Although the relationship between serum uric acid (SUA) and adiposity is well established, the direction of the causality is still unclear in the presence of conflicting evidences. We used a bidirectional Mendelian randomization approach to explore the nature and direction of causality between SUA and adiposity in a population-based study of Caucasians aged 35 to 75 years.rs6855911 within the SUA gene SLC2A9 in one direction, and combinations of SNPs within the adiposity genes FTO, MC4R and TMEM18 in the other direction. Adiposity markers included weight, body mass index, waist circumference and fat mass. We applied a two-stage least squares regression: a regression of SUA/adiposity markers on our instruments in the first stage and a regression of the response of interest on the fitted values from the first stage regression in the second stage. SUA explained by the SLC2A9 instrument was not associated to fat mass  contrasting with the ordinary least square estimate . By contrast, fat mass explained by genetic variants of the FTO, MC4R and TMEM18 genes was positively and significantly associated to SUA , similar to the ordinary least square estimate . Results were similar for the other adiposity markers.We used, as instrumental variables, Using a bidirectional Mendelian randomization approach in adult Caucasians, our findings suggest that elevated SUA is a consequence rather than a cause of adiposity. High serum uric acid (SUA) is known to co-exist with the components of metabolic syndrome including obesity Some hypothesized that SUA mediates obesity and other features of metabolic syndrome by reducing endothelial nitric oxide and decreasing insulin-mediated glucose uptake in skeletal muscle Conversely, others suggest that hyperinsulinemia  reduces urinary uric acid clearance with subsequent elevation of SUA levels As far as we are aware, the relationship between SUA and adiposity has not been previously explored using the principles of Mendelian randomization, a method that allows disentangling causation from association in the presence of confounding SLC2A9 to have a strong association with SUA levels FTO, MC4R and TMEM18, all of which have been recognized to be associated with obesity and explaining a variance of about 1–2% SUA is known to have a high (25 to 70%) heritability The CoLaus study is a cross-sectional population-based study conducted in Lausanne, Switzerland. Details of the study have been previously described Participants attended the outpatient clinic at Centre Hospitalier Universitaire Vaudois (CHUV) in the morning after an overnight fast. They were asked to continue taking their medication as usual. This examination included detailed questionnaire, physical examination with anthropometric measures by trained and certified field interviewers and laboratory testing. In the present analysis, smoking was defined as present if the participant reported to be current smoker at the time of examination and alcohol consumption was defined as present for participants who report drinking alcohol at least once a day. Diuretic use was assessed by recording all the prescribed drugs taken by the participants and was considered as present if participants were using drugs belonging to any class of diuretics. Height was measured to the nearest 5 mm using a Seca® height gauge , and weight was measured to the nearest 0.1 kg using a Seca® scale . These instruments were calibrated regularly. Body mass index was defined as weight divided by height in meter squared. Waist circumference was measured with a non-stretchable tape and the mean of two measurements expressed in centimeters was used for the analyses. Fat mass  was assessed by electrical bioimpedance using the Bodystat® 1500 analyzer . Fat mass (in kilograms) was calculated from the percentage of fat mass multiplied by weight.(−1.154)×age(−0.203)×F, where F = 1 for men and F = 0.742 for women Venous blood samples were collected after an overnight fasting. Most clinical assays were performed by the CHUV Clinical Laboratory on fresh blood samples. Serum creatinine was measured by Jaffe kinetic compensated method (2.9%−1.5% maximum inter and intra-batch coefficients of variation) and uric acid by uricase-PAP (1.0%−0.5%). Glomerular filtration rate (GFR) was estimated using the abbreviated Modification of the Diet in Renal Disease (MDRD) formula: 186×(serum creatinine [µmol/L]/88.4)FTO, MC4R, TMEM18 and SLC2A9 genes respectively, were considered for the present analyses.Nuclear DNA was extracted from whole blood for whole genome scan analysis. Genotyping was performed using Affymetrix 500 K SNP chip, as recommended by the manufacturer . Persons with less than 95% genotyping efficiency overall  and persons with possible gender inconsistencies (n = 5) were removed. Monomorphic SNPs, SNPs with less than 70% genotyping efficiency, SNPs with minor allele frequency less than 1%, and/or not in the Hardy-Weinberg proportions were excluded. A hundred and twenty-nine, 20, 56 and 124 SNPs, 100 kb upstream and downstream of the t test and χ2 test to compare the distribution of covariates according to sex.All tests were performed using Stata 11 . Continuous variables were summarized as mean (standard deviation [SD]) while categorical variables as number of subjects and percentages. We used SLC2A9 gene and SUA level, in the overall sample and separately by sex. We identified rs6855911, rs7442295 and rs7669607 as the best SNP in the overall sample, men and women respectively. These variants have been identified to be related to SUA in earlier studies FTO, MC4R and TMEM18 genes separately did not result in strong instruments. To identify sufficiently strong instruments (i.e. an F-statistics >10) FTO, MC4R and TMEM18, a score was created for every individual SNP, coded as 0-homozygote for the non-risk allele, 1-heterozygote and 2-homozygote for the risk allele. When combining the SNPs, we summed up their scores using an additive coding for the number of alleles associated with higher adiposity levels. This resulted in an ordinal variable with seven categories coded from 0 to 6. Further, we present the distribution of SUA across genotypes of the SLC2A9 rs6855911 and adiposity markers across adiposity-related SNPs individually or as genetic scores in the overall sample to see how the specific SNPs relate to the phenotype of interest in the CoLaus participants and used a non-parametric test to assess for trend. In the latter case when using genetic scores to check for trends, we combined participants having scores of 0, 1 and 2 since the number of participants in these categories was small. We also reported the associations of SNP/SNP scores with markers in the hypothesized pathway (i.e. SLC2A9 rs6855911 with adiposity markers and adiposity-related genetic variants with SUA).Pearson’s partial correlation coefficient test was used to estimate the correlation of SUA with adiposity markers and Fischer’s Z transformation to compare the correlation coefficients between men and women. We performed a bidirectional Mendelian randomization to 1) assess the causality in the direction of SUA causing elevated adiposity and 2) reverse causality i.e. elevated adiposity levels leading to hyperuricemia. In the former, we chose as instrumental variable the SNP with the best F-statistics  from theivregress function in Stata 11. To meet the assumptions for linear regression, we used the most appropriate transformations for both the dependent and independent variables . Further, to facilitate comparability between the coefficients and ease interpretation of the results, both the transformed dependent and independent variables were standardized and results from regression models expressed as 1 SD change in the outcome corresponding to a 1 SD increase in exposure (note that the significance of the results would remain the same without standardization). We tested for interaction by sex using the sex-specific results from the second stage and the following test statistic: (βmen-βwomen )/√ (S.Emen2+S.Ewomen2) where β and S.E is the standardized beta coefficient and standard error respectively.To explore the potential causal effect in both directions, we applied a Mendelian randomization approach, also called two-stage least squares (2 SLS) regression, using the instrumental variables that we identified. In the first stage, we conducted an ordinary least square (OLS) regression, regressing SUA/adiposity markers on our instruments , MC4R (748), TMEM18 (650–1442) and SLC2A9 (590–963). No significant difference with regards to the phenotype of interest i.e. adiposity markers and SUA was noted between participants with and without missing genetic data. Data was also missing for the adiposity markers: weight (n = 9), body mass index (n = 9), waist circumference (n = 9) and fat mass (n = 64).Of the 6184 participants, the range of missing genetic information varied across the different SNPs (chosen as instruments) of the P<0.001 in all) while women had higher fat mass (P<0.001).The main demographic and clinical characteristics of CoLaus participants according to sex are summarized in P<0.001). The correlations were stronger in women than in men for weight , BMI , waist circumference , and fat mass .We did not find any associations of the genetic variants with the other measured confounders , therebySLC2A9 SNPs used as instruments and SUA presented sufficient F-statistic values  in the overall sample. Both crude and adjusted analyses showed significant positive associations between SUA and all the selected adiposity markers (P<0.001) in the OLS regression. However, in the 2 SLS regression using instrumental variables, we observed no significant association with the adiposity traits. The results obtained from 2 SLS do not provide evidence of a causal effect of SUA on adiposity markers. This is further substantiated by the finding, in most cases, of a significant difference between the OLS and 2 SLS standardized coefficients, as shown by the P-value obtained from the Durbin-Hausman test. Similarly, conducting the same analyses but using rs7442295 as instrument in men (rs7669607 as instrument in women (The statistics from the first-stage regression between t in men  and rs76in women  resultedP<0.001) in the overall sample. The associations obtained from the 2 SLS regression were similar to the OLS regression both in magnitude (in most cases) and direction, and remained significant in the unadjusted analyses. In fat mass, the association was significant even after adjustment (P = 0.048). Sex-specific results are presented in P value from Durbin-Hausman test = 0.671). Of interest is the observation that the magnitude of both the crude and adjusted coefficients was very similar in most cases, this being more apparent upon stratification by sex. The large confidence intervals in the 2 SLS associations reflect the relative weakness of the instruments. Controlling for population stratification using principal components generated from genome-wide SNPs data as covariates into the multivariable models did not produce any relevant changes in the estimates (data not shown).For the relationship between SUA and adiposity markers in the reverse direction, where SUA was used as the dependent variable, we obtained different combinations of SNPs that produced large enough F-statistics for the different adiposity traits separately in the overall sample, in men and in women . Table 5SLC2A9 rs6855911 in the overall sample, by rs7442295 in men or by rs7669607 in women, was not associated with any of the selected adiposity markers; the second-stage estimates from the instrumental variable approach were close to zero. Thus, in the present study, we found no evidence to suggest