/HT and was used to measure population differentiation. Gene flow was calculated as Nm = 0.5(1 − GST)∕GST to estimate the level of gene drift among the populations  was performed based on q-value using false discovery rate . The phenotypic variation explained by the single associated marker (R2) indicated the fixed marker effects.The markers with minor allele frequency less than 5% were removed in order to reduce false positive associations. Relative kinship (K) among samples was calculated by TASSEL 2.1 software. The marker-trait association analysis was conducted to reveal associations between the interest traits and marker alleles using TASSEL 2.1 software along with the General Linear Model (GLM) and Mixed Linear Model (MLM) procedure , where h2 was calculated by using PROC MIXED . The h2 was calculated as follows: h2 = The genome-wide prediction was carried out by using the R package rrBLUP  and leaf length , indicating that a M. sinensis plant with a high number of tillers always followed with small tiller diameter and low leaf length. Plant height had a significant positive correlation with the main internode length . Significant positive correlations were also found between leaf width and tiller diameter and between flag leaf length and flag leaf width.Significant positive correlations of biomass yield (both fresh and dry) with TN, H, TD, NI, LI, LL, and WL were found, while no correlations were seen in with LF and WF Table . The higM. sinensis individuals while the other primers failed to amplify or did not produce clear bands. In total, 1059 bands were produced and 993 (93.8%) were polymorphic. For the SSR primers developed from M. sinensis, sorghum, sugarcane, maize, and conserved ESTs in grasses, the average of bands produced per primer was 7.8, 8.9, 5.8, 7.2, and 8.0, respectively. The production of ISAP primers had a similar result (6.5) with SSR, while the SRAP had a higher productive capacity (19.8). The mean of polymorphic information content ranged from 26.7% (SSR-4) to 39.0% (SSR-5), demonstrating a different discriminatory capacity for each kind of primer  of 0.60, the majority of the individuals were clearly assigned to the specific groups while admixture between groups referred to 18 individuals with Q < 0.6 (data not shown).Population structure of the 138 individuals was estimated under the Hardy-Weinberg Equilibrium by using STRUCTURE V2.3.3 software. After dropping the markers with minor allele frequency less than 5%, the total number of marker loci retained for structure and association analysis was 934. Based on maximum likelihood and delta K (ΔK) values, the number of optimum subgroups was three  values of 138 individuals ranged from 0.59 to 0.95 with an average of 0.67. The UPGMA dendrogram based on GS data obviously revealed three major clusters similar to the result from the population structure analysis when the GS value was equal to 0.67  was 0.35 ± 0.015, while gene diversity within groups (HS) was 0.33 ± 0.014 and gene diversity among groups (DST) was 0.016. The total Shannon's Information Index of Diversity (SII) among 138 individuals was 0.52 ± 0.015 with the average of SII within groups was 0.50. The mean genetic differentiation coefficient (GST) was estimated from the 933 bands with a value of 0.046. A higher level of genetic variation within the populations than among them suggested a high frequency of gene flow (Nm = 10.32) between the groups. The AMOVA analysis of the M. sinensis populations showed similar results, and both the genetic variations within (96.0%) and among (4.0%) groups were significant (P < 0.05)  estimates represented a normal distribution with approximately 98% of individuals from 0 to 0.5 . The averages of the phenotypic variations explained by the model for significant associations were 9.2% (S), 10.9% (Q), 46.8% (K), and 47.1% (Q+K), which was consistent with the model test results. For the significant associations detected by Q+K model, 4 markers were associated with plant height, 3 markers with flag leaf width, 1 marker with internode number, and 1 marker with fresh biomass yield (Table P > 0.001) in the Q+K model while they significant in the K model. The results were consistent with the model test above that the Q+K and K models had nearly the same capacity to detect the associations but the Q+K model seems a little better fit for biomass to control false positive associations. Specifically, one of associations (marker “793” for flag leaf width) reached genome-wide significant after Bonferroni correction for multiple testing (P < 0.05/934 ~ 5.35 × 10−5), with an estimated false discovery rate (FDR) < 0.05.A total of 21 significant associations were detected using a simple linear model, 18 using Q model, 15 using K model and 12 using Q+K model (2) equal to 0.56. Furthermore, the average of prediction accuracy and adjust prediction accuracy was 0.24 and 0.33, respectively. Although, the values of prediction accuracy seem lower, the adjust accuracy of genome-wide prediction for flag leaf width had moderate predictive ability (0.59). Interestingly, association analysis also showed that one marker was highly associated with flag leaf width on genome-wide significant level.For an overall measure of quality of the genotype and phenotype data, genome-wide prediction was conducted in this study Table . Most ofMiscanthus is a typical perennial grass species that requires a long period of time for establishment after transplanting clonal replicates prior to reaching the maximum growth for optimum and stable productivity . ISAP, as a very good complementary, is designed by using the highly conserved sequence of introns splice position as the core of the primer sequences to amplify the genes encoding areas, which could leading to a high association with expressed sequence. In this study, SRAP have a very high amplification capacity than other markers, demonstrating its values for use in the molecular marker system. The average number of alleles per loci produced in this study was similar to previous studies  and incomplete genetic map (28 linkage groups detected whereas M. sinensis has 19 chromosomes). More recently, Gifford et al. (M. sinensis population from China and using 23 SSR markers transferable from Brachypodium distachyon and 9 markers were significantly (P < 0.01) associated with heading date and biomass yield. A genome-wide association study was conducted in a 138 M. sinensis population by using 53,174 single-nucleotide variants (SNVs)  with phenology, morphology, and cell wall composition traits were detected. In our study, 12 significant associations of biomass yield with related traits were identified and marker “793” associated with flag leaf width reached genome-wide significant after Bonferroni correction for multiple testing. The possible reason why we obtained a number of significant associations similar to Slavov et al. (Miscanthus for improving genome-wide selection in a breeding program.Molecular markers have been used to evaluate the genetic relationship of accessions in d et al.  and Liu d et al.  conducted et al. , but ided et al.  conductev et al.  while usv et al. . Other fXZ, LH, and GN conceived the project and designed the experiments; GN, XW, and YZ performed the experiments; GN, XY, and XL analyzed the data; XZ, MT, and YJ finalized the manuscript; all authors discussed the results and reviewed the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.