Genotype imputation has become standard practice in modern genetic studies. As sequencing-based reference panels continue to grow, increasingly more markers are being well or better imputed but at the same time, even more markers with relatively low minor allele frequency are being imputed with low imputation quality. Here, we propose new methods that incorporate imputation uncertainty for downstream association analysis, with improved power and/or computational efficiency. We consider two scenarios: I) when posterior probabilities of all potential genotypes are estimated; and II) when only the one-dimensional summary statistic, imputed dosage, is available. For scenario I, we have developed an expectation-maximization likelihood-ratio test for association based on posterior probabilities. When only imputed dosages are available (scenario II), we first sample the genotype probabilities from its posterior distribution given the dosages, and then apply the EM-LRT on the sampled probabilities. Our simulations show that type I error of the proposed EM-LRT methods under both scenarios are protected. Compared with existing methods, EM-LRT-Prob (for scenario I) offers optimal statistical power across a wide spectrum of MAF and imputation quality. EM-LRT-Dose (for scenario II) achieves a similar level of statistical power as EM-LRT-Prob and, outperforms the standard Dosage method, especially for markers with relatively low MAF or imputation quality. Applications to two real data sets, the Cebu Longitudinal Health and Nutrition Survey study and the Women’s Health Initiative Study, provide further support to the validity and efficiency of our proposed methods. Genotype imputation has become standard practice in modern genetic studies Since association analysis with phenotypes of interest rather than genotype imputation per se is usually of the ultimate interest, development and evaluation of post-imputation association strategies have therefore attracted considerable attention from the research community Limited evaluations of existing methods (including methods that explicitly model posterior probabilities) on variants with low imputation quality suggest much reduced power compared with accurately imputed variants, for instance, as demonstrated in 2R, squared Pearson correlation between true and imputed genotypes) into account. This framework allows us to simulate data conditional on any desired 2R, making it straightforward to evaluate performance at any desired level of imputation quality. We then describe our EM-LRT in two scenarios, namely when posterior probabilities are available (Scenario I) and when only imputed dosages are available (Scenario II).We will first briefly review the Mixture method Yi and genotype probability vector The following mixture of regression model is fit for trait Zi is the covariate vector, i = 1, 2, …, N with N being the sample size. To estimate the parameters optim.2R), as well as a quantitative trait with which we test for genetic association. The model has three stages: the first stage generates genotype probabilities based on marker-specific information (genotype probability stage); the second stage employs a multinomial distribution with probabilities from the first stage to generate allele counts ; and the final stage fits a linear regression model to generate quantitative trait values (trait stage).We adopt a hierarchical model that generates posterior probabilities, imputed dosages, and true genotypes using marker-specific information including minor allele frequency (MAF) and imputation quality measure . We consider two common scenarios after genotype imputation: 1) when posterior probabilities of genotypes are available and 2) when only dosages are available.Our primary goal is to test for marker-trait association when marker genotypes iG is missing but genotype probability vector i = 1, 2, …, N with N being the sample size. Given the observations iy is independent of genotype probability vector if conditional on true genotype iG and true genotype is independent of covariates iz conditional on genotype probability vector. Therefore, with Gaussian distribution, the corresponding complete-data log-likelihood isUnder this scenario, the true genotype iG, namely iG and i2G, are not observed and will be replaced in the E-step by their conditional expectations given the observed data. Their conditional expectations are.In this complete data log-likelihood, terms involving true genotype M-step, the maximum likelihood (ML) estimates of the parameter In the E-step and M-step until convergence (Y using dosage D and covariates Z (a.k.a Dosage or standard method). Our EM-LRT-Prob approach shares some similarity with the seminar work by Lander and Botstein To speed up the EM algorithm, we suggest using the naïve parameter estimates as starting values, that is, the parameter estimates derived by fitting a simple linear regression on trait We propose a framework that first uses the conditional (on dosages) distribution to sample genotype probabilities given the imputed dosages, and then apply the EM algorithm detailed above in Scenario I.C′ is the normalizing constant, B(.) is the beta function .The drawback of the above rejection sampling approach is that it can be computationally rather expensive especially when the envelope function is large. Fortunately, we can use an approximation approach when MAF is not high. For example, when MAF is low enough, the probability of having two copies of the minor allele is close to zero. In that case, we adopt an approximation approach (referred hereafter as dosage approximation approach) by setting the probability of having one copy of the minor allele to dosage when MAF is below certain threshold depending on the imputation quality ), the p-value from EM-LRT-Dose  reached the conventionally employed genome-wide significance threshold of 5×10−8 while that from Dosage was marginally genome-wide insignificant .Here, we use meta-analysis results from our previous study as a gold standard to define true positives and investigate the It is crucial to take imputation uncertainty into consideration when performing association testing. Existing methods have focused on common variants, which have been the focus of the past wave of GWAS using HapMap-based imputation. With the deluge of next generation sequencing data being generated, increasingly denser reference panels are allowing imputation of a much larger number of variants, including an increasing number of relatively rare or poorly imputed variants. It is thus highly warranted to re-visit potential strategies for post-imputation association analysis and to seek more powerful or efficient statistical methods.In this work, we have proposed EM-LRT methods explicitly incorporating marker level imputation quality statistic into association tests. We considered two scenarios: when posterior probabilities of all potential genotypes are available and when only dosages are available. We evaluated the performance of the proposed methods along with existing alternatives using simulation studies and by application to real data sets.R function optim to find ML estimates. Technically, the optim function uses numerical differentiation to obtain ML estimates based on the score function and Hessian matrix, which is considerably slower than our proposed EM algorithm. To quantify the computational efficiency, we conducted association testing on a CLHNS data set of 1,717 subjects and 13,801 SNPs, using EM-LRT-Prob and Mixture methods with the same starting values . We observed that the association tests required 279 seconds computing time and 0.91 GB RAM for EM-LRT-Prob and 1,505 seconds computing time and 1.23 GB RAM for the Mixture method on a 2.93 GHz Intel Xeon Processor X5670. Computing time scales linearly with sample size for both EM-LRT-Prob and the Mixture method  and then to conduct association testing on the sampled probabilities using our proposed EM-LRT method.In scenario II, the Dosage method has been shown analytically as the optimal one dimensional summary statistic for association testing in a typical linear model 2R, the squared Pearson correlation between the imputed dosages and the unknown true genotypes. Since the calculation of 2R requires true genotypes, it is not available in practice and imputation software provides an estimate based on the observed dispersion in imputed genotypes over its expected value. Such an estimate  in combination with varying MAF . Specifically, for each 2R and MAF combination, we simulated A (A = 2500) (exchangeable) groups of data sets under the null hypothesis. For each group, we simulated B (B = 2000) data sets  and calculated the p-values by applying all methods to each of the B = 2000 simulated datasets. We then calculated the group-specific type I error as the proportion of B = 2000 p-values (in that group) below the significance threshold of 0.05. We therefore obtained A = 2500 type I error estimates. Finally, we conducted the following one-sample t-test on these 2500 type I error estimates 0H: type I error ≤0.05 vs. 1H: type I error >0.05. Significant results from the t-test indicate inflated type I error. Results are shown in 2R≤0.1, which is likely caused by the tendency of the mixture model over-fitting the data based on additional d.f. compared to the null model.Our simulations suggest an advantage of the proposed methods over the standard Dosage method when imputation quality is relatively low, where imputation quality is measured by http://www.unc.edu/∼yunmli/emlrt.html.In summary, we have proposed likelihood-ratio tests based on expectation maximization algorithms for post-imputation association testing. Simulation and real data analyses show our methods have protected type I error. In addition, simulation and real data results suggest slightly enhanced statistical power of our EM-LRT methods over a standard Dosage test, which has been shown to be the optimal one dimensional statistic for post-imputation association testing; and computationally more efficient (average more than fivefold reduction in computing time) than the Mixture method, which has been shown to be the most powerful at increased computational costs for variants imputed with high level of uncertainty. We anticipate our methods will replace the Mixture method for the analysis of low frequency variants or those imputed with high uncertainty. We envision our methods being applied on a larger scale for GWAS studies with imputation from sequencing based reference panels, including in the public domain, the 1000 Genomes Project Supplemental Data, which include two appendixes and three figures, can be found with the article.Figure S12). The MaCH estimated imputation quality Rsq (Y-axis) is plotted against the true imputation quality 2R (X-axis), which were calculated between genotype data from exome chip array and imputed genotype data (dosages). The red 45-degree line represents perfect estimation. A smooth density scatter plot is employed such that darker color corresponds to larger density and individual dots represent outliers.Estimated versus True Imputation Quality (Rsq vs. R(TIF)Click here for additional data file.Figure S2Variance of Dosages vs. Variance of Genotypes. The variance of dosages (Y-axis) is plotted against the variance of genotypes (X-axis) computed using imputed dosages and genotype data from exome chip array in the CLHNS study. The red 45-degree line represents perfect correlation.(TIF)Click here for additional data file.Figure S32R. The observed 2R (Y-axis) is shown across a spectrum of true 2R (X-axis) and MAF.Boxplot of Observed (TIF)Click here for additional data file.Appendix S12.Simulation of data with desired imputation quality R(PDF)Click here for additional data file.Appendix S2Derivation of the probability density function for the probability of having one copy of the minor allele conditioning on dosage.(PDF)Click here for additional data file.