that SUA causally impacts on adiposity. By contrast, using genetic variants of the FTO, MC4R and TMEM18 genes as instruments to explain the effect of adiposity on SUA, we observed a causal positive association of weight and fat mass with SUA in the overall sample; the association of fat mass with SUA was present in both men and women. This finding is not totally unexpected and is compatible with the hypothesis that hyperinsulinemia, a consequence of overweight and obesity, enhances renal proximal tubular reabsorption of uric acid with subsequent elevation of SUA levels Using a bidirectional Mendelian randomization approach in a population-based study of Caucasians aged 35 to 75 years, we tried to unravel the direction of causality between SUA and adiposity markers. SUA explained by To the best of our knowledge, this is one of the few population-based studies to use a bidirectional Mendelian randomization approach. Welsh et al. were among the first to have demonstrated the usefulness of a bidirectional Mendelian randomization approach in unraveling the directional link between adiposity and inflammation where the direction of relationship had not been otherwise proven SLC2A9) gene, encoding a putative hexose transporter, to be strongly associated with SUA SLC2A9 gene explains a substantial proportion (about 1–6%) of variance in SUA concentration SLC2A9 gene has urate transport activity and found the most significant SLC2A9 SNPs for SUA to be associated with a low fractional excretion of uric acid Recent genome-wide association studies have identified the solute carrier (SLC) family 2, member 9  like BMI 2) to fulfill the first assumption underlying the approach. However, we acknowledge that this practice can also lead to an increase in bias of the estimates FTO, MC4R and TMEM18 that are amongst the genes most strongly associated with obesity traits SLC2A9 variants as instruments.With respect to exploring causality in the other direction, i.e. SUA could be a consequence of excess fat accumulation, we took advantage of the fact that obesity has a strong genetic component with heritability estimates ranging from 65 to 80% The strengths of this study are its population-based design, the large sample size and accessibility to detailed and relevant information. However, our results have to be interpreted with caution since the validity of a Mendelian randomization approach in observational epidemiology relies partly on unverifiable assumptions. Some of the potential sources of residual confounding may arise due to pleiotropy and population stratification. Pleiotropy of genetic variants is difficult to address without examining all the biological pathways and this is often not possible because of the lack of understanding on the exact underlying mechanisms. However, we did not observe significant associations between any of the instruments and potential confounders suggesting that the associations are unlikely to be mediated through biological pathway involving the measured confounders. Similarly, it is reasonable to speculate that residual confounding from the association between the instruments and unmeasured confounders is minimal based on our findings of comparable crude and adjusted estimates (particularly in the direction of adiposity causing elevated SUA). We also did not find evidence of confounding by population stratification in our data.There are also other limitations in this study. First, the adiposity-related genetic variants used as instruments were weak, resulting in the estimates having wide confidence intervals and low precision. Second, the approach used here is not the classical Mendelian Randomization approach but a slight deviation from it Click here for additional data file.Table S2Distribution of adiposity markers across scores of adiposity-related SNPs.(DOC)Click here for additional data file.Table S3Distribution of SUA across scores of adiposity-related SNPs.(DOC)Click here for additional data file.Table S4Distribution of adiposity markers across genotypes of SLC2A9 rs6855911.(DOC)Click here for additional data file.Table S5Association between the SNP/SNP scores with potential confounders.(DOC)Click here for additional data file.Table S6Association of SUA (using rs7442295 from the SLC2A9 gene as instrument) with adiposity measures (dependent variable of interest) in men.(DOC)Click here for additional data file.Table S7Association of SUA (using rs7669607 from the SLC2A9 gene as instrument) with adiposity measures (dependent variable of interest) in women.(DOC)Click here for additional data file.Table S8Association of adiposity measures  with SUA (dependent variable of interest) in men.(DOC)Click here for additional data file.Table S9Association of adiposity measures  with SUA (dependent variable of interest) in women.(DOC)Click here for additional data file.


Components of (co)variance and genetic parameters were estimated for adjusted weights at ages 120 (W120), 240 (W240), 365 (W365) and 450 (W450) days of Polled Nellore cattle raised on pasture and born between 1987 and 2010. Analyses were performed using an animal model, considering fixed effects: herd-year-season of birth and calf sex as contemporary groups and the age of cow as a covariate. Gibbs Samplers were used to estimate (co)variance components, genetic parameters and additive genetic effects, which accounted for great proportion of total variation in these traits. High direct heritability estimates for the growth traits were revealed and presented mean 0.43, 0.61, 0.72 and 0.67 for W120, W240, W365 and W450, respectively. Maternal heritabilities were 0.07 and 0.08 for W120 and W240, respectively. Direct additive genetic correlations between the weight at 120, 240, 365 and 450 days old were strong and positive. These estimates ranged from 0.68 to 0.98. Direct-maternal genetic correlations were negative for W120 and W240. The estimates ranged from −0.31 to −0.54. Estimates of maternal heritability ranged from 0.056 to 0.092 for W120 and from 0.064 to 0.096 for W240. This study showed that genetic progress is possible for the growth traits we studied, which is a novel and favorable indicator for an upcoming and promising Polled Zebu breed in Tropical regions. Maternal effects influenced the performance of weight at 120 and 240 days old. These effects should be taken into account in genetic analyses of growth traits by fitting them as a genetic or a permanent environmental effect, or even both. In general, due to a medium-high estimate of environmental (co)variance components, management and feeding conditions for Polled Nellore raised at pasture in tropical regions of Brazil needs improvement and growth performance can be enhanced. Beef cattle production plays an important role in Brazilian agribusiness and is expected to increase 4.4% by 2015. In terms of beef cattle, Brazil ranks first worldwide in exportation, and is the second largest producer and the third largest food consumer in the world Growth traits are influenced both by direct additive genetic effects and by maternal effects The use of naturally polled breeds in modern cattle breeding has become an increasing trend, both because mechanical removal of horns and injuries from fights are undesirable and also because easier and safer animal management and cowhide integrity are preferred.In Brazil, the Polled Nellore breed has been raised separately from the common Nellore in many places and has for some time been considered as a distinct. Growth and weight data have enabled trait investigation in the common Nellore in many regions of Brazil Pedigree profiles and data information in general have limitations at some level, but estimates of genetic (co)variance components and genetic parameters are strategic The data set was provided by Guaporé Pecuária Company (OB brand) together with the National Association of Breeders and Researchers (ANCP) and consisted of weight records from the years between 1987 and 2010. The herds were reared in the municipality of Pontes e Lacerda, Mato Grosso State, Brazil. The climate is characterized by a humid tropical Cerrado-Amazon transition zone with two well defined seasons (rainy and dry). Average annual precipitation is 1500 mm and the altitude is 254 m.pre-WWG) was obtained for each animal using the birth weight (BW) and the weaning weight (WW) (pre-WWG = WW÷BW) and the formula bellow:Weight information from each and every animal allowed estimation of the adjusted weights for 120 (W120), 240 (W240), 365 (W365) and 450 (W450) days of age. This is done because all breeding programs in Brazil function with specific selection criteria, mainly standardized weights. For estimation of W120 and W240 average pre weaning daily weight gain (Estimation of W365 and W540 was done by obtaining the average post weaning daily weight gain (post-WWG), the latest adult weight record (AW) and using the age at the time of the last weighing (IA) (post-WWG = (AW−W240)÷(IA−W240). Estimation of W365 and W450 was done using the following formula: Breeding programs in Brazil do not work with average daily weight gain as selection criteria, so this trait was used only to estimate the adjusted weights at ages 120, 240, 365 and 450 days. The weaning weight (WW) and the latest adult weight record (AW) corresponds to the eighth or ninth month of age and to the seventeenth or eighteenth month of age, respectively. Based on the adjusted weights, consistency analysis was performed to exclude outliers from the data set. Any record presenting an adjusted weight smaller or greater than the average value in three standard deviation units was eliminated .Genetic analyses were carried out by fitting a model that included the following effects: cow age as covariate; sex of the calf coded into two levels ; season of birth coded into four levels , years of birth and the effect of herd (seven herds).General Linear Model) procedure of the Statistical Analysis System software The fixed effects included in the model were herd-year-season (HYS) of birth and sex of the calf. To define the fixed effects included in the contemporary groups, the GLM , vector y by the known matrix X and a, m and ep are the random effects vector  associated with records in y by the incidence matrix Z; and e, is the residuals vector. It is also assumed that both the systematic effects listed above and the (co)variance components included in the fitted model have a uniform a priori Gaussian distribution, whilst the conditional distributions of the direct additive, maternal, permanent environmental and residual variances present inverse Wishart distribution Where Multiple Trait using Gibbs Sampler under Animal Model) The marginal posterior distribution for each parameter was obtained via integration of multivariate density functions using a Gibbs sampling procedure, with a period of data collection for multi-traits of 1,500,000 iterates. The initial discard was 500,000 and the sampling interval was of 1000 iterations. Distributions (flat prior) were used to estimate (co)variance components. This was done using the MTGSAM software variance component estimates were accurate, tending to normal distribution (not shown). The symmetrical distributions of measures of central tendency indicated accurate analysis The (co)variance matrix of maternal and additive direct genetic effects, are shown in The estimates of direct heritability and genetic correlation for W120, W240, W365 and W450 are shown in The maternal effect on weight at ages 365 and 450 days was not included in the analysis due to insignificant effect. For weight at 120 and 240 days, the maternal effects accounted for a lower proportion of total variation than the direct additive genetic effects. In