The two main functions enable to run 1) replicated random cross validations within a training set of genotyped and phenotyped lines and 2) GEBV prediction, for a set of genotyped-only lines. Options are available for 1) missing data imputation, 2) markers and training set selection and 3) genomic prediction with 15 different methods, either parametric or semi-parametric. The usefulness and efficiency of BWGS are illustrated using a population of wheat lines from a real breeding programme. Adjusted yield data from historical trials  were used for testing the options of BWGS. On the whole, 760 candidate lines with adjusted phenotypes and genotypes for 47 839 robust SNP were used. With a simple desktop computer, we obtained results which compared with previously published results on wheat genomic selection. As predicted by the theory, factors that are most influencing predictive ability, for a given trait of moderate heritability, are the size of the training population and a minimum number of markers for capturing every QTL information. Missing data up to 40%, if randomly distributed, do not degrade predictive ability once imputed, and up to 80% randomly distributed missing data are still acceptable once imputed with Expectation-Maximization method of package rrBLUP. It is worth noticing that selecting markers that are most associated to the trait do improve predictive ability, compared with the whole set of markers, but only when marker selection is made on the whole population. When marker selection is made only on the sampled training set, this advantage nearly disappeared, since it was clearly due to overfitting. Few differences are observed between the 15 prediction models with this dataset. Although non-parametric methods that are supposed to capture non-additive effects have slightly better predictive accuracy, differences remain small. Finally, the GEBV from the 15 prediction models are all highly correlated to each other. These results are encouraging for an efficient use of genomic selection in applied breeding programmes and BWGS is a simple and powerful toolbox to apply in breeding programmes or training activities.We developed an integrated R library called BWGS to enable easy computation of Genomic Estimates of Breeding values (GEBV) for genomic selection. BWGS, for BreedWheat Genomic selection, was developed in the framework of a cooperative private-public partnership project called Breedwheat ( The use of molecular markers to provide selection criteria for quantitative traits was first proposed by . They inTo benefit from shorter cycles and increasing selection intensity, the genetic gain per cycle should be close to that expected from phenotypic selection (PS). This relative efficiency of GS vs PS thus relies on the ability of predicting the observed genetic value from the marker genotype. GEBV predictive ability is usually measured by the correlation between the predicted and observed values. Most reported studies on GS in plants have focused on measuring the accuracy of genomic predictions, usually assessed by cross-validation techniques –14.In the native theory, all available markers should be used without prior selection, since the statistical models were supposed to cope with the big data problem and avoid over parametrization. However, with the advance of molecular biology, the number of markers, e.g. single nucleotide polymorphisms (SNP), can be extremely high (up to some millions), particularly when compared to a few hundreds or thousands phenotypic observations. In this case, it seems reasonable to limit the number of markers to avoid too high over parametrization or over-representation of non-informative genomic regions, as well as speeding up computation. For example, one may wish to discard markers when they are in complete (or nearly complete) LD with another, thus bringing the very same information, or selecting markers which are evenly spaced, either physically or genetically, along the genome map. Since LD is related to  recombination, genetic distance seems to be more suited, whenever available, for selecting markers than physical distance.Molecular data often contain missing value, particularly with the so-called genotyping-by-sequencing (GBS), likely because the fraction of the genome which is re-sequenced is not exactly the same from one individual to another . Most prFinally, imputed data of (possibly selected) markers are used to predict GEBV using phenotypic observation in a training population. Several methods have been proposed to achieve GEBV prediction. They can be classified into parametric vs semi-parametric methods . In the The objectives of this manuscript are 1) to describe an integrated software (pipeline) which has been developed from open-source R functions available in various R libraries to enable the three steps to be performed easily and 2) to present an application of this software to carry out genomic predictions using historical data from a bread wheat breeding programme.www.breedwheat.fr), we developed an integrated pipeline based on R [In the framework of the French flagship programme BreedWheat .Once the “best” model has been chosen based on quality assessment, a second function, named bwgs.predict is used to build the BEST model using the whole training set (genotyping + phenotyping), then apply the model to the genotyping data of the target population to get GEBV of these new genotypes.Candidate lines of the target population can then be ranked according to GEBV for single trait (truncation) selection.Going into more details of the pipeline, the workflow comprises three main steps:MNI: missing data are replaced by the mean allele frequency of the given marker. This imputation method is only suited when there are a few missing values, typically in marker data from SNP chips or KasPAR.EMI: missing data are replaced using an expectation-maximization methods described in function A.mat of R-package rrBLUP . This alA step of (missing) genotyping data imputation. This option can be useful for sources of genotyping data such as GBS. The following options are available:RMR: Random sampling (without replacement) of a subset of markers. To be used with the parameter “reduct.marker.size”.LD (with r2 and MAP): enables “pruning” of markers which are in LD > r2. Only the marker with the least missing values is kept for each pair in LD>r2. To allow faster computation, r2 is estimated chromosome by chromosome, so a MAP file is required with information of marker assignation to chromosomes. The MAP file should contain at least three columns: marker_name, chromosome_name and distance_from_origin .ANO : one-way ANOVA are carried out with R function lm on trait “pheno” Every markers are tested one at a time, and only markers with pvalue<pval are kept for GEBV predictionANO+LD : combines a first step of marker selection with ANO, then a second step of pruning using LD option.For research or teaching purposes, an option for randomly sampling individuals has been added, although it is little useful in practical breeding applications. Options for selecting a subset of the training population are:RANDOM: a subset of sample.pop.size is randomly selected for training the model, and the unselected part of the population is used for validation. The process is repeated nFolds * nTimes to have the same number of replicates than with cross-validation.OPTI: the optimization algorithm based on CDmean  to selecA step of dimension reduction, i.e. reducing the number of markers. This reduction could be necessary to speed up computation on large datasets, depending on computer resources available. The following methods are availableA step of model building and cross validation.i.e. markers, (largely) exceeds the number of observations, making the classical linear model equation unsolvable. In a review, [In the general case of genomic selection, the number of explanatory variables,  review,  classifi review,  describeThe options available for genomic breeding value prediction are:GBLUP: performs G-BLUP using a marker-based relationship matrix, implemented through rrBLUP R-library. Equivalent to ridge regression (RR-BLUP) of marker effects.EGBLUP: performs EG-BLUP, i.e. BLUP using a “squared” relationship matrix to model epistatic 2x2 interactions, as described by  using thRR: ridge regression, using package glmnet . In theoLASSO: Least Absolute Shrinkage and Selection Operator is another penalized regression methods which yield more shrinked estimates than RR. Run by glmnet library.EN: Elastic Net  which isSeveral Bayesian methods, using the BGLR libraryBRR: Bayesian ridge regression: same as rr-blup, but Bayesian resolution. Induces homogeneous shrinkage of all markers effects towards zero with Gaussian distribution .BL: Bayesian LASSO: uses an exponential prior on marker variances priors, leading to double exponential distribution of marker effects .BA: Bayes A uses a scaled-t prior distribution of marker effects .BB: Bayes B, uses a mixture of distribution with a point mass at zero and with a slab of non-zero marker effects with a scaled-t distribution .BC: Bayes C same as Bayes B with Gaussian a distribution for non-zero marker effectshttp://genomics.cimmyt.org/BGLR-extdoc.pdf.) and in [A more detailed description of these methods can be found in the documentation of R-library “BGLR Bayesian Generalized Linear Regression”  and in Four semi-parametric methodsRKHS: reproductive kernel Hilbert space and multiple kernel MRKHS, using BGLR –31. BaseRF: Random forest regression, using randomForest library . This mehttps://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdfSVM: support vector machine, run by e1071 library. For details, see LIBSVM: a library for Support Vector Machines BRNN: Bayesian Regularization for feed-forward Neural Network, with the R-package BRNN . To keepThe criteria used to estimate model’s quality are 1) the Pearson correlation between adjusted phenotype and GEBV, computed over the whole data set  and 2) the square-root of the mean-squared error of prediction RMSEP. These criteria are provided for each replicate (nTimes) as well as mean and standard deviation over replicates. A table summarizes phenotype, estimated breeding value and its standard deviation, as well as the reliatibility for each individual GEBV computed as REL = 1-PEV/σ²g, where PEV is the prediction error variance for each individual and σ²g the genetic variance when the package used does provide estimate of prediction error variance of individual GEBV, as it is the case for BGLR.i.e. on average 45 single plot measurements per genotype, spread over 15 years (2000–2014), 10 locations in France and two managements (high vs low inputs). Of course these data are highly unbalanced, most genotypes being evaluated during one or two successive years, (on average 1.5 years) in 7.60 environments (site x year) with usually two replicates by management, and most connections in the design are between two successive years, with a few control varieties  being evaluated on a longer period to allow a higher connectivity in the dataset. These figures are for grain yield, the most documented trait. The whole dataset is described in [A training population was built by gathering historical