The use of genetically isolated populations can empower next-generation association studies. In this review, we discuss the advantages of this approach and review study design and analytical considerations of genetic association studies focusing on isolates. We cite successful examples of using population isolates in association studies and outline potential ways forward. Genome-wide association studies (GWAS) have met with widespread success in identifying common-frequency variants associated with complex diseases and medically relevant quantitative traits. Technological advances in genotyping and sequencing have recently enabled access to low-frequency and rare variant genotypes at the population level. The identification of modest effects at individual low frequency and rare variants requires very large sample sizes. Power can be boosted by using statistical approaches to aggregate rare variants across chromosomal regions or functional units. Power can also be increased by leveraging the unique characteristics of isolated, or founder, populations in genetic association studies. In the past, population isolates have typically been used in family-based genetic studies of Mendelian traits , 2. Advae or the effective number of individuals required to explain the observed genetic variability) [Population isolates can be defined as subpopulations derived from a small number of individuals who became isolated because of a founding event  and have stayed so for many generations. The resulting geographical and/or cultural isolation of these populations has genetic consequences, as, for example, endogamy (within community marriage) along with very restricted gene flow (immigration) from neighbouring populations can often be observed. Thus, genomes tend to show higher homogeneity in isolates compared with cosmopolitan populations, which is reflected by a reduced effective population size (Nability)   was recently replicated in an exome chip-wide association scan for lipid traits in ∼1200 individuals from a Greek population isolate [In population isolates, certain alleles reach fixation or extinction at a particular locus, thus reducing the extent of genetic variability , 28. Som isolate . To achiPopulation choice is an important consideration in designing a genetic association study focusing on isolates. Factors such as the number of founding haplotypes, age of divergence from the parent population, effective sample size and degree of admixture with neighbouring populations, all play a role in the population’s allelic architecture. For initial GWAS as well as rare Mendelian gene discovery, the study of young founder populations with recent expansion (e.g. late-settlement Finland) is a powerful strategy  because GWAS arrays by definition assay primarily variants selected to represent common frequency variation across the genome. Low-frequency and rare variants genome-wide cannot be easily captured on a genotyping array because of their large numbers and low levels of correlation. Recently, a genotyping chip focused on likely functional coding low-frequency and rare variants—the exome chip—has been used successfully in founder populations to associate rare variants with proinsulin  and HDL Whole exome sequencing has the advantage of reduced cost compared with whole-genome sequencing, but does not capture variation in non-genic regions. Previous experience from GWAS strongly indicates that the majority of complex trait signals reside outside of genes. High-depth sequencing is considered necessary to call high-quality variants across the allele frequency spectrum . HoweverWhen not all samples can be sequenced, whole-genome sequencing of a subset of cases and controls following an initial GWAS has proven to be a successful strategy for empowering rare variant association in dichotomous trait studies , 41. VarOne intrinsic consequence of genetic isolation is relatedness among individuals, which can conflict with the assumptions of independence of many commonly used analysis tools and inflate test statistics affecting association signals. An efficient approach is to account for relatedness in the association analysis through the use of a linear mixed model (LMM). Until recently, computation of an exact association test statistic such as the Wald statistic or likelihood ratio (implemented in EMMA ) was comSeveral methods have been proposed to improve on the power of single-point tests for rare variants by combining information across multiple sites in a chromosomal region and testing for association with the trait of interest . Relatedwww.1000genomes.org) and/or the UK10K study data (www.uk10k.org) to enhance the resolution of the study. This approach, where positions that were not genotyped in the sample are added using phase information in the reference set, is also relevant to refining genotype calls for low-depth sequencing data.When performing association based on genotyping arrays, it is common practice to impute untyped variants based on a reference panel , with other phased individuals. Relatedness is helpful for phasing because it increases the likelihood of finding a long IBS string of variants; the more related the samples, the more certain it is that these IBS sequences are actually inherited identical-by-descent (IBD), and the probability that unobserved positions are IBS becomes quantifiable , 59. KonP-value-based tests and effect size-based methods, which can be further subdivided into fixed or random effects models [The synthesis of data through meta-analysis can increase the power of association studies. Two different classes of methods have been typically applied in traditional meta-analysis of GWAS: s models . Fixed es models . In addis models , and a cNDST3 and was replicated in 11 independent cohorts of varying ethnicities. Recently, a Greek isolated population replicated a genome-wide significant association between R19X, a cardioprotective variant in APOC3, and low blood triglyceride levels [Several studies from Iceland have been successful in identifying low- /rare-frequency variants associated with sick sinus syndrome, gout, prostate cancer and Alzheimer’s disease , 66–68. e levels . This ste levels .Population isolates are uniquely positioned to usher in the new era of sequence-based association studies. The relative power advantages afforded by studying isolates have been well-documented and recently exemplified in the literature through successful identification of complex trait loci that replicate in other populations. Not all novel discoveries in isolates can be recapitulated in other populations. However, private variants detected in isolates can importantly point to novel biology, identifying potential pathways and loci involved in the aetiopathogenesis of clinically relevant traits. Large-scale efforts to synthesize genome-wide data across isolated populations are an intuitive next step in the field, and hold the promise of catalysing the discovery of further complex trait-associated variants.The population genetics characteristics of an isolate depend on demographic history and the number of bottlenecks/founding events, total number of founders, levels of endogamy, age and duration of isolation.Genetic consequences of isolation such as reduced haplotype diversity and genetic drift can enhance the power for locus identification in genetic association studies of complex traits.Wellcome Trust (098051) and the European Research Council (ERC-2011-StG 280559-SEPI).This work is supported by the


Moreover, genetic influences were significantly more pronounced under conditions of low parental knowledge or high peer deviance (unstandardized regression coefficients (b), p-values (p), and percent of variance (R2) accounted for by interaction terms: b = 1.54, p = 0.02, R2 = 0.33%; b = 0.94, p = 0.04, R2 = 0.30%, respectively). Supplementary set-based analyses indicated that the individual top single nucleotide polymorphisms (SNPs) contributing to the polygenic scores were not individually enriched for gene-environment interaction. Although the magnitude of the observed effects are small, this study illustrates the usefulness of polygenic approaches for understanding the pathways by which measured genetic predispositions come together with environmental factors to predict complex behavioral outcomes. Alcohol problems represent a classic example of a complex behavioral outcome that is likely influenced by many genes of small effect. A polygenic approach, which examines aggregate measured genetic effects, can have predictive power in cases where individual genes or genetic variants do not. In the current study, we first tested whether polygenic risk for alcohol problems—derived from genome-wide association estimates of an alcohol problems factor score from the age 18 assessment of the Avon Longitudinal Study of Parents and Children —predicted alcohol problems earlier in development (age 14) in an independent sample . We then tested whether environmental factors  moderated polygenic risk to predict alcohol problems in the FinnTwin12 sample. We found evidence for both polygenic association and for additive polygene-environment interaction. Higher polygenic scores predicted a greater number of alcohol problems (range of Pearson partial correlations 0.07–0.08, all  Higher polygenic scores were also modestly associated with higher peer deviance . This is consistent with previous evidence from twin studies showing that externalizing-spectrum behaviors such as alcohol use, tobacco use, and conduct problems are genetically correlated with environmental factors [We also tested whether there was evidence for gene-environment correlation by using Pearson correlations to examine the associations between polygenic scores and the parental knowledge and peer deviance environmental measures. As expected, higher polygenic scores were modestly associated with lower parental knowledge, although the effect was of a small magnitude and not significant [ factors ,8. These factors .p ≤ 0.05 threshold accounted for the greatest proportion of variance (0.70%) in age 14 alcohol problems, and we carried this score forward for the gene-by-environment analyses in view of earlier suggestions that SNPs having a nominal association with a phenotype are likely to be enriched for gene-by-environment interaction [The polygenic score using the eraction .Moderated multiple regression analyses indicated that parental knowledge and peer deviance moderated the associations of polygenic scores with age 14 alcohol problems . GeneticAlthough the effect sizes for the polygenic score X environment interactions were small, the pattern of effects is consistent with previous findings from the twin literature. Multiple independent twin studies find that parenting and peer environmental factors moderate latent genetic influences for alcohol use and related outcomes such that genetic influences increase under conditions of low parental knowledge and high peer deviance ,7,8,9,48In addition to these core analyses, we ran a series of supplementary analyses to examine the robustness of our effects after controlling for gene-environment correlation and after transforming our alcohol problems dependent variable to a logarithmic scale. Gene-environment correlation can produce spurious gene-environment interaction effects; likewise, interaction effects are known to be sensitive to scale. Accordingly, our supplementary analyses were intended to address concerns that our observed gene-environment interaction effects could be statistical artifacts.vice versa) and saved the residuals for use in the moderation models. Using residualized variables in this way statistically eliminates gene-environment correlation from the model because the genetic and environmental effects have been partialled from one another. We used the same method to calculate residualized polygenic score and peer deviance variables for our peer deviance analyses. The moderation effect for parental knowledge continued to be statistically significant; however, the moderation effect for peer deviance trended in the same direction but was not statistically significant (unstandardized regression coefficients (b) and p-values (p) for interaction terms: b = 1.33, p = 0.05 and b = 0.66, p = 0.14, respectively) when we used residualized values in our analyses. To control for gene-environment correlation in our parental knowledge analyses, we used residualized polygenic score and parental knowledge variables in our model. To calculate residualized variables, we regressed polygenic scores onto parental knowledge ) in our analyses. The interaction effects trended in the expected direction for parental knowledge and peer deviance, albeit failing to reach significance (unstandardized regression coefficients (b) and p-values (p) for interaction terms: b = 0.51, p = 0.07 and b = 0.30, p = 0.10, respectively). As a set, these