general, the samples obtained for the genetic correlations  showed nThe additive genetic effects accounted for a high proportion of total variation in the traits. This resulted in very high heritability estimates for growth traits, presenting upper limits of 0.49 (W120) and 0.76 (W365) (not shown). The direct additive genetic correlations between the weight at 120, 240, 365 and 450 days old were high and positive. In general, the estimates ranged from 0.68 to 0.98. The direct-maternal genetic correlations were negative for W120 and W240. The estimates ranged from −0.31 to −0.54. The estimates of maternal heritability ranged from 0.056 to 0.092 for W120 and from 0.064 to 0.096 for W240.High genetic variability was observed in this herd, which was indicated by high estimates of direct additive genetic variance and heritability coefficient. Thus, it is possible to improve the traits through a selection process Strong and positive estimates of the direct additive genetic correlation between weight at 120, 240, 365 and 450 days old were observed. This indicates that improvements in one trait will imply improvements of other traits. The estimates of the direct additive genetic correlations were stronger than 0.70 at most ages and are in accordance with other studies in Brazil The greatest weight gain interval is between calving and weaning Although the direct heritability estimates for W365 (0.72) and W450 (0.67) were greater than W120 (0.43) and W240 (0.61), selection for post weaning weights may increase the age at slaughter and the production costs throughout the years. More criteria for the selection of these traits are therefore necessary. Similar results and arguments were reported for adjusted weights at 205, 365 and 550 days in Nellore cattle in Northern Brazil Average weights for W120, W240, W365 and W450 were 121.6, 183.8, 215.5 and 248.5 kg, respectively. Average weight gain for pre-WWG and post-WWG were 0.645 kg (±0.124) and 0.288 kg (±0.162), respectively. Body weight is currently the selection criterion in Brazilian breeding programs, but if average weight gain was accounted for it would be best to select animals with greater values of W120 and W240. If correlations are strong in all ages and heritabilities are high, animal selection at the age of W120 and W240 will probably result in more precocious and heavier animals at the time of slaughter. The selection of cattle at older ages (W365 and W450) could result in animals with later growing profiles, whereas early maturing animals would maximize profits from cattle farming. The selection criteria should be based on weights, however, the decision of which weights should be chosen depends on the life period in which Polled Nellore gain more weight.Besides the great importance of the maternal effect in extensive breeding systems in tropical regions, selection has been focused over the years on heavier weights and on the genetic additive effects. As expected The genetic correlation between direct additive genetic and maternal additive genetic effects suggests genetic antagonism between both effects. Therefore, selection for direct additive genetic effects would worsen the maternal ability, making it difficult to conduct joint selection for W120 and W240 2T = 0.39 for W120 and h2T = 0.56 for W240) become smaller than the heritability for the direct additive effect (h2a = 0.43 for W120 and h2a = 0.61 for W240). This influences the expected genetic gain for the selection of W120 and W240, which is smaller and might be more appropriate. This influences the expected genetic gain for the selection of W120 and W240, which might be more appropriate as well as being smaller. However, negative values for the genetic correlation between direct additive effects and maternal effects may occur not only due to genetic antagonism, intrinsic to dam and progeny, but because of the introduction of bulls for preferential (assortative) mating and the sire x year interaction Negative regression coefficients for genetic value of daughters based on the dams phenotype were suggested by Prerequisites for accurate estimates of the genetic correlation between the direct additive and maternal effects have been proposed, like the need for a consistent data set In beef cattle farming the direct, maternal and permanent environmental effects have great importance in the pre-weaning growth rates This study has revealed that genetic progress is possible for the accounted traits. Maternal effects influenced the performance of weight at 120 and 240 days. Therefore, the maternal effect should be taken into account in genetic analyses of growth traits by fitting it as a genetic or a permanent environmental effect, or even both.In general, due to the medium-high estimate of environmental (co)variance components, management and feeding conditions for Polled Nellore raised on pasture in tropical regions of Brazil need improvement so that growth performance may be improved. This will possibly reinforce the competitiveness of Polled Nellore in the meat production industry in tropical regions and boost food supply worldwide. The strong and positive estimates of direct additive genetic correlation for weight at 120, 240, 365 and 450 days old indicate that improvements in a trait should involve improvements in other traits.


The genetic analysis of quantitative or complex traits has been based mainly on statistical quantities such as genetic variances and heritability. These analyses continue to be developed, for example in studies of natural populations. Genomic methods are having an impact on progress and prospects. Actual relationships of individuals can be estimated enabling novel quantitative analyses. Increasing precision of linkage mapping is feasible with dense marker panels and designed stocks allowing multiple generations of recombination, and large SNP panels enable the use of genome wide association analysis utilising historical recombination. Whilst such analyses are identifying many loci for disease genes and traits such as height, typically each individually contributes a small amount of the variation. Only by fitting all SNPs without regard to significance can a high proportion be accounted for, so a classical polygenic model with near infinitesimally small effects remains a useful one. Theory indicates that a high proportion of variants will have low minor allele frequency, making detection difficult. Genomic selection, based on simultaneously fitting very dense markers and incorporating these with phenotypic data in breeding value prediction is revolutionising breeding programmes in agriculture and has a major potential role in human disease prediction. The explanation of how traits could be both continuously distributed and yet depend on particulate inheritance was resolved within a few years of the Mendelian rediscovery. The model of effects of multiple genetic loci and the environment contributing to the phenotype accounts straightforwardly for the typically Gaussian phenotypic distribution of continuous traits, the observed correlations among relatives, inbreeding depression and heterosis, continuing responses to artificial selection, and evolutionary change. Analyses and predictions have been based almost entirely on phenotypic observations and their interpretation in terms of measures such as components of genetic variance and covariances, heritability, dominance, and changes in frequency of many genes -3. Many Research in recent decades has provided both direct and indirect evidence of the location and effects of individual loci affecting quantitative traits and, for a limited number of loci, knowledge of the causative change in the DNA. Progress has, however, been restricted by many factors. These include the inability to disentangle the effects of closely linked genes through limitations in amount and type of data, the difficulty of following the metabolic trail from a base substitution to a change in the trait, and the potential interactions among genes at the metabolic and phenotypic level. The rapid advance in genomic methods and other high throughput ‘-omic’ methods has both fuelled the recent developments and provides the opportunity for more. The advances are not restricted just to mapping and analysing individual loci but involve an increasing integration of molecular and statistical methods. With dense genetic markers used throughout the whole genome, the genetic covariances among relatives can be partitioned among genomic regions and incorporated into classical statistical analyses developed for phenotypic data to predict offspring performance in plant and animal improvement programmes and in the prevention of human disease. Nevertheless there is still a long way to go.Quantitative genetic understanding and application are also being informed by progress in the analysis, interpretation and utilisation of solely phenotypic data, facilitated by developments in statistical methods and computing power and by the availability of pedigree information in natural populations from long term records or genetic markers. Lest it be thought that, in the genomic era, quantitative genetic methods have been replaced rather than supplemented, I shall discuss these first. This review is from the perspective of a quantitative rather than molecular geneticist, concerned more with understanding the output of processes affect the phenotype rather than on what the processes are. It is not intended to be comprehensive. There is an expanding compendium in the work of Walsh and Lynch , and manA) and the additive genetic variance of the trait or covariance of a pair of traits. Although usually used in the context of the infinitesimal model of very many unlinked additive genes of small effect, it can also include discrete effects due to the genotype at individual loci. The basic descriptors of the phenotypic variability of quantitative traits have not changed greatly over recent decades, subsequent to the adoption of the ‘animal model’, led by Henderson, which generalises the description of the variances and covariances of all members of the population , 9-11. Ahttp://www.vsni.co.uk/downloads/asreml/). Bayesian methods are increasingly being employed, enabled by Markov Chain Monte Carlo Methods (MCMC) methods , i.e. U-shaped. Assuming two alleles at a locus, the heterozygosity and genetic variance for an additive gene are proportional to p(1 – p), and therefore they have a uniform distribution over the allele frequency range from 0 to 1. Most mutations affecting a trait are likely to be deleterious with respect to fitness, either through pleiotropic effects on other traits or directly on that trait, for example if there is an intermediate optimum, and consequently they are usually lost quickly from the population [The simplest model for maintenance of variation is a balance between genetic drift and mutation; although an oversimplification it is a useful reference point. The frequency density of alleles  is then proportional to 1/[pulation . Hence tD. melanogaster, for example, it is essentially all additive . This dvariance . This isvariance ) are nota on the trait is proportional to r2a2, where r2 is the squared LD correlation between QTL and marker alleles. The expected value of r2 depends not only on the closeness of the two but on their relative frequencies. Thus if a high proportion of trait genes have lower minor allele frequency than do the SNPs, many trait QTL are likely to be missed in GWAS studies, whether they are oriented at finding individual QTL [r2. In GWAS, power of detection of a QTL with effect dual QTL  or fittidual QTL , 52. ThuArtificial selection in a closed population can lead to long continued