data from the INRA-Agri-Obtentions breeding programme. To obtain robust phenotypes, we kept data from lines which have been evaluated in multisite trials (> 6 locations), usually for 1–2 years. They were developed from inbred lines crosses, followed by 6–8 generations of self-pollination. Few selection is made during the first 3 generations (i.e. F2 to F4), which are harvested in bulk, then visual selection on simple traits is made on plants or rows at generations F4 and F5. Selected F5 families are multiplied in field plots, which allow a rough evaluation of yield. Then enough seeds are available and selected lines are put into the evaluation network, usually at the F7 generation, when breeding lines are nearly homozygous. Typically, about 300–400 pair crosses are made each year, 120–150 F7 lines are put into first year multisite trials, then 50–60 are evaluated for a second year, and the very happy few (1–3) are then entering the official registration trials in France, to hopefully become a commercial variety, 10–12 years after the initial crosses. On the whole, our database for field data had 77,176 records on 1715 genotypes, ribed in  and is pThese highly unbalanced raw data require a pre-treatment step to correct for non-genetic factors as much as possible. The following mixed model was applied to estimate “corrected” genotypic means (BLUE) using the mixed model in R (library lme4):Y = lmer  + (1|year*site*geno)), where * stands for crossed factors (Year*site define environments) and: stands for nested effects, e.g. block within trial within year*site.i.e. when genotypes are also considered as random effects with identity matrix for modelling covariances). But BLUE are not shrinked, and thus can be more easily used in a second step involving mixed modelling.Where geno being the main genotypic effect (fixed) and all other effects being considered as random effects to be corrected for. Note that these genotypic “BLUE” are highly correlated (r = 0.94) with BLUP estimates (g / (σ²g + σ²ge /nenv + σ²e /nplot) σThe same model with geno as random effect was used to estimate variance components and rough estimate of broad sense heritability as σ²g is the genotypic variance component, σ²ge is the GxE variance component (i.e. year*site*geno) and σ²e is the residual variance. nenv is the average number of environments per genotype and nplot the average number of plots per genotype in the dataset used.Where σ²Out of these “HQ-phenotyped” lines, 760 were also genotyped with the TaBW280K SNP chip  and usedCross validation used randomly sampled 90% of the genotypes as the training set and the remaining 10% genotypes as the “validation set”. The resampling process was iterated 100 times to estimate an empirical mean and standard deviation. Since the true breeding value was unknown, predictive ability (PA) was measured by the Pearson correlation between GEBVs and the adjusted phenotypic values (BLUE) across all folds, i.e. the Hold accuracy, which is supposed to be less biased that a fold-by-fold estimate .Genotypic data have been used to estimate the additive relationships matrix according to  with theThe first step of data cleaning with MAF = 5% and maximum marker missing data = 20% led to retain 47,839 markers out of 60,912. Since adjusted Yield BLUE were complete for the 760 genotypes, the final dimension of geno matrix was 760 x 47,839. This SNP dataset contained on average 0.93% of missing data. Given the small proportion of missing data, no difference in predictive ability was observed between the two imputation methods. To test the efficiency of imputation methods on prediction accuracy, we generated new genotypic matrices with 20%, 40%; 60% and 80% of missing data, randomly distributed in the dataset. Imputation by mean allele frequency took a few seconds, while imputation by the EM algorithm of A.mat took around 100 times longer, whatever the proportion of missing data. Predictive ability for grain yield from imputed dataset using the two methods is presented in Results are also displayed in As expected, predictive ability increases with the number of randomly sampled markers, with a steady plateau above around 5000 markers and a best predictive ability of 0.525. This clearly shows that the extent of LD is large enough in such elite breeding lines to allow most QTLs for yield being captured with 5000–10000 markers, i.e. on average one every 1.7 Mbase. This is illustrated by marker selection based on LD-pruning, i.e. removing markers which are in LD > threshold with any other marker. The marker with the least number of missing value is conserved, otherwise the choice is random. The highest value of 0.544 is obtained with 4462 markers with pairwise LD >0.8. When marker selection is based on ANOVA carried out on the entire population, there is an optimum predictive ability of 0.614 achieved with 1820 markers . However, these results were only found when marker selection is made only once by association analysis on the whole dataset, which is then split into a training and a validation set. When marker selection is made within the loop only in the training set, then the predictive ability of the validation set is only slightly improved for N = 2000 and 5000 markers, compared to random sampling, but never exceeds the predictive ability achieved with the whole set of markers. This clearly illustrates that overfitting does occur in the first case.For this study, we used the whole set of 47,839 markers; which gave a predictive ability of 0.525 in 10-fold cross validation using the complete dataset of 760 breeding lines, i.e. with a training set of 684 lines and a validation set of 76 lines. Predictive ability was estimated with GBLUP using calibration set of size CS = 50, 100, 200, 300, 400, 500, 600 and 700, validation being made using the remaining lines. Two strategies were compared as illustrated in With again the whole set of 47,839 markers, we used all prediction methods available in BWGS to estimate prediction accuracy in 100 independent 10-fold cross validations. Computing time varied considerably from one method to another, as illustrated in In Perhaps as important as predictive ability is the consistency of GEBV estimated by different methods. Genomic selection programmes are now routinely used in dairy cow breeding, and benefit from huge phenotypic data recorded in past years on milk production of thousands of females, usually related by well-known pedigree relationships (i.e. mother and father known without error). This is however not yet the case in other species like sheep, although effort are being developed in so-called minor species. Therefore, using a relatively cheap genotyping, dairy cow breeders usually have very large population for training GS models, which led to highly accurate predictions. In many animal studies, the oldest animals are used as training set and the youngest as validation set.Contrasting with animal breeding, most plant breeding programmes do not have very large population sizes, although each breeding company manipulates hundreds or thousands of candidates, since companies are usually reluctant to share and merge datasets. Breeding companies have developed in-house biostatistical tools for calculating GEBV with semi-automated pipeline, since time is often short between data production (e.g. grain harvest) and selection decision (e.g. sowing next generation). There are also several publically available tools which have been developed, particularly as R libraries. Among the most popular we can mention glmnet , BGLR 1, rrBLUP In our highly unbalanced breeder’s dataset, when discarding control lines which were regularly replicated over years, the average number of environment per studied lines is 10.4, with two replicates by environment (site x year). This led to an estimate of broad sense heritability of 0.76 and therefore a theoretical upward limit of prediction accuracy of 0.872. When using random cross validation and the whole set of 47,839 markers, the achieved predictive ability is 0.525, which would correspond to an accuracy of 0.603 according to formula in . HoweverOur results compare with previously reported predictive ability estimated through random cross-validation, for example 0.36–0.53  and 0.32Although GS theory has been elaborated to cope with the over parameterization problem (number of markers >> number of observations), it empirically appears that adding more markers than needed does not improve predictive ability. This was already observed in many reports. Among other, a figure similar to our Fig 3 can be found in . In thisSelecting markers that are significantly associated with QTLs can achieve higher predictive ability than randomly selected markers, and surprisingly even higher predictive ability than using all markers. However, it is clear from In theory, the prediction accuracy is positively related to the training population size, as established by simulation studies –51. ThisPrediction methods have been classified into parametric and non-parametric (or semi-parametic) methods, and parametric methods sometimes split into penalized approach and Bayesian approach (for reviews see  and 55––55–57). Although there is no clear-cut separation between parametric vs non-parametric methods, nor between penalized vs Bayesian approach, it seems that methods that are supposed to better capture non additive and/or non-linear effects such as EGBLUP or RKHS gave higher PA, as already reported ,42,48,56In a simulation study,  showed tBut perhaps more important than the predictive ability is to know whether the different methods give similar values or at least similar rankings of GEBV for candidates. Indeed correlations between predicted GEBV with the 15 methods range from 0.85 to nearly 1. The highest values being observed between linear parametric methods, whatever based on penalized regression or Bayesian approaches. Machine learning methods such as SVM and BRNN are least related to others, except with MKRKHS. Given the close values of both predictive abilities and GEBV estimates among methods, it seems reasonable to keep the historical GBLUP as a reference method, for its simplicity and fastness, at least for polygenic traits such as grain yield chosen to illustrate this study.The R pipeline we have developed is based on publically available libraries and therefore offers a full freedom to operate. It is easy to handle and allows a wide range of options for missing data imputation, marker or training set selection and prediction methods. Its parameterization was fixed for medium sized datasets to make it easy to use for beginners or teaching. Applying this tool with defaults parameters to a set of elite breeding lines with historical data from yield trials allowed us to obtain similar results to those reported on other wheat populations. The options for subsampling markers and/or training set enabled us to illustrate theoretical expectations (e.g. ). The prFuture developments of BWGS are ongoing to address these challenges, particularly multitrait selection.https://forgemia.inra.fr/umr-gdec/bwgsThe source code of BWGS R functions as well as example files and notice are freely available on S1 Data(ZIP)Click here for additional data file.