supplementary analyses demonstrate that the moderation effects were modestly attenuated after controlling for gene-environment correlation and changing the scale of the alcohol problems outcome variable, but they continued to trend in the same direction and did not entirely go away. To test whether our interaction effects could be attributed to the scale of the alcohol problems measure, we used a log-transformed version of the measure (SCL6A4(5-HTT) who took part in a family-based prevention-intervention program aimed at increasing family cohesion were less likely to initiate risk behaviors  across a 29-month period compared to their counterparts in the control condition [Although the causal relationships among the genetic and environmental variables examined here are unknown, we note that early findings from genetically-informed randomized prevention studies suggest that efforts aimed at reducing environmental risk factors for adolescent alcohol use and related behavior problems may be particularly effective for those who are genetically predisposed toward developing such problems. For example, adolescents with either the short/short or short/long genotype of ondition . Examinip ≤ 0.0001) from the ALSPAC GWAS. We selected this relatively stringent p-value threshold in view of the computing resources required to perform the permutation analyses described below. Of the 311 SNPs meeting this threshold in ALSPAC, 279 (90%) were available in FinnTwin12. We pruned by LD in the FinnTwin12 sample in order to reduce the set to include only independent (r2 < 0.50) SNPs, which resulted in 76 SNPs. Because LD calculations should be made on independent individuals, we used a randomly-selected sample of independent individuals in the FinnTwin12 sample (n = 634) for this purpose. We then permuted the phenotypic and covariate information for these individuals 100,000 times while keeping the genotypic information (LD) unchanged. For each of these permuted datasets, we examined gene-environment interaction effects for parental knowledge and peer deviance. To calculate empirical p-values, we used the equation (R + 1)/(N + 1). R is the number of permutations where the sum of the absolute value of the t-scores for significant SNP interaction effects (p < 0.05) exceeded the sum of the absolute value of the t-scores for significant SNP interaction effects in the observed data. N is the number of permutations .The polygenic analyses indicated significant gene-environment interaction effects with parental knowledge and peer deviance, and we used set-based analyses to probe whether the individual top SNPs contributing to our polygenic scores were themselves enriched for gene-environment interaction. We examined this question using the set of top SNPs  predicted a higher number of adolescent alcohol problems in an independent, population-based sample. In addition, environmental factors in adolescence moderated these polygenic predispositions. Genetic predispositions were more important under conditions of low parental knowledge and high peer deviance. These gene-by-environment interactions, although small in magnitude, are consistent with previous findings from studies that show that environments low in social control or high in social opportunity permit the expression of genetic predispositions . In cont


K) was found. The GLM model was used to describe the association between SSR and agronomic traits. Fifty-one SSR markers associated with agronomic traits were observed. Twelve single-associated markers were associated with PH; six single-associated markers were associated with BCS; eight single-associated markers were associated with SW; five single-associated markers were associated with SC; seven single-associated markers were associated with SCP; three single-associated markers were associated with SL. Especially, we observed that the genetic variation of SW was explained 11.6 % by M37 marker. It is interesting to observe that nine markers  were associated with more than two agronomic traits. Notably, Branch count per spike (BCS) was explained by four markers  exceeding 10 %. These identified marker alleles associated with agronomic traits could provide important information and markers for molecular-assisted breeding that facilitate the breeding process in tall fescue.Tall fescue is widely used in temperate regions throughout the world as a dominant forage grass as well as a turfgrass, in pastoral and turf industry. However, the utilization of tall fescue was limited because of its leaf roughness, poor regeneration ability and poor stress resistance. New cultivars were desirable in modern pastoral industries exceed the potential of existing cultivars. Therefore, well understanding the agronomic traits and describing germplasms would help to overcome these constraints, and morphological evaluation of tall fescue germplasm is the key component in selecting rational parents for hybridization breeding. However, describing the morphological traits of tall fescue germplasm is costly and time-consuming. Fortunately, biotechnology approaches can supplement conventional breeding efforts for tall fescue improvement. Association mapping, as a powerful approach to identify association between agronomic traits and molecular markers has been widely used for enhancing the utilization, conservation and management of the tall fescue germplasms. Therefore, in the present research, 115 tall fescue accessions from different origins , were evaluated for agronomic traits and genetic diversity with 90 simple sequence repeat (SSR) markers. The panel displayed significant variation in spike count per plant (SCP) and spike weight (SW). However, BCS performed the lowest CV among all the observed agronomic traits. Three subpopulations were identified within the collections but no obvious relative kinship ( Festuca arundinacea Scherb.) is an important hexaploid (2n = 6x = 42) perennial cool-season grass  from the STRUCTURE output and an ad hoc statistic ΔK, which was based on the second-order rate of change in LnP(D) between successive K values [The Bayesian model-based clustering method carried out in STRUCTURE 2.3.1 software  was emplK values . 15 indeMean agronomic traits of the 7 quantitative traits  were subjected to association analysis with SSR loci, based on the whole set of 115 accessions used in this study.P<0.01).Association analysis between the markers and the agronomic traits were performed based on the general linear model by using the software TASSEL 2.0.1 . The datThe experiment was arranged in a completely randomized block design with four replications. All agronomic traits data were averaged over two years. All data were performed with the SPSS statistical software package .Significant genotypic variation in agronomic traits was determined for the tall fescue accessions . Among aNei’s genetic diversity was 0.255, and average of polymorphism information content was 0.211 [A total of 1010 SSR alleles were obtained from the 90 SSR markers across the 115 tall fescue accessions with an average of 11.22 alleles per locus. The allele numbers of SSR marker varied from 3 to 27 alleles per marker, and all of the individuals were successfully distinguished by these bands . The genas 0.211 .According to STRUCTURE analysis results based on Bayesian clustering approach model, a significant population structure was detected among the 115 gene bank accessions. Three structure groups  were identified in the collection of 115 tall fescue accessions .P < 0.05). With regard to agronomic traits, twelve single-associated markers were associated with PH , six single-associated markers were associated with BCS , eight single-associated markers were associated with SW , five single-associated markers were associated with SC , seven single-associated markers were associated with SCP , three single-associated markers were associated with SL . Especially, we observed that the genetic variation of SW was explained 11.6% by M37 marker.Association mapping was conducted to find SSR markers potentially associated or linked with agronomic traits. Many marker alleles were associated with agronomic traits in our study. P < 0.05, and nine multi-associated markers were associated with more than one trait. For example, M3 and M4 were associated with three traits . Four markers, M1, M2, M35 and M54, were associated with both BCS and SC. Meanwhile, three markers, M19, M40 and M193 were associated with two traits . With regard to agronomic traits, the number of loci associated with each trait ranged from one (PD SL and SCP) to six (BCS and SC). Two markers were associated with PH , six markers were associated with BCS and SC , three markers were associated with SW (explained by each marker ranged from 3.2% to 5.8%), and only one marker was associated with SL, PD, and SCP . Especially, the BCS variation was explained 12.6% by M1 marker.The estimation of genetic variation with species is crucial for their conservation and utilization . GeneticCV is a helpful indicator for selection of phenotypic variants of interest for breeding purpose. SCP and SW exhibited the greater CV, 55.6% and 48.3%, respectively, which in turn suggests good potential for selection in a breeding program. The SCP and SW are two determinative factors for high yield . Thus, tThe population structure separation from the genetic linkage as causes for marker-trait association is considered to be the key factor in association analysis. Accessions with diverse geographical origins, the germplasm panel may contain either population structure , or familiar relatedness (from the recent co-ancestry) . As popuWith regard to our research, population structure was observed among 115 tall fescue accessions. Three groups were divided among the entire tall fescue population. The main subpopulation was consisted of 87 wild accessions from European, America, and all commercial cultivars. The wild accessions from Africa and Asia were separated into the second subpopulation. The third subpopulation was only consisted 11 wild accessions, with 5 accessions from Africa. According to the previous studies, we can attribute this phenomenon to different evolutionary paths and ecological adaptation , 56.As the successful utilization in maize, durum wheat, spring wheat, sugar beet, rice, grape and forage grasses , 27, theOryza sativa L.), and reported that 76 significant (P<0.5) trait—marker associations were detected, and 11 significant associations had >10% explained ratio of genetic variation. 390 linseed (Linum usitatissimum L.) and 464 SSR markers were employed by Soto-Cerda et al. [Hordeum vulgare L.) germplasm, and seven marker locis were detected that associated with plant height, and only one marker was observed that associated with stem diameter. The percentage of the total variation explained by each marker ranged from 4.59% (HVM2 associated with plant height) to 17.48% (Bmac 90 associated with density of main spike) [Association mapping presents the opportunities to observe the genetic variation in natural population . Zhang en spike) . NotablyWith regard to agronomic traits analyzed in the present study, the following nine markers were associated with more than two agronomic traits: M1 and M2 (was associated with BCS and SC); M3 and M4 ; M19 (was associated with PH and PD); M35 (was associated with BCS and SC); M40 (was associated with SW and SCP); M54 (was associated with BCS and SC); M193 (was associated with PH and SL). These markers were considered for greatly utilization to correlate agronomic traits that have not been reported, alternatively, were confirmed the suspect of the correlation with agronomic traits, and these correlation need more investigation. Notably, M3 and M4 were associated with BCP, SW and SC, suggesting that these three traits might correlate to each other. All the six markers, M1, M2, M3, M4, M35 and M54 were associated with BCS and SC, suggesting that these two traits might have significant correlation. Four significant associations had > 10% explained ratio of genetic variation, and these markers should be well applied in future tall fescue breeding.K) was found. The GLM model was the best model to describe association between SSR and agronomic traits. Fifty-one SSR markers associated with agronomic traits were determined. It is interesting to observe that nine markers  were associated with more than two agronomic traits. Notably, BCS was explained by four markers  exceed 10%. It is necessary for tall fescue selection breeding because these markers would enhance efficiency in desirable allele selection.In summary, association mapping has become a powerful tool for identifying genes and markers linked to agronomic traits. Large variation in SCP, SW was found among accessions of tall fescue. Three subpopulations were identified in the collection but no obvious relative kinship Click here for additional data file.