response. Notably, the Illinois maize selection experiment for high and low oil content in the kernel has continued for over 100 generations (= years) and, although low lines have reached plateaux , the high lines have continued to respond and, for example, there was abundant variation present around generation 50 as evidenced by responses to reverse selection . Such coet al. [With the availability of dense markers it is possible to track changes in frequency and selective sweeps, albeit distinguishing selection and drift is not simple. Johansson et al.  analysedet al. .The indications are that broiler poultry still retain considerable variation as heritability for body weights remain around their ‘traditional’ 25%, despite over 50 generations of intensive and effective selection for growth and evidence from SNP analyses that variation was lost from native populations in the early periods of domestication prior to that . These rD. melanogaster, an infinitesimal model fitted almost as well as those developed with multiple loci of varying effects under models of maintenance of genetic variance, in each case taking account of selection in generating linkage disequilibrium [vs. low or control lines lacks power [Unsurprisingly, in view of the long continued responses to selection, we find from the GWAS and selection response analyses based not just on F2s that a very large number of genes are influencing the trait. Further, these studies do not detect substantial amounts of non-additivity and we do not expect much non-additive variance in any case in segregating populations . Whilst ilibrium . This inks power , unless ks power . Analysiks power  is likelvia mutation of individual genes, with insertional mutagenesis most convenient for subsequent analysis because the genetic lesion can be detected directly. QTL mapping using line crosses is not a definitive method to detect pleiotropy as it is difficult to disentangle it from close linkage of genes each affecting only one of the traits. GWAS is rather more definitive in that much smaller pieces of the genome can be isolated and with some degree of certainty pleiotropic effects detected. A more direct approach is D. melanogaster by Mackay, who reported [e.g. Many studies have been undertaken in ted e.g.  that of S. cerevisiae mutant for a different gene and the mean number of traits affected in each line was 21.6 and the median was 7. In another, for 4905 genes and 308 traits in mice, the mean was 8.2 and median 8. Thus Wagner and Zhang [In contrast, in two large studies of mutants, less pleiotropy was found . In one,nd Zhang  concludeWhilst applications of genomics have demonstrated clearly that many loci contribute to variation in quantitative traits, they have not yet resolved one of the so far intractable problems: explaining the magnitude of variation seen in natural or domesticated populations. For example the coefficient of variation is typically around 10% for juvenile growth rate, lower for mature size, and higher for reproductive rate. Heritability is typically 25% for juvenile growth rate, higher for mature size and lower for reproductive rate. It is simplistic to argue that the heritability of fitness associated traits such as reproductive rate is low as it is under stronger natural selection, because the CV of such traits is high, such that a standardised measure, the genetic coefficient of variation (evolvability), shows a much narrower range.Although there has been extensive analysis and discussion of what determines the level of genetic variation in populations , there is no clear resolution. There must be a trade-off between gain of variation from mutation  and loss by genetic drift and by most modes of selection. Basically it is hard to explain why levels of both genetic and environmental variation are as high as they are , 69, 70.The availability of dense genomic markers is revolutionising the methods being used in animal breeding and is increasingly so in plant breeding and in disease prediction in humans. The principles and methods for ‘Genomic selection’ were proposed in 2001 for livestock by Meuwissen, Hayes and Goddard . The baset al. [One simple view is to regard the predictor as simply that of improving estimation of the weighted proportion of genome or actual relationship (GBLUP) shared by relatives, which is equivalent to assuming effects of all SNPs are sampled from the same distribution, in essence an infinitesimal model (with linkage). The alternative proposed by Meuwissen et al.  is to aset al. , 73. GBLet al. . An essential component of assessment of methods of genomic prediction is some form of cross validation, whereby predictions of breeding values from a training set are checked by realisations on individuals which were not included in the initial predictions, the validation set. For example data from the most recent years can be excluded from the analyses leading to the predictions. Comparisons of accuracies of prediction under different models indicate that for some traits, such as proportion of white in the cattle coat, these are highest if there are assumed to be a relatively limited number of important genomic regions, whereas for milk yield GBLUP is accurate, suggesting more dispersed variation . The accAn alternative to genomic prediction fitting an additive quantitative genetic model or, exceptionally, dominance also within a Mendelian framework is to use non- or semi-parametric methods. With these any degree of epistatic interactions associated with the SNP marked effects can be fitted. Additive relationships can also be included for infinitesimal effects as in the usual model, but with a reproducing kernel Hilbert space regression fitted to the SNP associated affects . At its Incorporation of genomic prediction has the potential to greatly increase rates of genetic improvement of livestock and it is rapidly being taken up , 74, 78)Our understanding of quantitative genetic inheritance has largely been at the phenotypic level, summarised by variances and covariances using increasingly sophisticated statistical and computing methods. Developments continue in this area, notably in previously less well studied areas, such as of natural populations. These methods have been used successfully in effecting genetic change in livestock and plants. As QTL mapping has progressed with increasingly dense markers available with genomic techniques, now feasible using LD based genome wide association studies, so has information about the effects on the traits of individual loci accumulated and been utilised in marker assisted introgression and selection, to some extent in animal breeding and more in plants. These studies have, however, confirmed the multi-locus nature of quantitative genetic variation, previously largely inferred but not proven from selection and other experiments. At this stage genomic methods are therefore having their most impact in breeding practice in genomic prediction, expanding what is essentially a statistical approach. In view of the complexity of the genome and the evidence, unsurprising to most quantitative geneticists, that many genes contribute to variation, we must appreciate that exact descriptions even at the level of numbers of genes, distributions of gene effects and their interactions will be very hard to resolve. Fortunately, however, we can still make progress without all that detailed knowledge by introducing extra information from variation at the genome level into our descriptions and predictions.I have not dealt with the area here, but the hope and expectation is that techniques from genomics, transcriptomics and other ‘omics will increase our knowledge of how the genes that affect quantitative traits act in the organism to do so, and how they are regulated. Indeed these are likely to be informed by quantitative genetic approaches: for example transcript abundance is itself a quantitative trait. There is a hope that systems biological approaches will be fruitful in unravelling the chain from gene to phenotype. It seems likely that understanding will come first from the study of complex disease where understanding individual genes is a priority and it will probably be a slow process for continuous traits, for which only a few pathways are unlikely to predominate. In due course this will help us not only to make better predictions in health and breeding but also to understand more about how our species have evolved. We now have extensive information at the genomic level on species differences for example, but little on how the differences in quantitative measures such as size, longevity and behaviour have arisen. There is plenty to do.We now have the ability to record genomic sequences, obtain levels of expression of all genes in different environmental circumstances and manipulate the vast amounts of detail so as to construct pathways of gene action and interaction. The problem is how this information should be put together, both to understand how the system works and to use it to our benefit in improvement of food production, food quality, health, and the environment. Traditionally, and perhaps inevitably in view of the need to justify research grants, there has been long running optimism about understanding variation in quantitative traits. Much of it is still to be realised The quantitative nature of the traits and the polygenic influence on them will make this a very challenging task.


APOC3, with increased high-density lipoprotein and decreased triglycerides levels. Approximately 3.8% of individuals are heterozygous for this cardioprotective variant, which was previously thought to be private to the Amish founder population. R19X is rare (<0.05% frequency) in outbred European populations. The increased frequency of R19X enables discovery of this lipid traits signal at genome-wide significance in a small sample size. This work exemplifies the value of isolated populations in successfully detecting transferable rare variant associations of high medical relevance.Isolated populations can empower the identification of rare variation associated with complex traits through next generation association studies, but the generalizability of such findings remains unknown. Here we genotype 1,267 individuals from a Greek population isolate on the Illumina HumanExome Beadchip, in search of functional coding variants associated with lipids traits. We find genome-wide significant evidence for association between R19X, a functional variant in  Isolated populations may empower genetic association studies of complex traits. Here, the authors identify a rare cardioprotective APOC3 variant in a Greek population isolate and highlight the value of using population isolates to detect rare variants that confer disease risk. APOC3 variant, R19X, found to be associated with high-density lipoprotein (HDL) and triglycerides levels in the Amish was deemed to be population-specific as it was not detected in non-Amish individuals of European descent given available data at the timeThe escalating burden of cardiovascular and metabolic disease has growing health economics and societal implications. Blood lipid levels are measurable indices strongly associated with cardiometabolic disease endpoints. Genetic association studies have in the last few years substantially enhanced our understanding of factors underlying traits of high clinical importance, such as body mass index, lipid levels and blood pressure. Population isolates have been proposed as useful tools in genetic association studies for complex traits, as they are characterized by reduced phenotypic, environmental and genetic heterogeneityThere is growing empirical evidence that low-frequency and rare variants