Pearl millet, a nutritious food for around 100 million people in Africa and India, displays extensive genetic diversity and a high degree of admixture with wild relatives. Two major morphotypes can be distinguished in Senegal: early-flowering Souna and late-flowering Sanio. Phenotypic variabilities related to flowering time play an important role in the adaptation of pearl millet to climate variability. A better understanding of the genetic makeup of these variabilities would make it possible to breed pearl millet to suit regions with different climates. The aim of this study was to characterize the genetic basis of these phenotypic differences.P-value < 2.3E-06).We defined a core collection that captures most of the diversity of cultivated pearl millets in Senegal and includes 60 early-flowering Souna and 31 late-flowering Sanio morphotypes. Sixteen agro-morphological traits were evaluated in the panel in the 2016 and 2017 rainy seasons. Phenological and phenotypic traits related with yield, flowering time, and biomass helped differentiate early- and late-flowering morphotypes. Further, using genotyping-by-sequencing (GBS), 21,663 single nucleotide polymorphisms (SNPs) markers with more than 5% of minor allele frequencies were discovered. Sparse non-negative matrix factorization (sNMF) analysis confirmed the genetic structure in two gene pools associated with differences in flowering time. Two chromosomal regions on linkage groups (LG 3) (~ 89.7 Mb) and (LG 6) (~ 68.1 Mb) differentiated two clusters among the early-flowering Souna. A genome-wide association study (GWAS) was used to link phenotypic variation to the SNPs, and 18 genes were linked to flowering time, plant height, tillering, and biomass (The diversity of early- and late-flowering pearl millet morphotypes in Senegal was captured using a heuristic approach. Key phenological and phenotypic traits, SNPs, and candidate genes underlying flowering time, tillering, biomass yield and plant height of pearl millet were identified. Chromosome rearrangements in LG3 and LG6 were inferred as a source of variation in early-flowering morphotypes. Using candidate genes underlying these features between pearl millet morphotypes will be of paramount importance in breeding for resilience to climatic variability.The online version contains supplementary material available at 10.1186/s12864-020-07198-2. Pennisetum glaucum (L.) R. Br., syn Cenchrus americanus] is an integral part of the diet of many people, particularly in Africa and Asia. Pearl millet grain has a highly nutritional composition  than other cereals [Pearl millet [ cereals . Gluten  cereals .P. glaucum PHYTOCHROME C (PgPhyC) and MADS11 (PgMADS11) genes associated with yield and early flowering time [Pearl millet is an annual C4 plant that is cultivated in the driest environments. Due to its extensive genetic diversity combined with a high degree of admixture with wild relatives, pearl millet displays wide morphological and genetic diversity , 4. For ing time –8. Such In Senegal, two major varieties of pearl millet are cultivated: Souna morphotypes that flowers early, from 50 to 60 days after planting, and Sanio morphotype that flowers late, from 80 to 110 days after planting , 10. BetOne way to better characterize the genetic diversity of this large pearl millet collection is to define and use a representative subset of landraces in the panel . SeveralIn this study, a core collection of cultivated pearl millets from Senegal was first defined. The landraces were then field evaluated, GBS-sequenced, and used in a genome-wide association study (GWAS) aimed at identifying quantitative trait loci (QTLs) associated with phenological and phenotypic traits between early-flowering (Souna) and late-flowering (Sanio) landraces. Key SNPs as well as candidate genes underlying flowering time, tillering, biomass and plant height of pearl millet were identified. Their use in breeding program to address environmental challenges and farmer’s preferences are discussed.P-value = 0.49). Similarly, principal component analysis , tillering  and panicle exertion   (Table h2 ≥ 0.5)  Table . Six tra) Table h ≥ 0.5  was 0.213. Genome-wide structure analysis revealed two genetic pools that correspond to early- and late-flowering morphotypes Fig. . Further− 6 (Bonferroni correction). These SNPs were associated with biomass, flowering time, plant height and tillering. No SNPs associated with biomass were identified in the peak P-value, but they were squatter along the genome at SNPs with very low P-values. Flowering time, plant height, and tillering are each tightly associated with a single SNP per trait  plots showed that the mixed linear model (MLM) that accounts for the population structure effect and the kinship matrix was appropriate and had strong statistical power. The GWAS analysis detected 18 SNPs at the threshold of α = 2.3 × 10ait Fig. . For plaUsing a heuristic approach with phenotypic and genetic data, we built a core collection of Senegalese pearl millets. This subset of landraces is well distributed across cultivated areas and captures wide genetic diversity and phenotypic variations in the pearl millet morphotypes. The spatial pattern of diversity is correlated with the genetic structure, as previously reported  and matcTriticum aestivum) [Oryza sativa) [Allogamous species often have fewer redundancies due to gene flow between individuals. The great diversity of pearl millet was associated with dynamic gene flow and admixture between wild and cultivated pearl millet . This isestivum)  or rice  sativa) . Our res sativa) –10: an eDifferentiation of early- and late-flowering morphotypes may have occurred after the domestication of pearl millet (at least 4900 years ago), as a consequence of a center of specialization developing across the Sahel belt. This hypothesis is supported by previous reports of migrations, exchanges and gene flow that led to wider genetic diversity as an adaptive mechanism , 4. Thesh2 ≥ 0.5). There are two phenology features, i.e. heading and flowering time, that are correlated between themselves and also correlated with photoperiod sensitivity in pearl millet. The late-flowering morphotype is more sensitive to photoperiod. The phenology and photoperiod sensitivity are the two mechanisms help pearl millet to adapt to climate variability. Early flowering in pearl millet has been associated with a population adaptation mechanism [Among the 16 quantitative agro-morphological traits evaluated at many sites and across many years, six were highly discriminating between early- and late-flowering morphotypes with high heritability   Table . BiomassSequencing at the genome-wide scale revealed features at LG3 and LG6 in early-flowering millets, suggesting specific independent rearrangements at these regions for heading and flowering earliness. Chromosomes 3 and 6 might have undergone variations with breakpoints of 89.7 Mb and 68.1 Mb, respectively, large enough to look closely at gene insertion, deletion or synteny  induced by abscisic acid [PPR gene play a role in delaying flowering by mediating the expression of several genes during plant growth, or by repressing genes involved in the transition to the reproductive phase [PgPPR gene we identified could be a putative candidate gene involved in the control of flowering time between early- and late-flowering millets.The panel assembled is strongly structured between early-flowering Souna and late-flowering Sanio. Strongly structured diversity between early- and late-flowering millets has been considered to be a way of correcting for population structure and kinship. Correction through structure might be an obstacle to the identification of causal loci that are strongly differentiated between population. However, our results pinpoint some candidates for causal SNPs associated with the measured traits. Consequently, the power of GWAS is mainly identifying SNPs and phenotypic variation within each group. A panel with a more admixed genotype would have been preferable to identify phenotypic differences between early- and late-flowering millets. Biomass led to the identification of a large number of scattered SNPs with no standard peak in the sic acid  and in tsic acid , respectve phase . The PgPThe main traits that differentiate the morphotypes in the core collection are yield-related components, biomass, and flowering time. A north-south gradient of early- and late-flowering was identified across the Senegal . There aThe diversity of early- and late-flowering pearl millet landraces from Senegal was captured and a representative core set was defined using an effective heuristic approach. GWAS revealed key phenology and phenotypic traits between the two pearl millet morphotypes while identifying SNPs on genes underlying flowering time, tillering, biomass and plant height. The identification of two subgroups among early-flowering morphotypes on LG3 and LG6 suggests chromosome rearrangements as a source of variation for flowering earliness features. These marker-trait associations could be targeted in breeding programs for pearl millet improvement under erratic climatic conditions.https://sites.google.com/a/irri.org/bbi/products) with the formula:Y is the phenotype; μ, the mean; G the genetic effect; Y, the year effect; GY, the interaction between genotype and year; R the replication effect; B, the incomplete block and ε, the residual effect. The heritability of agro-morphological characters was calculated using a mixed linear model with random effects for individuals, using Plant Breeding Tools v 1.3 software (https://sites.google.com/a/irri.org/bbi/products), with the formula:y) year variance and r) replicates and (y) year.A total of 541 Senegalese pearl millet landraces were collected between 1992 and 2014 , 9, 11 fFor each trait, the adjusted mean of each individual in the 2016 and 2017 trials was calculated with fixed-effects for individuals, using Plant Breeding Tools v 1.3 software and was considered as the value of the individual for the trait concerned.P-value < 0.001 and which presented a high correlation (r) ≥ 0.7, with the axis of differentiation, were identified as discriminating characters between early- and late-flowering millets. This analysis was performed using XLStat 2014 software (http://www.xlstat.com) and the distribution of discriminating agro-morphological characters was plotted using R software v. 3.5.1 [https://www.qgis.org).A principal component analysis was performed using the adegenet v 2.1.1 package , R v 3.5v. 3.5.1 . The disPstI-MspI double-digestion was applied, and adapters were linked to each sample followed by mixing and amplification. The libraries were sequenced using Illumina HiSeq2500.Genotyping-by-sequencing (GBS) was performed on genomic DNA extracted as previously described  from a sThe quality of the reads was evaluated using FastQC v 0.72 and MultiQC v 1.6, and the sequences were then cleaned with FastQ Trimmer v 1.0.0. Only sequences of average quality (Q) ≥ 30 (Sanger format) were retained and the first 7 bases (5′ side) of each read were removed. The sequences were aligned with the pearl millet reference genome (GenBank Accession number GCA_002174835.2) using BWA v 1.2.3, before realignment of the sequences for insertions and deletions using RealignerTargetCreator v 0.0.4 and IndelAligner v 0.0.6. The binary alignment map (BAM) format files from the above procedures were merged using MergeBAM v 1.2.0 and SNP calling was performed using UnifiedGenotyper v 0.0.6. A total of 545,834 variants were called including 502,382 SNPs. Filtering was first performed based on mapping quality (MQ) and depth, applying hard filtering using VariantFiltration v 0.0.5 (MQ ≥ 40 divided by the depth of unfiltered samples > 0.1). A second filtering was performed according to the minor allele frequency (MAF) > 0.05, and the allowed maximum proportion of missing data was 0.05 for markers and 0.1 for individuals, using Plink v 1.9. Multi-allelic markers were then removed using Tassel v 5.2.48. The output file finally contained 21,663 SNPs and 78 individuals. This dataset was used for all subsequent analyses. All bioinformatics analysis  was carried out on the Galaxy v 18.0.5 platform , in the Genetic structure was evaluated using the sparse mixed linear model (sNMF) algorithm, through the LEA v 2.2.0 package implemented in R. The sNMF algorithm detects genetic clusters of individuals in the population sample. For this analysis, we used several populations ranging from K = 1 to K = 10, with ten repetitions for each K value. Discriminant analysis of principal components (DAPC) was also used through the adegenet v 2.1.1 package . The choAssociation analyses were conducted with a mixed linear model (MLM) correcting for population structure and kinship using Tassel v 5.2.48. Q-Q and Manhattan plots illustrating the results of GWAS were produced using the qqman package v 0.1.4 in R . The sigAdditional file 1: Table S1. List of accessions from the Senegalese pearl millet core collection. Table S2. Representativeness statistics of early-flowering Souna morphotype and late-flowering Sanio morphotypes within the core collection. %MD = Percentage of mean difference, %VD = Percentage of variance difference, %CR = Coincidence rate, and %VR = Variable rate. Table S3. Means, P-values and correlation with axis from the discriminant analysis (DA) of early- and late- flowering millets. Table S4. Significantly associated SNPs from GWAS, mapped in the pearl millet genome and associated with phenotypic traits. Figure S1. Genetic structure of Senegalese pearl millet landraces core collection at chromosome level (a-g) from chromosome 1 to chromosome 7, respectively. Figure S2. Discriminant analysis of (a) early-flowering  and (b) late-flowering morphotypes , from phenotypic traits of landraces from the core collection. Boxplots of (c) grain yield, (d) panicle yield, (e) spike thickness, (f) nodal tillering and (g) flag leaf length according to different subsets of early-flowering morphotype. Boxplots of (h) panicle yield, (i) grain yield and (j) nodal tillering according to different subsets of late-flowering morphotype.Additional file 2.Additional file 3.Additional file 4.Additional file 5.