Haematological traits are important traits that show associations with immune and metabolic status, as well as diseases in humans and animals. Mapping genome regions that affect the blood cell traits can contribute to the identification of genomic features useable as biomarkers for immune, disease and metabolic status. A genome-wide association study (GWAS) was conducted using PorcineSNP60 BeadChips. Single-marker and Bayesian multi-marker approaches were integrated to identify genomic regions and corresponding genes overlapping for both methods. GWAS was performed for haematological traits of 591 German Landrace pig. Heritability estimates for haematological traits were medium to high. In total 252 single SNPs associated with 12 haematological traits were identified . The Bayesian multi-marker approach revealed 102 QTL regions across the genome, indicated by 1-Mb windows with contribution to additive genetic variance above 0.5%. The integration of both methods resulted in 24 overlapping QTL regions. This study identified overlapping QTL regions from single- and multi-marker approaches for haematological traits. Identifying candidate genes that affect blood cell traits provides the first step towards the understanding of the molecular basis of haematological phenotypes. Genome-wide associational study (GWAS) has become a powerful genomics tool for mapping genetic loci associated with common diseases and quantitative traits. Haematological traits are important in the sense that they can reflect the immune status and healthy conditions and be utilized as biomarkers in human and animals . Pigs arHaematological traits include three components, leukocytes, erythrocytes and platelets, which are markers of immune and/or inflammatory responses , 5. A feIn the present study, we report GWAS for haematological traits of 591 performance-tested pigs from commercial herds of German Landrace (DL) using the PorcineSNP60 BeadChip . The Bayesian multi-marker approach was integrated with single-marker regression analyses to identify genomic regions and corresponding genes overlapping for both methods.Animal care and tissue collection procedures followed the guidelines of the German Law of Animal Protection, and the experimental protocol was approved by the Animal Care Committee of the Leibniz Institute for Farm Animal Biology (FBN). Animals (n = 591) of a German Landrace (DL) herdbook herd were kept at the Experimental Farm of the FBN. Animals were fed ad libitum. Samples were collected from the pigs at an average age of 170 days at the experimental slaughter facility of the FBN after electronarcosis followed by exsanguination. Veterinary inspection of the animals before and of carcasses and organs after slaughter ensured that only animals without any impairment, disease symptoms or inflammatory and pathological signs were considered, thus avoiding any bias of blood phenotypes. Haematological traits  according to the manufacturer's SNP Infinium HD assay protocol. In brief, 200 ng of DNA were amplified, fragmented, and hybridized to the PorcineSNP60 BeadChip containing 62,163 locus-specific 50-mers that are covalently linked to beads distributed on the microarray surface. Single-base extension of captured oligos allowed the incorporation of labelled nucleotides that were detected by Illumina iScan, and images were subsequently converted to intensity data. The intensity data were normalized and the cluster position, genotype, and quality score were assigned using the GenomeStudio software (Illumina Inc.). Quality control steps were applied including removing samples with call rates < 99%. Markers with low minor-allele frequency (< 5%) were excluded. Markers that strongly deviated from Hardy-Weinberg equilibrium (p < 0.0001) were also filtered out. The average call rate for all samples was 99.8% ± 0.2. All markers on the PorcineSNP60 BeadChip were mapped to the porcine reference genome, Sscrofa 10.2.Haematological traits were analysed for an association with SNPs using a mixed-model analysis of variance in JMP Genomics . Mixed-model analysis tests an association between traits and a single SNPs and simultaneously adjusts for population structure and family relatedness . The genPrior to the analyses, the genotype matrix was processed using fastPHASE (version 1.2) to impute missing genotype information . Bayesia591 pigs of commercial German Landrace (DL) were analysed for the following haematological traits: White blood cell count (WBC), Red Blood Cell Count (RBC), Haematocrit level (HCT), Haemoglobin concentration (HGB), Mean Corpuscular Volume (MCV), Mean Corpuscular Haemoglobin , Mean Corpuscular Haemoglobin Concentration , Red Distribution Width (RDW); Platelets (PLT); Mean Platelet Volume (MPV); Plateletcrit (PCT); and Lymphocytes count (LYM). Means of raw data, variance components and heritability estimates generated by the BayesC analyses for the haematological traits are listed in After filtering, 48,909 SNPs were retained for the subsequent GWAS by both single-marker analysis and Bayesian multi-marker approach. For single-marker analysis, a total of 252 SNPs associated with 12 haematological traits were revealed at significance levels of negative log 10 of p value > 5 See: , Fig 1. TOX2) gene locus (P = 1.6x10-6 and P = 2.7x10-6) as shown in Manhattan plots of WBC (DDX21) and storkhead box 1 (STOX1) and on SSC1 in lipase C, hepatic type (LIPC) (KDM6A) and micro RNA (miR-221 and miR-222) . The other 2 SNPs (ALGA0095413 and ASGA0091117) located on SSC17 (51.9–52.2 Mb) in the vicinity of the TOX high mobility group box family member 2 (s of WBC . Bayesias of WBC . The ovee (LIPC) . It is ne (LIPC) . The othmiR-222) .We performed GWAS for the following seven erythrocyte-related traits: RBC, HCT, HGB, MCV, MCH, MCHC and RDW. The single-marker approach revealed a number of genetic loci that were significantly associated with RBC (9 loci), HCT (1 loci), HGB (4 loci), MCV (98 loci), MCH (78 loci), MCHC (5 loci) and RDW (27 loci) at significance thresholds of FDR < 5% and NegLog10  > 5. The detailed information is shown in additional file 1: NUP214), and on SSC6 (136 Mb) in ribonucleoprotein, PTB binding 2 (RAVER2). The QTL region for RBC with the largest effect was found on SSC16 (5–5.9 Mb) explaining 1.36% of the additive genetic variance (MARCH11) and C7H14orf2. The SNPs on NUP214 also associated with HCT (p = 4.2x10-6). A region with the largest QTL effect for HCT accounting for 1.82% of the additive genetic variance encompassed the potential candidate genes family with sequence similarity 13 member A (FAM13A), HECT and RLD domain containing E3 ubiquitin protein ligase 3 (HERC3) and PIGY upstream reading frame (PYURF). The results also showed some discrepancies between the two GWAS analysis methods such as HGB of which most significant single SNPs were located on SSC15 (141.3 Mb), while multi-marker analysis detected significant loci on SSC5 (4.1–4.9 Mb) and SSC16 (5.0–5.9 Mb).Interestingly, overlaying the results from both analysis methods consistently discerned RBC associated regions located on SSC1 (305.1–305.2 Mb) physically linked to nucleoporin 214 (variance . This reEFNB1) gene (p = 9.3x10-10 for MCH and p = 5.6x10-8 for MCV), and on SSC1 (90–105 Mb), covering interesting genes like inhibitor of Bruton tyrosine kinase (IBTK) and CD109 molecule (CD109). On SSC17 (50.9–52.9 Mb) the region spanning the l(3)mbt-like 1 (L3MBTL1)- MYB proto-oncogene like 2 (MYBL2)-TOX2- serine incorporator 3 (SERINC3) locus showed effect on MCH as revealed by both single- and multi-marker analyses. For MCV a wide QTL region was identified on SSC2 including 4 significantly associated SNP and further supported by two windows exceeding the threshold in Bayesian GWAS , peaking in the ephrin B1  and ectodysplasin-A (EDA). By means of multi-marker analysis, a region on SSCX (2–2.8 Mb) explained the most of variance for MCV (1.57%), MCH (2.72%) and RDW (3.16%); the locus was physically linked with the transcript of neuroligin 4, X-linked (NLGN4X) (GRIA1), SSC4 (Fc receptor like 3 (FCRL3) and SSC14 (fibronectin type III and ankyrin repeat domains 1 (FANK1) associated with MCHC. In contrast, multi-marker analysis of MCHC revealed QTL on SSC7 (40–41 Mb) and SSC15 (140.0–140.9 Mb) which cover KN motif and ankyrin repeat domains 4 (KANK4) and eukaryotic translation initiation factor 2B subunit alpha (EIF2B1).Eleven SNPs significantly associated with MCV, MCH and RDW and located on SSCX (49.5–63.2 Mb), covering the region of (NLGN4X) . Further-7 and p = 9.1x10-8. By multi-marker analysis, QTL with the largest effect identified was on SSC18 at 0–1 Mb, follow by SSC1 at 284–285 Mb which explained 1.23% and 1.14% of the additive genetic variance of MPV, respectively. The SNP ASGA0030815 on bone morphogenetic protein 6 (BMP6) (SSC7) and MIGA0002922 on zinc finger, GATA-like protein 1 (ZGLP1) (SSC2) were associated with both PLT and PCT. Moreover, the region on SSC2 (69.1–69.8 Mb) was also confirmed by multi-marker analysis (IGF2R) gene (SSC1) was highly associated with PCT with p = 6.7x10-7; this region  was also confirmed by the multi-marker analysis with the greatest effect that explained 5.13% of the additive genetic variance and hosting 2 interesting transcripts IGF2R and MAS1 proto-oncogene (MAS1)  .The proportion of phenotypic variance explained by markers is considered as a measure of heritability and therefore indicates whether these traits are heritable. Heritability estimates for haematological traits in our study were shown to be moderate for WBC (0.23) which is in line with previous studies in Large White and Yorkshire pigs , 19. OurKDM6A, miR-221 and miR-222, and SSC14 (77–79 Mb) at STOX1 and DDX21. KDM6A (histone 3 lysine 27 demethylase UTX) was identified as a novel regulator for haematopoietic cell migration by using haematopoietic stem and progenitor cell [TOX2. TOX2 is a transcription factor belonging to the TOX family that shares a highly conserved high mobility group DNA-binding domain with the other TOX members. As recently reported, TOX2 regulates human natural killer cell development by controlling T-BET expression [TOX2 as a candidate gene for WBC counts.In total, the single-marker GWAS by GLM analysis revealed 252 SNPs associated with 12 haematological traits at significance levels of 5% FDR, whereas the Bayesian approach detected altogether 102 QTL regions across the genome for 12 haematological traits using a 1- Mb window and the genetic variance above 0.5%. The multi-markers method was challenged to avoid false positives and overestimation of QTL effects derived from single-SNP analysis; accordingly, Bayesian approaches involving SNP-windows to reflect QTL have been applied in many studies before –22. To ttor cell . Consistpression . RecentlQTL for MCH and MCV were reported in many pig chromosomes in particular on SSC2 at 55–60 Mb  and SSC8EFNB1, TLE4 and OPA1. In particular, identified regions on SSCX at 2.0–2.8 Mb and 62.1–62.7 Mb include NLGN4X and EFNB1 as interesting candidate genes. NLGN4X encodes a protein which belongs to a family of neuronal cell surface and causal factors for monogenic autism as well as directly impacts neurodevelopmental processes during the formation of neurons and their connections [EFNB1 encodes a membrane protein that acts as a ligand for Ephrin receptor tyrosine kinases and thus play a potential role in modulating blood pressure [EFNB1 was detected in leukaemia cell lines and bone marrow and was shown to be involved in normal haematopoietic development and tumorigenesis [TLE4. The TLE family of genes is a group of highly conserved transcriptional corepressors that are involved in myeloid cell proliferation and survival [OPA1 gene encodes a dynamin-like mitochondrial GTPase OPA1and plays a significant role in mitochondrial structure, maintenance and fusion. In addition, OPA1 –is involved in the regulation of energetic metabolism and cell death, which underscores its multiple physiological roles [MFN2 and OPA1 become up-regulated and functional to promote fusion [In this study, a region on SSC8 at 128 Mb was found significantly associated with MCV by means of single-marker analysis. Interestingly, Bayesian approaches persistently detected an associated effect for MCV, MCH, and RDW on SSC1, SSC13 and SSCX which constitute some plausible candidate genes contributing to red blood cell traits via haematopoietic mechanisms including nections , 33. EFNpressure . EFNB1 wigenesis , 36. Thesurvival . Tle4 knsurvival . Anothersurvival . The OPAal roles . Recentle fusion .NUP214. The gene is a member of the FG-repeat-containing nucleoporins and known to be fused with the DEK gene on chromosome 6 in a t-translocation associated with acute myeloid leukemia and myelodysplastic syndrome [In a previous study, the QTLs for RBC were mapped on the end of SSC1 . Regardisyndrome .IGF2R gene . The IGFs have an important role in physiologic and neoplastic processes as well as normal and malignant development of the haematopoietic system [ZGLP1  is a strong positional candidate gene for both PCT and PLT but there is still limited knowledge about the function of this gene. GWAS of PLT further revealed the BMP6 gene which is the key endogenous regulator of hepcidin, an iron homeostasis gene [The highly significant SNPs and regions, that both approaches detected s to be associated with platelet traits (PCT), were physically linked to the c system , 44. Recc system . ZGLP1  > 5 obtained by single-marker analysis. Haematological traits, marker name, chromosome, position according to Pig Genome Annotation 10.2, major/minor allele, % variance explained, p-value (NegLog10), positional candidate gene.(XLSX)Click here for additional data file.S2 TableList of 1-Mb windows with % variance explained > 0.5%. Haematological traits, chromosome, position of start and end of window according to Pig Genome Annotation 10.2, % of the additive genetic variation explained by the QTL regions associated with the traits, number of SNPs in the QTL regions, regions overlapping between the results of single-  and multi- (Bayesian approach) marker genome- wide association analyses.(XLSX)Click here for additional data file.