have an important role in complex human phenotypes. The Illumina HumanExome Beadchip has been developed as a cost-effective tool to study the role of exonic variants and has been successfully applied to the identification of new loci influencing insulin processing and secretionhttp://www.helic.org). The Mylopotamos villages’ residents anecdotally demonstrate high rates of longevity. Although Cretans have low mortality rates from cardiovascular disease6To identify potentially functional coding variants associated with cardiometabolic-related traits, we perform exome chip genotyping of 1,267 individuals from a Greek population isolate stemming from Anogia and the surrounding Mylopotamos villages on Crete (HELIC (Hellenic Isolated Cohorts) MANOLIS (Minoan Isolates) study, APOC3, and increased HDL and decreased triglycerides levels. R19X is rare (<0.05% frequency) in outbred European populations, whereas its frequency has increased to 1.9% in MANOLIS, which enables discovery of this lipid traits signal at genome-wide significance in a small sample size. Our findings additionally address the generalizability of associations discovered in population isolates, as this cardioprotective variant was previously thought to be private to the Amish founder population.We find genome-wide significant evidence for association between R19X, a functional variant in CETP , the well-established HDL-associated locus which has accrued robust evidence for association across multiple populations , which explains 2.9% of the trait variance  and explains 3.9% of trait variance  and was highly correlated to R19X in that founder population . In the MANOLIS cohort, we find that the two variants are poorly correlated (r2=0.001), whereas only three of the possible four haplotypes are observed .In an analysis of HDL levels using exome chip data in 1,256 individuals from the MANOLIS study , we findulations ; Table 1variance ; Fig. 2.variance ; Fig. 2.) and is not present in the 1,000 Genome Project datahttp://www.uk10k.org) (MAF=0.05%). Individuals with the R19X variant do not tend to be closely related in MANOLIS, with 97.3% of carrier pairs having ≤10% of alleles identical by descent exome array-wide  in 4,924 European-descent exomes from the NHLBI data et al.P=0.8; To examine patterns of haplotype sharing around R19X more broadly in the MANOLIS cohort, we identified stretches of maximal haplotype identity along chromosome 11 between pairs of samples using an adaptation of the long-range phasing algorithm of Kong APOC3 locus have previously been implicated in several traits related to triglycerides and HDL cholesterol levels171819202224P=0.76, likelihood ratio test).Functional coding mutations in the APOC3 and HDL and triglycerides levelsr2=0.01, D′=0.34) in the MANOLIS cohort  51.5 kb upstream of S cohort , indicatS cohort .APOC3 locus, in which common variants have also been found to be associated with lipid traits by genome-wide association study . It is located on mount Idi and is part of the municipality of Mylopotamos, which includes further isolated villages. The name Anogia means ‘Upper Earth’ and refers to the altitude at which the village is located (740 m). The residents of Anogia demonstrate characteristic Cretan resolve, great pride in their land and uphold ancient traditions. In 1593, the settlement numbered 911 citizens. Anogia is historically renowned for its residents’ resistance to conquerors. The village has been built three times, as it was burnt down twice by the Turks (in 1822 and 1866) and once by the Germans (in 1944). The local dialect is still strongly influenced by the ancient Dorian language.The HELIC-MANOLIS collection focuses on Anogia and surrounding Mylopotamos villages. Recruitment of this population-based sample was primarily carried out at the village medical centres. All individuals were older than 17 years and had to have at least one parent from the Mylopotamos area. The study includes biological sample collection for DNA extraction and lab-based blood measurements, and interview-based questionnaire filling. The phenotypes collected include anthropometric and biometric measurements, clinical evaluation data, biochemical and haematological profiles, self-reported medical history, demographic, socioeconomic and lifestyle information. Biochemical measurements were obtained using enzymatic colorimetric assays and included glucose (hexokinase method), total cholesterol (cholesterol oxidase – phenol aminophenazone method), HDL-cholesterol, triglycerides (glycerol-3-phosphate oxidase-phenol aminophenazone) and iron. Insulin and ferritin were measured via chemiluminescence and CRP through an immunoturbidimetric method. Low-density lipoprotein (LDL)-cholesterol levels were calculated according to Friedewald equationP<0.0001, cluster separation score <0.4). Sample QC was then carried out on the zCall data excluding samples that were ethnicity outliers or duplicates, that had a call rate <99%, or that were heterozygosity outliers, performed at two different MAF thresholds (MAF <1% and MAF ≥1%). We excluded variants with a zCall call rate <99%. We applied a final sample QC step examining again the multidimensional scaling results, MAF <1% heterozygosity distribution in combination with the number of singletons per sample, and excluded combined visual outliers and any samples that had a minor allele count of 1 for >100 variants. We also applied a final HWE step excluding variants with HWE P<0.0001. A total of 1,267 samples and 61,638 variants that were not monomorphic passed QC  as we might expect given the frequency of the allele and its age estimated from this frequency . We then constructed haplotypes for the 48 chromosomes with the stop allele in MANOLIS in the same way. One major inferred ancestral haplotype  in 99/1,870 individuals (5.3%) of the Amish cohort) with 53 SNPs from Illumina HumanExome Beadchip data was compared with the shared haplotype between UK10K and MANOLIS. Forty-five SNPs were shared among the three data sets. The Amish haplotype shares at least 571 kb  with the HELIC and UK10K haplotypes. There are no SNPs in the Amish data between 116,973,929 and 117,100,594 where the HELIC-UK10K shared haplotype ends, and the next SNP available in the Amish data shows that the haplotype is different from the UK10K haplotype. The shared haplotype between the Amish and HELIC-UK10K may extend some way beyond 117,091,609.et al.To identify stretches of extended haplotype sharing between individuals, we used a modification of the long-range phasing method of Kong Cohort collection: G.D., A.-E.F., C.K., E.T. and E.Z. Phenotype cleaning: A.-E.F., K.H., A.M., N.W.R., I.T. and E.Z. Bioinformatics: G.R.S.R., L.S., I.T. and E.Z. Genotype data processing and cleaning: K.H., A.M., K.P., N.W.R., J.S., L.S., I.T. and E.Z. Genotype–phenotype association testing: L.S., I.T. and E.Z. UK10K haplotype analyses: Y.C., UK10K Consortium, Chris Tyler-Smith, Yali Xue. Amish haplotype analysis: T.I.P., J.R.O’C., L.M.Y.-A. and Y.X. HELIC haplotype sharing: G.M., L.M. and D.K.X. Manuscript drafting: G.R.S.R., L.S., I.T., D.K.X., Y.X. and E.Z.How to cite this article: Tachmazidou, I. et al. A rare functional cardioprotective APOC3 variant has risen in frequency in distinct population isolates. Nat. Commun. 4:2872 doi: 10.1038/ncomms3872 (2013).Accession codes: The HELIC-MANOLIS study genotype data have been deposited to the European Genome-phenome Archive (EGA) under the accession code EGAS00001000630. UK10K sequence data have been deposited to the EGA under the accession codes EGAS00001000108 and EGAS00001000090.Supplementary Figures S1-S6, Supplementary Tables S1-S5 and Supplementary Note 1


A) derived from pedigrees to utilize information from relatives. For example, all pairs of full-sib offspring of a cross are assumed to share 50% of alleles in common. Such assumptions ignore variation in Mendelian segregation of alleles among progeny within family. With advances in marker genotyping technology and reduction in genotyping cost, it is now feasible to estimate genetic covariances from markers.Genetic merit can be considered the finite sum of thousands of allelic effects, each physically located at some place on the genome, whose transmission can be traced through molecular markers. Traditionally, best linear unbiased prediction (BLUP) of breeding values relies on average additive genetic covariances (the numerator relationship matrix G), which is used in place of the A matrix in solving the mixed model equations. This may allow more accurate estimation of individual breeding values than the traditional model based on average genetic covariances.Linear mixed models that utilize realized genomic relationship matrices could predict genomic estimated breeding values (GEBV) more accurately than those that use expected average genetic covariances derived from pedigrees. Dense markers can be used to trace identity by descent probabilities at each locus, and those probabilities used to construct an incidence matrix. The incidence matrix is used to estimate the genomic relationship matrix  = Iσ2m; where σ2m is the marker variance and I is the identity matrix. The term e is the vector of residuals. The dimension of Z is the number of individuals (n) by the number of loci (m). The regression method used to construct our G matrix did not require allele frequencies; instead, the inverse of the G matrix was generated by regressing ZZ’ as a dependent variable on the A matrix as the independent variable. Therefore, the expected value of G is A plus a constant matrix.We estimated realized genetic covariances between cloned progeny of a  G matrix. Clones were divided into a training group with both marker and phenotypic information and a validation group for which only marker genotypes were used. In one scenario ~90% of the clones (148) were sampled for training, either randomly selecting within each of the nine families or at random without family consideration. The remaining ~10% were used for validation (17 clones). In the second scenario, ~50% of clones (84) were sampled either within family or randomly from the whole population for training, and the remaining ~50% were used for validation (81 clones). The model parameters estimated in the training set were used to predict GEBV in the validation set. For each scenario, six independent samplings were carried out. The mean correlation between the GEBV based on G-BLUP and breeding values based on A-BLUP were determined for each scenario, along with the accuracy of the BLUP predictions for both G and A based models. The mean correlation varied from 0.37 to 0.61 across the four validation methods. The accuracies of the predictions for any validation scenario were always higher for G-BLUP (range of 0.65 to 0.75) than A-BLUP (0.60 to 0.62), which is related to the smaller standard error of the predicted G-BLUP for the validation clones (17 or 81) under the different scenarios.Different cross-validation methods were used to test performance of theEstimating realized genetic covariances based on the genotypes of biallelic markers and incorporating those estimates into G-BLUP helps to more accurately characterize Mendelian segregation of alleles, and could allow more accurate selection within family. Such a method would increase genetic gains in forest tree breeding. The major impact would be on reducing the need for expensive field testing, but it may also be possible to shorten the breeding cycle and thus increase genetic gain per unit time and cost. The impact of genomic selection applications in forest tree breeding may be greater than for other crop or animal species, because of the biology of trees and their much longer breeding cycles.