Fagus sylvatica L.) representing trees growing along steep precipitation gradients. The saplings were subjected to experimentally controlled drought treatments. Response of the saplings was assessed by the evaluation of stem diameter growth (SDG) and the chlorophyll fluorescence parameters FV/FM, PIabs, and PItot. The evaluation showed that saplings from xeric sites were less affected by the drought treatment. Five SNPs (7.14%) in three candidate genes were significantly associated with the evaluated traits; saplings with particular genotypes at these SNPs showed better performance under the drought treatment. The SNPs were located in the cytosolic class I small heat-shock protein, CTR/DRE binding transcription factor, and isocitrate dehydrogenase genes and explained 5.8–13.4% of the phenotypic variance. These findings provide insight into the genetic basis of traits related to drought tolerance in European beech and could support the development of forest conservation management strategies under future climatic conditions.Studies of genetic variation underlying traits related to drought tolerance in forest trees are of great importance for understanding their adaptive potential under a climate change scenario. In this study, using a candidate gene approach, associations between SNPs and drought related traits were assessed in saplings of European beech ( Fagus sylvatica L.) is one of the ecologically and economically most important forest tree species in Europe. Its natural distribution depends mainly on temperature and moisture availability1, and although it is widely distributed over the continent, it is predominant in Central Europe with relatively mild climatic conditions2. The climate change scenarios predict increase in frequency, duration, and intensity of summer droughts as a consequence of rising temperature and diminishing precipitation4 that could negatively affect survival of this species7. Indeed, some studies indicate that severe drought periods might be harmful for European beech, affecting growth, leaf conductance, photosynthetic activity, stem hydraulic conductivity, fine root vitality, and nutrient uptake14. Consequently, under a current climate change scenario, it is believed that European beech could lose its habitat to more drought tolerant species, such as Quercus petraea and Pinus sylvestris15.European beech  photochemistry that lead to reduction in electron transport21. Furthermore, changes in biomass allocation can also occur under drought stress leading to a change in the root/shoot ratio with a higher investment in root biomass to maximize soil water uptake22. In addition to minimizing water loss and maximizing water uptake, plants can also regulate cell osmotic potential to tolerate dehydration. It depends on such compounds as amino acids, hydrophilic proteins, and carbohydrates, which allow not only maintenance of water absorption and cell turgor under drought conditions, but also protect cell membrane and metabolic mechanisms. This osmotic adjustment is especially important in roots, allowing their growth under drought17.Under drought stress conditions plants respond by adjusting various physiological and morphological traits27. Some studies have shown distinct soil effects on the seasonal timing of leaf development and the metabolism of carbon and nitrogen30. All this may in turn affect the responsiveness of trees to drought through interacting effects on mechanisms controlling tree water balances and mediating physiological and molecular stress tolerance. Indeed, previous studies have shown such interacting effects of drought and soil on annual shoot growth, root development, nitrogen and carbon metabolism, leaf water status, synthesis of anti-oxidative compounds and osmotic stress defense 34. This demonstrates that the soil may play a significant role in the function of trees under water limited conditions and thus deserves further attention in experimental studies elaborating the drought tolerance of trees.It is well-known from the current literature that varying soil characteristics, such as texture, acidity, and nutrient availability, may greatly affect tree growth and its allocation to leaves, stem, and root organs5. Indeed, morphological and physiological data suggest that despite its presumed susceptibility to drought, populations of European beech from dry sites are more tolerant to drought than populations from wet sites38. For example, by monitoring physiological parameters such as leaf water potential and carbon isotopic discrimination during a 3 year period including the year 2003, one of the driest years in Europe, it was found that beech trees growing in a xeric Mediterranean environment did not demonstrate substantial signs of drought stress compared to beech trees in Central Europe39. These findings were further supported by dendroecological data41 and drought experiments with seedlings. When exposed to drought, not only water potential, transpiration rates and growth are less affected in seedlings from xeric sites, but also the root/shoot ratio is higher, which facilitates access to soil water43. Additionally, studies based on genetic markers such as AFLPs and SNPs have shown differences in genetic variation between beech populations growing in environments with different moisture availability45. Thus, although these results suggest that there is local adaptation to drought in European beech, little is still known about the genetic variation underlying drought tolerance in this species.Common garden experiments and provenance trials have shown that local adaptation is common in forest trees being reflected in differences in adaptive phenotypic traits that match local environmental conditions48. A common approach for the identification of genetic polymorphisms underlying adaptive traits is to test for associations between phenotypes and genetic variation50. Association studies can be classified as Genome-Wide Association Studies (GWAS) and candidate gene-based association studies51. GWAS allow searching for genetic associations with adaptive variation throughout the genome; however, it is still rather expensive and requires numerous genome-wide markers that often are noncoding or have unknown gene function53. In contrast, candidate gene-based association studies use genetic variation in genes that are likely directly involved in genetic control of adaptive traits of interest54. Several studies using this approach in plants have been able to successfully identify associations between SNPs in candidate genes and phenotypic traits such as disease resistance55, cold-hardiness56, bud and flower phenology58, and wood property traits59.The genetic variation underlying many traits in forest trees is likely to be polygenic, controlled by a large number of loci with small and moderate effects62. Recently, the development of SNP markers in candidate genes that are potentially involved in traits related to climate has been reported for European beech65. Not only associations between some of these SNPs and important traits, such as bud burst, bud set, growth rate, chlorophyll fluorescence, and transpiration have been detected67, but also their association with different geographic and environmental variables, such as elevation, temperature, precipitation, and aridity69, suggesting their relevance for the study of genetic variation important for adaptation to climate change in European beech.Among different genetic markers, single nucleotide polymorphisms (SNPs) are the preferred choice of molecular genetic markers for association studies, since they are the most common polymorphisms in the genome. They are found in both genic and intergenic regions and can represent mutations that might lead to changes in phenotype45 indicating the importance of further exploring genetic variation that could be involved in drought tolerance in these populations. In this study, the saplings from twelve beech populations along steep precipitation gradients in the upper Rhine and Rhône valleys in Switzerland were selected and subjected to simulated summer drought conditions. The drought simulation experiment with the selected saplings was conducted on two contrasting types of natural forest soil, with acidic or calcareous characteristic, respectively, to account for potentially interfering soil effects. Photosynthesis and growth are critical processes affected by drought18, therefore, they were used to evaluate the performance of the saplings under such conditions. Photosynthetic performance was measured using the following three parameters: maximum quantum efficiency of PSII (FV/FM), performance index of PSII on absorption basis (PIabs), and total performance index of PSII (PItot). Growth was evaluated as increment in stem diameter growth (SDG). The association between these traits and 70 SNPs in 23 candidate genes potentially involved in drought response was tested.Precipitation gradients might reflect differences in water availability that could promote local adaptation. Therefore, their study represents a good environmental approach to search for genetic variation underlying drought tolerance related traits. In this study, saplings of European beech from populations occurring along steep precipitation gradients in Switzerland were collected. A previous study showed signatures of natural selection in these saplings3 m−3 on acidic soil and to 0.07 and 0.11 m3 m−3 on calcareous soil in 2013 and 2014, respectively. Volumetric soil water content in regularly irrigated control plots ranged in both soil types always above 0.15 m3 m−3  = 2.69, P = 0.001), PIabs  = 7.79, P < 0.001) and PItot  = 6.81, P < 0.001). Likewise, the effect of treatment was significant for PIabs  = 15.24, P < 0.001) and PItot  = 33.69, P < 0.001): saplings under drought treatment demonstrated lower PIabs and PItot than control saplings  = 3.42, P < 0.01) and for PItot  = 5.36, P < 0.001). No significant differences were observed between populations under the drought treatment for the chlorophyll fluorescence parameters.Mixed-effects model analysis did not reveal significant differences among populations for the chlorophyll fluorescence traits  = 18.88, P < 0.001; SDG 2014: F = 97.36, P < 0.001; SDG 2013–2014: F = 58.46, P < 0.001); significant and close to significant regarding soil  = 18.57, P < 0.001; SDG 2013–2014: F = 4.11, P = 0.067); and significant or close to significant regarding population .Mixed-effects model analysis for SDG showed that differences among saplings growing in different chambers were not significant (Supplementary Tables t(373) = 4.15, P < 0.001), SDG 2014  = 9.43, P < 0.001) and SDG 2013–2014  = 7.50, P < 0.001). On calcareous soil, mean differences were also significant for SDG 2013  = 2.84, P < 0.01), SDG 2014  = 5.93, P < 0.001), and SDG 2013–2014  = 4.91, P < 0.001).Saplings under drought demonstrated lower SDG than control saplings Fig. . On acidt(339) = 1.78, P = 0.05) and drought  = 5.42, P < 0.001) conditions, and for SDG 2013–2014 under drought  = 2.75, P < 0.01). Indeed, the interaction between treatment and soil was close to significant for SDG 2014  = 3.49, P = 0.067) , and the results show that the effect of soil on SDG is more obvious under drought conditions, especially for SDG 2014 and SDG 2013–2014  = 4.217, P < 0.001; SDG 2014: F = 4.677, P < 0.001; SDG 2013–2014: F = 6.561, P < 0.001). The t-test for equality of means revealed that there were significant mean differences between Saxon, one of the most xeric populations, and other four populations with higher precipitation. In all cases, saplings from Saxon showed higher SDG . The comparison between GLM and MLM showed that both models produced similar results having similar distribution of P-values in the quantile–quantile plots , saplings collected underneath the same adult tree were more related having a higher proportion of the rQG coefficients above 0.1  showed a trend toward significant association with the FV/FM index (Table R2) with significant association was relatively high, between 5.8% and 13.4% . They were located in coding regions: one of them representing a non-synonymous substitution (50_39), and the rest, synonymous substitutions .The association analysis using MLM revealed that among the 70 SNPs analyzed, five SNPs (7.14%) showed significant association with the PI110_1_293 in the Cytosolic class I small heat-shock protein gene, which was associated with the PIabs index for chlorophyll fluorescence under control rather than drought conditions. Homozygous individuals TT for this SNP showed a mean PIabs by 9% higher than homozygotes AA and heterozygotes TA, even though the differences were not significant  than homozygotes AA  than AA (P < 0.001) and CC (P < 0.05) saplings  than homozygotes AA  and 2013–2014 (P < 0.01), and compared to heterozygotes TC in 2014 (P < 0.001) and 2013–2014  and 2013–2014 (P < 0.01), and also compared to heterozygotes GA in 2014 (P < 0.001) and 2013–2014  were used  could also inflate R2 of an individual marker, since it could jointly contribute to the trait together with other markers48. Among the significant SNPs, LD was found between pairs of SNPs in the same gene: between IDH_1 and IDH_4, and between 50_39 and 50_23245. Thus, an inflation of R2due to LD also cannot be ruled out. In addition, it is difficult to identify the likely causal SNP when it is in LD with other SNPs that are also significantly associated with a trait.In total, five SNPs  demonstrated significant association with the studied traits Table . The phe90. Thus, under control/acidic soil conditions, the 110_1_293 SNP in the Cytosolic class I small heat-shock protein gene was significantly associated with the chlorophyll fluorescence index PIabs. Nevertheless, the two alleles at this SNP seem to have low differences in their phenotypic effect, since the differences in the performance of the different genotypes were not significant  the discovery of SNPs throughout the genome of non-model species is now more feasible105, making possible the implementation of GWAS. This, together with the recent publication of a reference genome of European beech106, will help to get more insight into the genetic basis of drought tolerance in this species.It has been also estimated that the number of causative loci underlying polygenic traits in plants could be in the order of several hundred107. Heterozygosity was positively associated with growth, stress tolerance, and survival in different pine species108. However, heterozygosity did not always strongly correlate with fitness110. Indeed, our results show that only heterozygous individuals at 50_39 SNP in the CTR/DRE binding factor gene showed better performance over homozygotes  indicated a relatively weak correlation. Therefore, the better performance of some saplings cannot be attributed to the general level of heterozygosity in the studied SNPs. Further exploration including additional SNPs that provide a better coverage of the genome could lead to a better estimation of the whole-genome heterozygosity and shed light on its association with the performance of the saplings.Heterozygosity has usually been considered as advantageous for fitness. For example, highly heterozygous individuals in crops and domesticated animals can express heterosis—better vigor and yieldses Figs. a, and, ies Figs. . Only FV111. During stress conditions, epigenetic changes play an important role leading to acclimation responses via phenotypic plasticity111. Moreover, some epigenetic changes can be very stable and inherited from one generation to the next, and thus, facilitate adaptation in important traits such as drought tolerance113. Even though our results showed that some genotypes confer a better performance under drought conditions, which suggest genetic adaptation, it cannot be ruled out that acclimation, and thus, phenotypic plasticity, could have also played a significant role in the performance of the saplings. Indeed, the tested SNPs could represent adaptive genetic variation and also be involved in acclimation, since their genes participate in stress response. Although it is difficult to disentangle the relative contributions that phenotypic plasticity and genetic variation have on the phenotype114, future research should take into consideration both genetic and epigenetic variation, as both contribute to adaptation to environmental changing conditions.In addition to genetic variation, epigenetic variation could also be responsible for differences in phenotypic traits. Epigenetic marks could also explain some of the phenotypic variation that is usually attributed to genetic variation. Thus, if it has an effect on adaptive phenotypic traits and is stable enough for selection to act, epigenetic variation could provide long lasting selective advantage and be important for local adaptationTwelve beech populations along steep precipitation gradients in the upper Rhine and Rhône valleys in Switzerland were selected Table . From ea26. In a randomized design, two saplings per population were transplanted on each type of soil in each chamber. Two growing seasons (2011 and 2012) were used for saplings acclimatization. To exclude natural precipitation and control water irrigation, the sliding roofs of the chambers were closed from May to October. During that period, the saplings were irrigated every 2 or 3 days with 50 l of water per m−2 resembling ambient rainfall composition26. During hot periods, the amount and frequency of irrigation were incremented to keep soil water content above 0.15 m3 m−3 at 10 cm soil depth and counterbalance high evapotranspiration.The MODOEK facility is composed of 16 chambers equipped with an automated irrigation system for the control of water supply. Each chamber has a sliding roof and is split below ground in two concrete-walled lysimeters with a depth of 150 cm, each containing acidic (Haplic Alisol) or calcareous (Fluvisol) forest soil of similar texture with a pH of 4.0 and 6.9, respectivelyDrought conditions were imposed in eight of the chambers in 2013 and 2014 by excluding water irrigation from May to August. In particularly hot days, short irrigation pulses were applied to avoid too fast soil drying and irreversible damage of the saplings. The remaining eight chambers were used as control by not applying drought conditions and maintaining irrigation every 2 or 3 days as described above. A summary on the number of saplings from each population under each treatment (control and drought), type of soil , and chamber is provided in Supplementary Table Water relations of both soil and saplings was monitored to assess soil drought and physiological stress intensities. In each lysimeter, volumetric soil water content was measured at 10 cm soil depth using PC-controlled moisture probes and 5TM soil moisture and temperature sensor . Predawn leaf water potential was measured across a subset of 58 saplings on acidic soil (mesic population Mastrils and xeric population Saxon) using a Scholander pressure chamber M 600 .78 to evaluate the leaf physiological stress response of the transplanted saplings growing on acidic soil. In short, fast fluorescence kinetics were measured in the dark-adapted leaves between 11:00 and 12:00 using a portable plant efficiency analyzer . The increment in fluorescence was recorded after a saturating light pulse of 3500 μmol quanta m2/s of red light (650 nm). Based on the obtained fluorescence kinetics, different parameters describing the stages of the photochemical process with regard to energy absorption, trapping and electron transport are obtained providing information on stress-induced impairments of the photosystem II. We selected the following three fluorescence related parameters: (1) the maximum quantum efficiency of PSII (FV/FM), which provides information on the fraction of the total energy flux trapped by PSII reaction centers; (2) the performance index of PSII on absorption basis (PIabs), which provides information on energy conservation from photon trapping to the reduction of intersystem electron acceptors; and (3) the total performance index of PSII (PItot), which provides information on energy conservation from photon trapping to the reduction of final electron acceptors in PSI115.In July 2014, chlorophyll fluorescence was assessed as previously describedIn addition, the SDG was measured in all saplings as an indicator of morphological response to the simulated drought conditions. SDG was measured 5 cm above ground in March 2013, October 2013 and September 2014. Increment in SDG was calculated as the difference in stem diameter between March-October 2013 (SDG 2013), October 2013-September 2014 (SDG 2014) and March 2013-September 2014 (SDG 2013–2014).Leaves from the saplings were collected, dehydrated with silica gel and stored at room temperature until DNA extraction. DNA extraction was performed with the DNeasy 96 Plant Kit . To examine the amount and quality of the extracted DNA, electrophoresis in 1% agarose gel using 1X TAE as running buffer was performed. DNA was stained and visualized with Roti-Safe GelStain  under UV illumination, and compared with a Lambda DNA size ladder .Quercus robur—GOT066, FIR065 and FIR004116. The rest of the markers were random genomic microsatellites developed for F. crenata 117 and F. sylvatica 121. The markers were pooled in four multiplexes for the PCR amplification: all EST markers were included in the first multiplex; all sfc markers in the second multiplex; FS3-04 and msf11 markers in the third multiplex; and csolfagus and Fagsyl markers in the fourth multiplex. Primers for the GOT066, FIR065, sfc0018, sfc1143, FS3-04, and Fagsyl_002929 markers were labeled with 6-hexachlorofluorescein (HEX) fluorescent dye, whereas primers for the FIR004, sfc0161, sfc1063, mfs11, csolfagus_06, csolfagus_19, and Fagsyl_003994 markers were labeled with 6-carboxyfluorescein (FAM) fluorescent dye. The PCR amplifications were carried out in a final volume of 15 μL containing about 10 ng of genomic DNA, 1X reaction buffer 2SO4, 0.2% w/v Tween-20; Solis BioDyne, Tartu, Estonia), 2.5 mM MgCl2, 0.2 mM of each dNTP, 1 unit of Taq DNA polymerase , and 0.3 μM of each primer. The PCR protocol contained an initial denaturation at 95 °C for 15 min, 30 cycles of 1 min denaturation at 94 °C, 30 s annealing at 47 °C (first multiplex) or at 55 °C , and 1 min extension at 72 °C. The final extension step was performed at 72 °C for 20 min followed by the hold at 16 °C. The PCR fragments were separated in an ABI 3130xl Genetic Analyzer , and the fragment sizes were scored using the GeneMapper 4.1 software  based on the internal size standard GS 500 ROX .To establish the genetic relatedness among pairs of individuals, saplings were genotyped at 13 highly polymorphic microsatellite markers. Three of them were EST-linked markers originally developed for 45. In brief, based on previous studies on SNP discovery in European beech65, candidate genes that very likely participate in stress response according to the UniProt (www.uniprot.org) and the Arabidopsis Information Resource (TAIR) (www.arabidopsis.org) databases123 were selected. SNPs for genotyping in those genes were chosen based on their identification as haplotype tag SNPs by the software htSNPer 1.0124 or because they already showed signs of natural selection in previous studies68. For primer design, sequences surrounding the SNPs were sent to LGC Genomics Ltd. where SNP genotyping was performed using the PCR-based KASP genotyping assay . In total, 70 polymorphic SNPs in 23 candidate genes were genotyped in the saplings and represented 19 non-synonymous, 26 synonymous and 25 non-coding SNPs . FV/FM was not normally distributed for most of the groups (Supplementary Table https://www.minitab.com).Normality and homogeneity of variances for each trait was tested using Shapiro–Wilk test and Levene’s test, respectively, using the IBM SPSS statistics software .For the chlorophyll fluorescence traits Ft-tests for equality of means to compare saplings under control vs. drought treatment, saplings on acidic vs. calcareous soil and populations under drought treatment were carried out using the IBM SPSS statistics software .For the SDG traits, effects of population, treatment, soil and chamber were tested for significant differences. For the mixed-effects model, population was considered a random factor, whereas treatment, chamber, and soil were considered fixed factors. Besides, 125. For the analyses, individuals were grouped according to the experimental conditions: treatment and treatment/soil. Besides, the analysis for all saplings together regardless of experimental conditions was also performed. In this case, phenotypic data were normalized by dividing the data by the mean of each experimental condition. Association analyses were performed using the general linear model (GLM) and the mixed linear model (MLM). The GLM took into account population structure that potentially could be developed due to selectively neutral genetic processes such as genetic drift as a confounding factor (Q), where the Q-matrix was calculated using the tentatively selectively neutral microsatellite markers and the STRUCTURE 2.3.4 software126 as described in Cuervo-Alarcon et al.45. The MLM took into account both Q and kinship (K) as confounding factors. The K-matrix was calculated also based on the microsatellite genotypes as the pairwise relatedness coefficient rQG estimated according to Queller and Goodnight127 using the GenAlEx software129; negative values of rQG were set to zero. Two different methods were used to account for multiple testing in GLM and MLM analyses: Bonferroni correction using P-value cut-off equaled 0.00143 and adjustment of P-values using the false discovery rate130  implemented in the R function “p.adjust”131. Only SNPs with a minimum allele frequency (MAF) higher than 0.05 were used for the association analysis.Associations between SNPs and the phenotypic traits were tested using the TASSEL 5.0 software129. Spearman’s correlation analyses between individual heterozygosity and the traits were performed using the R 3.3.1 software131.Furthermore, relationships between heterozygosity and the measured traits were evaluated. Individual heterozygosity  was calculated using the GenAlEx 6.5 softwareSupplementary Information.


With the development of next generation sequencing (NGS) technology and genotype imputation methods, statistical methods have been proposed to test a set of genomic variants together to detect if any of them is associated with the phenotype or disease. In practice, within the set, there is an unknown proportion of variants truly causal or associated with the disease. There is a demand for statistical methods with high power in both dense and sparse scenarios, where the proportion of causal or associated variants is large or small respectively.We propose a new association test – weighted Adaptive Fisher (wAF) that can adapt to both dense and sparse scenarios by adding weights to the Adaptive Fisher (AF) method we developed before. Using simulation, we show that wAF enjoys comparable or better power to popular methods such as sequence kernel association tests (SKAT and SKAT-O) and adaptive SPU (aSPU) test. We apply wAF to a publicly available schizophrenia dataset, and successfully detect thirteen genes. Among them, three genes are supported by existing literature; six are plausible as they either relate to other neurological diseases or have relevant biological functions.The proposed wAF method is a powerful disease-variants association test in both dense and sparse scenarios. Both simulation studies and real data analysis indicate the potential of wAF for new biological findings. Compared to common SNVs, the number of rare SNVs is much larger, and their locations on the human genome are often unknown before genotyping all the study samples, which makes DNA hybridization-based genotyping technology (e.g. SNP array) less useful in genotyping rare SNVs. Thanks to the advent of NGS, researchers now are enabled to reliably measure rare SNVs. Furthermore, because of the development of fast imputation tools , while keep the other (1−π)K effects zeros. Trait of n=1,000 subjects are generated from model  is taken to be the logit link function. We increase the number of SNVs, K, from 50 to 500 with an increment 50, while holding the effect proportion π and the effect size δ constant. For the dense scenario, π=20% and δ=0.2. For the sparse scenario, π=2% and δ=1. Figure d follows behind with almost the same for small K and slightly inferior performance for large K. Min-P, on the other hand, is much less powerful than the other methods. For the sparse scenario, Min-P is the most powerful method. Our wAF has the second highest power, which is about 5% higher than the other methods including SKAT, SKAT-O and aSPU. wAF d has the third best performance, following tightly after wAF. For all these compared methods, the type I errors are well-controlled empirically as shown in the Additional file When generating binary trait, h(·) is taken to be the identity link function and random errors are standard normal random variables. Again, K is increased from 50 to 500 with an increment 50, while π and δ are held constants. For the dense scenario, π=20% and δ=0.1. For the sparse scenario, π=2% and δ=0.5. Based on power curves in Fig. d, and much better than Min-P. Whereas in the sparse scenario, Min-P is the most powerful method, seconded by wAF. wAF d has slightly less power than wAF, but has higher power than aSPU, SKAT and SKAT-O. Similar to the binary traits, all type I errors are well-controlled empirically.When generating continuous trait, βk’s are sampled from U distribution. The result shows that wAF d has large powers in both dense and sparse scenarios. wAF has almost the same high power with wAF d in the sparse scenario. In the second case, MAF’s are generated from U. wAF and wAF d work well especially in the sparse scenario. Power curves for these two cases are shown in Additional file We also consider two other cases where 1) all SNV effects are of the same direction; 2) all variants are rare variants (RVs). In the first case, nonzero d on European-American subjects from GAIN SCZ data. 2,548 subjects are selected after quality control, including 1,170 cases and 1,378 controls. Genotypes are imputed using Michigan Imputation Server [To further evaluate the performance of our methods, we apply wAF and wAF d methods based on model  rep repd. FAstudies  s) sd. FAMd. RPL5 is identified by International Multiple Sclerosis Genetics Consortium (IMSGC) [Gene RPL5 is the third significant by wAF and the second significant by wAF  (IMSGC)  and Rubi (IMSGC)  as a ris (IMSGC) , RPL5 is (IMSGC) , 42. ThiFor the other genes that we detect, Hek et al.  reports Among the thirteen genes listed in Table d with aSPU, SKAT and SKAT-O on these genes. It is noticeable that the five methods perform differently on gene CERCAM. wAF, wAF d and aSPU attain P-values that reach 1×10−4 while SKAT and SKAT-O can only reach 1×10−1. After calculating the marginal P-values for each of the 228 variants on CERCAM, we find that only 1 variant has a P-value smaller than 1×10−4 while the other P-values are all larger than 0.01  is a non-adaptive weight only depends on the genotype data, and w(Gk)=sd(Gk), and c∈{1,2,…,8,∞}; for wAF, c∈{1,2,…,K}, where c) are not adaptive to this proportion and only applies to either the dense or sparse scenario. In this paper, we proposed a new adaptive method wAF as an alternative to aSPU with better or comparable power. The adaptiveness of wAF allows it to perform better than current available methods in both dense and sparse scenarios, and to detect potential new genes associated with diseases.Additional file 1 Additional results for simulation studies and schizophrenia data application. Results for GAW 17 data application.


HS3ST4, C2CD2, 2p16.1, 10p15.1, and 18q12.2. Nevertheless, these five variants explain only a small fraction of the variation in the gut microbiota. We subsequently investigated the heritability of each of the 21 core genera and found that the abundances of six genera are heritable. We propose that the gut microbiota composition is affected by a highly polygenic architecture rather than several strongly associated variants in the Japanese population.Numerous host extrinsic and intrinsic factors affect the gut microbiota composition, but their cumulative effects do not sufficiently explain the variation in the microbiota, suggesting contributions of missing factors. The Japanese population possesses homogeneous genetic features suitable for genome-wide association study (GWAS). Here, we performed GWASs for human gut microbiota using 1068 healthy Japanese adults. To precisely evaluate genetic effects, we corrected for the impacts of numerous host extrinsic and demographic factors by introducing them as covariates, enabling us to discover five loci significantly associated with microbiome diversity measures:  Ishida et al. investigated genome-wide associations between 1068 healthy Japanese adults and their gut microbiota. They find that five loci account for a small variation of the human gut microbiota composition and that six genera are heritable. This study suggests that human gut microbiota composition is likely to be affected by multiple genetic loci. A significant portion of previous research has focused on discovering links between bacterial composition and various diseases such as metabolic syndromes, cancers and inflammatory bowel disease (IBD), while a fundamental understanding of factors shaping the gut microbiota composition of healthy individuals remains to be elucidated. Therefore, a major concern is how various types of host intrinsic and extrinsic factors contribute to the microbiota composition. Numerous host extrinsic factors, including diet, medication, physical activity, and health status, have been reported to affect the microbiota composition5. Conversely, the contribution of host genetics has only recently emerged and only a limited number of studies have been reported. In human studies, Goodrich et al. demonstrated that microbial communities in monozygotic twins were more similar than those in dizygotic twins, indicating that gut microbial composition was influenced by host genetics6. Previous studies have identified several heritable bacterial taxa, including Faecalibacterium, Unclassified Ruminococcaceae , Coprococcus, Bifidobacterium, Parabacteroides, and Bacteroides, using European and Korean populations11. The first genome-wide association study (GWAS) using 93 subjects from the Human Microbiome Project (HMP) identified several key host genes affecting the microbiota, including the LCT gene, whose variant correlated with the abundance of Bifidobacterium12. The effect of the LCT gene variant has been replicated in multiple studies using more than 1000 individuals with European ancestry, showing indubitable evidence14. Our previous targeted approach also indicated a possible contribution of monomorphic LCT gene variants associated with low lactase activity to the high abundance of Bifidobacterium in Japanese subjects15. However, genome-wide approaches to study the human gut microbiota have been restricted to Europeans, and the genomic basis for the microbiota in other populations remains unclear since genetic backgrounds differ among populations. Furthermore, most of the previous GWASs did not comprehensively consider numerous extrinsic factors for the precise evaluation of genetic factors.Recently, interest in the gut microbiome has increased because of its tremendous potential to impact our physiology16. In addition, the Japanese population has often been viewed as an Asian population with a more genetically homogeneous community than other populations such as Europeans17. This homogeneity is a suitable feature for GWAS because this approach assumes a homogeneous population in which the relationship between a SNP and a phenotype is random under the null hypothesis, and the presence of population stratification can cause spurious associations. Therefore, the GWAS analysis of the Japanese gut microbiota may reveal novel findings similar to those obtained from previous GWASs of several phenotypes19.Japanese people have a characteristic gut microbial composition compared to those of people from other nations that cannot be simply explained by differences in dietary habits aloneThis study reports the application of GWAS to identify associated loci related to Japanese gut microbiota for the first time to the best of our knowledge using 1068 healthy Japanese adults recruited from the customer base of MYCODE, a personal genome service in Japan. We incorporated 138 extrinsic and demographic variables as covariates for the analysis, which allowed us to precisely evaluate genetic associations. We also estimated the heritability of abundances of individual microbial genera to reveal a total contribution of common genetic factors.We utilized 16S rRNA gene sequencing to characterize the gut microbiota of 1068 healthy Japanese adults, whose background data are shown in Table To identify candidate covariates for GWAS analysis, we investigated the associations of various host extrinsic and demographic factors with gut microbiota parameters, the relative abundances of the 21 core genera and the bacterial diversity indices. A total of 138 variables were composed of nutrition (43 variables), food (74 variables), and nondietary groups (21 variables), which were sub-classified into five categories  (Supplementary Data P = 0.00387). Bifidobacterium, which is a well-known beneficial genus, showed positive associations with the intake amount of carbohydrates and full-fat milk, which contains lactose  with host microbiota parameters  , showing a nominally significant effect of Bristol stool scale (P = 1.13 × 10−4). The HS3ST4 locus was associated with the abundance of Faecalibacterium  in male subjects , and three variables: age, “soup consumed with noodles”, and potassium showed nominally significant effects . The abundance of Oscillospira was associated with the 10p15.1 locus in female subjects  and showed a lower p-value under a dominant model (P = 1.28 × 10−8) , and Bristol stool scale, bowel movement frequency, and coffee variables showed nominally significant effects . The C2CD2 gene locus was associated with the abundance of Erysipelotrichaceae;g  in female subjects . “Rice cakes and Japanese-style pancakes”, the second principal component of the host genotypic data, and age variables showed nominal significant effects . The alpha diversity index, Chao1 showed a significant association with the 18q12.2 locus, which was located in a large intergenic region, in female subjects , and the association p-value decreased slightly when the dominant model was applied (P = 5.12 × 10−8) , and three variables: “Rice cakes and Japanese-style pancakes”, the second principal component of the host genotypic data, and age showed nominal significant effects . The five newly identified loci showed significant sex differences in their effect sizes even though effect directions were the same in both sexes  Fig. . The inccts Fig. . The ass−8) Fig. . This sicts Fig. . This as−8) Fig. . This si5, which indicated that an ATXN1 gene region associates with the abundance of Ruminococcus. However, we obtained the association only in female subjects   of a multiple regression model incorporating lead SNPs we identified, were observed  by genome-wide SNPs using GEMMAThe gut microbiota composition shows large inter-individual variations, and identification of the factors contributing to gut microbial variations has been a major concern because previously identified factors could not sufficiently explain the variations. In this study, we performed the first GWAS for Japanese gut microbiota to the best of our knowledge using 1068 healthy Japanese adults to identify genetic loci related to the microbiota. We also investigated the heritability of Japanese gut microbiota using genome-wide SNPs.Prevotella, Faecalibacterium, Oscillospira, and Erysipelotrichaceae;g and the bacterial diversity Chao1 index . The protein expression of the gene has not yet been examined in human tissues . The mechanism underlying the relationship between the HS3ST4 gene variant and the gut microbiota should be further investigated. C2 domain-containing protein 2 encoded by the C2CD2 gene is expressed in the intestine and colon28  than the current GWAS standards (tens or hundreds of thousands). We should note here the following concerns in our study. One concern is that we used a more liberal threshold to extensively identify potential associations rather than a strict study-wide Bonferroni threshold, which was adjusted by not only the number of SNPs but also the number of phenotypes tested. The five loci identified with the threshold reached neither a strict study-wide Bonferroni threshold nor the usual genome-wide significance threshold of 5 × 10−8. The other concern is that our study lacked replications of the identified associations using independent cohorts because Japanese samples on a scale of a thousand are not currently available. These concerns may increase the false positive rate on the identified variants, and we cannot exclude the possibility that the above mentioned factors other than the population-specific genetics affected our results. A larger-scale analysis is required to confirm the population-specific genetic effects.The five loci identified in this study were not reported in previous European GWASs, and most reported associations from previous European GWASs were not replicated in our samples. These results indicate that population-specific genetics might affect the difference in the effect for each gut microbe. For one possible explanation, the different effect sizes of the SNPs might be due to the different gene-environment interactions or the difference in LD structure in the region between the two populations. Another possibility could be the low statistical power in the European population due to the low minor allele frequencies of the SNPs, such as rs1033781 (in the 10p15.1 locus), whose minor allele frequency is 0.004 in the HapMap CEU population. The 25. Sex hormone-related enzymes are already known to affect gut microbial differences between males and females23. Such sexual dimorphism in gene expression would explain sex-specific genetic effects. Thus, the sex-specific effects of the five loci should undergo further validation in independent cohorts.The five associations were significant only in the sex-stratified analysis, and significant sex differences were detected in their effects. The sex-specific differences in the gut microbiota were reported in multiple previous studies11. The most highly heritable genus in this study, Faecalibacterium, was also concluded to be heritable in these studies except for one European study11. For the second and third most highly heritable genera in our study, Clostridiales;Other and Ruminococcus, only one European study but not the other three agreed with our results9. The fourth most highly heritable genus in our study, Ruminococcaceae;g, was explored in only one European study, which concluded that it was heritable8. Concerning the other heritable genera in this study, Erysipelotrichaceae;g and Lachnospiraceae;Other, none of the four studies found significant heritability. Furthermore, 13 of the 15 heritable taxa reported previously did not show evidence of heritability in our study. Taken together, these results allow us to argue population-specific heritability, which would be caused by population-specific factors such as allele frequencies and variation in environmental conditions, in addition to heritability overriding population differences. However, we need more powerful studies to determine whether different conclusions for heritability estimates across studies result from population differences or other factors.We found four previous studies reporting heritability estimates, one of which studied Koreans, and the other three of which studied Europeans36. This consortium comprises 18 population-level worldwide cohorts for whom the gut microbiota, host genotype, and anthropometric, metabolic and disease-related individual outcomes, have been analyzed. This type of data set enables large-scale analyses and meta-analyses of diverse populations, which will generate new knowledge concerning the universal and population-specific effects of genetic factors on human gut microbiota.We demonstrated that combinatorial contributions of multiple common genetic variants affect the Japanese gut microbiota. However, our study design did not possess an efficient power to identify all the contributing loci. In addition, contributions of missing factors, such as rare genetic variants and gene-environment interactions, remain to be identified. To this end, we need to increase the number of samples. An open microbial consortium termed the MiBioGen consortium was recently initiatedHealthy Japanese participants were selected for this study in the following manner. After purchase of MYCODE , a personal genome service in Japan, the customers sent back their saliva samples for genetic testing to the DeNA Life Science’s (DLS’s) laboratory with a written application form. On the application form, they can choose whether to give consent to participate in MYCODE Research, in which their anonymized genetic data and/or health-related information would be used for scientific research purposes.15.Among participants in MYCODE Research, people between 20 and 64 years old were invited to participate in the specific research for this study by an email informing its overview. Potential participants expressing their interest provided informed consent for this study and completed a screening questionnaire online under a protocol approved by the ethics committee of DLS Inc. (protocol #20160727_1) and the ethical committee of Institute of Medical Science, the University of Tokyo  (IMSUT-IRB) (protocol #29-29-1125). In total, 1,907 people applied for the study over a four-day period, and 1083 turned out to be eligible for inclusion. The final 1068 subjects were selected through the quality control of subjects as described below, as well as in our previous studyp-value below 10−6, or a call rate under 95%. The quality control of subjects was performed using the following criteria. First, two subjects with call rates under 95% were excluded. Subsequently, nine subjects who were one of a pair with a proportion of identical by descent greater than 0.185 and one subject failing a sex check were removed.Subjects were genotyped on the Infinium OmniExpress-24+ BeadChip or Human OmniExpress-24+ BeadChip . All of these experiments were performed at the DLS laboratory. Quality control procedures for the genotype data included the exclusion of SNPs with a minor allele frequency under 0.01, a Hardy-Weinberg equilibrium 38. Three outlier subjects were removed from the analysis based on visual inspection of the top two principal components. Then, we confirmed that all the residual subjects had Japanese ancestry by PCA using their data along with the Phase 3 HapMap data   was performed based on the genotype data of the subjects after the quality control procedures described above using PLINK version 1.9The screening survey questionnaire contained questions to select eligible subjects according to the inclusion and exclusion criteria described above, as well as to ask bowel movement frequency, yogurt intake frequency, and whether the subjects took supplements containing probiotics, oligosaccharide, or fiber more than twice a week. Five variables for the analysis were obtained from certain questions, which are listed in Supplementary Data Subjects were provided with a fecal sample collection kit  along with the instructions for sample collection. Subjects were requested to complete an online questionnaire to ask health conditions on the day of fecal sampling. The questions utilized for the analysis in this study were whether subjects had symptoms of diarrhea and/or fever, whether they received medications within one month, and their stool consistency measured by the Bristol Stool Scale  41. The total stress response consisted of 18 items related to the psychological stress response, including manifestations such as lassitude, irritation, fatigue, anxiety, and depression, and 11 items related to physical stress response to somatic symptoms. Higher scores were related to a higher stress response.Each subject was asked to complete an online questionnaire about physical activity, smoking status, sleep duration, and stress response, and these variables were used for the analysis  was used to remove reads mapped to the PhiX 174 sequence and the Genome Reference Consortium human build 38 (GRCh38). Thereafter, the 3’ region of each read with a PHRED quality score less than 17 was trimmed. Trimmed reads less than 150 bp in length with an average quality score less than 25 or those lacking paired reads were also removed. The trimmed paired-end reads were combined by the fastq-join script in EA-Utils43 (ver. 1.1.2–537). Potential chimeric sequences were removed by reference-based chimaera checking in USEARCH44 (ver. 5.2.32) and the Genomes OnLine Database (GOLD) (http://drive5.com/otupipe/gold.tz).DNA extraction from fecal samples, subsequent sequencing of the V3-V4 region of bacterial 16S rRNA gene, and data processing of the sequences were conducted as described previously46. For genus-level analysis, the sequences were assigned to operational taxonomic units (OTUs) by open-reference OTU picking47 with a 97% pairwise identity threshold and the Greengenes reference database48.Non-chimeric sequences were analyzed via the QIIME software package version 1.8.0DNA sequences corresponding to 16S rRNA gene data were deposited in DDBJ under accession numbers DRA007985-DRA007990 , in which rows and columns were ordered based on a hierarchical clustering with the ward. D2 algorithm. The independence of the patterns of the two major genus clusters obtained from each hierarchical clustering based on association data of nutrition, food, and nondietary variables was evaluated by Fisher’s exact test using R. To investigate the association of each of the extrinsic and demographic variables with the beta diversity, non-metric multidimensional scaling (NMDS) was first performed using the metaMDS function in R (in the package ‘vegan’) for the weighted UniFrac metric data. Then, the associations between the variables and the NMDS ordination were analyzed using the envfit function in R (in the package ‘vegan’), and we found no variables with a significant association (P < 3.62 × 10−4).To investigate the associations of each of the 138 extrinsic and demographic variables with the relative abundances of genera or the alpha diversity, multivariable regression analyses were performed using the function lm in R and including sex and age variables as covariates. The 138 variables were classified into the following seven categories: (1) demographics, 2) physical characteristics, (3) nutrition, (4) food, (5) lifestyle, (6) stress response, and (7) health information. The nutrition variables and most of the food variables, which were equivalent to nutrient intake and food intake, respectively, were estimated from the BDHQ. The other variables were assessed by one of three questionnaires  or the BDHQ, as reported in Supplementary Data  physicalFaecalibacterium), demonstrating little inflation in p-values 54 as a reference. For the purpose of quality control, we used imputed variants with an Rsq ≥ 0.7 in the subsequent GWASs. The proportion of variance explained by an individual SNP was calculated by 2 f(1–f) a2, where f is the frequency of an allele and a is its standardized coefficient55. SNPs were annotated with SNPnexus (http://snp-nexus.org/index.html)56, and the protein expression data of overlapping genes in intestinal and colon tissues were obtained according to The Human Protein Atlas (http://www.proteinatlas.org/)28. Manhattan plots and quantile-quantile (Q-Q) plots were generated using the qqman R package57, and the Q-Q plots for the five significant associations indicated no remarkable discrepancy from the null hypothesis  from the PCA of the host genotypic data) in each GWAS. The number of PCs to be incorporated was determined based on examination of the screen plot of PC eigenvalues and assured by the genomic inflation factors close to 1. The first 20 PCs were subjected to inspection of the screen plot . The tag SNPs were selected using the --show-tags function in PLINK version 1.9. The statistical significance level was set to P < 2.28 × 10−4 based on multiple testing correction for the 219 SNPs analyzed.For each of the 303 genome-wide suggestive SNPs found, we analyzed nearby SNPs previously reported to be associated with the microbiota parameters studied hereThe predictive power of a multiple regression model with the GWAS-identified SNPs was calculated. The SNPs were selected via the elastic net from genome-wide suggestive SNPs (results of sex-combined GWAS and sex-stratified GWAS) using the cv.glmnet function in R (in the package ‘glmnet’).26 on sex-combined data as previously described7 with some modification. Briefly, each of the Box-Cox transformed relative abundances of genera was regressed on the sex variable in advance, and then the residual was used for the PVE calculation. A genus was considered heritable if the SE of its PVE measurements did not intersect zero.SNP heritability was estimated by calculating the PVE by genome-wide SNPs using GEMMA v0.94https://www.cog-genomics.org/plink/), microbiomeGWAS (https://github.com/lsncibb/microbiomeGWAS), GEMMA (https://github.com/genetics-statistics/GEMMA), and R (https://www.r-project.org/). The statistical significance levels were set to P < 3.62 × 10−4 and P < 8.9 × 10−8 for the association analysis of the gut microbiota with the extrinsic and demographic variables and host genetic variants, respectively, which adjust for multiple testing correction for the number of variables or genetic variants per phenotype. Replication was not discussed in this study.Software used for statistical calculation in this study: PLINK (Further information on research design is available in the Supplementary FiguresDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Reporting Summary