Owing to recent improvement of genotyping technology, large-scale genetic data can be utilized to identify disease susceptibility loci and this successful finding has substantially improved our understanding of complex diseases. However, in spite of these successes, most of the genetic effects for many complex diseases were found to be very small, which have been a big hurdle to build disease prediction model. Recently, many statistical methods based on penalized regressions have been proposed to tackle the so-called “large P and small N” problem. Penalized regressions including least absolute selection and shrinkage operator (LASSO) and ridge regression limit the space of parameters, and this constraint enables the estimation of effects for very large number of SNPs. Various extensions have been suggested, and, in this report, we compare their accuracy by applying them to several complex diseases. Our results show that penalized regressions are usually robust and provide better accuracy than the existing methods for at least diseases under consideration. Accurate disease prediction is a central goal of clinical genetics, and much effort has been made to utilize the large-scale genetic data for a disease prediction model for complex disease. However, except for the fully penetrant Mendelian disorders, effect sizes of most disease susceptibility loci identified by genome-wide association studies (GWAS) are usually modest  and the The statistical methods for the disease risk prediction at the early stage were based on gene scores –6. CausaAs an alternative to gene score-based approach, one may consider statistical learning methods in disease risk prediction. Statistical learning algorithms have been successful over the past decades in various learning tasks including text categorization, fraud detection, character and image recognition, natural language processing, and marketing. Disease risk prediction can be naturally posed as a classification problem, and support vector machines (SVMs)  and enseRecently, various penalized methods have been proposed to resolve the large P and small N problem. Examples include convex penalizations such as ridge –17 and LIn this report, we compare the performances of various nonpenalized and penalized methods in the prediction of diseases on data from Korea Association Resource (KARE) project that is a part of Korea Genome Epidemiology Study. We select individuals with extreme phenotypes among the participants in KARE project and consider the type 2 diabetes, obesity, hypertension, and three smoking-related phenotypes. The predictive performances of those nonpenalized and penalized methods are compared by area under the curve (AUC). Our results indicate that penalized methods tend to yield more accurate predictions than nonpenalized methods although their relative performances depend on particular diseases.p-values for Hardy-Weinberg equilibrium (HWE) less than 10−6, with genotype call rates less than 95%, or minor allele frequencies (MAF) less than 0.01, and 352,228 SNPs were left for subsequent analysis. Individuals with low call rates , high heterozygosity , gender inconsistencies (n = 41), or serious concomitant illness (n = 101) were excluded from analysis. We considered independent samples and excluded related or identical individuals whose computed average pairwise identical in state value was higher than that estimated from first-degree relatives of Korean sib-pair samples . In total, 8,842 individuals were analyzed. From randomly selected 20 duplicate samples, we found that genotype concordance rates exceeded 99.7%, with no single SNP excessively discordant. The population substructure was handled with EIGENSTRAT approach [The KARE project, with 10,038 participants living in Ansung  and Ansan (urban), was initiated in 2007 for large-scale GWAS based on the Korean population. Among the participants, 10,004 individuals were genotyped for 500,568 SNPs with the Affymetrix Genome-Wide Human SNP array 5.0. We discarded SNPs with approach  and we cT2D mainly occurs in people aged over 40, and it is diagnosed with level of glucose and hemoglobin a1c (hba1c) in blood. In our studies, individuals were selected as being affected with type 2 diabetes if their hba1c are larger than 6.5, fasting plasma glucoses are larger than or equal to 126, or 2-hour postprandial blood glucoses are larger than or equal to 200. In total, there were 1182 affected individuals, and 2364 individuals not satisfying the condition for type-2 diabetes and older than the other unaffected individuals were considered as controls. As environmental variables, we considered area (Ansan/Ansung), sex, age, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), triglyceride, and ten PC scores.Obesity status was determined by BMI. Individuals were considered as cases if their BMIs are larger than 27, and there were 1022 affected individuals in KARE cohort. We also selected 2325 individuals with BMIs less than 27 and older than the other unaffected individuals as controls. We considered area, sex, age, height, waist-hip ratio, SBP, DBP, high density lipoprotein, triglyceride, and ten PC scores as environmental variables.Hypertension status was determined by SBP and DBP. 1035 individuals with SBPs and DBPs larger than 140 and 80, respectively, were considered as cases. 2290 individuals whose SBPs and DBPs were less than 120 and 80, respectively, were selected as controls. Environmental variables considered were area, sex, age, BMI, and ten PC scores.For smoking-related phenotypes, we considered only male samples for predicting smoking behaviors because the number of female smokers was very small. CPD was defined to detect the nicotine dependence of each individual. Individuals whose number of cigarettes per day was larger than 20 were defined as being addicted to nicotine, and 333 individuals were selected as cases. Individuals were chosen as controls if the number of cigarettes per day was less than 10, and 375 males were chosen as controls. Environmental variables were area, age, BMI, waist-hip ratio, triglyceride, SBP, and ten PC scores.Smoking status for each individual has four categories: never smoked, former smoker, occasional smoker, and habitual smoker. Males who never smoked were defined as controls, and males who occasionally or habitually smoke were defined as cases. There were 3357 cases and 807 controls, and the same clinical variables as in CPD were environmental variables.SC was defined with smoking status as SI, but we used different categories for cases and controls. Males who never smoked were defined as controls, and males who occasionally or habitually smoke or smoked before were defined as cases. The numbers of cases and controls are 2064 and 1293, respectively, and environmental variables were the same clinical variables as in CPD.yi be a dichotomous phenotype for individual i, and affected and unaffected individuals are coded as 1 and 0, respectively. The sample size is denoted as n = na + nu, where na and nu denote the numbers of cases and controls, respectively. We assume that there are p1 genetic variants and p2 environmental variants including an intercept. Therefore the total number of variables is p = p1 + p2. xi denotes a vector with p covariates for individual i, and the coded genotypes of the kth variant and the lth environmental variable were denoted by xik1 and xil2, respectively. The coefficient vector of p covariates is denoted by β.Let n individuals where na cases and nu controls with extreme phenotypes were chosen, and the relative ratios of na to nu are assumed to be equal to their ratios between all available cases and controls in KARE cohorts. We evaluated the accuracies of the disease risk prediction models for different choices of n. Accuracies of the disease prediction models were assessed via 10-fold cross validation, and AUCs were calculated with 10 replicates. All individuals were randomly divided into 10 different subgroups with the same number of cases and controls. Each subgroup was used as test set once across ten replicates and therefore there is no overlap between test set in different replicates.To see the effect of sample size, we selected p1 genetic variants by the order of F-ratio from train set. If we let kth variant for individuals with phenotype l and denote the overall mean expression level of the kth variant by F-ratio of the kth variant [p1 genetic variants and p2 environmental variables on train set and apply the prediction model to test set.Numbers of available genetic markers seem to be related to the prediction accuracy, and different numbers of genetic variants were selected to build the disease risk prediction model. We choose the top  variant , 24 is dF-ratio [α = 0.05 level are summed to calculate GRS, and GRS and environmental variables were incorporated into the logistic regressions as covariates to build the final disease risk model on train set. The disease risk scores are calculated for individuals on test set and its accuracy of disease risk prediction model is evaluated.The marginal effects of covariates are tested with F-ratio , 24. ThePolygenic effects explained by available SNPs can be modeled by the linear mixed model whose variance covariance matrix is parameterized with the genetic relationship matrix –27, and p dimensional coefficient vector β = t can be estimated by minimizing the penalized negative log-likelihood:Jλ is a penalty function and λ is a vector of tuning parameter that can be determined by a search on an appropriate grid. Each penalized regression requires the estimation of λ, and 100 grid points of λ were considered from “glmnet” function in R for all the methods.Various penalized methods have been recently proposed, and we consider five penalized methods in our comparison: ridge , LASSO , elasticλ, and the resulting ridge estimates tend to have a smaller variance than least square estimates. In particular, ridge regression can be conducted even when p is much larger than n, where the least square method does not have a model identifiability. However, ridge estimates have a drawback in the interpretation of the final model because all the covariates are included in the final model regardless of the choice of λ. Hence, ridge regression must be conducted together with an extra selection process such as stepwise subset selection or truncation methods.In linear regression, estimates from least square method are quite unstable under severe multicollinearity because of their large variances. Ridge penaltyard zero . Ridge pλ [LASSO was proposed by Tibshirani  to achieameter λ . LASSO oameter λ , 36, andameter λ  because ameter λ –40. Howeameter λ –43. For ameter λ , and is ameter λ . To remeameter λ .a. Elastic-net has more desirable properties than LASSO and ridge. For instance, ridge tends to keep all the covariates in the final model and hence is undesirable when there are many noncausal variants. In contrast, LASSO cannot select larger number of covariates than the sample size and tends to select a single covariate among highly correlated covariates. However, by choosing appropriate λ and a, elastic-net enables us to have balanced estimates, producing a slightly more complex model than LASSO but far simpler model than ridge. Also it achieves a grouping effect [Elastic-net penalty proposed by Zou and Hastie  is a cong effect  on highlg effect .a = 50 is used for our own optimization algorithm. SCAD has several desirable properties over LASSO [The SCAD penalty introduced by Fan and Li  is(6)∂Jer