Initial genome-wide association study (GWAS) discoveries are being further explored through the use of large cohorts across multiple and diverse populations involving meta-analyses within large consortia and networks. Many of the additional studies characterize less than 100 single nucleotide polymorphisms (SNPs), often include multiple and correlated phenotypic measurements, and can include data from multiple-sites, multiple-studies, as well as multiple race/ethnicities. New approaches for visualizing resultant data are necessary in order to fully interpret results and obtain a broad view of the trends between DNA variation and phenotypes, as well as provide information on specific SNP and phenotype relationships.The Synthesis-View software tool was designed to visually synthesize the results of the aforementioned types of studies. Presented herein are multiple examples of the ways Synthesis-View can be used to report results from association studies of DNA variation and phenotypes, including the visual integration of p-values or other metrics of significance, allele frequencies, sample sizes, effect size, and direction of effect.https://chgr.mc.vanderbilt.edu/synthesisview.To truly allow a user to visually integrate multiple pieces of information typical of a genetic association study, innovative views are needed to integrate multiple pieces of information. As a result, we have created "Synthesis-View" software for the visualization of genotype-phenotype association data in multiple cohorts. Synthesis-View is freely available for non-commercial research institutions, for full details see  Significant GWAS findings are being further investigated for replication and characterization, both in the populations in which the initial GWAS findings were discovered (such as European-Americans) as well as in new cohorts and populations. To increase power, meta-analysis is often used to combine results from multiple research sites. Multiple independent and correlated phenotypic measurements may be included in these analyses, such as measurements of cardiovascular disease and related biomarkers . Many of these studies characterize less than 100 SNPs. Visualization of these data an integral part of interpreting as well as sharing the complex and multi-layered results of these follow-up studies. The software "Synthesis-View" has been developed to visually synthesize multiple pieces of information of interest from these studies with the flexibility to perform multiple types of data comparisons.Synthesis-View was extended from the previous software "LD-Plus" which also uses a flexible data display format of multiple data "tracks" that can be viewed . Within https://chgr.mc.vanderbilt.edu/synthesisview/create/setparams.Synthesis-View was developed in Ruby and utilizes the RMagick graphics library, and the software tool can be used within a web-interface or at the command line. Both the web interface Figure  and comm2.One file is necessary to produce a standard Synthesis-View plot. This file contains a column for SNP identification (such as rs number), a column with the corresponding chromosome for each SNP, and a column for SNP genomic location. The rest of the optional information will result in data tracks plotted if data are present, and can include p-values, odds ratios, allele frequencies, and sample size. If additional files are supplied, additional tracks are created including phenotypic summary information for continuous phenotypes, gene summary information, and linkage disequilibrium (LD) data plotted in Haploview style  format aProject name: Synthesis-ViewProject home page: http://chgr.mc.vanderbilt.edu/ritchielab/synthesisviewOperating systems(s): Linux, Mac OS X, WindowsProgramming language: RubyOther requirements: RMagickLicense: GNU General Public LicenseAny restrictions to use by non-academics:The use of Synthesis-View is restricted to academic and non-profit usersFigure Physical genome track. Synthesis-View provides information on the relative location of the SNP on a given chromosome and how that position relates to other SNPs in the same study. Lines lead from the chromosome locations to the IDs of each SNP. If the "Additional SNP locations" option is selected, the location of the SNPs within the chromosomal region are indicated. If SNPs are close together, the location of the first SNP in that group is indicated in the plot (to prevent text overlap). When the plot is first generated, an image of the plot is shown within the web browser that includes embedded links. If the results for a SNP are selected within this image, the NCBI SNP database page for that specific SNP is opened in the default web browser of the user.(1) (2) SNP presence/absence track. Not all SNPs may be available for all associations across study groups or populations. Thus, this track provides information on whether a SNP was used in the test of associations through the presence/absence of a colored box corresponding to the group, study, or phenotype.(3) Significance track. The resultant p-values are plotted as the negative log10 of the p-value. Grey vertical lines behind the points create an "abacus-view" that allows for the eye to follow from the SNP presence/absence track down through the lower data tracks. An optional red horizontal line can be applied at a significance level of choice  are plotted. This track allows you to view the similar effect sizes across race/ethnicity in Figure (4) Coded allele frequency track. The coded allele frequency (CAF), the allele chosen by the user to compare across groups or studies, is optionally plotted so trends and differences in the data can be observed.(5) Sample size track. Optional plotting of the sample size for each genotype-phenotype association for each group/study/phenotype is available so the relationships between sample size and other results of the study can be explored. To plot without sample size, omit sample size information columns from the input file. If sample size is provided only as entire group summary information, rather than for individual SNP/phenotype regressions, a separate box will appear at the bottom of the plot with this summary information graphically represented.(6) Phenotype summary plot. Summary information for a single phenotype across several groups is plotted if a separate file of phenotype summary information is included. This is currently a feature for quantitative traits/continuous data. Future versions will incorporate methods to characterize categorical/case-control phenotype summary information.(7) in preparation). Alternate colors for the data points can be specified by adding an additional column with the header of 'Color', along with the color selected for each group, in the phenotype summary file.When all SNPs are available for all tests of association, a legend appears indicating which colors correspond to which group, study, or phenotype. Figure Comparisons can also be made across study stages, such as Figure In Figure in preparation), and investigated for three race/ethnicities: Mexican Americans (MA); European Americans (EA); and African Americans (AF). Thus, in this plot, the results across multiple phenotypes are plotted with a track for each race/ethnicity allowing for multi-layered results to be viewed together.In some settings, studies have multiple phenotypes, as well as multiple groups (such as multiple race-ethnicities). In this case, Synthesis-View will plot the results for all the phenotypes for each group on separate tracks. Figure 2 plot in Haploview style format [in preparation). This plot will appear when D' or r2 data are provided in a separate file.Another feature available in Synthesis-View is the plotting of a D' or rSynthesis-View also has an option for plotting odds ratio (OR) results in forest plot format. Figure 1) The first track, like with the standard Synthesis-View plot, is a physical genome track, displaying the chromosome and relative location of each SNP used in the association tests.2) The next track is an optional significance track, displaying the p-values. A single color represents each group. In this case, a red line has been placed at a p-value of 0.05.3) The next three tracks are odds ratio/forest plot tracks. Squares represent the OR point estimate, with lines representing the upper and lower 95% confidence intervals. Here the similarity of the results between Stage 1 and Stage 2 are visible. An additional option, not shown, is available. If a result is significant , the square can be plotted in larger size, allowing for quick visual identification of significant results in forest plots with a large number of results.4) The second to last track is the CAF track. Colors match those of the groups of the previous tracks, allowing the user to identify trends in allele frequencies between groups which can aid in interpreting replication of results. The option of horizontal separation of overlapping points was also used here as the CAF measurements were very similar between the analyses.5) The last track is the sample size track. Case/control sample size can either be plotted in separate tracks, or, as shown here with closed circles indicating cases, and open circles indicating controls in the same track. The colors match those of the groups of the previous tracks. This option is also available when the CAF for cases vs. controls are provided.The upper panel of Figure An alternative way to view OR results is in stacked tracks where the eye moves from top to bottom, in more of the Synthesis-View standard format. If the forest plot option is not chosen in Synthesis-View, the default data plot is in this format. Unlike the forest plots of Figure To date, most replication, meta-analysis, and even top GWAS results, are presented in tabular form. There are additional ways to display these results. We developed and describe here a visualization tool for studies that have data for less than 100 SNPs, which is typical for a targeted genotyping study using thousands to tens of thousands of samples. We emphasize that Synthesis-View is especially effective when data are being investigated across phenotypes, studies, and multiple race/ethnicities. Through visually incorporating results, details of individual SNP-phenotype relationships as well as larger trends in the interplay between information such as SNP location, sample size, data stratification, and allele frequencies can be viewed.The authors declare that they have no competing interests.SAP carried out design of Synthesis-View, SMD programmed, DCC and MR participated in design and coordination and drafting of the manuscript. All authors read and approved the final manuscript.


It is necessary toevaluate alternatives to analyze discrete traits in a genome-wide predictioncontext.Genomic selection has gained much attention and the main goal is to increase thepredictive accuracy and the genetic gain in livestock using dense markerinformation. Most methods dealing with the large This study shows two threshold versions of Bayesian regressions (Bayes A andBayesian LASSO) and two machine learning algorithms (boosting and random forest)to analyze discrete traits in a genome-wide prediction context. These methods wereevaluated using simulated and field data to predict yet-to-be observed records.Performances were compared based on the models' predictive ability.The simulation showed that machine learning had some advantages over Bayesianregressions when a small number of QTL regulated the trait under pure additivity.However, differences were small and disappeared with a large number of QTL.Bayesian threshold LASSO and boosting achieved the highest accuracies, whereasRandom Forest presented the highest classification performance. Random Forest wasthe most consistent method in detecting resistant and susceptible animals, phicorrelation was up to 81% greater than Bayesian regressions. Random Forestoutperformed other methods in correctly classifying resistant and susceptibleanimals in the two pure swine lines evaluated. Boosting and Bayes A were moreaccurate with crossbred data.The results of this study suggest that the best method for genome-wide predictionmay depend on the genetic basis of the population analyzed. All methods were lessaccurate at correctly classifying intermediate animals than extreme animals. Amongthe different alternatives proposed to analyze discrete traits, machine-learningshowed some advantages over Bayesian regressions. Boosting with a pseudo Huberloss function showed high accuracy, whereas Random Forest produced more consistentresults and an interesting predictive ability. Nonetheless, the best method may becase-dependent and a initial evaluation of different methods is recommended todeal with a particular problem. The availability of thousands of markers from high throughput genotyping platformsoffers an exciting prospect to predict the outcome of complex traits in animal breedingusing genomic information  and in personalizedmedicine. Besides