Eucalyptus urograndis hybrid (E. grandis × E. urophylla). These traits directly impact the optimal use of wood for cellulose pulp, paper, and energy production. A population of 1,000 trees sampled in a progeny trial was phenotyped directly or following the development and use of near-infrared spectroscopy calibration models. Trees were genotyped with 33,398 SNPs and 24,001 DArT-seq genome-wide markers and genomic realized relationship matrices (GRM) were used for parameter estimation with an individual-tree additive-dominant mixed model. Wood chemical properties and wood density showed stronger genetic control than growth, cellulose and fiber traits. Additive effects are the main drivers of genetic variation for all traits, but dominance plays an equally or more important role for growth, singularly in this hybrid. GRM´s with >10,000 markers provided stable relationships estimates and more accurate parameters than pedigrees by capturing the full genetic relationships among individuals and disentangling the non-additive from the additive genetic component. Low correlations between growth and wood properties indicate that simultaneous selection for wood traits can be applied with minor effects on genetic gain for growth. Conversely, moderate to strong correlations between wood density and chemical traits exist, likely due to their interdependency on cell wall structure such that responses to selection will be connected for these traits. Our results illustrate the advantage of using genome-wide marker data to inform tree breeding in general and have important consequences for operational breeding of eucalypt urograndis hybrids.A thorough understanding of the heritability, genetic correlations and additive and non-additive variance components of tree growth and wood properties is a requisite for effective tree breeding. This knowledge is essential to maximize genetic gain, that is, the amount of increase in trait performance achieved annually through directional selection. Understanding the genetic attributes of traits targeted by breeding is also important to sustain decade-long genetic progress, that is, the progress made by increasing the average genetic value of the offspring as compared to that of the parental generation. In this study, we report quantitative genetic parameters for fifteen growth, wood chemical and physical traits for the world-famous  Eucalyptus L’Hér. (Myrtaceae) is the most globally planted genus of hardwood trees. The "big nine" species within subgenus Symphyomyrtus constitute over 95% of the world's eucalypt plantations [Eucalyptus grandis Hill ex Maiden, E. urophylla S.T. Blake, E. camaldulensis Dehnh and their hybrids are the main species planted in tropical regions, while E. globulus Labill and E. nitens H.Deane & Maiden are the most important species in temperate regions [ntations . Fast gr regions . The mas regions .E. grandis and E. urophylla–the celebrated urograndis hybrid–, although with a larger proportion of E. urophylla genome likely due to biased selection for disease and drought tolerance contributed by this species [E. grandis (>40 m3/ha/year) with the increased tolerance to biotic and abiotic stresses of E. urophylla. Furthermore, this hybrid displays good rooting ability and provides wood quality suitable to different industrial uses. Urograndis hybrids have been adopted in Congo [In Brazil, eucalypt forests comprise 5.67 million hectares, corresponding to 72% of the 7.84 million hectares of planted forests [in Congo , and havin Congo .1 urograndis hybrids have been available for several years [The expectation underlying the use of a clonally propagated high performing tree is the possibility of capturing all the additive and non-additive genetic variance components of its superiority, eventually resulting in a uniform, healthy and productive planted forest. Knowledge about the heritability, variance components and genetic correlations between growth and wood properties is therefore vital for effective breeding, both from the standpoint of maximizing and optimizing the potential genetic gain for multiple traits simultaneously, as well as ensuring a sustainable long-term genetic progress of the breeding program. While data on genetic parameters for growth traits in Fal years ,8,11–13,al years ,15, and The lack of genetic knowledge on wood properties has limited a better exploitation of the available inherent variation for these traits in breeding programs based on advanced generations of urograndis hybrids. While growth traits are easily measured in all trees of a progeny trial at half rotation age, about 3 to 4 years in tropical climates, the assessment of wood properties requires mature trees, such that adequate measurements are done typically closer to, or at rotation age. Moreover, standard methods are typically destructive, entail large samples, include the whole tree for some specific measurements, and are slow, laborious, and expensive. These constraints make wood analyses feasible only for a small number of trees already selected and deployed in final clonal trials of a selection cycle, and frequently target only wood density as a proxy for ultimate trait selection . However, the possibility of directly measuring chemical and physical traits has now become increasingly relevant especially as planted forests are also seen as the foundation of new industries replacing the use of fossil hydrocarbons for energy and industrial organic chemicals .Eucalyptus, a number of studies have successfully applied NIRS to predict chemical wood properties such as lignin content, syringyl/guaiacyl (S/G) lignin ratio, cellulose, pulp yield, and wood density in temperate species such as E. globulus, E. nitens and its hybrids [E. grandis [E. urophylla [To reduce time and cost of wood phenotyping, methods that can predict wood traits based on the development of calibration models using near-infrared reflectance spectroscopy (NIRS) have become increasingly common –19. Thes hybrids –25. Only grandis  and E. urophylla –29. Recerophylla . Some ofEucalyptus, selfing, pollen contamination, or even identification errors are common during controlled crosses, notwithstanding potential cryptic relatedness of parents especially in elite breeding populations involving hybrid parents, a common feature in urograndis hybrid-based programs. Inaccurate relationships may lead to incorrectly estimated genetic parameters, which in turn can bias the predicted genetic gains either up or downwardly. The value of using molecular markers for more precise estimation of genetic parameters in forest tree breeding has been documented, and shown initially with a limited number of microsatellites [Eucalyptus, realized genetic relationships have been estimated using thousands of genome-wide DArT [A and GRMs built with data from two alternative genome-wide genotyping platforms: DArT-seq, a restriction enzyme based complexity reduction followed by genotyping-by-sequencing and the fixed-content SNP array EuCHIP60K.Accurate assessment of genetic parameters relies on the estimation of variance and covariance components, which in turn are a function of the genetic relatedness of the individuals sampled. Genetic control of a trait is then estimated by correlating the phenotypic resemblance with the expected proportion of the genome that two relatives share identical by descent. An expected coefficient of relationship of 0.25 is assumed for half-siblings and 0.5 for full-sibs based on the pedigree information and presumed unrelatedness of parents. However, in the mixed mating system of tellites –33. A sotellites . With eatellites ,35–41, btellites –44 and gtellites . In specide DArT ,47 and Side DArT . In thisEucalyptus population employed in this study belongs to the breeding program of International Paper do Brasil. It involved a progeny trial encompassing almost exclusively E. grandis × E. urophylla F1, backcrosses, and F2 full-sib families. The trial was planted in randomized complete blocks, with five trees per plot comprising 58 full-sib families generated by an incomplete half-diallel mating design, totaling 2,784 trees planted in 2006 in Brotas, São Paulo State, Brazil (22°17′05″S 48°07′38″W). A subset of 1,000 trees out of the 2,784 in the trial was sampled for measuring growth and wood properties and DNA marker genotyping  of 19 backcross and F2 hybrid families of E. grandis and E. urophylla, and 24 (2%) trees of one E. grandis × E. camaldulensis hybrid family  at breast high (1.3 m), always in a north-south direction . Wood shCell wall chemical composition of wood samples was measured using a subsample of the same woodmeal samples previously scanned with NIRS. Cell wall carbohydrate and total lignin were measured as follows: woodmeal sample (40–60 mesh) was extracted with acetone in Soxhlet apparatus for 12 hours. Cell wall carbohydrates, namely cellulose (CEL) and hemicellulose (HC), and lignin traits, acid-soluble lignin (SL) and acid-insoluble lignin (IL), in combination forming the total lignin (TL) were determined as described  using th-1). Wood physical traits were ultimately measured on between 337 (MFA) and 