LASSO . First, er LASSO , achievier LASSO . Second,er LASSO ; that iser LASSO , its estλ and then truncate them with truncating level a. Hence TR declares the ridge coefficients whose absolute values smaller than a as zero and keeps the other large coefficients intact. An appropriate choice of truncating level enables us to identify a correct model while the final estimates still keep the same shrinkage property as ridge [a.As we mentioned above, ridge cannot be directly used in identifying important covariates. However, TR  can prodas ridge –35, and h2, explained by genotyped variants with GCTA program [h2 for binary traits was estimated with all available samples by using default options, and h2. In particular, the proportion between cases and controls for each phenotype is different from true prevalence, and the ascertainment bias often happens. However the performance of each method may be related to unadjusted estimates of h2 and ascertainment bias was not taken into account. According to h2 for CPD10 is large, and genetic components for all smoking-related phenotypes seem relatively less informative.To see the differences of penalized methods, we calculated AUCs of those methods on test set and the number of nonzero coefficients as a function of sample size.  program , 49. h2n and p1. Both approaches do not consider the joint effects among multiple causal SNPs. GRS assumes that effect sizes for causal SNPs are homogeneous and MultiBLUP assumes that sums of each SNP affect the normal distribution. However, penalized methods estimate individual effects of each SNP by shrinking each coefficient. This may explain the superiority of penalized methods over nonpenalized methods, but if those assumptions for nonpenalized methods are satisfied, they may perform better than penalized methods approaches. Interestingly MultiBLUP performs better than GRS except OB if n is larger, and AUC improvement of MultiBLUP for larger n is more substantial than GRS. Therefore, MultiBLUP seems to be more reasonable choice than GRS. Comparing overall performances of penalized methods, it can be seen that ridge and TR are the best, LASSO and elastic-net are the second, and SCAD is the last even though the performance of each method depends on specific diseases and the levels of n and p1. Regardless of n and p1, ridge was the best performer even for small n for all phenotypes except SI. For SI, it seems that the performance of ridge depends on n rather than p1. TR virtually has almost the same prediction accuracy, and a, is around 0.001, and thus the effect of truncation parameter on model complexity is almost negligible for these data sets. However, p1 and, in particular, are large even when n is small, which indicates that AUCs of TR depend less on n than ridge. Robustness of TR can be partially explained by smaller model complexity than ridge in n ≤ 800 and 46.6 SNPs for p1 ≤ 800) but achieves higher prediction accuracy than ridge when n is less than 800. However, when p1 = n = 1600, TR selects the same number of SNPs as ridge. Thus, we can conclude that the effect of truncation parameter diminishes for large n, which explains higher prediction accuracy when n is small.n and p1 in prediction accuracy and model complexity for whole phenotypes except SI, and their AUCs are proportionally related to n but inversely related to p1. Although their prediction accuracies are lower than those of ridge and TR for small n, they perform as well as ridge with small numbers of SNPs for large n. For instance, LASSO includes about 100 SNPs for n = 1600 and p1 = 200 and about 500 SNPs for n = 1600 and p1 = 1600, which indicates that we can construct prediction models without using the whole SNPs. Elastic-net tends to behave quite similarly as LASSO and it selects slightly larger number of SNPs for whole phenotypes except SI.LASSO and elastic-net show relatively large dependency on n = 1600, SCAD performs as well as other methods while still keeping small number of SNPs. For instance, for obesity, AUCs of SCAD are virtually the best and select extremely sparse models that have only 7.3 and 3.4 SNPs for p1 = 200 and p1 = 1600, respectively. Therefore, we can conclude that SCAD is appropriate as long as relatively large number of individuals is available.In terms of model complexity, there are substantial differences among penalized methods. In this study we have considered five penalized and two nonpenalized statistical methods with six case-control datasets that are computationally feasible at the genome-wide scale. Each method was utilized to build the disease risk prediction model with different sample sizes and numbers of variants, and the accuracy of disease risk prediction models was evaluated with cross validation. Cross validation tends to overestimate the prediction accuracy, and results should be interpreted with care. A more reliable but time-consuming way is to compare the methods on random partitions of data. However cross validation does not have a strong preference towards a specific method and it may give us a rough idea on prediction accuracies of methods. According to our results, dense methods such as ridge and TR are usually more accurate than sparse methods such as LASSO and SCAD. For a large sample size, prediction accuracies from penalized methods are expected to be similar to that from ridge , 35, 50.a = 10, the choice of a = 50 led to the better prediction accuracy. These findings suggest that most of SNPs have a small causal effect on diseases considered in this report. In this sense, sparse methods such as SCAD may not be preferred for infinitesimal model [However, in spite of our comprehensive evaluations, various factors such as filtering conditions for SNPs or individuals, test statistic for prescreening, and ways of obtaining tuning parameters can affect the accuracy of the final risk prediction model, and depending on their choices, accuracy of disease risk prediction model can be substantially different. In this context, the 1-standard deviation rule  for tunial model  unless tIn this report we have compared various penalized regression methods. However, we have not considered more recent methods such as bootstrapping methods , 52, 53.n and p1 TR indicates the truncated ridge.Figure 1: AUCs from train set for T2D, obesity and hypertension AUCs for T2D, obesity and hypertension from train set were calculated for different n and p1. TRindicates the truncated ridge.Figure 2: AUCs from train set for CPD, SC and SI. AUCs for CPD, SC and SI from train set were calculated for different


Brachypodium distachyon. Exploiting population genetics, landscape genomics and genome wide association studies, we evaluate B. distachyon role as a natural probe for identifying genomic loci involved in environmental adaptation.The local environment plays a major role in the spatial distribution of plant populations. Natural plant populations have an extremely poor displacing capacity, so their continued survival in a given environment depends on how well they adapt to local pedoclimatic conditions. Genomic tools can be used to identify adaptive traits at a DNA level and to further our understanding of evolutionary processes. Here we report the use of genotyping-by-sequencing on local groups of the sequenced monocot model species Brachypodium distachyon individuals were sampled in nine locations with different ecologies and characterized with 16,697 SNPs. Variations in sequencing depth showed consistent patterns at 8,072 genomic bins, which were significantly enriched in transposable elements. We investigated the structuration and diversity of this collection, and exploited climatic data to identify loci with adaptive significance through i) two different approaches for genome wide association analyses considering climatic variation, ii) an outlier loci approach, and iii) a canonical correlation analysis on differentially sequenced bins. A linkage disequilibrium-corrected Bonferroni method was applied to filter associations. The two association methods jointly identified a set of 15 genes significantly related to environmental adaptation. The outlier loci approach revealed that 5.7% of the loci analysed were under selection. The canonical correlation analysis showed that the distribution of some differentially sequenced regions was associated to environmental variation.B. distachyon adaptive variation, and may lead to the discovery of genes related to environmental adaptation in natural populations. Its application to a model species with a fully sequenced genome is a modular strategy that enables the stratification of biological material and thus improves our knowledge of the functional loci determining adaptation in near-crop species. When coupled with population genetics and measures of genomic structuration, methods coming from genome wide association studies may lead to the exploitation of model species as natural probes to identify loci related to environmental adaptation.We show that the multi-faceted approach used here targeted different components of The online version of this article (doi:10.1186/1471-2164-15-801) contains supplementary material, which is available to authorized users. One of the most ambitious objectives of natural variation studies is to provide a description of functional variability in natural populations . The abiDomestication and breeding, however, have caused a severe reduction of crop diversity, whose extant genetic variation is much smaller than that of their wild relatives , 11. ThiArabidopsis thaliana[Outlier detection relies on Wright’s fixation index Fst[association methods are based on marker - trait regressions and they directly target quantitative measures of the environment. The statistical framework of association methods is largely similar to that of genome-wide association studies (GWAS), which were originally developed in humans [The relation between genetic and climatic variation in natural populations has already been explored in humans , 15, andthaliana. Environthaliana, 18, thethaliana. This cathaliana. Outlierndex Fst to identndex Fst. Althougndex Fst, 24 and ndex Fst, outlierndex Fst. In addin humans  to map cn humans , 29, when humans .Populus[Teosinte[Medicago joined the association approach with an ex situ phenotypic evaluation, confirming the reliability of these methods [When considering organisms with limited displacing abilities such as plants, association methods might accommodate quantitative environmental data as a response variable rather than phenotypes, and map genomic associations with climate –33. WhilPopulus and TeosTeosinte, thus le methods . In all  methods . The deplandscape derives from geographical information systems (GISs), which can be used to couple quantitative geographical data with biological sampling [genomics, in fact, must first consider the disturbance caused by the many evolutionary forces other than selection [Merging population genomics and landscape data requires two sources of information. The sampling , 41 and sampling  link clielection , as wellelection . High-thelection  makes DNelection . Genotypelection  is a redelection .Brachypodium distachyon (L.) P. Beauv. Brachypodium distachyon is the leading model species for small grain monocots and temperate grasses [B. distachyon was deemed to have three distinct cytotypes of 2n = 10, 20 and 30 chromosomes: a recent study identified three different taxonomic entities, of which B. distachyon has the 2n = 10 chromosome set [B. distachyon genome (approximately 271 Mbp) was