production and other functional traits, genomic selection offers anovel challenge for discovering genetic variants affecting important diseases in humans,plants and livestock, and also for breeding resistant individuals to improve farmprofitability.p small n problem, the statisticaltreatment of the categorical nature of a trait may increase parameterization. So far,methods dealing with genome-assisted evaluations have focused on traits expressed orrecorded in a continuous and Gaussian manner [The statistical treatment of the genetic basis of these traits is not straightforwardbecause multiple genes, gene by gene interactions and gene by environment interactionsunderlie most complex traits and diseases. Capturing all marker signals is currentlychallenging. Besides the large n manner -3. Howevn manner -6. A lar2 Lloss function [Machine-learning is becoming more and more popular to deal with the difficulties statedabove, and has been previously applied in GWAS in humans  and livefunction . RF and The objective of this study was to present the threshold extension of two Bayesianregression methods that are used in genome-assisted evaluations (Bayes A and BayesianLASSO), a boosting algorithm for discrete traits, to describe more thoroughly the RFalternative to deal with discrete traits in a genome-wide prediction context, and toapply them to both simulated and real data to compare their predictive ability.y ={iy} be a vector of phenotypes recorded in a binaryfashion (0/1) from n animals genotyped for p markers X ={xi}. Four different methods were applied: two linearregressions using a Bayesian framework, and two machine-learning ensemblealgorithms.Let t):A threshold version of Bayes A (TBA) model was proposed here, which is an extensionof the Bayesian regression proposed by Meuwissen et al. . The traThe liability is taken as the response variable. The proposed modification consistsof the linear regression of the single nucleotide polymorphism (SNP) coefficients ona liability variable with Gaussian distribution. The TBA can be described asfollows:λ is the underlying liability variable vector for y,μ is the population mean, 1 is a column vector(n×1) of ones; b = {jb} corresponds to thevector for the regression coefficient estimates of the p markers or SNPassumed normally and independently distributed a priori as j. The prior distribution of j υ= 4 andX, of ordern × p, may be set up as for different additive, dominant orepistatic models. In the more practical scenario, it takes values -1, 0 or 1 formarker genotypes aa, Aa and AA, respectively. Theresiduals (e) are assumed to be distributed as where, n liabilities is:This method can be solved via the Gibbs sampler described in Meuwissen et al. , with thThe Bayesian LASSO described by Park and Casella  and its λ is the vector of liabilities for all individuals, μis the population mean, 1 is a column vector (n × 1) ofones; X as described for model TBA. As a modeling choice, e wasconsidered the vector of independently and identically distributed residuals, aswhere In a fully Bayesian context, the LASSO estimates l priors . Park anl priors  have proγ is aparameter controlling the shrinkage of the distribution. Inferences about γmay be done in different ways [2γ, with known rate (r) and shape(δ) hyper-parameters, as described by de los Campos et al. [where ent ways . To folls et al. . Sampless et al. , with thGradient boosting may be classified as an ensemble method . This al for m ∈ ) is applied consecutively to the residual from the committee formed bythe previous ones. This algorithm can be calculated using importance samplinglearning ensembles as follows:Each predictor , let the prediction of phenotypes beF0 = μ, with μ being thepopulation mean.: Given data m in {1 to M}, with M being large, calculate theloss function (L) for Then, for m jis the SNP (only one SNP is selected at each iteration)that minimizes m,h is the prediction of the observation using SNP jat the current iteration,mF-1(xi) is theupdated prediction at the previous iteration and L(·) is a given lossfunction. The updated prediction at each iteration m may be expressed asmF(xi) =mF-1(xi)+v·h with v being someshrinkage factor that, without loss of generality, can be assumed constant and small(0<v <1), but it may be optimized to balance predictive ability andcomputation time.where Therefore, after the initialization, the algorithm flows as follows:j to current residuals, whereν was set to 0.01.Step 1: Compute residuals as j, where Step 2: Select SNP mF(xi) =mF-1(xi)+ν·h, , where h is the estimate forindividual i obtained by regressing the current residual(ir) at iteration m on its genotype for the SNPselected in step 2.Step 3. Update predictions as m by 1, and repeat steps 2-4 until aconvergence criterion is reached.Step 4: Increase the iteration index h and two different loss functions: the 2 Llossfunction (L2B), which is a quadratic error term in the form)2, and apseudo-Huber loss function (LhB) in the form iy-mF) tends to infinite. Thechoice of the number of iterations, M, is a model comparison problem whichmay be overcome in many different ways [Here, we used ordinary least square regression as predictor ent ways ,20. Hereent ways . More deent ways , Friedmaent ways  and Gonzent ways .Random Forest can be viewed as a machine learning ensemble algorithm and was firstproposed by Breiman . It is mA RF algorithm aimed at genome-wide prediction is described next, in a more extensivemanner than the previous methods, as this is the first time that this algorithm isused in a genomic breeding value prediction context:y (n × 1) be the data vector consisting of discreteobservations for the outcome of a given trait, and X ={xi} where xi is a (p× 1) vector representing the genotype of each animal  for pSNP, to which T decision trees are built (see classification andregression tree theory e.g. [xi. Thisensemble can be described as an additive expansion of the form:Let ory e.g. ). Note tth for t∈) is distinct from any other in the ensemble as it is constructed fromn samples from the original data set selected at random with replacement,and at each node only a small group of SNP are randomly selected to create thesplitting rule. Each tree is grown to the largest extent possible until all theterminal nodes are maximally homogeneous. Then, ct is someshrinkage factor averaging the trees. The trees are independent identicallydistributed random vectors, each of them casting a unit vote for the most popularoutcome of the disease at a given combination of SNP genotypes.Each tree  may appearseveral times or not at all in the bootstrapped set Ψt).1. First, bootstrapped samples from the whole data set are drawn with replacement sothat realization , where2. Then, draw L) being a certain lossfunction. i.e. SNP j is the one that minimizes a given loss function at thecurrent node, and is selected in this step. The algorithm takes a fresh look at thedata that have arrived at each node and evaluate all possible splits. Many lossfunctions can be chosen . The behavior of a given lossfunction may depend on the nature of the problem. The squared loss function ispopular for continuous response variables, and the logit function for categoricalresponses.with j genotype that oneindividual may or may not have .3. Split the node in two child nodes according to SNP xi is the majority votefor the outcome at the terminal nodes .4. Repeat steps 2-3 until a minimum node size is reached . Thepredicted value of the genotype Finally, a large amount of trees are constructed repeating steps 1-4 to grow a randomforest. The forest may be stopped when the generalization error averaged across theout of bag samples (see section below) have converged. Convergence may be visuallytested but it may also be determined using traditional methods for convergencetesting of Monte Carlo Markov chains.Final predictions can be made by averaging the values predicted at each tree toobtain a probability of being susceptible. In a naïve 0 = non-susceptible/1 =susceptible scenario, individuals with probability <0.5 may be considered asnon-susceptible. To predict observations of new individuals, their marker genotypesare passed down each tree, and the estimate of the corresponding terminal nodes isassigned to the new individual in each tree. The predictions of each tree in the RFalgorithm are averaged for each animal to compute the final prediction.mtry).Generalized cross-validation strategies can be used to optimize mtry. Inhigh dimensional problems such as GWAS, Goldstein et al. [mtry to be fixed to >0.1 p. The algorithmmay speed up for smaller mtry values. Nonetheless, cross-validation can beused to determine the best value of mtry for each trait, although at anexpense of increasing computation time. Genetic background may influence the behaviorof this tuning parameter. The second aspect is the criterion to select the best SNPto split the node. As commented above, different criteria may be used and the bestchoice may depend on the nature of the problem. Entropy theory seems the mostappealing to evaluate genomic information on discrete traits . Other loss functions such as theL1-loss function or the misclassification rate could beimplemented in an easy manner. Without loss of generality we show how to implementthe entropy theory in the node splitting decision. The information gain (IG) for eachcovariate s drawn at random in a given node was calculated as described inLong et al. [There are two main aspects that can be tuned in random forest: the first one is thenumber of SNP or covariates sampled at random for each node  at each SNP covariate j xshowing y = 1(e.g. presence of disease) at such node, and jxcan be calculated as:Suppose there are y, and A is the set of all states that y can take .The SNP covariate with the highest IG at each node is used to split the node into twonew child nodes, each one containing the individuals from the parent node with therisk or the non-risk allele, respectively.where There are two features involved in the RF algorithm that deserve further attention:the out of bag samples, and the variable importance.n-fold cross validation, removing the need for a set aside test ortune test. Tuning of parameters can be done along the RF using the OOB, andgeneralization error can be calculated as the error rate of the OOB [The out of bag data (OOB) is an interesting feature of RF. Each tree is grownusing a bootstrapped sample of the data, which leaves roughly one third of theobservations out because some animals will appear more than once and others willnot appear at all. The samples that do not appear are called the OOB samples. TheOOB acts as a tuning/validation set at each tree and is almost identical to a the OOB ,24.L2 loss function). Then,genotypes for the pth SNP are permuted in the OOB, and the accuracy forthe permuted SNP is again calculated. The relative importance is calculated as thedifference between these prediction accuracies . This step is repeated for each covariate(SNP) and the decrease of accuracy is averaged over all trees in the randomforest. The variable importance may be expressed as a percentage of the accuracyobtained with the most important SNP, and provides insight in the level ofassociation of the SNP with the disease. The SNP with higher VI may be of interestfor prediction of trait susceptibility  atlow marker density, candidate gene studies or gene expression studies.RF may use the OOB to provide an importance measure of predictor variables . The relative variable importance (VI) is estimated asfollows. After each tree is constructed, the OOB are passed down the tree and theprediction accuracy of disease outcome is calculated using the chosen criterion. Ten replicates were analyzed,and the mean and standard deviations are presented.A field data set was used here to illustrate the behavior of the methods inclassification problems applied to genome-wide prediction of disease resistance inpigs. In this study we used one of the most important congenital diseases in pigindustry as response variable: scrotal hernia (SH). Most affected individualscannot feed effectively and consequently growth is affected . This lejth, we defined two covariates Data were provided by PIC North America, a Genus Plc company. The data setcontained records of scrotal hernia incidence (score 0 or 1) in 2768 animals fromthree different lines. Animals from two purebred lines (A and B) were born inelite genetic nuclei, where environmental conditions were better controlled andrisk of infections was lower. Animals from a crossbred line (C), from line A andother lines not used in this study, were born in commercial herds. Selectionemphasis in line A was placed on reproduction and lean growth efficiency. Line Bhas been selected mainly for reproductive traits. Selection against scrotal herniawas equally emphasized in both lines A and B. The prevalence of the disease rangedbetween 1 and 2% in all lines. Genotypes of all animals with phenotypic recordswere obtained for 6742 SNP located in different genomic regions identified ascandidate regions in previous studies ,30. A coAnalyses within each line were performed leaving out the 15% youngest individuals,as testing set. The raw phenotype was used as dependent variable in a control casedesign. Note that systematic effects were not included as covariates forsimplicity, although any covariate may be included in the algorithms without lossof generality. The predicted susceptibilities of animals in the testing set werethe percentages of trees in a random forest that a given animal was considered asaffected.Performance of the models was based on predictive ability to correctly predictgenetic susceptibility in the testing sets. The true genetic susceptibilities ofindividuals in the simulated data set are known. However, true genetic merits areunknown in the field data case. Therefore, predictive ability was evaluated in adifferent manner in the field data, as described below.i to be susceptible, for machine learningmethods. Pearson's correlations were calculated between true and predicted geneticsusceptibility merit for each model and simulated scenario.The true genetic susceptibilities were obtained from the simulations and followeda Gaussian distribution, whereas distributions of predicted susceptibilities weredependent on the model used. A Gaussian distribution was assumed for Bayesianregressions and an unknown distribution bounded between 0 and 1, representing theprobability of individual vs. false positive rate (1 − specificity)for a binary classifier system as its discrimination threshold changes [In addition, the area (AUC) under the receiving operating characteristic curve wascalculated for each model in each simulation. This curve is a graphical plot ofthe sensitivity, or true  changes . The AUCα percentile of theEBV distribution in each line, whereas those in the upper (1-α)percentile were considered as susceptible .Lower values of α selected the more extreme animals, thus a smallerapproximation error is expected.True genetic susceptibilities of individuals in the field data are unknown.Instead, estimated breeding values (EBV) for SH susceptibility obtained fromroutine genetic evaluation using the BLUP method  were assy) and predictions, whereas specificity measures the proportionof affected animals that are correctly identified as such (TP = true positives).Values of sensitivity and specificity closer to 1 are preferred. Specificity andsensitivity are more informative than raw rate of misclassification, as the latterdoes not differentiate if misclassification is on true healthy or true affectedanimals.Furthermore, all animals in the respective testing sets were used to calculate theAUC statistic, described above, for each method within a line. Animals with SHwere considered as positive examples, whereas animals without SH were considerednegative examples. As stated before, AUC measures predictive ability and may beconsidered as a model comparison criterion. Higher AUC values are desirable, asmentioned above.Table Bayesian regression showed larger Pearson correlations than ensemble algorithms inthe scenario with a larger number of QTL. Method BTL achieved the largest Pearsoncorrelation (0.38), followed by TBA and LhB (0.33). Method RF showed the smallestPearson correlation (0.22) in this simulated scenario and the largest AUC (0.72).This suggests that RF ranked individuals less accurately than other methods when alarge number of QTL affects additively the trait, but the method is more accuratethan other methods at discerning between healthy and affected individuals.It must be pointed out that the simulated scenarios are purely additive and othermore realistic scenarios with a more complex interaction between genes and biologicalpathways might provide different results.The three data sets had a disease occurrence of 50%. The relative predictiveimportance obtained with RF for each SNP covariate ϕr, sensitivity andspecificity were achieved at α = 0.05. RF and L2B achieved misclassification =0, ϕ r= 1, sensitivity = 1 and specificity = 1 at α =0.05 in lines A and B, respectively, which means a perfect classification of the mostextreme animals. At this α level, TBA and BTL showed misclassification = 17%,ϕ r= 0.71 in line A and misclassification = 14%,ϕ r= 0.75 in line B, and were either less sensitive orspecific than RF and L2B. RF outperformed BTL at α = 0.05, 0.25 and 0.50 inlines A and B, whereas TBL achieved better predictive accuracy at α = 0.10. RFdoubled the ϕ robtained with TBA at α = 0.50 in line A,and was 12% larger in Line B.Tables 1 loss function.None of the methods was clearly preferred in the crossbred (line C), where similarphi correlations were found for RF, TBA and boosting, with larger robustness for LhBat α < 0.50. No differences were found between RF, TBA and LhB to correctlydetect most extreme animals in the crossbred line. The Huber loss function was morerobust than the squared sum of residuals at analyzing binary traits, in accordancewith its resemblance with the LRF showed consistently larger AUC values than the other methods whichever line Table, whereasThe true genetic architecture of SH is obviously unknown and no conclusions on itsrelationship with the performance of the different methods can be extracted. Therewas no clear relationship between the preferred method and the number of relevantgenomic regions identified in each line Figure . The chonliability variables to be estimated in the model, increasing theparameterization of the models, and therefore hampering their predictive ability.Results showed that RF had the lowest risk, among methods used here, ofmisclassifying animals for low-medium heritability discrete traits in all lines,although all methods had considerable misclassification risks at α = 0.50.However, in a disease resistance genome-assisted prediction context, for instance, weare mainly interested in correctly detecting the most susceptible or resistantanimals , and RF seemed to perform slightly better than theBayesian regressions to detect susceptibility to SH in this population, mainly inline A. Note that the threshold versions presented here incorporate Results from the analyses of the crossbred line were not conclusive, as differentbehaviors between methods were found for different α values. This may beexplained by the larger genetic heterogeneity expected in line C which may not becaptured with only 5000 markers.A small number of animals was used in the testing set and only punctual estimates aregiven here. This may be important at low α levels with a smaller number ofrecords. Uncertainty about these estimates may be reported from their posteriordensities  in the cThe misclassification rate and the logit function were also used as splittingcriteria in RF but with poorer predictive ability (results not shown). Here,hyperparameters were set as fixed, although it is possible to assign them a priordistribution for their estimation . NonetheTwo Bayesian regressions (TBA and BTL) and two machine-learning algorithms (RF andboosting) were proposed here to analyze discrete traits in a genome-wide predictioncontext. Machine-learning performed better than Bayesian regression with a small numberof QTL with pure additive effects. RF seemed to outperform other methods in the fielddata sets, with better classification performance within and across data sets. It is anelegant method with an interesting predictive ability for studies on discrete traitsusing whole genome information. It is also easily interpretable as it is based onnaïve decision rules. The boosting algorithms may achieve high predictive accuracyif a case-specific loss function is used, although it may be influenced by geneticarchitecture. Comparison between Bayesian regressions was dependent on the data setused, although the threshold version of the Bayesian LASSO seemed to be preferred to thethreshold Bayes A.RF and boosting do not need an inheritance specification model and may account fornon-additive effects without increasing the number of covariates in the model orcomputing time. Results from this study showed some advantages in the use of machinelearning to analyze discrete traits in genome-wide prediction, although modelcomparisons for specific case problems are encouraged.The authors declare that they have no competing interests.SF participated in the statistical analyses, discussion of results, got access andedited the data, coordinated the study and helped writing the manuscript. OGRparticipated in the statistical analyses and editing of the data, development of thesoftware, discussion of the results and drafted the manuscript. Both authors read andapproved the final manuscript and contributed equally to this study.


Genomic selection (GS) involves selection decisions based on genomic breeding values estimated as the sum of the effects of genome-wide markers capturing most QTLs for the target trait(s). GS is revolutionizing breeding practice for complex trait in domestic animals. The same approach and concepts can be readily applied to forest tree breeding. Trees also have long generation times and late expressing traits. Differently from association genetics that aims at dissecting complex traits in their discrete components, GS precludes the discovery of individual marker-trait associations and focuses on prediction of performance. By capturing the "missing heritability" of complex quantitative traits beyond the few effect variants that association genetics has so far typically identified, GS might soon cause a paradigm shift in forest tree breeding. In a prior deterministic study we assessed the impact of linkage disequilibrium (modeled by Ne and inter-marker distance), the size of the training set, trait heritability and the number of QTL on the predicted accuracy of GS . ResultsE. grandis x E. urophylla hybrids clones therefore having an effective population size of Ne= 11. The second population was a progeny trial installed by FIBRIA (FIB) with 232 full-sib families involving 120 elite parents, the vast majority F1 hybrids of E. grandis, E. urophylla, E. globulus and E. maidenii. For the GS study however only the most phenotypically relevant 75 families were used derived from intercrossing 55 parents therefore limiting the effective population size to Ne= 55. A sample of trees was taken in a stratified way from all families to provide a balanced representation of all families with similar numbers of individuals per family. Bark or leaf samples were collected, DNA extracted and genotypes obtained for 783 and 920 trees from CEN and FIB respectively. Genotyping was carried out using a high-throughput genotyping platform developed earlier [Puccinia rust resistance were obtained. Single marker regression association analyses were initially performed with all the traits, treating the markers as fixed effects. Markers selected in the previous association analysis had their effects estimated adjusting all the allelic effects simultaneously using Random Regression Best Linear Unbiased Predictor. Each population was then divided in an training set comprising 90% of the total number of individuals and a validation set with the other 10%. The estimated effects of the markers were validated using a cross validation approach using random sub-sampling with ten replications so that all individuals had their phenotypes predicted by GS. Details of the overall analytical approach have been described [This study was carried out in two structured populations of Eucalyptus represented by two progeny trials of independent breeding programs. The first was a progeny trial developed by CENIBRA (CEN) with 43 full-sib families generated by an incomplete diallel mating design carried out by intercrossing 11 highly selected Eucalyptus tree breeding practice.To date results have been obtained for height, DBH and wood density while the remaining traits are under analysis. Realized accuracies by Genomic Estimated Breeding Values (GEBV) for H and DBH were 0.67 and 0.69 for CEN and 0.62 and 0.54 for FIB while for WD in FIB it was 0.53. These accuracies match or surpass those obtained by conventional BLUP (Best Linear Unbiased Prediction) based phenotypic selection. Not surprising GEBV accuracies were low (~0.18) across populations implying variable genotype-phenotype associations across backgrounds so that population-specific GS models will be necessary. GS-based reduction in breeding time by 50%, i.e. by reducing a breeding generation time from 12 to 6 years should provide gains ≥100% in selection efficiencies. If a reduction from 12 to 3 years using flower induction can be achieved, gains in selection efficiency theoretically could surpass 300%. These are among the first experimental results of GS in forest trees and in plants in general. With the technological advances and declining costs of genotyping methods we anticipate that GS will soon be implemented operationally and revolutionize