350 (WD) samples as some samples were lost in the procedure.Increment wood cores were collected for the 350 selected trees based on NIRS spectra variation. Increment cores (12 mm) were collected at breast height (1.3 m), in a north-south direction . The nor2p) following transformations, were calculated for the external validation set and used to compare model estimates. The phenotypes of the remaining 650 samples of the population were predicted only for those traits for which NIRS models showed satisfactory performance. In the case of traits for which NIRS models were deemed poor (see below), only data for the directly measured trees were used in quantitative parameter estimation.Calibrations of the NIRS models were attempted for all chemical and physical traits based on spectra outputs of the 40–60 mesh (0.297–0.420 mm) woodmeal isolates of wood shavings and their corresponding wet chemical and physical measurements. The calibration population, composed by trees with both spectra and wet-lab data, was randomly divided into two subsets, one for model estimation and one for model validation. The estimation/validation sample sizes were either N = 250/N = 100 for those traits for which 350 samples were measured, or N = 150/N = 50 for those traits for which only 200 samples could be measured . The Uns-1. DNA samples were genotyped at Geneseek  using an Infinium (Illumina) custom made chip for Eucalyptus (EucHIP60k.br) [Genomic DNA was extracted from xylem scrapings isolated from the sapwood at breast height (1.3m) using an optimized Sorbitol+CTAB method for high quality DNA from wood samples , quantifP60k.br) . DNA samP60k.br) . From thy is the vector of phenotypic data, β is the vector of fixed effects (block design effects); p is the vector of random plot effects following I is the identity matrix and a is the vector of random additive genetic effects following A is the average numerator relationship matrix and d is the vector of random dominance effects following D is the average dominance relationship matrix and e is the vector of the random residual effect following X, ZpZa, and Zd are incidence matrices relating fixed and random effects to measurements in vector y.The single-trait analysis was based on the following univariate individual-tree mixed model with additive and dominance genetic effects:A and dominance D effects of the previous mixed model [GA; based either on SNPs or DArT-seq data) and dominance  relationship matrices. The additive genomic relationship matrix (GA) from the co-dominant SNPs was calculated using the function ‘A.mat’, in R (http://www.R-project.org/) package rrBLUP, that uses the formula proposed earlier [W is the n × m  rescaled genotype matrix following M—P, where M is the genotype matrix containing genotypes coded as 0, 1, and 2 according to the number of alternative alleles, and P is a vector of twice the allelic frequency, pi. The additive genomic relationship matrix (GA) from the dominant DArT-seq markers was calculated following the formula proposed by [S is a n × m matrix  rescaled genotype matrix following Z—P, where Z is the genotype matrix that specifies the genotypes expressed as 1/0 denoting the presence/absence of the DArT-seq marker, and P is a matrix containing the allelic frequency of the code 1 at locus i, pi.In the marker-based approach, the pedigree-based relationship matrices for additive ed model  were sub earlier .GA=WW′2posed by GA=SS′∑pGD) relationship matrix. To that end, subsets of 500 (05K), 1,000 (1K), 3,000 (3K), 5,000 (5K), 10,000 (10K), 20,000 (20K), and 30,000 (30K) randomly selected SNPs, and subsets of 500 (05K), 1,000 (1K), 3,000 (3K), 5,000 (5K), 10,000 (10K), and 20,000 (20K) randomly selected DArT-seq markers were used to build the corresponding genomic additive relationship matrices. Then, a product-moment correlation coefficient was used to evaluate the connection between pairs of marker-based additive relationship matrices using each combination of marker (SNPs and DArT-seq) and the pedigree-based relationship matrix.We examined the effect of progressively reducing the number of SNP or DArT-seq markers on the estimate of relatedness coefficients and narrow-sense heritability using an additive-only model, i.e. model in Eq  without D) and SNPs-based (GD) relationship matrices were calculated in R (http://www.R-project.org/) using the package “AGHmatrix [GD, herein named GDVitezica and GDSu, respectively.The dominance pedigree-based , and individual trait narrow- and broad-sense heritability (Restricted maximum likelihood (REML)  was usedhttp://www.R-project.org/) package “physch”. Finally, Spearman correlations were also calculated to compare whether the ranking of predicted breeding values differed whether estimated using pedigrees and the two marker types used.Additionally, pairwise trait Pearson’s correlations (and its significance expressed in probability levels) were calculated using phenotypes, and breeding values estimated using pedigree (pedigree-based numerator relationship matrix), SNP-based  and DArT-seq-based  genomic relationship matrices, using the cor function in R and the function corr.test in R , but was still considered effective for predicting values and therefore increase sample size for the subsequent quantitative analyses. For all other traits  only between 200 and 339 lab-measured samples were used in subsequent quantitative genetic parameters analyses  ≥ 0.01, and 24,001 DArT-seq dominant (presence/absence) variants at a call rate ≥ 0.80 and estimated MAF ≥ 0.02 were used for the genetic analyses providing good genome-wide coverage of the 11 omosomes . To captomosomes . The aveomosomes . A compaomosomes .G) matrices built with variable numbers and types of DNA markers varied from 0.640 to 0.774, showing a very small variation with decreasing numbers of markers , while for the remaining wood traits heritabilities showed variation according to the estimation approach. Growth traits showed higher broad sense , revealing a substantial effect of non-additive genetic control on these traits , WD × S/G (-0.45), WD × CEL (0.32), WD × HC (-0.28), WD × FL (0.44), WD × COA (0.55), MFA × TL (-0.39), FL × CEL (0.35). Additive genetic and phenotypic correlations among the 15 traits were also estimated  and phenotypic correlations were estimated amongst the fifteen traits using a univariate analysis . Genotypstimated . Worth mstimated . HoweverEucalyptus urograndis in tropical regions, knowledge on the range of genetic variation and magnitude of quantitative parameters for key wood traits of this hybrid was rare until two recent studies reporting data for wood density and pulp yield [Despite the extensive worldwide use of lp yield ,15. Our lp yield , our stu-3 and lignin content and S/G ratio, as low as 24% and above 4.0 respectively  applied to wood properties in Eucalyptus is changing this scenario [p2 = 0.86) and reasonable models for the other lignin traits with Rp2 ≥ 0.70 , possibly impacted by the fact that NIRS spectra were collected on woodmeal samples and not on solid samples. Model for WD was, however, deemed adequate for the purpose of this study including the estimation of quantitative parameters and tree ranking, as both these data applications involve relative traits values. Better calibrations for WD in eucalypts were developed when NIRS readings were obtained on more elaborate samples of radial wood surfaces [Eucalyptus were reported using the same NIRS instrument as the one we employed in our study [Our study emphasizes that a better use of the existing phenotypic variation in breeding populations depends on efficient ways to collect data for wood property traits for large numbers of samples. Direct wet chemistry methods for thousands of tested trees in a progeny trial are usually not an option, in spite of faster analytical methods for some traits. In most scenario ,30,63,642 ≥ 0.70 . Very go species ,30,64–66 species  was obtasurfaces , althougsurfaces ,30. As aEucalyptus species [E. globulus and E. urophylla [Eucalyptus hybrid, confirming earlier studies that showed either a balance or a higher weight of dominant relative to additive effects [E. urohylla × E. tereticornis hybrids [E. globulus [E. nitens [E. globulus × E. nitens hybrids [Wood properties, particularly wood density and chemical traits, showed a moderate to stronger genetic control than growth traits , in agre species ,28,63. Orophylla ,63, whil effects ,15,38. F hybrids , E. globglobulus , E. nite. nitens  and E. g hybrids . These r hybrids . For alli.e., progeny model) with additive and dominance genetic effects instead of a parent mixed model with male and female effects and their interaction. The individual-tree mixed model used here assumes that the alleles controlling the traits would be common in the parental lines studied, that the variance genetic components in the segregating eucalypt hybrid population used are similar, and that epistasis is negligible. Identifying which genetic model provides the best description of a forest tree hybrid is a challenge [It is important to point out that in estimating genetic parameters, we used an individual-tree mixed model , cellulose content (-0.12) and fiber length (-0.18), and positive for soluble and insoluble lignin and S/G ratio (0.15) (E. globulus [E. urophylla [E. urophylla × E. tereticornis hybrids [E. globulus [Reported additive correlations between growth and wood traits have varied significantly across different o (0.15) . These fglobulus ,63 and Erophylla  consiste hybrids , or no cglobulus ,75. Takeglobulus ,28. On tglobulus ,27,29,633 is the target for trees for maximal efficiency in cellulose production, but higher density is aimed for energy . While low lignin is usually desired to improve pulp yield in cellulose production, high lignin content to enhance the calorific power of wood is pursued by industries that use eucalypt for charcoal or biomass. Our analysis showed that ample genetic variation is available for all traits in a typical urograndis hybrid breeding population. However, while additive variance represents essentially the exclusive source of genetic variation for all wood property traits, dominance variance plays at least an equally important role as the additive variance when growth traits are considered. Additionally, our results indicate a general pattern of low genetic correlations between growth and wood traits, indicating good potential for simultaneous genetic improvement of individual trees for higher volume growth and wood properties in any desired direction. Wood chemical traits, however, do show significant genetic correlations, and given their important influence on final production traits, they call for unique breeding tactics to manage the connected responses to selection.Urograndis hybrids have been and will continue to be the mainstay of the vast majority of eucalypt forest based industrial operations in the tropics, with an increasingly outstanding role in the world supply of sustainable forest-based products. By studying a typical urograndis breeding population, our report confirms some known patterns of variation for growth traits and brings novel quantitative genetics data for an array of wood properties. While increased volume growth is a common objective to all breeding programs, the relative importance and direction in which wood traits are selected varies. For example, a specific wood density window of approximately 500–550 kg.mE. grandis and E. urophylla was tentatively adopted by a number of industrial breeding programs in the 1980’s and 1990’s, based on models derived from hybrid breeding of annual crops. It proved, however, to be time-consuming, costly and inefficient. Recently, simple recurrent selection (SRS) in synthetic hybrid population has been adopted in urograndis programs due to its simplicity, higher speed and lower cost, with genetic gain per year projected to far exceed that of RRS [Our results have useful consequences for the current and future prospects of breeding urograndis hybrids. Reciprocal recurrent selection (RRS) between t of RRS ,76,77, at of RRS , while nFinally, our study adds to the recent stream of promising reports showing the positive impact of using genomic realized relationships to estimate heritability, genetic correlations and breeding values in essentially all mainstream forest trees . By effeS1 FigFor DNA extraction  pieces of wood were sampled. Wood dust samples  were collected for NIRS and chemical analyses. Wood cores samples  of 1.2mm were collected for physical analyses.(PDF)Click here for additional data file.S2 FigWood shavings were stored in paper envelopes and dried at room temperature . A Willey mill was used to grind the samples (c). Wood shavings classification in 60/40 mesh sieves (d).(PDF)Click here for additional data file.S3 FigDistribution of pairwise estimated relatedness for individuals with expected relationships: Full-sib (a), Half-sib (b), and Unrelated (c). From let to right estimates obtained using G matrices constructed from subsets of randomly selected of 500 (05K), 1,000 (1K), 3,000 (3K), 5,000 (5K), 10,000 (10K), 20,000 (20K), 30,000 (30K) and all 33K SNP markers, and 500 (05K), 1,000 (1K), 3,000 (3K), 5,000 (5K), 10,000 (10K), 20,000 (20K) and all 24K DArT-seq markers and using only the 10,501 (10.5K) DArT-seq markers mapped to the eleven Eucalyptus chromosome scaffolds.(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 TableMatrix G was constructed from all available SNPs (~ 33K) and DArT-seq (~ 24K) markers and from subsets of randomly selected of 500 (05K), 1,000 (1K), 3,000 (3K), 5,000 (5K), 10,000 (10K), 20,000 (20K), 30,000 (30K) SNP markers, and 500 (05K), 1,000 (1K), 3,000 (3K), 5,000 (5K), 10,000 (10K), 20,000 (20K) DArT-seq markers. Matrix G was also calculated using only the 10,501 (10.5K) DArT-seq markers mapped to the eleven chromosome scaffolds.(PDF)Click here for additional data file.S4 TableAbbreviations used for the number and types of marker were described in the caption of (PDF)Click here for additional data file.S5 TableSee text for traits´ abbreviation. Abbreviations used for the types and number of marker were described in the caption of (PDF)Click here for additional data file.S6 TableaCorrelation and their approximate standard errors were not estimated due to convergence problems.In each cell from top to bottom: genotypic correlation based on SNP-based realized relationship matrix; genotypic correlation based on DArTs-based realized relationship matrix; genotypic correlation based on pedigree-based relationship matrix; phenotypic correlations. NOTE: (PDF)Click here for additional data file.S7 Table(PDF)Click here for additional data file.


Population stratification and cryptic relationships have been the main sources of excessive false-positives and false-negatives in population-based association studies. Many methods have been developed to model these confounding factors and minimize their impact on the results of genome-wide association studies. In most of these methods, a two-stage approach is applied where: (1) methods are used to determine if there is a population structure in the sample dataset and (2) the effects of population structure are corrected either by modeling it or by running a separate analysis within each sub-population. The objective of this study was to evaluate the impact of population structure on the accuracy and power of genome-wide association studies using a Bayesian multiple regression method.We conducted a genome-wide association study in a stochastically simulated admixed population. The genome was composed of six chromosomes, each with 1000 markers. Fifteen segregating quantitative trait loci contributed to the genetic variation of a quantitative trait with heritability of 0.30. The impact of genetic relationships and breed composition (BC) on three analysis methods were evaluated: single marker simple regression (SMR), single marker mixed linear model (MLM) and Bayesian multiple-regression analysis (BMR). Each method was fitted with and without BC. Accuracy, power, false-positive rate and the positive predictive value of each method were calculated and used for comparison.SMR and BMR, both without BC, were ranked as the worst and the best performing approaches, respectively. Our results showed that, while explicit modeling of genetic relationships and BC is essential for models SMR and MLM, BMR can disregard them and yet result in a higher power without compromising its false-positive rate.This study showed that the Bayesian multiple-regression analysis is robust to population structure and to relationships among study subjects and performs better than a single marker mixed linear model approach. Like any other type of statistical association analysis, the purpose of a genetic association test is to establish an association between, or examine independence of, two variables: a trait of interest and a genetic marker , 2. If tMany approaches have been developed to account for PS and relatedness in a population-based GWAS, including genomic control (GC), structured association (SA), principal component analysis (PCA), mixed linear models (MLM) and multiple regression analysis.Without a doubt, the GC method  is simplThe PCA method , 48 is fOne might identify the above approaches as two-step methods for correcting for PS. They can eliminate the true association signals whenever the strength of association due to PS is comparable to that of a QTL. Simultaneous inference of PS and testing for association has the advantage of being able to separate the true and false signals from each other , 15. Yu In principle, PS simply can be adjusted for by including a set of ancestry-informative or null markers as covariates in the model. These markers or a function of them can effectively serve as proxies for the underlying PS , 32, 62.In recent years, genomic selection (GS)  has showe) of 1000. To simulate the four purebred populations , at generation 1001 four independent random samples of 100 animals were drawn from the base population and each was randomly mated for another 50 generations, with an Ne of 100. A previous study [A base population of unrelated individuals was stochastically simulated and used as described below to create four pure breeds, and admixed and crossbred populations based on these breeds. To generate LD, the base population was randomly mated for 1000 generations, with an effective size , (AB)A, (AB)C and (AB)(CD) populations. This resulted in eight different populations (including the four pure breeds) of size 1000. Finally, a random sample of 1000 individuals was drawn from the pool of all populations and used as the admixed population. The pure breed A and admixed datasets at generation 1053 (referred to as the training generation hereafter) were used as the resource populations for QTL mapping. PCA based on whole-genome marker genotypes was used to verify the population structure in the simulated admixed dataset.−5. A binomial map function was used to simulate recombination and interference was allowed for by setting the maximum number of uniformly and independently distributed crossovers on the chromosome to 4 [A genome of size 600 cM composed of six chromosomes that each had 5000 equally spaced markers was simulated. Markers were bi-allelic, with starting allele frequencies of 0.5 and a reversible random mutation rate of 2.5 × 10ome to 4 . At geneIn the training generation, 15 segregating markers (MAF > 0.02) that were closest to certain positions on chromosomes 1 to 3  scaled inverted Chi square variables with scale and shape parameters of iid and r et al. .A Gibbs sampler was used to generate a Markov chain Monte Carlo (MCMC) chain of 100,000 samples with a burn-in period of length 10,000. Convergence of the chain was examined using the R software package CODA  and visuThis model was similar to model  except t2, between consecutive loci 1 and 2 cM apart was about 5.5 and 3.5%, respectively. Generally speaking, the ± 2 cM interval is the preferred precision of interest for QTL detection or efficient implementation of QTL information in marker-assisted selection . The average LD, measured as Rn  and henion in R . We refeion in R , as the ion in R . This meion in R . For theion in R  was usedFor each replicate of a dataset, a QTL was declared detected if any of the markers within an interval of ± 2 cM of the QTL  had a P-value smaller than the 5% threshold P-value , or a PIP value larger than the 95% threshold PIP value . Power was defined as the proportion of times that a QTL was detected out of 20 replicates in that dataset.1 regions  were counted as true-positives (TP) and false-negatives (FN), respectively. Similarly, positive and negative results in H0 regions were counted as false-positives (FP) and true-negatives (TN). Then, accuracy and PPV were computed as:Excluding the ± 2 cM intervals harboring the QTL, the remaining parts of chromosomes 1, 2 and 3 were divided into 4-cM long segments, as intervals where the null hypothesis was correct. If a marker was declared significant in any of these intervals, it was regarded as a false-positive. FPR was the proportion of false-positives across the genome and then averaged over all replicates of a single dataset. Comparing power of methods that have different FPR could be misleading because positive results might be due to PS as well. Therefore, two other measurements, accuracy and PPV were also used to evaluate the performance of the different models . PositivAll performance measures, i.e., accuracy, power, FPR and PPV, were calculated on a per dataset basis and then averaged across all datasets. All QTL sizes showed a similar trend in the above performance measures, hence instead of calculating the performance measures for each QTL size, we report averages across all QTL in a dataset.PCA of the marker data for the purebred and admixed populations revealed distinct clusters of related animals within the admixed population, in contrast to the purebred population Fig. . In addiExamination of the Q–Q plots of P-values of the markers on null chromosomes showed spurious FP in the association analyses of both the purebred and the admixed populations when relationships and/or BC were not modeled properly Fig. .Fig. 2Q–Results of the SMA and MLM analyses of the ADMX population are in Tables Table In this simulation study, we compared three methods of GWAS in an admixed population: single marker simple regression, single marker mixed model and Bayesian multiple regression models, with- and without- fitting breed composition.FST statistic. PS will be a source of spurious associations if both allele frequencies and mean phenotypic values differ between the sub-populations [The PCA showed that there was a distinct PS in the ADMX dataset. In a study by Toosi, Fernando and Dekkers , who simulations . The effThe PB dataset showed spurious FP when the pedigree relationships in the population were ignored, as expected Fig. . UnequalIn the PB dataset, the MLM approach did control FPR at the nominal level on the null chromosomes, but it failed in the ADMX dataset without fitting BC. In this situation, any marker that has different allele frequencies between breeds shows association with the phenotype under study. The extent of FPR is a function of the extent to which the population is structured and not accounted for . TherefoIn a GWAS of a massively structured population consisting of 1800 bulls of the German Fleckvieh breed, Pausch et al.  applied BC models performed similar to the MLMBC, although the SMABC did not fully account for the kinship in the sample. It is possible that correcting for the PS has indirectly corrected some of the pedigree relationships between individuals in the sample and as a result, there were fewer spurious associations [The changes in the performance of SMA models with and without fitting BC were most evident with the SLIDE method of finding thresholds Table . On the ciations .BC when the NCHR method was used , power (0.72 and 0.30) and FPR (0.44 and 0.007). It is evident from these results that many of the significant results of the SMA are false positives. Also, the difference between the accuracies of the SMA and SMABC implies that modeling BC has dramatically increased the number of TN. However, preventing the confounding effect of PS by explicitly modeling it, comes at a cost of more FN [While there were no differences between accuracies of the SMA and MLM or the SMA and the SMAed Table , there w more FN , 89, 90. more FN . Anderso more FN  conducte more FN , 92.In our study, fitting BC resulted in a considerable drop of power of QTL detection when the SLIDE method was used, but this was compensated for by a significant drop in FPR. Further inspection showed that in most instances, the smallest QTL were missed. This agrees with the findings of Iwata et al. , who shoTo control family-wise type I error rate, SMA requires methods like Bonferroni correction for multiple testing. Such adjustments are usually too conservative, especially in a large scale SMA with extensive LD between linked markers, and thus they may cause true associations to be missed , 94–96. BC. While modeling of BC in the MLM approach improved power of association (Table Comparison of the performance of the BMR models with and without fitting BC Table  indicateon Table , it resuon Table . One majArabidopsis thaliana, showed that GWA yields unambiguous results for monogenic characters regardless of whether they corrected for PS or not. The authors concluded that the reason for this result was not that there were no confounding effects but because the true signals were showing the strongest associations. Therefore, they suggested that the problem of confounding due to PS in GWAS of complex traits might be better explained as a model misspecification, i.e., modeling a polygenic trait using a SMA that ignores the multi-factorial background of the trait. Not only SMA models result in spurious FP across the genome but they may also find the strongest associations on chromosomes that are completely devoid of QTL [Atwell et al. , in a GWd of QTL . Therefod of QTL  suggesteQ + K model with the K-only model, and with the GC, SA and PCA models. The K-only model outperformed all the other rivals. With high-density marker data, the K matrix contains all information on PS and hence the explicit modeling of PS might not be necessary [Our results agree with Setakis et al. , Iwata eecessary , 102. Apecessary , who argGu et al. , appliedUsing a multi-population sample the detection of QTL that cause between-population differences is possible. A combined analysis of data from several populations takes advantage of the between-population genetic variability and hence is more powerful than single-population association study , 107. A As we showed in this study, and in agreement with studies of Gu et al. , WurschuTeo et al.  showed tAs genomic selection approaches are gaining popularity, some recent GWAS in animal breeding dealing with multi-breed datasets have used methods similar to the BMR method we used here , 112. InIn conclusion, our results show the superiority of MMA models over SMA models. More specifically, our study confirms that MMA models are capable of automatically accounting for the confounding effects of kinship and population structure in GWAS, without compromising the power of QTL detection.


Advances in next generation sequencing technologies and statistical approaches enable genome-wide dissection of phenotypic traits via genome-wide association studies (GWAS). Although multiple statistical approaches for conducting GWAS are available, the power and cross-validation rates of many approaches have been mostly tested using simulated data. Empirical comparisons of single variant (SV) and multi-variant (MV) GWAS approaches have not been conducted to test if a single approach or a combination of SV and MV is effective, through identification and cross-validation of trait-associated loci. In this study, kernel row number (KRN) data were collected from a set of 6,230 entries derived from the Nested Association Mapping (NAM) population and related populations. Three different types of GWAS analyses were performed: 1) single-variant (SV), 2) stepwise regression (STR) and 3) a Bayesian-based multi-variant (BMV) model. Using SV, STR, and BMV models, 257, 300, and 442 KRN-associated variants (KAVs) were identified in the initial GWAS analyses. Of these, 231 KAVs were subjected to genetic validation using three unrelated populations that were not included in the initial GWAS. Genetic validation results suggest that the three GWAS approaches are complementary. Interestingly, KAVs in low recombination regions were more likely to exhibit associations in independent populations than KAVs in recombinationally active regions, probably as a consequence of linkage disequilibrium. The KAVs identified in this study have the potential to enhance our understanding of the genetic basis of ear development. Arabidopsis (Following the adoption of genome wide association studies (GWAS)  and multi-variant (MV) models. SV analysis compares the phenotypic distributions of alternative genotypes at each polymorphic site independently. They can be conducted without correction for population structure or with correction using techniques such as genomic control  or princMany models for conducting GWAS have been compared using simulated data  related to the nested association mapping (NAM) population, which combines the strengths of historical recombination events across a broad base of genetic diversity and recombination events that arise following experimental crosses  phenotype, which is both a component of yield and a model trait for genetic studies in maize , 231 of which were subjected to genetic validation tests using three unrelated populations that were not included in the initial GWAS. Approximately 60% of the validated KAVs were identified by only one of the three approaches.KRN phenotypes were collected from four related populations: 1) the nested association mapping  population which was composed of 25 recombinant inbred line (RIL) subpopulations  and in Molokai, HI (winter season), such that only a fraction of the full set of RILs were grown in a given environment. For each NAM RIL, five plants were grown within each row. KRN data were collected from the mature ears. In addition, we obtained KRN data from eight trials for NAM RILs from a previous study  were planted in Ames, IA in 12-plant rows. The B73 x RILs and Mo17x RILs were planted with two replications, and the diallel population was planted with three replications. For each replication, KRN data were collected from three mature ears. The obtained KRN phenotypic data were analyzed using a mixed linear model, where genotype was fitted as a fixed factor and replication was fitted as a random factor.http://www.ars.usda.gov/main/site_main.htm?modecode=36-25-12-00). During year 2011, these accessions were planted in three replications and observed for KRN phenotypes. About 7,000 of the maize accessions have been phenotyped for the KRN trait in the database of USDA PI Station. We selected the 225 accessions with the largest KRN values, the 208 accessions with the smallest KRN values, and 173 random accessions to serve as the second genetic validation population. Empirical KRN phenotypes were obtained from our previously published replicated field experiment  population were used for genetic validation of KAVs identified through GWAS. A total of 220 elite inbred lines, commercial lines that had formerly been subject to IP  protection via the plant variety protection act, were obtained from the USDA Plant Introduction (PI) Station in Ames, IA (https://www.panzea.org). These three sets of variants were merged using the consensus mode of PLINK  was identified via analysis of RNA-seq data from five tissues  on 26 NAM founder lines and Mo17 (https://www.panzea.org). Based on the GBS data and the known pedigrees, the https://github.com/yangjl/zmSNPtools) as previously described (Imputation of genotypes was performed as described below. NAM RILs had been directly genotyped using genotyping-by-sequencing (GBS) technology  KAVs. A total of 1,102 DNA samples from elite inbred lines (N = 208), extreme KRN lines from the USDA germplasm collection (N = 606) and individuals from the Iowa Long Ear Synthetic  were individually genotyped, by sequencing all multiplexed amplicons from all 1,102 samples in one HiSeq2000 lane. Informative variants, defined as those which were successfully genotyped, were polymorphic, and had a call rate of > 0.4 and a MAF > 0.05 were used for genetic validation.Joint linkage analysis was performed on NAM and IBM RILs using their corresponding genetic maps. A two-step composite interval mapping (CIM)  method wTo account for documented stratification effects, the statistical model included fixed effects for population and subpopulation for all three GWAS models described below.kth variant:l from the mixed linear model analysis; l is of GWAS population i and is 0 otherwise, and ith population in the model for variant k; l is from subpopulation j and 0 otherwise, j in the model for variant k; l genotype of the mth QTL detected by the joint linkage analyses, mth QTL in the model for variant k, k; kth variant in line l, kth variant; and Additional fixed effects were fitted in the model to control for effects of QTL on other chromosomes, while all variants on a single chromosome were scanned, resulting in the following model 1 for the T v2.3.0 .In the stepwise regression test, population and subpopulation effects were fitted first as fixed effects, and then markers were added in a stepwise manner. For each marker, kth variant in line l and kth variant; other terms in the model are as described in the SV model 1 except that neither the u, kth variant in the BMV model 2.A Bayesian-based MV model was constructed using the “BayesC” option of GenSel v4.1  and residual variance (resVariance = 1). In the 2nd step, we replaced the priors using the posteriors obtained from step 1 and ran a longer chain of simulations .We trained the BMV model using a two-step procedure. In the first step, we ran 1,000 iterations with 100 burn in of MCMC simulation using default priors, 10.6084/m9.figshare.6902144). R code for the analyses is available in the public GitHub repository (https://github.com/yangjl/KRN-GWAS). Supplemental material available at Figshare: 10.6084/m9.figshare.6902144.File S1 contains phenotypic data for the GWAS populations. File S2 contains the KAVs identified using the three GWAS models. File S3 contains genotypes of KAVs for the three validation populations. File S4 contains the KRN phenotypic data for the validation populations. In the table, “Internal_id” could be used as the identifier to match genotypic data. File S5 contains the genetic validation results. Stars indicate SNPs that were consistently associated with KRN in initial GWAS, and in at least one of the validation populations. All the supplementary files have been uploaded to Figshare . Best linear unbiased estimators (BLUE) of the KRN phenotype were calculated for each of the 6,230 entries in the four GWAS populations (File S1). In this combined analysis, KRN phenotypic values ranged from 9.1 to 23.6, with a mean of 14.9 rows, whereas the B73 inbred had an above average KRN phenotype of 17.1 rows. Density plots of the four GWAS populations exhibited the expected bell-shaped distributions .P value i.e., longer and shorter ears had smaller and larger KRN trait values, respectively , each of which contained one or more significant variants. Encouragingly, among these 764 bins, 66 (containing 169 variants) were detected by at least two models. Only one of these bins was detected by all three GWAS models. That bin (chr4:229.0-Mb) overlaps the most significant QTL peak detected in the joint QTL study. To estimate an upper bound for cross-validation rate for each approach and to determine whether the KAVs that were detected by more than one approach are more reliable, a set of 231 KAVs was selected for genetic validation. This set of KAVs included the 169 variants in the 66 bins detected by at least two approaches and 62 of the most significant one or two variants selected from 20 bins that had only been detected by one approach (approach-specific variants). Hence, in total 231 KAVs from a total of 126 bins (66 + 20 × 3) were selected for genetic validation.i.e., narrow-sense heritability Collectively, the 231 selected KAVs explained 64% of phenotypic variation in the initial GWAS population by fitting these KAVs simultaneously using an additive model  and enriched in variants discovered from the RNA-seq data  relative to the Note that a causal variant might be represented by multiple selected KAVs. The B73 variant-types were alleles for higher KRN for nearly three-quarters  of these KAVs, consistent with the high KRN value of the B73 genotype. Consistent with our previous study .To control for population structure in the elite inbred lines, a set of SNPs that had previously been used to genotype a subset (N = 91) of these lines was obtained  informative variants, 14/56 (25%) could be validated.Of the 51 informative KAVs in the BSLE population, 7/51 (14%) showed significant differences in allele frequency between C30 LE and C30 SE populations. To rule out the possibilities of genetic drift or stochastic sampling error, we conducted simulation to mimic the selection procedure. After simulation, one validated KAV did not pass the cutoff (FDR < 0.05) and was removed. Hence, even after accounting for drift and stochastic sampling errors, 6/51 (12%) KAVs were deemed to have been under divergent selection. We applied the simulation to the negative control variants, and they were indeed negative. Collectively, these loci account for 40% of the total between-population variance for KRN trait. Variants that are segregating in BSLE but not in GWAS populations or that were simply not detected as being KAVs in the GWAS populations may explain the remaining phenotypic variation between C30 LE and C30 SE.In summary, 40/77 (52%) of informative KAVs exhibited associations with the KRN trait in at least one of the genetic validation populations (File S5). The genetic validation results from the three GWAS models are illustrated in We also obtained genotyping data for 34 KAVs reported in an earlier GWAS .The amount of genetic recombination per Mb in maize varies substantially across the genome  of KAVs were cross-validated in at least one of three unrelated populations. Because KRN is mainly controlled by additive effect loci , each GWAS model identified validated KAVs that were not detected by the others. By definition, this means that the three GWAS models are complementary. Therefore, the use of multiple approaches or the development of a statistical model that combines their advantages, promises to enhance the power of GWAS.The genetic validation rate of KAVs identified in this experiment (40/77 = 52%) is higher than KAVs identified in an earlier KRN GWAS study (9/34 = 26%)  were 3.5 times more likely to be genetically validated than those in chromosomal regions with high rates of recombination per physical distance. This is probably a consequence of the relationship between recombination and LD (In conclusion, this study identified hundreds of KAVs that in combination explained 64% of phenotypic variation for KRN in lines that sample


N = 423) in Nellore bulls with high density SNP data, using different response variables. For this, solutions from double hierarchical generalized linear models (DHGLM) were used to provide the response variables, as follows: a DGHLM assuming non-null genetic correlation between mean and residual variance (rmv ≠ 0) to obtain deregressed EBV for mean (dEBVm) and residual variance (dEBVv); and a DHGLM assuming rmv = 0 to obtain two alternative response variables for residual variance, dEBVv_r0 and log-transformed variance of estimated residuals  showed a strong association (Bayes Factor > 20) with dEBVv and ln_v_r0. All overlapping 1-Mb windows among top 20 between dEBVm and dEBVv were previously associated with growth traits. The potential candidate genes for uniformity are involved in metabolism, stress, inflammatory and immune responses, mineralization, neuronal activity and bone formation.The dEBVmv to obtain genomic regions associated with uniformity that are not associated with the mean. Genes involved not only in metabolism, but also stress, inflammatory and immune responses, mineralization, neuronal activity and bone formation were the most promising biological candidates for uniformity of YW. Although no clear evidence of using a specific response variable was found, we recommend consider different response variables to study uniformity to increase evidence on candidate regions and biological mechanisms behind it.It is necessary to use a strategy like assuming null rThe online version of this article (10.1186/s12864-018-5003-4) contains supplementary material, which is available to authorized users. Uniformity is becoming increasingly important among livestock species. In meat production systems, this is more evident by the increasing adoption of economic incentives by slaughterhouses to stimulate farmers to deliver animals that meet specific carcass standards. If uniformity is partly under genetic control, genetic selection could be used to improve uniformity of animals. Genetic control of uniformity can be the result of how genotypes respond differently towards unknown micro-environmental factors , 2. SuchFew GWAS for uniformity traits have been performed in livestock and there is no consensus about the most appropriate phenotype to be used in such studies. Phenotypic standard deviation and coefficient of variation was used to address uniformity of egg weight in chickens by Wolc et al.  and birtIn beef cattle, growth traits are often used as selection criteria in breeding programs due to its economic impact in meat production. Several genes and genomic regions influencing the mean of these traits have been identified e.g. –16). In . In 16])mv ≠ 0) to obtain deregressed EBV for mean (dEBVm) and residual variance (dEBVv); and ii) rmv = 0 to obtain dEBVv_r0 and log-transformed variance of estimated residuals (ln_Our aim was to identify genomic regions (1-Mb windows among the top 20 that explained the largest proportion of genetic variance shared between response variables and 1-Mb windows with SNPs that showed a strong association by BF) associated with within-family residual variance of YW in Nellore cattle, using different response variables in GWAS. For this, we used solutions from DHGLM assuming: i) non-null genetic correlation between mean and residual variance of YW  1 , 3 (Chr3_42 and Chr3_45), 13 (Chr13_59), 14 (Chr14_24 to 26) and 16 (Chr16_21)  among them are shown in Table v . As a rv and dEBVv_r0 (0.53), which shared one window among the top 20, Chr12_5. On the other hand, the smallest correlation was between dEBVv and ln_v_r0 and ln_The response variables for residual variance of YW had low to moderate correlations with each other Table . The larm, dEBVv and ln_v and ln_v_r0. All these SNPs are located within the top 20 1-Mb windows that explained the largest proportion of genetic variance in each response variable. The top 20 windows explained together 34.6, 32.6, 4.4 and 20.0% of the genetic variance of dEBVm, dEBVv, dEBVv_r0 and ln_Based on the GWAS results, 40, 13 and 4 single nucleotide polymorphisms (SNPs) showed a strong association BF > 20) with dEBV0 with dEm and dEBVv, genes and QTL regions were mapped . Such findings highlight that most of the effects captured by dEBVv can be due to the strong rmv and could be potentially scale effects [Within the common windows shared by dEBVed Table . Several effects .m and dEBVv were divided by the mean of YW and mean of v than dEBVm indicating that these regions affect the residual variance beyond a simple scale effect. These results suggest that even with a high and strong rmv it is possible to find regions determining mean growth and its variability simultaneously, while the effects on the variance are larger than can be explained by a simple scale effect.In order to prove that these common regions can really affect the variance beyond a simple scale effect, we applied a similar procedure as described in Wolc et al. . For thiv_r0 and ln_NUDT9, SPARCL1 and MAN2B2), mineralization (DMP1 and DSPP) and neuronal activity  were considered potential candidates for uniformity , stress (IP6K2 and UCN2) and inflammatory and immune responses  (Table .In the shared windows by dEBVATP6V1B2 (Chr8), LPL (Chr8), OSBPL8 (Chr5) and GLI2 (Chr2); immune response: GLIPR1 (Chr5), neuronal plasticity: LZTS1 (Chr8) and stress response: SLC18A1 (Chr8), HIF1A (Chr10), BBS10 (Chr5) and CLASP1 (Chr2) were observed in the windows of the SNPs that showed strong association with ln_v, most of the genes in the regions harboring SNPs that showed strong association were related to metabolism: DPYD (Chr3), BTG1 (Chr5), AK7 (Chr21), ADIRF (Chr28), GLUD1 (Chr28), IP6K1, GMPPB, APEH, AMT, USP4, USP19, IMPDH2, NDUFAF3, SLC25A20, PRKAR2A, UQCRC1, PFKFB4 and SHISA5 all located on Chr22 (Table BDKRB2 and BDKRB1 (Chr21), and IP6K2 and UCN2 (Chr22); inflammatory and immune responses: EEA1 (Chr5), TCL1A and TCL1B (Chr21), UBA7, RNF123, MST1 and RHOA all on Chr11; neuronal plasticity: PLPPR4 and PLPPR5 (Chr3); and bone formation: BMPR1A (Chr28) were also found  seems to be the major gene due to its role in regulating insulin-like growth factors , neuropeptides B/W receptor 1 (NPBWR1) and proenkephalin (PENK), are also involved in processes that can contribute to determine uniformity of growth traits, like YW. RB1CC1 is involved in cell growth and differentiation, senescence, apoptosis and autophagy, and was one of the genes differentially expressed in heat stressed chickens [NPBWR1 modulates feeding behavior and energy homeostasis and PENK is responsible for producing enkephalins in response to stress [m and dEBVv showed effects beyond scale effects, we can conclude that the same gene can affect both, mean and its variability.All common regions shared by dEBVr breeds , 20–22. r breeds , 23–27. rs . Howevechickens , 30. NPBo stress , 32. BasATP6V1B2, LPL, OSBPL8 and GLI2), immune response (GLIPR1), neuronal plasticity (LZTS1) and stress response  were considered the most promising biological candidates for uniformity using ln_v, genes related to metabolism , stress response , inflammatory and immune responses , neuronal plasticity (PLPPR4 and PLPPR5) and bone formation (BMPR1A) were identified as potential candidates. Such findings support partially the hypothesis that most likely the mean of a trait and its uniformity share processes, like those related to metabolism. However, uniformity may be a trait which is even more complex and may be controlled by several processes and mechanisms beyond metabolism, in order to reach homeostasis and maintain the body in balance.Genes involved in metabolism  encodes a component of the vacuolar ATPase (V-ATPase), which is activated according to the energy level, like during glucose starvation [ATP6V1B2 gene was found to be associated with feed efficiency traits in beef cattle [LPL) and oxysterol binding protein-like 8 (OSBPL8) genes are involved in lipid metabolism. LPL is responsible for hydrolysis of circulating triglycerides and very low-density lipoprotein [LPL and growth and carcass quality traits were also reported previously [OSBPL8 modulates lipid homeostasis through sterol regulatory element binding proteins  gene is a transcription factor of the Hedgehog (Hh) signaling pathway, which is involved in muscle development mainly during embryonic development, but also in adult tissue homeostasis and repair [Among the metabolism-related genes for ln_arvation . Previouf cattle . Lipoprooprotein . Associaeviously –39. OSBP. In addd repair , 42.v, most of the genes are involved in metabolic pathways or related processes to it, such as metabolism of carbohydrate, energy and lipid, nucleotide and amino acid . Genes regulating muscle cell proliferation, differentiation and myogenesis (BTG1 [USP4 and USP19 [SHISA5), adipogenic differentiation (ADIRF [APEH) also showed metabolic functions that potentially can contribute to uniformity of YW.For dEBVis  and proGenes involved in the energy and protein metabolism may play an important role to keep body homeostasis against environmental perturbations. Usually, metabolic responses to these stressors involve catabolic processes such as energy mobilization and protein degradation , 48. In  and dEBVv . Stress is a response to new environmental conditions, like nutrition, housing or any stimuli, that threatens homeostasis. Therefore, stress genes may have a direct effect on uniformity, such that animals that are worse in dealing with environmental perturbations tend to be less uniform .SLC18A1) and urocortin 2 (UCN2) genes are involved in the hypothalamic-pituitary-adrenal (HPA) axis. SLC18A1 acts in the final stage of the HPA-axis, transporting catecholamines like dopamine, noradrenaline and adrenaline. Catecholamines are released as a physiological stress response [UCN2 is a member of the corticotropin-releasing hormone (CRH) family, which is a key mediator of the stress response by activating the HPA axis [Vesicular monoamine transporter 1  gene encodes a transcription factor that is induced by hypoxia, promoting angiogenesis, cell proliferation/survival and glucose/iron metabolism [HIF1A was highly expressed in the ruminal epithelium of highly-efficient animals [The hypoxia-inducible factor 1-alpha tabolism . Angioge animals , which cBBS10) gene, a member of heat shock protein (HSP) family, encodes a chaperonin-like protein [Hsp90 was considered a candidate gene for developmental stability of morphological traits in Drosophila melanogaster and Arabidopsis thaliana  and  and BBS1na (e.g. . It suppIP6K2), a metabolism related-gene that also plays a role in apoptosis [HSP90 gene [Another gene related to HSP family is the inositol hexakisphosphate kinase 2  and bradykinin receptor B1 and B2 (BDKRB1 and BDKRB2) genes are involved in regulation of wound healing and blood pressure regulation, mechanisms that can be used to deal with environmental stressors. Previously, Coble et al. [BDKRB1 among up-regulated genes during heat stress in chickens.Cytoplasmic linker associated protein 1  genes are involved in T- and B-cell development [MST1) gene regulates macrophage activity during inflammation [RHOA) is a small GTPases of the Rho family, which regulates several cellular processes including actin cytoskeleton reorganization and inflammation [Some genes involved in inflammatory and immune responses, elopment . The macammation , 66. Rasammation , 68.LZTS1), phospholipid phosphatase related 4 and 5 (PLPPR4 and PLPPR5) are involved in neuronal plasticity [BMPR1A) playing an important role in bone formation, which was recently found associated with body size in sheep [Four other potential candidate genes for uniformity were identified. Leucine zipper putative tumor suppressor 1  and mineralization of tissues like bones and teeth (DMP1 and DSPP [In the common regions between ln_and DSPP ), are liand DSPP , 73–75).PPP2R2C, JAKMIP1 and ADGRB3. Protein phosphatase 2, regulatory subunit B, gamma (PPP2R2C) and adhesion G protein-coupled receptor B3 (ADGRB3) were previously related to learning and memory (synaptic plasticity) and synaptogenesis in mice [. Janus kinase and microtubule-interacting protein 1 (JAKMIP1) was associated with social behavior in mice through its function on neuronal translation and was related to T cell-mediated cytotoxicity [v and ln_Drosophila [Additionally, both common regions (Chr6_105 and 9_8) contain genes with neuronal functions:  in mice , 77. Jantoxicity , 79. Theosophila –82. Therm and dEBVv resulted in 8 common regions affecting simultaneously mean and residual variance. Such findings are in line with Wolc et al. [The high correlation between dEBVc et al.  who founc et al.  also repv not only captured common effects with the mean of YW but also its residual variance by sharing regions (among top 20 by variance explained) with dEBVv_r0 and ln_m and dEBVv are involved in metabolism , a process most probably related to both, mean and uniformity.The dEBVm and the response variables that assumed null rmv emphasize the role of the latter to find genomic regions that are specifically affecting residual variance and not the mean of YW. Generally, data transformation is used as the main tool to reduce mean-variance relationship in studies on uniformity . Thi. Thim anKnowing the importance of considering potential scale effects and confounding between mean and residual variance, the next step is to discuss the suitability of the different response variables to be used in a GWAS for uniformity. Based on our results, we recommend considering the following aspects regarding the response variable and data structure. Firstly, we need to take into account the nature of the trait because small genetic variance and low heritabilities are often found for uniformity. Previously we estimated a heritability for residual variance of YW even smaller  than hamv were assumed to measure the influence of the mean and/or potential scale effects on the residual variance. Therefore, analyzing each response variable, we can conclude that: i) dEBVv was greatly affected by the mean; ii) dEBVv_r0 had low accuracy by assuming null rmv, which requires a large amount of phenotypic information and/or genotyped sires; and iii) ln_v, there may be in this case a small preference for this response variable as it behaved also better in the Markov chain Monte Carlo (MCMC). Furthermore, considering different response variables can be beneficial, windows that appear in multiple approaches may have a higher credibility and may help in understanding the role of genes affecting the mean, the residual variance or both and whether genes controlling the variance beyond scale effects.Secondly, we used two different procedures to obtain response variables to perform GWAS for uniformity. The first one was the deregressed EBV; deregression, which removes both the contribution of parents and the shrinkage present in the EBV. On the other hand, the second one resulted in a measure, ln_v_r0 and ln_Using solutions from a DHGLM that assumed null genetic correlation between mean and residual variance was a suitable strategy to identify genomic regions affecting uniformity less dependent on the mean. Thirteen and four SNPs were associated with dEBVThe phenotypic data used in this study are described in Iung et al. . Here, gy and ψ are vectors of response variables for the mean and residual variance models (denoted with the subscript v), respectively, b and bv are vectors of fixed effects and covariates , s and sv are vectors of random sire genetic effects, e and ev are vectors of random residual effects, X, Xv, Z and Zv are corresponding incidence matrices. This approach was chosen because of its better predictability compared to a two-step approach, as previously stated by Iung et al. [m obtained following the deregression procedure proposed by Garrick et al. [v: deregressed EBV of the residual variance from a DHGLM assuming non-null genetic correlation between mean and residual variance (rmv ≠ 0); ii) dEBVv_r0: deregressed EBV of the residual variance from a DHGLM assuming rmv = 0; and iii) ln_mv = 0. The log-transformation was used to reduce mean-variance relationship and correct for non-normality of the residual variances. Fitting the DHGLM assuming null rmv was intended to reduce the impact of the mean of YW may have on the EBV in the residual variance part of the model, given the strong estimate of rmv on YW previously reported for this population [v and EBVm would be even higher correlated than the rmv because of using the information on mean YW for both sets of EBV. When using null rmv, it is expected to have a higher possibility to find regions only affecting the residual variance. Descriptive statistics of each response variable are presented in Table All responses variables used solutions or residuals from DHGLM fitted in our previous study . The DHGg et al. . Mean ofk et al.  and usinmentclasspt{minimapulation . For insentclass1pt{minimap-value for Hardy-Weinberg equilibrium test (HWE) less than 10− 5 and highly correlated SNPs (r2 > 0.99) within a window of 100 consecutive SNPs were removed, so that 333,877 SNPs remained for analysis. All genotyped bulls had a call rate higher than 0.90, passing the quality control.Genotypic information from 423 Nellore bulls was used, being 415 genotyped with the Illumina® BovineHD chip (HD) and 8 with Illumina BovineSNP50 BeadChip  and then imputed to HD (777 k) using FImpute software . In the y is the vector of response variables . In addition, scaled inverse chi-squared prior distributions with v degrees of freedom and scale parameter S were assumed for The SNP effects were estimated using Bayes C method , a mixtuk et al. , whereasThe analyses were performed using the GS3 software . Chains 2) :ectively , 95. In http://www.ensembl.org), UMD3.1 bovine genome assembly [The shared 1-Mb windows (among the top 20 that explained the largest proportion of genetic variance) between response variables and windows with SNPs that showed a strong association (BF > 20) were screened to identify potential candidate genes using Ensembl Genome Browser  terms and pathways of each gene were obtained using Blast2GO and Reactome. (XLSX 99 kb)Additional file 2:Distribution of number of progeny per sire and responses variables. (DOCX 242 kb)Additional file 3:Manhattan plot and linkage disequilibrium (LD) map in each 1-Mb window discussed in our study. (PDF 419 kb)


We observed a similar effect in the Yup’ik cohort , and a non-significant effect in the same direction in the independent Greenlandic stage 2 cohort . The association remained genome-wide significant in meta-analysis of the Arctic cohorts (-0.10 SD (0.02), p = 4.7x10-8). Moreover, the variant was associated with a leaner body type  kg; waist circumference, -1.52 (0.33) cm; hip circumference, -0.85 (0.24) cm; lean mass, -0.84 (0.19) kg; fat mass and percent, -1.66 (0.33) kg and -1.39 (0.27) %; visceral adipose tissue, -0.30 (0.07) cm; subcutaneous adipose tissue, -0.16 (0.05) cm, all p<0.0002), lower insulin resistance , p = 0.00021), and favorable lipid levels  mmol/l, p = 0.025; HDL-cholesterol, 0.04 (0.01) mmol/l, p = 0.0015). In conclusion, we identified a novel variant, where the derived G-allele possibly associated with lower BMI in Arctic populations, and as a consequence also leaner body type, lower insulin resistance, and a favorable lipid profile.The genetic architecture of the small and isolated Greenlandic population is advantageous for identification of novel genetic variants associated with cardio-metabolic traits. We aimed to identify genetic loci associated with body mass index (BMI), to expand the knowledge of the genetic and biological mechanisms underlying obesity. Stage 1 BMI-association analyses were performed in 4,626 Greenlanders. Stage 2 replication and meta-analysis were performed in additional cohorts comprising 1,058 Yup’ik Alaska Native people, and 1,529 Greenlanders. Obesity-related traits were assessed in the stage 1 study population. We identified a common variant on chromosome 11, rs4936356, where the derived G-allele had a frequency of 24% in the stage 1 study population. The derived allele was genome-wide significantly associated with lower BMI (beta (SE), -0.14 SD (0.03), p = 3.2x10 The risk of developing obesity is strongly affected by lifestyle, particularly diet and level of physical activity, but also by genetic predisposition. Knowledge about the genes predisposing to obesity can inform about biological processes underlying this condition, and possibly identify targets for obesity treatment. In the present study, we take advantage of the genetic architecture of the Greenlandic population to identify genetic variants associated with alterations in body-mass index, as a measure of obesity. By examining more than 100,000 genetic variants in 4,626 Greenlanders we identify a specific variant, rs4936356, where the derived G-allele was associated with lower body-mass index, lower insulin resistance, and favorable lipid levels. We verified the association with body-mass index in a combined analysis including two additional Arctic cohorts. These results contribute to the understanding of the genetic predisposition to obesity, however, further studies are required to replicate these findings and to identify the gene through which the rs4936356 variant is affecting body-mass index. CREBRF and ADCY3 have been associated with obesity in Samoans and Greenlanders, respectively / 22.5, where insulin levels were expressed as pmol/l and glucose levels as mmol/l . InformaVisceral- and subcutaneous adiposity was assessed with ultrasonography according to a validated protocol, and defined as the depth in centimeters from the peritoneum to the lumbar spine, and from the skin to the linea alba, respectively. Coefficients for inter- and intra-observer variation were in the range 1.9–5.6% . Fat per-10), as well as SNPs which were polymorphic in the IHIT cohort but not in the B99 and BBH cohorts, and SNPs associated with sex (p<1.0x10-5). In total, 4,674 individuals  and 115,182 SNPs passed the quality control.The Greenlandic samples were genotyped on the Metabochip (Illumina), which contains 196,725 SNPs linked to metabolic, cardiovascular, or anthropometric traits . GenotypFor the Yup’ik cohort, detailed descriptions of genotyping procedures, pedigree analyses, and data cleaning to obtain ancestry information have previously been published . The rs4To fine map the locus identified based on Metabochip data, we imputed the region. The imputation was based on Omni5Marray (Illumina) genotype data from 20 Greenlandic trios. This data was phased using ShapeIt , and theTo account for relatedness and admixture, we applied a linear mixed model, implemented in the software GEMMA , for assThe Greenlandic population is an admixture of Inuit and Europeans, and we applied the asaMap method  to estimThe Yup’ik cohort was also analyzed with the GEMMA software . For thiAssociation testing in the independent Greenlandic stage-2 cohort was also done using the linear mixed effects model implemented in the GEMMA software  to accouWe performed a meta-analysis of the results from the stage 1 population and the two stage 2 replication cohorts based on the estimated effect sizes and their standard errors in METAL . HeterogWe estimated the allele frequency of rs4936356 separately for the Inuit and European ancestry components of the admixed Greenlandic population applying a two-step approach. In step 1, ancestry proportions for the Greenlandic individuals from the stage 1 study population, as well as for 50 Danish individuals, were estimated using ADMIXTURE v1.3.0 , assuminWhole transcriptome RNA was extracted in 2.5 ml peripheral blood from 499 Greenlanders from the stage 1 study population. The extraction was performed with the PAXgene Blood miRNA kit according to the manufacturer’s protocol, and subjected to on-column DNase I treatment with RNase-free DNase . The RNA quality and purity were assessed using an Agilent 2100 Bioanalyzer (Agilent RNA 6000 Nano Kit) and NanoDrop, respectively.TruSeq RNA Sample Prep Kit v2 (Illumina) was used to prepare the RNA sequencing library. Isolation of mRNA was carried out with oligo(dT) beads on 200 ng of total RNA, and fragmentation with Elute, Prime, Fragment Mix. First-Strand Mix and SuperScript II (Invitrogen) reverse transcription master mix was applied for generation of first-strand cDNA, and the second strand was synthesized by adding Second-Strand Master Mix. End-repairing and purification of the fragmented cDNA were performed with AMPure XP Beads (Agencourt), and A-Tailing Mix was added, and reactions were incubated. For adaptor ligation, Adenylate 3′ Ends DNA, RNA Index Adaptor and Ligation Mix were mixed and reactions were incubated. End-repaired DNA was purified with AMPure XP Beads (Agencourt). PCR amplification with PCR Primer Cocktail and PCR Master Mix were performed to enrich the cDNA fragments, and PCR products were purified with AMPure XP Beads (Agencourt). Agilent 2100 Bioanalyzer instrument (Agilent DNA 1000 Reagents) and by real-time qPCR (TaqMan Probe) were used to measure the average molecule length. The qualified libraries were amplified on a cBot to generate the cluster on the flow cell .Amplified libraries were sequenced using the BGI500 sequencing technology at BGI (100bp paired-end sequencing). We assessed the quality of the sequencing reads using FastQC , and inshttps://www.gtexportal.org/; accessed 14-06-2019) to assess possible effects of the genetic variant on the expression of nearby genes.The RegulomeDB  and Haplhttps://www.ebi.ac.uk/ega/home) under the accessions EGAS00001002641 and EGAS00001004127, respectively.The Greenlandic Metabochip-genotype data and the RNA sequencing data are deposited in the European Genome-phenome Archive Click here for additional data file.S2 FigP-values were calculated based on data transformed to a standard normal distribution.(PDF)Click here for additional data file.S3 Figr2) with the lead SNP.The association analysis was based on imputed data, and the dark red dot indicates the lead SNP in the region (rs4936356). The rest of the SNPs are colored according to the extent of correlation ((PDF)Click here for additional data file.