completely sequenced in the inbred line Bd21 [B. distachyon have already been extensively collected in Turkey, showing high intra-population homozygosity and a high level of inter-population genetic diversity [In this paper, we identify loci linked to environmental adaptation in Turkish accessions of the grass species  grasses , with an grasses . Until rsome set . B. distine Bd21 . Naturaliversity . This waB. distachyon individuals collected in nine locations scattered across a 1000-km transect in Turkey. By this, we wanted to exploit both methods developed in the landscape genomics field and in the GWAS community. Bringing landscape genomics closer to complex traits mapping, especially in an agronomical perspective, might open a significant perspective in the field. We employed a GBS approach to provide a genome-wide representation of molecular diversity in these B. distachyon individuals. The sampling locations were monitored on a GIS system to obtain climatic data for each individual, at the same time controlling for the spatial distribution of genetic diversity. The data was processed using the complementary characteristics of outlier and association approaches in order to identify signatures of adaptation at a molecular level.We explored the possibility of identifying the relation between climate and genomic features in a starting panel of 82 B. distachyon adaptation. We also found that transposable elements were differentially distributed across the genomes of local groups, some with a pattern matching the climatic diversity of the sampling transect.We found that the association and outlier methods mostly targeted soft and hard sweeps of selection, respectively. GWAS and landscape genomics method jointly identified 15 genes involved in B. distachyon could then be used as a natural probe to report functional variations in a broad set of environmental situations.Our method could be extended by including more genotypes and by targeting additional environments and environmental variables. Once the biological material is characterized, this might aggregate additional data and thus extend our capacity to understand the molecular bases of adaptation. Brachypodium distachyon local groups  [Additional file Nine Turkish ps Table  were samps Table  highlighx = 1,819.2; δ2 = 1,779.5). 26.48% of the full genome of Bd21 was masked when scanned with a library of B. distachyon specific transposable elements (TE). The masking proportion rose to 45.96% in the 8,072 P/A, and dropped to 14.52% when an equal number of non-P/A regions randomly drawn from the genome were considered  was 16,697. The comparison between the tested Bd21 inbred line and the Bd21 reference sequence produced only 148 polymorphisms (0.9%) of which 125 were due to heterozygous calls, thus supporting the correctness of SNP calling. The analysis of the distribution of reads showed that some genomic bins were consistently not sequenced in all samples sharing the same sampling area, whereas reads corresponding to the same bin were present in samples coming from other regions. 8,072 of such bins were characterized by either the presence or the absence of reads (P/A regions). Those regions were grouped into 4,911 continuous P/A regions spanning in length from the lowest arbitrary interval of 1,000 bp up to 22,000 bp  and easternmost (H) locales grouped with high confidence, in contrast to local groups D, F, G and partially E. Local groups C and C2, which were only 1.8 km apart, tightly clustered together but remained distinguishable, unveiling a low but detectable genetic differentiation at a small geographical scale.The full set of filtered SNPs was used to produce a phylogeny by neighbour joining (NJ) clustering of uncorrected P distances, which highlighted an unexpected convergence in distant geographical areas Figure . OverallThe 8,072 P/A regions were converted into binary markers on a local basis and used to calculate distances between local groups with Jaccard’s similarity index. The resulting tree Figure  is similst . This does not mean that the panel is not spatially clustered. In fact, quite the opposite is true: gene flow is low if not absent (as confirmed by Fst), especially between the two main clades also reported in the phylogeny. This is made clear by the incomplete population graph resulting from a spatial-aware molecular diversity analysis  were used to evaluate signals of environmental adaptation, controlling for false positives by considering the five cryptic genetic clusters identified by Structure. In parallel, a compressed mixed linear model (CMLM) considering kinship (K) and structure (Q) usually employed in GWAS analyses was also used to identify loci associated with climatic data. Kinship analysis confirmed the existence of two main genetic clusters already suggested by the previous analyses Figure . When thB. distachyon samples chosen for association analysis showed a genome organized into 654 LD blocks containing between 2 and 492 SNPs. Eighty SNPs were not associated with any LD block. After using Bonferroni correction over 734 independent tests, every SNP that yielded a p-value lower than 1.37 × 10−4  with environmental PC variables was deemed to be an environment-associated SNP (EAS). The manhattan plot in Figure http://www.ebi.ac.uk/interpro/), when available.The outcome of both the association analyses was filtered with a corrected Bonferroni criteria accounting for the dependency of statistical tests within linkage blocks identified by a linkage disequilibrium (LD) analysis. The set of 82 The LFMM approach identified alone 1035 genes, pointed by 901 genic EASs and 439 EASs in the 5 kb upstream predicted genes [Additional file The aim of our second approach was to identify genomic loci under selection by applying a Bayesian outlier detection method. This analysis identified 953 outlier loci at an FDR of 0.05 . A total of 708 unique loci were either 5kbp upstream (247) and/or inside (461) 490 unique genes [Additional file B. distachyon locales and environmental PCs. A canonical correlation analysis (CCA) was used to quantify whether the environment could explain the differential distribution of P/A regions. The triplot in Figure p < 2.2×10−16 after 999 permutations.The third approach focused on the relation between P/A regions differentially distributed among Bradi1g03700, a 60s ribosomal protein L36-3-like, is probably involved in expression control. In maize, the 60s subunit is involved in flooding responses [Bradi1g02575, bearing an oxidoreductase activity domain. PC1 also targets a MYB transcription factor (Bradi2g38560) a class of proteins involved in plant responses processes, including those to abiotic stresses. MYB are a strategic targets for crop improvement [Bradi1g71690, is likely involved in cellular signalling. Signalling is also contributed by Bradi3g28560 (transferase activity). This predicted gene encodes for a 3-ketoacyl-CoA synthase, whose elective biological processes include wax synthesis [http://www.uniprot.org). The energetic balance of the cell is possibly contributed by Bradi1g73170, a sucrose transmembrane transporter targeted by PC1, and Bradi4g04710, targeted by PC2 and involved in the mitochondrial respiration chain. Within 500 Kb of this locus, two outlier loci are found [Additional file We assayed the functional role of EASs detected by both association methods, as representatives of the strongest signal for adaptation Table . Environesponses  and mighrovement . Notablyynthesis  and respAlthough genome re-sequencing offers the most inclusive possible overview of the genomic variability of small genome species , 61, metIn our case, the persistence of phylogenetic relationships among samples when using P/A regions as genetic markers Figures  and 3 suIn both cases, the coverage of sequencing reads will show a gap as a result of the failure of the enzymatic cut. P/A regions are clearly enriched in TE, as demonstrated by the fact that the average TE content in those regions is significantly higher than that of the entire genome (45.96% versus 26.48%). The enrichment is even more dramatic when the TE content of regions not classified as P/A regions is taken into account . This evidence strongly suggests that TE displacement has a role in the P/A polymorphism.Two simple scenarios could be envisaged to explain the data: i) TEs inserted into the Bd21 reference genome after it separated from the other populations  thus giving rise to P/A polymorphism; ii) TEs are present in orthologous regions of both Bd21 and resequenced samples, but they are methylated only in some of the resequenced regions.B. distachyon natural populations. Our CCA analysis identified some of the P/A as being strongly related to environment, especially to PC1 and PC2 ? IBD is the direct consequence of the limited dispersal of alleles, causing populations that are spatially near to share more similarities with each other than populations far away . This phB. distachyon. This finding is in line with a previous survey performed with 43 SSR markers on 56 Turkish populations [B. distachyon accessions split into two distinct phylogenetic clades differing in terms of vernalization habits and morphological features without belonging clearly to a specific geographical area.Given the erratic nature of the sampling, we cannot rule out that patterns of gene flow between populations apply, at a finer scale, to IBD, as it is outside the scope of this work. However, the use of these results is a key feature for our association mapping approach. IBD, as in general spatial structuration, can mirror environmental association, leading to high rates of false positives. This was demonstrated in , where aulations , where BHowever, the absence of a diversity gradient did not rule out structuration. We thus performed our association approach by considering structuration in order to avoid overrepresentation of false positives. This was done both with a hierarchical structure and a PCA with LFMM and CMLM, respectively, and we showed that the two different approaches yield similar results though differing in magnitude in terms of the statistical association found.Our results are an empirical confirmation of what emerged in a simulation study testing the performance of five outlier-based and three correlation methods under explicit models for selection, demography and spatial relations . In thati.e. potentially involved in the regulation of gene expression) show an almost equal contribution to significant associations [In-gene polymorphisms are not the sole ones involved in environmental adaptation . In factciations . This alAn interesting point concerns the differences that emerged between outlier and association methods, which here report little loci in common. This result does not seem to fit the early tendency of seeing outlier loci as a confirmation for EAS validity and vice versa , 74. InsThe association approach is not dependent upon population genetic parameters, instead it targets a limited set of quantitative environmental characteristics. Complex traits targeted by means of correlative approaches, and especially those regarding climatic adaptation, are expected to reveal small changes in allele frequencies that push populations to a new optimum . In thisConversely, outlier methods do not depend, at least not directly, on environmental data. Loci identified by Bayescan but not by association methods might represent a set of loci under selection from factors not considered in the association analysis, such as fire regimes, soil composition, anthropic disturbance, grazing pressure, pathogens, and so on. Outlier methods are also affected by the assumptions about the null distribution used to compare loci, making the demographic history and structure of populations able to bias the outcome of the analysis , 80. We Brachypodium distachyon is currently based mostly on in silico models, and therefore needs a careful evaluation of the functional relevance of EASs, which was outside the scopes of our experiments. Yet, we identified a set of genes, including a MYB transcription factor pointed by association and outlier loci, which already suggests the potential downstream applicability of these methods. Owing to the nature of LD, however, a less-than complete coverage sequencing cannot achieve the single-gene definition in association: our analyses revealed that the genome of our B. distachyon collection could be split into 734 LD blocks. To achieve a higher definition, more recombination events should be sampled, i.e. more individuals are needed. This is one of the strengths of this approach: since it is modular it allows the stratification of environmental and biological data in an integrated framework to map for adaptation in B. distachyon.A closer evaluation of the genes related to EASs identified by both the association methods provided a varied set of putative functions Table . The annWe strongly support the application of next generation sequencing approaches to landscape genomics as a fast and modular tool for the discovery of adaptive traits, particularly in sequenced species. The application of landscape genomics to plants akin to crops can directly address adaptive variation that would be of great interest from an applied perspective. We noted that, when structuration is accounted for, the methodological effort to discover loci responsible for environmental adaptation might trace back to GWAS. This means that advances and statistics built by the complex trait mapping community could be exploited to gather information in the field.B. distachyon - like other model species - is thus not only an effective laboratory tool, but also a natural probe. By exploiting their geographical distribution, these model species could be used to identify functional variation, and ultimately genomic loci, whose evolution was shaped for survival well before artificial selection took place. We envisage this approach being directly applied to crops, focusing either on their wild relatives or landraces, to cleverly incorporate in agronomy the results of natural selection efforts.Our results derive from a modular method that can be extended in order to deal with any relevant environmental questions. Although our initial set of genotypes and environmental variables is limited, we believe that this and similar collections will soon be enlarged to provide a better capacity to map environmental adaptation. B. distachyon seeds collected in Turkey [B. distachyon populations spanning from the western Dardanelles strait to the eastern region beyond lake Tuz in order to cover a continuous and comprehensive environmental gradient. This region was analysed by coupling DIVA GIS and BioClim data derived from Worldclim 2.5 minutes data  [The plant material studied comes from n Turkey . We focu, ~5 Km) . The funi.e. had non-zero fitness), we collected seeds rather than green tissues. Collection points were associated with GPS coordinates (±6 m), hence WGS84 coordinates were used to extract local altitude values and BioClim data from the Worldclim 2.5 database. BioClim is made up of 19 variables, the result of processing raw measures of rainfall and temperature. Using the full set of BioClim variables in correlation analyses might result in augmented noise without any real information gain [A subset of nine local groups was chosen accordingly Figure ; Table 1ion gain , thus a ion gain  over theAt least five seeds from each spike were pooled, and all sample pools underwent the same germination routine. Seeds representing each original individual were sown in separate Petri dishes with moist turf and underwent six weeks of vernalization in the dark. Seeds were then transferred to 1:1 turf and pebbly soil, and germinated in separated pots in a growth chamber (16 h 25°C light/ 8 h 21°C dark). Green tissues were collected in equal proportions from the resulting seedlings, so as to reconstitute the full allelic set of each original natural accession. Genomic DNA was extracted using the GeneElute Plant Genomic DNA Miniprep extraction kit  following the suggested protocol. Four inbred lines developed by Dr. John Vogel in Albany, CA, USA, and the Bd21 inbred lines were added to the sample pool as reference. A total of 96 samples were selected for the following analyses.B. distachyon. Total genomic DNA was digested with ApeKI restriction enzyme . Adapters were titrated by ligating Bd21 genomic fragments to increasing concentrations of adapters in separate reactions, then piping them through GBS library construction. After the library quality had been evaluated on a Bioanalyzer 2100 , 6 ng of adapters per 100 ng of genomic DNA were deemed appropriate for all samples.The Genotyping-by-Sequencing (GBS) protocol is based on genome complexity reduction and multiplexed DNA sequencing for SNP discovery . The proAfter adapter ligation with T4 ligase  for 60’ at 22°C, then 30’ at 64°C, samples were pooled in two 48-plex cohorts and subjected to PCR amplification with high-fidelity Phusion DNA polymerase  using adapter-specific primers. The two 48-plex libraries were treated following the Illumina pair-end sequencing protocol, and then sequenced in separate lanes on a Genome Analyzer II  at IGA Services, Udine, Italy.ad hoc script, available upon request, was used to carry out the following process on GBS Illumina reads: i) reads were sorted according to their barcode, ii) barcodes were removed from reads, iii) reads were trimmed according to their overall quality using the rNA program [B. distachyon reference genome [i.e. allowing three mismatches, disallowing long gaps, and using a seed length of 28 nucleotides. The results were analysed using the GATK pipeline [http://picard.sourceforge.net). Samples below the 9th percentile of the distribution of read counts were discarded, thus reducing the number of individuals from 96 to 87, of which 82 were from field collection. Reads were mapped on the reference Bd21 genome sequence, and polymorphic positions were extracted.An  program . Trimmede genome  using BWe genome  run withpipeline . GATK wapipeline , 88. At pipeline  was not pipeline  and Picai.e. α< 0.001%). All polymorphic positions missing in over 20% of the samples were discarded, and loci were filtered for minor allele frequency (MAF) of 5%.The vcf files produced by GATK were parsed using a Perl script (available upon request): the analysis was limited to SNPs deemed as having PASSED by GATK .The reference genome was split into arbitrary 1,000 bp bins, and the amount of reads mapped per bin per sample was counted to assess the consistency of the distribution of the reads. We labeled as Presence/Absence (P/A) regions those bins that were present in the reference genome but did not produce any read in any of the samples from one to eight of the groups (A-H) tested. In “absence” bins, no samples sharing the same geographical origin mapped any read, whilst one or more of the other groups did . The content of transposable elements (TE) was assessed separately for P/A and non P/A regions using RepeatMasker  and a coA phylogeny comprising both natural accessions and inbred lines was derived from shared SNPs. SplitsTree4  was usedst) [i.e. unconnected nodes, represent populations lacking allelelic exchange. cGD values were regressed over spatial distances.Gene flow dynamics underlying the geographical sampling can affect the results of the analyses, and need to be considered in landscape genomics practises , 71. Genst) . The genst) , a measust) , which bst) , 98. In The spatial pattern of genetic diversity was explored at a finer scale with a spatial PCA  in R/adeF-tests between a full model against a reduced model at each marker. R/GAPIT enables a compressed mixed linear model (CMLM) [Association analysis was performed with two different methods on the full set of SNPs filtered for MAF > 0.05 against the three PCs accounting for environmental variation. LFMM software  was usedl (CMLM)  to deal l (CMLM)  and can l (CMLM) :Y is the vector of phenotypic/climate values, and X and Z are the known design matrix. The fixed effects ) are represented by the unknown vector β; random additive genetic effects are represented by the unknown vector u, while e represents the non-observed residuals. Kinship is included in the computation of u and e variance. The most significant PCs computed over molecular markers and the Structure clustering were evaluated as Q by assessing the normal fit of the model on quantile-quantile plots.Where e.g. 0.05) by the number of tests performed. However GWAS is not necessarily a collection of completely independent tests [D’ LD measure was used to identify LD blocks where strong LD was defined by an upper confidence bound of D’ > 0.98 and a lower confidence bound of D’ > 0.7. Strong evidence of recombination was provided wherever the upper bound of D’ was lower than 0.9, according to Gabriel's method [To control for false positives we applied an LD-corrected Bonferroni. The Bonferroni method is conservative in that it divides the target threshold  using Bayescan 2.1 [st values in a locus-specific component , and a population-specific component . The departure of α from the equilibrium suggests selection operating on a given locus. The 5% FDR threshold provided by Bayescan was used as a significance threshold. Brachypodium distachyon genome V1.2 annotation (ftp://ftpmips.helmholtz-muenchen.de/plants/brachypodium/v1.2) was used to locate EASs and outliers either more than 5 kb upstream, within 5 kb upstream, and within predicted genes with R/GenomicRanges [cis regulatory regions [The same dataset was tested to detect outlier loci   with R/vhttp://www.ebi.ac.uk/ena/) under accession no. PRJEB7130. Biological materials are available upon request. Climate data is publicly available at http://www.worldclim.org.All sequencing reads from this study have been submitted to the European Nucleotide Archive . Aside, the original meaning of BioClim variables. (XLSX 18 KB)Additional file 1:Association analysis quantile-quantile plots for environmental PC 1–3. Quantile-quantile plots generated by GAPIT model for PC 1 to 3. On the y axis, the distribution of calculated p-values. On the x axis, the expected distribution of association test statistics. A few, strong associations are present. (PDF 1 MB)Additional file 2:Genes detected by LFMM association method. Predicted genes identified by significant associations detected by LFMM approach. Entries also identified by outlier discovery approach are underlined and bold; entries also identified by CMLM association are highlighted in red. The three methods show interesting overlaps (discussed in text). (XLSX 82 KB)Additional file 3:Genes detected by CMLM association method. Predicted genes identified by significant associations detected by CMLM approach, with relative physical position and test significance. (XLSX 27 KB)Additional file 4:Genes detected by outlier loci analyses. List of predicted genes identified by outlier loci approach, with relative physical position and test significance. (XLSX 59 KB)Additional file 5: